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The role of accounting numbers in the "long-term" mispricing of initial public security offerings

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The role of accounting numbers in the "long-term" mispricing of initial public security offerings
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Neill, John D., 1959-
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Thesis (Ph. D.)--University of Florida, 1990.
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Includes bibliographical references (leaves 143-146).
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by John D. Neill, III.

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THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS





















By

JOHN D. NEILL, III


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNJI'.'ERSITY OF FLORIDA


1990




































Copyright 1990

by

John D. Neill, III
















ACKNOWLEDGEMENTS


There are a number of people whom I wish to thank for their

support and encouragement during the course of my graduate studies.

First and foremost, I want to express thanks to my dissertation

committee chairman Bipin Ajinkya for his advice in general and for his

insightful comments on this dissertation in particular. I will always

be appreciative of the tremendous amount of time he spent in directing

this dissertation research. I also want to thank the remainder of my

dissertation committee, David Brown, Miles Livingston, and Dan Smith,

for their encouragement and helpful suggestions and comments throughout

this project.

I also wish to acknowledge Jay Ritter and Chris James for their

contributions to this dissertation. The data that they supplied me with

proved to be invaluable in this research.

Finally, I want to especially thank my wife Karene for her

unfailing love and support over the past five years of graduate studies.

Without her continual encouragement, this dissertation might never have

become a reality.


iii


















TABLE OF CONTENTS

page

ACKNOWLEDGMENTS .............................................. iii

ABSTRACT ......................................................... vi

CHAPTERS

1 INTRODUCTION............................................ 1

2 PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS............. 9

Introduction ............................................ 9
Short-Term Mispricing of IPOs........................... 9
Long-Term Mispricing of IPOs........................... 16

3 HYPOTHESIS DEVELOPMENT................................. 24

Introduction........................................... 24
Assumptions ............................................ 24
Decomposition of Systematic and Unsystematic Risk
into Accounting and Business Risk Components......... 27
Hypotheses ............................................. 29

4 EMPIRICAL METHODOLOGY.................................. 33

Introduction........................................... 33
Three Measures of Accounting Risk....................... 33
Measurement of Risk-Adjusted
Abnormal Security Returns ............................ 42
Univariate Tests....................................... 47
Multivariate Tests..................................... 54
Summary of the Empirical Methodology.................... 65

5 DATA, SAMPLE SELECTION CRITERIA, AND RESULTS........... 67

Data Sources and Sample Selection Criteria............. 67
Descriptive Statistics................................. 69
Results of Univariate Tests............................ 77
Results of Multivariate Tests.......................... 87











6 CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH........ 109

Conclusions............................................ 109
Suggestions for Future Research........................ ll

APPENDICES

A SHORT-TERM UNDERPRICING AND ACCOUNTING RISK............ 113

B EMPIRICAL RESULTS BASED ON A FIVE YEAR TIME HORIZON.... 119

REFERENCES ................................................... 143

BIOGRAPHICAL SKETCH.......................................... .147
















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS

By

John D. Neill, III

August, 1990

Chairman: Bipin B. Ajinkya
Major Department: Fisher School of Accounting


This dissertation examines the impact of the quality of accounting

numbers supplied to potential investors on the "long-term" mispricing of

initial public offerings (IPOs) of securities. Such mispricing is

assessed in relation to the market's valuation after more information is

revealed. Initial offerings are of interest from an accounting research

perspective due to the lack of market information sources prior to the

IPO.

Three hypotheses relating accounting quality and long-term

mispricing are developed and empirically tested. First, the lack of

quality accounting numbers at the IPO date is predicted to have no

effect on the mean abnormal returns earned by investors. However, the

second hypothesis states that the variance of these abnormal returns is

greater in instances where quality accounting information is lacking.









Finally, the third hypothesis predicts that the influence of the quality

of accounting numbers on abnormal return variances diminishes over time.

Three empirical surrogates for accounting quality are employed.

The first is the reputation of the auditor utilized in the IPO process.

It is measured according to whether a Big Eight or a non-Big Eight

auditing firm is employed. The second is the potential managerial

manipulation of reported earnings as reflected in managers' accounting

procedure choices, measured by whether the inventory and depreciation

methods are income increasing or income decreasing. The third proxy is

whether the SEC registration statement filed is a Form S-1 or Form S-18

registration. This third surrogate measures the quantity of accounting

disclosures since Form S-1 registrations are more extensive.

The empirical results indicate that the three surrogates for

accounting quality have no appreciable impact upon the mean abnormal

returns of investors, as hypothesized. However, these same quality

surrogates, with the exception of auditor reputation, influence the

variance of abnormal returns, also as predicted. Thus, the results

indicate that uncertainty relating to accounting quality translates into

an increased variance of IPO abnormal returns, rather than a systematic

overvaluation or undervaluation. As for the third hypothesis, the

results do not reveal a diminution of the quality surrogates' ability to

explain abnormal return variances as time progresses.
















CHAPTER 1
INTRODUCTION


Initial public offerings (IPOs) of securities have been the

subject of academic research for over two decades.' An aspect of the

IPO market which makes it a particularly rich research area is the

relative scarcity of publicly available information about these

previously privately held firms.2 Specifically, potential IPO investors

are at an informational disadvantage compared to those who invest in

more established publicly traded firms, since there is no past series of

security prices and only very limited financial statement information is

available upon which to make determinations of IPO firm value.3 In

addition, IPO firms are not subject to the continuing scrutiny of the

financial analyst community.

The motivation of this dissertation is to understand the degree to

which accounting numbers influence IPO valuation. The preceding



1 See Smith [1986] and Ibbotson et al. [1988] for reviews of this
literature.

2 Privately held firms which have publicly traded debt outstanding
are notable exceptions. Publicly available financial statement
information is produced by these firms.

3 The Securities and Exchange Commission (SEC) requires the
inclusion of two (in a Form S-18 registration) or three (in a Form S-1
registration) years of audited financial statements in the registration
statement and accompanying prospectus. However, Weiss [1988] documents
that 529 out of her sample of 1510 IPOs reported less than two years of
earnings in the prospectus. These noncomplying firms are presumably
start-up firms which have operating histories of less than two years.











discussion indicates that accounting numbers potentially play a greater

role in the valuation of IPOs than in the valuation of more established

publicly traded firms, due to the reduced number of competing

information sources about IPOs.4 Thus, initial public offerings

represent an opportunity to explore the role of accounting numbers in

security valuation in instances of considerable uncertainty surrounding

the value of a firm. Specifically, this dissertation examines the

impact of the quality of accounting numbers available to investors on

the "long-term" mispricing of IPOs. In this thesis, accounting quality

refers to both the reliability and the quantity of the accounting

numbers supplied to investors. In other words, neither a sufficient

quantity of unreliable accounting numbers nor an insufficient quantity

of reliable accounting numbers are considered quality accounting

disclosures in this dissertation. The discussion to follow describes

the differences between "long-term" and "short-term" IPO mispricing, and

explains why the longer term perspective is adopted in this study.

Bower states that "there are two types of mispricing inherent in

initial offerings" (Bower [1989, p. 647]). The first type of mispricing







4 Prior research has consistently demonstrated that accounting
numbers and security prices are statistically associated. Please refer
to Beaver [1981, Chapter 5] and Watts and Zimmerman [1986, Chapter 3]
for comprehensive reviews of this literature. However, changes in
accounting numbers (i.e., "unexpected" earnings) typically only explain
five to ten percent of the variation in security returns. This low
level of explanatory power is presumably due to the abundance of
competing sources of information (e.g., financial analysts, industry
publications, the Wall Street Journal, etc.) about publicly traded
firms.











is the widely documented short-term underpricingg" phenomenon.

Underpricing refers to the empirical finding that, on average, the

offering price set by the underwriter6 (hereafter referred to as the IPO

price) is below the consensus price set by market participants shortly

after the securities begin trading publicly. On average, this

underpricing exceeds 15% (see Smith [1986]).7 Short-term underpricing

is typically calculated as the return that an investor could have earned

by purchasing the IPO from the underwriter and selling at the market

clearing price at the end of the first day of public trading.8 Thus,

the short-term mispricing research assesses the accuracy of

underwriters' pricing decisions by comparing an underwriter's IPO price

to the price which prevails at the close of the first day of public

trading (hereafter referred to as the first aftermarket price).






5 Short-term underpricing has been documented by Reilly and
Hatfield [1969], McDonald and Fisher [1972], Logue [1973], Ibbotson
[1975], Ibbotson and Jaffe [1975], Ritter [1984b, 1987], Beatty and
Ritter [1986], Miller and Reilly [1987], Tinic [1988], Weiss [1988], and
others. Reviews of these empirical findings are provided by Smith
[1986] and Ibbotson et al. [1988].

6 More precisely, the initial offering price is set by the
underwriter after consultation with the entrepreneur of the IPO firm.
Thus, the issuing firm may play a significant role in the setting of the
IPO price. Alternatively, the issuing firm may completely delegate the
pricing decision to the underwriter (as in Baron's [1982] model).

7 Proposed explanations of the short-term underpricing phenomenon
will be detailed in Chapter 2.

8 However, underpricing has also been calculated as the return over
the first week (e.g., Tinic [1988]) or month (e.g., Ibbotson [1975]) of
public trading. Such departures from the usual underpricing calculation
are typically due to the unavailability of the market price at the end
of the first trading day.











Bower describes the second type of IPO mispricing as follows:

The second type of mispricing is a longer term effect that
relates to the lack of publicly available information about
the firm. Firm value is not likely to be revealed
immediately, but rather investors' expectations about firm
value improve over a long period of time as more information
is revealed about the firm. (Bower [1989, p. 650])

This second type of IPO mispricing "does not deal with initial

underpricing, but rather is concerned with the change in the market

price from the immediate aftermarket to the long term" (Bower [1989, p.

656]). Hence this long-term perspective of mispricing relates to the

accuracy of market participants' initial consensus assessment of IPO

firm value (i.e., the first aftermarket price). The long-term

mispricing of the market's consensus valuation on the first day of

public trading9 is assessed in relation to the market's valuation of the

firm after more information is revealed, via examining abnormal

aftermarket security returns.

There are three principal reasons why the long-term perspective of

IPO mispricing is adopted in this study. The first is that scant

empirical evidence exists on the long-term mispricing of IPOs. In

contrast to the widely documented short-term underpricing by

underwriters, the existing empirical findings do not provide conclusive

evidence as to whether the market overvalues, undervalues, or accurately

values new security issues, on average.10 Further, the impact of




9 This first day of public trading is presumably a time of great
uncertainty about the true value of an IPO due to lack of quality
information about the firm.

10 The empirical findings on long-term mispricing of IPOs will be
reviewed in detail in Chapter 2.











accounting numbers on the long-term accuracy of IPO valuations has not

been examined.

Second, the aftermarket performance of initial offerings may

represent a more appropriate measure of the results of the typical

investor's experience with new issues than short-term assessments of

mispricing. Specifically, it is widely contended that investors may

only receive a small percentage (if any) of their desired number of

shares of an initial offering due to quantity rationing of typically

oversubscribed issues.11 If quantity rationing is indeed prevalent in

the IPO market, then the typical investor's experience with IPOs may not

be represented by the short-term underpricing of the IPO price since

many investors are unable to purchase shares at that price.

The third and most important reason for the long-term mispricing

perspective is that accounting numbers potentially play a greater role

in the valuation decisions of market participants than in underwriters'

pricing decisions. Specifically, in modern finance theory equilibrium

security prices such as an IPO's first aftermarket price equal the

present value of the firm's expected future cash flows. Therefore, if

accounting numbers are useful in estimating such cash flows, then

accounting numbers may be beneficial to market participants in their

assessment of the first aftermarket price. By contrast, prior IPO

research has proposed that the underpricing of the IPO price by

underwriters is due to a variety of nonaccounting factors. In




11 See Rock [1986] and Beatty and Ritter [1986] for analytical
explanations of short-term underpricing based on the quantity rationing
of IPOs.












particular, explanations of short-term underpricing based on potential

legal liabilities, information asymmetries, and the desire to receive a

higher price for subsequent seasoned security offerings have been

proposed.12 Therefore, from an accounting perspective, the examination

of the long-term mispricing of IPOs may be more appropriate.

Three hypotheses relating accounting quality and long-term IPO

mispricing are developed and empirically tested in this thesis. The

first hypothesis is that the lack of quality accounting numbers at the

IPO date has no effect on the mean abnormal aftermarket returns of IPO

investors. However, the second hypothesis states that the dispersion

(variance) of these abnormal returns is greater in instances where

quality accounting information is lacking. In other words, these first

two hypotheses suggest that investors are able to value IPOs in an

unbiased fashion (i.e., not systematically overvalue or undervalue) even

in the presence of considerable uncertainty due to insufficient and/or

unreliable accounting numbers. However, the lack of quality accounting

numbers influences the precision of investors' valuations of IPOs.

Thus, it is postulated that the absolute magnitude (rather than the

direction) of long-term IPO mispricing is related to the quality of

accounting disclosures provided to potential investors. The third

hypothesis provides the time path of Hypothesis 2. Specifically, it

predicts that the influence of the quality of accounting numbers

available at the IPO date on the variance of investors' abnormal returns

diminishes over time. Hypothesis 3 implies that the accounting


12 See footnote 7.











information supplied at the IPO date becomes less important in the

valuation of the firm over time as more timely information becomes known

about these newly public firms.

Three empirical surrogates for the quality of accounting

information available to IPO investors are employed in this study. The

first is the reputation (or quality) of the auditor utilized in the IPO

process. Auditor reputation is measured dichotomously according to

whether a Big Eight or a non-Big Eight auditing firm is utilized in the

going public process. A measure of the potential managerial

manipulation of reported accounting earnings as reflected in managers'

accounting procedure choices forms the second surrogate for quality.

This accounting procedure choice proxy is measured dichotomously

according to whether the methods utilized in accounting for inventory

and depreciation are income decreasing (i.e., "conservative") or income

increasing (i.e., "liberal"). The third proxy is whether the SEC

registration statement filed by the IPO issuer is a Form S-1 or Form

S-18 registration. This third surrogate measures the quantity component

of accounting quality since the accounting disclosure requirements of

Form S-1 registrations are much more extensive than those of Form S-18

filings.

The empirical results provide support for the first two

hypotheses. Specifically, the results indicate that certain accounting

choices which provide indications of accounting quality (i.e., the

quality of auditor utilized in the IPO process, the type of SEC

registration form filed, and the degree of conservatism inherent in

inventory and depreciation choices) have no appreciable impact upon the










8

mean abnormal aftermarket returns of IPO investors. However, these same

accounting choices (with the exception of auditor quality) influence the

variance of investors' aftermarket abnormal returns. Thus, the results

indicate that uncertainty relating to accounting quality translates into

an increased variance of IPO abnormal returns, rather than a systematic

overvaluation or undervaluation of IPOs by investors. However, the

empirical results do not support the third hypothesis. Specifically,

the results do not reveal a strong tendency for the accounting quality

proxy variables to decrease in their ability to explain abnormal return

variances as time progresses subsequent to the initial offering.
















CHAPTER 2
PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS


Introduction


Bower's [1989] specification of the two types of mispricing

inherent in IPO valuation is utilized in this chapter as an aid in

classifying and summarizing the relevant prior IPO research.' Thus,

this chapter will review in detail (1) the short-term underpricingg"

literature and (2) the extant long-term mispricing literature.


Short-Term Mispricing of IPOs


Summary Results


As indicated in Chapter 1, the empirical evidence on the short-

term average underpricing of initial offers is extensive. In his review

of this area of research, Smith [1986] reports that the average level of

underpricing exceeds 15%. In other words, on average, an investor could

purchase an initial offering at the price set by the underwriter and

then sell the security at the prevailing market price at the end of the



1 This classification scheme ignores all IPO research not
pertaining to the "mispricing" of the security issue. Thus, the major
line of research relating to the entrepreneur's ability to convey the
firm's "type" to investors via various signaling mechanisms will not be
reviewed here. The interested reader is referred to Leland and Pyle
[1977], Downes and Heinkel [1982], Ritter [1984a], Titman and Trueman
[1986], Hughes [1986], Feltham et al. [1988], Krinsky and Rotenberg
[1989], and Gale and Stiglitz [1989].











first day of public trading and earn a one day return (before

transactions costs) in excess of 15%.2 Thus, conclusive empirical

evidence exists concerning the short-term underpricing of initial offers

by underwriters. Table 2-1 summarizes the results of a number of

studies in this area.


Proposed Explanations of Short-Term Underpricing


Various analytical models have been proposed as explanations of

the short-term IPO underpricing phenomenon. Rock's [1986] model is

based on an asymmetry of information between "informed" and "uninformed"

investors. The informed investors have purchased perfect information

concerning the true value of an IPO. A major result of Rock's analysis

is that underpricing is required to entice the uninformed investors to

enter the IPO market. In the model, the informationally advantaged

investors only submit purchase orders for IPOs which are underpriced

(i.e., those in which the IPO price is less than the known true price).

By contrast, uninformed investors are unable to differentiate

underpriced and overpriced IPOs and must either not participate in the

IPO market or submit purchase orders for both underpriced and overpriced

issues. Thus, assuming uninformed demand is sufficient to fully

subscribe a new issue, uninformed investors are allocated 100% of the

shares in an overpriced issue and only a fraction of the shares in an

(oversubscribed) underpriced issue. This rationing of underpriced



2 It is important to note that on average IPOs are underpriced and
that any one particular initial offer could be overpriced. In fact,
evidence in Tinic [1988] and Miller and Reilly [1987] indicates that
approximately 30-40% of IPOs are not underpriced.

















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issues between informed and uninformed investors forms the basis of the

underpricing phenomenon in Rock's model. Beatty and Ritter [1986]

summarize the model's major result as follows:

Consequently, if an uninformed investor is allocated shares
in an initial public offering, there is a greater than usual
chance that the issue will start trading at a discount in
the aftermarket. In other words, for an uninformed
investor, the expected return conditional upon being
allocated shares is less than the expected return
conditional upon submitting a purchase order. But an
uninformed investor will participate in the market only if
the expected return conditional upon being allocated shares
is non-negative. This can only happen if, on average,
issuers underprice their shares. The owners of a firm going
public, who typically have a large proportion of their
wealth invested in the firm, would be willing to pay this
price if they are sufficiently risk-averse. (Beatty and
Ritter [1986, p. 228])

Beatty and Ritter's [1986] extension of Rock's model indicates

that the greater the ex ante uncertainty (i.e., uncertainty about the

true value of the issue), the greater the informed investors'

informational advantage, and thus the greater the level of expected

underpricing required to ensure uninformed investors' participation in

the offering. Thus, Beatty and Ritter conclude that ex ante uncertainty

and expected underpricing are positively related. This positive

association between ex ante uncertainty and underpricing has been

empirically verified by Beatty and Ritter [1986], Ritter [1984b], and

Miller and Reilly [1987].

Baron [1982] also attempts to explain the average short-term

underpricing of IPOs via asymmetric information arguments. In his model

the underwriter possesses an informational advantage over the IPO issuer

regarding investor demand for the upcoming security issue. In an

analytical principal-agent framework, Baron demonstrates that the











optimal contract between the issuer (i.e., the principal) and the

underwriter (i.e., the agent) involves the delegation of the offer price

decision to the better informed underwriter. This is in addition to the

underwriter's role as distributor of the issue. A major result of the

model is that the issuer will be willing to accept a lower offer price

decision from the underwriter (and hence greater underpricing) as the

issuer's uncertainty surrounding investor demand for the upcoming issue

increases. Thus, Baron's model also implies a positive association

between underpricing and uncertainty.

Tinic [1988] proposes an "implicit insurance" explanation of

underpricing. He summarizes the implicit insurance hypothesis as

follows:

Stated briefly, I demonstrate that gross underpricing serves
as an efficient form of protection against legal liabilities
and the associated damages to the reputations of both the
investment bankers and the issuers. In other words, it is a
form of implicit insurance against potential liabilities
that may arise from the "due-diligence" and disclosure
requirements of the federal securities regulations.
Although avenues for legal recompense against misinformation
and fraud existed before the Securities Act of 1933, the
Act, in effect, replaced the principle of caveat emptor with
the dictum: "Let the issuer and the investment banker
beware." (Tinic [1988, p. 790])

Even though Tinic's short-term underpricing explanation is not based on

informational asymmetries, his model is consistent with Beatty and

Ritter's [1986] conclusion that underpricing is positively associated

with ex ante uncertainty.

A recent line of research proposes that short-term underpricing

can be utilized as a signaling mechanism by entrepreneurs of high

quality IPO firms. In Welch's [1989] model, high quality firms











underprice their initial offering in order to obtain a higher price in

subsequent seasoned security offerings. A separating equilibrium

results due to the prohibitive costs which must be borne by lesser

quality firms attempting to imitate the underpricing signal of higher

quality firms. Thus, Welch's model is consistent with the hypothesis

that the purpose of underpricing is to "leave a good taste in investors'

mouths" (Ibbotson [1975, p. 264]).

Grinblatt and Hwang [1989] formulate a two signal model in which

underpricing and the percentage of ownership retained by the

entrepreneur serve as signals of the IPO firm's unobservable "intrinsic"

value and cash flow variance. Since underpricing signals the

"intrinsic" value of the IPO firm to investors, this model is also

consistent with the hypothesis that the underpricing of the initial

offering can be utilized by high quality firms in order to receive a

higher price in subsequent security offerings.

The prior paragraphs describe numerous proposed explanations for

the short-term underpricing phenomenon. However, it appears that

Ibbotson et al. [1988] are correct in their assessment of the current

state of underpricing research. They state: "A number of hypotheses

have been offered to explain the underpricing, but to date there is

still no persuasive, widely accepted, and test-supported explanation of

IPO underpricing" (Ibbotson et al. [1988, p. 37]).


Accounting Research Related to Short-Term Underpricing


Accounting research related to short-term IPO underpricing

examines the impact of auditor reputation on initial underpricing.












Beatty [1989a] and Balvers et al. [1988] hypothesize that auditor

reputation and initial underpricing are inversely related. Beatty

utilizes the major result of Beatty and Ritter [1986] (i.e., expected

underpricing is positively associated with ex ante uncertainty) in the

formulation of this hypothesis. Specifically, since the attestation of

an IPO's financial statements by a high reputation auditor serves to

reduce investors' ex ante uncertainty, then auditor reputation is

hypothesized to be inversely related to underpricing. Balvers et al.

extend the models of Rock [1986] and Beatty and Ritter [1986] by

explicitly considering the relationship between an IPO's auditor and

underwriter. In the model, high auditor reputation reduces investors'

uncertainty regarding an IPO's earnings and serves as a positive signal

of the reputation of the underwriter. Thus, auditor reputation is

inversely related to ex ante uncertainty, and therefore is hypothesized

to be inversely related to underpricing. Empirical support of this

hypothesized relationship between auditor reputation and short-term

underpricing is documented by Beatty [1989a, 1989b] and Balvers et al.

[1988].3 Thus, the results of prior research seem to suggest that an

entrepreneur of a firm contemplating an initial offering may be able to











3 However, Balvers et al. [1988] conclude that the impact of
auditor reputation on underpricing is reduced when a high reputation
underwriter is also involved in the registration process. Thus, auditor
and investment banker reputation appear to be (at least partial)
substitutes.












influence the short-term underpricing of the issue by his auditor

choice.4


Long-Term Mispricing of IPOs


Summary Results


As indicated in Chapter 1, long-term mispricing refers to the

accuracy of the market's initial valuation of an IPO. The accuracy of

this potentially "uninformed" first aftermarket price is assessed in

relation to security prices which prevail after additional information

about the firm is revealed, via examination of abnormal aftermarket

returns. In contrast to the extensive literature on short-term IPO

mispricing, little prior research exists on the long-term mispricing of

initial security offerings. Also, the evidence to date is not

conclusive as to whether, on average, market participants overvalue,

undervalue, or correctly value initial offerings of securities. Table

2-2 summarizes the results of prior examinations of long-term IPO

mispricing.

With the exception of Ritter [1989], cross-sectional explanations

of long-term mispricing are unavailable. Ritter documents considerable

variation in the level of long-term mispricing across industries and




'4 The impact of other accounting choices on initial underpricing
has not been examined. For example, prior research has not explored the
effect of the accounting procedures chosen by IPO firms or the amount
and content of accounting information disclosed on short-term
underpricing. Even though this dissertation examines the effect of
accounting numbers on long-term IPO mispricing, some exploratory results
of the impact of certain accounting choices on short-term underpricing
are provided in Appendix A.


















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year of IPO issuance. This dissertation contributes to the literature

by providing explanations of long-term IPO mispricing based on cross-

sectional differences in accounting quality.


Research on the Long-Term Aftermarket Performance of IPOs


Reilly and Hatfield hypothesize that underwriters' short-term

underpricing of IPOs continues into the long-term "as the market

continues to recognize and adjust for the underpricing" (Reilly and

Hatfield [1969, p. 74]). Thus, they argue that the market for initial

security offerings is inefficient in that the market initially

undervalues IPO firms, on average. They calculate short-term mispricing

as the percentage change from the IPO price to the prevailing price one

week (or month) following the IPO. Long-term performance is defined as

the return from one week (or month) following the IPO to one year

subsequent to the commencement of public trading. Reilly and Hatfield

do not empirically assess the average abnormal aftermarket returns of

their entire sample. Rather, they demonstrate that the subset of firms

which outperformed the market in the short-term also had above-market

long-term returns. Thus, Reilly and Hatfield's results do not provide

an indication of the average long-term mispricing of IPOs; however, the

results may indicate that short-term and long-term mispricing are

correlated.

McDonald and Fisher hypothesize that in an efficient market

"subsequent price behavior of the stock should be independent of the

initial rate of return at offering" (McDonald and Fisher [1972, p. 97]).

They calculate abnormal returns by subtracting the return on the market











portfolio from an IPO's return. Thus, this adjustment for overall

market performance does not vary across securities (i.e., the Capital

Asset Pricing Model (CAPM) beta parameter is assumed to equal one for

all firms). The authors' results support their contention that long-

term mispricing is independent of the level of short-term mispricing.

However, average abnormal returns of -18.1% are documented from one week

after the offering to one year after the offering. This negative

abnormal return implies that investors were overly optimistic in their

initial valuations of IPO firms, and therefore lost 18.1% of their

investment in the first year of public trading due to such incorrect

valuations. Thus, McDonald and Fisher's results indicate that the

market overvalued, on average, IPOs issued in the first quarter of 1969.

Ibbotson [1975] examines the abnormal aftermarket performance of a

sample of firms which went public between 1960 and 1969. Abnormal

returns are calculated over various holding periods up to 60 months

subsequent to the IPO by means of the two-parameter Sharpe [1964] and

Lintner [1965] CAPM. The empirical results demonstrate very few

statistically significant abnormal returns in the aftermarket. Thus,

Ibbotson concludes that IPO aftermarket returns do not deviate from

market efficiency. In other words, Ibbotson's results are consistent

with the hypothesis that the market utilizes all available information

in an unbiased fashion and hence, on average, accurately values new

issues.

The most extensive examination of the aftermarket performance of

IPOs is provided by Ritter [1989]. He explores the long-term mispricing

of 1,526 firms that made initial offerings between 1975 and 1984.











Monthly abnormal returns are calculated by subtracting the return on a

market index from an IPO's raw return for each of the first 36 months

following the IPO. Thus, market-adjusted rather than risk-adjusted

abnormal returns are computed. Ritter documents average abnormal

returns of -24.33% over the first three years of public trading. Thus,

the results are consistent with the hypothesis that the market

overvalued IPOs in the 1975-1984 period. However, considerable

variation in the level of this overvaluation across year of issuance and

industries is documented. In particular, Ritter states that the

overvaluation is "concentrated among relatively young growth companies,

especially those going public in the 1980s" (Ritter [1989, p. 3]).


Comparison of the Long-Term Mispricing of New Issues Before
and After the 1933 Securities Act


Stigler [1964] and Jarrell [1981] compare the long-term mispricing

of new security issues5 offered before and after the 1933 Securities Act

(the Act). Both researchers test the hypothesis that the period prior

to the disclosure provisions of the Act was characterized by exaggerated

claims of issuers and underwriters, resulting in overvaluation of new

issues by investors. Specifically, both Stigler and Jarrell examine

whether the disclosure requirements of the 1933 Securities Act resulted

in increased investor returns for post-1933 new issues as compared to

the returns earned on potentially overvalued pre-1933 issues.




5 These papers examine the aftermarket abnormal returns on all new
issues of securities. In other words, both IPOs and new security
offerings of existing publicly held corporations (i.e., seasoned issues)
are examined.











Stigler [1964] reports negative average market-adjusted returns

for both pre-1933 and post-1933 new security issues over each of the

first five years after issuance. Thus, Stigler concludes that the

disclosure requirements of the Act did not result in increased returns

for investors in the post-1933 era. Stigler controls for market

influences by comparing aftermarket prices to the value of a market

index. The pre-SEC sample is taken from the years 1923-1928, while

1949-1955 is utilized as the post-SEC period.

Jarrell [1981] also rejects the hypothesis that average

aftermarket performance improved significantly after passage of the Act.

He calculates abnormal returns by means of the two-factor CAPM. His

pre-SEC sample covers the years 1926-1933, while the post-SEC period

runs from 1934-1939. Jarrell's results indicate positive mean risk-

adjusted returns for both groups over a five year holding period.

Contrary to the hypothesis tested however, the cumulative returns for

the pre-SEC sample exceeded those of the post-SEC sample.

Simon [1989] also examines the aftermarket returns of pre-SEC

versus post-SEC new security issues. However, she hypothesizes that the

disclosure provisions of the 1933 Securities Act should have no effect

on the average abnormal returns earned by investors. In other words,

she postulates that rational investors were able to accurately value new

issues, on average, prior to the Act's increased disclosure requirements

since "the existence of substantial uncertainty about the true value of

a security need not imply that the issue will be, on average, overvalued

or undervalued" (Simon [1989, p. 295]). However, Simon theorizes that

the lack of quality information about the value of a firm undertaking a









22

new security issue which characterized the pre-SEC period increased the

level of riskiness associated with the purchase of the security. Thus,

she hypothesizes that the variance of aftermarket abnormal returns

decreased after the passage of the 1933 Securities Act.

Simon utilizes a multifactor asset pricing model in the

determination of risk-adjusted abnormal returns. In addition to the

return on the market, the model includes the industry return, the

unexpected variance of the market return, and a business cycle indicator

as explanators of equilibrium security returns. She classifies new

issues from 1926-1933 as pre-SEC issues, while 1934-1940 offerings are

deemed post-SEC issues. Contrary to expectations, Simon documents

significantly negative average abnormal returns for non-NYSE initial

public offerings in the pre-SEC period. Thus, it appears that investors

overvalued, on average, IPOs of non-NYSE firms prior to the passage of

the 1933 Securities Act. However, results consistent with the

hypothesis that average abnormal returns would not differ significantly

from zero are documented for NYSE issues and seasoned non-NYSE issues in

both the pre- and post-SEC periods. Further, in contrast to Stigler

[1964] and Jarrell [1981], Simon demonstrates a statistically

significant increase in average aftermarket abnormal returns for non-

NYSE initial public offerings from the pre-SEC to post-SEC periods. No

such increase was present for either NYSE issues or non-NYSE issues of

currently publicly traded corporations. Finally, the hypothesis that

the variance of aftermarket returns significantly decreased following

the disclosure requirements of the Act is empirically supported for each

portfolio of new security offerings.









23

Simon's study represents an intervention analysis of a time-series

nature (i.e., differences between the pre- and post-SEC periods are

examined). In contrast, the current study deals with information

variables from a cross-sectional perspective.


Summary of Long-term Mispricing Research


The results of prior research conflict as to whether, on average,

the market overvalues, undervalues, or accurately values new issues of

securities. In assessing the long-term performance of new issues, prior

researchers have employed diverse methods of calculating abnormal

returns and widely divergent sample years. It is possible that the

contradictory findings are time period specific and/or dependent upon

the asset pricing model utilized in the determination of abnormal

returns. This dissertation extends prior research by (1) reexamining

the long-term mispricing of initial public offerings by utilizing a

refined model of equilibrium security pricing6 on a sample of recent

IPOs7 and (2) testing hypotheses relating to the effect of accounting

numbers on such mispricing.







6 A modified version of Simon's [1989] multifactor asset pricing
model will be implemented in this study. The model will be described in
detail in Chapter 4.

7 In contrast to Stigler [1964], Jarrell [1981], and Simon [1989],
only initial public security offerings will be examined. Thus, new
seasoned issues will be excluded. Also, this study will only examine
the long-term mispricing of IPOs initiated since 1980. With the
exception of Ritter's [1989] 1975-1984 sample period, previous studies
have not utilized post-1970 IPOs.
















CHAPTER 3
HYPOTHESIS DEVELOPMENT


Introduction


The three hypotheses developed and empirically tested in this

dissertation relate to the impact of the quality of accounting

information available to IPO investors on long-term IPO mispricing.

With the aid of three assumptions, this chapter develops these

hypotheses in a Capital Asset Pricing Model (CAPM) framework.1


Assumptions


First, it is assumed that the total uncertainty surrounding an

initial offering arises from two sources. Specifically, assume that an

IPO's firm-specific risk may be decomposed2 as follows:

Firm-specific risk the uncertainty related to the firm's
production, investment, and financing activities + the
uncertainty related to the quality of the accounting numbers
supplied to potential investors. (la)

In mathematical terms, Equation (la) becomes

FSR1 BUSRISKi + ACCTRISKi, (lb)





SThe model utilized in this chapter to derive the hypotheses is an
adaptation of the model found in Jarrell [1981].

2 This partitioning of firm-specific risk is in addition to the
CAPM's systematic (i.e., nondiversifiable) versus unsystematic (i.e.,
diversifiable) decomposition.











where FSRj is firm specific risk, BUSRISKi equals firms i's business

risk, and ACCTRISK1 denotes accounting risk.

The first component of the preceding definition describes the cash

flow uncertainty associated with the firm's production, investment, and

financing activities, and will hereafter be referred to as business

risk. In other words, business risk represents the underlying

uncertainty related to the overall business activities (both operating

and financial) of the firm. The second component in the above

characterization of an IPO's firm-specific risk (hereafter referred to

as accounting risk) reflects the influence of accounting quality on IPO

firm uncertainty. According to Equation (Ib), the total level of

uncertainty surrounding an IPO equals the firm's business risk (i.e.,

the lower bound) only when there is no uncertainty surrounding the

quality of the accounting numbers supplied to potential investors.

However, when uncertainty surrounding accounting reliability exists, the

total uncertainty surrounding the security issue exceeds the IPO's

business risk, and increases as accounting risk increases.3




3 Equation (Ib) characterizes the total risk of an initial offering
as a linear and additive function of business risk and accounting risk.
Thus, the equation assumes the independence of the two concepts. This
assumption appears to be reasonable, since there is no a priori reason
to postulate the absence of any of the four possible combinations of
business and accounting risk. Specifically, one could reasonably expect
to find IPO firms in each of the following categories: (1) high
business risk and a high level of accounting reliability and accuracy,
(2) high business risk and a low level of accounting reliability, (3)
low business risk and a high level of accounting reliability, and (4)
low business risk and a low level of accounting reliability. Thus, it
seems reasonable to assume that the level of uncertainty surrounding an
IPO firm's production, investment, and financing activities (i.e., its
inherent cash flow uncertainty) is independent of the precision of its
accounting valuations.












Second, it is assumed that the two-factor Sharpe-Lintner CAPM

appropriately describes equilibrium security valuation.4 The model

characterizes the expected return on a security as a linear function of

the expected market return. The model (in expectation form) may be

written

E(Rit) RFt + Pi(E(RMt) RFt), (2)

where E the expectation operator, Rit the security return on IPO

firm i over time period t, RFt the risk-free rate for time t, Rmt the

time t return on the market portfolio, and i firm i's systematic

risk.

The CAPM is often utilized in its realized form for empirical

purposes. In realized form, the model is

Rit RFt + Pi(RMt RFt) + eit, (3)

where eit is an error term and all other terms are as previously

defined.

Systematic risk refers to that portion of firm-specific risk which

cannot be diversified away. In other words, Oi reflects the riskiness

of security i in relation to market-wide risk, and is calculated as

i Cov(Rit, RMt) / Var(Rmt). (4)

According to the CAPM, the remaining portion of firm-specific risk is

labelled unsystematic risk. Unsystematic risk is calculated as the

variance of the error terms in the realized formulation of the model




4 The assumption that the CAPM accurately reflects equilibrium
security valuation is implemented in this section for expositional
purposes only. In fact, in the empirical analysis to follow, a
multifactor model is implemented in an attempt to overcome certain
deficiencies of the CAPM.











(i.e., Var(eit)). In a CAPM framework, unsystematic risk does not

affect the expected return on a security since its effects may be

diversified away by holding a well-diversified portfolio.

Finally, this study makes use of the assumption that investors are

rational. In other words, this rational expectations assumption implies

that market participants form unbiased estimations of (1) an IPO's

systematic risk and (2) the future payouts arising from IPO ownership.5


Decomposition of Systematic and Unsystematic Risk into
Accounting and Business Risk Components


The CAPM partitions firm-specific risk into systematic and

unsystematic components. This section further decomposes the CAPM's

firm-specific risk components into the business risk and accounting risk

partition of Equation (Ib).

Specifically, i may be defined as

Pi = BiB + piA, (5)

where PiB systematic risk attributable to firm i's production,

investment, and financing activities, and piA = systematic risk

attributable to uncertainty surrounding the quality of the accounting

numbers supplied to investors.

The systematic risk attributable to accounting risk is modelled as

follows:

^- _i^(TQ), (6)






SThese expected future payouts can be thought of as either
dividend distributions or the firm's cash flows.











where T = time subsequent to the IPO and Q the quality of accounting

information disclosed to investors at the time of the IPO. It is

assumed that systematic accounting risk is a decreasing function of both

time and accounting quality at the IPO date. Specifically,

apiA/aT < 0 and (7a)

afiA/aQ < 0. (7b)

In other words, subsequent to the IPO, systematic accounting risk

decreases over time as more information is revealed about the firm.6

Also, the disclosure of a sufficient quantity of reliable accounting

information at the IPO date reduces systematic accounting risk.

Similarly, unsystematic risk may be divided into business and

accounting risk components as follows:

Var(ei) Var(ejB) + Var(eiA), (8)

where Var(ej) = firm i's unsystematic risk which is calculated as the

variance of the error terms in the realized formulation of the CAPM, and

Var(e B) and Var(eiA) equal the business risk and accounting risk

components of unsystematic risk, respectively. As was the case for

systematic accounting risk, unsystematic accounting risk is modelled as

a decreasing function of time subsequent to the IPO as well as

accounting quality at the IPO date. Specifically,

Var(e A) = Var(eiA)(T,Q), (9)





6 This additional information revealed in the periods following the
IPO may consist of both accounting (e.g., quarterly and/or yearly
financial statements or analysts' forecasts of earnings) and
nonaccounting (e.g., security prices or Wall Street Journal articles)
information. This additional information will reduce accounting risk
only if it is deemed reliable.










29

where aVar(eiA)/aT < 0 and (10a)

aVar(eiA)/aQ < 0. (10b)


Hypotheses


Overview


This section demonstrates the effects of changes in accounting

risk on the distribution of investors' aftermarket abnormal returns

(i.e., long-term mispricing). First, the effect of changes in

accounting risk on mean abnormal returns is explored. Next, accounting

risk's effects on the dispersion (variance) of abnormal risk-adjusted

returns is examined. Finally, the impact over time of accounting risk

on the variance of abnormal returns is portrayed.


Accounting Risk and Mean Abnormal Returns


An increase in accounting risk (ACCTRISK) leads to an increase in

both the systematic and unsystematic components of accounting risk.

According to Equations (5) and (8), such increases in iA and Var(eiA)

result in increases in both systematic and unsystematic risk. It is

evident from Equation (2) that such an increase in unsystematic risk has

no effect upon expected mean returns in a CAPM framework. The effects

of increases in unsystematic risk may be eliminated in a well-

diversified portfolio and thus do not affect mean expected security

returns.

In contrast, it is apparent from Equation (2) that an increase in

systematic risk increases investors' expected raw (i.e., unadjusted)











returns. However, assuming that investors are rational, it is readily

seen that an increase in systematic accounting risk should not cause

risk-adjusted abnormal returns to deviate from zero since the increased

raw returns demanded by investors merely compensate them for bearing

greater risks. The rational expectations assumption implies that

investors are able to form unbiased estimates of an IPO's future payouts

even in the presence of considerable uncertainty surrounding the value

of the firm (e.g., in instances of high accounting risk). In other

words, on average, rational investors do not overestimate or

underestimate a firm's cash flows due to unreliable and/or incomplete

accounting disclosures. Increased accounting risk merely induces

investors to demand a greater return per dollar of expected future

payout (cash flow) to compensate them for bearing increased risks.

Thus, on a risk-adjusted basis, IPOs are on average correctly valued

owing to the unbiased nature of the estimated value-relevant cash flows.

In other words, rational investors do not misprice IPOs, on average,

even in the presence of a high degree of systematic accounting risk.

Thus, neither unsystematic nor systematic accounting risk should

cause mean risk-adjusted aftermarket returns to deviate from zero. This

assertion gives rise to the first hypothesis, which may be formally

stated as follows:

Hypothesis 1: On average, the long-term mispricing of IPO
firms is zero regardless of the level of accounting risk
faced by investors.











Accounting Risk and the Variance of Abnormal Returns


The rational expectations assumption implies that even though

investors' estimates of future payouts are unbiased even in the presence

of high accounting risk, the precision (variance) of these estimates

decreases (increases) with accounting risk. This increase in the

variability of cash flow estimates should translate into greater

variability in returns. In fact, just such an increase in variability

can be demonstrated in a CAPM framework.

The variance of a security's returns according to the CAPM (in

realized form) is

Var(Ri) Oi2Var(RM) + Var(ei), (11)

where Var(Ri) the variance of security i's returns, Var(RP) the

variance of the return on the market portfolio, and the remaining terms

are as previously defined.

As previously noted, Equations (5) and (8) demonstrate that an

increase in accounting risk results in an increase in both systematic

(i.e., Pi) and unsystematic (i.e., Var(ei)) risk. Therefore, it follows

directly from Equation (11) that the variance of a security's returns

increases with accounting risk. The preceding assertion forms the basis

for the second hypothesis, which may be stated as

Hypothesis 2: The variance of IPO firms' abnormal returns
is positively associated with accounting risk.

Thus, it is consistent with rational expectations that the absolute

magnitude (rather than the direction) of IPO mispricing is related to

the level of accounting risk surrounding an issue, even though IPOs are

not systematically mispriced.











Accounting Risk and the Variance Over Time of Abnormal Returns


Equations (7a) and (10a) model systematic and unsystematic

accounting risk, respectively, as decreasing functions of time

subsequent to the IPO. Thus, this decrease in fliA and Var(ejA) over time

results in a reduction in both systematic (i.e., 1i) and unsystematic

(i.e., Var(ei)) risk. It then follows directly from Equation (11) that

such decreases in systematic and unsystematic risk result in an IPO

firm's abnormal return variance decreasing over time. The preceding

implication of the model gives rise to the third hypothesis, which may

be stated as follows:

Hypothesis 3: The variance of IPO firms' abnormal returns
decreases over time.

This third hypothesis is consistent with a diminishing effect of

accounting risk on the precision of IPO valuation (i.e., long-term

mispricing) over time as the information disclosed at the time of the

IPO takes on a lesser role in the valuation of the firm as new

information is revealed.7


7 See footnote 6.
















CHAPTER 4
EMPIRICAL METHODOLOGY


Introduction


The empirical methodology implemented in the testing of the three

hypotheses is described in this chapter. First, the three variables

utilized as empirical proxies for the accounting risk theoretical

construct are discussed. The measurement of abnormal risk-adjusted

aftermarket returns (i.e., long-term mispricing) is then illustrated.

Next, both univariate and multivariate tests of the three hypotheses are

detailed. Finally, a summary of the empirical methodology is provided.


Three Measures of Accounting Risk


Overview of the Three Accounting Risk Surrogates


The accounting risk theoretical construct is a multifaceted

concept which is difficult to empirically capture with any one proxy

variable. Thus, this dissertation utilizes three surrogates for

accounting risk, each of which is intended to capture a different aspect

of accounting riskiness. The three unique components of accounting risk

captured by this study's proxy variables are (1) the level of

credibility associated with the reported accounting numbers, (2) the

level of bias inherent in those numbers, and (3) the quantity of

accounting disclosures supplied to potential IPO investors. Auditor











reputation, the degree of conservatism inherent in a firm's inventory

and depreciation accounting procedure choices, and the type of SEC

registration statement filed, respectively, are employed in this study

as empirical surrogates for these three aspects of accounting risk. The

remainder of this section provides detailed descriptions of the three

accounting risk proxy variables.


Auditor Reputation (Quality) Proxy


The reputation (or quality) of the auditor utilized in the initial

public offering process serves as the first proxy for accounting risk.

The primary function of the auditor in the IPO process is to express an

opinion on the IPO firm's financial statements. Therefore, it is

reasonable to assume that a high quality audit lends credibility to the

financial statements, thereby reducing accounting risk. Conversely, a

low quality audit adds little credibility to the issuer's financial

statements, and thus investors' level of uncertainty surrounding the

reliability and accuracy of accounting numbers will be correspondingly

greater. Thus, audit quality is a surrogate for the level of

credibility associated with reported accounting numbers, which is an

aspect of accounting riskiness.

An IPO firm's accounting risk (based on auditor quality) is

measured in terms of whether the accounting numbers supplied to

potential investors at the time of the initial offer were audited by a









35

Big Eight or non-Big Eight auditing firm.' DeAngelo [1981] models audit

quality as a positive function of auditing firm size. She argues that

this relationship results from the greater level of independence which

is employed by large audit firms in an effort to maintain their

reputation capital. In an IPO context, this presumed higher quality of

large audit firms might also be due to larger firms' greater expertise

in industry-related accounting matters, greater experience in auditing

developmental stage companies, stricter internal control requirements,

etc. If audit quality is indeed positively associated with audit firm

size, accounting risk should be inversely related to auditor size. In

other words, the preceding arguments imply that Big Eight audited IPO

firms should possess less accounting risk than IPOs audited by non-Big

Eight firms.

However, a number of arguments against this presumed positive

association between audit quality and audit firm size can be raised.

First, DeAngelo's [1981] analytical model which predicts this

relationship has not been empirically verified. A second argument

against DeAngelo's hypothesized relationship is that individual audit




1 Non-Big Eight firms consist of both "national" (i.e., the mid-
sized firms with offices throughout the country) and local/regional
auditing firms. Thus, it is possible to classify auditing firms into
three categories, rather than the traditional Big Eight, non-Big Eight
dichotomy. For example, Simunic and Stein [1986] classify auditing
firms into three categories (i.e., Big Eight, "national," and
local/regional) and then discard the middle group in order to increase
the power of the test of auditor reputation on IPO valuation. Due to
the small number of non-Big Eight audited IPO firms in the sample
analyzed in this dissertation (see Chapter 5), a further breakdown of
this group into "national" and local/regional categories does not appear
to be a viable alternative in this study. Therefore, the traditional
Big Eight, non-Big Eight dichotomy is employed.












engagement partners in large decentralized auditing firms may have the

same incentives in regards to compromising their independence as

partners in small local/regional firms. In each case, a particular

audit client may represent a significant proportion of the revenue

generated by a partner. Therefore, audit engagement partners in large

decentralized firms may not consider the ramifications of certain

actions on the firm as a whole. Thus, the predictions of DeAngelo's

model may not hold in large decentralized audit firms. Finally, it is

possible to dispute the argument that large audit firms have greater

expertise in industry-related accounting matters. Specifically,

regional auditing firms may specialize in the predominant industry in

that region. For example, a regional auditing firm in Texas may have

greater expertise in the oil and gas industry than a large national firm

headquartered in New York. The preceding arguments against the presumed

positive association between audit quality and audit firm size imply

that the Big Eight, non-Big Eight dichotomy employed in this study may

not accurately proxy for accounting risk.


Accounting Procedure Choice Proxy


The second accounting risk surrogate utilized in this study is

measured according to whether the accounting numbers provided to

potential IPO investors were calculated by utilizing income decreasing

(i.e., "conservative") or income increasing (i.e., "liberal") accounting

methods. This proxy is utilized in recognition of the potential of a

firm's managers (or entrepreneurs in the IPO context) to manipulate











reported earnings via certain accounting procedure choices.2 An

entrepreneur of a firm about to undertake an initial public security

offering might be motivated to manipulate accounting numbers in order to

send an exaggerated (i.e., positively biased) signal to potential

investors concerning the earnings history (and potential) of his firm.

By definition, accounting risk (i.e., the uncertainty surrounding the

reliability and accuracy of accounting numbers) increases as the level

of managerial manipulation of accounting numbers increases. Thus, an

estimate of the degree of such manipulation is employed as a proxy for

accounting risk. This surrogate is intended to capture the component of

accounting risk which relates to the level of bias inherent in reported

accounting numbers.

Imhoff and Thomas [1989] provide empirical support for the

utilization of this conservative versus liberal accounting procedure

choice dichotomy as a proxy for accounting quality. They demonstrate a

statistically significant positive association between the ratings of

financial analysts regarding the accounting quality of firms in the

particular industry in which each analyst specializes and the degree of

conservatism of the accounting methods employed by these firms. Thus,

Imhoff and Thomas [1989] provide evidence that financial analysts view









2 Watts and Zimmerman [1986] review the literature (both
theoretical and empirical) which suggests that managerial manipulation
of earnings is related to the existence of political costs, debt
covenants, and bonus plans denominated in terms of accounting numbers.











firms which utilize conservative accounting methods as possessing higher

accounting quality than firms which utilize more liberal methods.3

In this study, the liberal versus conservative accounting

procedure choice proxy is measured based on whether an IPO firm's chosen

inventory and depreciation methods4 tend to increase or decrease

reported accounting earnings. Specifically, conservative accounting

choices are defined as the last-in, first-out (LIFO) inventory valuation





3 There are other possible explanations of this perceived
relationship besides the managerial manipulation of earnings explanation
utilized in this study. For example, it could be argued that accounting
risk is less when the "conservative" last-in, first-out (LIFO) inventory
valuation method is used in comparison to the more "liberal" first-in,
first-out (FIFO) method. Specifically, the LIFO method tends to smooth
reported income numbers in periods of rapidly changing prices, both
increasing and decreasing. These less volatile income numbers may be
perceived as being of higher quality than the more variable FIFO-based
numbers. Also, LIFO users often report in their financial statements
what income would have been under the FIFO method. However, the
opposite does not hold since FIFO users typically do not report LIFO-
based net income. Therefore, it is possible that LIFO-based numbers may
be considered co be of higher quality since more information is
typically available under LIFO than under FIFO.

4 Other accounting procedure choices have been examined in the
accounting literature. For example, the methods employed to account for
the investment tax credit (ITC) and oil and gas exploration costs have
been widely examined (see Watts and Zimmerman [1986] for a review and
Imhoff and Thomas [1989] for a recent example). These accounting
choices were also originally planned to be included in this study's
definition of conservative versus liberal accounting procedure choices.
However, only 2 out of the 372 sample firms used a procedure other than
the flow through ITC method and oil and gas exploration firms comprise
only 5% of the sample firms. Hence, these two accounting procedure
choices appear to be of little use in the current sample in classifying
firms into conservative versus liberal accounting procedure choice
groups. Thus, the level of conservatism of a firm's accounting
procedure choices is measured based only upon inventory and depreciation
method choices. The inability to examine other than inventory and
depreciation choices is considered to be only a minor limitation since
depreciation and inventory represent the most important (in dollar
terms) accounting procedure choices for most firms.











procedure and the utilization of any of a number of accelerated

depreciation methods (e.g., double-declining balance or sum-of-the-

years'-digits). Liberal (i.e., income increasing) choices are defined

as any inventory valuation method other than LIFO (e.g., first-in,

first-out (FIFO), weighted average, or specific identification)5 and the

straight-line depreciation method.

This accounting procedure choice proxy variable is measured

dichotomously. The current paragraph provides an overview of the

classification scheme utilized in the grouping of sample IPO firms into

high and low accounting risk portfolios based on accounting procedure

choices, while the following paragraph details the specifics of the

classification scheme. An IPO firm is considered to possess high

accounting risk if each of its available accounting method choices is

income increasing (i.e., "liberal"). Conversely, an IPO is considered

to be a low accounting risk firm if at least one of the two relevant

accounting procedure choices is income decreasing.

This accounting procedure choice variable was originally measured

over three levels. Specifically, firms were classified into accounting

risk portfolios based on whether their inventory and depreciation

choices were (1) consistently income increasing, (2) consistently income

decreasing, or (3) a combination of income increasing and income

decreasing methods. However, in the sample analyzed in this study (see

Chapter 5), only 10 out of the 372 sample firms used income decreasing




5 Consistent with current accounting practices, in the sample of
IPOs examined in this study (see Chapter 5), the vast majority of the
non-LIFO inventory choices are FIFO choices.











(i.e., conservative) methods for both inventory and depreciation.

Therefore, this consistently "conservative" group was combined with the

group whose accounting procedure choices consisted of a combination of

income increasing and income decreasing methods to form the low

accounting risk portfolio in the dichotomous classification scheme.

Further, a number of the sample firms did not have both inventories and

long-term depreciable assets. In cases where a firm possessed no

inventory (long-term depreciable assets), the measurement of this

accounting procedure choice variable was based strictly upon the firm's

depreciation (inventory valuation) choice.

In summary, the high accounting risk firms either (1) utilized

income increasing methods for both inventories and long-term depreciable

assets or (2) employed an income increasing method for the only

applicable accounting choice. Conversely, the low accounting risk firms

either (1) utilized income decreasing methods for both accounting

choices, (2) used a combination of income increasing and income

decreasing methods, or (3) employed an income decreasing method for the

only applicable accounting choice.6















6 Various methods of classifying firms into accounting risk groups
based on the firms' accounting procedure choices were employed in the
empirical analysis, with no qualitative differences in results.











SEC Registration Form Proxy


The final accounting risk surrogate employed in this dissertation

is whether the registration statement7 filed with the SEC was on Form

S-18 or Form S-1. Form S-18 offerings were instituted by the SEC in

April 1979. Prior to that time all initial offerings (with the

exception of unregistered Regulation A offerings which had a limit on

the amount of proceeds raised of $1.5 million) were required to be

registered on Form S-1. The limit on the amount of capital raised with

an S-18 offering is $7.5 million.8

Important differences exist in the disclosure requirements in

these two types of registration statements. Form S-1 registrations are

more comprehensive both in terms of the number of years of data required

and in the extent of data required per year. Specifically, Form S-18

allows the issuer to present two years of audited financial statements,

rather than the three required in an S-1 offering. Further, "Form S-18

relaxes or eliminates the disclosure requirements for several areas,

including property, segment data, foreign operations, order backlogs,

and research and development" (Manegold and Arnold [1986 p. 29]). The

elimination of these potentially valuable accounting disclosures in an




7 Before securities may be sold to investors in a public offering,
the SEC must approve an issuing firm's registration statement. The
registration statement contains information about the proposed offering,
as well as information about the issuing firm itself (e.g., audited
financial statements). Similar information about the offering and the
issuer is provided to potential investors in a prospectus.

8 For a detailed account of the differences between the two types
of registrations, please see Manegold [1986] or Manegold and Arnold
[1986].












S-18 offering should lead to an increase in accounting risk, and hence

the type of SEC registration statement filed comprises the third proxy

for accounting risk. This third surrogate is intended to capture the

quantity (i.e., extent of disclosure) aspect of the accounting risk

theoretical construct.


Measurement of Risk-Adjusted Abnormal Security Returns


In order to test Hypotheses 1 through 3, aftermarket abnormal

security returns (i.e., long-term mispricing) must be calculated. A

modified version of Simon's [1989] multifactor security pricing model is

employed in the measurement of such excess returns.9 The model utilized

is

Rit RFt i(RMt RFt) + 6i(INDUSTRYit RMt RFt)
3
+ EjiDjt + Eit, (12)


where Rit -the return on firm i in time period t, RFt = the return on

riskless assets over time t, RM = the time t return on the market

portfolio, INDUSTRYit = the equally-weighted average return of firms in

the same industry (based on 2-digit SIC codes) as firm i during time

period t, Djt are time period specific dummy variables which are

designed to capture abnormal returns, 6j, 6j, and 8ji are regression

parameters, and eit is an error term.






SSimon's model includes factors relating to business cycle
activity and the unanticipated portion of the variance in market returns
in addition to the variables employed in this study in the specification
of the equilibrium return generating process.












The Rit terms in Equation (12) represent IPO firm i's raw (i.e.,

unadjusted) returns over month t following the initial offering. These

monthly returns are constructed from the CRSP NASDAQ daily returns file

utilizing a procedure similar to that employed by Ritter [1989]. The

return for month 1 is calculated as the multiplicative return over the

21 trading day period beginning with the day following the IPO.10 Thus,

returns are calculated based on "event time" (with the event in question

being the IPO), rather than calendar time. The monthly returns for

months subsequent to month 1 are then calculated using the returns on

successive 21 trading day periods. In equation form, Rit is calculated

21+21(t-1)
Rit n (1 + Rid) 1, (13)
d=l+21(t-l)

where Rid firm i's raw return on day d where d-1 represents the day

following the IPO, and t denotes the number of months subsequent to the

initial offering.

A monthly market portfolio return series (i.e., Rmt) is

constructed for each IPO firm corresponding to the firm's "event time"

monthly periods. This monthly market return series is calculated in the

same manner as the Rit series, with the exception that the daily returns

utilized in the construction of the monthly returns are the returns on

the NASDAQ Composite Index, rather than an individual IPO firm's raw

returns. Since each of the sample firms examined in this study is

traded over-the-counter, the NASDAQ Composite Index appears to be a more



10 It is important to note that the return on the first day of
public trading is the commonly examined one-day underpricing return.
Therefore, the empirically documented short-term underpricing phenomenon
is not included in the long-term returns examined here.











appropriate measure of the "market" return than either the CRSP Value-

Weighted or CRSP Equally-Weighted Indices (both of which consist of New

York and American Stock Exchange firms) which are often utilized in

capital markets research.

The INDUSTRYit terms present in Equation (12) represent the

average equally-weighted return of all firms on the NASDAQ daily returns

file possessing the same 2-digit SIC code as IPO firm i. Again, for

each sample IPO firm, this INDUSTRYit series is calculated over the same

"event time" months as the Ri series.

The RFt terms in Equation (12) represent the prevailing "risk-free

rate" at time t. The proxy employed for the unobservable true "risk-

free rate" is the return on United States Treasury Bills. The monthly

total return series on U.S. Treasury Bills from January 1980 through

December 1988 was collected from Ibbotson Associates [1989]. Then this

monthly risk-free rate series was aligned with the Rit series in order

to create the RFt series for each IPO firm.

Equation (12) is a generalization of the two-factor CAPM utilized

in the derivation of Hypotheses 1 through 3 in Chapter 3. There are two

principal reasons the multifactor specification of security pricing

found in Equation (12) is employed in the empirical testing rather than

the traditionally utilized two-factor CAPM or single-factor market

model. First, prior research suggests that factors other than the

return on the market portfolio are needed to explain equilibrium

security returns.11 In this study, industry returns as well as overall



11 For example, see Ross [1976] for a theoretical development of
the multifactor Arbitrage Pricing Theory (APT).









45

market returns are employed in the specification of equilibrium security

returns.

Industry returns are included in Equation (12) to control for

abnormal return performance arising from business risk changes over

time. Assuming firms in the same industry face similar production,

investment, and financing environments, then INDUSTRYij represents an

appropriate proxy for business risk and 65 reflects return performance

attributable to changing business risk conditions. Thus, Equation (12)

abstracts from both overall market performance and unexpected industry

performance (i.e., business risk) in the generation of firm-specific

abnormal returns (i.e., the 6ji parameters). Since the effects of both

overall market risk and firm-specific business risk are taken away, it

is assumed that the abnormal returns captured by the 6ji terms are

attributable to accounting risk.

The second reason why the traditional CAPM or market model

formulation is not utilized in this study is due to the institutional

features of the IPO market. In traditional capital markets "event"

studies, the CAPM or market model is estimated over a period of time

prior to the "event" in question. Then the estimated parameters are

utilized in the determination of abnormal returns during the event

period. Such an approach is infeasible for IPOs since return data are

unavailable prior to the event in question (i.e., the initial offer).

Thus, the relevant parameters must be estimated during the event period.

The time period specific dummy variables included in Equation (12) allow

the researcher to pick up abnormal returns over specified periods during












the estimation period.12'13 Specifically, Dlt is coded 1 during the

first 12 months following the IPO and 0 otherwise. Similarly, D2t

equals 1 only for months 13-24, while D3U is coded 1 for months 25-36

and 0 otherwise. Thus, the parameters on the dummy variables (i.e., 911

to 63) represent an IPO firm's abnormal returns over the first, second,

and third year of public trading, respectively.

Equation (12) is estimated for each firm on a time-series basis

over the first 36 months of public trading. Cumulative abnormal

performance over the entire 36 month estimation period may be computed

by simply adding the parameter estimates on the three time period

specific dummy variables. In other words,

3
CAR36i Z ji, (14)
j-l

where CAR361 firm i's 36 month cumulative abnormal return.14 This


12 In a typical event study, the error terms (i.e., the it terms)
represent risk-adjusted abnormal returns. However, when the model's
parameters must be estimated over the event period, the sum of the error
terms is constrained to equal zero by the OLS regression procedure and
thus the errors cannot be utilized as measures of abnormal performance.

13 See Schipper and Thompson [1983] for an example of the
utilization of time period specific dummy variables to measure abnormal
performance surrounding different events.

14 The return generating process described by Equation (12) does
not include an intercept term. Instead, it includes a time period
specific dummy variable for each of the first three years of public
trading. Alternatively, Equation (12) could be estimated by utilizing
an intercept term along with dummy variables relating only to the first
two years of public trading. The model would then be written as
follows:
Rit- RFt ai + i(RMt RFt) + 6i(INDUSTRYt RMt RFt)
2
+ Z jiDjt + Eit,
j=1
where ai is an intercept term and all other terms are as previously
defined. In this alternative specification, ai picks up abnormal











cumulative abnormal return, as well as the individual abnormal returns

pertaining to the first three years of public trading, is utilized in a

cross-sectional format to test Hypothesis 1. Further, the variances of

these abnormal return measures are used to cross-sectionally test

Hypotheses 2 and 3.


Univariate Tests


Univariate Tests of Hypothesis 1


Hypothesis 1 states that market participants do not misprice IPOs,

on average, regardless of the level of accounting risk they face. In

other words, average aftermarket abnormal returns are predicted to equal

zero (i.e., they are unbiased) and are hypothesized not to be influenced

by accounting risk.

Univariate tests of Hypothesis 1 consist of constructing

portfolios of IPOs based on the firms' levels of the three previously

described accounting risk measures and then comparing the average

abnormal returns of each portfolio over various holding periods (1) to

the hypothesized value of zero and (2) to the average abnormal returns




performance over the period of estimation not accounted for by the dummy
variables (i.e., year 3) and the parameter estimates on the two dummy
variables measure differences between the abnormal performance of years
1 and 2, respectively, and year 3. Thus, the abnormal returns relating
to year 1 would be calculated by adding the dummy variable parameter
estimate pertaining to year 1 (i.e., 81i) to aj. The abnormal returns
of year 2 would be calculated in a similar manner. The cumulative 36
month return would then be calculated by adding the measured abnormal
returns over years 1 through 3. See Maddala [1977, Chapter 9] for a
more detailed discussion of the interpretation of the estimated dummy
variable coefficients in instances when an intercept term (1) is and (2)
is not included in the model.











of the remaining portfolios. This procedure will now be described in

detail for the case of the auditor quality accounting risk surrogate.15

Two portfolios of IPO firms are constructed based on the

reputation (quality) of the auditor utilized in the IPO process.

Specifically, portfolios are devised according to whether an IPO firm

was audited by a Big Eight (i.e., low accounting risk) or non-Big Eight

(i.e., high accounting risk) auditing firm.

The mean abnormal return for a given accounting risk portfolio

over a specific time horizon is calculated as

N
MARIt Z ARit / N, (15)
i=l

where MARpt the mean abnormal return of portfolio p over time period

t, ARit firm i's abnormal return over time period t as generated from

Equation (12),16 and N the number of firms in portfolio p. Portfolio

mean returns are calculated for the first, second, and third year of

public trading, as well as a three year cumulative return.

Two sets of statistical tests are conducted on these mean

portfolio abnormal returns to test Hypothesis 1. First, each MARpt is

tested via a t test to determine whether it differs significantly from

the hypothesized value of zero. Second, a two-population t test is then

conducted to see if MARpt differs across the auditor quality based




15 A similar analysis is also conducted for the other two
accounting risk surrogates.

16 For example, ARij equals the estimated 811 parameter from
Equation (12). Similarly, ARi2 = e2i and ARA = 631. Also, the abnormal
return covering the cumulative 36 month period is the CAR36i variable
defined in Equation (14).












accounting risk portfolios over each of the four relevant time

horizons.17 Table 4-1 illustrates these tests.


Univariate Tests of Hypothesis 2


Hypothesis 2 states that the dispersion (i.e., variance) of IPO

firms' aftermarket abnormal returns is positively associated with

accounting risk. In other words, the precision of market participants'

pricing decisions is hypothesized to decrease with accounting risk.

Univariate tests of Hypothesis 2 utilize the accounting risk

portfolios constructed for testing Hypothesis 1. However, a portfolio's

mean abnormal return variance over various holding periods is now the

statistic of interest, rather than the cross-sectional mean abnormal

return employed in the empirical testing of Hypothesis 1.

An individual IPO firm's variance of period t abnormal returns is

defined as

VARit = ARit2, (16)

where ARit is the abnormal return over time period t as provided by

Equation (12). Then, an accounting risk portfolio's mean abnormal

return variance over a specific time period is defined as


17 A two-population t test assumes that the data being tested comes
from two independent populations. This assumption appears reasonable in
this case since there is no a priori reason to hypothesize that high
accounting risk and low accounting risk portfolios are not independent.
Each of these t tests is the mathematical equivalent of a one-way
analysis of variance (ANOVA) since each accounting risk proxy only
possesses two levels (i.e., high and low). Even though the statistical
tests are equivalent, Neter et al. state that "the t test generally is
to be preferred since it can be used to conduct both two-sided and one-
sided tests" (Neter et al. [1985, p. 544]). By contrast, the F test
employed in a one-way ANOVA "can only be used for two-sided tests"
(Neter et al. [1985, p. 544]). Since many of this study's hypotheses
are one-directional, the t test procedure is employed in this research.












Table 4-1
Univariate (Portfolio) Tests of Hypothesis 1

Time High Risk Low Risk Difference
Horizon Portfolioa Portfoliob High-Low

Year 1 MARic, d MARLld MAR 1-MARLje

Year 2 MARCH2 MARL2 MAR2 MARL2

Year 3 MARH3 MARL3 MARCH3 MARL3

3 Year
Cumulative MARHc MARLC MARc MARLC

a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.

b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-1 registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.

MARpt portfolio p's mean abnormal return over time horizon t. Thus,
MARHi the mean abnormal return of the high accounting risk portfolio
over the first year of public trading, MARH2 = the high risk portfolio's
second year mean abnormal return, etc.

d A two-tailed t test is conducted to determine whether each MARt in
this column differs significantly from the hypothesized value of zero.

e A two-tailed two-population t test is conducted to determine whether
each difference statistic in this column differs significantly from the
hypothesized value of zero.












N
MVARt Z VARit / N, (17)
i=1l

where MVARt the mean abnormal return variance over time period t of

the firms comprising portfolio p, and the remaining variables are as

previously defined. This mean portfolio variance figure is computed

separately for the first, second, and third years of public trading, as

well as over a 36 month cumulative period. Then, the empirical tests of

Hypothesis 2 merely involve assessing via two-population t tests18

whether MVARPt is significantly greater for high accounting risk

portfolios than for low accounting risk portfolios over each of the four

time horizons. Table 4-2 illustrates these tests.


Univariate Tests of Hypothesis 3


Hypothesis 3 states that the variance of IPO firms' aftermarket

abnormal returns decreases over time. This decrease in abnormal return

variance is hypothesized to be attributable to decreasing accounting

risk over time as more information becomes known about these newly

publicly held firms.

Univariate tests of Hypothesis 3 employ the same accounting risk

portfolios constructed for the testing of Hypotheses 1 and 2. The

empirical tests of the third hypothesis seek to determine whether the

mean abnormal return variance of each portfolio decreases over the three

year time horizon. Hypothesis 3 implies a monotonic decrease in the


18 See footnote 17.











Table 4-2
Univariate (Portfolio) Tests of Hypothesis 2

Time High Risk Low Risk Difference
Horizon Portfolioa Portfoliob High-Low

Year 1 MVARHl MVARi MVARHi-MVARL Id

Year 2 MVARH2 MVARL2 MVARH2 -ARL2

Year 3 MVARH3 MVARL3 MVARH3 MVARL3

3 Year
Cumulative MVARHC MVARLc MVARHc-MVARLc

a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-1 registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.

MVARpt portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH, the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
the high risk portfolio's second year mean abnormal return variance,
etc.
d A two-population t test is conducted to determine whether each
difference statistic in this column differs significantly from zero.
Hypothesis 2 predicts these differences to be significantly greater than
zero. Thus, one-sided significance tests are called for in this case.









53

variance over the three years. For example, the hypothesis implies that

the mean abnormal return variance of Big Eight audited IPO firms is

greater in the first year of trading than in the second year of trading,

and that the second year variance exceeds that of the third year. Each

of the remaining accounting risk portfolios is predicted to behave in a

similar fashion.

Even though Hypothesis 3 implies that the mean abnormal return

variances decrease monotonically (however not necessarily in a linear

fashion) over time, the hypothesis may be testing by examining whether

the variances exhibit the presence of a decreasing linear trend over the

three year period.19 Dawes and Corrigan [1974] demonstrate the

usefulness of linear models in cases such as this. They state that

"linear models are good approximations to all multivariate models that

are conditionally monotone in each predictor variable" (Dawes and

Corrigan [1974, p. 98]).

Since an individual IPO firm's abnormal return variance over year

1 is not independent of its return variance in other years, a

statistical procedure which accounts for these dependencies is required.

An often utilized procedure in cases such as this is a repeated measures

ANOVA, where time represents the repeated measure. A test for the

presence of a significant linear trend over the time horizon may be

conducted in such an analysis. In instances such as this one in which

the repeated measure (i.e., time) takes on only three values, the linear




19 Keppel [1982, Chapter 7] provides a detailed description of
trend analysis, and provides details of empirical procedures which test
for the presence of a significant linear trend.











trend test is mathematically equivalent to a test of the difference in

the variable of interest (i.e., MVAR) between time period 1 and time

period 3.20

In these specific circumstances in which the repeated measure only

takes on three possible values, a matched-pair (i.e., paired comparison)

t test of the difference between year 1 and year 3 mean abnormal return

variances is mathematically equivalent to the repeated measures ANOVA

test of linear trend. Since Hypothesis 3 is a one-directional

hypothesis, the t test procedure is implemented since it allows one-

sided hypothesis tests, whereas the F statistics produced by the ANOVA

procedure are solely two-sided tests. Table 4-3 illustrates these

tests.


Multivariate Tests


Multiple Regression Test of Hypothesis 1


Hypothesis 1 may alternatively be tested via the following cross-

sectional regression models:

AR(t)i = a + PiAUDITORj + fi2FORMi + 03APCHOICEi

+ -y1SIZEi + -y2LNRISKSi + Y3UE(t)j + ei, (18)



20 Keppel states that "trend analysis requires a different set of
special coefficients for each of the orthogonal trend components to be
extracted from the data" (Keppel [1982, p. 135]). The special
coefficients required to test for a decreasing linear trend when the
repeated measure takes on only three values are +1, 0, and -1.
Specifically, a portion of the calculated test statistic consists of the
sum of +1 times the observation for year 1, 0 times the year 2
observation, and -1 times the year three observation. The resulting
statistical test is mathematically equivalent to a test of the
difference between period 1 and period 3 observations.











Table 4-3
Univariate (Portfolio) Tests of Hypothesis 3

Difference
Portfolio Year 1 Year 2 Year 3 Yearl-Year3

High Riska MVARlc MVARH2 MVARg3 MVARHi-MVARHsd,

Low Riskb MVARLI MVARL MVARL3 MVARLi -M 4VARL3

a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.

b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-1 registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.

SMVARpt portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH, the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
= the high risk portfolio's second year mean abnormal return variance,
etc.

d A matched-pair (i.e., paired comparison) t test is conducted to
determine whether each difference statistic in this column differs
significantly from zero. Hypothesis 3 predicts these differences to be
significantly greater than zero. Thus, one-sided significance tests are
called for in this case.

S This matched-pair t test of the difference between the MVAR of years 1
and 3 is mathematically equivalent to a test for the presence of a
significant linear trend over time in a repeated measures ANOVA
analysis.











where AR(t)i firm i's abnormal return over holding period t as

generated from Equation (12) (i.e., Ot), AUDITOR1 the quality of

auditor utilized in the IPO process, FORMi the registration statement

type filed by IPO firm i, APCHOICE1 the level of conservatism

surrounding firm i's accounting procedure choices for inventory and

depreciation, SIZE1 firm size at the initial offering, LNRISKS1 the

natural logarithm of one plus the number of risk factors listed in firm

i's prospectus, UE(t)i firm i's unexpected earnings for period t, and

ej is an error term. The coefficients a, 01 to 03, and 71 to 73 are

regression parameters.

In Equation (18), AUDITORJ, FORMi, and APCHOICE1 are empirical

proxies for accounting risk, while SIZE1, LNRISKS1, and UE(t)i are

included as control variables. The control variables are designed to

control for potential differences among firms' abnormal returns which

are unrelated to differences in accounting risk. Thus, Equation (18)

allows one to test the impact of accounting risk on long-term

mispricing, while controlling for potential confounding influences.

SIZEi and LNRISKS1 are included in the regression models to

control for business risk (i.e., uncertainty related to a firm's

production, investment, and financing activities). It is important to

control for firm size since it has been empirically documented21 that

larger IPO firms are less speculative than small initial offering firms.

The number of risk factors supplied by an IPO firm in its prospectus may

also proxy for the inherent cash flow riskiness of the firm. The SEC


21 See Beatty [1989b].











requires firms undertaking IPOs to enumerate important "risk factors"

which will aid prospective investors in assessing the riskiness of the

offering. The SEC may choose to delay an offering through a "deficiency

letter" until the firm provides adequate details concerning the

riskiness of its operations, its financing obligations, etc. Hence,

Beatty [1989b] argues that riskier IPO firms provide greater disclosure

in the "risk factors" section of the prospectus than less risky firms.

Finally, UE(t)i is included in the regression models to control for the

widely documented empirical result that abnormal security returns are

positively associated with "unexpected" accounting earnings.

Equation (18) is estimated cross-sectionally for four different

specifications of the dependent variable (i.e., long-term mispricing).

Specifically, the equation is separately estimated for abnormal returns

calculated over the first, second, and third year of public trading, as

well as a 36 month cumulative return.

AUDITORj is measured as a dichotomous variable. It is coded 1 in

cases where non-Big Eight auditing firms are utilized in the IPO process

and 0 for Big Eight firms. FORMi is similarly measured in a categorical

fashion. The variable is coded 1 for S-18 registrations and 0 for S-1

registrations. APCHOICEj is assigned a value of 1 for firms which

employ consistently income increasing (i.e., "liberal") inventory and

depreciation accounting procedure choices and 0 otherwise. SIZEi is

calculated as the natural logarithm of IPO firm i's total book value of

assets as provided in the prospectus, and LNRISKSi is defined as the

natural logarithm of one plus the number of risk factors listed in firm

i's prospectus.









58

Each of the previously described independent variables is measured

based on the most recently available data at the time of the IPO (i.e.,

that available in the prospectus). In other words, these independent

variables remain constant across the four cross-sectional regressions.

Thus, the regressions are intended to determine the impact of accounting

risk at the time of the IPO on various specifications of long-term IPO

mispricing. Hypothesis 1 predicts zero coefficients on the three

accounting risk variables in each regression.22

In contrast to the other independent variables, UE(t)i is measured

over the same time horizon as the dependent variable in each of the four

cross-sectional regressions. Unexpected earnings is calculated using a

variant of the commonly utilized random walk earnings expectation model.

An alternative specification of the random walk model is required in

this study due to the misalignment of the abnormal security return

series and the earnings series. Specifically, an IPO's abnormal returns

are generated via Equation (12) based on monthly returns beginning on

the day following the IPO date. In other words, the return series is

based on "event time," with the event in question being the initial

offering. By contrast, the annual earnings series is based on each

firm's fiscal year end (FYE). Therefore, unless a firm went public on

(or very near) its fiscal year end date, the returns and earnings series

utilized in this study are not properly aligned.





22 It is easily verified that the model presented in Chapter 3 also
predicts nonsignificant coefficients for the business risk control
variables (i.e., SIZEi and LNRISKSi) since the abnormal security returns
are already risk-adjusted.









59

To alleviate this potential misalignment problem, the random walk

earnings expectation model is adjusted in order that the earnings series

becomes aligned with the security returns series. Specifically, IPO

firm i's unexpected earnings for period t is calculated as follows:

UE(t)i [(EPSit EPSit1) (Nlit / 12) + (EPSit+1 EPSi) (N2it / 12)]

/ PRICEit, (19)

where EPS signifies annual earnings per share, PRICE designates the

security price per share,23 and N1 and N2 are adjustment factors

utilized to bring the earnings series into alignment with the returns

series.

As an example of how Equation (19) is used in the determination of

unexpected earnings, the calculation of firm i's unexpected earnings

over the first year of public trading (i.e., UElI) will be illustrated.

In the calculation of UEli, the time subscript t equals 1 throughout

Equation (19). EPSil signifies firm i's annual earnings per share as of

the first fiscal year end subsequent to the IPO. For example, if a

December 31 FYE firm went public on August 1, 1980, EPSil would

represent the firm's earnings per share for the year ended December 31,

1980. The period of time (rounded to the nearest month) which elapses

between the IPO date and this first FYE subsequent to the initial offer

is designated Nlil months. In the above example, Nlil equals the five

month period from August 1, 1980 to December 31, 1980. N2i1 then

represents the number of months from this first FYE following the IPO to

the date one year subsequent to the IPO (i.e., the end of the first year



23 Both the earnings per share and the price per share series are
adjusted for stock dividends and stock splits.











of trading in "event time"). In the above example, N2j, equals the

seven month period from December 31, 1980 to August 1, 1981.

Therefore, Nli1/12 (i.e., 5/12 in the example) of the

traditionally calculated random walk based "unexpected earnings"

accruing from the FYE preceding the IPO to the first FYE subsequent to

the IPO (i.e., from December 31, 1979 to December 31, 1980) is included

in the calculation of UEli. Further, N2j/12 (i.e., 7/12 in the

example) of the traditionally calculated "unexpected earnings" based on

the random walk model accruing from the first FYE following the IPO to

the second FYE subsequent to the IPO (i.e., from December 31, 1980 to

December 31, 1981) is included in the calculation of UEli. Thus, the

N1/12 and N2/12 factors serve to transform the FYE based earnings series

into a series compatible with the "event time" returns series.

Therefore, as is customary in a random walk annual earnings expectation

model, UEli is calculated based on 12 months of earnings. However, this

12 months of earnings represents a weighted average of the "unexpected

earnings" pertaining to two fiscal years (i.e., the fiscal years ended

December 31, 1980 and December 31, 1981 in the above example).24

Consistent with the widely documented result that unexpected

accounting earnings and abnormal security returns are positively

associated, the parameter estimate on UE(t)i is predicted to be positive

in each of the four cross-sectional regressions.


24 The previously described method of estimating unexpected
earnings is utilized to determine unexpected earnings over the first,
second, and third year of public trading. However, unexpected earnings
corresponding to the cumulative three year holding period is calculated
as the sum of the undeflated unexpected earnings calculated for each of
the first three years deflated by the security price at the end of year
three.











Multiple Regression Test of Hypothesis 2


Hypothesis 2 may alternatively be examined via the following

cross-sectional regression models:

VAR(t)i a + PIAUDITORj + A2F0ORM + A3APCHOICEi

+ -1SIZEi + 721!NRISKSi + 73ABSUE(t)i + ej, (20)

where ABSUE(t)i the absolute value of firm i's period t unexpected

earnings, and all remaining variables are as previously defined. The

absolute value specification of unexpected earnings is utilized in

Equation (20) since the absolute magnitude (rather than the signed

magnitude) of unexpected earnings should impact the variance of returns.

As was the case for Equation (18), Equation (20) is estimated

separately across all sample firms for four different specifications of

the dependent variable (i.e., abnormal return variances over four

different time horizons). Specifically, an IPO firm's abnormal return

variance is measured over the first, second, and third years of public

trading, as well as over the cumulative 36 month period. As previously

indicated by Equation (16), an IPO's variance of period t abnormal

returns (i.e., VARit or equivalently VAR(t)i) is calculated as the

abnormal return over time t squared (i.e., AR 2).

According to Hypothesis 2, the estimated coefficients on the

accounting risk variables (i.e., 01 to P3) are predicted to be positive

for each of the four cross-sectional regressions.25 Similarly, the




25 However, Hypothesis 3 predicts that the ability of these
variables to explain the variation in abnormal return variances
decreases over time. Empirical tests of Hypothesis 3 utilizing a
multiple regression approach are detailed in the following section.









62

parameter estimate on the unexpected earnings variable is also expected

to be positive. If business risk is adequately controlled for in the

return generating process described by Equation (12), the coefficients

on the business risk control variables (i.e., SIZEj and LNRISKSj) should

not differ significantly from zero. However, 71 and 72 may be

significantly different from zero if INDUSTRYit does not adequately

control for business risk. In such a case, the coefficient on SIZEit is

predicted to be negative (since smaller firms correspond to a higher

level of business risk and hence should experience a higher variance of

returns), while the coefficient on LNRISKS is predicted to be positive

(since the number of risk factors enumerated in the prospectus is

positively associated with business risk).


Multiple Regression Test of Hypothesis 3


Hypothesis 3 may alternatively be tested via the following pooled

cross-sectional time-series regression model:

VARit + 6iAUDITORit + fi'AUDITORit*Ilit + 3i' 'AUDITORit*I2it

+ 2FORMit + /2'FORMit*Ilit + 2' 'FORMit*I2it

+ 83APCHOICEit + 13'APCHOICEit*Ilit + 03' 'APCHOICEit*I2it

+ 7iSIZEit + 71'SIZEjt*Iljt + 7y' 'SIZEit*I2jt

+ 72LNRISKSjt + 72'LNRISKSit*Iljt + 72' 'LNRISKSit*I2it

+ 73ABSUEjt + 73'ABSUEit*Ilit + 73' ABSUEjt*I2it

+ eit, (21)

where Ilit is an indicator variable which is coded 1 for observations

relating to the second year following the IPO and 0 otherwise, I2jt is

an indicator variable which takes on the value of 1 for third year









63

observations and 0 otherwise, and all other variables are as previously

defined.

As stated earlier, Equation (21) is a pooled cross-sectional time-

series model. Specifically, the equation is estimated with three

observations per sample firm. These three observations per firm pertain

to the first, second, and third year of public trading, respectively.

Thus, Equation (21) is estimated with three times as many observations

as the separate year by year cross-sectional regressions utilized in the

testing of Hypotheses 1 and 2.

The interaction terms employing the indicator variables llit and

12t are included in the model so that tests concerning changes in the

regression coefficients over time may be conducted.26 Specifically,

since both the indicator variables are coded 0 for observations relating

to the first year following the IPO, the regression coefficients 01 to

03 and 71 to 73 represent the respective coefficients for year 1.

Similarly, since 1lit equals 1 for year 2 only and 12it equals 0 for year

2, then the regression coefficients #I' to 03' and 71' to 73' represent

the changes in the respective coefficients for year 2 as compared to the

base year (i.e., year 1). In a similar fashion, since llit is coded 0

for year 3 and 12it takes on the value 1 only for year 3, then the

regression coefficients 81' to 083'' and -yj'' to 73'' represent changes

in the respective coefficients for year 3 in comparison to year 1.





26 It should be noted that the model specified in Equation (21)
assumes that the intercept does not vary across sample years but that
the slopes differ according to whether the first, second, or third year
of public trading is being examined.











Hypothesis 3 implies that the accounting risk variables explain

less of the variation in abnormal return variances as more information

is revealed as time progresses from the IPO date. Thus, the 01' to 03'

and 1' to P3'' coefficients predicted to be negative.27

Even though the coefficients on the interaction terms in Equation

(21) represent differences in the coefficients for year 2 and year 3,

respectively, from the coefficients pertaining to year 1, they provide

no indication of the change in the parameters from year 2 to year 3.

The change in the regression coefficients from year 2 to year 3 is

tested by examining the significance levels of the following linear

hypotheses regarding the coefficients: 01' i1'' 0, 02' P2'' 0,

03' 03' 0, 71' 7' 0'' 72' 72'' 0, and 73' 73'' 0. Each

of these tests seeks to determine whether a particular year 2 regression





27 A priori, it is difficult to predict the sign of the
coefficients on the interaction terms which contain the control
variables. On the one hand, it could be postulated that the business
risk control variables (i.e., SIZEi and LNRISKSj) will decrease in
significance over time since these variables are measured as of the IPO
date and remain constant across the three time periods examined.
However, it could also be hypothesized that the business risk proxies
will increase in significance as the accounting risk variables lose
explanatory power. Similarly, one could reasonably argue that the
unexpected earnings control variable could either increase, decrease, or
remain constant over time. Specifically, it can be hypothesized that
unexpected earnings will increase in explanatory power over time as the
accounting risk variables decrease in importance. However, it could
also be argued that the unexpected earnings variables will decrease in
explanatory power over time since earnings may play a greater role in
firm valuation early in the life of a public firm (i.e., before other
competing sources of information become readily available). Finally,
one could also reasonably argue that the unexpected earnings variable
should not vary in significance over time since the relationship of
earnings and returns should not change significantly from year to year.
Therefore, predictions are not made relating to the hypothesized signs
of the 71' to 73' and -y7' to 73'' coefficients.











coefficient is significantly greater than the corresponding year 3

coefficient.28


Summary of the Empirical Methodology


This chapter describes both univariate and multivariate approaches

employed in the empirical testing of Hypotheses 1 through 3. The

univariate tests allow a straightforward assessment of the three

hypotheses. The univariate (portfolio) approach is particularly useful

in the examination of Hypothesis 1 since it permits a direct

determination of whether mean abnormal returns differ significantly from

zero for (and across) portfolios of IPOs which differ according to

accounting risk. However, the portfolio approach suffers from the

limitation that it is univariate in nature. In other words, the

portfolio approach is limited in that the analysis must be conducted

separately for portfolios based on each of the three accounting risk

proxies. Thus, the approach does not allow an assessment of the

incremental impact of various measures of accounting risk on long-term

IPO mispricing. A potentially more important limitation is that the

univariate tests do not control for factors other than accounting risk

which may influence mean aftermarket abnormal returns and mean abnormal

return variances. Thus, it is not possible to conclude from these



28 According to Hypothesis 3, the regression coefficients on the
three accounting risk variables should be greater in year 2 than in year
3. Thus, the differences in the estimated year 2 and year 3 regression
parameters pertaining to AUDITOR, FORM, and APCHOICE are expected to be
significantly greater than zero. As explained in footnote 26, no
predictions are made in regards to the signs of the differences between
the regression coefficients of years 2 and 3 for the .control variables
(i.e., SIZE, LNRISKS, and ABSUE).










66

univariate tests that it is accounting risk that is responsible for any

observed empirical relationships, rather than some other factor such as

IPO firm size or inherent cash flow riskiness.

In contrast, the multiple regression techniques are multivariate

analyses which allow the determination of the incremental effects of

this study's three measures of accounting risk on long-term mispricing.

The regression approach also allows for the controlling of potentially

confounding influences on the relationships of interest. The technique

also allows a direct assessment of Hypothesis 3, the change in

explanatory power of the accounting risk variables over time. However,

the regression method provides only an indirect test of the validity of

Hypothesis 1. Specifically, while the regression technique indicates

whether accounting risk and abnormal returns are significantly

correlated, it provides no indication of whether IPOs are mispriced on

average.

Thus, there are advantages and disadvantages associated with both

the univariate (portfolio) and multivariate (regression) approaches.

Due to the complementary nature of the two approaches, both are employed

in the testing of the three hypotheses.
















CHAPTER 5
DATA, SAMPLE SELECTION CRITERIA, AND RESULTS


Data Sources and Sample Selection Criteria


The major source of data employed in this study consists of a

collection of microfiche IPO registration statements (which include

preliminary prospectuses) which were obtained from Disclosure Inc.1

This database contains registration statements for a number of firm

commitment2 IPOs of nonfinancial firms which occurred between January

1980 and December 1983. The database contains registration statements

and preliminary prospectuses for 511 firms which made initial public

offerings of equity securities (unit offerings which include warrants

are excluded) during this 1980-1983 time period. Only firms for which

at least three years of security price data are available on the CRSP



1 I wish to thank Professor Chris James for allowing me access to
these registration statements. Please see James and Wier [1989] for a
complete description of the criteria utilized in assembling this
microfiche database.

2 The two major types of underwriting contracts utilized in the
going public process are "firm commitment" and "best efforts" contracts.
In a firm commitment offering, the underwriter purchases the entire
security issue from the issuer and then must resell the shares to
investors. Thus, the underwriter makes a "firm commitment" to purchase
the offering at a specified price (i.e., the IPO price less the
underwriter's discount), and bears the risk of not being able to sell
the entire issue at the IPO price. Conversely, in a best efforts
offering, the underwriter's only commitment is to make his "best
efforts" to sell the issuer's security offering to investors. In this
case, the issuer bears the risk of not being able to sell the entire
issue at the IPO price.












NASDAQ file are included in the final sample.3 This three year of

security return availability criterion is the only criterion utilized in

the sample selection process and results in the exclusion of 139 firms.

Thus, the final sample consists of 372 firms. Table 5-1 summarizes the

sample selection process employed in this study.


Table 5-1
Sample Selection Procedures

Total Available Microfiche Prospectuses of Non-Unit IPOs 511
Number of Firms Removed Due to a Lack of 3 Years of Data
on the 1988 Daily CRSP NASDAQ File 139

Final Sample 372


Much of the data required for this study was gathered from this

collection of microfiche registration statements. For example, the data

required to calculate the AUDITOR, FORM, and SIZE variables have been

collected from these registration statements and accompanying

prospectuses. Further, this microfiche collection was utilized in

conjunction with the Compustat Full-Coverage file in the collection of

the sample firms' accounting procedure choices for inventory and

depreciation.

Another significant portion of the required data was gathered from

"Ritter's 1975-1984 IPO Database."4 This database contains extensive



3 The entire empirical analysis was repeated using a more
restrictive five years of CRSP NASDAQ data availability criterion. This
more restrictive criterion resulted in a final sample of 299 firms.
Summary results of this additional analysis are reported in Appendix B.

4 I wish to express thanks to Professor Jay Ritter for providing me
this extensive amount of data on IPO firms. Please see Ritter [1984b]
and Beatty and Ritter [1986] for detailed descriptions of the criteria
utilized in building this database.










69

detail on 2,567 initial public offerings which occurred between 1975 and

1984. Each of the 372 sample initial offerings is contained in Ritter's

IPO database. This database was utilized to collect information

concerning the number of risk factors listed in an IPO's prospectus, the

date of the initial offering, the IPO price, the first available closing

bid price following the IPO, the number of shares sold in the offering,

and the year that the firm was first organized. Thus, this data allowed

the calculation of the LNRISKS variable, the amount of short-term

underpricing, the gross proceeds of the offering, and the age of the

firm at the time of the IPO. Descriptive statistics on these and other

variables will be provided in the following section.

The required security returns data were collected from the CRSP

NASDAQ file. Specifically, the NASDAQ daily returns file was utilized

in order to gather raw returns for each sample firm, the returns on the

NASDAQ Composite Index, and the returns required in the calculation of

the industry return variable. Finally, the information required to

calculate the unexpected earnings variables was gathered from the

COMPUSTAT Full-Coverage file in conjunction with Standard and Poor's

Corporation Records.


Descriptive Statistics


Sample Firm and Offering Characteristics


Table 5-2 provides descriptive statistics relating to certain

characteristics of the 372 sample firms and their respective initial

offerings. Panel A provides information about the sample as a whole,












Table 5-2
Descriptive Statistics

Panel A
Sample Firm and Initial Offering Descriptive Statistics

Variable Mean Median Standard Deviation Nb

PROCEEDS 15.62 9.06 18.34 372
BKASSETS 15.56 6.92 25.78 372
SIZE 8.66 8.84 1.65 372
RISKS 4.98 0.00 6.50 366
LNRISKS 1.09 0.00 1.22 366
AGE 10.80 7.00 13.57 370


Panel B
Summary Firm and Offerings Statistics By Year of Offering

Mean Mean
Year Number of Offers PROCEEDS BKASSETS

1980 35 10.11 13.89
1981 114 10.81 12.31
1982 25 12.70 12.01
1983 198 19.72 18.18

a The variables examined in this table relate to certain characteristics
of the 372 sample firms and their initial offerings. The variables of
interest are defined as follows:
PROCEEDS the gross proceeds of the initial offering measured in
millions of dollars. Gross proceeds are calculated as the actual number
of shares sold in the offering multiplied by the offering price per
share.
BKASSETS the book value of total assets (measured in millions of
dollars) prior to the IPO. This variable is collected from the latest
audited balance sheet supplied in the prospectus.
SIZE = the natural logarithm of BKASSETS.
RISKS the number of risk factors listed in the prospectus.
LNRISKS the natural logarithm of (1 + RISKS).
AGE the age of the firm in years prior to the initial offering.

b N = the number of firms involved in the calculation of the
mean, median, and standard deviation statistics.












while Panel B provides descriptive statistics by year of initial

offering.

Panel A demonstrates that the average offering gross proceeds

(i.e., PROCEEDS) raised by the 372 sample firms equals $15.62 million.

This very closely approximates the sample firms' mean book value of

total assets (i.e., BKASSETS) prior to the offering. Thus, on average,

the sample firms raised an amount approximately equal to their currently

prevailing book value of assets via an IPO. Panel A also reveals that

the mean (median) number of risk factors (i.e., RISKS) enumerated in the

offering prospectus is approximately equal to 5 (0).5

One other variable reported in Panel A of Table 5-2 merits

mention. The average age of the sample firms prior to the IPO is

slightly less than 11 years. Thus, it is apparent that the sample is

not made up entirely of start-up firms (i.e., firms with less than two

years of operations).

Panel B reports the number of offers, the mean gross proceeds, and

the mean book value of assets (prior to the IPO) by sample year. It is

apparent that the number of offers is not spread evenly across the

sample years. In fact, the sample is dominated by firms undertaking

IPOs in 1981 and 1983. This is consistent with evidence provided in



5 Panel A also indicates that the logarithmic transformations of
BKASSETS and RISKS which are employed as business risk control variables
(i.e., SIZE and LNRISKS, respectively) in the cross-sectional
regressions reported later have greatly reduced standard deviations in
comparison to the original variables. However, even though these
logarithmic transformations are utilized in the regression models rather
than the original variables, the error terms of the various regressions
are heteroscedastic. Later sections will provide (1) details of the
severity of the heteroscedasticity and (2) the means utilized in
correcting the problems caused by heteroscedastic error terms.











Ibbotson et al. [1988] that more initial offers were undertaken in the

years 1981 and 1983 than in 1980 or 1982. Thus, the makeup of the

sample does not appear to differ significantly from the population as a

whole. Panel B also illustrates that the average offering gross

proceeds increases over the sample period. The same relationship also

generally holds for the sample firms' book value of assets prior to the

offering.


Long-Term Mispricing. Short-Term Mispricing, and Unexpected Earnings


Table 5-3 provides additional descriptive statistics relating to

various measures of long-term and short-term IPO mispricing, as well as

to the unexpected earnings variables employed in this study. The mean

short-term underpricing (i.e., UP) of the 372 sample firms is 11.70%.

As is evidenced by Table 2-1, this 11.70% level of underpricing

approximates findings in other research.

Descriptive statistics pertaining to the sample firms' risk-

adjusted abnormal returns over the first, second, and third year of

public trading, as well as a 36 month cumulative return are provided in

Table 5-3. These measures of long-term IPO mispricing are generated

from the return generating process described in Equation (12), and are

designated as AR1, AR2, AR3, and CAR36 respectively. The table

indicates that only AR1 (i.e., first year abnormal returns) exceeds 1%

(in absolute value) on average. Further, AR1 is the only one of the

four risk-adjusted long-term mispricing measures which differs

significantly from zero at conventional significance levels, with a












Table 5-3
Descriptive Statistics Relating to Measures of
Long-Term and Short-Term Mispricing, and Unexpected Earnings

Variable Mean Median Standard Deviation Nb

UP 11.70% 2.78% 0.27 372
AR1 1.21% 1.49% 0.05 372
AR2 -0.35% -0.29% 0.05 372
AR3 -0.35% -0.63% 0.05 372
CAR36 0.50% 0.39% 0.08 372
RIMRM1 2.12% -0.30% 0.59 372
RIMRM2 -4.19% -3.15% 0.61 372
RIMRM3 -6.35% -6.90% 0.59 372
RIMRM36 -8.42% -10.95% 0.97 372
UE1 -0.44 0.00 8.29 371
UE2 -0.04 -0.01 0.11 366
UE3 0.01 0.00 0.39 337
UE36 -0.95 -0.02 15.05 337


a The variables examined in this table relate to various measures of
short-term and long-term IPO mispricing, as well as measures of
unexpected accounting earnings for the 372 sample firms. The variables
of interest are defined as follows:
UP short-term underpricing of the initial offering price by the
underwriter. UP is calculated as: (Pl-PIPO)/PIPO, where PI refers to
the first available closing bid price following the IPO and PIPO equals
the initial offering price set by the underwriter.
AR1, AR2, and AR3 represent the risk-adjusted abnormal returns as
generated from Equation (12) for the first, second, and third years of
public trading, respectively.
CAR36 = the cumulative risk-adjusted abnormal return over the
first 36 months of public trading as generated from Equation (12).
CAR36 is defined as the sum of ARI, AR2, and AR3.
RIMRM1, RIMRM2, and RIMRM3 represent market adjusted security
returns over the first, second, and third years of public trading,
respectively. These variables are calculated by subtracting the return
on the NASDAQ Composite (Market) Index from an individual IPO's raw
security returns over the appropriate time periods.
RIMRM36 the sum of RIMRMI, RIMRM2, and RIMRM3. Thus, the
variable represents the cumulative market-adjusted return over the first
36 months of trading.
UE1, UE2, UE3, and UE36 are defined as unexpected accounting
earnings over the first year, second year, third year, and 36 month
cumulative time horizon, respectively.

b N = the number of firms involved in the calculation of the mean,
median, and standard deviation statistics.









74

P-Value of less than .01. Thus, the assertion of Hypothesis 1 that mean

abnormal returns should not differ from zero is generally supported for

the sample as a whole. However, the hypothesis is not supported for the

first year of public trading time horizon.6

Table 5-3 also lists alternative measures of long-term mispricing

based on simple market-adjusted (rather than risk-adjusted) returns.

Specifically, RIMRMl to RIMRM3 and RIMRM36 represent market-adjusted

security returns over the first, second, and third years of public

trading, as well as a 36 month cumulative market-adjusted return. The

market adjustments consist of subtracting the return on the NASDAQ

Composite Index from an IPO's unadjusted raw return over the

corresponding time period in the construction of monthly returns. Then,

yearly market-adjusted returns are constructed from these monthly

returns via an additive process. Table 5-3 shows that the absolute

magnitudes of these market-adjusted returns are much greater than those

of the risk-adjusted returns previously described. However, only RIMRM3

is significantly different from zero at the .05 level, while the 36

month cumulative return is marginally significant at .10.7


6 More formal statistical tests of Hypothesis 1 will be provided in
later sections.

7 Market-adjusted returns are employed by Ritter [1989] in the
examination of long-term IPO mispricing. Ritter finds average market-
adjusted returns of -24.33% over the first three years of trading. This
compares with the -8.42% cumulative return of the sample examined in
this study. One potential reason for the much more negative mean
market-adjusted returns of Ritter's sample firms is that the market
index employed differs between the two studies. Ritter uses an index
based on NYSE and AMEX firms, while the current study uses the NASDAQ
Composite Index. Average market-adjusted returns for the current sample
were also estimated using the market index employed by Ritter (i.e., the
CRSP Value-Weighted Index based on NYSE and AMEX firms). The results of
such an estimation process indicate three year cumulative mean market-









75

Empirical tests of Hypotheses 1 through 3 were performed utilizing

both the risk-adjusted and market-adjusted definitions of long-term

mispricing, with virtually no qualitative differences in results.8

Hence, only the results pertaining to the risk-adjusted measures are

reported in subsequent sections.

Unexpected accounting earnings over the first three years of

public trading, as well a 36 month cumulative unexpected earnings

measure are also provided in Table 5-3. With the exception of year 2

which has a P-Value of less than .01, the mean unexpected earnings

measures do not differ significantly from zero.


Makeup of Accounting Risk Portfolios


Table 5-4 reports the number of firms comprising each accounting

risk category (portfolio). For example, the table illustrates that 294

out of the 372 firms employed Big Eight auditing firms in the going

public process. Thus, the low accounting risk portfolio (based on

auditor quality) consists of 294 firms. Conversely, the high accounting

risk portfolio consists of the 78 firms which utilized non-Big Eight

auditors. Thus, the sample is dominated by Big Eight audited IPO firms.






adjusted returns of -25.14%. Thus, it appears that the differences
between the market-adjusted returns reported by Ritter and those
reported in Table 5-3 of the current study are largely due to
differences in the market indices employed in the two studies.

8 The Pearson correlation coefficients between the corresponding
market-adjusted and risk-adjusted long-term mispricing measures are in
the .75-.95 range. Thus, the two methods of calculating long-term
mispricing appear to be capturing the same phenomena.












Table 5-4
Number of Sample Firms
Comprising Each Accounting Risk Category


Number of Firms in Number of Firms in
High Accounting Risk Low Accounting Risk
Accounting Risk Proxy Categorya Category


AUDITOR 78 294


FORM 78 294


APCHOICE 264 95


a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.

b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-1 registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.











Similarly, Table 5-4 shows that the sample is dominated by IPO

firms which filed S-1 SEC registration statements. Again, 294 out of

the 372 sample firms are classified in the low accounting risk category

(based on the SEC registration statement type filed), and only 78 are

classified as high accounting risk firms.

However, when accounting procedure choices are utilized in the

classification of high versus low accounting risk sample firms, the

sample is dominated by high risk firms. Specifically, 264 out of the

359 sample firms which had data available on the accounting methods

chosen utilized consistently income increasing (i.e., "liberal")

accounting procedures for inventory and depreciation. By contrast, only

95 out of these 359 firms employed at least one income reducing method.

Therefore, the common practice among the sample firms was to utilize

straight-line depreciation and an inventory method other than LIFO for

financial reporting purposes.


Results of Univariate Tests


Results of Univariate Tests of Hypothesis 1


Hypothesis 1 predicts that the mean abnormal return (MAR) of each

accounting risk portfolio will not significantly deviate from zero over

any of the four time horizons. Further, the hypothesis states that the

mean abnormal return of the high risk portfolios should not differ

significantly from the MAR of the low risk portfolios over each time

period examined. Table 5-5 provides the results of the univariate

(portfolio) tests of Hypothesis 1. Panels A, B, and C supply the












Table 5-5
Univariate (Portfolio) Tests of Hypothesis 1


Time
Horizon

Year 1

Year 2

Year 3

3 Year
Cumulative




Time
Horizon

Year 1

Year 2

Year 3

3 Year
Cumulative


Panel A
Accounting Risk Proxy Variable AUDITOR

High Risk Low Risk
Portfolio MARa Portfolio MAR

0.94% 1.28%**

0.48% -0.57%*

-0.56% -0.30%


0.86%


0.41%


Panel B
Accounting Risk Proxy Variable FORM

High Risk Low Risk
Portfolio MAR Portfolio MAR

1.53% 1.12%**

0.09% -0.47%

-0.35% -0.35%


1.27%


0.30%


Difference
High-Low

-0.34%

1.05%

-0.26%


0.45%




Difference
High-Low

0.41%

0.56%

0.00%


0.97%


Panel C
Accounting Risk Proxy Variable = APCHOICE

Time High Risk Low Risk
Horizon Portfolio MAR Portfolio MAR

Year 1 1.02%** 1.12%*

Year 2 -0.26% -0.08%

Year 3 -0.48% -0.40%

3 Year
Cumulative 0.29% 0.64%

a MAR signifies mean abnormal return.

** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests


Difference
High-Low

-0.10%

-0.18%

-0.08%


-0.35%












results of the univariate tests when AUDITOR, FORM, and APCHOICE,

respectively, are employed as proxies for accounting risk.

Panel A indicates that the mean abnormal return of the high

accounting risk portfolio based on auditor quality (i.e., non-Big Eight

audited IPO firms) does not differ significantly from the hypothesized

value of zero over any of the four time horizons. By contrast, the mean

abnormal return of the low accounting risk auditor portfolio (i.e., Big

Eight audited IPOs) is significantly greater than zero in year 1 and

significantly less than zero in year 2. However, the low risk

portfolio's MAR conforms to the predictions of Hypothesis 1 in year 3

and over the 36 month cumulative period. Finally, consistent with

Hypothesis 1, the differences between the mean abnormal returns of the

high risk and low risk portfolios do not differ significantly from zero

for any of the time periods studied. Thus, the prediction of Hypothesis

1 that accounting risk should not influence mean abnormal returns is

supported for the case of the auditor quality accounting risk surrogate.

Panel B provides similar results for the SEC registration

statement type accounting risk proxy. Again, the hypothesis that the

MAR of the high risk portfolio (i.e., IPOs which filed S-18

registrations) equals zero in each time period cannot be rejected at

conventional levels. The hypothesis can be rejected for the low risk

group (i.e., Form S-1 filers) only for the first year of public trading,

when the mean abnormal return is significantly greater than zero.

However, the differences between the MARs of the high and low risk

portfolios do not differ significantly from the hypothesized value of









80

zero. As was the case for the AUDITOR proxy, Hypothesis 1 is supported

when FORM is utilized as a proxy for accounting risk.

Panel C illustrates that Hypothesis 1 is also supported when

APCHOICE is utilized as a proxy for accounting riskiness. Specifically,

the mean abnormal returns of both the high risk (i.e., firms utilizing

consistently "liberal" accounting methods for inventory and

depreciation) and low risk (i.e., firms utilizing at least one

"conservative" method) groups differ significantly from zero in only the

first year of public trading. Further, consistent with the predictions

of Hypothesis 1, none of the MAR differences between the accounting risk

groups differs significantly from zero.

In summary, the results reported in Table 5-5 strongly support the

prediction of Hypothesis 1 that IPO firms' mean abnormal returns do not

vary according to the level of accounting risk faced by potential

investors, since none of the difference statistics in the table differs

significantly from zero. Thus, the results are consistent with the

hypothesis that investors do not systematically misprice IPOs because of

excessive accounting riskiness. However, Table 5-5 also reveals that in

certain isolated cases a given portfolio's MAR differs from the zero

hypothesized value, even though none of the portfolios possess

statistically significant 36 month cumulative mean abnormal returns.

Therefore, Hypothesis l's prediction of zero abnormal returns over all

time horizons for each portfolio (i.e., no systematic mispricing of IPOs

by investors) is not uniformly supported even though the MARs do not

differ significantly from zero in the vast majority of cases.











Results of Univariate Tests of Hypothesis 2


Hypothesis 2 predicts that the mean abnormal return variance

(MVAR) of high accounting risk firms will exceed that of low accounting

risk firms in each of the time horizons examined. Panels A, B, and C of

Table 5-6 report the results of examining Hypothesis 2 using the

AUDITOR, FORM, and APCHOICE accounting risk proxy variables,

respectively.9

Panel A reveals that when AUDITOR is utilized in the

classification of firms into high and low accounting risk categories,

the mean abnormal return variance of the high accounting risk group

exceeds that of the low risk group in each of the first three years.

The difference is significant at the .05 level only for year 2.

However, Hypothesis 2 is marginally supported in years 1 and 3.

Specifically, the differences in mean abnormal return variances are

significant at the .06 and .13 levels in years 1 and 3, respectively.

Thus, Hypothesis 2 is supported in year 2 and marginally supported in

years 1 and 3 when auditor quality is utilized as a surrogate for

accounting risk.

Panel B demonstrates that Hypothesis 2 is supported in years 1 and

2, as well as over the cumulative 36 month period, when the FORM

accounting risk proxy variable is utilized to classify firms into high



9 It is important to note that in Table 5-6 a given portfolio's
cumulative 36 month MVAR does not equal the sum of year 1 through year 3
MVARs. As indicated by Equation (16), abnormal return variances over a
given time period are calculated as the abnormal return over that same
time period squared. Therefore, the cumulative abnormal return variance
equals the cumulative abnormal return squared. Hence, for each firm
(and portfolio) VAR36 = CAR362 rather than AR12 + AR22 + AR32.


















Time
Horizon

Year 1

Year 2

Year 3

3 Year
Cumulative




Time
Horizon

Year 1

Year 2

Year 3

3 Year
Cumulative


Table 5-6
Univariate (Portfolio) Tests of Hypothesis 2

Panel A
Accounting Risk Proxy Variable AUDITOR

High Risk Low Risk
Portfolio MVAR Portfolio MVAR

0.00347 0.00240

0.00313 0.00222

0.00254 0.00207


0.00642


0.00649


Panel B
Accounting Risk Proxy Variable FORM

High Risk Low Risk
Portfolio MVAR Portfolio MVAR

0.00501 0.00199

0.00436 0.00190

0.00229 0.00213


0.01013


0.00550


Difference
High-Low

0.00107

0.00091*

0.00047


-0.00007




Difference
High-Low

0.00302**

0.00246**

0.00016


0.00463**


Panel C
Accounting Risk Proxy Variable APCHOICE

Time High Risk Low Risk
Horizon Portfolio MVAR Portfolio MVAR

Year 1 0.00212 0.00279

Year 2 0.00250 0.00175

Year 3 0.00237 0.00136

3 Year
Cumulative 0.00614 0.00645

a MVAR signifies mean abnormal return variance.

** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests


Difference
High-Low

-0.00067

0.00075*

0.00101**


-0.00031











versus low accounting risk portfolios. Specifically, the MVAR of the

high risk firms exceeds that of the low risk firms in each year, and

this difference is statistically significant in all but the third year.

Thus, Hypothesis 2 is strongly supported when FORM is used as a

surrogate for accounting risk.

Panel C provides evidence that supports Hypothesis 2 for only two

of the four time horizons when APCHOICE is employed to proxy for

accounting riskiness. Specifically, the high risk group's mean abnormal

return variance exceeds that of the low risk group at conventional

significance levels for years 2 and 3. In contrast, the MVAR difference

over year 1 and over the 36 month cumulative period is insignificantly

different from zero. Thus, Hypothesis 2 can be supported for only years

2 and 3 when APCHOICE is used to proxy for accounting risk.

In summary, the univariate tests of Hypothesis 2 reveal that the

hypothesis can be supported very strongly for the FORM proxy, and can be

marginally supported for the other two proxy variables. Therefore, the

results generally support the prediction of Hypothesis 2 that accounting

risk and the variance of IPO firms' aftermarket abnormal returns are

positively correlated. Hence, it appears that accounting risk

influences the absolute magnitude of long-term IPO mispricing, as

predicted by Hypothesis 2.


Results of Univariate Tests of Hypothesis 3


Hypothesis 3 states that the variance of IPO firms' abnormal

returns will decrease over time subsequent to the IPO. This reduction

in variance is attributed to a reduction in accounting risk over time as












more information is learned about these newly public firms. Table 5-7

reveals the results of univariate (portfolio) tests of this hypothesis.

Panels A, B, and C, respectively, provide the results obtained when

AUDITOR, FORM, and APCHOICE are used to proxy for accounting risk.

Panel A discloses that, consistent with the prediction of

Hypothesis 3, the mean abnormal return variance of the high accounting

risk firms (based on auditor quality) in year 1 exceeds that of the same

firms' MVAR in year 3. However, this difference is not statistically

significant. The same relationship holds true for the low accounting

risk firms. Thus, Hypothesis 3 is not supported for auditor quality

based accounting risk portfolios.

By contrast, Panel B reports a statistically significant decrease

in the high accounting risk (based on the FORM proxy) portfolio's MVAR

from year 1 to year 3. Thus, a significant decreasing linear trend over

the three year time horizon is documented for the high accounting risk

portfolio's MVAR, consistent with the prediction of Hypothesis 3.

However, an insignificant difference between the low accounting risk

portfolio's year 1 and year 3 MVAR is documented. Thus, Hypothesis 3 is

supported only for the high accounting risk firms when the SEC

registration statement type proxy variable is employed.

Finally, Panel C demonstrates empirical support for Hypothesis 3

only for the low accounting risk (based on APCHOICE) firms. The high

accounting risk portfolio's MVAR is demonstrated not to decrease over

the year 1 to year 3 time period. Thus, inconsistent results are found

when the accounting procedure choice proxy variable is used to test

Hypothesis 3.












Table 5-7
Univariate (Portfolio) Tests of Hypothesis 3


Accounting

Year 1
MVARa

0.00347

0.00240


Panel A
Risk Proxy Variable AUDITOR

Year 2 Year 3
MVAR MVAR

0.00313 0.00254

0.00222 0.00207


Difference
Yearl-Year3

0.00093

0.00033


Accounting

Year 1
MVAR

0.00501

0.00199


Panel B
Risk Proxy

Year 2
MVAR

0.00436

0.00190


Variable FORM

Year 3
MVAR

0.00229

0.00213


Difference
Yearl-Year3

0.00272**

-0.00014


Accounting

Year 1


Panel C
Risk Proxy Variable APCHOICE

Year 2 Year 3


Portfolio MVAR MVAR MVAR

High Risk 0.00212 0.00250 0.00237

Low Risk 0.00279 0.00175 0.00136

a MVAR signifies mean abnormal return variance.

** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests


Difference
Yearl-Year3

-0.00025

0.00143*


Portfolio

High Risk

Low Risk


Portfolio

High Risk

Low Risk











In summary, Hypothesis 3 cannot be strongly supported based on

these univariate tests. When AUDITOR is used to surrogate for

accounting risk, no significant differences are documented. When the

other two proxies are analyzed, inconsistent results are found.

Specifically, for both the FORM and APCHOICE proxies, the results

pertaining to one of the accounting risk portfolios support Hypothesis

3, while the other portfolio provides insignificant results. Hence, the

results reported in Table 5-7 do not provide convincing evidence that

IPO firms' abnormal return variances decrease over the first three years

of public trading. One possible reason for these results may relate to

the short time horizon examined in this study. Specifically, it is

possible that the effect predicted by Hypothesis 3 may occur gradually

over time, in which case it would not be detected over a short (e.g.,

three year) time horizon.10


Summary of Univariate Tests


The results of the univariate tests indicate that Hypothesis 1 can

be strongly supported for each accounting risk proxy variable.

Hypothesis 2 is strongly supported for the FORM proxy and marginally

supported for the AUDITOR and APCHOICE surrogates. Finally, the

empirical results support Hypothesis 3 only in isolated instances. As

is implied by the name "univariate tests," these tests are univariate in



10 Appendix B reports the results of testing Hypothesis 3 over a
longer five year time horizon. Over this longer time horizon,
univariate test results are documented which strongly support Hypothesis
3. Thus, there is some indication that the effect predicted by
Hypothesis 3 may only be empirically verified over a long time horizon.











nature and the results may not hold in a multivariate setting. In other

words, the results attributed to accounting risk in this section may

actually be the result of another factor (e.g., IPO firm size or

business riskiness) not explicitly controlled for in these univariate

analyses. The next section describes the results of multivariate

examinations of the three hypotheses.


Results of Multivariate Tests


Results of Multivariate Tests of Hypothesis 1


The multivariate tests of Hypothesis 1 consist of estimating the

cross-sectional multiple regression model described by Equation (18)

over four separate time horizons. Table 5-8 provides evidence of the

correlations among the independent variables utilized in the various

specifications of the model.11 The table indicates that a number of the

independent variables are correlated in a statistically significant

sense. However, only three of the correlations (excluding correlations

among the unexpected earnings variables) exceed .50 in absolute value,

and various model specification checks (to be described in more detail

later) indicated no serious multicollinearity problems among the

independent variables.

Table 5-9 provides details of the estimation of Equation (18) over

the four different time horizons. Specifically, Panels A, B, C, and D

report the results of estimating the model over the first year, second



11 It should be noted that the correlations among the four measures
of unexpected earnings are not of concern since only one of these
variables is utilized in each of the four estimated models.















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Table 5-9
Regression Approach to Testing Hypothesis 1


Model: ARIi a


Panel A
)i1AUDITORi + 02FORMi + 63APCHOICEi
71SIZEi + 72LNRISKSi + 3UEli + ei


t
Statistic

3.275**
-0.139
1.092
-0.243
-2.613**
3.030**
6.458**


Heteroscedasticity
Adjusted X2
Test Statistic


5.277#
0.017
0.882
0.054
3.414
8.760##
16.456##


Test of the Model Goodness of Fit: F-Value-8.535 P-Value=.0001

White's [1980] Homoscedasticity Test: x2-Value-183.59 P-Value-.000l

Model Adjusted R2 .1141 N-352


Panel B
Model: AR2i a + 01AUDITORi + P2FORMi + 83APCHOICEi
+ -y1SIZEi + 7-2LNRISKSi + 73UE2i + ei


Standard
Error


.021
.006
.007
.006
.002
.003
.022


t
Statistic

-0.539
1.565
0.981
-0.600
0.577
1.366
6.936**


Heteroscedasticity
Adjusted X2
Test Statistic


0.321
2.078
0.632
0.511
0.364
2.041
33.596##


Test of the Model Goodness of Fit: F-Value-9.441 P-Value-.000l

White's [1980] Homoscedasticity Test: x2-Value-281.08 P-Value=.000l


Model Adjusted R2 = .1277 N=347


Variable

INTERCEPT
AUDITOR
FORM
APCHOICE
SIZE
LNRISKS
UE1


Parameter
Estimate


.067
.001
.008
.001
.005
-.008
.187


Standard
Error


.020
.006
.007
.005
.002
.003
.029


Variable

INTERCEPT
AUDITOR
FORM
APCHOICE
SIZE
LNRISKS
UE2


Parameter
Estimate


.011
.010
.007
.003
.001
.004
150












Table 5-9--continued


Model: AR31 = a


Panel C
+ fi1AUDITORi + 02FORMi + P3APCHOICEi
+ 7y1SIZEi + y2LNRISKSi + fy3UE3i + ei


Standard
Error


.022
.007
.008
.006
.002
.003
.007


t
Statistic


1.113
-0.486
-0.732
-0.011
-1.281
-0.374
1.217


Heteroscedasticity
Adjusted x2
Test Statistic


1.483
0.213
0.487
0.000
1.983
0.116
0.460


Test of the Model Goodness of Fit: F-Value-0.542 P-Value=.7759

White's [1980] Homoscedasticity Test: x2-Value-195.85 P-Value-.000l

Model Adjusted R2 -.0087 N-318


Model: CAR36i -


Panel D
a + PIAUDITORi + 02FORMi + 03APCHOICEi
+ -y1SIZEi + -y2LNRISKSi + y3UE36i + ei


Standard
Error


.035
.011
.012
.010
.003
.005
.007


t
Statistic

1.713
0.031
0.539
0.232
-1.492
-1.088
6.061**


Heteroscedasticity
Adjusted X2
Test Statistic


2.007
0.001
0.256
0.054
1.537
1.246
12.586##


Test of the Model Goodness of Fit: F-Value-6.624 P-Value=.0001

White's [1980] Homoscedasticity Test: X2-Value-298.73 P-Value=.O00l

Model Adjusted R2 .0962 N=318

** significant at .01 level, based on two-tailed t tests
* significant at .05 level, based on two-tailed t tests
## significant at .01 level, based on X2 tests
# significant at .05 level, based on X2 tests


Variable

INTERCEPT
AUDITOR
FORM
APCHOICE
SIZE
LNRISKS
UE3


Parameter
Estimate


.025
-.003
-.006
-.000
-.003
-.001
.008


Variable

INTERCEPT
AUDITOR
FORM
APCHOICE
SIZE
LNRISKS
UE36


Parameter
Estimate


.060
.000
.007
.002
.005
.005
.041









91

year, third year, and 36 month cumulative time horizon.12 In each case,

White's [1980] test for the presence of heteroscedasticity revealed that

the null hypothesis of homoscedasticity could be rejected at the .0001

level. Thus, the error terms in the four specifications of the model

are heteroscedastic. In such cases, the estimated parameters are

unbiased but inefficient (i.e., they are not minimum variance

estimators), and the resulting parameter estimate covariance matrix is

inconsistent. Therefore, Table 5-9 reports test statistics based both

on an inconsistent covariance matrix (i.e., the typically utilized t

statistics) and on a heteroscedasticity consistent covariance matrix

(i.e., the heteroscedasticity adjusted X2 statistics based on White's

[1980] correction). The significance tests performed using the

heteroscedasticity consistent covariance matrix are asymptotic tests.

Unless otherwise noted, only the tests based on the heteroscedasticity

adjusted test statistics will be discussed in this section.13

The four specifications of Equation (18) were also examined for

the presence of multicollinearity. Diagnostic tests based on tolerance




12 It is important to note that the 36 month cumulative time
horizon is not independent of the other three time horizons since
cumulative abnormal returns are calculated as the sum of the abnormal
returns accruing over the first three years. Therefore, the four
regression models are not independent.

13 Weighted least squares (WLS) regressions were also estimated in
an attempt to overcome the heteroscedasticity problem. The residuals of
the estimated OLS models were examined in an attempt to determine the
nature of the heteroscedasticity and thus to aid in the determination of
the appropriate weighting factors. However, proper weighting factors
were not identified as a result of these residual analyses. Thus,
White's [1980] correction is utilized since its utilization appears to
be superior to employing WLS regressions with weighting factors chosen
on an ad hoc basis.









92

factors revealed no serious multicollinearity problems. An independent

variable's tolerance factor is calculated as l-R2k, where R2k is the

coefficient of multiple correlation which results when the independent

variable is regressed on all the other independent variables in the

model. This tolerance factor is often used "to detect instances where

an X variable should not be allowed into the fitted regression model

because of excessively high interdependence between this variable and

the other X variables in the model" (Neter et al. [1985, p. 393]).

Neter et al. continue by stating that frequently utilized tolerance

limits are .01, .001, and .0001, below which the independent variable

should be discarded from the model. In other words, unless the

calculated tolerance factor is below the chosen tolerance limit, the

independent variable is typically not dropped from the model since it is

not considered to possess excessive collinearity with any other

independent variable. In the four regressions reported in Table 5-9,

the lowest tolerance factor calculated for any independent variable is

.54. Thus, multicollinearity does not appear to pose a serious threat

in the four estimated regressions.14

Panel A of Table 5-9 provides the results of estimating Equation

(18) for the first year of public trading time horizon. As predicted by

Hypothesis 1, none of the accounting risk proxy variables is

statistically significant at conventional levels. Thus, consistent with



14 Analysis of the estimated residuals, as well as an examination
of Cook's D distance measures, was also performed in an effort to detect
outlying observations. The number of identified possible "outliers" was
very small in each regression. Equation (18) was then reestimated with
these potentially outlying observations dropped from the analysis. The
results did not qualitatively change from those reported in Table 5-9.











Hypothesis 1, accounting risk is not significantly correlated with IPO

firms' first year abnormal returns.

All three control variables exhibit (at least marginally)

significant relationships with the dependent variable. Consistent with

prior research, year 1 unexpected earnings is positively associated with

year 1 abnormal security returns. Also, firms which list a greater

number of risk factors in their prospectus (i.e., firms with greater

business risk) experience lower abnormal returns than firms which

enumerate fewer risk factors. However, SIZE is negatively associated

(at the .10 level) with first year abnormal returns. Specifically,

Panel A demonstrates that smaller firms (i.e., those with greater

business risk) experience greater abnormal first year returns. Thus,

conflicting results concerning the relationship of business risk and

first year abnormal returns are documented. These conflicting results

are consistent with the theory presented in Chapter 3 which indicates

that mean risk-adjusted abnormal returns should not be systematically

related to measures of risk.

Panel B shows that the three accounting risk variables also are

insignificant in the explanation of year 2 abnormal returns. Thus,

Hypothesis 1 is also supported for year 2 risk-adjusted returns. Again,

the widely documented positive association between unexpected earnings

and abnormal returns is demonstrated. In fact, unexpected earnings is

the only significant variable among the three control variables.

Therefore, neither SIZE nor LNRISKS contributes to the explanation of

second year abnormal returns.




Full Text
75
Empirical tests of Hypotheses 1 through 3 were performed utilizing
both the risk-adjusted and market-adjusted definitions of long-term
mispricing, with virtually no qualitative differences in results.8
Hence, only the results pertaining to the risk-adjusted measures are
reported in subsequent sections.
Unexpected accounting earnings over the first three years of
public trading, as well a 36 month cumulative unexpected earnings
measure are also provided in Table 5-3. With the exception of year 2
which has a P-Value of less than .01, the mean unexpected earnings
measures do not differ significantly from zero.
Makeup of Accounting Risk Portfolios
Table 5-4 reports the number of firms comprising each accounting
risk category (portfolio). For example, the table illustrates that 294
out of the 372 firms employed Big Eight auditing firms in the going
public process. Thus, the low accounting risk portfolio (based on
auditor quality) consists of 294 firms. Conversely, the high accounting
risk portfolio consists of the 78 firms which utilized non-Big Eight
auditors. Thus, the sample is dominated by Big Eight audited IPO firms.
adjusted returns of -25.14%. Thus, it appears that the differences
between the market-adjusted returns reported by Ritter and those
reported in Table 5-3 of the current study are largely due to
differences in the market indices employed in the two studies.
8 The Pearson correlation coefficients between the corresponding
market-adjusted and risk-adjusted long-term mispricing measures are in
the .75-,95 range. Thus, the two methods of calculating long-term
mispricing appear to be capturing the same phenomena.


127
five year time horizon, accounting risk portfolios based on the auditor
quality proxy no longer conform to the predictions of Hypothesis 2.
Table B-5 provides details of the univariate (portfolio) tests of
Hypothesis 3. Panels A, B, and C, respectively, provide the results
obtained when AUDITOR, FORM, and APCHOICE are utilized as surrogates for
accounting risk. The empirical methodology employed tests for a
significantly decreasing linear trend over the five year time horizon
based on a repeated measures ANOVA analysis. As stated in Chapter 4,
such a test is mathematically equivalent to a matched-pair (i.e., paired
comparison) t test of the difference between a portfolio's year 1 and
year 3 mean abnormal return variance in the special case where the
repeated measure (i.e., time in this case) takes on only three values.
However, in the more general case such as this one where the repeated
measure takes on more than three possible values, the linear trend test
is no longer equivalent to a matched-pair t test. The statistical tests
reported in Table B-5 are based on the F statistics which result from a
repeated measures ANOVA test for the presence of a significant linear
trend over the five year time horizon.
Table B-5 reveals that over this five year time horizon, a
significantly decreasing linear trend is documented for five of the six
accounting risk portfolios. Further, the remaining portfolio is
marginally significant, with a P-Value of .11. These results provide
much stronger support for Hypothesis 3 than those reported in Table 5-7
for the three year time horizon. Therefore, there is some indication
that the effect predicted by Hypothesis 3 is a gradual effect which is


120
Table B-2 reports the number of firms comprising each accounting
risk category (portfolio) in this 299 firm sample. The table is
consistent with the corresponding table for the 372 firm sample (i.e.,
Table 5-4) since in each case the sample is dominated by IPO firms which
(1) employ Big Eight auditors, (2) file SEC registration statement S-l,
and (3) utilize consistently income increasing accounting procedures for
inventory and depreciation.
Table B-3 reports the results of the univariate (portfolio) tests
of Hypothesis 1. Panels A, B, and C supply the results when AUDITOR,
FORM, and APCHOICE, respectively, are employed as proxies for accounting
risk. The results are qualitatively similar to those reported in Table
5-5 for the larger 372 firm sample. Specifically, the results reported
in Table B-3 strongly support the prediction of Hypothesis 1 that mean
IPO aftermarket abnormal returns will not differ based on the level of
accounting risk faced by investors, since in only one case do mean
abnormal returns differ significantly across accounting risk portfolios.
The results based on this five year data availability criterion
also generally support the prediction of Hypothesis 1 that the mean
abnormal return of each accounting risk portfolio will not differ
significantly from zero in each time horizon examined. As indicated by
Table B-3, only 9 out of 36 (i.e., 25%) portfolio mean abnormal returns
differ significantly from the hypothesized value of zero. Also, in
those instances where portfolio MARs do significantly differ from zero,
there appears to be an approximately equal chance that the return will
be greater than or less than zero. Thus, the portfolio mean abnormal


86
In summary, Hypothesis 3 cannot be strongly supported based on
these univariate tests. When AUDITOR is used to surrogate for
accounting risk, no significant differences are documented. When the
other two proxies are analyzed, inconsistent results are found.
Specifically, for both the FORM and APCHOICE proxies, the results
pertaining to one of the accounting risk portfolios support Hypothesis
3, while the other portfolio provides insignificant results. Hence, the
results reported in Table 5-7 do not provide convincing evidence that
IPO firms' abnormal return variances decrease over the first three years
of public trading. One possible reason for these results may relate to
the short time horizon examined in this study. Specifically, it is
possible that the effect predicted by Hypothesis 3 may occur gradually
over time, in which case it would not be detected over a short (e.g.,
three year) time horizon.10
Summary of Univariate Tests
The results of the univariate tests indicate that Hypothesis 1 can
be strongly supported for each accounting risk proxy variable.
Hypothesis 2 is strongly supported for the FORM proxy and marginally
supported for the AUDITOR and APCHOICE surrogates. Finally, the
empirical results support Hypothesis 3 only in isolated instances. As
is implied by the name "univariate tests," these tests are univariate in
10 Appendix B reports the results of testing Hypothesis 3 over a
longer five year time horizon. Over this longer time horizon,
univariate test results are documented which strongly support Hypothesis
3. Thus, there is some indication that the effect predicted by
Hypothesis 3 may only be empirically verified over a long time horizon.


50
Table 4-1
Univariate (Portfolio) Tests of Hypothesis 1
Time
Horizon
High Risk
Portfolio3
Low Risk
Portfolio15
Difference
High-Low
Year 1
MARHiCd
MARL1d
MARHi-MARL1'
Year 2
MARH2
MARL2
marH2 marl2
Year 3
marH3
marL3
marH3-marL3
3 Year
Cumulative
marhc
marlc
marhc marlc
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
c MARpt = portfolio p's mean abnormal return over time horizon t. Thus,
MARH1 = the mean abnormal return of the high accounting risk portfolio
over the first year of public trading, MARH2 the high risk portfolio's
second year mean abnormal return, etc.
d A two-tailed t test is conducted to determine whether each MARpt in
this column differs significantly from the hypothesized value of zero.
e A two-tailed two-population t test is conducted to determine whether
each difference statistic in this column differs significantly from the
hypothesized value of zero.


133
Table B-7
Regression Approach to Testing Hypothesis 2
Using 299 Firm Sample
Panel A
Model: VARlt = a + ^AUDITORi + ^2FORMi + /^APCHOICEi
+ y^IZEi + yjjLNRISKSi + 73ABSUE11 + e
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0093
.0021
4.526**
6.229##
AUDITOR
.0002
.0006
0.382
0.107
FORM
.0011
.0007
1.548
1.624
APCHOICE
-.0009
.0005
-1.709*
1.287
SIZE
-.0007
.0002
-3.582**
4.901#
LNRISKS
-.0002
.0003
-0.791
0.869
ABSUE1
-.0007
.0030
-0.243
0.112
Test of the Model Goodness of Fit: F-Value=5.086 P-Value=.0001
White's [1980] Homoscedasticity Test: x2'Value=792.17 P-Value=.0001
Model Adjusted R2 = .0790 N=287
Panel B
Model: VAR2t = a + ^AUDITORi + /^FORMi + ^APCHOICEi
+ y^IZEi + yzLNRISKSi + 73ABSUE2! + ei
Variable
Parameter
Estimate
Standard
Error
t
Statistic
Heteroscedasticity
Adjusted x2
Test Statistic
INTERCEPT
.0040
.0019
2.141*
4.536#
AUDITOR
.0001
.0006
0.195
0.030
FORM
.0003
.0007
0.506
0.153
APCHOICE
.0006
.0005
1.192
2.897@
SIZE
-.0003
.0002
-1.869*
3.336@
LNRISKS
.0002
.0002
0.898
1.039
ABSUE2
.0053
.0025
2.098*
3.437@
Test of the
Model Goodness
of Fit: F-
-Value=3.641
P-Value=.0017
White's [1980] Homoscedasticity Test: x2'^alue=206.65 P-Value=.0001
Model Adjusted R2 =
.0527 N=286


Table 5-3
Descriptive Statistics Relating to Measures of
Long-Term and Short-Term Mispricing, and Unexpected Earnings
73
Variable3
Mean
Median
Standard Deviation
Nb
UP
11.70%
2.78%
0.27
372
AR1
1.21%
1.49%
0.05
372
AR2
-0.35%
-0.29%
0.05
372
AR3
-0.35%
-0.63%
0.05
372
CAR36
0.50%
0.39%
0.08
372
RIMRM1
2.12%
-0.30%
0.59
372
RIMRM2
-4.19%
-3.15%
0.61
372
RIMRM3
-6.35%
-6.90%
0.59
372
RIMRM36
-8.42%
-10.95%
0.97
372
UE1
-0.44
0.00
8.29
371
UE2
-0.04
-0.01
0.11
366
UE3
0.01
0.00
0.39
337
UE36
-0.95
-0.02
15.05
337
3 The variables examined
in this table relate to various measures of
short-term
and long-term
IPO mispricing,
as well as measures
of
unexpected accounting earnings for the 372 sample firms. The variables
of interest are defined as follows:
UP = short-term underpricing of the initial offering price by the
underwriter. UP is calculated as: (P1-PIP0)/PIP0, where PI refers to
the first available closing bid price following the IPO and PIPO equals
the initial offering price set by the underwriter.
AR1, AR2, and AR3 represent the risk-adjusted abnormal returns as
generated from Equation (12) for the first, second, and third years of
public trading, respectively.
CAR36 = the cumulative risk-adjusted abnormal return over the
first 36 months of public trading as generated from Equation (12).
CAR36 is defined as the sum of AR1, AR2, and AR3.
RIMRM1, RIMRM2, and RIMRM3 represent market adjusted security
returns over the first, second, and third years of public trading,
respectively. These variables are calculated by subtracting the return
on the NASDAQ Composite (Market) Index from an individual IPO's raw
security returns over the appropriate time periods.
RIMRM36 = the sum of RIMRM1, RIMRM2, and RIMRM3. Thus, the
variable represents the cumulative market-adjusted return over the first
36 months of trading.
UE1, UE2, UE3, and UE36 are defined as unexpected accounting
earnings over the first year, second year, third year, and 36 month
cumulative time horizon, respectively.
b N = the number of firms involved in the calculation of the mean,
median, and standard deviation statistics.


THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS
By
JOHN D. NEILL, III
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1990


68
NASDAQ file are included in the final sample.3 This three year of
security return availability criterion is the only criterion utilized in
the sample selection process and results in the exclusion of 139 firms.
Thus, the final sample consists of 372 firms. Table 5-1 summarizes the
sample selection process employed in this study.
Table 5-1
Sample Selection Procedures
Total Available Microfiche Prospectuses of Non-Unit IPOs 511
Number of Firms Removed Due to a Lack of 3 Years of Data
on the 1988 Daily CRSP NASDAQ File 139
Final Sample 372
Much of the data required for this study was gathered from this
collection of microfiche registration statements. For example, the data
required to calculate the AUDITOR, FORM, and SIZE variables have been
collected from these registration statements and accompanying
prospectuses. Further, this microfiche collection was utilized in
conjunction with the Compustat Full-Coverage file in the collection of
the sample firms' accounting procedure choices for inventory and
depreciation.
Another significant portion of the required data was gathered from
"Ritter's 1975-1984 IPO Database.This database contains extensive
3 The entire empirical analysis was repeated using a more
restrictive five years of CRSP NASDAQ data availability criterion. This
more restrictive criterion resulted in a final sample of 299 firms.
Summary results of this additional analysis are reported in Appendix B.
A I wish to express thanks to Professor Jay Ritter for providing me
this extensive amount of data on IPO firms. Please see Ritter [1984b]
and Beatty and Ritter [1986] for detailed descriptions of the criteria
utilized in building this database.


Table B-4
Univariate (Portfolio) Tests of Hypothesis 2
Using 299 Firm Sample
125
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MVARa
Low Risk
Portfolio MVAR
Difference
Hizh-Low
Year 1
0.00305
0.00238
0.00067
Year 2
0.00251
0.00198
0.00053
Year 3
0.00215
0.00182
0.00033
Year 4
0.00145
0.00151
-0.00006
Year 5
0.00187
0.00164
0.00023
5 Year
Cumulative
0.00790
0.00918
-0.00128
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hizh-Low
Year 1
0.00478
0.00197
0.00281**
Year 2
0.00310
0.00184
0.00126
Year 3
0.00145
0.00199
-0.00054*
Year 4
0.00190
0.00140
0.00050
Year 5
0.00191
0.00163
0.00028
5 Year
Cumulative
0.01306
0.00796
0.00510**


93
Hypothesis 1, accounting risk is not significantly correlated with IPO
firms' first year abnormal returns.
All three control variables exhibit (at least marginally)
significant relationships with the dependent variable. Consistent with
prior research, year 1 unexpected earnings is positively associated with
year 1 abnormal security returns. Also, firms which list a greater
number of risk factors in their prospectus (i.e., firms with greater
business risk) experience lower abnormal returns than firms which
enumerate fewer risk factors. However, SIZE is negatively associated
(at the .10 level) with first year abnormal returns. Specifically,
Panel A demonstrates that smaller firms (i.e., those with greater
business risk) experience greater abnormal first year returns. Thus,
conflicting results concerning the relationship of business risk and
first year abnormal returns are documented. These conflicting results
are consistent with the theory presented in Chapter 3 which indicates
that mean risk-adjusted abnormal returns should not be systematically
related to measures of risk.
Panel B shows that the three accounting risk variables also are
insignificant in the explanation of year 2 abnormal returns. Thus,
Hypothesis 1 is also supported for year 2 risk-adjusted returns. Again,
the widely documented positive association between unexpected earnings
and abnormal returns is demonstrated. In fact, unexpected earnings is
the only significant variable among the three control variables.
Therefore, neither SIZE nor LNRISKS contributes to the explanation of
second year abnormal returns.


43
The Rit terms in Equation (12) represent IPO firm i's raw (i.e.,
unadjusted) returns over month t following the initial offering. These
monthly returns are constructed from the CRSP NASDAQ daily returns file
utilizing a procedure similar to that employed by Ritter [1989]. The
return for month 1 is calculated as the multiplicative return over the
21 trading day period beginning with the day following the IPO.10 Thus,
returns are calculated based on "event time" (with the event in question
being the IPO), rather than calendar time. The monthly returns for
months subsequent to month 1 are then calculated using the returns on
successive 21 trading day periods. In equation form ^it is calculated
21+21(t-l)
Rit n (1 + Rid) 1, (13)
d=l+21(t-l)
where Rid = firm i's raw return on day d where d=l represents the day
following the IPO, and t denotes the number of months subsequent to the
initial offering.
A monthly market portfolio return series (i.e., R^,.) is
constructed for each IPO firm corresponding to the firm's "event time"
monthly periods. This monthly market return series is calculated in the
same manner as the Rit series, with the exception that the daily returns
utilized in the construction of the monthly returns are the returns on
the NASDAQ Composite Index, rather than an individual IPO firm's raw
returns. Since each of the sample firms examined in this study is
traded over-the-counter, the NASDAQ Composite Index appears to be a more
10 It is important to note that the return on the first day of
public trading is the commonly examined one-day underpricing return.
Therefore, the empirically documented short-term underpricing phenomenon
is not included in the long-term returns examined here.


106
year 1. Thus, Hypothesis 3 is not supported for the accounting
procedure choice risk proxy.
Panel A also allows the determination of the change in explanatory
power of the control variables over the three year time horizon. The
significantly negative coefficients on the SIZE interaction terms
indicate that the estimated SIZE parameter becomes more negative over
time. Since SIZE is predicted to be negative (i.e., smaller firms
should have greater abnormal return variability since they possess
greater business risk), these results indicate that SIZE increases in
explanatory power over the three year period. However, the estimated
parameters on the other business risk control variable LNRISKS do not
significantly differ in years 2 and 3 from year 1. Finally, the
unexpected earnings variable increases in explanatory power in years 2
and 3 in relation to year 1, even though the difference is significant
only in year 2.
The results reported in Panel A indicate changes in the regression
parameters pertaining to years 2 and 3 from those of year 1. However,
the table provides no indication of the changes in the parameters from
year 2 to year 3. Panel B of Table 5-12 provides this additional
information by reporting the results of tests of linear hypotheses which
examine whether the estimated regression parameters pertaining to year 2
exceed those of year 3. Significant differences in the estimated
parameters of years 2 and 3 are demonstrated only for the FORM, SIZE,
and ABSUE variables. Specifically, consistent with Hypothesis 3, the
SEC registration form proxy explains less of third year abnormal return
variances than second year variances. Similarly, the unexpected


59
To alleviate this potential misalignment problem, the random walk
earnings expectation model is adjusted in order that the earnings series
becomes aligned with the security returns series. Specifically, IPO
firm i's unexpected earnings for period t is calculated as follows:
UEit), = [ (EPSit EPS^) (Nlit / 12) + (EPSit+1 EPSit) (N2it / 12)]
/ PRICEit, (19)
where EPS signifies annual earnings per share, PRICE designates the
security price per share,23 and N1 and N2 are adjustment factors
utilized to bring the earnings series into alignment with the returns
series.
As an example of how Equation (19) is used in the determination of
unexpected earnings, the calculation of firm i's unexpected earnings
over the first year of public trading (i.e., UEl^ will be illustrated.
In the calculation of UE^, the time subscript t equals 1 throughout
Equation (19). EPS^ signifies firm i's annual earnings per share as of
the first fiscal year end subsequent to the IPO. For example, if a
December 31 FYE firm went public on August 1, 1980, EPSil would
represent the firm's earnings per share for the year ended December 31,
1980. The period of time (rounded to the nearest month) which elapses
between the IPO date and this first FYE subsequent to the initial offer
is designated Nln months. In the above example, Nlu equals the five
month period from August 1, 1980 to December 31, 1980. N2U then
represents the number of months from this first FYE following the IPO to
the date one year subsequent to the IPO (i.e., the end of the first year
23 Both the earnings per share and the price per share series are
adjusted for stock dividends and stock splits.


8
mean abnormal aftermarket returns of IPO investors. However, these same
accounting choices (with the exception of auditor quality) influence the
variance of investors' aftermarket abnormal returns. Thus, the results
indicate that uncertainty relating to accounting quality translates into
an increased variance of IPO abnormal returns, rather than a systematic
overvaluation or undervaluation of IPOs by investors. However, the
empirical results do not support the third hypothesis. Specifically,
the results do not reveal a strong tendency for the accounting quality
proxy variables to decrease in their ability to explain abnormal return
variances as time progresses subsequent to the initial offering.


36
engagement partners in large decentralized auditing firms may have the
same incentives in regards to compromising their independence as
partners in small local/regional firms. In each case, a particular
audit client may represent a significant proportion of the revenue
generated by a partner. Therefore, audit engagement partners in large
decentralized firms may not consider the ramifications of certain
actions on the firm as a whole. Thus, the predictions of DeAngelo's
model may not hold in large decentralized audit firms. Finally, it is
possible to dispute the argument that large audit firms have greater
expertise in industry-related accounting matters. Specifically,
regional auditing firms may specialize in the predominant industry in
that region. For example, a regional auditing firm in Texas may have
greater expertise in the oil and gas industry than a large national firm
headquartered in New York. The preceding arguments against the presumed
positive association between audit quality and audit firm size imply
that the Big Eight, non-Big Eight dichotomy employed in this study may
not accurately proxy for accounting risk.
Accounting Procedure Choice Proxy
The second accounting risk surrogate utilized in this study is
measured according to whether the accounting numbers provided to
potential IPO investors were calculated by utilizing income decreasing
(i.e., "conservative") or income increasing (i.e., "liberal") accounting
methods. This proxy is utilized in recognition of the potential of a
firm's managers (or entrepreneurs in the IPO context) to manipulate


107
earnings variable possesses higher explanatory power in year 2 than in
year 3. Finally, the estimated parameter on the size variable is more
negative in year 2 than in year 3. Thus, this business risk proxy loses
explanatory power as time progresses from the second to the third year
subsequent to the IPO (since SIZE is predicted to be negative).
In summary, the results reported in Table 5-12 support Hypothesis
3 only for the FORM accounting risk proxy. The explanatory power of
FORM is demonstrated to decrease from year 1 to year 3, as predicted by
Hypothesis 3. Thus, accounting risk (based on the SEC registration type
proxy) becomes a less important factor in explaining IPO abnormal return
variances over time as more information about these firms becomes
publicly known. However, the AUDITOR and APCHOICE accounting risk
proxies do not conform to the predictions of Hypothesis 3. Thus, with
the exception of the FORM accounting risk proxy, the data do not support
Hypothesis 3 in this multivariate setting.20
Summary of Multivariate Tests
The results reported in this section demonstrate that the evidence
obtained via univariate tests of Hypotheses 1 through 3 also generally
hold in a multivariate environment. Specifically, Hypothesis 1 is
strongly supported in a cross-sectional multiple regression analysis.
Thus, even after controlling for possible confounding factors (e.g.,
20
Appendix B reports the results of testing Hypothesis 3 in a
multivariate setting over a longer five year time horizon. The results
based on this longer five year time horizon are consistent with those
documented in Table 5-12 for the three year time horizon. Specifically,
Hypothesis 3 is not supported in a multivariate setting even over a
longer time horizon.


54
trend test is mathematically equivalent to a test of the difference in
the variable of interest (i.e., MVAR) between time period 1 and time
period 3.20
In these specific circumstances in which the repeated measure only
takes on three possible values, a matched-pair (i.e., paired comparison)
t test of the difference between year 1 and year 3 mean abnormal return
variances is mathematically equivalent to the repeated measures ANOVA
test of linear trend. Since Hypothesis 3 is a one-directional
hypothesis, the t test procedure is implemented since it allows one
sided hypothesis tests, whereas the F statistics produced by the ANOVA
procedure are solely two-sided tests. Table 4-3 illustrates these
tests.
Multivariate Tests
Multiple Regression Test of Hypothesis 1
Hypothesis 1 may alternatively be tested via the following cross-
sectional regression models:
AR(t)i = a + ^AUDITORi + )92FORMi + ^3APCHOICEi
+ yiSIZEi + yzLNRISKSi + 73UE(t)i + e^ (18)
20 Keppel states that "trend analysis requires a different set of
special coefficients for each of the orthogonal trend components to be
extracted from the data" (Keppel [1982, p. 135]). The special
coefficients required to test for a decreasing linear trend when the
repeated measure takes on only three values are +1, 0, and -1.
Specifically, a portion of the calculated test statistic consists of the
sum of +1 times the observation for year 1, 0 times the year 2
observation, and -1 times the year three observation. The resulting
statistical test is mathematically equivalent to a test of the
difference between period 1 and period 3 observations.


31
Accounting Risk and the Variance of Abnormal Returns
The rational expectations assumption implies that even though
investors' estimates of future payouts are unbiased even in the presence
of high accounting risk, the precision (variance) of these estimates
decreases (increases) with accounting risk. This increase in the
variability of cash flow estimates should translate into greater
variability in returns. In fact, just such an increase in variability
can be demonstrated in a CAPM framework.
The variance of a security's returns according to the CAPM (in
realized form) is
Var(Ri) = ^Vard^) + Vare,) (11)
where Var(R,) = the variance of security i's returns, Var(Rfl) = the
variance of the return on the market portfolio, and the remaining terms
are as previously defined.
As previously noted, Equations (5) and (8) demonstrate that an
increase in accounting risk results in an increase in both systematic
(i.e., £,) and unsystematic (i.e., Var(e,)) risk. Therefore, it follows
directly from Equation (11) that the variance of a security's returns
increases with accounting risk. The preceding assertion forms the basis
for the second hypothesis, which may be stated as
Hypothesis 2: The variance of IPO firms' abnormal returns
is positively associated with accounting risk.
Thus, it is consistent with rational expectations that the absolute
magnitude (rather than the direction) of IPO mispricing is related to
the level of accounting risk surrounding an issue, even though IPOs are
not systematically mispriced.


Table 5-11
Regression Approach to Testing Hypothesis 2
97
Panel A
Model: VAR1 = a + ^AUDITORi + )92FORMi + ^APCHOICEi
+ yjSIZEj^ + 72LNRISKS + 73ABSUE1 + e
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0071
.0017
4.148**
6.132##
AUDITOR
.0001
.0005
0.246
0.044
FORM
.0011
.0006
1.891*
2.596
APCHOICE
-.0009
.0005
-1.898*
1.670
SIZE
-.0005
.0002
-2.990**
4.088#
LNRISKS
-.0000
.0002
-0.062
0.006
ABSUE1
.0003
.0027
0.120
0.017
Test of the Model Goodness of Fit: F-Value=5.476 P-Value=.0001
White's [1980] Homoscedasticity Test: x2-Value-235.44 P-Value-.0001
Model Adjusted R2 .0711 N-352
Panel B
Model: VAR2t a + ^AUDITOR* + ^FORM* + ^APCHOICEi
+ 71SIZEi + yzLNRISKSi + 73ABSUE2 + eA
Heteroscedasticity
Variable
Parameter
Estimate
Standard
Error
t
Statistic
Adjusted x2
Test Statistic
INTERCEPT
.0040
.0018
2.176*
4.391#
AUDITOR
-.0001
.0005
-0.100
0.008
FORM
.0011
.0006
1.742*
1.496
APCHOICE
.0005
.0005
1.089
2.208
SIZE
-.0004
.0002
-2.004*
3.637@
LNRISKS
.0002
.0002
1.040
1.169
ABSUE2
.0100
.0021
4.671**
7.876##
Test of the
Model Goodness
of Fit: F-
Value9.194
P-Value.0001
White's [1980] Homoscedasticity Test: xz-Value-242.52 P-Value=.0001
Model Adjusted R2 =
.1244 N-347


66
univariate tests that it is accounting risk that is responsible for any
observed empirical relationships, rather than some other factor such as
IPO firm size or inherent cash flow riskiness.
In contrast, the multiple regression techniques are multivariate
analyses which allow the determination of the incremental effects of
this study's three measures of accounting risk on long-term mispricing.
The regression approach also allows for the controlling of potentially
confounding influences on the relationships of interest. The technique
also allows a direct assessment of Hypothesis 3, the change in
explanatory power of the accounting risk variables over time. However,
the regression method provides only an indirect test of the validity of
Hypothesis 1. Specifically, while the regression technique indicates
whether accounting risk and abnormal returns are significantly
correlated, it provides no indication of whether IPOs are mispriced on
average.
Thus, there are advantages and disadvantages associated with both
the univariate (portfolio) and multivariate (regression) approaches.
Due to the complementary nature of the two approaches, both are employed
in the testing of the three hypotheses.


51
N
MVARpt Z VARit / N, (17)
where MVARpt the mean abnormal return variance over time period t of
the firms comprising portfolio p, and the remaining variables are as
previously defined. This mean portfolio variance figure is computed
separately for the first, second, and third years of public trading, as
well as over a 36 month cumulative period. Then, the empirical tests of
Hypothesis 2 merely involve assessing via two-population t tests18
whether MVARpt is significantly greater for high accounting risk
portfolios than for low accounting risk portfolios over each of the four
time horizons. Table 4-2 illustrates these tests.
Univariate Tests of Hypothesis 3
Hypothesis 3 states that the variance of IPO firms' aftermarket
abnormal returns decreases over time. This decrease in abnormal return
variance is hypothesized to be attributable to decreasing accounting
risk over time as more information becomes known about these newly
publicly held firms.
Univariate tests of Hypothesis 3 employ the same accounting risk
portfolios constructed for the testing of Hypotheses 1 and 2. The
empirical tests of the third hypothesis seek to determine whether the
mean abnormal return variance of each portfolio decreases over the three
year time horizon. Hypothesis 3 implies a monotonic decrease in the
18
See footnote 17.


135
Table B-7--continued
Panel E
Model: VAi^ = a + ^AUDITORi + ^92FORMi + /^APCHOICE^
+ y^IZEj. + 72LNRISKS + yjABSUESi +
Heteroscedasticity
Parameter
Standard
t
Adjusted *2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0004
.0012
0.316
0.085
AUDITOR
.0003
.0003
0.901
0.697
FORM
.0001
.0004
0.149
0.014
APCHOICE
.0002
.0003
0.840
0.780
SIZE
.0000
.0001
0.347
0.115
LNRISKS
.0002
.0001
1.495
2.480
ABSUE5
.0004
.0001
4.000**
12.341##
Test of the
Model Goodness
of Fit: F-
Value4.264
P-Value-.0004
White's [1980] Homoscedasticity Test:
X2-Value829
.74 P-Value-.0001
Model Adjusted R2 = .0752
N-242
Panel
F
Model: VAR60i = a
+ ^AUDITORi + /^FORMi +
^3APCHOICEi
+
yiSIZEi + yjjLNRISKSi + yaABSUEOi + et
Heteroscedasticity
Parameter
Standard
t
Adjusted xZ
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0254
.0061
4.173**
13.926##
AUDITOR
-.0039
.0018
-2.159*
6.326##
FORM
.0039
.0021
1.862*
2.919@
APCHOICE
-.0029
.0016
-1.850*
3.027@
SIZE
-.0019
.0006
-3.109**
8.346##
LNRISKS
-.0006
.0008
-0.776
0.703
ABSUE60
.0039
.0006
6.423**
56.817##
Test of the
Model Goodness
of Fit: F-
Value-12.338
P-Value.0001
White's [1980] Homoscedasticity Test: x2_Value=670.12 P-Value-.0001
Model Adjusted R2 = .2201 N-242
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on \Z tests
# significant at .05 level, based on \Z tests
@ significant at .10 level, based on *2 tests


16
influence the short-term underpricing of the issue by his auditor
choice.4
Long-Term Mispricing of IPOs
Summary Results
As indicated in Chapter 1, long-term mispricing refers to the
accuracy of the market's initial valuation of an IPO. The accuracy of
this potentially "uninformed" first aftermarket price is assessed in
relation to security prices which prevail after additional information
about the firm is revealed, via examination of abnormal aftermarket
returns. In contrast to the extensive literature on short-term IPO
mispricing, little prior research exists on the long-term mispricing of
initial security offerings. Also, the evidence to date is not
conclusive as to whether, on average, market participants overvalue,
undervalue, or correctly value initial offerings of securities. Table
2-2 summarizes the results of prior examinations of long-term IPO
mispricing.
With the exception of Ritter [1989], cross-sectional explanations
of long-term mispricing are unavailable. Ritter documents considerable
variation in the level of long-term mispricing across industries and
4 The impact of other accounting choices on initial underpricing
has not been examined. For example, prior research has not explored the
effect of the accounting procedures chosen by IPO firms or the amount
and content of accounting information disclosed on short-term
underpricing. Even though this dissertation examines the effect of
accounting numbers on long-term IPO mispricing, some exploratory results
of the impact of certain accounting choices on short-term underpricing
are provided in Appendix A.


6 CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
109
Conclusions 109
Suggestions for Future Research Ill
APPENDICES
A SHORT-TERM UNDERPRICING AND ACCOUNTING RISK 113
B EMPIRICAL RESULTS BASED ON A FIVE YEAR TIME HORIZON.... 119
REFERENCES 143
BIOGRAPHICAL SKETCH 147
v


CHAPTER 2
PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS
Introduction
Bower's [1989] specification of the two types of mispricing
inherent in IPO valuation is utilized in this chapter as an aid in
classifying and summarizing the relevant prior IPO research.1 Thus,
this chapter will review in detail (1) the short-term "underpricing"
literature and (2) the extant long-term mispricing literature.
Short-Term Mispricing of IPOs
Summary Results
As indicated in Chapter 1, the empirical evidence on the short
term average underpricing of initial offers is extensive. In his review
of this area of research, Smith [1986] reports that the average level of
underpricing exceeds 15%. In other words, on average, an investor could
purchase an initial offering at the price set by the underwriter and
then sell the security at the prevailing market price at the end of the
1 This classification scheme ignores all IPO research not
pertaining to the "mispricing" of the security issue. Thus, the major
line of research relating to the entrepreneur's ability to convey the
firm's "type" to investors via various signaling mechanisms will not be
reviewed here. The interested reader is referred to Leland and Pyle
[1977], Downes and Heinkel [1982], Ritter [1984a], Titman and Trueman
[1986], Hughes [1986], Feltham et al. [1988], Krinsky and Rotenberg
[1989], and Gale and Stiglitz [1989].
9


CHAPTER 6
CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
Conclusions
This dissertation extends prior research in two ways. First, the
long-term mispricing of initial public security offerings is reexamined
by utilizing a refined model of equilibrium security pricing on a sample
of recent IPOs. Second, the impact of accounting numbers on long-term
IPO mispricing is explored.
The hypotheses are developed (and empirically tested) that
uncertainty surrounding the quality of the accounting numbers available
to estimate IPO firm value (i.e., accounting risk) affects the variance
rather than the mean of investors' aftermarket abnormal returns. Thus,
accounting risk is hypothesized to influence the precision (i.e., the
absolute magnitude of mispricing) of investors' initial valuations of
IPOs even though such valuations are unbiased (i.e., not systematically
overvalued or undervalued). However, the third hypothesis predicts that
the variance of investors' aftermarket abnormal returns will decrease
over time. This third hypothesis is predicated on the assumption that
accounting risk declines over time as more information becomes publicly
known about these previously privately held companies.
The research conducted in this dissertation is intended to provide
an indication of the role that accounting reliability plays in the
109


Table B-5
Univariate (Portfolio) Tests of Hypothesis 3 Using 299 Firm Sample
Panel A
Accounting Risk Proxy Variable = AUDITOR
Portfolio
Year 1
MVARa
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00305
0.00238
0.00251
0.00198
0.00215
0.00182
0.00145
0.00151
0.00187
0.00164
4.05*
6.16**
Accounting Risk
Panel B
Proxy Variable
= FORM
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00478
0.00197
0.00310
0.00184
0.00145
0.00199
0.00190
0.00140
0.00191
0.00163
7.01**
3.35@
Panel C
Accounting Risk Proxy Variable
= APCHOICE
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00206
0.00285
0.00222
0.00160
0.00209
0.00131
0.00169
0.00093
0.00181
0.00131
2.64
4.78*
MVAR signifies mean abnormal return variance.
*
* significant at .05 level @ significant at .10 level oo
significant at .01 level


126
Table B-4--continued
Panel C
Accounting Risk Proxy Variable APCHOICE
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00206
0.00285
-0.00079
Year 2
0.00222
0.00160
0.00062*
Year 3
0.00209
0.00131
0.00078**
Year 4
0.00169
0.00093
0.00076**
Year 5
0.00181
0.00131
0.00050
5 Year
Cumulative
0.00810
0.00977
-0.00167
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests


105
variables II and 12 are utilized to represent observations relating to
the second and third year subsequent to initial offering, respectively.
Thus, the estimated parameters on the noninteraction terms represent the
parameter estimates for year 1. As expected, the direction and
significance levels of these noninteraction parameters are consistent
with those reported in Panel A of Table 5-11, since that panel also
reports the results of the abnormal return variance regression for
year 1 observations.
In the indicator variable configuration described by Equation
(21), the parameters on the II (12) interaction terms represent chanees
in the respective parameter estimates from year 1 to year 2 (3). Panel
A indicates that the estimated parameters on the AUDITOR accounting risk
proxy variable are not significantly different in either year 2 or 3
than in year 1. Therefore, Hypothesis 3 is not supported for the audit
quality variable. Similarly, the table shows that the estimated FORM
parameter is significantly lower in year 3 than in year 1. Thus, the
FORM variable explains less of third year abnormal return variances than
first year variances. This result is consistent with the assertion of
Hypothesis 3 that accounting risk should play a lesser role in
explaining abnormal return variances over time as more information about
IPO firms becomes publicly known. Finally, the estimated interaction
terms relating to the APCHOICE variable indicate that the change in
explanatory power of APCHOICE over time runs counter to the prediction
of Hypothesis 3. Specifically, APCHOICE is shown to explain more of the
variation in the dependent variable in both years 2 and 3 than in


34
reputation, the degree of conservatism inherent in a firm's inventory
and depreciation accounting procedure choices, and the type of SEC
registration statement filed, respectively, are employed in this study
as empirical surrogates for these three aspects of accounting risk. The
remainder of this section provides detailed descriptions of the three
accounting risk proxy variables.
Auditor Reputation (Quality) Proxy
The reputation (or quality) of the auditor utilized in the initial
public offering process serves as the first proxy for accounting risk.
The primary function of the auditor in the IPO process is to express an
opinion on the IPO firm's financial statements. Therefore, it is
reasonable to assume that a high quality audit lends credibility to the
financial statements, thereby reducing accounting risk. Conversely, a
low quality audit adds little credibility to the issuer's financial
statements, and thus investors' level of uncertainty surrounding the
reliability and accuracy of accounting numbers will be correspondingly
greater. Thus, audit quality is a surrogate for the level of
credibility associated with reported accounting numbers, which is an
aspect of accounting riskiness.
An IPO firm's accounting risk (based on auditor quality) is
measured in terms of whether the accounting numbers supplied to
potential investors at the time of the initial offer were audited by a


103
Table 5-12--continued
Panel B
Tests of Linear Hypotheses to Determine Whether the Independent
Variables Explain More of Year 2 Variances Than Year 3 Variances
Linear
Hypothesis
Difference
in Parameters
Non-Hetero-
scedasticity
Adjusted F
Test Statistic
Hetero-
scedasticity
Adjusted xZ
Test Statistic
AUDITOR*I1-AUDITOR*I2=0
-.0004
0.286
0.271
FORM*11- FORM*12=0
.0019
5.116#
3.010@
APCH0ICE*I1-APCHOICE*I2=0
-.0002
0.069
0.127
SIZE*I1-SIZE*I2=0
-.0001
1.443
3.535@
LNRISKS*I1-LNRISKS*I2=0
.0002
0.814
0.670
ABSUE*I1-ABSUE*I2-0
.0080
14.907##
4.743#
**
significant
at
.01
level,
based
on
one-tailed t
*
significant
at
.05
level,
based
on
one-tailed t
##
significant
at
.01
level,
based
on
x2
or F
tests
#
significant
at
.05
level,
based
on
x2
or F
tests
significant
at
.10
level,
based
on
x2
or F
tests
tests


134
Table B-7--continued
Panel C
Model: VAR3 = a + ^AUDITORi + ^2F0RMi + sAPCHOICEi
+ y^IZEi + 72LNRISKS + 73ABSUE3i + et
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0009
.0016
0.550
0.367
AUDITOR
.0004
.0005
0.909
0.812
FORM
-.0014
.0006
-2.398**
4.744#
APCHOICE
.0008
.0004
1.775*
6.610##
SIZE
.0000
.0002
0.014
0.000
LNRISKS
.0005
.0002
2.489**
4.233#
ABSUE3
.0007
.0007
1.053
0.618
Test of the Model Goodness of Fit: F-Value=2.776 P-Value=.0123
White's [1980] Horaoscedasticity Test: x2'Value=570.63 P-Value=.0001
Model Adjusted R2 .0364 N=283
Panel D
Model: VAR4a = a + ^AUDITORi + ^2F0RMi + ^APCHOICEi
+ 7XSIZE + 72LNRISKS + 73ABSUE4 + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0029
.0012
2.301**
6.771##
AUDITOR
-.0004
.0004
-0.924
0.653
FORM
.0001
.0005
0.185
0.017
APCHOICE
.0006
.0003
1.876*
5.552#
SIZE
-.0002
.0001
-1.809*
4.015#
LNRISKS
.0001
.0002
0.533
0.260
ABSUE4
.0009
.0003
2.452**
12.008##
Test of the
Model Goodness
of Fit: F-
-Value=3.533
P-Value=.0022
White's [1980] Homoscedasticity Test: x2_^alue=204.27 P-Value=.0001
Model Adjusted R2 =
.0522 N=277


57
requires firms undertaking IPOs to enumerate important "risk factors"
which will aid prospective investors in assessing the riskiness of the
offering. The SEC may choose to delay an offering through a "deficiency
letter" until the firm provides adequate details concerning the
riskiness of its operations, its financing obligations, etc. Hence,
Beatty [1989b] argues that riskier IPO firms provide greater disclosure
in the "risk factors" section of the prospectus than less risky firms.
Finally, UE(t)i is included in the regression models to control for the
widely documented empirical result that abnormal security returns are
positively associated with "unexpected" accounting earnings.
Equation (18) is estimated cross-sectionally for four different
specifications of the dependent variable (i.e., long-term mispricing).
Specifically, the equation is separately estimated for abnormal returns
calculated over the first, second, and third year of public trading, as
well as a 36 month cumulative return.
AUDITOR is measured as a dichotomous variable. It is coded 1 in
cases where non-Big Eight auditing firms are utilized in the IPO process
and 0 for Big Eight firms. FORMi is similarly measured in a categorical
fashion. The variable is coded 1 for S-18 registrations and 0 for S-l
registrations. APCHOICEj^ is assigned a value of 1 for firms which
employ consistently income increasing (i.e., "liberal") inventory and
depreciation accounting procedure choices and 0 otherwise. SIZEi is
calculated as the natural logarithm of IPO firm i's total book value of
assets as provided in the prospectus, and LNRISKSj^ is defined as the
natural logarithm of one plus the number of risk factors listed in firm
i's prospectus.


139
Table B-8--continued
Panel B
Tests of Linear Hypotheses to Determine Whether the Independent
Variables Explain More of the Variances
in Early Years Than in Later Years
Non-Hetero-
Hetero-
scedasticity
scedasticity
Linear
Difference
Adjusted F
Adjusted x2
HvDothesis
in Parameters
Test Statistic
Test Statistic
AUDITOR*I1-AUDITOR*I2=0
-.0002
0.098
0.077
AUDITOR*I1-AUDITOR*I3=0
.0005
0.518
0.444
AUDITOR*I1-AUDITOR*14=0
.0000
0.007
0.007
AUDITOR*I2-AUDITOR*I3=0
.0007
1.045
1.189
AUDITOR*12 -AUDITOR*I4=0
.0002
0.046
0.063
AUDIT0R*I3-AUDITOR*I4=0
-.0005
0.594
0.957
F0RM*I1-F0RM*I2=0
.0019
6.219##
3.386@
FORM*I1-FORM*13=0
.0003
0.205
0.114
FORM*I1-FORM*I4=0
.0006
0.624
0.427
F0RM*I2 -F0RM*I3=0
-.0016
4.018#
3.331@
F0RM*I2-FORM*I4=0
-.0013
2.629
2.945@
FORM*13 -FORM*14=0
.0003
0.116
0.133
APCH0ICE*I1-APCHOICE*I2=0
.0001
0.008
0.012
APCHOIC E*I1-APCHOIC E*13=0
.0001
0.008
0.014
APCH0ICE*I1-APCHOICE*I4=0
.0006
0.787
1.493
APCHOICE*I2-APCHOICE*I3=0
.0000
0.000
0.000
APCHOICE*I2-APCHOICE*I4=0
.0005
0.641
1.593
APCHOICE*I3-APCHOICE*I4=0
.0005
0.628
1.835
SIZE*I1-SIZE*I2=0
.0000
0.596
1.282
SIZE*I1-SIZE*I3=0
.0000
0.037
0.076
SIZE*I1-SIZE*I4=0
.0000
0.377
0.910
SIZE*I2-SIZE*I3=0
.0000
0.350
0.856
SIZE*I2-SIZE*I4=0
.0000
0.019
0.058
SIZE*I3-SIZE*I4=0
.0000
0.189
0.524
LNRISKS*I1-LNRISKS*I2=0
-.0001
0.109
0.081
LNRISKS*I1-LNRISKS*I3=0
.0002
0.699
0.788
LNRISKS*I1-LNRISKS*I4=0
.0003
0.906
1.300
LNRISKS*I2 -LNRISKS*I3=0
.0003
1.351
1.178
LNRISKS*I2-LNRISKS*I4=0
.0004
1.611
1.705
LNRISKS*I3-LNRISKS*I4=0
.0001
0.019
0.038
ABSUE*I1-ABSUE*I2=0
.0046
4.078#
2.512
ABSUE*I1-ABSUE*I3=0
.0045
4.169#
2.661@
ABSUE*I1-ABSUE*I4=0
.0051
5.424#
3.347@
ABSUE*I2-ABSUE*I3=0
-.0001
0.009
0.007
ABSUE*I2-ABSUE*I4=0
.0005
0.410
0.268
ABSUE*I3-ABSUE*I4=0
.0006
1.231
4.391#
## significant at .01 level, based on *2 or F tests
# significant at .05 level, based on x2 r F tests
@ significant at .10 level, based on \2 or F tests


42
S-18 offering should lead to an increase in accounting risk, and hence
the type of SEC registration statement filed comprises the third proxy
for accounting risk. This third surrogate is intended to capture the
quantity (i.e., extent of disclosure) aspect of the accounting risk
theoretical construct.
Measurement of Risk-Adiusted Abnormal Security Returns
In order to test Hypotheses 1 through 3, aftermarket abnormal
security returns (i.e., long-term mispricing) must be calculated. A
modified version of Simon's [1989] multifactor security pricing model is
employed in the measurement of such excess returns.9 The model utilized
is
Rit Rft 0i(Rnt RFt> + M industry,t Rt RFt)
+ S QjiDjt + it, (12)
ji
where Rit = the return on firm i in time period t, RFt = the return on
riskless assets over time t, R^,. = the time t return on the market
portfolio, INDUSTRY,,. = the equally-weighted average return of firms in
the same industry (based on 2-digit SIC codes) as firm i during time
period t, Djt are time period specific dummy variables which are
designed to capture abnormal returns, /?,, 5,, and 9j, are regression
parameters, and e,t is an error term.
9 Simon's model includes factors relating to business cycle
activity and the unanticipated portion of the variance in market returns
in addition to the variables employed in this study in the specification
of the equilibrium return generating process.


Ill
The results documented in this study indicate that investors do
not systematically overvalue or undervalue IPOs even in the face of
great uncertainty concerning the quality of the accounting numbers
supplied to them in the offering firm's prospectus. However, the data
are consistent with the hypothesis that the variance of IPO investors'
abnormal aftermarket returns increases as the level of uncertainty
surrounding the quality of accounting disclosures increases. Thus, the
precision of investors' valuation decisions is shown to decrease as
accounting risk increases. Finally, the data do not support the
prediction of Hypothesis 3 that accounting risk becomes less important
in the explanation of abnormal return variances over IPO firms' initial
three years of public trading.
Suggestions for Future Research
Future research could extend this study by more directly examining
the role of accounting numbers in the initial valuation of IPOs.
Specifically, an equity valuation model could be developed which
utilizes certain accounting numbers (e.g., earnings per share, book
value of assets, or book value of equity) as determinants of IPO firm
value. This is in contrast to the current study which examines the
association between accounting quality and various measures of long-term
IPO mispricing.
Another avenue for future accounting research in the IPO area
appears to be the development of a model of the accounting procedure
choices of initial public offering firms. Since IPO firms differ in
many respects from established publicly traded firms, the managerial


12
issues between informed and uninformed investors forms the basis of the
underpricing phenomenon in Rock's model. Beatty and Ritter [1986]
summarize the' model's major result as follows:
Consequently, if an uninformed investor is allocated shares
in an initial public offering, there is a greater than usual
chance that the issue will start trading at a discount in
the aftermarket. In other words, for an uninformed
investor, the expected return conditional upon being
allocated shares is less than the expected return
conditional upon submitting a purchase order. But an
uninformed investor will participate in the market only if
the expected return conditional upon being allocated shares
is non-negative. This can only happen if, on average,
issuers underprice their shares. The owners of a firm going
public, who typically have a large proportion of their
wealth invested in the firm, would be willing to pay this
price if they are sufficiently risk-averse. (Beatty and
Ritter [1986, p. 228])
Beatty and Ritter's [1986] extension of Rock's model indicates
that the greater the ex ante uncertainty (i.e., uncertainty about the
true value of the issue), the greater the informed investors'
informational advantage, and thus the greater the level of expected
underpricing required to ensure uninformed investors' participation in
the offering. Thus, Beatty and Ritter conclude that ex ante uncertainty
and expected underpricing are positively related. This positive
association between ex ante uncertainty and underpricing has been
empirically verified by Beatty and Ritter [1986], Ritter [1984b], and
Miller and Reilly [1987].
Baron [1982] also attempts to explain the average short-term
underpricing of IPOs via asymmetric information arguments. In his model
the underwriter possesses an informational advantage over the IPO issuer
regarding investor demand for the upcoming security issue. In an
analytical principal-agent framework, Baron demonstrates that the


58
Each of the previously described independent variables is measured
based on the most recently available data at the time of the IPO (i.e.,
that available in the prospectus). In other words, these independent
variables remain constant across the four cross-sectional regressions.
Thus, the regressions are intended to determine the impact of accounting
risk at the time of the IPO on various specifications of long-term IPO
mispricing. Hypothesis 1 predicts zero coefficients on the three
accounting risk variables in each regression.22
In contrast to the other independent variables, UE(t)i is measured
over the same time horizon as the dependent variable in each of the four
cross-sectional regressions. Unexpected earnings is calculated using a
variant of the commonly utilized random walk earnings expectation model.
An alternative specification of the random walk model is required in
this study due to the misalignment of the abnormal security return
series and the earnings series. Specifically, an IPO's abnormal returns
are generated via Equation (12) based on monthly returns beginning on
the day following the IPO date. In other words, the return series is
based on "event time," with the event in question being the initial
offering. By contrast, the annual earnings series is based on each
firm's fiscal year end (FYE). Therefore, unless a firm went public on
(or very near) its fiscal year end date, the returns and earnings series
utilized in this study are not properly aligned.
22 It is easily verified that the model presented in Chapter 3 also
predicts nonsignificant coefficients for the business risk control
variables (i.e., SIZEi and LNRISKS^ since the abnormal security returns
are already risk-adjusted.


BIOGRAPHICAL SKETCH
John D. Neill, III, was born in Abilene, Texas, on April 27, 1959.
He received a B.B.A. degree in accounting from Abilene Christian
University in 1980. After working for Pennzoil Company in Houston,
Texas, as a Corporate Accountant and as a Programmer Analyst from
December, 1980, until August, 1983, he returned to Abilene Christian
University and was awarded the M.S. degree in religious education in
1984. He entered the accounting Ph.D. program at the University of
Florida in August, 1985. After completion of the Ph.D. degree, he will
be employed as an Assistant Professor at Florida State University.
147


TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iii
ABSTRACT vi
CHAPTERS
1 INTRODUCTION 1
2 PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS 9
Introduction 9
Short-Term Mispricing of IPOs 9
Long-Term Mispricing of IPOs 16
3 HYPOTHESIS DEVELOPMENT 24
Introduction 24
Assumptions 24
Decomposition of Systematic and Unsystematic Risk
into Accounting and Business Risk Components 27
Hypotheses 29
4 EMPIRICAL METHODOLOGY 33
Introduction 33
Three Measures of Accounting Risk 33
Measurement of Risk-Adjusted
Abnormal Security Returns 42
Univariate Tests 47
Multivariate Tests 54
Summary of the Empirical Methodology 65
5 DATA, SAMPLE SELECTION CRITERIA, AND RESULTS 67
Data Sources and Sample Selection Criteria 67
Descriptive Statistics 69
Results of Univariate Tests 77
Results of Multivariate Tests 87
IV


37
reported earnings via certain accounting procedure choices.2 An
entrepreneur of a firm about to undertake an initial public security
offering might be motivated to manipulate accounting numbers in order to
send an exaggerated (i.e., positively biased) signal to potential
investors concerning the earnings history (and potential) of his firm.
By definition, accounting risk (i.e., the uncertainty surrounding the
reliability and accuracy of accounting numbers) increases as the level
of managerial manipulation of accounting numbers increases. Thus, an
estimate of the degree of such manipulation is employed as a proxy for
accounting risk. This surrogate is intended to capture the component of
accounting risk which relates to the level of bias inherent in reported
accounting numbers.
Imhoff and Thomas [1989] provide empirical support for the
utilization of this conservative versus liberal accounting procedure
choice dichotomy as a proxy for accounting quality. They demonstrate a
statistically significant positive association between the ratings of
financial analysts regarding the accounting quality of firms in the
particular industry in which each analyst specializes and the degree of
conservatism of the accounting methods employed by these firms. Thus,
Imhoff and Thomas [1989] provide evidence that financial analysts view
2 Watts and Zimmerman [1986] review the literature (both
theoretical and empirical) which suggests that managerial manipulation
of earnings is related to the existence of pol-itical costs, debt
covenants, and bonus plans denominated in terms of accounting numbers.


61
Multiple Regression Test of Hypothesis 2
Hypothesis 2 may alternatively be examined via the following
cross-sectional regression models:
VAR( t) i = a + ^AUDITORi + £2F0RJiii + ^APCHOICEi
+ y^IZEi + 72LNRISKS + 73ABSUE(t)i + eif (20)
where ABSUEit^ = the absolute value of firm i's period t unexpected
earnings, and all remaining variables are as previously defined. The
absolute value specification of unexpected earnings is utilized in
Equation (20) since the absolute magnitude (rather than the signed
magnitude) of unexpected earnings should impact the variance of returns.
As was the case for Equation (18), Equation (20) is estimated
separately across all sample firms for four different specifications of
the dependent variable (i.e., abnormal return variances over four
different time horizons). Specifically, an IPO firm's abnormal return
variance is measured over the first, second, and third years of public
trading, as well as over the cumulative 36 month period. As previously
indicated by Equation (16), an IPO's variance of period t abnormal
returns (i.e., VARit or equivalently VAR(t)i) is calculated as the
abnormal return over time t squared (i.e., ARit2) .
According to Hypothesis 2, the estimated coefficients on the
accounting risk variables (i.e., to /S3) are predicted to be positive
for each of the four cross-sectional regressions.25 Similarly, the
25 However, Hypothesis 3 predicts that the ability of these
variables to explain the variation in abnormal return variances
decreases over time. Empirical tests of Hypothesis 3 utilizing a
multiple regression approach are detailed in the following section.


124
returns reported in Table B-3 are not systematically greater than or
less than zero, as predicted by Hypothesis 1.
In summary, the results documented in Table B-3 are consistent
with those reported earlier for the 372 firm sample based on the less
restrictive sample selection criterion. In both cases, the data lend
support to the predictions of Hypothesis 1.
Panels A, B, and C of Table B-4 report the results of the
univariate (portfolio) tests of Hypothesis 2 using the 299 firm sample
when AUDITOR, FORM, and APCHOICE, respectively, are employed as proxies
for accounting risk. The results detailed in Table B-4 are generally
consistent with those which were obtained using the 372 firm sample
(i.e., those reported in Table 5-6). However, the auditor reputation
variable is no longer significant when this five year sample selection
criterion is employed. Hypothesis 2 is still generally supported for
the FORM and APCHOICE accounting risk proxies. In particular, the mean
abnormal return variance of the high accounting risk group based on the
FORM variable is (at least marginally) significantly greater than that
of the low risk group in each time horizon except years 3 and 5. The
significance levels for years 2 and 4 are .07 and .11, respectively.
Similarly, Panel C of Table B-4 demonstrates that Hypothesis 2 is
supported for the APCHOICE proxy in the second, third, and fourth years
of public trading, and is marginally supported (.07 level) in the fifth
year.
In summary, consistent with the results described earlier for the
372 firm sample, the results reported in Table B-4 are supportive of
Hypothesis 2 for the FORM and APCHOICE variables. However, over this


136
time horizon, respectively. Since heteroscedastic error terms are
discovered in each of these six cross-sectional regressions, only the
statistical tests based on the heteroscedasticity adjusted test
statistics are discussed.
The empirical results reported in Table B-7 are not as supportive
of Hypothesis 2 as those obtained when the less restrictive three year
data availability criterion was utilized (i.e., those reported in Table
5-11). Contrary to the results of Table 5-11, the FORM accounting risk
proxy only conforms to the predictions of Hypothesis 2 over the
cumulative 60 month period. The results based on the APCHOICE variable
support Hypothesis 2 for years 2, 3, and 4. However, the variable is
significant in the direction opposite to that predicted by Hypothesis 2
over the cumulative 60 month period. Finally, consistent with Table 5-
11, the AUDITOR variable is not significant in the explanation of
abnormal return variances in any of the first five years of public
trading. However, the auditor quality variable is significant in the
direction opposite to that predicted by Hypothesis 2 over the cumulative
60 month time horizon.
In summary, the cross-sectional multiple regression tests of
Hypothesis 2 do not provide strong support for the hypothesis that
accounting risk is positively associated with abnormal return variances
when the 299 firm sample is examined. This is in contrast to the much
more supportive results documented for the 372 firm sample based on a
less restrictive sample selection criterion.
Table B-8 provides the results of testing Hypothesis 3 via a
pooled time-series cross-sectional regression model for the 299 firm
i


114
conclude that the impact of auditor reputation on underpricing is
reduced when a high reputation underwriter is also involved in the
registration process. Thus, auditor and investment banker reputation
appear to be (at least partial) substitutes.
Therefore, the results of prior accounting research pertaining to
short-term mispricing of IPOs suggest that an entrepreneur of a firm
contemplating an initial offering may be able to influence the level of
underpricing associated with the issue by his auditor choice. However,
the impact of other accounting choices on initial underpricing has not
been examined. For example, prior research has not explored the effect
of the accounting procedures chosen by IPO firms or the amount and
content of accounting information disclosed to potential investors on
short-term underpricing. This appendix reports the results of an
exploratory analysis which investigates these issues.
In order to examine the impact of certain accounting choices on
short-term underpricing the following cross-sectional regression model
is estimated:
UPt = a + ^AUDITORi + /32FORMi + ^33APCHOICEi
+ yiSIZEi + 72LNRISKSi + ei, (22)
where UPi = the short-term underpricing of firm i's initial offering,
and all other variables are as previously defined. Short-term
underpricing is calculated as:
UPi = (Plt PIPOi) / PIPOi, (23)
where Pli = firm i's first available closing bid price following the
initiation of public trading, and PIPOi = firm i's initial offering


38
firms which utilize conservative accounting methods as possessing higher
accounting quality than firms which utilize more liberal methods.3
In this study, the liberal versus conservative accounting
procedure choice proxy is measured based on whether an IPO firm's chosen
inventory and depreciation methods4 tend to increase or decrease
reported accounting earnings. Specifically, conservative accounting
choices are defined as the last-in, first-out (LIFO) inventory valuation
3 There are other possible explanations of this perceived
relationship besides the managerial manipulation of earnings explanation
utilized in this study. For example, it could be argued that accounting
risk is less when the "conservative" last-in, first-out (LIFO) inventory
valuation method is used in comparison to the more "liberal" first-in,
first-out (FIFO) method. Specifically, the LIFO method tends to smooth
reported income numbers in periods of rapidly changing prices, both
increasing and decreasing. These less volatile income numbers may be
perceived as being of higher quality than the more variable FIFO-based
numbers. Also, LIFO users often report in their financial statements
what income would have been under the FIFO method. However, the
opposite does not hold since FIFO users typically do not report LIFO-
based net income. Therefore, it is possible that LIFO-based numbers may
be considered co be of higher quality since more information is
typically available under LIFO than under FIFO.
4 Other accounting procedure choices have been examined in the
accounting literature. For example, the methods employed to account for
the investment tax credit (ITC) and oil and gas exploration costs have
been widely examined (see Watts and Zimmerman [1986] for a review and
Imhoff and Thomas [1989] for a recent example). These accounting
choices were also originally planned to be included in this study's
definition of conservative versus liberal accounting procedure choices.
However, only 2 out of the 372 sample firms used a procedure other than
the flow through ITC method and oil and gas exploration firms comprise
only 5% of the sample firms. Hence, these two accounting procedure
choices appear to be of little use in the current sample in classifying
firms into conservative versus liberal accounting procedure choice
groups. Thus, the level of conservatism of a firm's accounting
procedure choices is measured based only upon inventory and depreciation
method choices. The inability to examine other than inventory and
depreciation choices is considered to be only a minor limitation since
depreciation and inventory represent the most important (in dollar
terms) accounting procedure choices for most firms.


APPENDIX B
EMPIRICAL RESULTS BASED ON A FIVE YEAR TIME HORIZON
The empirical results reported in Chapter 5 are based on a sample
of 372 firms which had undertaken initial public offerings in the 1980-
1983 time period. These 372 sample firms were selected out of a
possible 511 initial offering firms since they met the sample selection
criterion of having at least three years of security return data
available on the CRSP NASDAQ file. The entire empirical analysis was
also conducted using a more restrictive five year data availability
sample selection criterion. This appendix presents summary results of
the testing of Hypotheses 1 through 3 using this more restrictive
criterion.
Table B-l reveals that a five year data availability criterion
results in a loss of an additional 73 firms beyond the less restrictive
three year criterion. Thus, a sample of 299 IPO firms remain which have
at least five years of security returns available on NASDAQ.
Table B-l
Sample Selection Procedures Using a Five Year Criterion
Total Available Microfiche Prospectuses of Non-Unit IPOs
511
Number of Firms Removed Due to a Lack of 3 Years of Data
on the 1988 Daily CRSP NASDAQ File
139
Number of Additional Firms Removed Due to a Lack of 5 Years
of Data on the 1988 Daily CRSP NASDAQ File
73
Final Sample Based on a Five Year Criterion
299
119


2
discussion indicates that accounting numbers potentially play a greater
role in the valuation of IPOs than in the valuation of more established
publicly traded firms, due to the reduced number of competing
information sources about IPOs.4 Thus, initial public offerings
represent an opportunity to explore the role of accounting numbers in
security valuation in instances of considerable uncertainty surrounding
the value of a firm. Specifically, this dissertation examines the
impact of the quality of accounting numbers available to investors on
the "long-term" mispricing of IPOs. In this thesis, accounting quality
refers to both the reliability and the quantity of the accounting
numbers supplied to investors. In other words, neither a sufficient
quantity of unreliable accounting numbers nor an insufficient quantity
of reliable accounting numbers are considered quality accounting
disclosures in this dissertation. The discussion to follow describes
the differences between "long-term" and "short-term" IPO mispricing, and
explains why the longer term perspective is adopted in this study.
Bower states that "there are two types of mispricing inherent in
initial offerings" (Bower [1989, p. 647]). The first type of mispricing
4 Prior research has consistently demonstrated that accounting
numbers and security prices are statistically associated. Please refer
to Beaver [1981, Chapter 5] and Watts and Zimmerman [1986, Chapter 3]
for comprehensive reviews of this literature. However, changes in
accounting numbers (i.e., "unexpected" earnings) typically only explain
five to ten percent of the variation in security returns. This low
level of explanatory power is presumably due to the abundance of
competing sources of information (e.g., financial analysts, industry
publications, the Wall Street Journal, etc.) about publicly traded
firms.


108
business risk), accounting risk is demonstrated not to significantly
influence mean IPO aftermarket abnormal returns. Similarly, the
univariate results pertaining to Hypothesis 2 (with the exception of the
results for the auditor quality variable) are generally confirmed in
this multivariate setting. Thus, accounting risk is demonstrated to be
positively associated with IPO abnormal return variances even after
controlling for firm size, the number of risk factors enumerated in the
prospectus, and unexpected accounting earnings. Finally, as was the
case for the univariate tests, Hypothesis 3 is not supported in a
multivariate environment. Thus, there is little indication that
accounting risk loses its ability to explain IPO aftermarket abnormal
return variances over the first three years of public trading.


80
zero. As was the case for the AUDITOR proxy, Hypothesis 1 is supported
when FORM is utilized as a proxy for accounting risk.
Panel C illustrates that Hypothesis 1 is also supported when
APCHOICE is utilized as a proxy for accounting riskiness. Specifically,
the mean abnormal returns of both the high risk (i.e., firms utilizing
consistently "liberal" accounting methods for inventory and
depreciation) and low risk (i.e., firms utilizing at least one
"conservative" method) groups differ significantly from zero in only the
first year of public trading. Further, consistent with the predictions
of Hypothesis 1, none of the MAR differences between the accounting risk
groups differs significantly from zero.
In summary, the results reported in Table 5-5 strongly support the
prediction of Hypothesis 1 that IPO firms' mean abnormal returns do not
vary according to the level of accounting risk faced by potential
investors, since none of the difference statistics in the table differs
significantly from zero. Thus, the results are consistent with the
hypothesis that investors do not systematically misprice IPOs because of
excessive accounting riskiness. However, Table 5-5 also reveals that in
certain isolated cases a given portfolio's MAR differs from the zero
hypothesized value, even though none of the portfolios possess
statistically significant 36 month cumulative mean abnormal returns.
Therefore, Hypothesis l's prediction of zero abnormal returns over all
time horizons for each portfolio (i.e., no systematic mispricing of IPOs
by investors) is not uniformly supported even though the MARs do not
differ significantly from zero in the vast majority of cases.


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS
By
John D. Neill, III
August, 1990
Chairman: Bipin B. Ajinkya
Major Department: Fisher School of Accounting
This dissertation examines the impact of the quality of accounting
numbers supplied to potential investors on the "long-term" mispricing of
initial public offerings (IPOs) of securities. Such mispricing is
assessed in relation to the market's valuation after more information is
revealed. Initial offerings are of interest from an accounting research
perspective due to the lack of market information sources prior to the
IPO.
Three hypotheses relating accounting quality and long-term
mispricing are developed and empirically tested. First, the lack of
quality accounting numbers at the IPO date is predicted to have no
effect on the mean abnormal returns earned by investors. However, the
second hypothesis states that the variance of these abnormal returns is
greater in instances where quality accounting information is lacking.
vi


Table 5-2
Descriptive Statistics
70
Panel A
Sample Firm and Initial Offering Descriptive Statistics
Variable
Mean
Median
Standard Deviation
Nb
PROCEEDS
15.62
9.06
18.34
372
BKASSETS
15.56
6.92
25.78
372
SIZE
8.66
8.84
1.65
372
RISKS
4.98
0.00
6.50
366
LNRISKS
1.09
0.00
1.22
366
AGE
10.80
7.00
13.57
370
Year
Summary Firm and
Number of
Panel B
Offerings Statistics By Year
Mean
Offers PROCEEDS
of Offering
Mean
BKASSETS
1980
35
10.11
13.89
1981
114
10.81
12.31
1982
25
12.70
12.01
1983
198
19.72
18.18
a The variables examined in this table relate to certain characteristics
of the 372 sample firms and their initial offerings. The variables of
interest are defined as follows:
PROCEEDS = the gross proceeds of the initial offering measured in
millions of dollars. Gross proceeds are calculated as the actual number
of shares sold in the offering multiplied by the offering price per
share.
BKASSETS = the book value of total assets (measured in millions of
dollars) prior to the IPO. This variable is collected from the latest
audited balance sheet supplied in the prospectus.
SIZE = the natural logarithm of BKASSETS.
RISKS = the number of risk factors listed in the prospectus.
LNRISKS the natural logarithm of (1 + RISKS).
AGE = the age of the firm in years prior to the initial offering.
b N = the number of firms involved in the calculation of the
mean, median, and standard deviation statistics.


40
(i.e., conservative) methods for both inventory and depreciation.
Therefore, this consistently "conservative" group was combined with the
group whose accounting procedure choices consisted of a combination of
income increasing and income decreasing methods to form the low
accounting risk portfolio in the dichotomous classification scheme.
Further, a number of the sample firms did not have both inventories and
long-term depreciable assets. In cases where a firm possessed no
inventory (long-term depreciable assets), the measurement of this
accounting procedure choice variable was based strictly upon the firm's
depreciation (inventory valuation) choice.
In summary, the high accounting risk firms either (1) utilized
income increasing methods for both inventories and long-term depreciable
assets or (2) employed an income increasing method for the only
applicable accounting choice. Conversely, the low accounting risk firms
either (1) utilized income decreasing methods for both accounting
choices, (2) used a combination of income increasing and income
decreasing methods, or (3) employed an income decreasing method for the
only applicable accounting choice.6
6 Various methods of classifying firms into accounting risk groups
based on the firms' accounting procedure choices were employed in the
empirical analysis, with no qualitative differences in results.


146
Simon, C. J. 1989. The effect of the 1933 securities act on investor
information and the performance of new issues. American
Economic Review 19 (June): 295-318.
Simunic, D. A. and M. Stein. 1987. Product Differentiation in Auditing:
Auditor Choice in the Market for Unseasoned New Issues.
Vancouver, BC: Canadian Certified General Accountants'
Research Foundation.
Smith, C. W. 1986. Investment banking and the capital acquisition
process. Journal of Financial Economics 15
(January/February): 3-29.
Stigler, G. 1964. Public regulation of the securities markets. Journal
of Business 37 (April): 117-42.
Tinic, S. M. 1988. Anatomy of initial public offerings of common stock.
Journal of Finance 43 (September): 789-822.
Titman, S. and B. Trueman. 1986. Information quality and the valuation
of new issues. Journal of Accounting and Economics 8 (June):
159-72.
Watts, R. L. and J. L. Zimmerman. 1986. Positive Accounting Theory.
Englewood Cliffs, NJ: Prentice-Hall.
Weiss, K. A. 1988. Initial public offerings and the cost of information.
Ph.D. dissertation, University of Florida. (
Welch, I. 1989. Seasoned offerings, imitation costs, and the
underpricing of initial public offerings. Journal of Finance
44 (June): 421-49.
White, H. 1980. A heteroskedasticity-consistent covariance matrix
estimator and a direct test of heteroskedasticity.
Econometrica 48 (May): 817-38.


7
information supplied at the IPO date becomes less important in the
valuation of the firm over time as more timely information becomes known
about these newly public firms.
Three empirical surrogates for the quality of accounting
information available to IPO investors are employed in this study. The
first is the reputation (or quality) of the auditor utilized in the IPO
process. Auditor reputation is measured dichotomously according to
whether a Big Eight or a non-Big Eight auditing firm is utilized in the
going public process. A measure of the potential managerial
manipulation of reported accounting earnings as reflected in managers'
accounting procedure choices forms the second surrogate for quality.
This accounting procedure choice proxy is measured dichotomously
according to whether the methods utilized in accounting for inventory
and depreciation are income decreasing (i.e., "conservative") or income
increasing (i.e., "liberal"). The third proxy is whether the SEC
registration statement filed by the IPO issuer is a Form S-l or Form
S-18 registration. This third surrogate measures the quantity component
of accounting quality since the accounting disclosure requirements of
Form S-l registrations are much more extensive than those of Form S-18
filings.
The empirical results provide support for the first two
hypotheses. Specifically, the results indicate that certain accounting
choices which provide indications of accounting quality (i.e., the
quality of auditor utilized in the IPO process, the type of SEC
registration form filed, and the degree of conservatism inherent in
inventory and depreciation choices) have no appreciable impact upon the


63
observations and 0 otherwise, and all other variables are as previously-
defined.
As stated earlier, Equation (21) is a pooled cross-sectional time-
series model. Specifically, the equation is estimated with three
observations per sample firm. These three observations per firm pertain
to the first, second, and third year of public trading, respectively.
Thus, Equation (21) is estimated with three times as many observations
as the separate year by year cross-sectional regressions utilized in the
testing of Hypotheses 1 and 2.
The interaction terms employing the indicator variables Ilit and
I2it are included in the model so that tests concerning changes in the
regression coefficients over time may be conducted.26 Specifically,
since both the indicator variables are coded 0 for observations relating
to the first year following the IPO, the regression coefficients to
and yx to y3 represent the respective coefficients for year 1.
Similarly, since Ilit equals 1 for year 2 only and I2it equals 0 for year
2, then the regression coefficients /3X' to /33' and y1' to y3' represent
the changes in the respective coefficients for year 2 as compared to the
base year (i.e., year 1). In a similar fashion, since Ilit is coded 0
for year 3 and I2it takes on the value 1 only for year 3, then the
regression coefficients /91'' to /33'' and yx'' to y3'' represent changes
in the respective coefficients for year 3 in comparison to year 1.
26 It should be noted that the model specified in Equation (21)
assumes that the intercept does not vary across sample years but that
the slopes differ according to whether the first, second, or third year
of public trading is being examined.


98
Table 5-11--continued
Panel C
Model: VAR3i = a + ^AUDITORi + P2Fomi + ftAPCHOICEj,
+ y^IZEi + 72LNRISKS + 73ABSUE3 + eA
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0004
.0015
0.285
0.112
AUDITOR
.0005
.0005
0.998
1.032
FORM
-.0005
.0005
-0.874
0.649
APCHOICE
.0009
.0004
2.233**
10.817##
SIZE
.0001
.0002
0.340
0.156
LNRISKS
.0003
.0002
1.281
1.190
ABSUE3
.0020
.0005
4.002**
4.738#
Test of the
Model Goodness
of Fit: F-
Value=4.548
P-Value=.0002
White's [1980] Homoscedasticity Test:
X2-Value245
.62 P-Value=.0001
Model Adjusted R2 = .0629
N-318
Panel
D
Model: VAR3 = a
+ ^AUDITOR
i + £2F0RMi +
^APCHOICEi
+
yiSIZEi + yzLNRISKSi + 73ABSUE3 + eA
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0125
.0040
3.170**
7.254##
AUDITOR
-.0007
.0012
-0.595
0.380
FORM
.0018
.0014
1.328
1.524
APCHOICE
-.0007
.0011
-0.691
0.417
SIZE
-.0008
.0004
-2.109*
3.541@
LNRISKS
.0002
.0005
0.417
0.191
ABSUE36
.0043
.0008
5.505**
66.666##
Test of the Model Goodness of Fit: F-Value=7.839 P-Value=.0001
White's [1980] Homoscedasticity Test: x2_Value=284.25 P-Value=.0001
Model Adjusted R2 = .1146 N=*318
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on \2 tests
# significant at .05 level, based on *2 tests
@ significant at .10 level, based on x2 tests


130
Table B-6
Regression Approach to Testing Hypothesis 1
Using 299 Firm Sample
Panel A
Model: ARlt = a + ^AUDITORi + /32FORMi + 0SAPCHOICE1
+ y^IZEi + yjjLNRISKSi + 73UEli + et
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.064
.023
2.785**
3.731#
AUDITOR
.005
.007
0.725
0.504
FORM
.013
.008
1.610
1.970
APCHOICE
- .003
.006
-0.466
0.196
SIZE
- .005
.002
-2.380*
2.803
LNRISKS
- .007
.003
-2.301*
5.058#
UE1
.156
.031
5.107**
12.987##
Test of the Model Goodness
of Fit: F-
-Value=5.834
P-Value=.0001
White's [1980]
Homoscedasticity Test:
XZ-Value=216.
85 P-Value=.0001
Model Adjusted
R2 = .0921
N-287
Panel
B
Model: AR2t =
a + /JjAUDITORi
+ /^FORMi +
^3APCH0ICEi
+ 'YiSIZE^ + *
YLNRISKS +
73UE2i + e
Heteroscedasticity
Parameter
Standard
t
Adjusted \2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.014
.022
-0.617
0.429
AUDITOR
.012
.007
1.836
2.849
FORM
.005
.008
0.646
0.319
APCHOICE
- .001
.006
-0.207
0.061
SIZE
.001
.002
0.636
0.456
LNRISKS
.001
.003
0.466
0.236
UE2
.157
.026
6.141**
33.717##
Test of the Model Goodness of Fit: F-Value=7.528 P-Value=.0001
White's [1980] Homoscedasticity Test: x2_Value=518.55 P-Value=.0001
Model Adjusted R2 =
.1208 N=286


6
particular, explanations of short-term underpricing based on potential
legal liabilities, information asymmetries, and the desire to receive a
higher price for subsequent seasoned security offerings have been
proposed.12 Therefore, from an accounting perspective, the examination
of the long-term mispricing of IPOs may be more appropriate.
Three hypotheses relating accounting quality and long-term IPO
mispricing are developed and empirically tested in this thesis. The
first hypothesis is that the lack of quality accounting numbers at the
IPO date has no effect on the mean abnormal aftermarket returns of IPO
investors. However, the second hypothesis states that the dispersion
(variance) of these abnormal returns is greater in instances where
quality accounting information is lacking. In other words, these first
two hypotheses suggest that investors are able to value IPOs in an
unbiased fashion (i.e., not systematically overvalue or undervalue) even
in the presence of considerable uncertainty due to insufficient and/or
unreliable accounting numbers. However, the lack of quality accounting
numbers influences the precision of investors' valuations of IPOs.
Thus, it is postulated that the absolute magnitude (rather than the
direction) of long-term IPO mispricing is related to the quality of
accounting disclosures provided to potential investors. The third
hypothesis provides the time path of Hypothesis 2. Specifically, it
predicts that the influence of the quality of accounting numbers
available at the IPO date on the variance of investors' abnormal returns
diminishes over time. Hypothesis 3 implies that the accounting
12
See footnote 7.


123
Table B-3--continued
Panel C
Accounting Risk Proxy Variable APCHOICE
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
0.82%**
1.03%
-0.21%
Year 2
-0.28%
-0.29%
0.01%
Year 3
0.08%
-0.16%
0.24%
Year 4
-0.50%
-0.63%
0.13%
Year 5
-1.07%**
-0.19%
-0.88%
5 Year
Cumulative
-0.95%
-0.25%
-0.70%
a MAR signifies mean abnormal return.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests


CHAPTER 5
DATA, SAMPLE SELECTION CRITERIA, AND RESULTS
Data Sources and Sample Selection Criteria
The major source of data employed in this study consists of a
collection of microfiche IPO registration statements (which include
preliminary prospectuses) which were obtained from Disclosure Inc.1
This database contains registration statements for a number of firm
commitment2 IPOs of nonfinancial firms which occurred between January
1980 and December 1983. The database contains registration statements
and preliminary prospectuses for 511 firms which made initial public
offerings of equity securities (unit offerings which include warrants
are excluded) during this 1980-1983 time period. Only firms for which
at least three years of security price data are available on the CRSP
1 I wish to thank Professor Chris James for allowing me access to
these registration statements. Please see James and Wier [1989] for a
complete description of the criteria utilized in assembling this
microfiche database.
2 The two major types of underwriting contracts utilized in the
going public process are "firm commitment" and "best efforts" contracts.
In a firm commitment offering, the underwriter purchases the entire
security issue from the issuer and then must resell the shares to
investors. Thus, the underwriter makes a "firm commitment" to purchase
the offering at a specified price (i.e., the IPO price less the
underwriter's discount), and bears the risk of not being able to sell
the entire issue at the IPO price. Conversely, in a best efforts
offering, the underwriter's only commitment is to make his "best
efforts" to sell the issuer's security offering to investors. In this
case, the issuer bears the risk of not being able to sell the entire
issue at the IPO price.
67


20
Monthly abnormal returns are calculated by subtracting the return on a
market index from an IPO's raw return for each of the first 36 months
following the IPO. Thus, market-adjusted rather than risk-adjusted
abnormal returns are computed. Ritter documents average abnormal
returns of -24.33% over the first three years of public trading. Thus,
the results are consistent with the hypothesis that the market
overvalued IPOs in the 1975-1984 period. However, considerable
variation in the level of this overvaluation across year of issuance and
industries is documented. In particular, Ritter states that the
overvaluation is "concentrated among relatively young growth companies,
especially those going public in the 1980s" (Ritter [1989, p. 3]).
Comparison of the Long-Term Mispricing of New Issues Before
and After the 1933 Securities Act
Stigler [1964] and Jarrell [1981] compare the long-term mispricing
of new security issues5 offered before and after the 1933 Securities Act
(the Act). Both researchers test the hypothesis that the period prior
to the disclosure provisions of the Act was characterized by exaggerated
claims of issuers and underwriters, resulting in overvaluation of new
issues by investors. Specifically, both Stigler and Jarrell examine
whether the disclosure requirements of the 1933 Securities Act resulted
in increased investor returns for post-1933 new issues as compared to
the returns earned on potentially overvalued pre-1933 issues.
5 These papers examine the aftermarket abnormal returns on all new
issues of securities. In other words, both IPOs and new security
offerings of existing publicly held corporations (i.e., seasoned issues)
are examined.


Copyright 1990
by
John D. Neill, III


18
year of IPO issuance. This dissertation contributes to the literature
by providing explanations of long-term IPO mispricing based on cross-
sectional differences in accounting quality.
Research on the Long-Term Aftermarket Performance of IPOs
Reilly and Hatfield hypothesize that underwriters' short-term
underpricing of IPOs continues into the long-term "as the market
continues to recognize and adjust for the underpricing" (Reilly and
Hatfield [1969, p. 74]). Thus, they argue that the market for initial
security offerings is inefficient in that the market initially
undervalues IPO firms, on average. They calculate short-term mispricing
as the percentage change from the IPO price to the prevailing price one
week (or month) following the IPO. Long-term performance is defined as
the return from one week (or month) following the IPO to one year
subsequent to the commencement of public trading. Reilly and Hatfield
do not empirically assess the average abnormal aftermarket returns of
their entire sample. Rather, they demonstrate that the subset of firms
which outperformed the market in the short-term also had above-market
long-term returns. Thus, Reilly and Hatfield's results do not provide
an indication of the average long-term mispricing of IPOs; however, the
results may indicate that short-term and long-term mispricing are
correlated.
McDonald and Fisher hypothesize that in an efficient market
"subsequent price behavior of the stock should be independent of the
initial rate of return at offering" (McDonald and Fisher [1972, p. 97]).
They calculate abnormal returns by subtracting the return on the market


110
valuation of IPOs. Specifically, the empirical tests are designed to
examine the impact of accounting accuracy and reliability on how
precisely an IPO firm is valued. Thus, an indication of the
"usefulness" of accounting information for valuation purposes, in a
context of scarcity of alternative information sources, is provided.
Three empirical surrogates for the accounting risk theoretical
construct are employed in this study. The three surrogates are intended
to capture three unique aspects of the multifaceted accounting risk
concept. The type of SEC registration statement filed by the IPO issuer
is utilized to measure the quantity (i.e., extent of disclosure)
component of accounting quality. The conservative versus liberal
accounting procedure choice variable is employed to capture the level of
bias present in the reported accounting numbers due to managerial
manipulation of earnings. Finally, the auditor reputation proxy is used
to measure the level of credibility associated with the reported
accounting numbers.
The empirical results reported in Chapter 5 strongly support the
prediction of Hypothesis 1 that accounting risk does not affect
investors' mean abnormal returns in the three years subsequent to the
IPO. Hypothesis 2 is also supported by the data. However, the results
are not as compelling as those pertaining to Hypothesis 1.
Specifically, Hypothesis 2 is (at least marginally) supported for two of
the years examined for both the FORM and APCHOICE accounting risk
proxies. However, the hypothesis is not supported for the AUDITOR
accounting risk proxy in a multivariate context. Finally, the empirical
results demonstrate very little evidence in support -of Hypothesis 3.


30
returns. However, assuming that investors are rational, it is readily
seen that an increase in systematic accounting risk should not cause
risk-adjusted abnormal returns to deviate from zero since the increased
raw returns demanded by investors merely compensate them for bearing
greater risks. The rational expectations assumption implies that
investors are able to form unbiased estimates of an IPO's future payouts
even in the presence of considerable uncertainty surrounding the value
of the firm (e.g., in instances of high accounting risk). In other
words, on average, rational investors do not overestimate or
underestimate a firm's cash flows due to unreliable and/or incomplete
accounting disclosures. Increased accounting risk merely induces
investors to demand a greater return per dollar of expected future
payout (cash flow) to compensate them for bearing increased risks.
Thus, on a risk-adjusted basis, IPOs are on average correctly valued
owing to the unbiased nature of the estimated value-relevant cash flows.
In other words, rational investors do not misprice IPOs, on average,
even in the presence of a high degree of systematic accounting risk.
Thus, neither unsystematic nor systematic accounting risk should
cause mean risk-adjusted aftermarket returns to deviate from zero. This
assertion gives rise to the first hypothesis, which may be formally
stated as follows:
Hypothesis 1: On average, the long-term mispricing of IPO
firms is zero regardless of the level of accounting risk
faced by investors.


72
Ibbotson et al. [1988] that more initial offers were undertaken in the
years 1981 and 1983 than in 1980 or 1982. Thus, the makeup of the
sample does not appear to differ significantly from the population as a
whole. Panel B also illustrates that the average offering gross
proceeds increases over the sample period. The same relationship also
generally holds for the sample firms' book value of assets prior to the
offering.
Long-Term Mispricing. Short-Term Mispricing, and Unexpected Earnings
Table 5-3 provides additional descriptive statistics relating to
various measures of long-term and short-term IPO mispricing, as well as
to the unexpected earnings variables employed in this study. The mean
short-term underpricing (i.e., UP) of the 372 sample firms is 11.70%.
As is evidenced by Table 2-1, this 11.70% level of underpricing
approximates findings in other research.
Descriptive statistics pertaining to the sample firms' risk-
adjusted abnormal returns over the first, second, and third year of
public trading, as well as a 36 month cumulative return are provided in
Table 5-3. These measures of long-term IPO mispricing are generated
from the return generating process described in Equation (12) and are
designated as AR1, AR2, AR3, and CAR36 respectively. The table
indicates that only AR1 (i.e., first year abnormal returns) exceeds 1%
(in absolute value) on average. Further, AR1 is the only one of the
four risk-adjusted long-term mispricing measures which differs
significantly from zero at conventional significance levels, with a


55
Table 4-3
Univariate (Portfolio) Tests of Hypothesis 3
Portfolio
Year 1
Year 2
Year 3
Difference
Yearl-Year3
High Risk*
MVARH1C
MVARh2
hVARH3
MVARH1-MVARH3d,e
Low Riskb
MVARL1
mvarL2
mvarL3
MVARL1-HVARL3
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
0 MVARpt = portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH1 = the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
= the high risk portfolio's second year mean abnormal return variance,
etc.
d A matched-pair (i.e., paired comparison) t test is conducted to
determine whether each difference statistic in this column differs
significantly from zero. Hypothesis 3 predicts these differences to be
significantly greater than zero. Thus, one-sided significance tests are
called for in this case.
0 This matched-pair t test of the difference between the MVAR of years 1
and 3 is mathematically equivalent to a test for the presence of a
significant linear trend over time in a repeated measures ANOVA
analysis.


14
underprice their initial offering in order to obtain a higher price in
subsequent seasoned security offerings. A separating equilibrium
results due to the prohibitive costs which must be borne by lesser
quality firms attempting to imitate the underpricing signal of higher
quality firms. Thus, Welch's model is consistent with the hypothesis
that the purpose of underpricing is to "leave a good taste in investors'
mouths" (Ibbotson [1975, p. 264]).
Grinblatt and Hwang [1989] formulate a two signal model in which
underpricing and the percentage of ownership retained by the
entrepreneur serve as signals of the IPO firm's unobservable "intrinsic"
value and cash flow variance. Since underpricing signals the
"intrinsic" value of the IPO firm to investors, this model is also
consistent with the hypothesis that the underpricing of the initial
offering can be utilized by high quality firms in order to receive a
higher price in subsequent security offerings.
The prior paragraphs describe numerous proposed explanations for
the short-term underpricing phenomenon. However, it appears that
Ibbotson et al. [1988] are correct in their assessment of the current
state of underpricing research. They state: "A number of hypotheses
have been offered to explain the underpricing, but to date there is
still no persuasive, widely accepted, and test-supported explanation of
IPO underpricing" (Ibbotson et al. [1988, p. 37]).
Accounting Research Related to Short-Term Underpricing
Accounting research related to short-term IPO underpricing
examines the impact of auditor reputation on initial underpricing.


27
(i.e., Var(eit)). In a CAPM framework, unsystematic risk does not
affect the expected return on a security since its effects may be
diversified away by holding a well-diversified portfolio.
Finally, this study makes use of the assumption that investors are
rational. In other words, this rational expectations assumption implies
that market participants form unbiased estimations of (1) an IPO's
systematic risk and (2) the future payouts arising from IPO ownership.5
Decomposition of Systematic and Unsystematic Risk into
Accounting and Business Risk Components
The CAPM partitions firm-specific risk into systematic and
unsystematic components. This section further decomposes the CAPM's
firm-specific risk components into the business risk and accounting risk
partition of Equation (lb).
Specifically, may be defined as
h = /*iB + £i\ (5)
where fiB = systematic risk attributable to firm i's production,
investment, and financing activities, and f3AA = systematic risk
attributable to uncertainty surrounding the quality of the accounting
numbers supplied to investors.
The systematic risk attributable to accounting risk is modelled as
follows:
/?iA ^a(T,Q), (6)
5 These expected future payouts can be thought of as either
dividend distributions or the firm's cash flows.


53
variance over the three years. For example, the hypothesis implies that
the mean abnormal return variance of Big Eight audited IPO firms is
greater in the first year of trading than in the second year of trading,
and that the second year variance exceeds that of the third year. Each
of the remaining accounting risk portfolios is predicted to behave in a
similar fashion.
Even though Hypothesis 3 implies that the mean abnormal return
variances decrease monotonically (however not necessarily in a linear
fashion) over time, the hypothesis may be testing by examining whether
the variances exhibit the presence of a decreasing linear trend over the
three year period.19 Dawes and Corrigan [1974] demonstrate the
usefulness of linear models in cases such as this. They state that
"linear models are good approximations to all multivariate models that
are conditionally monotone in each predictor variable" (Dawes and
Corrigan [1974, p. 98]).
Since an individual IPO firm's abnormal return variance over year
1 is not independent of its return variance in other years, a
statistical procedure which accounts for these dependencies is required.
An often utilized procedure in cases such as this is a repeated measures
ANOVA, where time represents the repeated measure. A test for the
presence of a significant linear trend over the time horizon may be
conducted in such an analysis. In instances such as this one in which
the repeated measure (i.e., time) takes on only three values, the linear
19 Keppel [1982, Chapter 7] provides a detailed description of
trend analysis, and provides details of empirical procedures which test
for the presence of a significant linear trend.


Table B-3
Univariate (Portfolio) Tests of Hypothesis 1
Using 299 Firm Sample
122
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MARa
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.14%
1.01%**
0.13%
Year 2
0.86%
-0.63%*
1.49%*
Year 3
-0.03%
0.06%
-0.09%
Year 4
-0.90%
-0.41%
-0.49%
Year 5
-0.62%
-0.91%**
0.29%
5 Year
Cumulative
0.45%
-0.88%
1.33%
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
2.04%*
0.80%**
1.24%
Year 2
0.21%
-0.48%
0.69%
Year 3
0.16%
0.01%
0.15%
Year 4
-0.71%
-0.46%
-0.25%
Year 5
-1.34%*
-0.74%**
-0.60%
5 Year
Cumulative
0.36%
-0.86%
1.22%


Table 5-8
Pearson Correlations Among the Independent Variables
Employed in Testing Hypothesis 1 Via the Regression Approach
FORM
APCH0ICE
SIZE
LNRISKS
UE1
UE2
UE3
UE36
AUDITOR
. 24**
.04
-.23**
. 22**
.02
- .06
.05
.03
(3 7 2 ) a
(359)
(372)
(366)
(371)
(366)
(337)
(337)
FORM
.03
-.50**
.51**
.03
- .03
.10
.03
(359)
(372)
(366)
(371)
(366)
(337)
(337)
APCH0ICE
-.11*
.11*
.09
- .00
.06
.10
(359)
(353)
(358)
(353)
(324)
(324)
SIZE
-.66**
-.00
.08
- .05
- .01
(366)
(371)
(366)
(337)
(337)
LNRISKS
- .08
- .05
.12*
.02
(365)
(360)
(331)
(331)
UE1
-.01
.00
. 99**
(365)
(337)
(337)
UE2 .02 .00
(337) (337)
UE3 .02
(337)
a The numbers in parentheses represent the number of firms utilized in the determination of the correlation
coefficient.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests
00
oo


APPENDIX A
SHORT-TERM UNDERPRICING AND ACCOUNTING RISK
Prior accounting research on the short-term underpricing of
initial offers has explored the impact of auditor reputation on initial
underpricing. Beatty [1989a] and Balvers et al. [1988] hypothesize that
auditor reputation and initial underpricing are inversely related.
Beatty utilizes the major result of Beatty and Ritter [1986] that
expected IPO underpricing is positively associated with ex ante
uncertainty (i.e., uncertainty about the true value of an IPO) in the
formulation of this hypothesis. Specifically, since the attestation of
an IPO firm's financial statements by a high reputation auditor serves
to reduce investors' ex ante uncertainty, then auditor reputation is
hypothesized to be inversely related to underpricing.
Balvers et al. [1988] extend the models of Rock [1986] and Beatty
and Ritter [1986] by explicitly considering the relationship between an
IPO firm's auditor and underwriter. In the model, high auditor
reputation reduces investors' uncertainty regarding an IPO's earnings
and serves as a positive signal of the reputation of the underwriter.
Thus, auditor reputation is inversely related to ex ante uncertainty,
and is therefore hypothesized to be inversely related to underpricing.
Empirical support for this hypothesized relationship between
auditor reputation and short-term underpricing is documented by Beatty
[1989a, 1989b] and Balvers et al. [1988]. However, Balvers et al.
113


Table 2-1
Summary of the Results of Various Examinations
of Short-Term IPO Underpricing
Study
Definition of Return
Mean Return
Samle Period
Ritter [1989]
1st Day
14.32%
1975-1984
Ibbotson et al. [1988]
1st Calendar Month for 1960-1976
16.37%
1960-1987
1st Day for 1977-1987
Weiss [1988]
1st Day
8.77%
12/83-9/87
Miller and Reilly [1987]
1st Day
9.87%
1982-1983
Ibbotson [1975]
1st Calendar Month
11.4%
1960-1969
McDonald and Fisher [1972]
1st Week
28.5%
1/69-3/69
Reilly and Hatfield [1969]
1st Calendar Week
9.9%
12/63-8/64
1/65-6/65


142
supported over a long time horizon. However, Hypothesis 3 is not
supported over the five year time horizon when a multivariate test of
the hypothesis is conducted. Second, Hypothesis 2 is not as strongly
supported when the smaller sample based on the more restrictive sample
selection criterion is employed. Therefore, it is possible that
Hypothesis 2 may only be strongly supported in a large sample.


64
Hypothesis 3 implies that the accounting risk variables explain
less of the variation in abnormal return variances as more information
is revealed as time progresses from the IPO date. Thus, the f}x' to /?3'
and fix' to f}3'' coefficients predicted to be negative.27
Even though the coefficients on the interaction terms in Equation
(21) represent differences in the coefficients for year 2 and year 3,
respectively, from the coefficients pertaining to year 1, they provide
no indication of the change in the parameters from year 2 to year 3.
The change in the regression coefficients from year 2 to year 3 is
tested by examining the significance levels of the following linear
hypotheses regarding the coefficients: /3- f)x' = 0, f}2' f3z'' = 0,
/V /V' = 0, 7i' 7i" = 0, 72' 72" 0, and 73' 73" 0. Each
of these tests seeks to determine whether a particular year 2 regression
27 A priori, it is difficult to predict the sign of the
coefficients on the interaction terms which contain the control
variables. On the one hand, it could be postulated that the business
risk control variables (i.e., SIZEt and LNRISKSi) will decrease in
significance over time since these variables are measured as of the IPO
date and remain constant across the three time periods examined.
However, it could also be hypothesized that the business risk proxies
will increase in significance as the accounting risk variables lose
explanatory power. Similarly, one could reasonably argue that the
unexpected earnings control variable could either increase, decrease, or
remain constant over time. Specifically, it can be hypothesized that
unexpected earnings will increase in explanatory power over time as the
accounting risk variables decrease in importance. However, it could
also be argued that the unexpected earnings variables will decrease in
explanatory power over time since earnings may play a greater role in
firm valuation early in the life of a public firm (i.e., before other
competing sources of information become readily available). Finally,
one could also reasonably argue that the unexpected earnings variable
should not vary in significance over time since the relationship of
earnings and returns should not change significantly from year to year.
Therefore, predictions are not made relating to the hypothesized signs
of the 7X' to 73' and y1'' to 73'' coefficients.


ACKNOWLEDGEMENTS
There are a number of people whom I wish to thank for their
support and encouragement during the course of my graduate studies.
First and foremost, I want to express thanks to my dissertation
committee chairman Bipin Ajinkya for his advice in general and for his
insightful comments on this dissertation in particular. I will always
be appreciative of the tremendous amount of time he spent in directing
this dissertation research. I also want to thank the remainder of my
dissertation committee, David Brown, Miles Livingston, and Dan Smith,
for their encouragement and helpful suggestions and comments throughout
this project.
I also wish to acknowledge Jay Ritter and Chris James for their
contributions to this dissertation. The data that they supplied me with
proved to be invaluable in this research.
Finally, I want to especially thank my wife Karene for her
unfailing love and support over the past five years of graduate studies.
Without her continual encouragement, this dissertation might never have
become a reality.
iii


140
sample. Two additional indicator variables are required when one
employs a five year rather than a three year time horizon.
Specifically, the revised regression model includes indicator variables
13 and 14, which are coded 1 for observations pertaining to years 4 and
5, respectively, and 0 otherwise. As in Equation (21), the estimated
parameters on the noninteraction terms relate to year 1 observations.
Since heteroscedastic error terms are discovered in the estimation of
this revised model, only the statistical tests based on a
heteroscedasticity consistent covariance matrix are described.
The results reported in Table B-8 are generally consistent with
those found for the 372 firm sample (i.e., those reported in Table 5-
12). Specifically, Hypothesis 3 is not supported for the AUDITOR
accounting risk variable since Table B-8 does not demonstrate any
significant differences between the estimated auditor quality parameters
of any of the five years. The significance levels of the test
statistics relating to changes in the estimated regression parameters on
the APCHOICE proxy over time are less than those reported in Table 5-12.
However, when changes between years are found to be significant, these
changes are in the direction opposite to that predicted by Hypothesis 3.
Finally, consistent with the results reported in Table 5-12, the FORM
accounting risk variable appears to lose explanatory power over the
first three years of public trading. However, Panel B of Table B-8
demonstrates that the estimated regression parameters on the FORM
variable significantly increase over the year 3 to year 5 time period.
Thus, the results pertaining to the FORM variable only support
Hypothesis 3 over the initial three year time horizon.


101
univariate tests are generally confirmed in this multivariate context,
with the notable exception that the auditor quality variable loses
significance once business risk and other measures of accounting risk
are explicitly controlled for. Even though the auditor proxy is
insignificant, the results pertaining to the other two accounting risk
proxies generally support the prediction of Hypothesis 2 that accounting
risk is positively associated with IPO aftermarket abnormal return
variances. The lack of support on the auditor variable may be due to
one or more of the arguments enumerated in Chapter 4 that disputed the
presumed positive association between audit quality and audit firm size.
Multivariate Tests of Hypothesis 3
The multivariate tests of Hypothesis 3 consist of estimating the
pooled time-series cross-sectional regression model described by
Equation (21). Table 5-12 reports the results of the estimation of the
model. Panel A provides details regarding the statistical significance
across all time horizons. Thus, it may be appropriate to think of
accounting risk as a vector of component parts in which a different
combination of the vector components is needed in order to explain
abnormal return variances across different time horizons.
In an attempt to capture the totality of the accounting risk
theoretical construct in one empirical variable, this study's three
accounting risk proxies were collapsed into one overall measure of
accounting riskiness. Since each of the proxies is measured
dichotomously with high accounting risk firms coded 1 and low risk firms
coded 0, the new accounting risk measure was simply calculated as the
sum of AUDITOR, FORM, and APCHOICE. In a multiple regression test of
Hypothesis 2 employing this overall accounting risk measure, accounting
risk was found to be marginally significant in years 2 and 3, and
insignificant over the other two time horizons.


THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS
By
JOHN D. NEILL, III
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1990

Copyright 1990
by
John D. Neill, III

ACKNOWLEDGEMENTS
There are a number of people whom I wish to thank for their
support and encouragement during the course of my graduate studies.
First and foremost, I want to express thanks to my dissertation
committee chairman Bipin Ajinkya for his advice in general and for his
insightful comments on this dissertation in particular. I will always
be appreciative of the tremendous amount of time he spent in directing
this dissertation research. I also want to thank the remainder of my
dissertation committee, David Brown, Miles Livingston, and Dan Smith,
for their encouragement and helpful suggestions and comments throughout
this project.
I also wish to acknowledge Jay Ritter and Chris James for their
contributions to this dissertation. The data that they supplied me with
proved to be invaluable in this research.
Finally, I want to especially thank my wife Karene for her
unfailing love and support over the past five years of graduate studies.
Without her continual encouragement, this dissertation might never have
become a reality.
iii

TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iii
ABSTRACT vi
CHAPTERS
1 INTRODUCTION 1
2 PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS 9
Introduction 9
Short-Term Mispricing of IPOs 9
Long-Term Mispricing of IPOs 16
3 HYPOTHESIS DEVELOPMENT 24
Introduction 24
Assumptions 24
Decomposition of Systematic and Unsystematic Risk
into Accounting and Business Risk Components 27
Hypotheses 29
4 EMPIRICAL METHODOLOGY 33
Introduction 33
Three Measures of Accounting Risk 33
Measurement of Risk-Adjusted
Abnormal Security Returns 42
Univariate Tests 47
Multivariate Tests 54
Summary of the Empirical Methodology 65
5 DATA, SAMPLE SELECTION CRITERIA, AND RESULTS 67
Data Sources and Sample Selection Criteria 67
Descriptive Statistics 69
Results of Univariate Tests 77
Results of Multivariate Tests 87
IV

6 CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
109
Conclusions 109
Suggestions for Future Research Ill
APPENDICES
A SHORT-TERM UNDERPRICING AND ACCOUNTING RISK 113
B EMPIRICAL RESULTS BASED ON A FIVE YEAR TIME HORIZON.... 119
REFERENCES 143
BIOGRAPHICAL SKETCH 147
v

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE ROLE OF ACCOUNTING NUMBERS IN THE "LONG-TERM"
MISPRICING OF INITIAL PUBLIC SECURITY OFFERINGS
By
John D. Neill, III
August, 1990
Chairman: Bipin B. Ajinkya
Major Department: Fisher School of Accounting
This dissertation examines the impact of the quality of accounting
numbers supplied to potential investors on the "long-term" mispricing of
initial public offerings (IPOs) of securities. Such mispricing is
assessed in relation to the market's valuation after more information is
revealed. Initial offerings are of interest from an accounting research
perspective due to the lack of market information sources prior to the
IPO.
Three hypotheses relating accounting quality and long-term
mispricing are developed and empirically tested. First, the lack of
quality accounting numbers at the IPO date is predicted to have no
effect on the mean abnormal returns earned by investors. However, the
second hypothesis states that the variance of these abnormal returns is
greater in instances where quality accounting information is lacking.
vi

Finally, the third hypothesis predicts that the influence of the quality
of accounting numbers on abnormal return variances diminishes over time.
Three empirical surrogates for accounting quality are employed.
The first is the reputation of the auditor utilized in the IPO process.
It is measured according to whether a Big Eight or a non-Big Eight
auditing firm is employed. The second is the potential managerial
manipulation of reported earnings as reflected in managers' accounting
procedure choices, measured by whether the inventory and depreciation
methods are income increasing or income decreasing. The third proxy is
whether the SEC registration statement filed is a Form S-l or Form S-18
registration. This third surrogate measures the quantity of accounting
disclosures since Form S-l registrations are more extensive.
The empirical results indicate that the three surrogates for
accounting quality have no appreciable impact upon the mean abnormal
returns of investors, as hypothesized. However, these same quality
surrogates, with the exception of auditor reputation, influence the
variance of abnormal returns, also as predicted. Thus, the results
indicate that uncertainty relating to accounting quality translates into
an increased variance of IPO abnormal returns, rather than a systematic
overvaluation or undervaluation. As for the third hypothesis, the
results do not reveal a diminution of the quality surrogates' ability to
explain abnormal return variances as time progresses.
vii

CHAPTER 1
INTRODUCTION
Initial public offerings (IPOs) of securities have been the
subject of academic research for over two decades.1 An aspect of the
IPO market which makes it a particularly rich research area is the
relative scarcity of publicly available information about these
previously privately held firms.2 Specifically, potential IPO investors
are at an informational disadvantage compared to those who invest in
more established publicly traded firms, since there is no past series of
security prices and only very limited financial statement information is
available upon which to make determinations of IPO firm value.3 In
addition, IPO firms are not subject to the continuing scrutiny of the
financial analyst community.
The motivation of this dissertation is to understand the degree to
which accounting numbers influence IPO valuation. The preceding
1 See Smith [1986] and Ibbotson et al. [1988] for reviews of this
literature.
2 Privately held firms which have publicly traded debt outstanding
are notable exceptions. Publicly available financial statement
information is produced by these firms.
3 The Securities and Exchange Commission (SEC) requires the
inclusion of two (in a Form S-18 registration) or three (in a Form S-l
registration) years of audited financial statements in the registration
statement and accompanying prospectus. However, Weiss [1988] documents
that 529 out of her sample of 1510 IPOs reported less than two years of
earnings in the prospectus. These noncomplying firms are presumably
start-up firms which have operating histories of less than two years.
1

2
discussion indicates that accounting numbers potentially play a greater
role in the valuation of IPOs than in the valuation of more established
publicly traded firms, due to the reduced number of competing
information sources about IPOs.4 Thus, initial public offerings
represent an opportunity to explore the role of accounting numbers in
security valuation in instances of considerable uncertainty surrounding
the value of a firm. Specifically, this dissertation examines the
impact of the quality of accounting numbers available to investors on
the "long-term" mispricing of IPOs. In this thesis, accounting quality
refers to both the reliability and the quantity of the accounting
numbers supplied to investors. In other words, neither a sufficient
quantity of unreliable accounting numbers nor an insufficient quantity
of reliable accounting numbers are considered quality accounting
disclosures in this dissertation. The discussion to follow describes
the differences between "long-term" and "short-term" IPO mispricing, and
explains why the longer term perspective is adopted in this study.
Bower states that "there are two types of mispricing inherent in
initial offerings" (Bower [1989, p. 647]). The first type of mispricing
4 Prior research has consistently demonstrated that accounting
numbers and security prices are statistically associated. Please refer
to Beaver [1981, Chapter 5] and Watts and Zimmerman [1986, Chapter 3]
for comprehensive reviews of this literature. However, changes in
accounting numbers (i.e., "unexpected" earnings) typically only explain
five to ten percent of the variation in security returns. This low
level of explanatory power is presumably due to the abundance of
competing sources of information (e.g., financial analysts, industry
publications, the Wall Street Journal, etc.) about publicly traded
firms.

3
is the widely documented short-term "underpricing" phenomenon.5
Underpricing refers to the empirical finding that, on average, the
offering price set by the underwriter6 (hereafter referred to as the IPO
price) is below the consensus price set by market participants shortly
after the securities begin trading publicly. On average, this
underpricing exceeds 15% (see Smith [1986]).7 Short-term underpricing
is typically calculated as the return that an investor could have earned
by purchasing the IPO from the underwriter and selling at the market
clearing price at the end of the first day of public trading.8 Thus,
the short-term mispricing research assesses the accuracy of
underwriters' pricing decisions by comparing an underwriter's IPO price
to the price which prevails at the close of the first day of public
trading (hereafter referred to as the first aftermarket price).
5 Short-term underpricing has been documented by Reilly and
Hatfield [1969], McDonald and Fisher [1972], Logue [1973], Ibbotson
[1975], Ibbotson and Jaffe [1975], Ritter [1984b, 1987], Beatty and
Ritter [1986], Miller and Reilly [1987], Tinic [1988], Weiss [1988], and
others. Reviews of these empirical findings are provided by Smith
[1986] and Ibbotson et al. [1988].
6 More precisely, the initial offering price is set by the
underwriter after consultation with the entrepreneur of the IPO firm.
Thus, the issuing firm may play a significant role in the setting of the
IPO price. Alternatively, the issuing firm may completely delegate the
pricing decision to the underwriter (as in Baron's [1982] model).
7 Proposed explanations of the short-term underpricing phenomenon
will be detailed in Chapter 2.
8 However, underpricing has also been calculated as the return over
the first week (e.g., Tinic [1988]) or month (e.g., Ibbotson [1975]) of
public trading. Such departures from the usual underpricing calculation
are typically due to the unavailability of the market price at the end
of the first trading day.

4
Bower describes the second type of IPO mispricing as follows:
The second type of mispricing is a longer term effect that
relates to the lack of publicly available information about
the firm. Firm value is not likely to be revealed
immediately, but rather investors' expectations about firm
value improve over a long period of time as more information
is revealed about the firm. (Bower [1989, p. 650])
This second type of IPO mispricing "does not deal with initial
underpricing, but rather is concerned with the change in the market
price from the immediate aftermarket to the long term" (Bower [1989, p.
656]). Hence this long-term perspective of mispricing relates to the
accuracy of market participants' initial consensus assessment of IPO
firm value (i.e., the first aftermarket price). The long-term
mispricing of the market's consensus valuation on the first day of
public trading9 is assessed in relation to the market's valuation of the
firm after more information is revealed, via examining abnormal
aftermarket security returns.
There are three principal reasons why the long-term perspective of
IPO mispricing is adopted in this study. The first is that scant
empirical evidence exists on the long-term mispricing of IPOs. In
contrast to the widely documented short-term underpricing by
underwriters, the existing empirical findings do not provide conclusive
evidence as to whether the market overvalues, undervalues, or accurately
values new security issues, on average.10 Further, the impact of
9 This first day of public trading is presumably a time of great
uncertainty about the true value of an IPO due to lack of quality
information about the firm.
10 The empirical findings on long-term mispricing of IPOs will be
reviewed in detail in Chapter 2.

5
accounting numbers on the long-term accuracy of IPO valuations has not
been examined.
Second, the aftermarket performance of initial offerings may
represent a more appropriate measure of the results of the typical
investor's experience with new issues than short-term assessments of
mispricing. Specifically, it is widely contended that investors may
only receive a small percentage (if any) of their desired number of
shares of an initial offering due to quantity rationing of typically
oversubscribed issues.11 If quantity rationing is indeed prevalent in
the IPO market, then the typical investor's experience with IPOs may not
be represented by the short-term underpricing of the IPO price since
many investors are unable to purchase shares at that price.
The third and most important reason for the long-term mispricing
perspective is that accounting numbers potentially play a greater role
in the valuation decisions of market participants than in underwriters'
pricing decisions. Specifically, in modern finance theory equilibrium
security prices such as an IPO's first aftermarket price equal the
present value of the firm's expected future cash flows. Therefore, if
accounting numbers are useful in estimating such cash flows, then
accounting numbers may be beneficial to market participants in their
assessment of the first aftermarket price. By contrast, prior IPO
research has proposed that the underpricing of the IPO price by
underwriters is due to a variety of nonaccounting factors. In
11 See Rock [1986] and Beatty and Ritter [1986] for analytical
explanations of short-term underpricing based on the quantity rationing
of IPOs.

6
particular, explanations of short-term underpricing based on potential
legal liabilities, information asymmetries, and the desire to receive a
higher price for subsequent seasoned security offerings have been
proposed.12 Therefore, from an accounting perspective, the examination
of the long-term mispricing of IPOs may be more appropriate.
Three hypotheses relating accounting quality and long-term IPO
mispricing are developed and empirically tested in this thesis. The
first hypothesis is that the lack of quality accounting numbers at the
IPO date has no effect on the mean abnormal aftermarket returns of IPO
investors. However, the second hypothesis states that the dispersion
(variance) of these abnormal returns is greater in instances where
quality accounting information is lacking. In other words, these first
two hypotheses suggest that investors are able to value IPOs in an
unbiased fashion (i.e., not systematically overvalue or undervalue) even
in the presence of considerable uncertainty due to insufficient and/or
unreliable accounting numbers. However, the lack of quality accounting
numbers influences the precision of investors' valuations of IPOs.
Thus, it is postulated that the absolute magnitude (rather than the
direction) of long-term IPO mispricing is related to the quality of
accounting disclosures provided to potential investors. The third
hypothesis provides the time path of Hypothesis 2. Specifically, it
predicts that the influence of the quality of accounting numbers
available at the IPO date on the variance of investors' abnormal returns
diminishes over time. Hypothesis 3 implies that the accounting
12
See footnote 7.

7
information supplied at the IPO date becomes less important in the
valuation of the firm over time as more timely information becomes known
about these newly public firms.
Three empirical surrogates for the quality of accounting
information available to IPO investors are employed in this study. The
first is the reputation (or quality) of the auditor utilized in the IPO
process. Auditor reputation is measured dichotomously according to
whether a Big Eight or a non-Big Eight auditing firm is utilized in the
going public process. A measure of the potential managerial
manipulation of reported accounting earnings as reflected in managers'
accounting procedure choices forms the second surrogate for quality.
This accounting procedure choice proxy is measured dichotomously
according to whether the methods utilized in accounting for inventory
and depreciation are income decreasing (i.e., "conservative") or income
increasing (i.e., "liberal"). The third proxy is whether the SEC
registration statement filed by the IPO issuer is a Form S-l or Form
S-18 registration. This third surrogate measures the quantity component
of accounting quality since the accounting disclosure requirements of
Form S-l registrations are much more extensive than those of Form S-18
filings.
The empirical results provide support for the first two
hypotheses. Specifically, the results indicate that certain accounting
choices which provide indications of accounting quality (i.e., the
quality of auditor utilized in the IPO process, the type of SEC
registration form filed, and the degree of conservatism inherent in
inventory and depreciation choices) have no appreciable impact upon the

8
mean abnormal aftermarket returns of IPO investors. However, these same
accounting choices (with the exception of auditor quality) influence the
variance of investors' aftermarket abnormal returns. Thus, the results
indicate that uncertainty relating to accounting quality translates into
an increased variance of IPO abnormal returns, rather than a systematic
overvaluation or undervaluation of IPOs by investors. However, the
empirical results do not support the third hypothesis. Specifically,
the results do not reveal a strong tendency for the accounting quality
proxy variables to decrease in their ability to explain abnormal return
variances as time progresses subsequent to the initial offering.

CHAPTER 2
PRIOR RESEARCH ON INITIAL PUBLIC OFFERINGS
Introduction
Bower's [1989] specification of the two types of mispricing
inherent in IPO valuation is utilized in this chapter as an aid in
classifying and summarizing the relevant prior IPO research.1 Thus,
this chapter will review in detail (1) the short-term "underpricing"
literature and (2) the extant long-term mispricing literature.
Short-Term Mispricing of IPOs
Summary Results
As indicated in Chapter 1, the empirical evidence on the short
term average underpricing of initial offers is extensive. In his review
of this area of research, Smith [1986] reports that the average level of
underpricing exceeds 15%. In other words, on average, an investor could
purchase an initial offering at the price set by the underwriter and
then sell the security at the prevailing market price at the end of the
1 This classification scheme ignores all IPO research not
pertaining to the "mispricing" of the security issue. Thus, the major
line of research relating to the entrepreneur's ability to convey the
firm's "type" to investors via various signaling mechanisms will not be
reviewed here. The interested reader is referred to Leland and Pyle
[1977], Downes and Heinkel [1982], Ritter [1984a], Titman and Trueman
[1986], Hughes [1986], Feltham et al. [1988], Krinsky and Rotenberg
[1989], and Gale and Stiglitz [1989].
9

10
first day of public trading and earn a one day return (before
transactions costs) in excess of 15%.2 Thus, conclusive empirical
evidence exists concerning the short-term underpricing of initial offers
by underwriters. Table 2-1 summarizes the results of a number of
studies in this area.
Proposed Explanations of Short-Term Underpricing
Various analytical models have been proposed as explanations of
the short-term IPO underpricing phenomenon. Rock's [1986] model is
based on an asymmetry of information between "informed" and "uninformed"
investors. The informed investors have purchased perfect information
concerning the true value of an IPO. A major result of Rock's analysis
is that underpricing is required to entice the uninformed investors to
enter the IPO market. In the model, the informationally advantaged
investors only submit purchase orders for IPOs which are underpriced
(i.e., those in which the IPO price is less than the known true price).
By contrast, uninformed investors are unable to differentiate
underpriced and overpriced IPOs and must either not participate in the
IPO market or submit purchase orders for both underpriced and overpriced
issues. Thus, assuming uninformed demand is sufficient to fully
subscribe a new issue, uninformed investors are allocated 100% of the
shares in an overpriced issue and only a fraction of the shares in an
(oversubscribed) underpriced issue. This rationing of underpriced
2
It is important to note that on average IPOs are underpriced and
that any one particular initial offer could be overpriced. In fact,
evidence in Tinic [1988] and Miller and Reilly [1987] indicates that
approximately 30-40% of IPOs are not underpriced.

Table 2-1
Summary of the Results of Various Examinations
of Short-Term IPO Underpricing
Study
Definition of Return
Mean Return
Samle Period
Ritter [1989]
1st Day
14.32%
1975-1984
Ibbotson et al. [1988]
1st Calendar Month for 1960-1976
16.37%
1960-1987
1st Day for 1977-1987
Weiss [1988]
1st Day
8.77%
12/83-9/87
Miller and Reilly [1987]
1st Day
9.87%
1982-1983
Ibbotson [1975]
1st Calendar Month
11.4%
1960-1969
McDonald and Fisher [1972]
1st Week
28.5%
1/69-3/69
Reilly and Hatfield [1969]
1st Calendar Week
9.9%
12/63-8/64
1/65-6/65

12
issues between informed and uninformed investors forms the basis of the
underpricing phenomenon in Rock's model. Beatty and Ritter [1986]
summarize the' model's major result as follows:
Consequently, if an uninformed investor is allocated shares
in an initial public offering, there is a greater than usual
chance that the issue will start trading at a discount in
the aftermarket. In other words, for an uninformed
investor, the expected return conditional upon being
allocated shares is less than the expected return
conditional upon submitting a purchase order. But an
uninformed investor will participate in the market only if
the expected return conditional upon being allocated shares
is non-negative. This can only happen if, on average,
issuers underprice their shares. The owners of a firm going
public, who typically have a large proportion of their
wealth invested in the firm, would be willing to pay this
price if they are sufficiently risk-averse. (Beatty and
Ritter [1986, p. 228])
Beatty and Ritter's [1986] extension of Rock's model indicates
that the greater the ex ante uncertainty (i.e., uncertainty about the
true value of the issue), the greater the informed investors'
informational advantage, and thus the greater the level of expected
underpricing required to ensure uninformed investors' participation in
the offering. Thus, Beatty and Ritter conclude that ex ante uncertainty
and expected underpricing are positively related. This positive
association between ex ante uncertainty and underpricing has been
empirically verified by Beatty and Ritter [1986], Ritter [1984b], and
Miller and Reilly [1987].
Baron [1982] also attempts to explain the average short-term
underpricing of IPOs via asymmetric information arguments. In his model
the underwriter possesses an informational advantage over the IPO issuer
regarding investor demand for the upcoming security issue. In an
analytical principal-agent framework, Baron demonstrates that the

13
optimal contract between the issuer (i.e., the principal) and the
underwriter (i.e., the agent) involves the delegation of the offer price
decision to the better informed underwriter. This is in addition to the
underwriter's role as distributor of the issue. A major result of the
model is that the issuer will be willing to accept a lower offer price
decision from the underwriter (and hence greater underpricing) as the
issuer's uncertainty surrounding investor demand for the upcoming issue
increases. Thus, Baron's model also implies a positive association
between underpricing and uncertainty.
Tinic [1988] proposes an "implicit insurance" explanation of
underpricing. He summarizes the implicit insurance hypothesis as
follows:
Stated briefly, I demonstrate that gross underpricing serves
as an efficient form of protection against legal liabilities
and the associated damages to the reputations of both the
investment bankers and the issuers. In other words, it is a
form of implicit insurance against potential liabilities
that may arise from the "due-diligence" and disclosure
requirements of the federal securities regulations.
Although avenues for legal recompense against misinformation
and fraud existed before the Securities Act of 1933, the
Act, in effect, replaced the principle of caveat emptor with
the dictum: "Let the issuer and the investment banker
beware." (Tinic [1988, p. 790])
Even though Tinic's short-term underpricing explanation is not based on
informational asymmetries, his model is consistent with Beatty and
Ritter's [1986] conclusion that underpricing is positively associated
with ex ante uncertainty.
A recent line of research proposes that short-term underpricing
can be utilized as a signaling mechanism by entrepreneurs of high
quality IPO firms. In Welch's [1989] model, high quality firms

14
underprice their initial offering in order to obtain a higher price in
subsequent seasoned security offerings. A separating equilibrium
results due to the prohibitive costs which must be borne by lesser
quality firms attempting to imitate the underpricing signal of higher
quality firms. Thus, Welch's model is consistent with the hypothesis
that the purpose of underpricing is to "leave a good taste in investors'
mouths" (Ibbotson [1975, p. 264]).
Grinblatt and Hwang [1989] formulate a two signal model in which
underpricing and the percentage of ownership retained by the
entrepreneur serve as signals of the IPO firm's unobservable "intrinsic"
value and cash flow variance. Since underpricing signals the
"intrinsic" value of the IPO firm to investors, this model is also
consistent with the hypothesis that the underpricing of the initial
offering can be utilized by high quality firms in order to receive a
higher price in subsequent security offerings.
The prior paragraphs describe numerous proposed explanations for
the short-term underpricing phenomenon. However, it appears that
Ibbotson et al. [1988] are correct in their assessment of the current
state of underpricing research. They state: "A number of hypotheses
have been offered to explain the underpricing, but to date there is
still no persuasive, widely accepted, and test-supported explanation of
IPO underpricing" (Ibbotson et al. [1988, p. 37]).
Accounting Research Related to Short-Term Underpricing
Accounting research related to short-term IPO underpricing
examines the impact of auditor reputation on initial underpricing.

15
Beatty [1989a] and Balvers et al. [1988] hypothesize that auditor
reputation and initial underpricing are inversely related. Beatty
utilizes the major result of Beatty and Ritter [1986] (i.e., expected
underpricing is positively associated with ex ante uncertainty) in the
formulation of this hypothesis. Specifically, since the attestation of
an IPO's financial statements by a high reputation auditor serves to
reduce investors' ex ante uncertainty, then auditor reputation is
hypothesized to be inversely related to underpricing. Balvers et al.
extend the models of Rock [1986] and Beatty and Ritter [1986] by
explicitly considering the relationship between an IPO's auditor and
underwriter. In the model, high auditor reputation reduces investors'
uncertainty regarding an IPO's earnings and serves as a positive signal
of the reputation of the underwriter. Thus, auditor reputation is
inversely related to ex ante uncertainty, and therefore is hypothesized
to be inversely related to underpricing. Empirical support of this
hypothesized relationship between auditor reputation and short-term
underpricing is documented by Beatty [1989a, 1989b] and Balvers et al.
[1988],3 Thus, the results of prior research seem to suggest that an
entrepreneur of a firm contemplating an initial offering may be able to
3 However, Balvers et al. [1988] conclude that the impact of
auditor reputation on underpricing is reduced when a high reputation
underwriter is also involved in the registration process. Thus, auditor
and investment banker reputation appear to be (at least partial)
substitutes.

16
influence the short-term underpricing of the issue by his auditor
choice.4
Long-Term Mispricing of IPOs
Summary Results
As indicated in Chapter 1, long-term mispricing refers to the
accuracy of the market's initial valuation of an IPO. The accuracy of
this potentially "uninformed" first aftermarket price is assessed in
relation to security prices which prevail after additional information
about the firm is revealed, via examination of abnormal aftermarket
returns. In contrast to the extensive literature on short-term IPO
mispricing, little prior research exists on the long-term mispricing of
initial security offerings. Also, the evidence to date is not
conclusive as to whether, on average, market participants overvalue,
undervalue, or correctly value initial offerings of securities. Table
2-2 summarizes the results of prior examinations of long-term IPO
mispricing.
With the exception of Ritter [1989], cross-sectional explanations
of long-term mispricing are unavailable. Ritter documents considerable
variation in the level of long-term mispricing across industries and
4 The impact of other accounting choices on initial underpricing
has not been examined. For example, prior research has not explored the
effect of the accounting procedures chosen by IPO firms or the amount
and content of accounting information disclosed on short-term
underpricing. Even though this dissertation examines the effect of
accounting numbers on long-term IPO mispricing, some exploratory results
of the impact of certain accounting choices on short-term underpricing
are provided in Appendix A.

Table 2-2
Summary of Prior Examinations of
Long-Term IPO Underpricing
Study
Return Time Horizon
Mean Return
Sample Peri'
Ritter [1989]
3 Years
-24.33%
1975-1984
Simon [1989]
Pre-1933 Non-NYSEa IPOs
5 Years
-52.61%
1926-1933
Pre-1933 NYSE IPOs
5 Years
Insignificant
1926-1933
Post-1933 Non-NYSE IPOs
5 Years
Insignificant
1934-1939
Post-1933 NYSE IPOs
5 Years
Insignificant
1934-1939
Jarrell [1981]
Pre-1933 New Issues
5 Years
64%
1926-1933
Post-1933 New Issues
5 Years
25%
1934-1939
Ibbotson [1975]
5 Years
Insignificant
1960-1969
McDonald and Fisher [1972]
1 Year
-18.1%
1/69-3/69
NYSE refers to New York Stock Exchange listed firms.

18
year of IPO issuance. This dissertation contributes to the literature
by providing explanations of long-term IPO mispricing based on cross-
sectional differences in accounting quality.
Research on the Long-Term Aftermarket Performance of IPOs
Reilly and Hatfield hypothesize that underwriters' short-term
underpricing of IPOs continues into the long-term "as the market
continues to recognize and adjust for the underpricing" (Reilly and
Hatfield [1969, p. 74]). Thus, they argue that the market for initial
security offerings is inefficient in that the market initially
undervalues IPO firms, on average. They calculate short-term mispricing
as the percentage change from the IPO price to the prevailing price one
week (or month) following the IPO. Long-term performance is defined as
the return from one week (or month) following the IPO to one year
subsequent to the commencement of public trading. Reilly and Hatfield
do not empirically assess the average abnormal aftermarket returns of
their entire sample. Rather, they demonstrate that the subset of firms
which outperformed the market in the short-term also had above-market
long-term returns. Thus, Reilly and Hatfield's results do not provide
an indication of the average long-term mispricing of IPOs; however, the
results may indicate that short-term and long-term mispricing are
correlated.
McDonald and Fisher hypothesize that in an efficient market
"subsequent price behavior of the stock should be independent of the
initial rate of return at offering" (McDonald and Fisher [1972, p. 97]).
They calculate abnormal returns by subtracting the return on the market

19
portfolio from an IPO's return. Thus, this adjustment for overall
market performance does not vary across securities (i.e., the Capital
Asset Pricing Model (CAPM) beta parameter is assumed to equal one for
all firms). The authors' results support their contention that long
term mispricing is independent of the level of short-term mispricing.
However, average abnormal returns of -18.1% are documented from one week
after the offering to one year after the offering. This negative
abnormal return implies that investors were overly optimistic in their
initial valuations of IPO firms, and therefore lost 18.1% of their
investment in the first year of public trading due to such incorrect
valuations. Thus, McDonald and Fisher's results indicate that the
market overvalued, on average, IPOs issued in the first quarter of 1969.
Ibbotson [1975] examines the abnormal aftermarket performance of a
sample of firms which went public between 1960 and 1969. Abnormal
returns are calculated over various holding periods up to 60 months
subsequent to the IPO by means of the two-parameter Sharpe [1964] and
Lintner [1965] CAPM. The empirical results demonstrate very few
statistically significant abnormal returns in the aftermarket. Thus,
Ibbotson concludes that IPO aftermarket returns do not deviate from
market efficiency. In other words, Ibbotson's results are consistent
with the hypothesis that the market utilizes all available information
in an unbiased fashion and hence, on average, accurately values new
issues.
The most extensive examination of the aftermarket performance of
IPOs is provided by Ritter [1989], He explores the long-term mispricing
of 1,526 firms that made initial offerings between 1975 and 1984.

20
Monthly abnormal returns are calculated by subtracting the return on a
market index from an IPO's raw return for each of the first 36 months
following the IPO. Thus, market-adjusted rather than risk-adjusted
abnormal returns are computed. Ritter documents average abnormal
returns of -24.33% over the first three years of public trading. Thus,
the results are consistent with the hypothesis that the market
overvalued IPOs in the 1975-1984 period. However, considerable
variation in the level of this overvaluation across year of issuance and
industries is documented. In particular, Ritter states that the
overvaluation is "concentrated among relatively young growth companies,
especially those going public in the 1980s" (Ritter [1989, p. 3]).
Comparison of the Long-Term Mispricing of New Issues Before
and After the 1933 Securities Act
Stigler [1964] and Jarrell [1981] compare the long-term mispricing
of new security issues5 offered before and after the 1933 Securities Act
(the Act). Both researchers test the hypothesis that the period prior
to the disclosure provisions of the Act was characterized by exaggerated
claims of issuers and underwriters, resulting in overvaluation of new
issues by investors. Specifically, both Stigler and Jarrell examine
whether the disclosure requirements of the 1933 Securities Act resulted
in increased investor returns for post-1933 new issues as compared to
the returns earned on potentially overvalued pre-1933 issues.
5 These papers examine the aftermarket abnormal returns on all new
issues of securities. In other words, both IPOs and new security
offerings of existing publicly held corporations (i.e., seasoned issues)
are examined.

21
Stigler [1964] reports negative average market-adjusted returns
for both pre-1933 and post-1933 new security issues over each of the
first five years after issuance. Thus, Stigler concludes that the
disclosure requirements of the Act did not result in increased returns
for investors in the post-1933 era. Stigler controls for market
influences by comparing aftermarket prices to the value of a market
index. The pre-SEC sample is taken from the years 1923-1928, while
1949-1955 is utilized as the post-SEC period.
Jarrell [1981] also rejects the hypothesis that average
aftermarket performance improved significantly after passage of the Act.
He calculates abnormal returns by means of the two-factor CAPM. His
pre-SEC sample covers the years 1926-1933, while the post-SEC period
runs from 1934-1939. Jarrell's results indicate positive mean risk-
adjusted returns for both groups over a five year holding period.
Contrary to the hypothesis tested however, the cumulative returns for
the pre-SEC sample exceeded those of the post-SEC sample.
Simon [1989] also examines the aftermarket returns of pre-SEC
versus post-SEC new security issues. However, she hypothesizes that the
disclosure provisions of the 1933 Securities Act should have no effect
on the average abnormal returns earned by investors. In other words,
she postulates that rational investors were able to accurately value new
issues, on average, prior to the Act's increased disclosure requirements
since "the existence of substantial uncertainty about the true value of
a security need not imply that the issue will be, on average, overvalued
or undervalued" (Simon [1989, p. 295]). However, Simon theorizes that
the lack of quality information about the value of a firm undertaking a

22
new security issue which characterized the pre-SEC period increased the
level of riskiness associated with the purchase of the security. Thus,
she hypothesizes that the variance of aftermarket abnormal returns
decreased after the passage of the 1933 Securities Act.
Simon utilizes a multifactor asset pricing model in the
determination of risk-adjusted abnormal returns. In addition to the
return on the market, the model includes the industry return, the
unexpected variance of the market return, and a business cycle indicator
as explanators of equilibrium security returns. She classifies new
issues from 1926-1933 as pre-SEC issues, while 1934-1940 offerings are
deemed post-SEC issues. Contrary to expectations, Simon documents
significantly negative average abnormal returns for non-NYSE initial
public offerings in the pre-SEC period. Thus, it appears that investors
overvalued, on average, IPOs of non-NYSE firms prior to the passage of
the 1933 Securities Act. However, results consistent with the
hypothesis that average abnormal returns would not differ significantly
from zero are documented for NYSE issues and seasoned non-NYSE issues in
both the pre- and post-SEC periods. Further, in contrast to Stigler
[1964] and Jarrell [1981], Simon demonstrates a statistically
significant increase in average aftermarket abnormal returns for non-
NYSE initial public offerings from the pre-SEC to post-SEC periods. No
such increase was present for either NYSE issues or non-NYSE issues of
currently publicly traded corporations. Finally, the hypothesis that
the variance of aftermarket returns significantly decreased following
the disclosure requirements of the Act is empirically supported for each
portfolio of new security offerings.

23
Simon's study represents an intervention analysis of a time-series
nature (i.e., differences between the pre- and post-SEC periods are
examined). In contrast, the current study deals with information
variables from a cross-sectional perspective.
Summary of Long-term Mispricing Research
The results of prior research conflict as to whether, on average,
the market overvalues, undervalues, or accurately values new issues of
securities. In assessing the long-term performance of new issues, prior
researchers have employed diverse methods of calculating abnormal
returns and widely divergent sample years. It is possible that the
contradictory findings are time period specific and/or dependent upon
the asset pricing model utilized in the determination of abnormal
returns. This dissertation extends prior research by (1) reexamining
the long-term mispricing of initial public offerings by utilizing a
refined model of equilibrium security pricing6 on a sample of recent
IPOs7 and (2) testing hypotheses relating to the effect of accounting
numbers on such mispricing.
6 A modified version of Simon's [1989] multifactor asset pricing
model will be implemented in this study. The model will be described in
detail in Chapter 4.
7 In contrast to Stigler [1964], Jarrell [1981], and Simon [1989],
only initial public security offerings will be examined. Thus, new
seasoned issues will be excluded. Also, this study will only examine
the long-term mispricing of IPOs initiated since 1980. With the
exception of Ritter's [1989] 1975-1984 sample period, previous studies
have not utilized post-1970 IPOs.

CHAPTER 3
HYPOTHESIS DEVELOPMENT
Introduction
The three hypotheses developed and empirically tested in this
dissertation relate to the impact of the quality of accounting
information available to IPO investors on long-term IPO mispricing.
With the aid of three assumptions, this chapter develops these
hypotheses in a Capital Asset Pricing Model (CAPM) framework.1
Assumptions
First, it is assumed that the total uncertainty surrounding an
initial offering arises from two sources. Specifically, assume that an
IPO's firm-specific risk may be decomposed2 as follows:
Firm-specific risk = the uncertainty related to the firm's
production, investment, and financing activities + the
uncertainty related to the quality of the accounting numbers
supplied to potential investors. (la)
In mathematical terms, Equation (la) becomes
FSRi = BUSRISiq + ACCTRISKi, (lb)
1 The model utilized in this chapter to derive the hypotheses is an
adaptation of the model found in Jarrell [1981].
2 This partitioning of firm-specific risk is in addition to the
CAPM's systematic (i.e., nondiversifiable) versus unsystematic (i.e.,
diversifiable) decomposition.
24

25
where FSRt is firm specific risk, BUSRISKi equals firms i's business
risk, and ACCTRISKi denotes accounting risk.
The first component of the preceding definition describes the cash
flow uncertainty associated with the firm's production, investment, and
financing activities, and will hereafter be referred to as business
risk. In other words, business risk represents the underlying
uncertainty related to the overall business activities (both operating
and financial) of the firm. The second component in the above
characterization of an IPO's firm-specific risk (hereafter referred to
as accounting risk) reflects the influence of accounting quality on IPO
firm uncertainty. According to Equation (lb), the total level of
uncertainty surrounding an IPO equals the firm's business risk (i.e.,
the lower bound) only when there is no uncertainty surrounding the
quality of the accounting numbers supplied to potential investors.
However, when uncertainty surrounding accounting reliability exists, the
total uncertainty surrounding the security issue exceeds the IPO's
business risk, and increases as accounting risk increases.3
3 Equation (lb) characterizes the total risk of an initial offering
as a linear and additive function of business risk and accounting risk.
Thus, the equation assumes the independence of the two concepts. This
assumption appears to be reasonable, since there is no a priori reason
to postulate the absence of any of the four possible combinations of
business and accounting risk. Specifically, one could reasonably expect
to find IPO firms in each of the following categories: (1) high
business risk and a high level of accounting reliability and accuracy,
(2) high business risk and a low level of accounting reliability, (3)
low business risk and a high level of accounting reliability, and (4)
low business risk and a low level of accounting reliability. Thus, it
seems reasonable to assume that the level of uncertainty surrounding an
IPO firm's production, investment, and financing activities (i.e., its
inherent cash flow uncertainty) is independent of the precision of its
accounting valuations.

26
Second, it is assumed that the two-factor Sharpe-Lintner CAPM
appropriately describes equilibrium security valuation.4 The model
characterizes the expected return on a security as a linear function of
the expected market return. The model (in expectation form) may be
written
E(Rit) RFt + iCECRut) RFt), (2)
where E = the expectation operator, Rit the security return on IPO
firm i over time period t, RFt = the risk-free rate for time t, R^ = the
time t return on the market portfolio, and /3t = firm i's systematic
risk.
The CAPM is often utilized in its realized form for empirical
purposes. In realized form, the model is
E-it = RFt + EFt) + eit, (3)
where eit is an error term and all other terms are as previously
defined.
Systematic risk refers to that portion of firm-specific risk which
cannot be diversified away. In other words, f)L reflects the riskiness
of security i in relation to market-wide risk, and is calculated as
Pi Cov(Rit, R^) / VarCR,,,.). (4)
According to the CAPM, the remaining portion of firm-specific risk is
labelled unsystematic risk. Unsystematic risk is calculated as the
variance of the error terms in the realized formulation of the model
4 The assumption that the CAPM accurately reflects equilibrium
security valuation is implemented in this section for expositional
purposes only. In fact, in the empirical analysis to follow, a
multifactor model is implemented in an attempt to overcome certain
deficiencies of the CAPM.

27
(i.e., Var(eit)). In a CAPM framework, unsystematic risk does not
affect the expected return on a security since its effects may be
diversified away by holding a well-diversified portfolio.
Finally, this study makes use of the assumption that investors are
rational. In other words, this rational expectations assumption implies
that market participants form unbiased estimations of (1) an IPO's
systematic risk and (2) the future payouts arising from IPO ownership.5
Decomposition of Systematic and Unsystematic Risk into
Accounting and Business Risk Components
The CAPM partitions firm-specific risk into systematic and
unsystematic components. This section further decomposes the CAPM's
firm-specific risk components into the business risk and accounting risk
partition of Equation (lb).
Specifically, may be defined as
h = /*iB + £i\ (5)
where fiB = systematic risk attributable to firm i's production,
investment, and financing activities, and f3AA = systematic risk
attributable to uncertainty surrounding the quality of the accounting
numbers supplied to investors.
The systematic risk attributable to accounting risk is modelled as
follows:
/?iA ^a(T,Q), (6)
5 These expected future payouts can be thought of as either
dividend distributions or the firm's cash flows.

28
where T = time subsequent to the IPO and Q the quality of accounting
information disclosed to investors at the time of the IPO. It is
assumed that systematic accounting risk is a decreasing function of both
time and accounting quality at the IPO date. Specifically,
dfif/dT < 0 and (7a)
apf/BQ < 0. (7b)
In other words, subsequent to the IPO, systematic accounting risk
decreases over time as more information is revealed about the firm.6
Also, the disclosure of a sufficient quantity of reliable accounting
information at the IPO date reduces systematic accounting risk.
Similarly, unsystematic risk may be divided into business and
accounting risk components as follows:
Var(ei) Var(eiB) + Vare/), (8)
where Var(et) = firm i's unsystematic risk which is calculated as the
variance of the error terms in the realized formulation of the CAPM, and
Vare/) and Var(e/) equal the business risk and accounting risk
components of unsystematic risk, respectively. As was the case for
systematic accounting risk, unsystematic accounting risk is modelled as
a decreasing function of time subsequent to the IPO as well as
accounting quality at the IPO date. Specifically,
Var(e/) = Var(e/) (T,Q) (9)
6 This additional information revealed in the periods following the
IPO may consist of both accounting (e.g., quarterly and/or yearly
financial statements or analysts' forecasts of earnings) and
nonaccounting (e.g., security prices or Wall Street Journal articles)
information. This additional information will reduce accounting risk
only if it is deemed reliable.

29
where SVarCe^/ST < 0 and
3Var(eiA)/3Q < 0.
(10a)
(10b)
Hypotheses
Overview
This section demonstrates the effects of changes in accounting
risk on the distribution of investors' aftermarket abnormal returns
(i.e., long-term mispricing). First, the effect of changes in
accounting risk on mean abnormal returns is explored. Next, accounting
risk's effects on the dispersion (variance) of abnormal risk-adjusted
returns is examined. Finally, the impact over time of accounting risk
on the variance of abnormal returns is portrayed.
Accountine Risk and Mean Abnormal Returns
An increase in accounting risk (ACCTRISK) leads to an increase in
both the systematic and unsystematic components of accounting risk.
According to Equations (5) and (8), such increases in and Var(etA)
result in increases in both systematic and unsystematic risk. It is
evident from Equation (2) that such an increase in unsystematic risk has
no effect upon expected mean returns in a CAPM framework. The effects
of increases in unsystematic risk may be eliminated in a well-
diversified portfolio and thus do not affect mean expected security
returns.
In contrast, it is apparent from Equation (2) that an increase in
systematic risk increases investors' expected raw (i.e., unadjusted)

30
returns. However, assuming that investors are rational, it is readily
seen that an increase in systematic accounting risk should not cause
risk-adjusted abnormal returns to deviate from zero since the increased
raw returns demanded by investors merely compensate them for bearing
greater risks. The rational expectations assumption implies that
investors are able to form unbiased estimates of an IPO's future payouts
even in the presence of considerable uncertainty surrounding the value
of the firm (e.g., in instances of high accounting risk). In other
words, on average, rational investors do not overestimate or
underestimate a firm's cash flows due to unreliable and/or incomplete
accounting disclosures. Increased accounting risk merely induces
investors to demand a greater return per dollar of expected future
payout (cash flow) to compensate them for bearing increased risks.
Thus, on a risk-adjusted basis, IPOs are on average correctly valued
owing to the unbiased nature of the estimated value-relevant cash flows.
In other words, rational investors do not misprice IPOs, on average,
even in the presence of a high degree of systematic accounting risk.
Thus, neither unsystematic nor systematic accounting risk should
cause mean risk-adjusted aftermarket returns to deviate from zero. This
assertion gives rise to the first hypothesis, which may be formally
stated as follows:
Hypothesis 1: On average, the long-term mispricing of IPO
firms is zero regardless of the level of accounting risk
faced by investors.

31
Accounting Risk and the Variance of Abnormal Returns
The rational expectations assumption implies that even though
investors' estimates of future payouts are unbiased even in the presence
of high accounting risk, the precision (variance) of these estimates
decreases (increases) with accounting risk. This increase in the
variability of cash flow estimates should translate into greater
variability in returns. In fact, just such an increase in variability
can be demonstrated in a CAPM framework.
The variance of a security's returns according to the CAPM (in
realized form) is
Var(Ri) = ^Vard^) + Vare,) (11)
where Var(R,) = the variance of security i's returns, Var(Rfl) = the
variance of the return on the market portfolio, and the remaining terms
are as previously defined.
As previously noted, Equations (5) and (8) demonstrate that an
increase in accounting risk results in an increase in both systematic
(i.e., £,) and unsystematic (i.e., Var(e,)) risk. Therefore, it follows
directly from Equation (11) that the variance of a security's returns
increases with accounting risk. The preceding assertion forms the basis
for the second hypothesis, which may be stated as
Hypothesis 2: The variance of IPO firms' abnormal returns
is positively associated with accounting risk.
Thus, it is consistent with rational expectations that the absolute
magnitude (rather than the direction) of IPO mispricing is related to
the level of accounting risk surrounding an issue, even though IPOs are
not systematically mispriced.

32
Accounting Risk and the Variance Over Time of Abnormal Returns
Equations (7a) and (10a) model systematic and unsystematic
accounting risk, respectively, as decreasing functions of time
subsequent to the IPO. Thus, this decrease in and Varie^) over time
results in a reduction in both systematic (i.e., and unsystematic
(i.e., Var(e)) risk. It then follows directly from Equation (11) that
such decreases in systematic and unsystematic risk result in an IPO
firm's abnormal return variance decreasing over time. The preceding
implication of the model gives rise to the third hypothesis, which may
be stated as follows:
Hypothesis 3: The variance of IPO firms' abnormal returns
decreases over time.
This third hypothesis is consistent with a diminishing effect of
accounting risk on the precision of IPO valuation (i.e., long-term
mispricing) over time as the information disclosed at the time of the
IPO takes on a lesser role in the valuation of the firm as new
information is revealed.7
7
See footnote 6.

CHAPTER 4
EMPIRICAL METHODOLOGY
Introduction
The empirical methodology implemented in the testing of the three
hypotheses is described in this chapter. First, the three variables
utilized as empirical proxies for the accounting risk theoretical
construct are discussed. The measurement of abnormal risk-adjusted
aftermarket returns (i.e., long-term mispricing) is then illustrated.
Next, both univariate and multivariate tests of the three hypotheses are
detailed. Finally, a summary of the empirical methodology is provided.
Three Measures of Accounting Risk
Overview of the Three Accounting Risk Surrogates
The accounting risk theoretical construct is a multifaceted
concept which is difficult to empirically capture with any one proxy
variable. Thus, this dissertation utilizes three surrogates for
accounting risk, each of which is intended to capture a different aspect
of accounting riskiness. The three unique components of accounting risk
captured by this study's proxy variables are (1) the level of
credibility associated with the reported accounting numbers, (2) the
level of bias inherent in those numbers, and (3) the quantity of
accounting disclosures supplied to potential IPO investors. Auditor
33

34
reputation, the degree of conservatism inherent in a firm's inventory
and depreciation accounting procedure choices, and the type of SEC
registration statement filed, respectively, are employed in this study
as empirical surrogates for these three aspects of accounting risk. The
remainder of this section provides detailed descriptions of the three
accounting risk proxy variables.
Auditor Reputation (Quality) Proxy
The reputation (or quality) of the auditor utilized in the initial
public offering process serves as the first proxy for accounting risk.
The primary function of the auditor in the IPO process is to express an
opinion on the IPO firm's financial statements. Therefore, it is
reasonable to assume that a high quality audit lends credibility to the
financial statements, thereby reducing accounting risk. Conversely, a
low quality audit adds little credibility to the issuer's financial
statements, and thus investors' level of uncertainty surrounding the
reliability and accuracy of accounting numbers will be correspondingly
greater. Thus, audit quality is a surrogate for the level of
credibility associated with reported accounting numbers, which is an
aspect of accounting riskiness.
An IPO firm's accounting risk (based on auditor quality) is
measured in terms of whether the accounting numbers supplied to
potential investors at the time of the initial offer were audited by a

35
Big Eight or non-Big Eight auditing firm.1 DeAngelo [1981] models audit
quality as a positive function of auditing firm size. She argues that
this relationship results from the greater level of independence which
is employed by large audit firms in an effort to maintain their
reputation capital. In an IPO context, this presumed higher quality of
large audit firms might also be due to larger firms' greater expertise
in industry-related accounting matters, greater experience in auditing
developmental stage companies, stricter internal control requirements,
etc. If audit quality is indeed positively associated with audit firm
size, accounting risk should be inversely related to auditor size. In
other words, the preceding arguments imply that Big Eight audited IPO
firms should possess less accounting risk than IPOs audited by non-Big
Eight firms.
However, a number of arguments against this presumed positive
association between audit quality and audit firm size can be raised.
First, DeAngelo's [1981] analytical model which predicts this
relationship has not been empirically verified. A second argument
against DeAngelo's hypothesized relationship is that individual audit
1 Non-Big Eight firms consist of both "national" (i.e., the mid
sized firms with offices throughout the country) and local/regional
auditing firms. Thus, it is possible to classify auditing firms into
three categories, rather than the traditional Big Eight, non-Big Eight
dichotomy. For example, Simunic and Stein [1986] classify auditing
firms into three categories (i.e., Big Eight, "national," and
local/regional) and then discard the middle group in order to increase
the power of the test of auditor reputation on IPO valuation. Due to
the small number of non-Big Eight audited IPO firms in the sample
analyzed in this dissertation (see Chapter 5), a further breakdown of
this group into "national" and local/regional categories does not appear
to be a viable alternative in this study. Therefore, the traditional
Big Eight, non-Big Eight dichotomy is employed.

36
engagement partners in large decentralized auditing firms may have the
same incentives in regards to compromising their independence as
partners in small local/regional firms. In each case, a particular
audit client may represent a significant proportion of the revenue
generated by a partner. Therefore, audit engagement partners in large
decentralized firms may not consider the ramifications of certain
actions on the firm as a whole. Thus, the predictions of DeAngelo's
model may not hold in large decentralized audit firms. Finally, it is
possible to dispute the argument that large audit firms have greater
expertise in industry-related accounting matters. Specifically,
regional auditing firms may specialize in the predominant industry in
that region. For example, a regional auditing firm in Texas may have
greater expertise in the oil and gas industry than a large national firm
headquartered in New York. The preceding arguments against the presumed
positive association between audit quality and audit firm size imply
that the Big Eight, non-Big Eight dichotomy employed in this study may
not accurately proxy for accounting risk.
Accounting Procedure Choice Proxy
The second accounting risk surrogate utilized in this study is
measured according to whether the accounting numbers provided to
potential IPO investors were calculated by utilizing income decreasing
(i.e., "conservative") or income increasing (i.e., "liberal") accounting
methods. This proxy is utilized in recognition of the potential of a
firm's managers (or entrepreneurs in the IPO context) to manipulate

37
reported earnings via certain accounting procedure choices.2 An
entrepreneur of a firm about to undertake an initial public security
offering might be motivated to manipulate accounting numbers in order to
send an exaggerated (i.e., positively biased) signal to potential
investors concerning the earnings history (and potential) of his firm.
By definition, accounting risk (i.e., the uncertainty surrounding the
reliability and accuracy of accounting numbers) increases as the level
of managerial manipulation of accounting numbers increases. Thus, an
estimate of the degree of such manipulation is employed as a proxy for
accounting risk. This surrogate is intended to capture the component of
accounting risk which relates to the level of bias inherent in reported
accounting numbers.
Imhoff and Thomas [1989] provide empirical support for the
utilization of this conservative versus liberal accounting procedure
choice dichotomy as a proxy for accounting quality. They demonstrate a
statistically significant positive association between the ratings of
financial analysts regarding the accounting quality of firms in the
particular industry in which each analyst specializes and the degree of
conservatism of the accounting methods employed by these firms. Thus,
Imhoff and Thomas [1989] provide evidence that financial analysts view
2 Watts and Zimmerman [1986] review the literature (both
theoretical and empirical) which suggests that managerial manipulation
of earnings is related to the existence of pol-itical costs, debt
covenants, and bonus plans denominated in terms of accounting numbers.

38
firms which utilize conservative accounting methods as possessing higher
accounting quality than firms which utilize more liberal methods.3
In this study, the liberal versus conservative accounting
procedure choice proxy is measured based on whether an IPO firm's chosen
inventory and depreciation methods4 tend to increase or decrease
reported accounting earnings. Specifically, conservative accounting
choices are defined as the last-in, first-out (LIFO) inventory valuation
3 There are other possible explanations of this perceived
relationship besides the managerial manipulation of earnings explanation
utilized in this study. For example, it could be argued that accounting
risk is less when the "conservative" last-in, first-out (LIFO) inventory
valuation method is used in comparison to the more "liberal" first-in,
first-out (FIFO) method. Specifically, the LIFO method tends to smooth
reported income numbers in periods of rapidly changing prices, both
increasing and decreasing. These less volatile income numbers may be
perceived as being of higher quality than the more variable FIFO-based
numbers. Also, LIFO users often report in their financial statements
what income would have been under the FIFO method. However, the
opposite does not hold since FIFO users typically do not report LIFO-
based net income. Therefore, it is possible that LIFO-based numbers may
be considered co be of higher quality since more information is
typically available under LIFO than under FIFO.
4 Other accounting procedure choices have been examined in the
accounting literature. For example, the methods employed to account for
the investment tax credit (ITC) and oil and gas exploration costs have
been widely examined (see Watts and Zimmerman [1986] for a review and
Imhoff and Thomas [1989] for a recent example). These accounting
choices were also originally planned to be included in this study's
definition of conservative versus liberal accounting procedure choices.
However, only 2 out of the 372 sample firms used a procedure other than
the flow through ITC method and oil and gas exploration firms comprise
only 5% of the sample firms. Hence, these two accounting procedure
choices appear to be of little use in the current sample in classifying
firms into conservative versus liberal accounting procedure choice
groups. Thus, the level of conservatism of a firm's accounting
procedure choices is measured based only upon inventory and depreciation
method choices. The inability to examine other than inventory and
depreciation choices is considered to be only a minor limitation since
depreciation and inventory represent the most important (in dollar
terms) accounting procedure choices for most firms.

39
procedure and the utilization of any of a number of accelerated
depreciation methods (e.g., double-declining balance or sum-of-the-
years'-digits). Liberal (i.e., income increasing) choices are defined
as any inventory valuation method other than LIFO (e.g., first-in,
first-out (FIFO), weighted average, or specific identification)5 and the
straight-line depreciation method.
This accounting procedure choice proxy variable is measured
dichotomously. The current paragraph provides an overview of the
classification scheme utilized in the grouping of sample IPO firms into
high and low accounting risk portfolios based on accounting procedure
choices, while the following paragraph details the specifics of the
classification scheme. An IPO firm is considered to possess high
accounting risk if each of its available accounting method choices is
income increasing (i.e., "liberal"). Conversely, an IPO is considered
to be a low accounting risk firm if at least one of the two relevant
accounting procedure choices is income decreasing.
This accounting procedure choice variable was originally measured
over three levels. Specifically, firms were classified into accounting
risk portfolios based on whether their inventory and depreciation
choices were (1) consistently income increasing, (2) consistently income
decreasing, or (3) a combination of income increasing and income
decreasing methods. However, in the sample analyzed in this study (see
Chapter 5), only 10 out of the 372 sample firms used income decreasing
5 Consistent with current accounting practices, in the sample of
IPOs examined in this study (see Chapter 5), the vast majority of the
non-LIFO inventory choices are FIFO choices.

40
(i.e., conservative) methods for both inventory and depreciation.
Therefore, this consistently "conservative" group was combined with the
group whose accounting procedure choices consisted of a combination of
income increasing and income decreasing methods to form the low
accounting risk portfolio in the dichotomous classification scheme.
Further, a number of the sample firms did not have both inventories and
long-term depreciable assets. In cases where a firm possessed no
inventory (long-term depreciable assets), the measurement of this
accounting procedure choice variable was based strictly upon the firm's
depreciation (inventory valuation) choice.
In summary, the high accounting risk firms either (1) utilized
income increasing methods for both inventories and long-term depreciable
assets or (2) employed an income increasing method for the only
applicable accounting choice. Conversely, the low accounting risk firms
either (1) utilized income decreasing methods for both accounting
choices, (2) used a combination of income increasing and income
decreasing methods, or (3) employed an income decreasing method for the
only applicable accounting choice.6
6 Various methods of classifying firms into accounting risk groups
based on the firms' accounting procedure choices were employed in the
empirical analysis, with no qualitative differences in results.

41
SEC Registration Form Proxy
The final accounting risk surrogate employed in this dissertation
is whether the registration statement7 filed with the SEC was on Form
S-18 or Form S-l. Form S-18 offerings were instituted by the SEC in
April 1979. Prior to that time all initial offerings (with the
exception of unregistered Regulation A offerings which had a limit on
the amount of proceeds raised of $1.5 million) were required to be
registered on Form S-l. The limit on the amount of capital raised with
an S-18 offering is $7.5 million.8
Important differences exist in the disclosure requirements in
these two types of registration statements. Form S-l registrations are
more comprehensive both in terms of the number of years of data required
and in the extent of data required per year. Specifically, Form S-18
allows the issuer to present two years of audited financial statements,
rather than the three required in an S-l offering. Further, "Form S-18
relaxes or eliminates the disclosure requirements for several areas,
including property, segment data, foreign operations, order backlogs,
and research and development" (Manegold and Arnold [1986 p. 29]). The
elimination of these potentially valuable accounting disclosures in an
7 Before securities may be sold to investors in a public offering,
the SEC must approve an issuing firm's registration statement. The
registration statement contains information about the proposed offering,
as well as information about the issuing firm itself (e.g., audited
financial statements). Similar information about the offering and the
issuer is provided to potential investors in a prospectus.
8 For a detailed account of the differences between the two types
of registrations, please see Manegold [1986] or Manegold and Arnold
[1986].

42
S-18 offering should lead to an increase in accounting risk, and hence
the type of SEC registration statement filed comprises the third proxy
for accounting risk. This third surrogate is intended to capture the
quantity (i.e., extent of disclosure) aspect of the accounting risk
theoretical construct.
Measurement of Risk-Adiusted Abnormal Security Returns
In order to test Hypotheses 1 through 3, aftermarket abnormal
security returns (i.e., long-term mispricing) must be calculated. A
modified version of Simon's [1989] multifactor security pricing model is
employed in the measurement of such excess returns.9 The model utilized
is
Rit Rft 0i(Rnt RFt> + M industry,t Rt RFt)
+ S QjiDjt + it, (12)
ji
where Rit = the return on firm i in time period t, RFt = the return on
riskless assets over time t, R^,. = the time t return on the market
portfolio, INDUSTRY,,. = the equally-weighted average return of firms in
the same industry (based on 2-digit SIC codes) as firm i during time
period t, Djt are time period specific dummy variables which are
designed to capture abnormal returns, /?,, 5,, and 9j, are regression
parameters, and e,t is an error term.
9 Simon's model includes factors relating to business cycle
activity and the unanticipated portion of the variance in market returns
in addition to the variables employed in this study in the specification
of the equilibrium return generating process.

43
The Rit terms in Equation (12) represent IPO firm i's raw (i.e.,
unadjusted) returns over month t following the initial offering. These
monthly returns are constructed from the CRSP NASDAQ daily returns file
utilizing a procedure similar to that employed by Ritter [1989]. The
return for month 1 is calculated as the multiplicative return over the
21 trading day period beginning with the day following the IPO.10 Thus,
returns are calculated based on "event time" (with the event in question
being the IPO), rather than calendar time. The monthly returns for
months subsequent to month 1 are then calculated using the returns on
successive 21 trading day periods. In equation form ^it is calculated
21+21(t-l)
Rit n (1 + Rid) 1, (13)
d=l+21(t-l)
where Rid = firm i's raw return on day d where d=l represents the day
following the IPO, and t denotes the number of months subsequent to the
initial offering.
A monthly market portfolio return series (i.e., R^,.) is
constructed for each IPO firm corresponding to the firm's "event time"
monthly periods. This monthly market return series is calculated in the
same manner as the Rit series, with the exception that the daily returns
utilized in the construction of the monthly returns are the returns on
the NASDAQ Composite Index, rather than an individual IPO firm's raw
returns. Since each of the sample firms examined in this study is
traded over-the-counter, the NASDAQ Composite Index appears to be a more
10 It is important to note that the return on the first day of
public trading is the commonly examined one-day underpricing return.
Therefore, the empirically documented short-term underpricing phenomenon
is not included in the long-term returns examined here.

44
appropriate measure of the "market" return than either the CRSP Value-
Weighted or CRSP Equally-Weighted Indices (both of which consist of New
York and American Stock Exchange firms) which are often utilized in
capital markets research.
The INDUSTRYit terms present in Equation (12) represent the
average equally-weighted return of all firms on the NASDAQ daily returns
file possessing the same 2-digit SIC code as IPO firm i. Again, for
each sample IPO firm, this INDUSTRYit series is calculated over the same
"event time" months as the Rit series.
The RFt terms in Equation (12) represent the prevailing "risk-free
rate" at time t. The proxy employed for the unobservable true "risk
free rate" is the return on United States Treasury Bills. The monthly
total return series on U.S. Treasury Bills from January 1980 through
December 1988 was collected from Ibbotson Associates [1989] Then this
monthly risk-free rate series was aligned with the Rit series in order
to create the RFt series for each IPO firm.
Equation (12) is a generalization of the two-factor CAPM utilized
in the derivation of Hypotheses 1 through 3 in Chapter 3. There are two
principal reasons the multifactor specification of security pricing
found in Equation (12) is employed in the empirical testing rather than
the traditionally utilized two-factor CAPM or single-factor market
model. First, prior research suggests that factors other than the
return on the market portfolio are needed to explain equilibrium
security returns.11 In this study, industry returns as well as overall
11 For example, see Ross [1976] for a theoretical development of
the multifactor Arbitrage Pricing Theory (APT).

45
market returns are employed in the specification of equilibrium security
returns.
Industry returns are included in Equation (12) to control for
abnormal return performance arising from business risk changes over
time. Assuming firms in the same industry face similar production,
investment, and financing environments, then INDUSTRYit represents an
appropriate proxy for business risk and Si reflects return performance
attributable to changing business risk conditions. Thus, Equation (12)
abstracts from both overall market performance and unexpected industry
performance (i.e., business risk) in the generation of firm-specific
abnormal returns (i.e., the 9^ parameters). Since the effects of both
overall market risk and firm-specific business risk are taken away, it
is assumed that the abnormal returns captured by the 8^ terms are
attributable to accounting risk.
The second reason why the traditional CAPM or market model
formulation is not utilized in this study is due to the institutional
features of the IPO market. In traditional capital markets "event"
studies, the CAPM or market model is estimated over a period of time
prior to the "event" in question. Then the estimated parameters are
utilized in the determination of abnormal returns during the event
period. Such an approach is infeasible for IPOs since return data are
unavailable prior to the event in question (i.e., the initial offer).
Thus, the relevant parameters must be estimated during the event period.
The time period specific dummy variables included in Equation (12) allow
the researcher to pick up abnormal returns over specified periods during

46
the estimation period.12,13 Specifically, Dlt is coded 1 during the
first 12 months following the IPO and 0 otherwise. Similarly > Dzt
equals 1 only for months 13-24, while D3t is coded 1 for months 25-36
and 0 otherwise. Thus, the parameters on the dummy variables (i.e., 9U
to 03i) represent an IPO firm's abnormal returns over the first, second,
and third year of public trading, respectively.
Equation (12) is estimated for each firm on a time-series basis
over the first 36 months of public trading. Cumulative abnormal
performance over the entire 36 month estimation period may be computed
by simply adding the parameter estimates on the three time period
specific dummy variables. In other words,
3
CAR36i = 2 9ji., (14)
where CAR36i = firm i's 36 month cumulative abnormal return.14 This
12 In a typical event study, the error terms (i.e., the eit terms)
represent risk-adjusted abnormal returns. However, when the model's
parameters must be estimated over the event period, the sum of the error
terms is constrained to equal zero by the OLS regression procedure and
thus the errors cannot be utilized as measures of abnormal performance.
13 See Schipper and Thompson [1983] for an example of the
utilization of time period specific dummy variables to measure abnormal
performance surrounding different events.
14The return generating process described by Equation (12) does
not include an intercept term. Instead, it includes a time period
specific dummy variable for each of the first three years of public
trading. Alternatively, Equation (12) could be estimated by utilizing
an intercept term along with dummy variables relating only to the first
two years of public trading. The model would then be written as
follows:
Rit RFt = or, + ^(Rfit RFt) + JV9JiDJt
j=l
+ i
where ai is an intercept term and all other terms are as previously
defined. In this alternative specification, aq picks up abnormal

47
cumulative abnormal return, as well as the individual abnormal returns
pertaining to the first three years of public trading, is utilized in a
cross-sectional format to test Hypothesis 1. Further, the variances of
these abnormal return measures are used to cross-sectionally test
Hypotheses 2 and 3.
Univariate Tests
Univariate Tests of Hypothesis 1
Hypothesis 1 states that market participants do not misprice IPOs,
on average, regardless of the level of accounting risk they face. In
other words, average aftermarket abnormal returns are predicted to equal
zero (i.e., they are unbiased) and are hypothesized not to be influenced
by accounting risk.
Univariate tests of Hypothesis 1 consist of constructing
portfolios of IPOs based on the firms' levels of the three previously
described accounting risk measures and then comparing the average
abnormal returns of each portfolio over various holding periods (1) to
the hypothesized value of zero and (2) to the average abnormal returns
performance over the period of estimation not accounted for by the dummy
variables (i.e., year 3) and the parameter estimates on the two dummy
variables measure differences between the abnormal performance of years
1 and 2, respectively, and year 3. Thus, the abnormal returns relating
to year 1 would be calculated by adding the dummy variable parameter
estimate pertaining to year 1 (i.e., 9U) to ai. The abnormal returns
of year 2 would be calculated in a similar manner. The cumulative 36
month return would then be calculated by adding the measured abnormal
returns over years 1 through 3. See Maddala [1977, Chapter 9] for a
more detailed discussion of the interpretation of the estimated dummy
variable coefficients in instances when an intercept term (1) is and (2)
is not included in the model.

48
of the remaining portfolios. This procedure will now be described in
detail for the case of the auditor quality accounting risk surrogate.15
Two portfolios of IPO firms are constructed based on the
reputation (quality) of the auditor utilized in the IPO process.
Specifically, portfolios are devised according to whether an IPO firm
was audited by a Big Eight (i.e., low accounting risk) or non-Big Eight
(i.e., high accounting risk) auditing firm.
The mean abnormal return for a given accounting risk portfolio
over a specific time horizon is calculated as
MAR^t = S ARit / N, (15)
i*l
where MARpt = the mean abnormal return of portfolio p over time period
t, ARit firm i's abnormal return over time period t as generated from
Equation (12),16 and N = the number of firms in portfolio p. Portfolio
mean returns are calculated for the first, second, and third year of
public trading, as well as a three year cumulative return.
Two sets of statistical tests are conducted on these mean
portfolio abnormal returns to test Hypothesis 1. First, each MARpfc is
tested via a t test to determine whether it differs significantly from
the hypothesized value of zero. Second, a two-population t test is then
conducted to see if MARpt differs across the auditor quality based
15 A similar analysis is also conducted for the other two
accounting risk surrogates.
16 For example, ARU equals the estimated 0U parameter from
Equation (12). Similarly, ARi2 = 02i and ARi3 03i. Also, the abnormal
return covering the cumulative 36 month period is the CAR3 variable
defined in Equation (14).

49
accounting risk portfolios over each of the four relevant time
horizons.17 Table 4-1 illustrates these tests.
Univariate Tests of Hypothesis 2
Hypothesis 2 states that the dispersion (i.e., variance) of IPO
firms' aftermarket abnormal returns is positively associated with
accounting risk. In other words, the precision of market participants'
pricing decisions is hypothesized to decrease with accounting risk.
Univariate tests of Hypothesis 2 utilize the accounting risk
portfolios constructed for testing Hypothesis 1. However, a portfolio's
mean abnormal return variance over various holding periods is now the
statistic of interest, rather than the cross-sectional mean abnormal
return employed in the empirical testing of Hypothesis 1.
An individual IPO firm's variance of period t abnormal returns is
defined as
VARit = ARit2, (16)
where ARit is the abnormal return over time period t as provided by
Equation (12). Then, an accounting risk portfolio's mean abnormal
return variance over a specific time period is defined as
17 A two-population t test assumes that the data being tested comes
from two independent populations. This assumption appears reasonable in
this case since there is no a priori reason to hypothesize that high
accounting risk and low accounting risk portfolios are not independent.
Each of these t tests is the mathematical equivalent of a one-way
analysis of variance (ANOVA) since each accounting risk proxy only
possesses two levels (i.e., high and low). Even though the statistical
tests are equivalent, Neter et al. state that "the t test generally is
to be preferred since it can be used to conduct both two-sided and one
sided tests" (Neter et al. [1985, p. 544]). By contrast, the F test
employed in a one-way ANOVA "can only be used for two-sided tests"
(Neter et al. [1985, p. 544]). Since many of this study's hypotheses
are one-directional, the t test procedure is employed in this research.

50
Table 4-1
Univariate (Portfolio) Tests of Hypothesis 1
Time
Horizon
High Risk
Portfolio3
Low Risk
Portfolio15
Difference
High-Low
Year 1
MARHiCd
MARL1d
MARHi-MARL1'
Year 2
MARH2
MARL2
marH2 marl2
Year 3
marH3
marL3
marH3-marL3
3 Year
Cumulative
marhc
marlc
marhc marlc
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
c MARpt = portfolio p's mean abnormal return over time horizon t. Thus,
MARH1 = the mean abnormal return of the high accounting risk portfolio
over the first year of public trading, MARH2 the high risk portfolio's
second year mean abnormal return, etc.
d A two-tailed t test is conducted to determine whether each MARpt in
this column differs significantly from the hypothesized value of zero.
e A two-tailed two-population t test is conducted to determine whether
each difference statistic in this column differs significantly from the
hypothesized value of zero.

51
N
MVARpt Z VARit / N, (17)
where MVARpt the mean abnormal return variance over time period t of
the firms comprising portfolio p, and the remaining variables are as
previously defined. This mean portfolio variance figure is computed
separately for the first, second, and third years of public trading, as
well as over a 36 month cumulative period. Then, the empirical tests of
Hypothesis 2 merely involve assessing via two-population t tests18
whether MVARpt is significantly greater for high accounting risk
portfolios than for low accounting risk portfolios over each of the four
time horizons. Table 4-2 illustrates these tests.
Univariate Tests of Hypothesis 3
Hypothesis 3 states that the variance of IPO firms' aftermarket
abnormal returns decreases over time. This decrease in abnormal return
variance is hypothesized to be attributable to decreasing accounting
risk over time as more information becomes known about these newly
publicly held firms.
Univariate tests of Hypothesis 3 employ the same accounting risk
portfolios constructed for the testing of Hypotheses 1 and 2. The
empirical tests of the third hypothesis seek to determine whether the
mean abnormal return variance of each portfolio decreases over the three
year time horizon. Hypothesis 3 implies a monotonic decrease in the
18
See footnote 17.

52
Table 4-2
Univariate (Portfolio) Tests of Hypothesis 2
Time
Horizon
High Risk
Portfolio
Low Risk
Portfolio15
Difference
Hizh-Low
Year 1
MVARH1C
MVARL1
MVARH1-MVARL1d
Year 2
mvarL2
MVARH2-MVARL2
Year 3
mvarH3
mvarL3
mvarH3-mvarL3
3 Year
Cumulative
MVARhc
MVARlc
MVARhc-MVARlc
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
0 MVARpt = portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH1 = the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
= the high risk portfolio's second year mean abnormal return variance,
etc.
d A two-population t test is conducted to determine whether each
difference statistic in this column differs significantly from zero.
Hypothesis 2 predicts these differences to be significantly greater than
zero. Thus, one-sided significance tests are called for in this case.

53
variance over the three years. For example, the hypothesis implies that
the mean abnormal return variance of Big Eight audited IPO firms is
greater in the first year of trading than in the second year of trading,
and that the second year variance exceeds that of the third year. Each
of the remaining accounting risk portfolios is predicted to behave in a
similar fashion.
Even though Hypothesis 3 implies that the mean abnormal return
variances decrease monotonically (however not necessarily in a linear
fashion) over time, the hypothesis may be testing by examining whether
the variances exhibit the presence of a decreasing linear trend over the
three year period.19 Dawes and Corrigan [1974] demonstrate the
usefulness of linear models in cases such as this. They state that
"linear models are good approximations to all multivariate models that
are conditionally monotone in each predictor variable" (Dawes and
Corrigan [1974, p. 98]).
Since an individual IPO firm's abnormal return variance over year
1 is not independent of its return variance in other years, a
statistical procedure which accounts for these dependencies is required.
An often utilized procedure in cases such as this is a repeated measures
ANOVA, where time represents the repeated measure. A test for the
presence of a significant linear trend over the time horizon may be
conducted in such an analysis. In instances such as this one in which
the repeated measure (i.e., time) takes on only three values, the linear
19 Keppel [1982, Chapter 7] provides a detailed description of
trend analysis, and provides details of empirical procedures which test
for the presence of a significant linear trend.

54
trend test is mathematically equivalent to a test of the difference in
the variable of interest (i.e., MVAR) between time period 1 and time
period 3.20
In these specific circumstances in which the repeated measure only
takes on three possible values, a matched-pair (i.e., paired comparison)
t test of the difference between year 1 and year 3 mean abnormal return
variances is mathematically equivalent to the repeated measures ANOVA
test of linear trend. Since Hypothesis 3 is a one-directional
hypothesis, the t test procedure is implemented since it allows one
sided hypothesis tests, whereas the F statistics produced by the ANOVA
procedure are solely two-sided tests. Table 4-3 illustrates these
tests.
Multivariate Tests
Multiple Regression Test of Hypothesis 1
Hypothesis 1 may alternatively be tested via the following cross-
sectional regression models:
AR(t)i = a + ^AUDITORi + )92FORMi + ^3APCHOICEi
+ yiSIZEi + yzLNRISKSi + 73UE(t)i + e^ (18)
20 Keppel states that "trend analysis requires a different set of
special coefficients for each of the orthogonal trend components to be
extracted from the data" (Keppel [1982, p. 135]). The special
coefficients required to test for a decreasing linear trend when the
repeated measure takes on only three values are +1, 0, and -1.
Specifically, a portion of the calculated test statistic consists of the
sum of +1 times the observation for year 1, 0 times the year 2
observation, and -1 times the year three observation. The resulting
statistical test is mathematically equivalent to a test of the
difference between period 1 and period 3 observations.

55
Table 4-3
Univariate (Portfolio) Tests of Hypothesis 3
Portfolio
Year 1
Year 2
Year 3
Difference
Yearl-Year3
High Risk*
MVARH1C
MVARh2
hVARH3
MVARH1-MVARH3d,e
Low Riskb
MVARL1
mvarL2
mvarL3
MVARL1-HVARL3
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
0 MVARpt = portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH1 = the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
= the high risk portfolio's second year mean abnormal return variance,
etc.
d A matched-pair (i.e., paired comparison) t test is conducted to
determine whether each difference statistic in this column differs
significantly from zero. Hypothesis 3 predicts these differences to be
significantly greater than zero. Thus, one-sided significance tests are
called for in this case.
0 This matched-pair t test of the difference between the MVAR of years 1
and 3 is mathematically equivalent to a test for the presence of a
significant linear trend over time in a repeated measures ANOVA
analysis.

56
where AR(t)i = firm i's abnormal return over holding period t as
generated from Equation (12) (i.e., 9ti) AUDITOR = the quality of
auditor utilized in the IPO process, FORMj^ = the registration statement
type filed by IPO firm i, APCHOICEj^ the level of conservatism
surrounding firm i's accounting procedure choices for inventory and
depreciation, SIZEi = firm size at the initial offering, LNRISKSi the
natural logarithm of one plus the number of risk factors listed in firm
i's prospectus, UE(t)i = firm i's unexpected earnings for period t, and
e is an error term. The coefficients a, to /S3, and to y3 are
regression parameters.
In Equation (18), AUDITOR^, FORMi, and APCHOICEj^ are empirical
proxies for accounting risk, while SIZE^ LNRISKS^ and UE(t)i are
included as control variables. The control variables are designed to
control for potential differences among firms' abnormal returns which
are unrelated to differences in accounting risk. Thus, Equation (18)
allows one to test the impact of accounting risk on long-term
mispricing, while controlling for potential confounding influences.
SIZEj^ and LNRISKSi are included in the regression models to
control for business risk (i.e., uncertainty related to a firm's
production, investment, and financing activities). It is important to
control for firm size since it has been empirically documented21 that
larger IPO firms are less speculative than small initial offering firms.
The number of risk factors supplied by an IPO firm in its prospectus may
also proxy for the inherent cash flow riskiness of the firm. The SEC
21
See Beatty [1989b].

57
requires firms undertaking IPOs to enumerate important "risk factors"
which will aid prospective investors in assessing the riskiness of the
offering. The SEC may choose to delay an offering through a "deficiency
letter" until the firm provides adequate details concerning the
riskiness of its operations, its financing obligations, etc. Hence,
Beatty [1989b] argues that riskier IPO firms provide greater disclosure
in the "risk factors" section of the prospectus than less risky firms.
Finally, UE(t)i is included in the regression models to control for the
widely documented empirical result that abnormal security returns are
positively associated with "unexpected" accounting earnings.
Equation (18) is estimated cross-sectionally for four different
specifications of the dependent variable (i.e., long-term mispricing).
Specifically, the equation is separately estimated for abnormal returns
calculated over the first, second, and third year of public trading, as
well as a 36 month cumulative return.
AUDITOR is measured as a dichotomous variable. It is coded 1 in
cases where non-Big Eight auditing firms are utilized in the IPO process
and 0 for Big Eight firms. FORMi is similarly measured in a categorical
fashion. The variable is coded 1 for S-18 registrations and 0 for S-l
registrations. APCHOICEj^ is assigned a value of 1 for firms which
employ consistently income increasing (i.e., "liberal") inventory and
depreciation accounting procedure choices and 0 otherwise. SIZEi is
calculated as the natural logarithm of IPO firm i's total book value of
assets as provided in the prospectus, and LNRISKSj^ is defined as the
natural logarithm of one plus the number of risk factors listed in firm
i's prospectus.

58
Each of the previously described independent variables is measured
based on the most recently available data at the time of the IPO (i.e.,
that available in the prospectus). In other words, these independent
variables remain constant across the four cross-sectional regressions.
Thus, the regressions are intended to determine the impact of accounting
risk at the time of the IPO on various specifications of long-term IPO
mispricing. Hypothesis 1 predicts zero coefficients on the three
accounting risk variables in each regression.22
In contrast to the other independent variables, UE(t)i is measured
over the same time horizon as the dependent variable in each of the four
cross-sectional regressions. Unexpected earnings is calculated using a
variant of the commonly utilized random walk earnings expectation model.
An alternative specification of the random walk model is required in
this study due to the misalignment of the abnormal security return
series and the earnings series. Specifically, an IPO's abnormal returns
are generated via Equation (12) based on monthly returns beginning on
the day following the IPO date. In other words, the return series is
based on "event time," with the event in question being the initial
offering. By contrast, the annual earnings series is based on each
firm's fiscal year end (FYE). Therefore, unless a firm went public on
(or very near) its fiscal year end date, the returns and earnings series
utilized in this study are not properly aligned.
22 It is easily verified that the model presented in Chapter 3 also
predicts nonsignificant coefficients for the business risk control
variables (i.e., SIZEi and LNRISKS^ since the abnormal security returns
are already risk-adjusted.

59
To alleviate this potential misalignment problem, the random walk
earnings expectation model is adjusted in order that the earnings series
becomes aligned with the security returns series. Specifically, IPO
firm i's unexpected earnings for period t is calculated as follows:
UEit), = [ (EPSit EPS^) (Nlit / 12) + (EPSit+1 EPSit) (N2it / 12)]
/ PRICEit, (19)
where EPS signifies annual earnings per share, PRICE designates the
security price per share,23 and N1 and N2 are adjustment factors
utilized to bring the earnings series into alignment with the returns
series.
As an example of how Equation (19) is used in the determination of
unexpected earnings, the calculation of firm i's unexpected earnings
over the first year of public trading (i.e., UEl^ will be illustrated.
In the calculation of UE^, the time subscript t equals 1 throughout
Equation (19). EPS^ signifies firm i's annual earnings per share as of
the first fiscal year end subsequent to the IPO. For example, if a
December 31 FYE firm went public on August 1, 1980, EPSil would
represent the firm's earnings per share for the year ended December 31,
1980. The period of time (rounded to the nearest month) which elapses
between the IPO date and this first FYE subsequent to the initial offer
is designated Nln months. In the above example, Nlu equals the five
month period from August 1, 1980 to December 31, 1980. N2U then
represents the number of months from this first FYE following the IPO to
the date one year subsequent to the IPO (i.e., the end of the first year
23 Both the earnings per share and the price per share series are
adjusted for stock dividends and stock splits.

60
of trading in "event time"). In the above example, N2iX equals the
seven month period from December 31, 1980 to August 1, 1981.
Therefore, Nlu/12 (i.e., 5/12 in the example) of the
traditionally calculated random walk based "unexpected earnings"
accruing from the FYE preceding the IPO to the first FYE subsequent to
the IPO (i.e., from December 31, 1979 to December 31, 1980) is included
in the calculation of UEl^ Further, N2U/12 (i.e., 7/12 in the
example) of the traditionally calculated "unexpected earnings" based on
the random walk model accruing from the first FYE following the IPO to
the second FYE subsequent to the IPO (i.e., from December 31, 1980 to
December 31, 1981) is included in the calculation of UEl^ Thus, the
Nl/12 and N2/12 factors serve to transform the FYE based earnings series
into a series compatible with the "event time" returns series.
Therefore, as is customary in a random walk annual earnings expectation
model, UEli is calculated based on 12 months of earnings. However, this
12 months of earnings represents a weighted average of the "unexpected
earnings" pertaining to two fiscal years (i.e., the fiscal years ended
December 31, 1980 and December 31, 1981 in the above example).24
Consistent with the widely documented result that unexpected
accounting earnings and abnormal security returns are positively
associated, the parameter estimate on UE(t)i is predicted to be positive
in each of the four cross-sectional regressions.
24 The previously described method of estimating unexpected
earnings is utilized to determine unexpected earnings over the first,
second, and third year of public trading. However, unexpected earnings
corresponding to the cumulative three year holding period is calculated
as the sum of the undeflated unexpected earnings calculated for each of
the first three years deflated by the security price at the end of year
three.

61
Multiple Regression Test of Hypothesis 2
Hypothesis 2 may alternatively be examined via the following
cross-sectional regression models:
VAR( t) i = a + ^AUDITORi + £2F0RJiii + ^APCHOICEi
+ y^IZEi + 72LNRISKS + 73ABSUE(t)i + eif (20)
where ABSUEit^ = the absolute value of firm i's period t unexpected
earnings, and all remaining variables are as previously defined. The
absolute value specification of unexpected earnings is utilized in
Equation (20) since the absolute magnitude (rather than the signed
magnitude) of unexpected earnings should impact the variance of returns.
As was the case for Equation (18), Equation (20) is estimated
separately across all sample firms for four different specifications of
the dependent variable (i.e., abnormal return variances over four
different time horizons). Specifically, an IPO firm's abnormal return
variance is measured over the first, second, and third years of public
trading, as well as over the cumulative 36 month period. As previously
indicated by Equation (16), an IPO's variance of period t abnormal
returns (i.e., VARit or equivalently VAR(t)i) is calculated as the
abnormal return over time t squared (i.e., ARit2) .
According to Hypothesis 2, the estimated coefficients on the
accounting risk variables (i.e., to /S3) are predicted to be positive
for each of the four cross-sectional regressions.25 Similarly, the
25 However, Hypothesis 3 predicts that the ability of these
variables to explain the variation in abnormal return variances
decreases over time. Empirical tests of Hypothesis 3 utilizing a
multiple regression approach are detailed in the following section.

62
parameter estimate on the unexpected earnings variable is also expected
to be positive. If business risk is adequately controlled for in the
return generating process described by Equation (12), the coefficients
on the business risk control variables (i.e., SIZEi and LNRISKSt) should
not differ significantly from zero. However, 7l and y2 may be
significantly different from zero if INDUSTRYit does not adequately
control for business risk. In such a case, the coefficient on SIZEit is
predicted to be negative (since smaller firms correspond to a higher
level of business risk and hence should experience a higher variance of
returns), while the coefficient on LNRISKS is predicted to be positive
(since the number of risk factors enumerated in the prospectus is
positively associated with business risk).
Multiple Regression Test of Hypothesis 3
Hypothesis 3 may alternatively be tested via the following pooled
cross-sectional time-series regression model:
VARit = a + /^AUDITOR^ + 0X' AUDITORit*Ilit + 0X" AUDIT0Rit*I2it
+ /32FORMit + 02' FORMit*Ilit + 02" F0RMit*I2it
+ £3APCHOICEit + APCHOICEit*Ilit + 03 APCH0ICEit*I2it
+ 7iSIZEit + 7l'SIZEit*Ilit + 7i"SIZEit*I2it
+ 72LNRISKSit + 72' LNRISKSit*Ilit + 72" LNRISKSit*I2it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73 ABSUEit*I2it
+ eit, (21)
where Ilit is an indicator variable which is coded 1 for observations
relating to the second year following the IPO and 0 otherwise, I2it is
an indicator variable which takes on the value of 1 for third year

63
observations and 0 otherwise, and all other variables are as previously-
defined.
As stated earlier, Equation (21) is a pooled cross-sectional time-
series model. Specifically, the equation is estimated with three
observations per sample firm. These three observations per firm pertain
to the first, second, and third year of public trading, respectively.
Thus, Equation (21) is estimated with three times as many observations
as the separate year by year cross-sectional regressions utilized in the
testing of Hypotheses 1 and 2.
The interaction terms employing the indicator variables Ilit and
I2it are included in the model so that tests concerning changes in the
regression coefficients over time may be conducted.26 Specifically,
since both the indicator variables are coded 0 for observations relating
to the first year following the IPO, the regression coefficients to
and yx to y3 represent the respective coefficients for year 1.
Similarly, since Ilit equals 1 for year 2 only and I2it equals 0 for year
2, then the regression coefficients /3X' to /33' and y1' to y3' represent
the changes in the respective coefficients for year 2 as compared to the
base year (i.e., year 1). In a similar fashion, since Ilit is coded 0
for year 3 and I2it takes on the value 1 only for year 3, then the
regression coefficients /91'' to /33'' and yx'' to y3'' represent changes
in the respective coefficients for year 3 in comparison to year 1.
26 It should be noted that the model specified in Equation (21)
assumes that the intercept does not vary across sample years but that
the slopes differ according to whether the first, second, or third year
of public trading is being examined.

64
Hypothesis 3 implies that the accounting risk variables explain
less of the variation in abnormal return variances as more information
is revealed as time progresses from the IPO date. Thus, the f}x' to /?3'
and fix' to f}3'' coefficients predicted to be negative.27
Even though the coefficients on the interaction terms in Equation
(21) represent differences in the coefficients for year 2 and year 3,
respectively, from the coefficients pertaining to year 1, they provide
no indication of the change in the parameters from year 2 to year 3.
The change in the regression coefficients from year 2 to year 3 is
tested by examining the significance levels of the following linear
hypotheses regarding the coefficients: /3- f)x' = 0, f}2' f3z'' = 0,
/V /V' = 0, 7i' 7i" = 0, 72' 72" 0, and 73' 73" 0. Each
of these tests seeks to determine whether a particular year 2 regression
27 A priori, it is difficult to predict the sign of the
coefficients on the interaction terms which contain the control
variables. On the one hand, it could be postulated that the business
risk control variables (i.e., SIZEt and LNRISKSi) will decrease in
significance over time since these variables are measured as of the IPO
date and remain constant across the three time periods examined.
However, it could also be hypothesized that the business risk proxies
will increase in significance as the accounting risk variables lose
explanatory power. Similarly, one could reasonably argue that the
unexpected earnings control variable could either increase, decrease, or
remain constant over time. Specifically, it can be hypothesized that
unexpected earnings will increase in explanatory power over time as the
accounting risk variables decrease in importance. However, it could
also be argued that the unexpected earnings variables will decrease in
explanatory power over time since earnings may play a greater role in
firm valuation early in the life of a public firm (i.e., before other
competing sources of information become readily available). Finally,
one could also reasonably argue that the unexpected earnings variable
should not vary in significance over time since the relationship of
earnings and returns should not change significantly from year to year.
Therefore, predictions are not made relating to the hypothesized signs
of the 7X' to 73' and y1'' to 73'' coefficients.

65
coefficient is significantly greater than the corresponding year 3
coefficient.28
Summary of the Empirical Methodology
This chapter describes both univariate and multivariate approaches
employed in the empirical testing of Hypotheses 1 through 3. The
univariate tests allow a straightforward assessment of the three
hypotheses. The univariate (portfolio) approach is particularly useful
in the examination of Hypothesis 1 since it permits a direct
determination of whether mean abnormal returns differ significantly from
zero for (and across) portfolios of IPOs which differ according to
accounting risk. However, the portfolio approach suffers from the
limitation that it is univariate in nature. In other words, the
portfolio approach is limited in that the analysis must be conducted
separately for portfolios based on each of the three accounting risk
proxies. Thus, the approach does not allow an assessment of the
incremental impact of various measures of accounting risk on long-term
IPO mispricing. A potentially more important limitation is that the
univariate tests do not control for factors other than accounting risk
which may influence mean aftermarket abnormal returns and mean abnormal
return variances. Thus, it is not possible to conclude from these
28 According to Hypothesis 3, the regression coefficients on the
three accounting risk variables should be greater in year 2 than in year
3. Thus, the differences in the estimated year 2 and year 3 regression
parameters pertaining to AUDITOR, FORM, and APCHOICE are expected to be
significantly greater than zero. As explained in footnote 26, no
predictions are made in regards to the signs of the differences between
the regression coefficients of years 2 and 3 for the .control variables
(i.e., SIZE, LNRISKS, and ABSUE).

66
univariate tests that it is accounting risk that is responsible for any
observed empirical relationships, rather than some other factor such as
IPO firm size or inherent cash flow riskiness.
In contrast, the multiple regression techniques are multivariate
analyses which allow the determination of the incremental effects of
this study's three measures of accounting risk on long-term mispricing.
The regression approach also allows for the controlling of potentially
confounding influences on the relationships of interest. The technique
also allows a direct assessment of Hypothesis 3, the change in
explanatory power of the accounting risk variables over time. However,
the regression method provides only an indirect test of the validity of
Hypothesis 1. Specifically, while the regression technique indicates
whether accounting risk and abnormal returns are significantly
correlated, it provides no indication of whether IPOs are mispriced on
average.
Thus, there are advantages and disadvantages associated with both
the univariate (portfolio) and multivariate (regression) approaches.
Due to the complementary nature of the two approaches, both are employed
in the testing of the three hypotheses.

CHAPTER 5
DATA, SAMPLE SELECTION CRITERIA, AND RESULTS
Data Sources and Sample Selection Criteria
The major source of data employed in this study consists of a
collection of microfiche IPO registration statements (which include
preliminary prospectuses) which were obtained from Disclosure Inc.1
This database contains registration statements for a number of firm
commitment2 IPOs of nonfinancial firms which occurred between January
1980 and December 1983. The database contains registration statements
and preliminary prospectuses for 511 firms which made initial public
offerings of equity securities (unit offerings which include warrants
are excluded) during this 1980-1983 time period. Only firms for which
at least three years of security price data are available on the CRSP
1 I wish to thank Professor Chris James for allowing me access to
these registration statements. Please see James and Wier [1989] for a
complete description of the criteria utilized in assembling this
microfiche database.
2 The two major types of underwriting contracts utilized in the
going public process are "firm commitment" and "best efforts" contracts.
In a firm commitment offering, the underwriter purchases the entire
security issue from the issuer and then must resell the shares to
investors. Thus, the underwriter makes a "firm commitment" to purchase
the offering at a specified price (i.e., the IPO price less the
underwriter's discount), and bears the risk of not being able to sell
the entire issue at the IPO price. Conversely, in a best efforts
offering, the underwriter's only commitment is to make his "best
efforts" to sell the issuer's security offering to investors. In this
case, the issuer bears the risk of not being able to sell the entire
issue at the IPO price.
67

68
NASDAQ file are included in the final sample.3 This three year of
security return availability criterion is the only criterion utilized in
the sample selection process and results in the exclusion of 139 firms.
Thus, the final sample consists of 372 firms. Table 5-1 summarizes the
sample selection process employed in this study.
Table 5-1
Sample Selection Procedures
Total Available Microfiche Prospectuses of Non-Unit IPOs 511
Number of Firms Removed Due to a Lack of 3 Years of Data
on the 1988 Daily CRSP NASDAQ File 139
Final Sample 372
Much of the data required for this study was gathered from this
collection of microfiche registration statements. For example, the data
required to calculate the AUDITOR, FORM, and SIZE variables have been
collected from these registration statements and accompanying
prospectuses. Further, this microfiche collection was utilized in
conjunction with the Compustat Full-Coverage file in the collection of
the sample firms' accounting procedure choices for inventory and
depreciation.
Another significant portion of the required data was gathered from
"Ritter's 1975-1984 IPO Database.This database contains extensive
3 The entire empirical analysis was repeated using a more
restrictive five years of CRSP NASDAQ data availability criterion. This
more restrictive criterion resulted in a final sample of 299 firms.
Summary results of this additional analysis are reported in Appendix B.
A I wish to express thanks to Professor Jay Ritter for providing me
this extensive amount of data on IPO firms. Please see Ritter [1984b]
and Beatty and Ritter [1986] for detailed descriptions of the criteria
utilized in building this database.

69
detail on 2,567 initial public offerings which occurred between 1975 and
1984. Each of the 372 sample initial offerings is contained in Ritter's
IPO database. This database was utilized to collect information
concerning the number of risk factors listed in an IPO's prospectus, the
date of the initial offering, the IPO price, the first available closing
bid price following the IPO, the number of shares sold in the offering,
and the year that the firm was first organized. Thus, this data allowed
the calculation of the LNRISKS variable, the amount of short-term
underpricing, the gross proceeds of the offering, and the age of the
firm at the time of the IPO. Descriptive statistics on these and other
variables will be provided in the following section.
The required security returns data were collected from the CRSP
NASDAQ file. Specifically, the NASDAQ daily returns file was utilized
in order to gather raw returns for each sample firm, the returns on the
NASDAQ Composite Index, and the returns required in the calculation of
the industry return variable. Finally, the information required to
calculate the unexpected earnings variables was gathered from the
COMPUSTAT Full-Coverage file in conjunction with Standard and Poor's
Corporation Records.
Descriptive Statistics
Sample Firm and Offering Characteristics
Table 5-2 provides descriptive statistics relating to certain
characteristics of the 372 sample firms and their respective initial
offerings. Panel A provides information about the sample as a whole,

Table 5-2
Descriptive Statistics
70
Panel A
Sample Firm and Initial Offering Descriptive Statistics
Variable
Mean
Median
Standard Deviation
Nb
PROCEEDS
15.62
9.06
18.34
372
BKASSETS
15.56
6.92
25.78
372
SIZE
8.66
8.84
1.65
372
RISKS
4.98
0.00
6.50
366
LNRISKS
1.09
0.00
1.22
366
AGE
10.80
7.00
13.57
370
Year
Summary Firm and
Number of
Panel B
Offerings Statistics By Year
Mean
Offers PROCEEDS
of Offering
Mean
BKASSETS
1980
35
10.11
13.89
1981
114
10.81
12.31
1982
25
12.70
12.01
1983
198
19.72
18.18
a The variables examined in this table relate to certain characteristics
of the 372 sample firms and their initial offerings. The variables of
interest are defined as follows:
PROCEEDS = the gross proceeds of the initial offering measured in
millions of dollars. Gross proceeds are calculated as the actual number
of shares sold in the offering multiplied by the offering price per
share.
BKASSETS = the book value of total assets (measured in millions of
dollars) prior to the IPO. This variable is collected from the latest
audited balance sheet supplied in the prospectus.
SIZE = the natural logarithm of BKASSETS.
RISKS = the number of risk factors listed in the prospectus.
LNRISKS the natural logarithm of (1 + RISKS).
AGE = the age of the firm in years prior to the initial offering.
b N = the number of firms involved in the calculation of the
mean, median, and standard deviation statistics.

71
while Panel B provides descriptive statistics by year of initial
offering.
Panel A demonstrates that the average offering gross proceeds
(i.e., PROCEEDS) raised by the 372 sample firms equals $15.62 million.
This very closely approximates the sample firms' mean book value of
total assets (i.e., BKASSETS) prior to the offering. Thus, on average,
the sample firms raised an amount approximately equal to their currently
prevailing book value of assets via an IPO. Panel A also reveals that
the mean (median) number of risk factors (i.e., RISKS) enumerated in the
offering prospectus is approximately equal to 5 (0).5
One other variable reported in Panel A of Table 5-2 merits
mention. The average age of the sample firms prior to the IPO is
slightly less than 11 years. Thus, it is apparent that the sample is
not made up entirely of start-up firms (i.e., firms with less than two
years of operations).
Panel B reports the number of offers, the mean gross proceeds, and
the mean book value of assets (prior to the IPO) by sample year. It is
apparent that the number of offers is not spread evenly across the
sample years. In fact, the sample is dominated by firms undertaking
IPOs in 1981 and 1983. This is consistent with evidence provided in
5 Panel A also indicates that the logarithmic transformations of
BKASSETS and RISKS which are employed as business risk control variables
(i.e., SIZE and LNRISKS, respectively) in the cross-sectional
regressions reported later have greatly reduced standard deviations in
comparison to the original variables. However, even though these
logarithmic transformations are utilized in the regression models rather
than the original variables, the error terms of the various regressions
are heteroscedastic. Later sections will provide (1) details of the
severity of the heteroscedasticity and (2) the means utilized in
correcting the problems caused by heteroscedastic error terms.

72
Ibbotson et al. [1988] that more initial offers were undertaken in the
years 1981 and 1983 than in 1980 or 1982. Thus, the makeup of the
sample does not appear to differ significantly from the population as a
whole. Panel B also illustrates that the average offering gross
proceeds increases over the sample period. The same relationship also
generally holds for the sample firms' book value of assets prior to the
offering.
Long-Term Mispricing. Short-Term Mispricing, and Unexpected Earnings
Table 5-3 provides additional descriptive statistics relating to
various measures of long-term and short-term IPO mispricing, as well as
to the unexpected earnings variables employed in this study. The mean
short-term underpricing (i.e., UP) of the 372 sample firms is 11.70%.
As is evidenced by Table 2-1, this 11.70% level of underpricing
approximates findings in other research.
Descriptive statistics pertaining to the sample firms' risk-
adjusted abnormal returns over the first, second, and third year of
public trading, as well as a 36 month cumulative return are provided in
Table 5-3. These measures of long-term IPO mispricing are generated
from the return generating process described in Equation (12) and are
designated as AR1, AR2, AR3, and CAR36 respectively. The table
indicates that only AR1 (i.e., first year abnormal returns) exceeds 1%
(in absolute value) on average. Further, AR1 is the only one of the
four risk-adjusted long-term mispricing measures which differs
significantly from zero at conventional significance levels, with a

Table 5-3
Descriptive Statistics Relating to Measures of
Long-Term and Short-Term Mispricing, and Unexpected Earnings
73
Variable3
Mean
Median
Standard Deviation
Nb
UP
11.70%
2.78%
0.27
372
AR1
1.21%
1.49%
0.05
372
AR2
-0.35%
-0.29%
0.05
372
AR3
-0.35%
-0.63%
0.05
372
CAR36
0.50%
0.39%
0.08
372
RIMRM1
2.12%
-0.30%
0.59
372
RIMRM2
-4.19%
-3.15%
0.61
372
RIMRM3
-6.35%
-6.90%
0.59
372
RIMRM36
-8.42%
-10.95%
0.97
372
UE1
-0.44
0.00
8.29
371
UE2
-0.04
-0.01
0.11
366
UE3
0.01
0.00
0.39
337
UE36
-0.95
-0.02
15.05
337
3 The variables examined
in this table relate to various measures of
short-term
and long-term
IPO mispricing,
as well as measures
of
unexpected accounting earnings for the 372 sample firms. The variables
of interest are defined as follows:
UP = short-term underpricing of the initial offering price by the
underwriter. UP is calculated as: (P1-PIP0)/PIP0, where PI refers to
the first available closing bid price following the IPO and PIPO equals
the initial offering price set by the underwriter.
AR1, AR2, and AR3 represent the risk-adjusted abnormal returns as
generated from Equation (12) for the first, second, and third years of
public trading, respectively.
CAR36 = the cumulative risk-adjusted abnormal return over the
first 36 months of public trading as generated from Equation (12).
CAR36 is defined as the sum of AR1, AR2, and AR3.
RIMRM1, RIMRM2, and RIMRM3 represent market adjusted security
returns over the first, second, and third years of public trading,
respectively. These variables are calculated by subtracting the return
on the NASDAQ Composite (Market) Index from an individual IPO's raw
security returns over the appropriate time periods.
RIMRM36 = the sum of RIMRM1, RIMRM2, and RIMRM3. Thus, the
variable represents the cumulative market-adjusted return over the first
36 months of trading.
UE1, UE2, UE3, and UE36 are defined as unexpected accounting
earnings over the first year, second year, third year, and 36 month
cumulative time horizon, respectively.
b N = the number of firms involved in the calculation of the mean,
median, and standard deviation statistics.

74
P-Value of less than .01. Thus, the assertion of Hypothesis 1 that mean
abnormal returns should not differ from zero is generally supported for
the sample as a whole. However, the hypothesis is not supported for the
first year of public trading time horizon.6
Table 5-3 also lists alternative measures of long-term mispricing
based on simple market-adjusted (rather than risk-adjusted) returns.
Specifically, RIMRM1 to RIMRM3 and RIMRM36 represent market-adjusted
security returns over the first, second, and third years of public
trading, as well as a 36 month cumulative market-adjusted return. The
market adjustments consist of subtracting the return on the NASDAQ
Composite Index from an IPO's unadjusted raw return over the
corresponding time period in the construction of monthly returns. Then,
yearly market-adjusted returns are constructed from these monthly
returns via an additive process. Table 5-3 shows that the absolute
magnitudes of these market-adjusted returns are much greater than those
of the risk-adjusted returns previously described. However, only RIMRM3
is significantly different from zero at the .05 level, while the 36
month cumulative return is marginally significant at .10.7
6 More formal statistical tests of Hypothesis 1 will be provided in
later sections.
7 Market-adjusted returns are employed by Ritter [1989] in the
examination of long-term IPO mispricing. Ritter finds average market-
adjusted returns of -24.33% over the first three years of trading. This
compares with the -8.42% cumulative return of the sample examined in
this study. One potential reason for the much more negative mean
market-adjusted returns of Ritter's sample firms is that the market
index employed differs between the two studies. Ritter uses an index
based on NYSE and AMEX firms, while the current study uses the NASDAQ
Composite Index. Average market-adjusted returns for the current sample
were also estimated using the market index employed by Ritter (i.e., the
CRSP Value-Weighted Index based on NYSE and AMEX firms). The results of
such an estimation process indicate three year cumulative mean market-

75
Empirical tests of Hypotheses 1 through 3 were performed utilizing
both the risk-adjusted and market-adjusted definitions of long-term
mispricing, with virtually no qualitative differences in results.8
Hence, only the results pertaining to the risk-adjusted measures are
reported in subsequent sections.
Unexpected accounting earnings over the first three years of
public trading, as well a 36 month cumulative unexpected earnings
measure are also provided in Table 5-3. With the exception of year 2
which has a P-Value of less than .01, the mean unexpected earnings
measures do not differ significantly from zero.
Makeup of Accounting Risk Portfolios
Table 5-4 reports the number of firms comprising each accounting
risk category (portfolio). For example, the table illustrates that 294
out of the 372 firms employed Big Eight auditing firms in the going
public process. Thus, the low accounting risk portfolio (based on
auditor quality) consists of 294 firms. Conversely, the high accounting
risk portfolio consists of the 78 firms which utilized non-Big Eight
auditors. Thus, the sample is dominated by Big Eight audited IPO firms.
adjusted returns of -25.14%. Thus, it appears that the differences
between the market-adjusted returns reported by Ritter and those
reported in Table 5-3 of the current study are largely due to
differences in the market indices employed in the two studies.
8 The Pearson correlation coefficients between the corresponding
market-adjusted and risk-adjusted long-term mispricing measures are in
the .75-,95 range. Thus, the two methods of calculating long-term
mispricing appear to be capturing the same phenomena.

76
Table 5-4
Number of Sample Firms
Comprising Each Accounting Risk Category
Number of Firms in
High Accounting Risk
Number of Firms in
Low Accounting Risk
Accounting Risk Proxy
Category8
Category*3
AUDITOR
78
294
FORM
78
294
APCHOICE
264
95
8 When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.

77
Similarly, Table 5-4 shows that the sample is dominated by IPO
firms which filed S-l SEC registration statements. Again, 294 out of
the 372 sample firms are classified in the low accounting risk category
(based on the SEC registration statement type filed), and only 78 are
classified as high accounting risk firms.
However, when accounting procedure choices are utilized in the
classification of high versus low accounting risk sample firms, the
sample is dominated by high risk firms. Specifically, 264 out of the
359 sample firms which had data available on the accounting methods
chosen utilized consistently income increasing (i.e., "liberal")
accounting procedures for inventory and depreciation. By contrast, only
95 out of these 359 firms employed at least one income reducing method.
Therefore, the common practice among the sample firms was to utilize
straight-line depreciation and an inventory method other than LIFO for
financial reporting purposes.
Results of Univariate Tests
Results of Univariate Tests of Hypothesis 1
Hypot.hesis 1 predicts that the mean abnormal return (MAR) of each
accounting risk portfolio will not significantly deviate from zero over
any of the four time horizons. Further, the hypothesis states that the
mean abnormal return of the high risk portfolios should not differ
significantly from the MAR of the low risk portfolios over each time
period examined. Table 5-5 provides the results of the univariate
(portfolio) tests of Hypothesis 1. Panels A, B, and C supply the

78
Table 5-5
Univariate (Portfolio) Tests of Hypothesis 1
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
0.94%
1.28%**
-0.34%
Year 2
0.48%
-0.57%*
1.05%
Year 3
-0.56%
-0.30%
-0.26%
3 Year
Cumulative
0.86%
0.41%
0.45%
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.53%
1.12%**
0.41%
Year 2
0.09%
-0.47%
0.56%
Year 3
-0.35%
-0.35%
0.00%
3 Year
Cumulative
1.27%
0.30%
0.97%
Panel C
Accounting Risk Proxy Variable = APCHOICE
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.02%**
1.12%*
-0.10%
Year 2
-0.26%
-0.08%
-0.18%
Year 3
-0.48%
-0.40%
-0.08%
3 Year
Cumulative
0.29%
0.64%
-0.35%
MAR signifies
mean abnormal return.
** significant
* significant
at .01 level, based on
at .05 level, based on
two-tailed tests
two-tailed tests

79
results of the univariate tests when AUDITOR, FORM, and APCHOICE,
respectively, are employed as proxies for accounting risk.
Panel A indicates that the mean abnormal return of the high
accounting risk portfolio based on auditor quality (i.e., non-Big Eight
audited IPO firms) does not differ significantly from the hypothesized
value of zero over any of the four time horizons. By contrast, the mean
abnormal return of the low accounting risk auditor portfolio (i.e., Big
Eight audited IPOs) is significantly greater than zero in year 1 and
significantly less than zero in year 2. However, the low risk
portfolio's MAR conforms to the predictions of Hypothesis 1 in year 3
and over the 36 month cumulative period. Finally, consistent with
Hypothesis 1, the differences between the mean abnormal returns of the
high risk and low risk portfolios do not differ significantly from zero
for any of the time periods studied. Thus, the prediction of Hypothesis
1 that accounting risk should not influence mean abnormal returns is
supported for the case of the auditor quality accounting risk surrogate.
Panel B provides similar results for the SEC registration
statement type accounting risk proxy. Again, the hypothesis that the
MAR of the high risk portfolio (i.e., IPOs which filed S-18
registrations) equals zero in each time period cannot be rejected at
conventional levels. The hypothesis can be rejected for the low risk
group (i.e., Form S-l filers) only for the first year of public trading,
when the mean abnormal return is significantly greater than zero.
However, the differences between the MARs of the high and low risk
portfolios do not differ significantly from the hypothesized value of

80
zero. As was the case for the AUDITOR proxy, Hypothesis 1 is supported
when FORM is utilized as a proxy for accounting risk.
Panel C illustrates that Hypothesis 1 is also supported when
APCHOICE is utilized as a proxy for accounting riskiness. Specifically,
the mean abnormal returns of both the high risk (i.e., firms utilizing
consistently "liberal" accounting methods for inventory and
depreciation) and low risk (i.e., firms utilizing at least one
"conservative" method) groups differ significantly from zero in only the
first year of public trading. Further, consistent with the predictions
of Hypothesis 1, none of the MAR differences between the accounting risk
groups differs significantly from zero.
In summary, the results reported in Table 5-5 strongly support the
prediction of Hypothesis 1 that IPO firms' mean abnormal returns do not
vary according to the level of accounting risk faced by potential
investors, since none of the difference statistics in the table differs
significantly from zero. Thus, the results are consistent with the
hypothesis that investors do not systematically misprice IPOs because of
excessive accounting riskiness. However, Table 5-5 also reveals that in
certain isolated cases a given portfolio's MAR differs from the zero
hypothesized value, even though none of the portfolios possess
statistically significant 36 month cumulative mean abnormal returns.
Therefore, Hypothesis l's prediction of zero abnormal returns over all
time horizons for each portfolio (i.e., no systematic mispricing of IPOs
by investors) is not uniformly supported even though the MARs do not
differ significantly from zero in the vast majority of cases.

81
Results of Univariate Tests of Hypothesis 2
Hypothesis 2 predicts that the mean abnormal return variance
(MVAR) of high accounting risk firms will exceed that of low accounting
risk firms in each of the time horizons examined. Panels A, B, and C of
Table 5-6 report the results of examining Hypothesis 2 using the
AUDITOR, FORM, and APCHOICE accounting risk proxy variables,
respectively.9
Panel A reveals that when AUDITOR is utilized in the
classification of firms into high and low accounting risk categories,
the mean abnormal return variance of the high accounting risk group
exceeds that of the low risk group in each of the first three years.
The difference is significant at the .05 level only for year 2.
However, Hypothesis 2 is marginally supported in years 1 and 3.
Specifically, the differences in mean abnormal return variances are
significant at the .06 and .13 levels in years 1 and 3, respectively.
Thus, Hypothesis 2 is supported in year 2 and marginally supported in
years 1 and 3 when auditor quality is utilized as a surrogate for
accounting risk.
Panel B demonstrates that Hypothesis 2 is supported in years 1 and
2, as well as over the cumulative 36 month period, when the FORM
accounting risk proxy variable is utilized to classify firms into high
9 It is important to note that in Table 5-6 a given portfolio's
cumulative 36 month MVAR does not equal the sum of year 1 through year 3
MVARs. As indicated by Equation (16), abnormal return variances over a
given time period are calculated as the abnormal return over that same
time period squared. Therefore, the cumulative abnormal return variance
equals the cumulative abnormal return squared. Hence, for each firm
(and portfolio) VAR36 = CAR362 rather than AR12 + AR22 + AR32.

82
Table 5-6
Univariate (Portfolio) Tests of Hypothesis 2
Panel A
Accounting Risk Proxy Variable = AUDITOR
Time
Horizon
High Risk
Portfolio MVARa
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00347
0.00240
0.00107
Year 2
0.00313
0.00222
0.00091*
Year 3
0.00254
0.00207
0.00047
3 Year
Cumulative
0.00642
0.00649
-0.00007
Panel B
Accounting Risk Proxy
Variable = FORM
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00501
0.00199
0.00302**
Year 2
0.00436
0.00190
0.00246**
Year 3
0.00229
0.00213
0.00016
3 Year
Cumulative
0.01013
0.00550
0.00463**
Panel C
Accounting Risk Proxy Variable = APCHOICE
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
High-Low
Year 1
0.00212
0.00279
-0.00067
Year 2
0.00250
0.00175
0.00075*
Year 3
0.00237
0.00136
0.00101**
3 Year
Cumulative
0.00614
0.00645
-0.00031
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests

83
versus low accounting risk portfolios. Specifically, the MVAR of the
high risk firms exceeds that of the low risk firms in each year, and
this difference is statistically significant in all but the third year.
Thus, Hypothesis 2 is strongly supported when FORM is used as a
surrogate for accounting risk.
Panel C provides evidence that supports Hypothesis 2 for only two
of the four time horizons when APCHOICE is employed to proxy for
accounting riskiness. Specifically, the high risk group's mean abnormal
return variance exceeds that of the low risk group at conventional
significance levels for years 2 and 3. In contrast, the MVAR difference
over year 1 and over the 36 month cumulative period is insignificantly
different from zero. Thus, Hypothesis 2 can be supported for only years
2 and 3 when APCHOICE is used to proxy for accounting risk.
In summary, the univariate tests of Hypothesis 2 reveal that the
hypothesis can be supported very strongly for the FORM proxy, and can be
marginally supported for the other two proxy variables. Therefore, the
results generally support the prediction of Hypothesis 2 that accounting
risk and the variance of IPO firms' aftermarket abnormal returns are
positively correlated. Hence, it appears that accounting risk
influences the absolute magnitude of long-term IPO mispricing, as
predicted by Hypothesis 2.
Results of Univariate Tests of Hypothesis 3
Hypothesis 3 states that the variance of IPO firms' abnormal
returns will decrease over time subsequent to the IPO. This reduction
in variance is attributed to a reduction in accounting risk over time as

84
more information is learned about these newly public firms. Table 5-7
reveals the results of univariate (portfolio) tests of this hypothesis.
Panels A, B, and C, respectively, provide the results obtained when
AUDITOR, FORM, and APCHOICE are used to proxy for accounting risk.
Panel A discloses that, consistent with the prediction of
Hypothesis 3, the mean abnormal return variance of the high accounting
risk firms (based on auditor quality) in year 1 exceeds that of the same
firms' MVAR in year 3. However, this difference is not statistically
significant. The same relationship holds true for the low accounting
risk firms. Thus, Hypothesis 3 is not supported for auditor quality
based accounting risk portfolios.
By contrast, Panel B reports a statistically significant decrease
in the high accounting risk (based on the FORM proxy) portfolio's MVAR
from year 1 to year 3. Thus, a significant decreasing linear trend over
the three year time horizon is documented for the high accounting risk
portfolio's MVAR, consistent with the prediction of Hypothesis 3.
However, an insignificant difference between the low accounting risk
portfolio's year 1 and year 3 MVAR is documented. Thus, Hypothesis 3 is
supported only for the high accounting risk firms when the SEC
registration statement type proxy variable is employed.
Finally, Panel C demonstrates empirical support for Hypothesis 3
only for the low accounting risk (based on APCHOICE) firms. The high
accounting risk portfolio's MVAR is demonstrated not to decrease over
the year 1 to year 3 time period. Thus, inconsistent results are found
when the accounting procedure choice proxy variable is used to test
Hypothesis 3.

Table 5-7
Univariate (Portfolio) Tests of Hypothesis 3
85
Panel A
Accounting Risk Proxy Variable AUDITOR
Portfolio
Year 1
MVARa
Year 2
MVAR
Year 3
MVAR
Difference
Year1-Year3
High Risk
0.00347
0.00313
0.00254
0.00093
Low Risk
0.00240
0.00222
0.00207
0.00033
Accounting
Panel B
Risk Proxy Variable FORM
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Difference
Yearl-Year3
High Risk
0.00501
0.00436
0.00229
0.00272**
Low Risk
0.00199
0.00190
0.00213
-0.00014
Panel C
Accounting Risk Proxy Variable APCHOICE
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Difference
Yearl-Year3
High Risk
0.00212
0.00250
0.00237
-0.00025
Low Risk
0.00279
0.00175
0.00136
0.00143*
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests

86
In summary, Hypothesis 3 cannot be strongly supported based on
these univariate tests. When AUDITOR is used to surrogate for
accounting risk, no significant differences are documented. When the
other two proxies are analyzed, inconsistent results are found.
Specifically, for both the FORM and APCHOICE proxies, the results
pertaining to one of the accounting risk portfolios support Hypothesis
3, while the other portfolio provides insignificant results. Hence, the
results reported in Table 5-7 do not provide convincing evidence that
IPO firms' abnormal return variances decrease over the first three years
of public trading. One possible reason for these results may relate to
the short time horizon examined in this study. Specifically, it is
possible that the effect predicted by Hypothesis 3 may occur gradually
over time, in which case it would not be detected over a short (e.g.,
three year) time horizon.10
Summary of Univariate Tests
The results of the univariate tests indicate that Hypothesis 1 can
be strongly supported for each accounting risk proxy variable.
Hypothesis 2 is strongly supported for the FORM proxy and marginally
supported for the AUDITOR and APCHOICE surrogates. Finally, the
empirical results support Hypothesis 3 only in isolated instances. As
is implied by the name "univariate tests," these tests are univariate in
10 Appendix B reports the results of testing Hypothesis 3 over a
longer five year time horizon. Over this longer time horizon,
univariate test results are documented which strongly support Hypothesis
3. Thus, there is some indication that the effect predicted by
Hypothesis 3 may only be empirically verified over a long time horizon.

87
nature and the results may not hold in a multivariate setting. In other
words, the results attributed to accounting risk in this section may
actually be the result of another factor (e.g., IPO firm size or
business riskiness) not explicitly controlled for in these univariate
analyses. The next section describes the results of multivariate
examinations of the three hypotheses.
Results of Multivariate Tests
Results of Multivariate Tests of Hypothesis 1
The multivariate tests of Hypothesis 1 consist of estimating the
cross-sectional multiple regression model described by Equation (18)
over four separate time horizons. Table 5-8 provides evidence of the
correlations among the independent variables utilized in the various
specifications of the model.11 The table indicates that a number of the
independent variables are correlated in a statistically significant
sense. However, only three of the correlations (excluding correlations
among the unexpected earnings variables) exceed .50 in absolute value,
and various model specification checks (to be described in more detail
later) indicated no serious multicollinearity problems among the
independent variables.
Table 5-9 provides details of the estimation of Equation (18) over
the four different time horizons. Specifically, Panels A, B, C, and D
report the results of estimating the model over the first year, second
11 It should be noted that the correlations among the four measures
of unexpected earnings are not of concern since only one of these
variables is utilized in each of the four estimated models.

Table 5-8
Pearson Correlations Among the Independent Variables
Employed in Testing Hypothesis 1 Via the Regression Approach
FORM
APCH0ICE
SIZE
LNRISKS
UE1
UE2
UE3
UE36
AUDITOR
. 24**
.04
-.23**
. 22**
.02
- .06
.05
.03
(3 7 2 ) a
(359)
(372)
(366)
(371)
(366)
(337)
(337)
FORM
.03
-.50**
.51**
.03
- .03
.10
.03
(359)
(372)
(366)
(371)
(366)
(337)
(337)
APCH0ICE
-.11*
.11*
.09
- .00
.06
.10
(359)
(353)
(358)
(353)
(324)
(324)
SIZE
-.66**
-.00
.08
- .05
- .01
(366)
(371)
(366)
(337)
(337)
LNRISKS
- .08
- .05
.12*
.02
(365)
(360)
(331)
(331)
UE1
-.01
.00
. 99**
(365)
(337)
(337)
UE2 .02 .00
(337) (337)
UE3 .02
(337)
a The numbers in parentheses represent the number of firms utilized in the determination of the correlation
coefficient.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests
00
oo

Table 5-9
Regression Approach to Testing Hypothesis 1
89
Panel A
Model: ARlt = a +
/SjAUDITORi +
/32FORMi +
y93APCHOICEi
+
yiSIZEi + yzLNRISKSi +
73UE1i + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted \2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.067
.020
3.275**
5.277#
AUDITOR
-.001
.006
-0.139
0.017
FORM
.008
.007
1.092
0.882
APCHOICE
-.001
.005
-0.243
0.054
SIZE
- .005
.002
-2.613**
3.414
LNRISKS
-.008
.003
-3.030**
8.760##
UE1
.187
.029
6.458**
16.456##
Test of the Model Goodness of Fit: F-Value=8.535
P-Value=.0001
White's [1980] Homoscedasticity Test: \2
Model Adjusted R2 .1141 N-352
Panel B
-Value183
.59 P-Value.0001
Model: AR2a = a +
^AUDITORi +
^2F0RMt +
^APCHOICEi
+
yiSIZEi + yzLNRISKSi + 73UE2i + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.011
.021
-0.539
0.321
AUDITOR
.010
.006
1.565
2.078
FORM
.007
.007
0.981
0.632
APCHOICE
- .003
.006
-0.600
0.511
SIZE
.001
.002
0.577
0.364
LNRISKS
.004
.003
1.366
2.041
UE2
.150
.022
6.936**
33.596##
Test of the Model Goodness of Fit: F-Value=9.441 P-Value=.0001
White's [1980] Homoscedasticity Test: x2-Value=-281.08 P-Value=.0001
Model Adjusted R2 =
.1277 N=347

90
Table 5-9--continued
Panel C
Model: AR^ = a + ^AUDITORi + ^FORMi + ^APCHOICEi
+ 71SIZEi + 72LNRISKS + 73UE3i + ei
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.025
.022
1.113
1.483
AUDITOR
-.003
.007
-0.486
0.213
FORM
-.006
.008
-0.732
0.487
APCHOICE
-.000
.006
-0.011
0.000
SIZE
-.003
.002
-1.281
1.983
LNRISKS
- .001
.003
-0.374
0.116
UE3
.008
.007
1.217
0.460
Test of the
Model Goodness
of Fit: F
-Value=0.542
P-Value=.7759
White's [1980] Homoscedasticity Test:
X2-Value195
.85 P-Value=.0001
Model Adjusted R2 = -.0087
N-318
Panel
D
Model: CAR36i = a
-t
+ ^AUDITORi + )32FORMi + ^APCHOICE*
y^IZEj^ + 72LNRISKSi + 73UE36A + eA
Parameter
Standard
t
Heteroscedasticity
Adjusted xZ
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.060
.035
1.713
2.007
AUDITOR
.000
.011
0.031
0.001
FORM
.007
.012
0.539
0.256
APCHOICE
.002
.010
0.232
0.054
SIZE
-.005
.003
-1.492
1.537
LNRISKS
- .005
.005
-1.088
1.246
UE36
.041
.007
6.061**
12.586##
Test of the
Model Goodness
of Fit: F-
Value=6.624
P-Value=.0001
White's [1980] Homoscedasticity Test: xz-Value=298.73 P-Value=.0001
Model Adjusted R2 .0962 N=318
** significant at .01 level, based on two-tailed t tests
* significant at .05 level, based on two-tailed t tests
## significant at .01 level, based on *2 tests
# significant at .05 level, based on *2 tests

91
year, third year, and 36 month cumulative time horizon.12 In each case,
White's [1980] test for the presence of heteroscedasticity revealed that
the null hypothesis of homoscedasticity could be rejected at the .0001
level. Thus, the error terms in the four specifications of the model
are heteroscedastic. In such cases, the estimated parameters are
unbiased but inefficient (i.e., they are not minimum variance
estimators), and the resulting parameter estimate covariance matrix is
inconsistent. Therefore, Table 5-9 reports test statistics based both
on an inconsistent covariance matrix (i.e., the typically utilized t
statistics) and on a heteroscedasticity consistent covariance matrix
(i.e., the heteroscedasticity adjusted xZ statistics based on White's
[1980] correction). The significance tests performed using the
heteroscedasticity consistent covariance matrix are asymptotic tests.
Unless otherwise noted, only the tests based on the heteroscedasticity
adjusted test statistics will be discussed in this section.13
The four specifications of Equation (18) were also examined for
the presence of multicollinearity. Diagnostic tests based on tolerance
12 It is important to note that the 36 month cumulative time
horizon is not independent of the other three time horizons since
cumulative abnormal returns are calculated as the sum of the abnormal
returns accruing over the first three years. Therefore, the four
regression models are not independent.
13 Weighted least squares (WLS) regressions were also estimated in
an attempt to overcome the heteroscedasticity problem. The residuals of
the estimated OLS models were examined in an attempt to determine the
nature of the heteroscedasticity and thus to aid in the determination of
the appropriate weighting factors. However, proper weighting factors
were not identified as a result of these residual analyses. Thus,
White's [1980] correction is utilized since its utilization appears to
be superior to employing WLS regressions with weighting factors chosen
on an ad hoc basis.

92
factors revealed no serious multicollinearity problems. An independent
variable's tolerance factor is calculated as 1-R2k, where R2k is the
coefficient of multiple correlation which results when the independent
variable is regressed on all the other independent variables in the
model. This tolerance factor is often used "to detect instances where
an X variable should not be allowed into the fitted regression model
because of excessively high interdependence between this variable and
the other X variables in the model" (Neter et al. [1985, p. 393]).
Neter et al. continue by stating that frequently utilized tolerance
limits are .01, .001, and .0001, below which the independent variable
should be discarded from the model. In other words, unless the
calculated tolerance factor is below the chosen tolerance limit, the
independent variable is typically not dropped from the model since it is
not considered to possess excessive collinearity with any other
independent variable. In the four regressions reported in Table 5-9,
the lowest tolerance factor calculated for any independent variable is
.54. Thus, multicollinearity does not appear to pose a serious threat
in the four estimated regressions.14
Panel A of Table 5-9 provides the results of estimating Equation
(18) for the first year of public trading time horizon. As predicted by
Hypothesis 1, none of the accounting risk proxy variables is
statistically significant at conventional levels. Thus, consistent with
14 Analysis of the estimated residuals, as well as an examination
of Cook's D distance measures, was also performed in an effort to detect
outlying observations. The number of identified possible "outliers" was
very small in each regression. Equation (18) was then reestimated with
these potentially outlying observations dropped from the analysis. The
results did not qualitatively change from those reported in Table 5-9.

93
Hypothesis 1, accounting risk is not significantly correlated with IPO
firms' first year abnormal returns.
All three control variables exhibit (at least marginally)
significant relationships with the dependent variable. Consistent with
prior research, year 1 unexpected earnings is positively associated with
year 1 abnormal security returns. Also, firms which list a greater
number of risk factors in their prospectus (i.e., firms with greater
business risk) experience lower abnormal returns than firms which
enumerate fewer risk factors. However, SIZE is negatively associated
(at the .10 level) with first year abnormal returns. Specifically,
Panel A demonstrates that smaller firms (i.e., those with greater
business risk) experience greater abnormal first year returns. Thus,
conflicting results concerning the relationship of business risk and
first year abnormal returns are documented. These conflicting results
are consistent with the theory presented in Chapter 3 which indicates
that mean risk-adjusted abnormal returns should not be systematically
related to measures of risk.
Panel B shows that the three accounting risk variables also are
insignificant in the explanation of year 2 abnormal returns. Thus,
Hypothesis 1 is also supported for year 2 risk-adjusted returns. Again,
the widely documented positive association between unexpected earnings
and abnormal returns is demonstrated. In fact, unexpected earnings is
the only significant variable among the three control variables.
Therefore, neither SIZE nor LNRISKS contributes to the explanation of
second year abnormal returns.

94
Panel C reports the results of estimating Equation (18) over the
third year subsequent to initial offering. The overall model is
insignificant in this case. Thus, the hypothesis that each of the
estimated parameters equals zero cannot be rejected (P-Value=.7759).
However, the results reported in this panel provide support for
Hypothesis 1, since each of the three accounting risk surrogates is
insignificant. Over this third year time horizon, a positive
association between unexpected earnings and abnormal returns is
supported via a one-tailed t test at the .11 level. However, such a
positive relationship is not supported by the xZ test.
Finally, Panel D relates the results of estimating Equation (18)
over the cumulative 36 month time horizon. Once again, the accounting
risk proxy variables are insignificant. In fact, only the unexpected
earnings variable is a significant explanator of CAR36.
In summary, the results reported in Table 5-9 strongly support the
prediction of Hypothesis 1 concerning the insignificant relationship
between accounting risk and mean abnormal returns. Therefore,
Hypothesis 1 is supported in this multivariate setting, as well as in
the univariate settings previously described. Further, Table 5-9 is
consistent with prior research since the widely documented positive
association between unexpected accounting earnings and abnormal returns
is documented in three out of the four time horizons.
Mutlivariate Tests of Hypothesis 2
The multivariate tests of Hypothesis 2 consist of estimating the
cross-sectional multiple regression model described in Equation (20)

95
over four separate time horizons. Table 5-10 reports the correlations
among the independent variables utilized in the various specifications
of the model.15 The table indicates that certain of the independent
variables are correlated in a statistically significant sense. Again,
only three of the correlations (excluding correlations among the
unexpected earnings variables) exceed .50 in absolute value, and the
estimated tolerance factors do not indicate a serious multicollinearity
problem among the independent variables.
Table 5-11 reports the results of the estimation of Equation (20)
over the four different time horizons. Specifically, Panels A, B, C,
and D report the results of estimating the model over the first, second,
and third year, and over the 36 month cumulative time horizon.16 As was
the case for the regressions reported in Table 5-9, White's [1980] test
for the presence of heteroscedasticity revealed that the null hypothesis
of homoscedasticity could be rejected at the .0001 level for each of the
four specifications of the model. Therefore, Table 5-11 reports test
statistics based (1) on an inconsistent covariance matrix (i.e., the
typically utilized t statistics) and (2) on a heteroscedasticity
consistent covariance matrix (i.e., the heteroscedasticity adjusted x2
statistics based on White's [1980] correction). Unless otherwise noted,
only the statistical tests based on the heteroscedasticity adjusted test
statistics will be discussed in this section.17
15 See footnote 11.
16 See footnote 12.
17
See footnote 13.

Table 5-10
Pearson Correlations Among the Independent Variables
Employed in
Testing Hypothesis
2 Via the
Regression
Approach
FORM
APCHOICE
SIZE
LNRISKS
ABSUE1
ABSUE2
ABSUE3
ABSUE36
AUDITOR
. 24**
.04
-.23**
. 22**
- .03
.09
.02
-.03
(372)a
(359)
(372)
(366)
(371)
(366)
(337)
(337)
FORM
.03
-.50**
. 51**
-.03
.06
.07
-.03
(359)
(372)
(366)
(371)
(366)
(337)
(337)
APCHOICE
- .11*
.11*
-.09
.01
.08
-.09
(359)
(353)
(358)
(353)
(324)
(324)
SIZE
-.66**
.00
-.09
-.03
.01
(366)
(371)
(366)
(337)
(337)
LNRISKS
. 13**
.10
.12*
.01
(365)
(360)
(331)
(331)
ABSUE1
-.03
-.02
. 99**
(365)
(337)
(337)
ABSUE2
.45**
-.02
(337)
(337)
ABSUE3
.01
(337)
a The numbers in parentheses represent the number of firms utilized in the determination of the correlation
coefficient.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests
VO
O'

Table 5-11
Regression Approach to Testing Hypothesis 2
97
Panel A
Model: VAR1 = a + ^AUDITORi + )92FORMi + ^APCHOICEi
+ yjSIZEj^ + 72LNRISKS + 73ABSUE1 + e
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0071
.0017
4.148**
6.132##
AUDITOR
.0001
.0005
0.246
0.044
FORM
.0011
.0006
1.891*
2.596
APCHOICE
-.0009
.0005
-1.898*
1.670
SIZE
-.0005
.0002
-2.990**
4.088#
LNRISKS
-.0000
.0002
-0.062
0.006
ABSUE1
.0003
.0027
0.120
0.017
Test of the Model Goodness of Fit: F-Value=5.476 P-Value=.0001
White's [1980] Homoscedasticity Test: x2-Value-235.44 P-Value-.0001
Model Adjusted R2 .0711 N-352
Panel B
Model: VAR2t a + ^AUDITOR* + ^FORM* + ^APCHOICEi
+ 71SIZEi + yzLNRISKSi + 73ABSUE2 + eA
Heteroscedasticity
Variable
Parameter
Estimate
Standard
Error
t
Statistic
Adjusted x2
Test Statistic
INTERCEPT
.0040
.0018
2.176*
4.391#
AUDITOR
-.0001
.0005
-0.100
0.008
FORM
.0011
.0006
1.742*
1.496
APCHOICE
.0005
.0005
1.089
2.208
SIZE
-.0004
.0002
-2.004*
3.637@
LNRISKS
.0002
.0002
1.040
1.169
ABSUE2
.0100
.0021
4.671**
7.876##
Test of the
Model Goodness
of Fit: F-
Value9.194
P-Value.0001
White's [1980] Homoscedasticity Test: xz-Value-242.52 P-Value=.0001
Model Adjusted R2 =
.1244 N-347

98
Table 5-11--continued
Panel C
Model: VAR3i = a + ^AUDITORi + P2Fomi + ftAPCHOICEj,
+ y^IZEi + 72LNRISKS + 73ABSUE3 + eA
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0004
.0015
0.285
0.112
AUDITOR
.0005
.0005
0.998
1.032
FORM
-.0005
.0005
-0.874
0.649
APCHOICE
.0009
.0004
2.233**
10.817##
SIZE
.0001
.0002
0.340
0.156
LNRISKS
.0003
.0002
1.281
1.190
ABSUE3
.0020
.0005
4.002**
4.738#
Test of the
Model Goodness
of Fit: F-
Value=4.548
P-Value=.0002
White's [1980] Homoscedasticity Test:
X2-Value245
.62 P-Value=.0001
Model Adjusted R2 = .0629
N-318
Panel
D
Model: VAR3 = a
+ ^AUDITOR
i + £2F0RMi +
^APCHOICEi
+
yiSIZEi + yzLNRISKSi + 73ABSUE3 + eA
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0125
.0040
3.170**
7.254##
AUDITOR
-.0007
.0012
-0.595
0.380
FORM
.0018
.0014
1.328
1.524
APCHOICE
-.0007
.0011
-0.691
0.417
SIZE
-.0008
.0004
-2.109*
3.541@
LNRISKS
.0002
.0005
0.417
0.191
ABSUE36
.0043
.0008
5.505**
66.666##
Test of the Model Goodness of Fit: F-Value=7.839 P-Value=.0001
White's [1980] Homoscedasticity Test: x2_Value=284.25 P-Value=.0001
Model Adjusted R2 = .1146 N=*318
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on \2 tests
# significant at .05 level, based on *2 tests
@ significant at .10 level, based on x2 tests

99
Panel A of Table 5-11 provides the results of estimating Equation
(20) for the first year of public trading. The three accounting risk
proxy variables are predicted to be positive according to Hypothesis 2.
However, only FORM is significantly greater than zero at the .05 (.11)
level, based on the t (x2) statistic. Among the control variables, only
SIZE is significant. The negative coefficient on size indicates that
smaller firms (i.e., those possessing greater business risk) have higher
abnormal return variances. Thus, the results of Panel A of Table 5-11
support Hypothesis 2 for only the FORM accounting risk proxy. The
results indicate that IPO firms which file SEC Form S-18 registrations
experience higher first year abnormal return variances than Form S-l
filers. Therefore, a significant positive association between
accounting risk (based on the FORM proxy) and first year abnormal return
variances is documented in this multivariate setting, just as it was in
the univariate setting described previously. However, this same
significant positive association is not documented for the other two
accounting risk surrogates when business risk and other measures of
accounting risk are explicitly controlled for.
In Panel B, the AUDITOR accounting risk proxy variable is again
insignificant. Consistent with the prediction of Hypothesis 2, both the
FORM and APCHOICE variables are positive. However, the significance
levels of these two variables are marginal. Specifically, Hypothesis 2
is supported at the .05 (.22) level based on the t (x2) statistic for
FORM and at the .14 level based on either test statistic for APCHOICE.
Thus, Hypothesis 2 is marginally supported for two out of the three
accounting risk surrogates. Two of the control variables are also

100
significant. Firm size is negative, and as expected, ABSUE2 is
positively associated with second year abnormal return variances.
Panel C reports the results of estimating Equation (20) for the
third year time horizon. Hypothesis 2 is supported for the APCHOICE
accounting risk measure only. The only other significant variable in
the model is the absolute value of unexpected earnings.
Finally, Panel D demonstrates the results of estimating Equation
(20) over the cumulative 36 month time horizon. Over this period, among
the accounting risk surrogates only the FORM variable approaches
conventional significance levels, with a P-Value of .09 (.22) based on
the t (x2) statistic. Thus, Hypothesis 2 cannot be strongly supported
for any of the accounting risk measures over this 36 month cumulative
period. However, two of the control variables are useful in the
explanation of the 36 month cumulative abnormal return variances. SIZE
is negatively related to VAR36 and the typical positive association
between unexpected earnings and abnormal return variances is
demonstrated.
In summary, the results reported in Table 5-11 support Hypothesis
2 for the FORM proxy variable for years 1 and 2, support the APCHOICE
proxy for years 2 and 3, and do not lend support for the AUDITOR proxy
over any of the four time horizons.18 Hence, the results of the
18 As indicated in Chapter 4, the accounting risk theoretical
construct is a multifaceted concept which is difficult to empirically
capture with any one proxy variable. In this study, three accounting
risk proxies are employed in an attempt to capture three unique aspects
of accounting riskiness. The results reported in Table 5-11 indicate
that different combinations of these proxy variables are effective in
explaining abnormal return variances as the time horizon examined
changes. In other words, it appears that none of these empirical
surrogates adequately captures the accounting risk theoretical construct

101
univariate tests are generally confirmed in this multivariate context,
with the notable exception that the auditor quality variable loses
significance once business risk and other measures of accounting risk
are explicitly controlled for. Even though the auditor proxy is
insignificant, the results pertaining to the other two accounting risk
proxies generally support the prediction of Hypothesis 2 that accounting
risk is positively associated with IPO aftermarket abnormal return
variances. The lack of support on the auditor variable may be due to
one or more of the arguments enumerated in Chapter 4 that disputed the
presumed positive association between audit quality and audit firm size.
Multivariate Tests of Hypothesis 3
The multivariate tests of Hypothesis 3 consist of estimating the
pooled time-series cross-sectional regression model described by
Equation (21). Table 5-12 reports the results of the estimation of the
model. Panel A provides details regarding the statistical significance
across all time horizons. Thus, it may be appropriate to think of
accounting risk as a vector of component parts in which a different
combination of the vector components is needed in order to explain
abnormal return variances across different time horizons.
In an attempt to capture the totality of the accounting risk
theoretical construct in one empirical variable, this study's three
accounting risk proxies were collapsed into one overall measure of
accounting riskiness. Since each of the proxies is measured
dichotomously with high accounting risk firms coded 1 and low risk firms
coded 0, the new accounting risk measure was simply calculated as the
sum of AUDITOR, FORM, and APCHOICE. In a multiple regression test of
Hypothesis 2 employing this overall accounting risk measure, accounting
risk was found to be marginally significant in years 2 and 3, and
insignificant over the other two time horizons.

102
Table 5-12
Regression Approach to Testing Hypothesis 3
Panel A
Model: VARit a + ^AUDITOR^ + AUDITORit*Ilit + AUDIT0Rit*I2it
+ 02FORMit + P2' F0RMit*Ilit + P2" F0RMit*I2it
+ /93APCHOICEit + /93' APCHOICEit*Ilit + 03" APCH0ICEit*I2it
+ 7iSIZEit + 7i'SIZEit*Ilit + 7i"SIZEit*I2it
+ 72LNRISKSit + 72'LNRISKSit*Ilit + 72" LNRISKSit*I2it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73" ABSUEit*I2it + e,
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0039
.0010
3.974**
9.441##
AUDITOR
.0002
.0005
0.501
0.184
FORM
.0014
.0006
2.437**
2.939@
APCHOICE
-.0007
.0004
-1.506
1.306
SIZE
-.0002
.0001
-1.946*
3.520@
LNRISKS
.0002
.0002
1.044
2.120
ABSUE
.0005
.0026
0.204
0.052
AUDIT0R*I1
-.0003
.0007
-0.429
0.135
AUDIT0R*I2
.0001
.0007
0.119
0.014
FORM*11
-.0003
.0008
-0.351
0.051
FORM*I2
-.0022
.0008
-2.616**
4.155#
APCH0ICE*I1
.0012
.0006
1.934*
3.434@
APCHOICE*I2
.0014
.0006
2.141*
5.177#
SIZE*I1
-.0002
.0001
-2.656**
9.159##
SIZE*I2
-.0001
.0001
-1.384
3.268@
LNRISKS*I1
.0000
.0003
0.167
0.028
LNRISKS*I2
-.0002
.0003
-0.738
0.611
ABSUE*I1
.0095
.0033
2.866**
5.022#
ABSUE*I2
.0015
.0027
0.558
0.354
Test of the Model Goodness
of Fit: F-
-Value=6.223 P-
Value-.0001
White's [1980]
Homoscedasticity Test:
X2-Value=650.79
P-Value-.0001
Model Adjusted
R2 .0847
N1,017

103
Table 5-12--continued
Panel B
Tests of Linear Hypotheses to Determine Whether the Independent
Variables Explain More of Year 2 Variances Than Year 3 Variances
Linear
Hypothesis
Difference
in Parameters
Non-Hetero-
scedasticity
Adjusted F
Test Statistic
Hetero-
scedasticity
Adjusted xZ
Test Statistic
AUDITOR*I1-AUDITOR*I2=0
-.0004
0.286
0.271
FORM*11- FORM*12=0
.0019
5.116#
3.010@
APCH0ICE*I1-APCHOICE*I2=0
-.0002
0.069
0.127
SIZE*I1-SIZE*I2=0
-.0001
1.443
3.535@
LNRISKS*I1-LNRISKS*I2=0
.0002
0.814
0.670
ABSUE*I1-ABSUE*I2-0
.0080
14.907##
4.743#
**
significant
at
.01
level,
based
on
one-tailed t
*
significant
at
.05
level,
based
on
one-tailed t
##
significant
at
.01
level,
based
on
x2
or F
tests
#
significant
at
.05
level,
based
on
x2
or F
tests
significant
at
.10
level,
based
on
x2
or F
tests
tests

104
of the differences between the regression parameters relating to years 2
and 3 with those pertaining to year 1, while Panel B provides tests of
the differences in regression coefficients between years 2 and 3.
As was the case for each of the regressions reported in Tables
5-9 and 5-11, White's [1980] test for the presence of heteroscedasticity
revealed that the null hypothesis of homoscedasticity could be rejected
at the .0001 level. Thus, Table 5-12 Panel A reports both the typically
utilized t statistics and the heteroscedasticity adjusted \z statistics
based on White's [1980] correction. In a similar fashion, Panel B
reports two sets of test statistics. One set of reported statistics
ignores the presence of heteroscedasticity while the second set adjusts
for heteroscedastic error terms. Unless otherwise noted, only the
statistical tests based on the heteroscedasticity adjusted test
statistics will be described in this section.19
Panel A of Table 5-12 reports the results of estimating Equation
(21) over the entire three year time horizon. As was indicated in
Chapter 4, Equation (21) is a pooled time-series cross-sectional model
in which each sample firm contributes three observations. In other
words, observations relating to the first, second, and third year of
public trading are included for each sample firm. The indicator
19 The tolerance factors resulting from the estimation of Equation
(21) were also examined in order to assess whether collinear independent
variables might be unduly influencing the estimated parameters. The
tolerance factors for the ABSUE and ABSUE*I2 variables each approximate
.04. Thus, these variables appear to possess a high degree of
collinearity with the other independent variables. However, these .04
tolerance factors are still above the most restrictive tolerance limit
(i.e., .01) typically utilized as a guide for discarding severely
collinear variables. Thus, the tolerance factor diagnostic check did
not reveal severe multicollinearity problems.

105
variables II and 12 are utilized to represent observations relating to
the second and third year subsequent to initial offering, respectively.
Thus, the estimated parameters on the noninteraction terms represent the
parameter estimates for year 1. As expected, the direction and
significance levels of these noninteraction parameters are consistent
with those reported in Panel A of Table 5-11, since that panel also
reports the results of the abnormal return variance regression for
year 1 observations.
In the indicator variable configuration described by Equation
(21), the parameters on the II (12) interaction terms represent chanees
in the respective parameter estimates from year 1 to year 2 (3). Panel
A indicates that the estimated parameters on the AUDITOR accounting risk
proxy variable are not significantly different in either year 2 or 3
than in year 1. Therefore, Hypothesis 3 is not supported for the audit
quality variable. Similarly, the table shows that the estimated FORM
parameter is significantly lower in year 3 than in year 1. Thus, the
FORM variable explains less of third year abnormal return variances than
first year variances. This result is consistent with the assertion of
Hypothesis 3 that accounting risk should play a lesser role in
explaining abnormal return variances over time as more information about
IPO firms becomes publicly known. Finally, the estimated interaction
terms relating to the APCHOICE variable indicate that the change in
explanatory power of APCHOICE over time runs counter to the prediction
of Hypothesis 3. Specifically, APCHOICE is shown to explain more of the
variation in the dependent variable in both years 2 and 3 than in

106
year 1. Thus, Hypothesis 3 is not supported for the accounting
procedure choice risk proxy.
Panel A also allows the determination of the change in explanatory
power of the control variables over the three year time horizon. The
significantly negative coefficients on the SIZE interaction terms
indicate that the estimated SIZE parameter becomes more negative over
time. Since SIZE is predicted to be negative (i.e., smaller firms
should have greater abnormal return variability since they possess
greater business risk), these results indicate that SIZE increases in
explanatory power over the three year period. However, the estimated
parameters on the other business risk control variable LNRISKS do not
significantly differ in years 2 and 3 from year 1. Finally, the
unexpected earnings variable increases in explanatory power in years 2
and 3 in relation to year 1, even though the difference is significant
only in year 2.
The results reported in Panel A indicate changes in the regression
parameters pertaining to years 2 and 3 from those of year 1. However,
the table provides no indication of the changes in the parameters from
year 2 to year 3. Panel B of Table 5-12 provides this additional
information by reporting the results of tests of linear hypotheses which
examine whether the estimated regression parameters pertaining to year 2
exceed those of year 3. Significant differences in the estimated
parameters of years 2 and 3 are demonstrated only for the FORM, SIZE,
and ABSUE variables. Specifically, consistent with Hypothesis 3, the
SEC registration form proxy explains less of third year abnormal return
variances than second year variances. Similarly, the unexpected

107
earnings variable possesses higher explanatory power in year 2 than in
year 3. Finally, the estimated parameter on the size variable is more
negative in year 2 than in year 3. Thus, this business risk proxy loses
explanatory power as time progresses from the second to the third year
subsequent to the IPO (since SIZE is predicted to be negative).
In summary, the results reported in Table 5-12 support Hypothesis
3 only for the FORM accounting risk proxy. The explanatory power of
FORM is demonstrated to decrease from year 1 to year 3, as predicted by
Hypothesis 3. Thus, accounting risk (based on the SEC registration type
proxy) becomes a less important factor in explaining IPO abnormal return
variances over time as more information about these firms becomes
publicly known. However, the AUDITOR and APCHOICE accounting risk
proxies do not conform to the predictions of Hypothesis 3. Thus, with
the exception of the FORM accounting risk proxy, the data do not support
Hypothesis 3 in this multivariate setting.20
Summary of Multivariate Tests
The results reported in this section demonstrate that the evidence
obtained via univariate tests of Hypotheses 1 through 3 also generally
hold in a multivariate environment. Specifically, Hypothesis 1 is
strongly supported in a cross-sectional multiple regression analysis.
Thus, even after controlling for possible confounding factors (e.g.,
20
Appendix B reports the results of testing Hypothesis 3 in a
multivariate setting over a longer five year time horizon. The results
based on this longer five year time horizon are consistent with those
documented in Table 5-12 for the three year time horizon. Specifically,
Hypothesis 3 is not supported in a multivariate setting even over a
longer time horizon.

108
business risk), accounting risk is demonstrated not to significantly
influence mean IPO aftermarket abnormal returns. Similarly, the
univariate results pertaining to Hypothesis 2 (with the exception of the
results for the auditor quality variable) are generally confirmed in
this multivariate setting. Thus, accounting risk is demonstrated to be
positively associated with IPO abnormal return variances even after
controlling for firm size, the number of risk factors enumerated in the
prospectus, and unexpected accounting earnings. Finally, as was the
case for the univariate tests, Hypothesis 3 is not supported in a
multivariate environment. Thus, there is little indication that
accounting risk loses its ability to explain IPO aftermarket abnormal
return variances over the first three years of public trading.

CHAPTER 6
CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
Conclusions
This dissertation extends prior research in two ways. First, the
long-term mispricing of initial public security offerings is reexamined
by utilizing a refined model of equilibrium security pricing on a sample
of recent IPOs. Second, the impact of accounting numbers on long-term
IPO mispricing is explored.
The hypotheses are developed (and empirically tested) that
uncertainty surrounding the quality of the accounting numbers available
to estimate IPO firm value (i.e., accounting risk) affects the variance
rather than the mean of investors' aftermarket abnormal returns. Thus,
accounting risk is hypothesized to influence the precision (i.e., the
absolute magnitude of mispricing) of investors' initial valuations of
IPOs even though such valuations are unbiased (i.e., not systematically
overvalued or undervalued). However, the third hypothesis predicts that
the variance of investors' aftermarket abnormal returns will decrease
over time. This third hypothesis is predicated on the assumption that
accounting risk declines over time as more information becomes publicly
known about these previously privately held companies.
The research conducted in this dissertation is intended to provide
an indication of the role that accounting reliability plays in the
109

110
valuation of IPOs. Specifically, the empirical tests are designed to
examine the impact of accounting accuracy and reliability on how
precisely an IPO firm is valued. Thus, an indication of the
"usefulness" of accounting information for valuation purposes, in a
context of scarcity of alternative information sources, is provided.
Three empirical surrogates for the accounting risk theoretical
construct are employed in this study. The three surrogates are intended
to capture three unique aspects of the multifaceted accounting risk
concept. The type of SEC registration statement filed by the IPO issuer
is utilized to measure the quantity (i.e., extent of disclosure)
component of accounting quality. The conservative versus liberal
accounting procedure choice variable is employed to capture the level of
bias present in the reported accounting numbers due to managerial
manipulation of earnings. Finally, the auditor reputation proxy is used
to measure the level of credibility associated with the reported
accounting numbers.
The empirical results reported in Chapter 5 strongly support the
prediction of Hypothesis 1 that accounting risk does not affect
investors' mean abnormal returns in the three years subsequent to the
IPO. Hypothesis 2 is also supported by the data. However, the results
are not as compelling as those pertaining to Hypothesis 1.
Specifically, Hypothesis 2 is (at least marginally) supported for two of
the years examined for both the FORM and APCHOICE accounting risk
proxies. However, the hypothesis is not supported for the AUDITOR
accounting risk proxy in a multivariate context. Finally, the empirical
results demonstrate very little evidence in support -of Hypothesis 3.

Ill
The results documented in this study indicate that investors do
not systematically overvalue or undervalue IPOs even in the face of
great uncertainty concerning the quality of the accounting numbers
supplied to them in the offering firm's prospectus. However, the data
are consistent with the hypothesis that the variance of IPO investors'
abnormal aftermarket returns increases as the level of uncertainty
surrounding the quality of accounting disclosures increases. Thus, the
precision of investors' valuation decisions is shown to decrease as
accounting risk increases. Finally, the data do not support the
prediction of Hypothesis 3 that accounting risk becomes less important
in the explanation of abnormal return variances over IPO firms' initial
three years of public trading.
Suggestions for Future Research
Future research could extend this study by more directly examining
the role of accounting numbers in the initial valuation of IPOs.
Specifically, an equity valuation model could be developed which
utilizes certain accounting numbers (e.g., earnings per share, book
value of assets, or book value of equity) as determinants of IPO firm
value. This is in contrast to the current study which examines the
association between accounting quality and various measures of long-term
IPO mispricing.
Another avenue for future accounting research in the IPO area
appears to be the development of a model of the accounting procedure
choices of initial public offering firms. Since IPO firms differ in
many respects from established publicly traded firms, the managerial

112
incentives which influence the selection of accounting procedures most
likely differ between the two types of firms. Thus, the development of
an explanatory model of the economic incentives of IPO managers in the
selection of accounting procedures appears to represent a fruitful area
for future research.
Finally, future research could extend this study by providing
cross-sectional explanations of long-term IPO mispricing based on firms'
level of business risk. This is in contrast to the current study which
attempts to explain long-term mispricing from an accounting risk
perspective.

APPENDIX A
SHORT-TERM UNDERPRICING AND ACCOUNTING RISK
Prior accounting research on the short-term underpricing of
initial offers has explored the impact of auditor reputation on initial
underpricing. Beatty [1989a] and Balvers et al. [1988] hypothesize that
auditor reputation and initial underpricing are inversely related.
Beatty utilizes the major result of Beatty and Ritter [1986] that
expected IPO underpricing is positively associated with ex ante
uncertainty (i.e., uncertainty about the true value of an IPO) in the
formulation of this hypothesis. Specifically, since the attestation of
an IPO firm's financial statements by a high reputation auditor serves
to reduce investors' ex ante uncertainty, then auditor reputation is
hypothesized to be inversely related to underpricing.
Balvers et al. [1988] extend the models of Rock [1986] and Beatty
and Ritter [1986] by explicitly considering the relationship between an
IPO firm's auditor and underwriter. In the model, high auditor
reputation reduces investors' uncertainty regarding an IPO's earnings
and serves as a positive signal of the reputation of the underwriter.
Thus, auditor reputation is inversely related to ex ante uncertainty,
and is therefore hypothesized to be inversely related to underpricing.
Empirical support for this hypothesized relationship between
auditor reputation and short-term underpricing is documented by Beatty
[1989a, 1989b] and Balvers et al. [1988]. However, Balvers et al.
113

114
conclude that the impact of auditor reputation on underpricing is
reduced when a high reputation underwriter is also involved in the
registration process. Thus, auditor and investment banker reputation
appear to be (at least partial) substitutes.
Therefore, the results of prior accounting research pertaining to
short-term mispricing of IPOs suggest that an entrepreneur of a firm
contemplating an initial offering may be able to influence the level of
underpricing associated with the issue by his auditor choice. However,
the impact of other accounting choices on initial underpricing has not
been examined. For example, prior research has not explored the effect
of the accounting procedures chosen by IPO firms or the amount and
content of accounting information disclosed to potential investors on
short-term underpricing. This appendix reports the results of an
exploratory analysis which investigates these issues.
In order to examine the impact of certain accounting choices on
short-term underpricing the following cross-sectional regression model
is estimated:
UPt = a + ^AUDITORi + /32FORMi + ^33APCHOICEi
+ yiSIZEi + 72LNRISKSi + ei, (22)
where UPi = the short-term underpricing of firm i's initial offering,
and all other variables are as previously defined. Short-term
underpricing is calculated as:
UPi = (Plt PIPOi) / PIPOi, (23)
where Pli = firm i's first available closing bid price following the
initiation of public trading, and PIPOi = firm i's initial offering

115
price as set by the underwriter. Thus, UPt measures the return (before
transaction costs) that an investor could have earned by purchasing an
IPO from the underwriter at the initial offering price and then selling
it at the end of the first day of public trading.
Equation (22) expresses short-term underpricing as a function of
accounting risk and business risk variables. Thus, asstuning that Beatty
and Ritter's [1986] ex ante uncertainty construct can be decomposed into
accounting risk and business risk components, Equation (22) follows the
predictions of Beatty and Ritter's model. In other words, short-term
underpricing should be a positive function of both accounting risk and
business risk, since it is a positive function of ex ante uncertainty.
Hence, each of the variables in Equation (22) is predicted to be
positive except for the size variable which is predicted to be negative
(since SIZE is inversely related to business risk).
Table A-l reports the results of estimating Equation (22).
White's [1980] test for the presence of heteroscedasticity revealed that
the null hypothesis of homoscedasticity could be rejected at the .0001
level. Therefore, Table A-l reports test statistics based (1) on the
typically utilized t statistics and (2) on heteroscedasticity adjusted
X2 statistics based on White's [1980] correction. Unless otherwise
noted, only the statistical tests based on the heteroscedasticity
adjusted test statistics will be discussed in this appendix.
Consistent with the predictions of the model, the table reveals
that firm size and the accounting procedure choice variable are
significant in the explanation of short-term underpricing.

Table A-l
Short-Term Underpricing Results
116
UPi = a + ^AUDITORi + ^FORl^ + ^3APCHOICEi
+ 71SIZEi + 72LNRISKSi + ej^
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.472
.103
4.599**
9.499##
AUDITOR
.001
.030
0.049
0.002
FORM
- .053
.036
-1.486
1.108
AP CHOICE
.041
.027
1.523
3.622@
SIZE
- .042
.010
-4.170**
7.370##
LNRISKS
- .011
.013
-0.802
0.771
Test of the Model Goodness of Fit: F-Value=4.829 P-Value=.0003
White's [1980] Homoscedasticity Test: x2Value=105.88 P-Value=.0001
Model Adjusted R2 = .0516 N=353
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on x2 tests
# significant at .05 level, based on x2 tests
@ significant at .10 level, based on x2 tests

117
Specifically, firm size is shown to be negatively associated with
underpricing. Thus, smaller firms (i.e., those which possess greater
business risk) experience a greater amount of underpricing of the
initial offering price. Table A-l also reveals that firms which utilize
less conservative accounting procedures for inventory and depreciation
(i.e., those firms with greater accounting risk) also experience a
greater level of underpricing.
However, the table demonstrates that none of the remaining
accounting risk or business risk proxy variables is significant in the
explanation of short-term underpricing. Thus, neither the reputation of
the auditor employed in the going public process nor the amount and
content of accounting disclosures supplied to potential investors (as
proxied by the FORM accounting risk variable) aid in the explanation of
the variation in IPO firms' initial underpricing. Similarly, LNRISKS,
the remaining business risk variable does not conform to the prediction
of the model.
The results reported in this appendix are of interest in a number
of respects. First, the results are consistent with prior research
which demonstrates that ex ante uncertainty and short-term IPO
mispricing are positively related, since both accounting risk (in the
form of APCHOICE) and business risk (in the form of SIZE) confirm the
prior findings.
Second, the results of the estimation of Equation (22) are not
consistent with previous findings which demonstrate an inverse
relationship between auditor reputation and underpricing. In the
present study, the AUDITOR variable is found to be insignificant. Thus,

118
it is possible that auditor reputation loses its explanatory power once
other measures of accounting risk, as well as business risk variables,
are included in the analysis.
Possibly the most interesting result of this exploratory analysis
is the result pertaining to the APCHOICE variable. In particular, Table
A-l demonstrates a significant positive association between the degree
that income increasing (i.e., "liberal") accounting methods are utilized
for inventory and depreciation and the level of short-term underpricing
experienced by IPO firms. Thus, these results indicate that an
entrepreneur of a firm contemplating an initial offering may be able to
influence the short-term underpricing of the issue by his accounting
procedure choices pertaining to depreciation and inventory.
In summary, the results reported in this appendix demonstrate that
accounting choices other than the reputation of the auditor employed in
the IPO process are significantly associated with the level of short
term underpricing experienced by IPO firms. In particular, this
appendix shows that the degree of conservatism inherent in a firm's
depreciation and inventory accounting methods is related to short-term
IPO mispricing.

APPENDIX B
EMPIRICAL RESULTS BASED ON A FIVE YEAR TIME HORIZON
The empirical results reported in Chapter 5 are based on a sample
of 372 firms which had undertaken initial public offerings in the 1980-
1983 time period. These 372 sample firms were selected out of a
possible 511 initial offering firms since they met the sample selection
criterion of having at least three years of security return data
available on the CRSP NASDAQ file. The entire empirical analysis was
also conducted using a more restrictive five year data availability
sample selection criterion. This appendix presents summary results of
the testing of Hypotheses 1 through 3 using this more restrictive
criterion.
Table B-l reveals that a five year data availability criterion
results in a loss of an additional 73 firms beyond the less restrictive
three year criterion. Thus, a sample of 299 IPO firms remain which have
at least five years of security returns available on NASDAQ.
Table B-l
Sample Selection Procedures Using a Five Year Criterion
Total Available Microfiche Prospectuses of Non-Unit IPOs
511
Number of Firms Removed Due to a Lack of 3 Years of Data
on the 1988 Daily CRSP NASDAQ File
139
Number of Additional Firms Removed Due to a Lack of 5 Years
of Data on the 1988 Daily CRSP NASDAQ File
73
Final Sample Based on a Five Year Criterion
299
119

120
Table B-2 reports the number of firms comprising each accounting
risk category (portfolio) in this 299 firm sample. The table is
consistent with the corresponding table for the 372 firm sample (i.e.,
Table 5-4) since in each case the sample is dominated by IPO firms which
(1) employ Big Eight auditors, (2) file SEC registration statement S-l,
and (3) utilize consistently income increasing accounting procedures for
inventory and depreciation.
Table B-3 reports the results of the univariate (portfolio) tests
of Hypothesis 1. Panels A, B, and C supply the results when AUDITOR,
FORM, and APCHOICE, respectively, are employed as proxies for accounting
risk. The results are qualitatively similar to those reported in Table
5-5 for the larger 372 firm sample. Specifically, the results reported
in Table B-3 strongly support the prediction of Hypothesis 1 that mean
IPO aftermarket abnormal returns will not differ based on the level of
accounting risk faced by investors, since in only one case do mean
abnormal returns differ significantly across accounting risk portfolios.
The results based on this five year data availability criterion
also generally support the prediction of Hypothesis 1 that the mean
abnormal return of each accounting risk portfolio will not differ
significantly from zero in each time horizon examined. As indicated by
Table B-3, only 9 out of 36 (i.e., 25%) portfolio mean abnormal returns
differ significantly from the hypothesized value of zero. Also, in
those instances where portfolio MARs do significantly differ from zero,
there appears to be an approximately equal chance that the return will
be greater than or less than zero. Thus, the portfolio mean abnormal

Table B-2
Number of Sample Firms
Comprising Each Accounting Risk Category
121
Accountinz Risk Proxv
Number of Firms in
High Accounting Risk
Catezorv3
Number of Finns in
Low Accounting Risk
Cateeorvb
AUDITOR
57
242
FORM
57
242
APCHOICE
216
77
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.

Table B-3
Univariate (Portfolio) Tests of Hypothesis 1
Using 299 Firm Sample
122
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MARa
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.14%
1.01%**
0.13%
Year 2
0.86%
-0.63%*
1.49%*
Year 3
-0.03%
0.06%
-0.09%
Year 4
-0.90%
-0.41%
-0.49%
Year 5
-0.62%
-0.91%**
0.29%
5 Year
Cumulative
0.45%
-0.88%
1.33%
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
2.04%*
0.80%**
1.24%
Year 2
0.21%
-0.48%
0.69%
Year 3
0.16%
0.01%
0.15%
Year 4
-0.71%
-0.46%
-0.25%
Year 5
-1.34%*
-0.74%**
-0.60%
5 Year
Cumulative
0.36%
-0.86%
1.22%

123
Table B-3--continued
Panel C
Accounting Risk Proxy Variable APCHOICE
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
0.82%**
1.03%
-0.21%
Year 2
-0.28%
-0.29%
0.01%
Year 3
0.08%
-0.16%
0.24%
Year 4
-0.50%
-0.63%
0.13%
Year 5
-1.07%**
-0.19%
-0.88%
5 Year
Cumulative
-0.95%
-0.25%
-0.70%
a MAR signifies mean abnormal return.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests

124
returns reported in Table B-3 are not systematically greater than or
less than zero, as predicted by Hypothesis 1.
In summary, the results documented in Table B-3 are consistent
with those reported earlier for the 372 firm sample based on the less
restrictive sample selection criterion. In both cases, the data lend
support to the predictions of Hypothesis 1.
Panels A, B, and C of Table B-4 report the results of the
univariate (portfolio) tests of Hypothesis 2 using the 299 firm sample
when AUDITOR, FORM, and APCHOICE, respectively, are employed as proxies
for accounting risk. The results detailed in Table B-4 are generally
consistent with those which were obtained using the 372 firm sample
(i.e., those reported in Table 5-6). However, the auditor reputation
variable is no longer significant when this five year sample selection
criterion is employed. Hypothesis 2 is still generally supported for
the FORM and APCHOICE accounting risk proxies. In particular, the mean
abnormal return variance of the high accounting risk group based on the
FORM variable is (at least marginally) significantly greater than that
of the low risk group in each time horizon except years 3 and 5. The
significance levels for years 2 and 4 are .07 and .11, respectively.
Similarly, Panel C of Table B-4 demonstrates that Hypothesis 2 is
supported for the APCHOICE proxy in the second, third, and fourth years
of public trading, and is marginally supported (.07 level) in the fifth
year.
In summary, consistent with the results described earlier for the
372 firm sample, the results reported in Table B-4 are supportive of
Hypothesis 2 for the FORM and APCHOICE variables. However, over this

Table B-4
Univariate (Portfolio) Tests of Hypothesis 2
Using 299 Firm Sample
125
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MVARa
Low Risk
Portfolio MVAR
Difference
Hizh-Low
Year 1
0.00305
0.00238
0.00067
Year 2
0.00251
0.00198
0.00053
Year 3
0.00215
0.00182
0.00033
Year 4
0.00145
0.00151
-0.00006
Year 5
0.00187
0.00164
0.00023
5 Year
Cumulative
0.00790
0.00918
-0.00128
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hizh-Low
Year 1
0.00478
0.00197
0.00281**
Year 2
0.00310
0.00184
0.00126
Year 3
0.00145
0.00199
-0.00054*
Year 4
0.00190
0.00140
0.00050
Year 5
0.00191
0.00163
0.00028
5 Year
Cumulative
0.01306
0.00796
0.00510**

126
Table B-4--continued
Panel C
Accounting Risk Proxy Variable APCHOICE
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00206
0.00285
-0.00079
Year 2
0.00222
0.00160
0.00062*
Year 3
0.00209
0.00131
0.00078**
Year 4
0.00169
0.00093
0.00076**
Year 5
0.00181
0.00131
0.00050
5 Year
Cumulative
0.00810
0.00977
-0.00167
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests

127
five year time horizon, accounting risk portfolios based on the auditor
quality proxy no longer conform to the predictions of Hypothesis 2.
Table B-5 provides details of the univariate (portfolio) tests of
Hypothesis 3. Panels A, B, and C, respectively, provide the results
obtained when AUDITOR, FORM, and APCHOICE are utilized as surrogates for
accounting risk. The empirical methodology employed tests for a
significantly decreasing linear trend over the five year time horizon
based on a repeated measures ANOVA analysis. As stated in Chapter 4,
such a test is mathematically equivalent to a matched-pair (i.e., paired
comparison) t test of the difference between a portfolio's year 1 and
year 3 mean abnormal return variance in the special case where the
repeated measure (i.e., time in this case) takes on only three values.
However, in the more general case such as this one where the repeated
measure takes on more than three possible values, the linear trend test
is no longer equivalent to a matched-pair t test. The statistical tests
reported in Table B-5 are based on the F statistics which result from a
repeated measures ANOVA test for the presence of a significant linear
trend over the five year time horizon.
Table B-5 reveals that over this five year time horizon, a
significantly decreasing linear trend is documented for five of the six
accounting risk portfolios. Further, the remaining portfolio is
marginally significant, with a P-Value of .11. These results provide
much stronger support for Hypothesis 3 than those reported in Table 5-7
for the three year time horizon. Therefore, there is some indication
that the effect predicted by Hypothesis 3 is a gradual effect which is

Table B-5
Univariate (Portfolio) Tests of Hypothesis 3 Using 299 Firm Sample
Panel A
Accounting Risk Proxy Variable = AUDITOR
Portfolio
Year 1
MVARa
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00305
0.00238
0.00251
0.00198
0.00215
0.00182
0.00145
0.00151
0.00187
0.00164
4.05*
6.16**
Accounting Risk
Panel B
Proxy Variable
= FORM
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00478
0.00197
0.00310
0.00184
0.00145
0.00199
0.00190
0.00140
0.00191
0.00163
7.01**
3.35@
Panel C
Accounting Risk Proxy Variable
= APCHOICE
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Year 4
MVAR
Year 5
MVAR
Test of
Linear Trend
F Statistic
High Risk
Low Risk
0.00206
0.00285
0.00222
0.00160
0.00209
0.00131
0.00169
0.00093
0.00181
0.00131
2.64
4.78*
MVAR signifies mean abnormal return variance.
*
* significant at .05 level @ significant at .10 level oo
significant at .01 level

129
difficult to detect over a short three year time horizon, but which
becomes evident over a longer five year period.
Table B-6 provides the results of a multiple regression test of
Hypothesis 1 using the more restrictive five year data availability
sample selection criterion. Panels A through F report the results of
separate cross-sectional regressions pertaining to the first through
fifth year and a cumulative 60 month time horizon, respectively. Since
heteroscedastic error terms are discovered in each of these six cross-
sectional regressions, only the statistical tests based on a
heteroscedasticity consistent covariance matrix are described.
Consistent with the results reported for the 372 firm sample in
Table 5-9, the results shown in Table B-6 provide empirical support for
Hypothesis 1. Specifically, the accounting risk proxy variables are
demonstrated not to be significant in the explanation of IPO aftermarket
abnormal returns in any of the first five years of public trading.
However, AUDITOR is demonstrated to be statistically associated with
cumulative 60 month abnormal returns. It is interesting to note that
over this five year time horizon, the unexpected earnings variables are
(at least marginally) significant only for years 1 through 3, and over
the cumulative 60 month time period. Thus, the unexpected earnings
variables do not conform to the results of prior research in years 4
and 5.
Table B-7 relates the results of testing Hypothesis 2 for the 299
firm sample in a multiple regression format. Panels A through F report
the results of separate cross-sectional regressions which relate to the
first through fifth year of public trading and a cumulative 60 month

130
Table B-6
Regression Approach to Testing Hypothesis 1
Using 299 Firm Sample
Panel A
Model: ARlt = a + ^AUDITORi + /32FORMi + 0SAPCHOICE1
+ y^IZEi + yjjLNRISKSi + 73UEli + et
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.064
.023
2.785**
3.731#
AUDITOR
.005
.007
0.725
0.504
FORM
.013
.008
1.610
1.970
APCHOICE
- .003
.006
-0.466
0.196
SIZE
- .005
.002
-2.380*
2.803
LNRISKS
- .007
.003
-2.301*
5.058#
UE1
.156
.031
5.107**
12.987##
Test of the Model Goodness
of Fit: F-
-Value=5.834
P-Value=.0001
White's [1980]
Homoscedasticity Test:
XZ-Value=216.
85 P-Value=.0001
Model Adjusted
R2 = .0921
N-287
Panel
B
Model: AR2t =
a + /JjAUDITORi
+ /^FORMi +
^3APCH0ICEi
+ 'YiSIZE^ + *
YLNRISKS +
73UE2i + e
Heteroscedasticity
Parameter
Standard
t
Adjusted \2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.014
.022
-0.617
0.429
AUDITOR
.012
.007
1.836
2.849
FORM
.005
.008
0.646
0.319
APCHOICE
- .001
.006
-0.207
0.061
SIZE
.001
.002
0.636
0.456
LNRISKS
.001
.003
0.466
0.236
UE2
.157
.026
6.141**
33.717##
Test of the Model Goodness of Fit: F-Value=7.528 P-Value=.0001
White's [1980] Homoscedasticity Test: x2_Value=518.55 P-Value=.0001
Model Adjusted R2 =
.1208 N=286

131
Table B-6--continued
Panel C
Model: AR3t = a
+ ^AUDITORi + ^32FORMi + ^APCHOICE*
+ 71SIZEi +
72LNRISKS + 73UE3i
+ ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT .016
.022
0.735
0.647
AUDITOR
- .001
.007
-0.108
0.011
FORM
- .003
.008
-0.363
0.128
APCHOICE
.002
.006
0.361
0.187
SIZE
-.002
.002
-0.910
1.024
LNRISKS
.000
.003
0.057
0.003
UE3
.022
.009
2.456**
1.392
Test of
the Model Goodness
of Fit: F
-Value=1.274 P-Value=.2694
White's
[1980] Homoscedasticity Test:
X2-Value=183.83
P-Value=.0001
Model Adjusted R2 .0058
N=283
Panel
D
Model: AR4 =
a + /JjAUDITORi
+ ^FORMi +
y33APCHOICEi
+ 7^SIZE^ +
72LNRISKS +
73UE4i + eL
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic Test Statistic
INTERCEPT
- .026
.020
-1.278
1.552
AUDITOR
- .003
.006
-0.423
0.187
FORM
.003
.007
0.433
0.140
APCHOICE
.003
.005
0.584
0.492
SIZE
.002
.002
1.037
0.997
LNRISKS
- .001
.003
-0.209
0.041
UE4
.005
.005
0.864
0.190
Test of the Model Goodness of Fit: F-Value=0.603 P-Value=.7282
White's [1980] Homoscedasticity Test: x2"Value=584.99 P-Value=.0001
Model Adjusted R2 =
-.0087 N=277

132
Table B-6--continued
Panel E
Model: AR^ = a + ^AUDITORi + ^FORM* + ^3APCHOICEi
+ 71SIZEi + 72LNRISKS + 73UE5 + eL
Heteroscedasticity
Parameter
Standard
t
Adjusted \Z
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.015
.022
-0.681
0.408
AUDITOR
.006
.006
1.009
0.796
FORM
-.008
.007
-1.180
1.095
APCH0ICE
-.006
.005
-1.077
1.253
SIZE
.001
.002
0.438
0.171
LNRISKS
.004
.003
1.324
1.852
UE5
-.001
.002
-0.481
0.111
Test of the Model Goodness of Fit: F-Value=0.849 P-Value=.5333
White's [1980] Homoscedasticity Test: x2-Value=1038.28 P-Value=.0001
Model Adjusted R2 = -.0038 N=242
Panel F
Model: CAROi = a + y^AUDITORi + ^FORMi + ^APCHOICE
+ y^IZEi + 72LNRISKSi + 73E60 + e
Heteroscedasticity
Parameter
Standard
t
Adjusted \Z
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.047
.047
1.001
1.091
AUDITOR
.028
.014
1.988*
4.735#
FORM
.006
.016
0.365
0.116
APCHOICE
- .004
.012
-0.330
0.098
SIZE
-.005
.005
-0.999
1.184
LNRISKS
-.004
.006
-0.750
0.651
UE60
.031
.005
6.877**
54.635##
Test of the Model Goodness of Fit:
F
-Value=8.941
P-Value=.0001
White's [1980]
Homoscedasticity Test:
xz-
-Value=1056
.41 P-Value=.0001
Model Adjusted
R2 = .1651 ]
N=242
** significant
at .01 level,
based
on
two-tailed t
tests
* significant
at .05 level,
based
on
two-tailed t
tests
## significant
at .01 level,
based
on
x2
tests
# significant
at .05 level,
based
on
X2
tests

133
Table B-7
Regression Approach to Testing Hypothesis 2
Using 299 Firm Sample
Panel A
Model: VARlt = a + ^AUDITORi + ^2FORMi + /^APCHOICEi
+ y^IZEi + yjjLNRISKSi + 73ABSUE11 + e
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0093
.0021
4.526**
6.229##
AUDITOR
.0002
.0006
0.382
0.107
FORM
.0011
.0007
1.548
1.624
APCHOICE
-.0009
.0005
-1.709*
1.287
SIZE
-.0007
.0002
-3.582**
4.901#
LNRISKS
-.0002
.0003
-0.791
0.869
ABSUE1
-.0007
.0030
-0.243
0.112
Test of the Model Goodness of Fit: F-Value=5.086 P-Value=.0001
White's [1980] Homoscedasticity Test: x2'Value=792.17 P-Value=.0001
Model Adjusted R2 = .0790 N=287
Panel B
Model: VAR2t = a + ^AUDITORi + /^FORMi + ^APCHOICEi
+ y^IZEi + yzLNRISKSi + 73ABSUE2! + ei
Variable
Parameter
Estimate
Standard
Error
t
Statistic
Heteroscedasticity
Adjusted x2
Test Statistic
INTERCEPT
.0040
.0019
2.141*
4.536#
AUDITOR
.0001
.0006
0.195
0.030
FORM
.0003
.0007
0.506
0.153
APCHOICE
.0006
.0005
1.192
2.897@
SIZE
-.0003
.0002
-1.869*
3.336@
LNRISKS
.0002
.0002
0.898
1.039
ABSUE2
.0053
.0025
2.098*
3.437@
Test of the
Model Goodness
of Fit: F-
-Value=3.641
P-Value=.0017
White's [1980] Homoscedasticity Test: x2'^alue=206.65 P-Value=.0001
Model Adjusted R2 =
.0527 N=286

134
Table B-7--continued
Panel C
Model: VAR3 = a + ^AUDITORi + ^2F0RMi + sAPCHOICEi
+ y^IZEi + 72LNRISKS + 73ABSUE3i + et
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0009
.0016
0.550
0.367
AUDITOR
.0004
.0005
0.909
0.812
FORM
-.0014
.0006
-2.398**
4.744#
APCHOICE
.0008
.0004
1.775*
6.610##
SIZE
.0000
.0002
0.014
0.000
LNRISKS
.0005
.0002
2.489**
4.233#
ABSUE3
.0007
.0007
1.053
0.618
Test of the Model Goodness of Fit: F-Value=2.776 P-Value=.0123
White's [1980] Horaoscedasticity Test: x2'Value=570.63 P-Value=.0001
Model Adjusted R2 .0364 N=283
Panel D
Model: VAR4a = a + ^AUDITORi + ^2F0RMi + ^APCHOICEi
+ 7XSIZE + 72LNRISKS + 73ABSUE4 + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0029
.0012
2.301**
6.771##
AUDITOR
-.0004
.0004
-0.924
0.653
FORM
.0001
.0005
0.185
0.017
APCHOICE
.0006
.0003
1.876*
5.552#
SIZE
-.0002
.0001
-1.809*
4.015#
LNRISKS
.0001
.0002
0.533
0.260
ABSUE4
.0009
.0003
2.452**
12.008##
Test of the
Model Goodness
of Fit: F-
-Value=3.533
P-Value=.0022
White's [1980] Homoscedasticity Test: x2_^alue=204.27 P-Value=.0001
Model Adjusted R2 =
.0522 N=277

135
Table B-7--continued
Panel E
Model: VAi^ = a + ^AUDITORi + ^92FORMi + /^APCHOICE^
+ y^IZEj. + 72LNRISKS + yjABSUESi +
Heteroscedasticity
Parameter
Standard
t
Adjusted *2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0004
.0012
0.316
0.085
AUDITOR
.0003
.0003
0.901
0.697
FORM
.0001
.0004
0.149
0.014
APCHOICE
.0002
.0003
0.840
0.780
SIZE
.0000
.0001
0.347
0.115
LNRISKS
.0002
.0001
1.495
2.480
ABSUE5
.0004
.0001
4.000**
12.341##
Test of the
Model Goodness
of Fit: F-
Value4.264
P-Value-.0004
White's [1980] Homoscedasticity Test:
X2-Value829
.74 P-Value-.0001
Model Adjusted R2 = .0752
N-242
Panel
F
Model: VAR60i = a
+ ^AUDITORi + /^FORMi +
^3APCHOICEi
+
yiSIZEi + yjjLNRISKSi + yaABSUEOi + et
Heteroscedasticity
Parameter
Standard
t
Adjusted xZ
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0254
.0061
4.173**
13.926##
AUDITOR
-.0039
.0018
-2.159*
6.326##
FORM
.0039
.0021
1.862*
2.919@
APCHOICE
-.0029
.0016
-1.850*
3.027@
SIZE
-.0019
.0006
-3.109**
8.346##
LNRISKS
-.0006
.0008
-0.776
0.703
ABSUE60
.0039
.0006
6.423**
56.817##
Test of the
Model Goodness
of Fit: F-
Value-12.338
P-Value.0001
White's [1980] Homoscedasticity Test: x2_Value=670.12 P-Value-.0001
Model Adjusted R2 = .2201 N-242
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on \Z tests
# significant at .05 level, based on \Z tests
@ significant at .10 level, based on *2 tests

136
time horizon, respectively. Since heteroscedastic error terms are
discovered in each of these six cross-sectional regressions, only the
statistical tests based on the heteroscedasticity adjusted test
statistics are discussed.
The empirical results reported in Table B-7 are not as supportive
of Hypothesis 2 as those obtained when the less restrictive three year
data availability criterion was utilized (i.e., those reported in Table
5-11). Contrary to the results of Table 5-11, the FORM accounting risk
proxy only conforms to the predictions of Hypothesis 2 over the
cumulative 60 month period. The results based on the APCHOICE variable
support Hypothesis 2 for years 2, 3, and 4. However, the variable is
significant in the direction opposite to that predicted by Hypothesis 2
over the cumulative 60 month period. Finally, consistent with Table 5-
11, the AUDITOR variable is not significant in the explanation of
abnormal return variances in any of the first five years of public
trading. However, the auditor quality variable is significant in the
direction opposite to that predicted by Hypothesis 2 over the cumulative
60 month time horizon.
In summary, the cross-sectional multiple regression tests of
Hypothesis 2 do not provide strong support for the hypothesis that
accounting risk is positively associated with abnormal return variances
when the 299 firm sample is examined. This is in contrast to the much
more supportive results documented for the 372 firm sample based on a
less restrictive sample selection criterion.
Table B-8 provides the results of testing Hypothesis 3 via a
pooled time-series cross-sectional regression model for the 299 firm
i

137
'I
Table B-8
Regression Approach to Testing Hypothesis 3
Using 299 Firm Sample
Panel A
Model: VARit a + ^AUDITOR*,. + /VAUDITORit*Ilit + P^'AUDIT0Rit*I2it
+ p^" 'AUDIT0Rit*I3it + P-l" "AUDIT0Rit*I4it
+ £2FORMit + p2' FORMit*Ilit + P2" F0RMit*I2it
+ P2' ' F0RMit*I3it + p2" "F0RMit*I4it
+ /33APCHOICEit + /33' APCHOICEit*Ilit + p3 APCH0ICEit*l2it
+ P3" 'APCH0ICEit*I3it + P3" "APCH0ICEit*I4it
+ 7lSIZEit + 7i'SIZEit*Ilit + 7i"SIZEit*I2it
+ 7i"'SIZEit*l3it + 7i" ' SIZEit*I4it
+ 72LNRISKSit + 72'LNRISKSlt*Ilit + 72" LNRISKSit*I2it
+ 72'"LNRISKSit*I3it + 72" "LNRISKSit*I4it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73" ABSUEit*I2it
+ 73"'ABSUEit*I3it + 73' ' ABSUEit*!4it fe,
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0037
.0008
4.911**
13.473##
AUDITOR
.0004
.0005
0.923
0.402
FORM
.0016
.0006
2.864**
2.213
APCHOICE
-.0006
.0004
-1.322
0.670
SIZE
-.0002
.0001
-2.322**
5.719#
LNRISKS
.0002
.0002
0.933
1.239
ABSUE
-.0003
.0024
-0.106
0.016
AUDITORS 1
-.0003
.0007
-0.473
0.122
AUDIT0R*I2
-.0001
.0007
-0.154
0.016
AUDIT0R*I3
-.0008
.0007
-1.188
0.997
AUDIT0R*I4
-.0003
.0007
-0.365
0.109
FORM*11
-.0013
.0008
-1.579
0.756
FORM*I2
-.0032
.0008
-4.068**
6.178##
FORM*I3
-.0016
.0008
-2.007*
1.526
FORM*I4
-.0019
.0008
-2.315**
2.347
APCH0ICE*I1
.0012
.0006
1.983*
2.534
APCH0ICE*I2
.0011
.0006
1.889*
2.535
APCH0ICE*I3
.0011
.0006
1.875*
2.630@
APCH0ICE*I4
.0006
.0006
1.013
0.818
SIZE*I1
-.0001
.0001
-2.089*
4.666#
SIZE*I2
-.0001
.0001
-1.350
2.035
SIZE*I3
-.0001
.0001
-1.939*
4.089#
SIZE*I4
- .0001
.0001
-1.449
2.542

138
Table B-8--continued
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
LNRISKS*I1
.0001
.0003
0.259
0.066
LNRISKS*I2
.0002
.0003
0.589
0.272
LNRISKS*I3
-.0001
.0003
-0.579
0.418
LNRISKS*I4
-.0002
.0003
-0.701
0.801
ABSUE*I1
.0056
.0032
1.733*
2.743@
ABSUE*I2
.0010
.0025
0.400
0.201
ABSUE*I3
.0011
.0024
0.441
0.278
ABSUE*I4
.0005
.0024
0.226
0.071
Test of the Model Goodness of Fit: F-Value=3.746 P-Value=.0001
White's [1980] Homoscedasticity Test: xz-Value=1252.83 P-Value=.0001
Model Adjusted R2 = .0566 N=l,375
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on xZ tests
# significant at .05 level, based on xZ tests
@ significant at .10 level, based on xZ tests

139
Table B-8--continued
Panel B
Tests of Linear Hypotheses to Determine Whether the Independent
Variables Explain More of the Variances
in Early Years Than in Later Years
Non-Hetero-
Hetero-
scedasticity
scedasticity
Linear
Difference
Adjusted F
Adjusted x2
HvDothesis
in Parameters
Test Statistic
Test Statistic
AUDITOR*I1-AUDITOR*I2=0
-.0002
0.098
0.077
AUDITOR*I1-AUDITOR*I3=0
.0005
0.518
0.444
AUDITOR*I1-AUDITOR*14=0
.0000
0.007
0.007
AUDITOR*I2-AUDITOR*I3=0
.0007
1.045
1.189
AUDITOR*12 -AUDITOR*I4=0
.0002
0.046
0.063
AUDIT0R*I3-AUDITOR*I4=0
-.0005
0.594
0.957
F0RM*I1-F0RM*I2=0
.0019
6.219##
3.386@
FORM*I1-FORM*13=0
.0003
0.205
0.114
FORM*I1-FORM*I4=0
.0006
0.624
0.427
F0RM*I2 -F0RM*I3=0
-.0016
4.018#
3.331@
F0RM*I2-FORM*I4=0
-.0013
2.629
2.945@
FORM*13 -FORM*14=0
.0003
0.116
0.133
APCH0ICE*I1-APCHOICE*I2=0
.0001
0.008
0.012
APCHOIC E*I1-APCHOIC E*13=0
.0001
0.008
0.014
APCH0ICE*I1-APCHOICE*I4=0
.0006
0.787
1.493
APCHOICE*I2-APCHOICE*I3=0
.0000
0.000
0.000
APCHOICE*I2-APCHOICE*I4=0
.0005
0.641
1.593
APCHOICE*I3-APCHOICE*I4=0
.0005
0.628
1.835
SIZE*I1-SIZE*I2=0
.0000
0.596
1.282
SIZE*I1-SIZE*I3=0
.0000
0.037
0.076
SIZE*I1-SIZE*I4=0
.0000
0.377
0.910
SIZE*I2-SIZE*I3=0
.0000
0.350
0.856
SIZE*I2-SIZE*I4=0
.0000
0.019
0.058
SIZE*I3-SIZE*I4=0
.0000
0.189
0.524
LNRISKS*I1-LNRISKS*I2=0
-.0001
0.109
0.081
LNRISKS*I1-LNRISKS*I3=0
.0002
0.699
0.788
LNRISKS*I1-LNRISKS*I4=0
.0003
0.906
1.300
LNRISKS*I2 -LNRISKS*I3=0
.0003
1.351
1.178
LNRISKS*I2-LNRISKS*I4=0
.0004
1.611
1.705
LNRISKS*I3-LNRISKS*I4=0
.0001
0.019
0.038
ABSUE*I1-ABSUE*I2=0
.0046
4.078#
2.512
ABSUE*I1-ABSUE*I3=0
.0045
4.169#
2.661@
ABSUE*I1-ABSUE*I4=0
.0051
5.424#
3.347@
ABSUE*I2-ABSUE*I3=0
-.0001
0.009
0.007
ABSUE*I2-ABSUE*I4=0
.0005
0.410
0.268
ABSUE*I3-ABSUE*I4=0
.0006
1.231
4.391#
## significant at .01 level, based on *2 or F tests
# significant at .05 level, based on x2 r F tests
@ significant at .10 level, based on \2 or F tests

140
sample. Two additional indicator variables are required when one
employs a five year rather than a three year time horizon.
Specifically, the revised regression model includes indicator variables
13 and 14, which are coded 1 for observations pertaining to years 4 and
5, respectively, and 0 otherwise. As in Equation (21), the estimated
parameters on the noninteraction terms relate to year 1 observations.
Since heteroscedastic error terms are discovered in the estimation of
this revised model, only the statistical tests based on a
heteroscedasticity consistent covariance matrix are described.
The results reported in Table B-8 are generally consistent with
those found for the 372 firm sample (i.e., those reported in Table 5-
12). Specifically, Hypothesis 3 is not supported for the AUDITOR
accounting risk variable since Table B-8 does not demonstrate any
significant differences between the estimated auditor quality parameters
of any of the five years. The significance levels of the test
statistics relating to changes in the estimated regression parameters on
the APCHOICE proxy over time are less than those reported in Table 5-12.
However, when changes between years are found to be significant, these
changes are in the direction opposite to that predicted by Hypothesis 3.
Finally, consistent with the results reported in Table 5-12, the FORM
accounting risk variable appears to lose explanatory power over the
first three years of public trading. However, Panel B of Table B-8
demonstrates that the estimated regression parameters on the FORM
variable significantly increase over the year 3 to year 5 time period.
Thus, the results pertaining to the FORM variable only support
Hypothesis 3 over the initial three year time horizon.

141
Table B-8 also allows one to assess the change in explanatory
power of the control variables over the five year time horizon. Neither
LNRISKS nor SIZE (with two exceptions) appears to significantly change
in explanatory power over the five years. By contrast, the unexpected
earnings variables first increase and then decrease in explanatory power
over the time horizon studied. Specifically, Panel A demonstrates that
the ABSUE variable explains more of year 2 abnormal return variances
than year 1 variances. However, Panel B further shows that ABSUE
possesses more explanatory power in year 2 than in either year 4 or 5,
and that the explanatory power of the variable is greater in year 4 than
in year 5.
In summary, the results of testing Hypothesis 3 in a pooled cross-
sectional time-series regression format for the 299 firm sample
generally conform to the corresponding results relating to the 372 firm
sample. Thus, the results reported in Table B-8 do not provide strong
support for the prediction of Hypothesis 3 that accounting risk should
become less important in the explanation of abnormal return variances
over time.
This appendix reports the results of testing Hypotheses 1 through
3 using a more restrictive five years of data availability sample
selection criterion. The results generally confirm those documented for
the 372 firm sample based on the less restrictive three year criterion.
However, the results differ in two important respects. First, stronger
support is documented for Hypothesis 3 in the univariate (portfolio)
tests when the five year time horizon is employed. Thus, it is
conceivable that the predictions of Hypothesis 3 may only be empirically

142
supported over a long time horizon. However, Hypothesis 3 is not
supported over the five year time horizon when a multivariate test of
the hypothesis is conducted. Second, Hypothesis 2 is not as strongly
supported when the smaller sample based on the more restrictive sample
selection criterion is employed. Therefore, it is possible that
Hypothesis 2 may only be strongly supported in a large sample.

REFERENCES
Balvers, R. J., B. McDonald, and R. E. Miller. 1988. Underpricing of new
issues and the choice of auditor as a signal of investment
banker reputation. The Accounting Review 63 (October): 605-
22.
Baron, D. P. 1982. A model of the demand for investment banking advising
and distribution services for new issues. Journal of Finance
37 (September): 955-76.
Beatty, R. P. 1989a. Auditor reputation and the pricing of initial
public offerings. The Accounting Review 64 (October): 693-
709.
. 1989b. The initial public offerings market for auditing
services. In Auditing Research Symposium 1986. edited by
R. E. Ziegler and F. L. Neumann, 1-40. Champaign, IL:
University of Illinois Office of Accounting Research.
, and J. R. Ritter. 1986. Investment banking, reputation, and the
underpricing of initial public offerings. Journal of
Financial Economics 15 (January/February): 213-32.
Beaver, W. H. 1981. Financial Reporting: An Accounting Revolution.
Englewood Cliffs, NJ: Prentice-Hall.
Bower, N. L. 1989. Firm value and the choice of offering method in
initial public offerings. Journal of Finance 44 (July): 647-
62.
Dawes, R. M. and B. Corrigan. 1974. Linear models in decision making.
Psychological Bulletin 81 (February): 95-105.
DeAngelo, L. 1981. Auditor size and audit quality. Journal of Accounting
and Economics 3 (December): 183-99.
Downes, D. and R. Heinkel. 1982. Signaling and the valuation of
unseasoned new issues. Journal of Finance 37 (March): 1-10.
Feltham, G. A., J. S. Hughes, and D. A. Simunic. 1988. Empirical
assessment of the impact of auditor quality on the valuation
of new issues. Working paper, University of British
Columbia.
143

Gale, I. and J. E. Stiglitz. 1989. The information content of initial
public offerings. Journal of Finance 44 (June): 469-77.
144
Grinblatt, M. and C. Y. Hwang. 1989. Signalling and the pricing of new
issues. Journal of Finance 44 (June): 393-420.
Hughes, P. J. 1986. Signalling by direct disclosure under asymmetric
information. Journal of Accounting and Economics 8 (June):
119-42.
Ibbotson Associates. 1989. Stocks Bonds Bills and Inflation. Chicago:
Ibbotson Associates.
Ibbotson, R. 1975. Price performance of common stock issues. Journal of
Financial Economics 2 (September): 235-72.
and J. F. Jaffe. 1975. "Hot issue" markets. Journal of Finance
30 (September): 1027-42.
, J. L. Sindelar, and J. R. Ritter. 1988. Initial public
offerings. Journal of Applied Corporate Finance 1 (Summer):
37-45.
Imhoff, E. A. and J. K. Thomas. 1989. Accounting quality. Working paper,
University of Michigan.
James, C. and P. Wier. 1989. Borrowing relationships, intermediation and
the cost of issuing public securities. Working paper,
University of Florida.
Jarrell, G. A. 1981. The economic effects of federal regulation of the
market for new security issues. Journal of Law and Economics
24 (December): 613-75.
Keppel, G. 1982. Design and Analysis. 2nd ed. Englewood Cliffs, NJ:
Prentice-Hall.
Krinsky, I. and W. Rotenberg. 1989. The valuation of initial public
offerings. Contemporary Accounting Research 5 (Spring): 501-
15.
Leland, H. and D. Pyle. 1977. Informational asymmetries, financial
structure, and financial intermediation. Journal of Finance
32 (May): 371-87.
Lintner, J. 1965. The valuation of risk assets and the selection of
risky investments in stock portfolios and capital budgets.
Review of Economics and Statistics 47 (February): 13-37.
Logue, D. 1973. On the pricing of unseasoned equity issues: 1965-1969.
Journal of Financial and Quantitative Analysis 8 (January):
91-103.

145
Maddala, G. S. 1977. Econometrics. New York: McGraw-Hill.
Manegold, J. G. 1986. Small-company initial public offerings: The impact
of SEC registration form S-18. Journal of Accounting.
Auditing & Finance 1 (Summer): 206-21.
and J. L. Arnold. 1986. An easier way to go public. Harvard
Business Review 64 (January-February): 28-30.
McDonald, J. and A. Fisher. 1972. New issue stock price behavior.
Journal of Finance 27 (March): 97-102.
Miller, R. E. and F. K. Reilly. 1987. An examination of mispricing,
returns, and uncertainty for initial public offerings.
Financial Management 16 (Summer): 33-8.
Neter, J., W. Wasserman, and M. H. Kutner. 1985. 2nd ed. Applied Linear
Statistical Models. Homewood, IL: Irwin.
Reilly, F. K. and K. Hatfield. 1969. Investor experience with new stock
issues. Financial Analysts Journal 25 (September-October):
73-80.
Ritter, J. R. 1984a. Signaling and the valuation of unseasoned new
issues: A comment. Journal of Finance 39 (September): 1231-
37.
. 1984b. The "hot issue" market of 1980. Journal of Business 57
(April): 215-40.
. 1987. The costs of going public. Journal of Financial Economics
19 (December): 269-81.
. 1989. The long run performance of initial public offerings.
Working paper, University of Illinois.
Rock, K. 1986. Why new issues are underpriced. Journal of Financial
Economics 15 (January/February): 187-212.
Ross, S. A. 1976. The arbitrage theory of capital asset pricing. Journal
of Economic Theory 13 (December): 341-60.
Schipper, K. and R. Thompson. 1983. The impact of merger-related
regulations on the shareholders of acquiring firms. Journal
of Accounting Research 21 (Spring): 184-221.
Sharpe, W. F. 1964. Capital asset prices: A theory of market equilibrium
under conditions of risk. Journal of Finance 19 (September):
425-42.

146
Simon, C. J. 1989. The effect of the 1933 securities act on investor
information and the performance of new issues. American
Economic Review 19 (June): 295-318.
Simunic, D. A. and M. Stein. 1987. Product Differentiation in Auditing:
Auditor Choice in the Market for Unseasoned New Issues.
Vancouver, BC: Canadian Certified General Accountants'
Research Foundation.
Smith, C. W. 1986. Investment banking and the capital acquisition
process. Journal of Financial Economics 15
(January/February): 3-29.
Stigler, G. 1964. Public regulation of the securities markets. Journal
of Business 37 (April): 117-42.
Tinic, S. M. 1988. Anatomy of initial public offerings of common stock.
Journal of Finance 43 (September): 789-822.
Titman, S. and B. Trueman. 1986. Information quality and the valuation
of new issues. Journal of Accounting and Economics 8 (June):
159-72.
Watts, R. L. and J. L. Zimmerman. 1986. Positive Accounting Theory.
Englewood Cliffs, NJ: Prentice-Hall.
Weiss, K. A. 1988. Initial public offerings and the cost of information.
Ph.D. dissertation, University of Florida. (
Welch, I. 1989. Seasoned offerings, imitation costs, and the
underpricing of initial public offerings. Journal of Finance
44 (June): 421-49.
White, H. 1980. A heteroskedasticity-consistent covariance matrix
estimator and a direct test of heteroskedasticity.
Econometrica 48 (May): 817-38.

BIOGRAPHICAL SKETCH
John D. Neill, III, was born in Abilene, Texas, on April 27, 1959.
He received a B.B.A. degree in accounting from Abilene Christian
University in 1980. After working for Pennzoil Company in Houston,
Texas, as a Corporate Accountant and as a Programmer Analyst from
December, 1980, until August, 1983, he returned to Abilene Christian
University and was awarded the M.S. degree in religious education in
1984. He entered the accounting Ph.D. program at the University of
Florida in August, 1985. After completion of the Ph.D. degree, he will
be employed as an Assistant Professor at Florida State University.
147

I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
' 1 \
wv' wti U
f
Bipin B. Ajinkya, Chairman
Professor of Accounting
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
E. Dan Smith
Professor of Accounting
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
^7l(A 8<
Miles B. Livingston
Associate Professor of Finance,
Insurance, and Real Estate
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
r.
David T. Brown
Assistant Professor of Finance,
Insurance, and Real Estate
This dissertation was submitted to the Graduate Faculty of the
Fisher School of Accounting in the College of Business Administration
and to the Graduate School and was accepted as partial fulfillment of
the requirements for the degree of Doctor of Philosophy.
August, 1990
Dean, Graduate School



26
Second, it is assumed that the two-factor Sharpe-Lintner CAPM
appropriately describes equilibrium security valuation.4 The model
characterizes the expected return on a security as a linear function of
the expected market return. The model (in expectation form) may be
written
E(Rit) RFt + iCECRut) RFt), (2)
where E = the expectation operator, Rit the security return on IPO
firm i over time period t, RFt = the risk-free rate for time t, R^ = the
time t return on the market portfolio, and /3t = firm i's systematic
risk.
The CAPM is often utilized in its realized form for empirical
purposes. In realized form, the model is
E-it = RFt + EFt) + eit, (3)
where eit is an error term and all other terms are as previously
defined.
Systematic risk refers to that portion of firm-specific risk which
cannot be diversified away. In other words, f)L reflects the riskiness
of security i in relation to market-wide risk, and is calculated as
Pi Cov(Rit, R^) / VarCR,,,.). (4)
According to the CAPM, the remaining portion of firm-specific risk is
labelled unsystematic risk. Unsystematic risk is calculated as the
variance of the error terms in the realized formulation of the model
4 The assumption that the CAPM accurately reflects equilibrium
security valuation is implemented in this section for expositional
purposes only. In fact, in the empirical analysis to follow, a
multifactor model is implemented in an attempt to overcome certain
deficiencies of the CAPM.


52
Table 4-2
Univariate (Portfolio) Tests of Hypothesis 2
Time
Horizon
High Risk
Portfolio
Low Risk
Portfolio15
Difference
Hizh-Low
Year 1
MVARH1C
MVARL1
MVARH1-MVARL1d
Year 2
mvarL2
MVARH2-MVARL2
Year 3
mvarH3
mvarL3
mvarH3-mvarL3
3 Year
Cumulative
MVARhc
MVARlc
MVARhc-MVARlc
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.
0 MVARpt = portfolio p's mean abnormal return variance over time horizon
t. Thus, MVARH1 = the mean abnormal return variance of the high
accounting risk portfolio over the first year of public trading, MVARH2
= the high risk portfolio's second year mean abnormal return variance,
etc.
d A two-population t test is conducted to determine whether each
difference statistic in this column differs significantly from zero.
Hypothesis 2 predicts these differences to be significantly greater than
zero. Thus, one-sided significance tests are called for in this case.


95
over four separate time horizons. Table 5-10 reports the correlations
among the independent variables utilized in the various specifications
of the model.15 The table indicates that certain of the independent
variables are correlated in a statistically significant sense. Again,
only three of the correlations (excluding correlations among the
unexpected earnings variables) exceed .50 in absolute value, and the
estimated tolerance factors do not indicate a serious multicollinearity
problem among the independent variables.
Table 5-11 reports the results of the estimation of Equation (20)
over the four different time horizons. Specifically, Panels A, B, C,
and D report the results of estimating the model over the first, second,
and third year, and over the 36 month cumulative time horizon.16 As was
the case for the regressions reported in Table 5-9, White's [1980] test
for the presence of heteroscedasticity revealed that the null hypothesis
of homoscedasticity could be rejected at the .0001 level for each of the
four specifications of the model. Therefore, Table 5-11 reports test
statistics based (1) on an inconsistent covariance matrix (i.e., the
typically utilized t statistics) and (2) on a heteroscedasticity
consistent covariance matrix (i.e., the heteroscedasticity adjusted x2
statistics based on White's [1980] correction). Unless otherwise noted,
only the statistical tests based on the heteroscedasticity adjusted test
statistics will be discussed in this section.17
15 See footnote 11.
16 See footnote 12.
17
See footnote 13.


29
where SVarCe^/ST < 0 and
3Var(eiA)/3Q < 0.
(10a)
(10b)
Hypotheses
Overview
This section demonstrates the effects of changes in accounting
risk on the distribution of investors' aftermarket abnormal returns
(i.e., long-term mispricing). First, the effect of changes in
accounting risk on mean abnormal returns is explored. Next, accounting
risk's effects on the dispersion (variance) of abnormal risk-adjusted
returns is examined. Finally, the impact over time of accounting risk
on the variance of abnormal returns is portrayed.
Accountine Risk and Mean Abnormal Returns
An increase in accounting risk (ACCTRISK) leads to an increase in
both the systematic and unsystematic components of accounting risk.
According to Equations (5) and (8), such increases in and Var(etA)
result in increases in both systematic and unsystematic risk. It is
evident from Equation (2) that such an increase in unsystematic risk has
no effect upon expected mean returns in a CAPM framework. The effects
of increases in unsystematic risk may be eliminated in a well-
diversified portfolio and thus do not affect mean expected security
returns.
In contrast, it is apparent from Equation (2) that an increase in
systematic risk increases investors' expected raw (i.e., unadjusted)


145
Maddala, G. S. 1977. Econometrics. New York: McGraw-Hill.
Manegold, J. G. 1986. Small-company initial public offerings: The impact
of SEC registration form S-18. Journal of Accounting.
Auditing & Finance 1 (Summer): 206-21.
and J. L. Arnold. 1986. An easier way to go public. Harvard
Business Review 64 (January-February): 28-30.
McDonald, J. and A. Fisher. 1972. New issue stock price behavior.
Journal of Finance 27 (March): 97-102.
Miller, R. E. and F. K. Reilly. 1987. An examination of mispricing,
returns, and uncertainty for initial public offerings.
Financial Management 16 (Summer): 33-8.
Neter, J., W. Wasserman, and M. H. Kutner. 1985. 2nd ed. Applied Linear
Statistical Models. Homewood, IL: Irwin.
Reilly, F. K. and K. Hatfield. 1969. Investor experience with new stock
issues. Financial Analysts Journal 25 (September-October):
73-80.
Ritter, J. R. 1984a. Signaling and the valuation of unseasoned new
issues: A comment. Journal of Finance 39 (September): 1231-
37.
. 1984b. The "hot issue" market of 1980. Journal of Business 57
(April): 215-40.
. 1987. The costs of going public. Journal of Financial Economics
19 (December): 269-81.
. 1989. The long run performance of initial public offerings.
Working paper, University of Illinois.
Rock, K. 1986. Why new issues are underpriced. Journal of Financial
Economics 15 (January/February): 187-212.
Ross, S. A. 1976. The arbitrage theory of capital asset pricing. Journal
of Economic Theory 13 (December): 341-60.
Schipper, K. and R. Thompson. 1983. The impact of merger-related
regulations on the shareholders of acquiring firms. Journal
of Accounting Research 21 (Spring): 184-221.
Sharpe, W. F. 1964. Capital asset prices: A theory of market equilibrium
under conditions of risk. Journal of Finance 19 (September):
425-42.


102
Table 5-12
Regression Approach to Testing Hypothesis 3
Panel A
Model: VARit a + ^AUDITOR^ + AUDITORit*Ilit + AUDIT0Rit*I2it
+ 02FORMit + P2' F0RMit*Ilit + P2" F0RMit*I2it
+ /93APCHOICEit + /93' APCHOICEit*Ilit + 03" APCH0ICEit*I2it
+ 7iSIZEit + 7i'SIZEit*Ilit + 7i"SIZEit*I2it
+ 72LNRISKSit + 72'LNRISKSit*Ilit + 72" LNRISKSit*I2it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73" ABSUEit*I2it + e,
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0039
.0010
3.974**
9.441##
AUDITOR
.0002
.0005
0.501
0.184
FORM
.0014
.0006
2.437**
2.939@
APCHOICE
-.0007
.0004
-1.506
1.306
SIZE
-.0002
.0001
-1.946*
3.520@
LNRISKS
.0002
.0002
1.044
2.120
ABSUE
.0005
.0026
0.204
0.052
AUDIT0R*I1
-.0003
.0007
-0.429
0.135
AUDIT0R*I2
.0001
.0007
0.119
0.014
FORM*11
-.0003
.0008
-0.351
0.051
FORM*I2
-.0022
.0008
-2.616**
4.155#
APCH0ICE*I1
.0012
.0006
1.934*
3.434@
APCHOICE*I2
.0014
.0006
2.141*
5.177#
SIZE*I1
-.0002
.0001
-2.656**
9.159##
SIZE*I2
-.0001
.0001
-1.384
3.268@
LNRISKS*I1
.0000
.0003
0.167
0.028
LNRISKS*I2
-.0002
.0003
-0.738
0.611
ABSUE*I1
.0095
.0033
2.866**
5.022#
ABSUE*I2
.0015
.0027
0.558
0.354
Test of the Model Goodness
of Fit: F-
-Value=6.223 P-
Value-.0001
White's [1980]
Homoscedasticity Test:
X2-Value=650.79
P-Value-.0001
Model Adjusted
R2 .0847
N1,017


82
Table 5-6
Univariate (Portfolio) Tests of Hypothesis 2
Panel A
Accounting Risk Proxy Variable = AUDITOR
Time
Horizon
High Risk
Portfolio MVARa
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00347
0.00240
0.00107
Year 2
0.00313
0.00222
0.00091*
Year 3
0.00254
0.00207
0.00047
3 Year
Cumulative
0.00642
0.00649
-0.00007
Panel B
Accounting Risk Proxy
Variable = FORM
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
Hieh-Low
Year 1
0.00501
0.00199
0.00302**
Year 2
0.00436
0.00190
0.00246**
Year 3
0.00229
0.00213
0.00016
3 Year
Cumulative
0.01013
0.00550
0.00463**
Panel C
Accounting Risk Proxy Variable = APCHOICE
Time
Horizon
High Risk
Portfolio MVAR
Low Risk
Portfolio MVAR
Difference
High-Low
Year 1
0.00212
0.00279
-0.00067
Year 2
0.00250
0.00175
0.00075*
Year 3
0.00237
0.00136
0.00101**
3 Year
Cumulative
0.00614
0.00645
-0.00031
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests


21
Stigler [1964] reports negative average market-adjusted returns
for both pre-1933 and post-1933 new security issues over each of the
first five years after issuance. Thus, Stigler concludes that the
disclosure requirements of the Act did not result in increased returns
for investors in the post-1933 era. Stigler controls for market
influences by comparing aftermarket prices to the value of a market
index. The pre-SEC sample is taken from the years 1923-1928, while
1949-1955 is utilized as the post-SEC period.
Jarrell [1981] also rejects the hypothesis that average
aftermarket performance improved significantly after passage of the Act.
He calculates abnormal returns by means of the two-factor CAPM. His
pre-SEC sample covers the years 1926-1933, while the post-SEC period
runs from 1934-1939. Jarrell's results indicate positive mean risk-
adjusted returns for both groups over a five year holding period.
Contrary to the hypothesis tested however, the cumulative returns for
the pre-SEC sample exceeded those of the post-SEC sample.
Simon [1989] also examines the aftermarket returns of pre-SEC
versus post-SEC new security issues. However, she hypothesizes that the
disclosure provisions of the 1933 Securities Act should have no effect
on the average abnormal returns earned by investors. In other words,
she postulates that rational investors were able to accurately value new
issues, on average, prior to the Act's increased disclosure requirements
since "the existence of substantial uncertainty about the true value of
a security need not imply that the issue will be, on average, overvalued
or undervalued" (Simon [1989, p. 295]). However, Simon theorizes that
the lack of quality information about the value of a firm undertaking a


76
Table 5-4
Number of Sample Firms
Comprising Each Accounting Risk Category
Number of Firms in
High Accounting Risk
Number of Firms in
Low Accounting Risk
Accounting Risk Proxy
Category8
Category*3
AUDITOR
78
294
FORM
78
294
APCHOICE
264
95
8 When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.


Table 5-7
Univariate (Portfolio) Tests of Hypothesis 3
85
Panel A
Accounting Risk Proxy Variable AUDITOR
Portfolio
Year 1
MVARa
Year 2
MVAR
Year 3
MVAR
Difference
Year1-Year3
High Risk
0.00347
0.00313
0.00254
0.00093
Low Risk
0.00240
0.00222
0.00207
0.00033
Accounting
Panel B
Risk Proxy Variable FORM
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Difference
Yearl-Year3
High Risk
0.00501
0.00436
0.00229
0.00272**
Low Risk
0.00199
0.00190
0.00213
-0.00014
Panel C
Accounting Risk Proxy Variable APCHOICE
Portfolio
Year 1
MVAR
Year 2
MVAR
Year 3
MVAR
Difference
Yearl-Year3
High Risk
0.00212
0.00250
0.00237
-0.00025
Low Risk
0.00279
0.00175
0.00136
0.00143*
a MVAR signifies mean abnormal return variance.
** significant at .01 level, based on one-tailed tests
* significant at .05 level, based on one-tailed tests


78
Table 5-5
Univariate (Portfolio) Tests of Hypothesis 1
Panel A
Accounting Risk Proxy Variable AUDITOR
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
0.94%
1.28%**
-0.34%
Year 2
0.48%
-0.57%*
1.05%
Year 3
-0.56%
-0.30%
-0.26%
3 Year
Cumulative
0.86%
0.41%
0.45%
Panel B
Accounting Risk Proxy Variable = FORM
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.53%
1.12%**
0.41%
Year 2
0.09%
-0.47%
0.56%
Year 3
-0.35%
-0.35%
0.00%
3 Year
Cumulative
1.27%
0.30%
0.97%
Panel C
Accounting Risk Proxy Variable = APCHOICE
Time
Horizon
High Risk
Portfolio MAR
Low Risk
Portfolio MAR
Difference
Hieh-Low
Year 1
1.02%**
1.12%*
-0.10%
Year 2
-0.26%
-0.08%
-0.18%
Year 3
-0.48%
-0.40%
-0.08%
3 Year
Cumulative
0.29%
0.64%
-0.35%
MAR signifies
mean abnormal return.
** significant
* significant
at .01 level, based on
at .05 level, based on
two-tailed tests
two-tailed tests


87
nature and the results may not hold in a multivariate setting. In other
words, the results attributed to accounting risk in this section may
actually be the result of another factor (e.g., IPO firm size or
business riskiness) not explicitly controlled for in these univariate
analyses. The next section describes the results of multivariate
examinations of the three hypotheses.
Results of Multivariate Tests
Results of Multivariate Tests of Hypothesis 1
The multivariate tests of Hypothesis 1 consist of estimating the
cross-sectional multiple regression model described by Equation (18)
over four separate time horizons. Table 5-8 provides evidence of the
correlations among the independent variables utilized in the various
specifications of the model.11 The table indicates that a number of the
independent variables are correlated in a statistically significant
sense. However, only three of the correlations (excluding correlations
among the unexpected earnings variables) exceed .50 in absolute value,
and various model specification checks (to be described in more detail
later) indicated no serious multicollinearity problems among the
independent variables.
Table 5-9 provides details of the estimation of Equation (18) over
the four different time horizons. Specifically, Panels A, B, C, and D
report the results of estimating the model over the first year, second
11 It should be noted that the correlations among the four measures
of unexpected earnings are not of concern since only one of these
variables is utilized in each of the four estimated models.


3
is the widely documented short-term "underpricing" phenomenon.5
Underpricing refers to the empirical finding that, on average, the
offering price set by the underwriter6 (hereafter referred to as the IPO
price) is below the consensus price set by market participants shortly
after the securities begin trading publicly. On average, this
underpricing exceeds 15% (see Smith [1986]).7 Short-term underpricing
is typically calculated as the return that an investor could have earned
by purchasing the IPO from the underwriter and selling at the market
clearing price at the end of the first day of public trading.8 Thus,
the short-term mispricing research assesses the accuracy of
underwriters' pricing decisions by comparing an underwriter's IPO price
to the price which prevails at the close of the first day of public
trading (hereafter referred to as the first aftermarket price).
5 Short-term underpricing has been documented by Reilly and
Hatfield [1969], McDonald and Fisher [1972], Logue [1973], Ibbotson
[1975], Ibbotson and Jaffe [1975], Ritter [1984b, 1987], Beatty and
Ritter [1986], Miller and Reilly [1987], Tinic [1988], Weiss [1988], and
others. Reviews of these empirical findings are provided by Smith
[1986] and Ibbotson et al. [1988].
6 More precisely, the initial offering price is set by the
underwriter after consultation with the entrepreneur of the IPO firm.
Thus, the issuing firm may play a significant role in the setting of the
IPO price. Alternatively, the issuing firm may completely delegate the
pricing decision to the underwriter (as in Baron's [1982] model).
7 Proposed explanations of the short-term underpricing phenomenon
will be detailed in Chapter 2.
8 However, underpricing has also been calculated as the return over
the first week (e.g., Tinic [1988]) or month (e.g., Ibbotson [1975]) of
public trading. Such departures from the usual underpricing calculation
are typically due to the unavailability of the market price at the end
of the first trading day.


112
incentives which influence the selection of accounting procedures most
likely differ between the two types of firms. Thus, the development of
an explanatory model of the economic incentives of IPO managers in the
selection of accounting procedures appears to represent a fruitful area
for future research.
Finally, future research could extend this study by providing
cross-sectional explanations of long-term IPO mispricing based on firms'
level of business risk. This is in contrast to the current study which
attempts to explain long-term mispricing from an accounting risk
perspective.


39
procedure and the utilization of any of a number of accelerated
depreciation methods (e.g., double-declining balance or sum-of-the-
years'-digits). Liberal (i.e., income increasing) choices are defined
as any inventory valuation method other than LIFO (e.g., first-in,
first-out (FIFO), weighted average, or specific identification)5 and the
straight-line depreciation method.
This accounting procedure choice proxy variable is measured
dichotomously. The current paragraph provides an overview of the
classification scheme utilized in the grouping of sample IPO firms into
high and low accounting risk portfolios based on accounting procedure
choices, while the following paragraph details the specifics of the
classification scheme. An IPO firm is considered to possess high
accounting risk if each of its available accounting method choices is
income increasing (i.e., "liberal"). Conversely, an IPO is considered
to be a low accounting risk firm if at least one of the two relevant
accounting procedure choices is income decreasing.
This accounting procedure choice variable was originally measured
over three levels. Specifically, firms were classified into accounting
risk portfolios based on whether their inventory and depreciation
choices were (1) consistently income increasing, (2) consistently income
decreasing, or (3) a combination of income increasing and income
decreasing methods. However, in the sample analyzed in this study (see
Chapter 5), only 10 out of the 372 sample firms used income decreasing
5 Consistent with current accounting practices, in the sample of
IPOs examined in this study (see Chapter 5), the vast majority of the
non-LIFO inventory choices are FIFO choices.


69
detail on 2,567 initial public offerings which occurred between 1975 and
1984. Each of the 372 sample initial offerings is contained in Ritter's
IPO database. This database was utilized to collect information
concerning the number of risk factors listed in an IPO's prospectus, the
date of the initial offering, the IPO price, the first available closing
bid price following the IPO, the number of shares sold in the offering,
and the year that the firm was first organized. Thus, this data allowed
the calculation of the LNRISKS variable, the amount of short-term
underpricing, the gross proceeds of the offering, and the age of the
firm at the time of the IPO. Descriptive statistics on these and other
variables will be provided in the following section.
The required security returns data were collected from the CRSP
NASDAQ file. Specifically, the NASDAQ daily returns file was utilized
in order to gather raw returns for each sample firm, the returns on the
NASDAQ Composite Index, and the returns required in the calculation of
the industry return variable. Finally, the information required to
calculate the unexpected earnings variables was gathered from the
COMPUSTAT Full-Coverage file in conjunction with Standard and Poor's
Corporation Records.
Descriptive Statistics
Sample Firm and Offering Characteristics
Table 5-2 provides descriptive statistics relating to certain
characteristics of the 372 sample firms and their respective initial
offerings. Panel A provides information about the sample as a whole,


35
Big Eight or non-Big Eight auditing firm.1 DeAngelo [1981] models audit
quality as a positive function of auditing firm size. She argues that
this relationship results from the greater level of independence which
is employed by large audit firms in an effort to maintain their
reputation capital. In an IPO context, this presumed higher quality of
large audit firms might also be due to larger firms' greater expertise
in industry-related accounting matters, greater experience in auditing
developmental stage companies, stricter internal control requirements,
etc. If audit quality is indeed positively associated with audit firm
size, accounting risk should be inversely related to auditor size. In
other words, the preceding arguments imply that Big Eight audited IPO
firms should possess less accounting risk than IPOs audited by non-Big
Eight firms.
However, a number of arguments against this presumed positive
association between audit quality and audit firm size can be raised.
First, DeAngelo's [1981] analytical model which predicts this
relationship has not been empirically verified. A second argument
against DeAngelo's hypothesized relationship is that individual audit
1 Non-Big Eight firms consist of both "national" (i.e., the mid
sized firms with offices throughout the country) and local/regional
auditing firms. Thus, it is possible to classify auditing firms into
three categories, rather than the traditional Big Eight, non-Big Eight
dichotomy. For example, Simunic and Stein [1986] classify auditing
firms into three categories (i.e., Big Eight, "national," and
local/regional) and then discard the middle group in order to increase
the power of the test of auditor reputation on IPO valuation. Due to
the small number of non-Big Eight audited IPO firms in the sample
analyzed in this dissertation (see Chapter 5), a further breakdown of
this group into "national" and local/regional categories does not appear
to be a viable alternative in this study. Therefore, the traditional
Big Eight, non-Big Eight dichotomy is employed.


Table A-l
Short-Term Underpricing Results
116
UPi = a + ^AUDITORi + ^FORl^ + ^3APCHOICEi
+ 71SIZEi + 72LNRISKSi + ej^
Heteroscedasticity
Parameter
S tandard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.472
.103
4.599**
9.499##
AUDITOR
.001
.030
0.049
0.002
FORM
- .053
.036
-1.486
1.108
AP CHOICE
.041
.027
1.523
3.622@
SIZE
- .042
.010
-4.170**
7.370##
LNRISKS
- .011
.013
-0.802
0.771
Test of the Model Goodness of Fit: F-Value=4.829 P-Value=.0003
White's [1980] Homoscedasticity Test: x2Value=105.88 P-Value=.0001
Model Adjusted R2 = .0516 N=353
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on x2 tests
# significant at .05 level, based on x2 tests
@ significant at .10 level, based on x2 tests


141
Table B-8 also allows one to assess the change in explanatory
power of the control variables over the five year time horizon. Neither
LNRISKS nor SIZE (with two exceptions) appears to significantly change
in explanatory power over the five years. By contrast, the unexpected
earnings variables first increase and then decrease in explanatory power
over the time horizon studied. Specifically, Panel A demonstrates that
the ABSUE variable explains more of year 2 abnormal return variances
than year 1 variances. However, Panel B further shows that ABSUE
possesses more explanatory power in year 2 than in either year 4 or 5,
and that the explanatory power of the variable is greater in year 4 than
in year 5.
In summary, the results of testing Hypothesis 3 in a pooled cross-
sectional time-series regression format for the 299 firm sample
generally conform to the corresponding results relating to the 372 firm
sample. Thus, the results reported in Table B-8 do not provide strong
support for the prediction of Hypothesis 3 that accounting risk should
become less important in the explanation of abnormal return variances
over time.
This appendix reports the results of testing Hypotheses 1 through
3 using a more restrictive five years of data availability sample
selection criterion. The results generally confirm those documented for
the 372 firm sample based on the less restrictive three year criterion.
However, the results differ in two important respects. First, stronger
support is documented for Hypothesis 3 in the univariate (portfolio)
tests when the five year time horizon is employed. Thus, it is
conceivable that the predictions of Hypothesis 3 may only be empirically


5
accounting numbers on the long-term accuracy of IPO valuations has not
been examined.
Second, the aftermarket performance of initial offerings may
represent a more appropriate measure of the results of the typical
investor's experience with new issues than short-term assessments of
mispricing. Specifically, it is widely contended that investors may
only receive a small percentage (if any) of their desired number of
shares of an initial offering due to quantity rationing of typically
oversubscribed issues.11 If quantity rationing is indeed prevalent in
the IPO market, then the typical investor's experience with IPOs may not
be represented by the short-term underpricing of the IPO price since
many investors are unable to purchase shares at that price.
The third and most important reason for the long-term mispricing
perspective is that accounting numbers potentially play a greater role
in the valuation decisions of market participants than in underwriters'
pricing decisions. Specifically, in modern finance theory equilibrium
security prices such as an IPO's first aftermarket price equal the
present value of the firm's expected future cash flows. Therefore, if
accounting numbers are useful in estimating such cash flows, then
accounting numbers may be beneficial to market participants in their
assessment of the first aftermarket price. By contrast, prior IPO
research has proposed that the underpricing of the IPO price by
underwriters is due to a variety of nonaccounting factors. In
11 See Rock [1986] and Beatty and Ritter [1986] for analytical
explanations of short-term underpricing based on the quantity rationing
of IPOs.


I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
I certify that I
conforms to acceptable
adequate, in scope and
Doctor of Philosophy.
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
' 1 \
wv' wti U
f
Bipin B. Ajinkya, Chairman
Professor of Accounting
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
E. Dan Smith
Professor of Accounting
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
^7l(A 8<
Miles B. Livingston
Associate Professor of Finance,
Insurance, and Real Estate
have read this study and that in my opinion it
standards of scholarly presentation and is fully
quality, as a dissertation for the degree of
r.
David T. Brown
Assistant Professor of Finance,
Insurance, and Real Estate
This dissertation was submitted to the Graduate Faculty of the
Fisher School of Accounting in the College of Business Administration
and to the Graduate School and was accepted as partial fulfillment of
the requirements for the degree of Doctor of Philosophy.
August, 1990
Dean, Graduate School


71
while Panel B provides descriptive statistics by year of initial
offering.
Panel A demonstrates that the average offering gross proceeds
(i.e., PROCEEDS) raised by the 372 sample firms equals $15.62 million.
This very closely approximates the sample firms' mean book value of
total assets (i.e., BKASSETS) prior to the offering. Thus, on average,
the sample firms raised an amount approximately equal to their currently
prevailing book value of assets via an IPO. Panel A also reveals that
the mean (median) number of risk factors (i.e., RISKS) enumerated in the
offering prospectus is approximately equal to 5 (0).5
One other variable reported in Panel A of Table 5-2 merits
mention. The average age of the sample firms prior to the IPO is
slightly less than 11 years. Thus, it is apparent that the sample is
not made up entirely of start-up firms (i.e., firms with less than two
years of operations).
Panel B reports the number of offers, the mean gross proceeds, and
the mean book value of assets (prior to the IPO) by sample year. It is
apparent that the number of offers is not spread evenly across the
sample years. In fact, the sample is dominated by firms undertaking
IPOs in 1981 and 1983. This is consistent with evidence provided in
5 Panel A also indicates that the logarithmic transformations of
BKASSETS and RISKS which are employed as business risk control variables
(i.e., SIZE and LNRISKS, respectively) in the cross-sectional
regressions reported later have greatly reduced standard deviations in
comparison to the original variables. However, even though these
logarithmic transformations are utilized in the regression models rather
than the original variables, the error terms of the various regressions
are heteroscedastic. Later sections will provide (1) details of the
severity of the heteroscedasticity and (2) the means utilized in
correcting the problems caused by heteroscedastic error terms.


Table 5-10
Pearson Correlations Among the Independent Variables
Employed in
Testing Hypothesis
2 Via the
Regression
Approach
FORM
APCHOICE
SIZE
LNRISKS
ABSUE1
ABSUE2
ABSUE3
ABSUE36
AUDITOR
. 24**
.04
-.23**
. 22**
- .03
.09
.02
-.03
(372)a
(359)
(372)
(366)
(371)
(366)
(337)
(337)
FORM
.03
-.50**
. 51**
-.03
.06
.07
-.03
(359)
(372)
(366)
(371)
(366)
(337)
(337)
APCHOICE
- .11*
.11*
-.09
.01
.08
-.09
(359)
(353)
(358)
(353)
(324)
(324)
SIZE
-.66**
.00
-.09
-.03
.01
(366)
(371)
(366)
(337)
(337)
LNRISKS
. 13**
.10
.12*
.01
(365)
(360)
(331)
(331)
ABSUE1
-.03
-.02
. 99**
(365)
(337)
(337)
ABSUE2
.45**
-.02
(337)
(337)
ABSUE3
.01
(337)
a The numbers in parentheses represent the number of firms utilized in the determination of the correlation
coefficient.
** significant at .01 level, based on two-tailed tests
* significant at .05 level, based on two-tailed tests
VO
O'


132
Table B-6--continued
Panel E
Model: AR^ = a + ^AUDITORi + ^FORM* + ^3APCHOICEi
+ 71SIZEi + 72LNRISKS + 73UE5 + eL
Heteroscedasticity
Parameter
Standard
t
Adjusted \Z
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.015
.022
-0.681
0.408
AUDITOR
.006
.006
1.009
0.796
FORM
-.008
.007
-1.180
1.095
APCH0ICE
-.006
.005
-1.077
1.253
SIZE
.001
.002
0.438
0.171
LNRISKS
.004
.003
1.324
1.852
UE5
-.001
.002
-0.481
0.111
Test of the Model Goodness of Fit: F-Value=0.849 P-Value=.5333
White's [1980] Homoscedasticity Test: x2-Value=1038.28 P-Value=.0001
Model Adjusted R2 = -.0038 N=242
Panel F
Model: CAROi = a + y^AUDITORi + ^FORMi + ^APCHOICE
+ y^IZEi + 72LNRISKSi + 73E60 + e
Heteroscedasticity
Parameter
Standard
t
Adjusted \Z
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.047
.047
1.001
1.091
AUDITOR
.028
.014
1.988*
4.735#
FORM
.006
.016
0.365
0.116
APCHOICE
- .004
.012
-0.330
0.098
SIZE
-.005
.005
-0.999
1.184
LNRISKS
-.004
.006
-0.750
0.651
UE60
.031
.005
6.877**
54.635##
Test of the Model Goodness of Fit:
F
-Value=8.941
P-Value=.0001
White's [1980]
Homoscedasticity Test:
xz-
-Value=1056
.41 P-Value=.0001
Model Adjusted
R2 = .1651 ]
N=242
** significant
at .01 level,
based
on
two-tailed t
tests
* significant
at .05 level,
based
on
two-tailed t
tests
## significant
at .01 level,
based
on
x2
tests
# significant
at .05 level,
based
on
X2
tests


22
new security issue which characterized the pre-SEC period increased the
level of riskiness associated with the purchase of the security. Thus,
she hypothesizes that the variance of aftermarket abnormal returns
decreased after the passage of the 1933 Securities Act.
Simon utilizes a multifactor asset pricing model in the
determination of risk-adjusted abnormal returns. In addition to the
return on the market, the model includes the industry return, the
unexpected variance of the market return, and a business cycle indicator
as explanators of equilibrium security returns. She classifies new
issues from 1926-1933 as pre-SEC issues, while 1934-1940 offerings are
deemed post-SEC issues. Contrary to expectations, Simon documents
significantly negative average abnormal returns for non-NYSE initial
public offerings in the pre-SEC period. Thus, it appears that investors
overvalued, on average, IPOs of non-NYSE firms prior to the passage of
the 1933 Securities Act. However, results consistent with the
hypothesis that average abnormal returns would not differ significantly
from zero are documented for NYSE issues and seasoned non-NYSE issues in
both the pre- and post-SEC periods. Further, in contrast to Stigler
[1964] and Jarrell [1981], Simon demonstrates a statistically
significant increase in average aftermarket abnormal returns for non-
NYSE initial public offerings from the pre-SEC to post-SEC periods. No
such increase was present for either NYSE issues or non-NYSE issues of
currently publicly traded corporations. Finally, the hypothesis that
the variance of aftermarket returns significantly decreased following
the disclosure requirements of the Act is empirically supported for each
portfolio of new security offerings.


48
of the remaining portfolios. This procedure will now be described in
detail for the case of the auditor quality accounting risk surrogate.15
Two portfolios of IPO firms are constructed based on the
reputation (quality) of the auditor utilized in the IPO process.
Specifically, portfolios are devised according to whether an IPO firm
was audited by a Big Eight (i.e., low accounting risk) or non-Big Eight
(i.e., high accounting risk) auditing firm.
The mean abnormal return for a given accounting risk portfolio
over a specific time horizon is calculated as
MAR^t = S ARit / N, (15)
i*l
where MARpt = the mean abnormal return of portfolio p over time period
t, ARit firm i's abnormal return over time period t as generated from
Equation (12),16 and N = the number of firms in portfolio p. Portfolio
mean returns are calculated for the first, second, and third year of
public trading, as well as a three year cumulative return.
Two sets of statistical tests are conducted on these mean
portfolio abnormal returns to test Hypothesis 1. First, each MARpfc is
tested via a t test to determine whether it differs significantly from
the hypothesized value of zero. Second, a two-population t test is then
conducted to see if MARpt differs across the auditor quality based
15 A similar analysis is also conducted for the other two
accounting risk surrogates.
16 For example, ARU equals the estimated 0U parameter from
Equation (12). Similarly, ARi2 = 02i and ARi3 03i. Also, the abnormal
return covering the cumulative 36 month period is the CAR3 variable
defined in Equation (14).


60
of trading in "event time"). In the above example, N2iX equals the
seven month period from December 31, 1980 to August 1, 1981.
Therefore, Nlu/12 (i.e., 5/12 in the example) of the
traditionally calculated random walk based "unexpected earnings"
accruing from the FYE preceding the IPO to the first FYE subsequent to
the IPO (i.e., from December 31, 1979 to December 31, 1980) is included
in the calculation of UEl^ Further, N2U/12 (i.e., 7/12 in the
example) of the traditionally calculated "unexpected earnings" based on
the random walk model accruing from the first FYE following the IPO to
the second FYE subsequent to the IPO (i.e., from December 31, 1980 to
December 31, 1981) is included in the calculation of UEl^ Thus, the
Nl/12 and N2/12 factors serve to transform the FYE based earnings series
into a series compatible with the "event time" returns series.
Therefore, as is customary in a random walk annual earnings expectation
model, UEli is calculated based on 12 months of earnings. However, this
12 months of earnings represents a weighted average of the "unexpected
earnings" pertaining to two fiscal years (i.e., the fiscal years ended
December 31, 1980 and December 31, 1981 in the above example).24
Consistent with the widely documented result that unexpected
accounting earnings and abnormal security returns are positively
associated, the parameter estimate on UE(t)i is predicted to be positive
in each of the four cross-sectional regressions.
24 The previously described method of estimating unexpected
earnings is utilized to determine unexpected earnings over the first,
second, and third year of public trading. However, unexpected earnings
corresponding to the cumulative three year holding period is calculated
as the sum of the undeflated unexpected earnings calculated for each of
the first three years deflated by the security price at the end of year
three.


84
more information is learned about these newly public firms. Table 5-7
reveals the results of univariate (portfolio) tests of this hypothesis.
Panels A, B, and C, respectively, provide the results obtained when
AUDITOR, FORM, and APCHOICE are used to proxy for accounting risk.
Panel A discloses that, consistent with the prediction of
Hypothesis 3, the mean abnormal return variance of the high accounting
risk firms (based on auditor quality) in year 1 exceeds that of the same
firms' MVAR in year 3. However, this difference is not statistically
significant. The same relationship holds true for the low accounting
risk firms. Thus, Hypothesis 3 is not supported for auditor quality
based accounting risk portfolios.
By contrast, Panel B reports a statistically significant decrease
in the high accounting risk (based on the FORM proxy) portfolio's MVAR
from year 1 to year 3. Thus, a significant decreasing linear trend over
the three year time horizon is documented for the high accounting risk
portfolio's MVAR, consistent with the prediction of Hypothesis 3.
However, an insignificant difference between the low accounting risk
portfolio's year 1 and year 3 MVAR is documented. Thus, Hypothesis 3 is
supported only for the high accounting risk firms when the SEC
registration statement type proxy variable is employed.
Finally, Panel C demonstrates empirical support for Hypothesis 3
only for the low accounting risk (based on APCHOICE) firms. The high
accounting risk portfolio's MVAR is demonstrated not to decrease over
the year 1 to year 3 time period. Thus, inconsistent results are found
when the accounting procedure choice proxy variable is used to test
Hypothesis 3.


129
difficult to detect over a short three year time horizon, but which
becomes evident over a longer five year period.
Table B-6 provides the results of a multiple regression test of
Hypothesis 1 using the more restrictive five year data availability
sample selection criterion. Panels A through F report the results of
separate cross-sectional regressions pertaining to the first through
fifth year and a cumulative 60 month time horizon, respectively. Since
heteroscedastic error terms are discovered in each of these six cross-
sectional regressions, only the statistical tests based on a
heteroscedasticity consistent covariance matrix are described.
Consistent with the results reported for the 372 firm sample in
Table 5-9, the results shown in Table B-6 provide empirical support for
Hypothesis 1. Specifically, the accounting risk proxy variables are
demonstrated not to be significant in the explanation of IPO aftermarket
abnormal returns in any of the first five years of public trading.
However, AUDITOR is demonstrated to be statistically associated with
cumulative 60 month abnormal returns. It is interesting to note that
over this five year time horizon, the unexpected earnings variables are
(at least marginally) significant only for years 1 through 3, and over
the cumulative 60 month time period. Thus, the unexpected earnings
variables do not conform to the results of prior research in years 4
and 5.
Table B-7 relates the results of testing Hypothesis 2 for the 299
firm sample in a multiple regression format. Panels A through F report
the results of separate cross-sectional regressions which relate to the
first through fifth year of public trading and a cumulative 60 month


94
Panel C reports the results of estimating Equation (18) over the
third year subsequent to initial offering. The overall model is
insignificant in this case. Thus, the hypothesis that each of the
estimated parameters equals zero cannot be rejected (P-Value=.7759).
However, the results reported in this panel provide support for
Hypothesis 1, since each of the three accounting risk surrogates is
insignificant. Over this third year time horizon, a positive
association between unexpected earnings and abnormal returns is
supported via a one-tailed t test at the .11 level. However, such a
positive relationship is not supported by the xZ test.
Finally, Panel D relates the results of estimating Equation (18)
over the cumulative 36 month time horizon. Once again, the accounting
risk proxy variables are insignificant. In fact, only the unexpected
earnings variable is a significant explanator of CAR36.
In summary, the results reported in Table 5-9 strongly support the
prediction of Hypothesis 1 concerning the insignificant relationship
between accounting risk and mean abnormal returns. Therefore,
Hypothesis 1 is supported in this multivariate setting, as well as in
the univariate settings previously described. Further, Table 5-9 is
consistent with prior research since the widely documented positive
association between unexpected accounting earnings and abnormal returns
is documented in three out of the four time horizons.
Mutlivariate Tests of Hypothesis 2
The multivariate tests of Hypothesis 2 consist of estimating the
cross-sectional multiple regression model described in Equation (20)


45
market returns are employed in the specification of equilibrium security
returns.
Industry returns are included in Equation (12) to control for
abnormal return performance arising from business risk changes over
time. Assuming firms in the same industry face similar production,
investment, and financing environments, then INDUSTRYit represents an
appropriate proxy for business risk and Si reflects return performance
attributable to changing business risk conditions. Thus, Equation (12)
abstracts from both overall market performance and unexpected industry
performance (i.e., business risk) in the generation of firm-specific
abnormal returns (i.e., the 9^ parameters). Since the effects of both
overall market risk and firm-specific business risk are taken away, it
is assumed that the abnormal returns captured by the 8^ terms are
attributable to accounting risk.
The second reason why the traditional CAPM or market model
formulation is not utilized in this study is due to the institutional
features of the IPO market. In traditional capital markets "event"
studies, the CAPM or market model is estimated over a period of time
prior to the "event" in question. Then the estimated parameters are
utilized in the determination of abnormal returns during the event
period. Such an approach is infeasible for IPOs since return data are
unavailable prior to the event in question (i.e., the initial offer).
Thus, the relevant parameters must be estimated during the event period.
The time period specific dummy variables included in Equation (12) allow
the researcher to pick up abnormal returns over specified periods during


49
accounting risk portfolios over each of the four relevant time
horizons.17 Table 4-1 illustrates these tests.
Univariate Tests of Hypothesis 2
Hypothesis 2 states that the dispersion (i.e., variance) of IPO
firms' aftermarket abnormal returns is positively associated with
accounting risk. In other words, the precision of market participants'
pricing decisions is hypothesized to decrease with accounting risk.
Univariate tests of Hypothesis 2 utilize the accounting risk
portfolios constructed for testing Hypothesis 1. However, a portfolio's
mean abnormal return variance over various holding periods is now the
statistic of interest, rather than the cross-sectional mean abnormal
return employed in the empirical testing of Hypothesis 1.
An individual IPO firm's variance of period t abnormal returns is
defined as
VARit = ARit2, (16)
where ARit is the abnormal return over time period t as provided by
Equation (12). Then, an accounting risk portfolio's mean abnormal
return variance over a specific time period is defined as
17 A two-population t test assumes that the data being tested comes
from two independent populations. This assumption appears reasonable in
this case since there is no a priori reason to hypothesize that high
accounting risk and low accounting risk portfolios are not independent.
Each of these t tests is the mathematical equivalent of a one-way
analysis of variance (ANOVA) since each accounting risk proxy only
possesses two levels (i.e., high and low). Even though the statistical
tests are equivalent, Neter et al. state that "the t test generally is
to be preferred since it can be used to conduct both two-sided and one
sided tests" (Neter et al. [1985, p. 544]). By contrast, the F test
employed in a one-way ANOVA "can only be used for two-sided tests"
(Neter et al. [1985, p. 544]). Since many of this study's hypotheses
are one-directional, the t test procedure is employed in this research.


44
appropriate measure of the "market" return than either the CRSP Value-
Weighted or CRSP Equally-Weighted Indices (both of which consist of New
York and American Stock Exchange firms) which are often utilized in
capital markets research.
The INDUSTRYit terms present in Equation (12) represent the
average equally-weighted return of all firms on the NASDAQ daily returns
file possessing the same 2-digit SIC code as IPO firm i. Again, for
each sample IPO firm, this INDUSTRYit series is calculated over the same
"event time" months as the Rit series.
The RFt terms in Equation (12) represent the prevailing "risk-free
rate" at time t. The proxy employed for the unobservable true "risk
free rate" is the return on United States Treasury Bills. The monthly
total return series on U.S. Treasury Bills from January 1980 through
December 1988 was collected from Ibbotson Associates [1989] Then this
monthly risk-free rate series was aligned with the Rit series in order
to create the RFt series for each IPO firm.
Equation (12) is a generalization of the two-factor CAPM utilized
in the derivation of Hypotheses 1 through 3 in Chapter 3. There are two
principal reasons the multifactor specification of security pricing
found in Equation (12) is employed in the empirical testing rather than
the traditionally utilized two-factor CAPM or single-factor market
model. First, prior research suggests that factors other than the
return on the market portfolio are needed to explain equilibrium
security returns.11 In this study, industry returns as well as overall
11 For example, see Ross [1976] for a theoretical development of
the multifactor Arbitrage Pricing Theory (APT).


83
versus low accounting risk portfolios. Specifically, the MVAR of the
high risk firms exceeds that of the low risk firms in each year, and
this difference is statistically significant in all but the third year.
Thus, Hypothesis 2 is strongly supported when FORM is used as a
surrogate for accounting risk.
Panel C provides evidence that supports Hypothesis 2 for only two
of the four time horizons when APCHOICE is employed to proxy for
accounting riskiness. Specifically, the high risk group's mean abnormal
return variance exceeds that of the low risk group at conventional
significance levels for years 2 and 3. In contrast, the MVAR difference
over year 1 and over the 36 month cumulative period is insignificantly
different from zero. Thus, Hypothesis 2 can be supported for only years
2 and 3 when APCHOICE is used to proxy for accounting risk.
In summary, the univariate tests of Hypothesis 2 reveal that the
hypothesis can be supported very strongly for the FORM proxy, and can be
marginally supported for the other two proxy variables. Therefore, the
results generally support the prediction of Hypothesis 2 that accounting
risk and the variance of IPO firms' aftermarket abnormal returns are
positively correlated. Hence, it appears that accounting risk
influences the absolute magnitude of long-term IPO mispricing, as
predicted by Hypothesis 2.
Results of Univariate Tests of Hypothesis 3
Hypothesis 3 states that the variance of IPO firms' abnormal
returns will decrease over time subsequent to the IPO. This reduction
in variance is attributed to a reduction in accounting risk over time as


56
where AR(t)i = firm i's abnormal return over holding period t as
generated from Equation (12) (i.e., 9ti) AUDITOR = the quality of
auditor utilized in the IPO process, FORMj^ = the registration statement
type filed by IPO firm i, APCHOICEj^ the level of conservatism
surrounding firm i's accounting procedure choices for inventory and
depreciation, SIZEi = firm size at the initial offering, LNRISKSi the
natural logarithm of one plus the number of risk factors listed in firm
i's prospectus, UE(t)i = firm i's unexpected earnings for period t, and
e is an error term. The coefficients a, to /S3, and to y3 are
regression parameters.
In Equation (18), AUDITOR^, FORMi, and APCHOICEj^ are empirical
proxies for accounting risk, while SIZE^ LNRISKS^ and UE(t)i are
included as control variables. The control variables are designed to
control for potential differences among firms' abnormal returns which
are unrelated to differences in accounting risk. Thus, Equation (18)
allows one to test the impact of accounting risk on long-term
mispricing, while controlling for potential confounding influences.
SIZEj^ and LNRISKSi are included in the regression models to
control for business risk (i.e., uncertainty related to a firm's
production, investment, and financing activities). It is important to
control for firm size since it has been empirically documented21 that
larger IPO firms are less speculative than small initial offering firms.
The number of risk factors supplied by an IPO firm in its prospectus may
also proxy for the inherent cash flow riskiness of the firm. The SEC
21
See Beatty [1989b].


65
coefficient is significantly greater than the corresponding year 3
coefficient.28
Summary of the Empirical Methodology
This chapter describes both univariate and multivariate approaches
employed in the empirical testing of Hypotheses 1 through 3. The
univariate tests allow a straightforward assessment of the three
hypotheses. The univariate (portfolio) approach is particularly useful
in the examination of Hypothesis 1 since it permits a direct
determination of whether mean abnormal returns differ significantly from
zero for (and across) portfolios of IPOs which differ according to
accounting risk. However, the portfolio approach suffers from the
limitation that it is univariate in nature. In other words, the
portfolio approach is limited in that the analysis must be conducted
separately for portfolios based on each of the three accounting risk
proxies. Thus, the approach does not allow an assessment of the
incremental impact of various measures of accounting risk on long-term
IPO mispricing. A potentially more important limitation is that the
univariate tests do not control for factors other than accounting risk
which may influence mean aftermarket abnormal returns and mean abnormal
return variances. Thus, it is not possible to conclude from these
28 According to Hypothesis 3, the regression coefficients on the
three accounting risk variables should be greater in year 2 than in year
3. Thus, the differences in the estimated year 2 and year 3 regression
parameters pertaining to AUDITOR, FORM, and APCHOICE are expected to be
significantly greater than zero. As explained in footnote 26, no
predictions are made in regards to the signs of the differences between
the regression coefficients of years 2 and 3 for the .control variables
(i.e., SIZE, LNRISKS, and ABSUE).


77
Similarly, Table 5-4 shows that the sample is dominated by IPO
firms which filed S-l SEC registration statements. Again, 294 out of
the 372 sample firms are classified in the low accounting risk category
(based on the SEC registration statement type filed), and only 78 are
classified as high accounting risk firms.
However, when accounting procedure choices are utilized in the
classification of high versus low accounting risk sample firms, the
sample is dominated by high risk firms. Specifically, 264 out of the
359 sample firms which had data available on the accounting methods
chosen utilized consistently income increasing (i.e., "liberal")
accounting procedures for inventory and depreciation. By contrast, only
95 out of these 359 firms employed at least one income reducing method.
Therefore, the common practice among the sample firms was to utilize
straight-line depreciation and an inventory method other than LIFO for
financial reporting purposes.
Results of Univariate Tests
Results of Univariate Tests of Hypothesis 1
Hypot.hesis 1 predicts that the mean abnormal return (MAR) of each
accounting risk portfolio will not significantly deviate from zero over
any of the four time horizons. Further, the hypothesis states that the
mean abnormal return of the high risk portfolios should not differ
significantly from the MAR of the low risk portfolios over each time
period examined. Table 5-5 provides the results of the univariate
(portfolio) tests of Hypothesis 1. Panels A, B, and C supply the


131
Table B-6--continued
Panel C
Model: AR3t = a
+ ^AUDITORi + ^32FORMi + ^APCHOICE*
+ 71SIZEi +
72LNRISKS + 73UE3i
+ ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT .016
.022
0.735
0.647
AUDITOR
- .001
.007
-0.108
0.011
FORM
- .003
.008
-0.363
0.128
APCHOICE
.002
.006
0.361
0.187
SIZE
-.002
.002
-0.910
1.024
LNRISKS
.000
.003
0.057
0.003
UE3
.022
.009
2.456**
1.392
Test of
the Model Goodness
of Fit: F
-Value=1.274 P-Value=.2694
White's
[1980] Homoscedasticity Test:
X2-Value=183.83
P-Value=.0001
Model Adjusted R2 .0058
N=283
Panel
D
Model: AR4 =
a + /JjAUDITORi
+ ^FORMi +
y33APCHOICEi
+ 7^SIZE^ +
72LNRISKS +
73UE4i + eL
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic Test Statistic
INTERCEPT
- .026
.020
-1.278
1.552
AUDITOR
- .003
.006
-0.423
0.187
FORM
.003
.007
0.433
0.140
APCHOICE
.003
.005
0.584
0.492
SIZE
.002
.002
1.037
0.997
LNRISKS
- .001
.003
-0.209
0.041
UE4
.005
.005
0.864
0.190
Test of the Model Goodness of Fit: F-Value=0.603 P-Value=.7282
White's [1980] Homoscedasticity Test: x2"Value=584.99 P-Value=.0001
Model Adjusted R2 =
-.0087 N=277


19
portfolio from an IPO's return. Thus, this adjustment for overall
market performance does not vary across securities (i.e., the Capital
Asset Pricing Model (CAPM) beta parameter is assumed to equal one for
all firms). The authors' results support their contention that long
term mispricing is independent of the level of short-term mispricing.
However, average abnormal returns of -18.1% are documented from one week
after the offering to one year after the offering. This negative
abnormal return implies that investors were overly optimistic in their
initial valuations of IPO firms, and therefore lost 18.1% of their
investment in the first year of public trading due to such incorrect
valuations. Thus, McDonald and Fisher's results indicate that the
market overvalued, on average, IPOs issued in the first quarter of 1969.
Ibbotson [1975] examines the abnormal aftermarket performance of a
sample of firms which went public between 1960 and 1969. Abnormal
returns are calculated over various holding periods up to 60 months
subsequent to the IPO by means of the two-parameter Sharpe [1964] and
Lintner [1965] CAPM. The empirical results demonstrate very few
statistically significant abnormal returns in the aftermarket. Thus,
Ibbotson concludes that IPO aftermarket returns do not deviate from
market efficiency. In other words, Ibbotson's results are consistent
with the hypothesis that the market utilizes all available information
in an unbiased fashion and hence, on average, accurately values new
issues.
The most extensive examination of the aftermarket performance of
IPOs is provided by Ritter [1989], He explores the long-term mispricing
of 1,526 firms that made initial offerings between 1975 and 1984.


92
factors revealed no serious multicollinearity problems. An independent
variable's tolerance factor is calculated as 1-R2k, where R2k is the
coefficient of multiple correlation which results when the independent
variable is regressed on all the other independent variables in the
model. This tolerance factor is often used "to detect instances where
an X variable should not be allowed into the fitted regression model
because of excessively high interdependence between this variable and
the other X variables in the model" (Neter et al. [1985, p. 393]).
Neter et al. continue by stating that frequently utilized tolerance
limits are .01, .001, and .0001, below which the independent variable
should be discarded from the model. In other words, unless the
calculated tolerance factor is below the chosen tolerance limit, the
independent variable is typically not dropped from the model since it is
not considered to possess excessive collinearity with any other
independent variable. In the four regressions reported in Table 5-9,
the lowest tolerance factor calculated for any independent variable is
.54. Thus, multicollinearity does not appear to pose a serious threat
in the four estimated regressions.14
Panel A of Table 5-9 provides the results of estimating Equation
(18) for the first year of public trading time horizon. As predicted by
Hypothesis 1, none of the accounting risk proxy variables is
statistically significant at conventional levels. Thus, consistent with
14 Analysis of the estimated residuals, as well as an examination
of Cook's D distance measures, was also performed in an effort to detect
outlying observations. The number of identified possible "outliers" was
very small in each regression. Equation (18) was then reestimated with
these potentially outlying observations dropped from the analysis. The
results did not qualitatively change from those reported in Table 5-9.


79
results of the univariate tests when AUDITOR, FORM, and APCHOICE,
respectively, are employed as proxies for accounting risk.
Panel A indicates that the mean abnormal return of the high
accounting risk portfolio based on auditor quality (i.e., non-Big Eight
audited IPO firms) does not differ significantly from the hypothesized
value of zero over any of the four time horizons. By contrast, the mean
abnormal return of the low accounting risk auditor portfolio (i.e., Big
Eight audited IPOs) is significantly greater than zero in year 1 and
significantly less than zero in year 2. However, the low risk
portfolio's MAR conforms to the predictions of Hypothesis 1 in year 3
and over the 36 month cumulative period. Finally, consistent with
Hypothesis 1, the differences between the mean abnormal returns of the
high risk and low risk portfolios do not differ significantly from zero
for any of the time periods studied. Thus, the prediction of Hypothesis
1 that accounting risk should not influence mean abnormal returns is
supported for the case of the auditor quality accounting risk surrogate.
Panel B provides similar results for the SEC registration
statement type accounting risk proxy. Again, the hypothesis that the
MAR of the high risk portfolio (i.e., IPOs which filed S-18
registrations) equals zero in each time period cannot be rejected at
conventional levels. The hypothesis can be rejected for the low risk
group (i.e., Form S-l filers) only for the first year of public trading,
when the mean abnormal return is significantly greater than zero.
However, the differences between the MARs of the high and low risk
portfolios do not differ significantly from the hypothesized value of


13
optimal contract between the issuer (i.e., the principal) and the
underwriter (i.e., the agent) involves the delegation of the offer price
decision to the better informed underwriter. This is in addition to the
underwriter's role as distributor of the issue. A major result of the
model is that the issuer will be willing to accept a lower offer price
decision from the underwriter (and hence greater underpricing) as the
issuer's uncertainty surrounding investor demand for the upcoming issue
increases. Thus, Baron's model also implies a positive association
between underpricing and uncertainty.
Tinic [1988] proposes an "implicit insurance" explanation of
underpricing. He summarizes the implicit insurance hypothesis as
follows:
Stated briefly, I demonstrate that gross underpricing serves
as an efficient form of protection against legal liabilities
and the associated damages to the reputations of both the
investment bankers and the issuers. In other words, it is a
form of implicit insurance against potential liabilities
that may arise from the "due-diligence" and disclosure
requirements of the federal securities regulations.
Although avenues for legal recompense against misinformation
and fraud existed before the Securities Act of 1933, the
Act, in effect, replaced the principle of caveat emptor with
the dictum: "Let the issuer and the investment banker
beware." (Tinic [1988, p. 790])
Even though Tinic's short-term underpricing explanation is not based on
informational asymmetries, his model is consistent with Beatty and
Ritter's [1986] conclusion that underpricing is positively associated
with ex ante uncertainty.
A recent line of research proposes that short-term underpricing
can be utilized as a signaling mechanism by entrepreneurs of high
quality IPO firms. In Welch's [1989] model, high quality firms


91
year, third year, and 36 month cumulative time horizon.12 In each case,
White's [1980] test for the presence of heteroscedasticity revealed that
the null hypothesis of homoscedasticity could be rejected at the .0001
level. Thus, the error terms in the four specifications of the model
are heteroscedastic. In such cases, the estimated parameters are
unbiased but inefficient (i.e., they are not minimum variance
estimators), and the resulting parameter estimate covariance matrix is
inconsistent. Therefore, Table 5-9 reports test statistics based both
on an inconsistent covariance matrix (i.e., the typically utilized t
statistics) and on a heteroscedasticity consistent covariance matrix
(i.e., the heteroscedasticity adjusted xZ statistics based on White's
[1980] correction). The significance tests performed using the
heteroscedasticity consistent covariance matrix are asymptotic tests.
Unless otherwise noted, only the tests based on the heteroscedasticity
adjusted test statistics will be discussed in this section.13
The four specifications of Equation (18) were also examined for
the presence of multicollinearity. Diagnostic tests based on tolerance
12 It is important to note that the 36 month cumulative time
horizon is not independent of the other three time horizons since
cumulative abnormal returns are calculated as the sum of the abnormal
returns accruing over the first three years. Therefore, the four
regression models are not independent.
13 Weighted least squares (WLS) regressions were also estimated in
an attempt to overcome the heteroscedasticity problem. The residuals of
the estimated OLS models were examined in an attempt to determine the
nature of the heteroscedasticity and thus to aid in the determination of
the appropriate weighting factors. However, proper weighting factors
were not identified as a result of these residual analyses. Thus,
White's [1980] correction is utilized since its utilization appears to
be superior to employing WLS regressions with weighting factors chosen
on an ad hoc basis.


Table B-2
Number of Sample Firms
Comprising Each Accounting Risk Category
121
Accountinz Risk Proxv
Number of Firms in
High Accounting Risk
Catezorv3
Number of Finns in
Low Accounting Risk
Cateeorvb
AUDITOR
57
242
FORM
57
242
APCHOICE
216
77
a When auditor quality is utilized as a proxy for accounting risk, the
high risk portfolio consists of IPO firms audited by non-Big Eight
auditing firms. For the SEC registration statement form proxy, the high
risk portfolio consists of IPOs which filed an S-18 registration.
Finally, the high accounting risk firms based on the accounting
procedure choice proxy are those firms which utilized consistently
income increasing (i.e., "liberal") methods for accounting for inventory
and depreciation.
b When auditor quality is utilized as a proxy for accounting risk, the
low risk portfolio consists of IPO firms audited by Big Eight auditors.
For the registration statement type proxy, the low risk portfolio
consists of IPOs which filed S-l registration statements. Finally, the
low accounting risk firms based on the accounting procedure choice proxy
are those firms which utilized at least one income decreasing (i.e.,
"conservative") method for accounting for inventory and depreciation.


41
SEC Registration Form Proxy
The final accounting risk surrogate employed in this dissertation
is whether the registration statement7 filed with the SEC was on Form
S-18 or Form S-l. Form S-18 offerings were instituted by the SEC in
April 1979. Prior to that time all initial offerings (with the
exception of unregistered Regulation A offerings which had a limit on
the amount of proceeds raised of $1.5 million) were required to be
registered on Form S-l. The limit on the amount of capital raised with
an S-18 offering is $7.5 million.8
Important differences exist in the disclosure requirements in
these two types of registration statements. Form S-l registrations are
more comprehensive both in terms of the number of years of data required
and in the extent of data required per year. Specifically, Form S-18
allows the issuer to present two years of audited financial statements,
rather than the three required in an S-l offering. Further, "Form S-18
relaxes or eliminates the disclosure requirements for several areas,
including property, segment data, foreign operations, order backlogs,
and research and development" (Manegold and Arnold [1986 p. 29]). The
elimination of these potentially valuable accounting disclosures in an
7 Before securities may be sold to investors in a public offering,
the SEC must approve an issuing firm's registration statement. The
registration statement contains information about the proposed offering,
as well as information about the issuing firm itself (e.g., audited
financial statements). Similar information about the offering and the
issuer is provided to potential investors in a prospectus.
8 For a detailed account of the differences between the two types
of registrations, please see Manegold [1986] or Manegold and Arnold
[1986].


138
Table B-8--continued
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
LNRISKS*I1
.0001
.0003
0.259
0.066
LNRISKS*I2
.0002
.0003
0.589
0.272
LNRISKS*I3
-.0001
.0003
-0.579
0.418
LNRISKS*I4
-.0002
.0003
-0.701
0.801
ABSUE*I1
.0056
.0032
1.733*
2.743@
ABSUE*I2
.0010
.0025
0.400
0.201
ABSUE*I3
.0011
.0024
0.441
0.278
ABSUE*I4
.0005
.0024
0.226
0.071
Test of the Model Goodness of Fit: F-Value=3.746 P-Value=.0001
White's [1980] Homoscedasticity Test: xz-Value=1252.83 P-Value=.0001
Model Adjusted R2 = .0566 N=l,375
** significant at .01 level, based on one-tailed t tests
* significant at .05 level, based on one-tailed t tests
## significant at .01 level, based on xZ tests
# significant at .05 level, based on xZ tests
@ significant at .10 level, based on xZ tests


104
of the differences between the regression parameters relating to years 2
and 3 with those pertaining to year 1, while Panel B provides tests of
the differences in regression coefficients between years 2 and 3.
As was the case for each of the regressions reported in Tables
5-9 and 5-11, White's [1980] test for the presence of heteroscedasticity
revealed that the null hypothesis of homoscedasticity could be rejected
at the .0001 level. Thus, Table 5-12 Panel A reports both the typically
utilized t statistics and the heteroscedasticity adjusted \z statistics
based on White's [1980] correction. In a similar fashion, Panel B
reports two sets of test statistics. One set of reported statistics
ignores the presence of heteroscedasticity while the second set adjusts
for heteroscedastic error terms. Unless otherwise noted, only the
statistical tests based on the heteroscedasticity adjusted test
statistics will be described in this section.19
Panel A of Table 5-12 reports the results of estimating Equation
(21) over the entire three year time horizon. As was indicated in
Chapter 4, Equation (21) is a pooled time-series cross-sectional model
in which each sample firm contributes three observations. In other
words, observations relating to the first, second, and third year of
public trading are included for each sample firm. The indicator
19 The tolerance factors resulting from the estimation of Equation
(21) were also examined in order to assess whether collinear independent
variables might be unduly influencing the estimated parameters. The
tolerance factors for the ABSUE and ABSUE*I2 variables each approximate
.04. Thus, these variables appear to possess a high degree of
collinearity with the other independent variables. However, these .04
tolerance factors are still above the most restrictive tolerance limit
(i.e., .01) typically utilized as a guide for discarding severely
collinear variables. Thus, the tolerance factor diagnostic check did
not reveal severe multicollinearity problems.


99
Panel A of Table 5-11 provides the results of estimating Equation
(20) for the first year of public trading. The three accounting risk
proxy variables are predicted to be positive according to Hypothesis 2.
However, only FORM is significantly greater than zero at the .05 (.11)
level, based on the t (x2) statistic. Among the control variables, only
SIZE is significant. The negative coefficient on size indicates that
smaller firms (i.e., those possessing greater business risk) have higher
abnormal return variances. Thus, the results of Panel A of Table 5-11
support Hypothesis 2 for only the FORM accounting risk proxy. The
results indicate that IPO firms which file SEC Form S-18 registrations
experience higher first year abnormal return variances than Form S-l
filers. Therefore, a significant positive association between
accounting risk (based on the FORM proxy) and first year abnormal return
variances is documented in this multivariate setting, just as it was in
the univariate setting described previously. However, this same
significant positive association is not documented for the other two
accounting risk surrogates when business risk and other measures of
accounting risk are explicitly controlled for.
In Panel B, the AUDITOR accounting risk proxy variable is again
insignificant. Consistent with the prediction of Hypothesis 2, both the
FORM and APCHOICE variables are positive. However, the significance
levels of these two variables are marginal. Specifically, Hypothesis 2
is supported at the .05 (.22) level based on the t (x2) statistic for
FORM and at the .14 level based on either test statistic for APCHOICE.
Thus, Hypothesis 2 is marginally supported for two out of the three
accounting risk surrogates. Two of the control variables are also


74
P-Value of less than .01. Thus, the assertion of Hypothesis 1 that mean
abnormal returns should not differ from zero is generally supported for
the sample as a whole. However, the hypothesis is not supported for the
first year of public trading time horizon.6
Table 5-3 also lists alternative measures of long-term mispricing
based on simple market-adjusted (rather than risk-adjusted) returns.
Specifically, RIMRM1 to RIMRM3 and RIMRM36 represent market-adjusted
security returns over the first, second, and third years of public
trading, as well as a 36 month cumulative market-adjusted return. The
market adjustments consist of subtracting the return on the NASDAQ
Composite Index from an IPO's unadjusted raw return over the
corresponding time period in the construction of monthly returns. Then,
yearly market-adjusted returns are constructed from these monthly
returns via an additive process. Table 5-3 shows that the absolute
magnitudes of these market-adjusted returns are much greater than those
of the risk-adjusted returns previously described. However, only RIMRM3
is significantly different from zero at the .05 level, while the 36
month cumulative return is marginally significant at .10.7
6 More formal statistical tests of Hypothesis 1 will be provided in
later sections.
7 Market-adjusted returns are employed by Ritter [1989] in the
examination of long-term IPO mispricing. Ritter finds average market-
adjusted returns of -24.33% over the first three years of trading. This
compares with the -8.42% cumulative return of the sample examined in
this study. One potential reason for the much more negative mean
market-adjusted returns of Ritter's sample firms is that the market
index employed differs between the two studies. Ritter uses an index
based on NYSE and AMEX firms, while the current study uses the NASDAQ
Composite Index. Average market-adjusted returns for the current sample
were also estimated using the market index employed by Ritter (i.e., the
CRSP Value-Weighted Index based on NYSE and AMEX firms). The results of
such an estimation process indicate three year cumulative mean market-


15
Beatty [1989a] and Balvers et al. [1988] hypothesize that auditor
reputation and initial underpricing are inversely related. Beatty
utilizes the major result of Beatty and Ritter [1986] (i.e., expected
underpricing is positively associated with ex ante uncertainty) in the
formulation of this hypothesis. Specifically, since the attestation of
an IPO's financial statements by a high reputation auditor serves to
reduce investors' ex ante uncertainty, then auditor reputation is
hypothesized to be inversely related to underpricing. Balvers et al.
extend the models of Rock [1986] and Beatty and Ritter [1986] by
explicitly considering the relationship between an IPO's auditor and
underwriter. In the model, high auditor reputation reduces investors'
uncertainty regarding an IPO's earnings and serves as a positive signal
of the reputation of the underwriter. Thus, auditor reputation is
inversely related to ex ante uncertainty, and therefore is hypothesized
to be inversely related to underpricing. Empirical support of this
hypothesized relationship between auditor reputation and short-term
underpricing is documented by Beatty [1989a, 1989b] and Balvers et al.
[1988],3 Thus, the results of prior research seem to suggest that an
entrepreneur of a firm contemplating an initial offering may be able to
3 However, Balvers et al. [1988] conclude that the impact of
auditor reputation on underpricing is reduced when a high reputation
underwriter is also involved in the registration process. Thus, auditor
and investment banker reputation appear to be (at least partial)
substitutes.


100
significant. Firm size is negative, and as expected, ABSUE2 is
positively associated with second year abnormal return variances.
Panel C reports the results of estimating Equation (20) for the
third year time horizon. Hypothesis 2 is supported for the APCHOICE
accounting risk measure only. The only other significant variable in
the model is the absolute value of unexpected earnings.
Finally, Panel D demonstrates the results of estimating Equation
(20) over the cumulative 36 month time horizon. Over this period, among
the accounting risk surrogates only the FORM variable approaches
conventional significance levels, with a P-Value of .09 (.22) based on
the t (x2) statistic. Thus, Hypothesis 2 cannot be strongly supported
for any of the accounting risk measures over this 36 month cumulative
period. However, two of the control variables are useful in the
explanation of the 36 month cumulative abnormal return variances. SIZE
is negatively related to VAR36 and the typical positive association
between unexpected earnings and abnormal return variances is
demonstrated.
In summary, the results reported in Table 5-11 support Hypothesis
2 for the FORM proxy variable for years 1 and 2, support the APCHOICE
proxy for years 2 and 3, and do not lend support for the AUDITOR proxy
over any of the four time horizons.18 Hence, the results of the
18 As indicated in Chapter 4, the accounting risk theoretical
construct is a multifaceted concept which is difficult to empirically
capture with any one proxy variable. In this study, three accounting
risk proxies are employed in an attempt to capture three unique aspects
of accounting riskiness. The results reported in Table 5-11 indicate
that different combinations of these proxy variables are effective in
explaining abnormal return variances as the time horizon examined
changes. In other words, it appears that none of these empirical
surrogates adequately captures the accounting risk theoretical construct


28
where T = time subsequent to the IPO and Q the quality of accounting
information disclosed to investors at the time of the IPO. It is
assumed that systematic accounting risk is a decreasing function of both
time and accounting quality at the IPO date. Specifically,
dfif/dT < 0 and (7a)
apf/BQ < 0. (7b)
In other words, subsequent to the IPO, systematic accounting risk
decreases over time as more information is revealed about the firm.6
Also, the disclosure of a sufficient quantity of reliable accounting
information at the IPO date reduces systematic accounting risk.
Similarly, unsystematic risk may be divided into business and
accounting risk components as follows:
Var(ei) Var(eiB) + Vare/), (8)
where Var(et) = firm i's unsystematic risk which is calculated as the
variance of the error terms in the realized formulation of the CAPM, and
Vare/) and Var(e/) equal the business risk and accounting risk
components of unsystematic risk, respectively. As was the case for
systematic accounting risk, unsystematic accounting risk is modelled as
a decreasing function of time subsequent to the IPO as well as
accounting quality at the IPO date. Specifically,
Var(e/) = Var(e/) (T,Q) (9)
6 This additional information revealed in the periods following the
IPO may consist of both accounting (e.g., quarterly and/or yearly
financial statements or analysts' forecasts of earnings) and
nonaccounting (e.g., security prices or Wall Street Journal articles)
information. This additional information will reduce accounting risk
only if it is deemed reliable.


Table 2-2
Summary of Prior Examinations of
Long-Term IPO Underpricing
Study
Return Time Horizon
Mean Return
Sample Peri'
Ritter [1989]
3 Years
-24.33%
1975-1984
Simon [1989]
Pre-1933 Non-NYSEa IPOs
5 Years
-52.61%
1926-1933
Pre-1933 NYSE IPOs
5 Years
Insignificant
1926-1933
Post-1933 Non-NYSE IPOs
5 Years
Insignificant
1934-1939
Post-1933 NYSE IPOs
5 Years
Insignificant
1934-1939
Jarrell [1981]
Pre-1933 New Issues
5 Years
64%
1926-1933
Post-1933 New Issues
5 Years
25%
1934-1939
Ibbotson [1975]
5 Years
Insignificant
1960-1969
McDonald and Fisher [1972]
1 Year
-18.1%
1/69-3/69
NYSE refers to New York Stock Exchange listed firms.


CHAPTER 3
HYPOTHESIS DEVELOPMENT
Introduction
The three hypotheses developed and empirically tested in this
dissertation relate to the impact of the quality of accounting
information available to IPO investors on long-term IPO mispricing.
With the aid of three assumptions, this chapter develops these
hypotheses in a Capital Asset Pricing Model (CAPM) framework.1
Assumptions
First, it is assumed that the total uncertainty surrounding an
initial offering arises from two sources. Specifically, assume that an
IPO's firm-specific risk may be decomposed2 as follows:
Firm-specific risk = the uncertainty related to the firm's
production, investment, and financing activities + the
uncertainty related to the quality of the accounting numbers
supplied to potential investors. (la)
In mathematical terms, Equation (la) becomes
FSRi = BUSRISiq + ACCTRISKi, (lb)
1 The model utilized in this chapter to derive the hypotheses is an
adaptation of the model found in Jarrell [1981].
2 This partitioning of firm-specific risk is in addition to the
CAPM's systematic (i.e., nondiversifiable) versus unsystematic (i.e.,
diversifiable) decomposition.
24


90
Table 5-9--continued
Panel C
Model: AR^ = a + ^AUDITORi + ^FORMi + ^APCHOICEi
+ 71SIZEi + 72LNRISKS + 73UE3i + ei
Parameter
Standard
t
Heteroscedasticity
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.025
.022
1.113
1.483
AUDITOR
-.003
.007
-0.486
0.213
FORM
-.006
.008
-0.732
0.487
APCHOICE
-.000
.006
-0.011
0.000
SIZE
-.003
.002
-1.281
1.983
LNRISKS
- .001
.003
-0.374
0.116
UE3
.008
.007
1.217
0.460
Test of the
Model Goodness
of Fit: F
-Value=0.542
P-Value=.7759
White's [1980] Homoscedasticity Test:
X2-Value195
.85 P-Value=.0001
Model Adjusted R2 = -.0087
N-318
Panel
D
Model: CAR36i = a
-t
+ ^AUDITORi + )32FORMi + ^APCHOICE*
y^IZEj^ + 72LNRISKSi + 73UE36A + eA
Parameter
Standard
t
Heteroscedasticity
Adjusted xZ
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.060
.035
1.713
2.007
AUDITOR
.000
.011
0.031
0.001
FORM
.007
.012
0.539
0.256
APCHOICE
.002
.010
0.232
0.054
SIZE
-.005
.003
-1.492
1.537
LNRISKS
- .005
.005
-1.088
1.246
UE36
.041
.007
6.061**
12.586##
Test of the
Model Goodness
of Fit: F-
Value=6.624
P-Value=.0001
White's [1980] Homoscedasticity Test: xz-Value=298.73 P-Value=.0001
Model Adjusted R2 .0962 N=318
** significant at .01 level, based on two-tailed t tests
* significant at .05 level, based on two-tailed t tests
## significant at .01 level, based on *2 tests
# significant at .05 level, based on *2 tests


62
parameter estimate on the unexpected earnings variable is also expected
to be positive. If business risk is adequately controlled for in the
return generating process described by Equation (12), the coefficients
on the business risk control variables (i.e., SIZEi and LNRISKSt) should
not differ significantly from zero. However, 7l and y2 may be
significantly different from zero if INDUSTRYit does not adequately
control for business risk. In such a case, the coefficient on SIZEit is
predicted to be negative (since smaller firms correspond to a higher
level of business risk and hence should experience a higher variance of
returns), while the coefficient on LNRISKS is predicted to be positive
(since the number of risk factors enumerated in the prospectus is
positively associated with business risk).
Multiple Regression Test of Hypothesis 3
Hypothesis 3 may alternatively be tested via the following pooled
cross-sectional time-series regression model:
VARit = a + /^AUDITOR^ + 0X' AUDITORit*Ilit + 0X" AUDIT0Rit*I2it
+ /32FORMit + 02' FORMit*Ilit + 02" F0RMit*I2it
+ £3APCHOICEit + APCHOICEit*Ilit + 03 APCH0ICEit*I2it
+ 7iSIZEit + 7l'SIZEit*Ilit + 7i"SIZEit*I2it
+ 72LNRISKSit + 72' LNRISKSit*Ilit + 72" LNRISKSit*I2it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73 ABSUEit*I2it
+ eit, (21)
where Ilit is an indicator variable which is coded 1 for observations
relating to the second year following the IPO and 0 otherwise, I2it is
an indicator variable which takes on the value of 1 for third year


137
'I
Table B-8
Regression Approach to Testing Hypothesis 3
Using 299 Firm Sample
Panel A
Model: VARit a + ^AUDITOR*,. + /VAUDITORit*Ilit + P^'AUDIT0Rit*I2it
+ p^" 'AUDIT0Rit*I3it + P-l" "AUDIT0Rit*I4it
+ £2FORMit + p2' FORMit*Ilit + P2" F0RMit*I2it
+ P2' ' F0RMit*I3it + p2" "F0RMit*I4it
+ /33APCHOICEit + /33' APCHOICEit*Ilit + p3 APCH0ICEit*l2it
+ P3" 'APCH0ICEit*I3it + P3" "APCH0ICEit*I4it
+ 7lSIZEit + 7i'SIZEit*Ilit + 7i"SIZEit*I2it
+ 7i"'SIZEit*l3it + 7i" ' SIZEit*I4it
+ 72LNRISKSit + 72'LNRISKSlt*Ilit + 72" LNRISKSit*I2it
+ 72'"LNRISKSit*I3it + 72" "LNRISKSit*I4it
+ 73ABSUEit + 73'ABSUEit*Ilit + 73" ABSUEit*I2it
+ 73"'ABSUEit*I3it + 73' ' ABSUEit*!4it fe,
Heteroscedasticity
Parameter
Standard
t
Adjusted x
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.0037
.0008
4.911**
13.473##
AUDITOR
.0004
.0005
0.923
0.402
FORM
.0016
.0006
2.864**
2.213
APCHOICE
-.0006
.0004
-1.322
0.670
SIZE
-.0002
.0001
-2.322**
5.719#
LNRISKS
.0002
.0002
0.933
1.239
ABSUE
-.0003
.0024
-0.106
0.016
AUDITORS 1
-.0003
.0007
-0.473
0.122
AUDIT0R*I2
-.0001
.0007
-0.154
0.016
AUDIT0R*I3
-.0008
.0007
-1.188
0.997
AUDIT0R*I4
-.0003
.0007
-0.365
0.109
FORM*11
-.0013
.0008
-1.579
0.756
FORM*I2
-.0032
.0008
-4.068**
6.178##
FORM*I3
-.0016
.0008
-2.007*
1.526
FORM*I4
-.0019
.0008
-2.315**
2.347
APCH0ICE*I1
.0012
.0006
1.983*
2.534
APCH0ICE*I2
.0011
.0006
1.889*
2.535
APCH0ICE*I3
.0011
.0006
1.875*
2.630@
APCH0ICE*I4
.0006
.0006
1.013
0.818
SIZE*I1
-.0001
.0001
-2.089*
4.666#
SIZE*I2
-.0001
.0001
-1.350
2.035
SIZE*I3
-.0001
.0001
-1.939*
4.089#
SIZE*I4
- .0001
.0001
-1.449
2.542


REFERENCES
Balvers, R. J., B. McDonald, and R. E. Miller. 1988. Underpricing of new
issues and the choice of auditor as a signal of investment
banker reputation. The Accounting Review 63 (October): 605-
22.
Baron, D. P. 1982. A model of the demand for investment banking advising
and distribution services for new issues. Journal of Finance
37 (September): 955-76.
Beatty, R. P. 1989a. Auditor reputation and the pricing of initial
public offerings. The Accounting Review 64 (October): 693-
709.
. 1989b. The initial public offerings market for auditing
services. In Auditing Research Symposium 1986. edited by
R. E. Ziegler and F. L. Neumann, 1-40. Champaign, IL:
University of Illinois Office of Accounting Research.
, and J. R. Ritter. 1986. Investment banking, reputation, and the
underpricing of initial public offerings. Journal of
Financial Economics 15 (January/February): 213-32.
Beaver, W. H. 1981. Financial Reporting: An Accounting Revolution.
Englewood Cliffs, NJ: Prentice-Hall.
Bower, N. L. 1989. Firm value and the choice of offering method in
initial public offerings. Journal of Finance 44 (July): 647-
62.
Dawes, R. M. and B. Corrigan. 1974. Linear models in decision making.
Psychological Bulletin 81 (February): 95-105.
DeAngelo, L. 1981. Auditor size and audit quality. Journal of Accounting
and Economics 3 (December): 183-99.
Downes, D. and R. Heinkel. 1982. Signaling and the valuation of
unseasoned new issues. Journal of Finance 37 (March): 1-10.
Feltham, G. A., J. S. Hughes, and D. A. Simunic. 1988. Empirical
assessment of the impact of auditor quality on the valuation
of new issues. Working paper, University of British
Columbia.
143


CHAPTER 1
INTRODUCTION
Initial public offerings (IPOs) of securities have been the
subject of academic research for over two decades.1 An aspect of the
IPO market which makes it a particularly rich research area is the
relative scarcity of publicly available information about these
previously privately held firms.2 Specifically, potential IPO investors
are at an informational disadvantage compared to those who invest in
more established publicly traded firms, since there is no past series of
security prices and only very limited financial statement information is
available upon which to make determinations of IPO firm value.3 In
addition, IPO firms are not subject to the continuing scrutiny of the
financial analyst community.
The motivation of this dissertation is to understand the degree to
which accounting numbers influence IPO valuation. The preceding
1 See Smith [1986] and Ibbotson et al. [1988] for reviews of this
literature.
2 Privately held firms which have publicly traded debt outstanding
are notable exceptions. Publicly available financial statement
information is produced by these firms.
3 The Securities and Exchange Commission (SEC) requires the
inclusion of two (in a Form S-18 registration) or three (in a Form S-l
registration) years of audited financial statements in the registration
statement and accompanying prospectus. However, Weiss [1988] documents
that 529 out of her sample of 1510 IPOs reported less than two years of
earnings in the prospectus. These noncomplying firms are presumably
start-up firms which have operating histories of less than two years.
1


32
Accounting Risk and the Variance Over Time of Abnormal Returns
Equations (7a) and (10a) model systematic and unsystematic
accounting risk, respectively, as decreasing functions of time
subsequent to the IPO. Thus, this decrease in and Varie^) over time
results in a reduction in both systematic (i.e., and unsystematic
(i.e., Var(e)) risk. It then follows directly from Equation (11) that
such decreases in systematic and unsystematic risk result in an IPO
firm's abnormal return variance decreasing over time. The preceding
implication of the model gives rise to the third hypothesis, which may
be stated as follows:
Hypothesis 3: The variance of IPO firms' abnormal returns
decreases over time.
This third hypothesis is consistent with a diminishing effect of
accounting risk on the precision of IPO valuation (i.e., long-term
mispricing) over time as the information disclosed at the time of the
IPO takes on a lesser role in the valuation of the firm as new
information is revealed.7
7
See footnote 6.


Gale, I. and J. E. Stiglitz. 1989. The information content of initial
public offerings. Journal of Finance 44 (June): 469-77.
144
Grinblatt, M. and C. Y. Hwang. 1989. Signalling and the pricing of new
issues. Journal of Finance 44 (June): 393-420.
Hughes, P. J. 1986. Signalling by direct disclosure under asymmetric
information. Journal of Accounting and Economics 8 (June):
119-42.
Ibbotson Associates. 1989. Stocks Bonds Bills and Inflation. Chicago:
Ibbotson Associates.
Ibbotson, R. 1975. Price performance of common stock issues. Journal of
Financial Economics 2 (September): 235-72.
and J. F. Jaffe. 1975. "Hot issue" markets. Journal of Finance
30 (September): 1027-42.
, J. L. Sindelar, and J. R. Ritter. 1988. Initial public
offerings. Journal of Applied Corporate Finance 1 (Summer):
37-45.
Imhoff, E. A. and J. K. Thomas. 1989. Accounting quality. Working paper,
University of Michigan.
James, C. and P. Wier. 1989. Borrowing relationships, intermediation and
the cost of issuing public securities. Working paper,
University of Florida.
Jarrell, G. A. 1981. The economic effects of federal regulation of the
market for new security issues. Journal of Law and Economics
24 (December): 613-75.
Keppel, G. 1982. Design and Analysis. 2nd ed. Englewood Cliffs, NJ:
Prentice-Hall.
Krinsky, I. and W. Rotenberg. 1989. The valuation of initial public
offerings. Contemporary Accounting Research 5 (Spring): 501-
15.
Leland, H. and D. Pyle. 1977. Informational asymmetries, financial
structure, and financial intermediation. Journal of Finance
32 (May): 371-87.
Lintner, J. 1965. The valuation of risk assets and the selection of
risky investments in stock portfolios and capital budgets.
Review of Economics and Statistics 47 (February): 13-37.
Logue, D. 1973. On the pricing of unseasoned equity issues: 1965-1969.
Journal of Financial and Quantitative Analysis 8 (January):
91-103.


4
Bower describes the second type of IPO mispricing as follows:
The second type of mispricing is a longer term effect that
relates to the lack of publicly available information about
the firm. Firm value is not likely to be revealed
immediately, but rather investors' expectations about firm
value improve over a long period of time as more information
is revealed about the firm. (Bower [1989, p. 650])
This second type of IPO mispricing "does not deal with initial
underpricing, but rather is concerned with the change in the market
price from the immediate aftermarket to the long term" (Bower [1989, p.
656]). Hence this long-term perspective of mispricing relates to the
accuracy of market participants' initial consensus assessment of IPO
firm value (i.e., the first aftermarket price). The long-term
mispricing of the market's consensus valuation on the first day of
public trading9 is assessed in relation to the market's valuation of the
firm after more information is revealed, via examining abnormal
aftermarket security returns.
There are three principal reasons why the long-term perspective of
IPO mispricing is adopted in this study. The first is that scant
empirical evidence exists on the long-term mispricing of IPOs. In
contrast to the widely documented short-term underpricing by
underwriters, the existing empirical findings do not provide conclusive
evidence as to whether the market overvalues, undervalues, or accurately
values new security issues, on average.10 Further, the impact of
9 This first day of public trading is presumably a time of great
uncertainty about the true value of an IPO due to lack of quality
information about the firm.
10 The empirical findings on long-term mispricing of IPOs will be
reviewed in detail in Chapter 2.


117
Specifically, firm size is shown to be negatively associated with
underpricing. Thus, smaller firms (i.e., those which possess greater
business risk) experience a greater amount of underpricing of the
initial offering price. Table A-l also reveals that firms which utilize
less conservative accounting procedures for inventory and depreciation
(i.e., those firms with greater accounting risk) also experience a
greater level of underpricing.
However, the table demonstrates that none of the remaining
accounting risk or business risk proxy variables is significant in the
explanation of short-term underpricing. Thus, neither the reputation of
the auditor employed in the going public process nor the amount and
content of accounting disclosures supplied to potential investors (as
proxied by the FORM accounting risk variable) aid in the explanation of
the variation in IPO firms' initial underpricing. Similarly, LNRISKS,
the remaining business risk variable does not conform to the prediction
of the model.
The results reported in this appendix are of interest in a number
of respects. First, the results are consistent with prior research
which demonstrates that ex ante uncertainty and short-term IPO
mispricing are positively related, since both accounting risk (in the
form of APCHOICE) and business risk (in the form of SIZE) confirm the
prior findings.
Second, the results of the estimation of Equation (22) are not
consistent with previous findings which demonstrate an inverse
relationship between auditor reputation and underpricing. In the
present study, the AUDITOR variable is found to be insignificant. Thus,


Finally, the third hypothesis predicts that the influence of the quality
of accounting numbers on abnormal return variances diminishes over time.
Three empirical surrogates for accounting quality are employed.
The first is the reputation of the auditor utilized in the IPO process.
It is measured according to whether a Big Eight or a non-Big Eight
auditing firm is employed. The second is the potential managerial
manipulation of reported earnings as reflected in managers' accounting
procedure choices, measured by whether the inventory and depreciation
methods are income increasing or income decreasing. The third proxy is
whether the SEC registration statement filed is a Form S-l or Form S-18
registration. This third surrogate measures the quantity of accounting
disclosures since Form S-l registrations are more extensive.
The empirical results indicate that the three surrogates for
accounting quality have no appreciable impact upon the mean abnormal
returns of investors, as hypothesized. However, these same quality
surrogates, with the exception of auditor reputation, influence the
variance of abnormal returns, also as predicted. Thus, the results
indicate that uncertainty relating to accounting quality translates into
an increased variance of IPO abnormal returns, rather than a systematic
overvaluation or undervaluation. As for the third hypothesis, the
results do not reveal a diminution of the quality surrogates' ability to
explain abnormal return variances as time progresses.
vii


23
Simon's study represents an intervention analysis of a time-series
nature (i.e., differences between the pre- and post-SEC periods are
examined). In contrast, the current study deals with information
variables from a cross-sectional perspective.
Summary of Long-term Mispricing Research
The results of prior research conflict as to whether, on average,
the market overvalues, undervalues, or accurately values new issues of
securities. In assessing the long-term performance of new issues, prior
researchers have employed diverse methods of calculating abnormal
returns and widely divergent sample years. It is possible that the
contradictory findings are time period specific and/or dependent upon
the asset pricing model utilized in the determination of abnormal
returns. This dissertation extends prior research by (1) reexamining
the long-term mispricing of initial public offerings by utilizing a
refined model of equilibrium security pricing6 on a sample of recent
IPOs7 and (2) testing hypotheses relating to the effect of accounting
numbers on such mispricing.
6 A modified version of Simon's [1989] multifactor asset pricing
model will be implemented in this study. The model will be described in
detail in Chapter 4.
7 In contrast to Stigler [1964], Jarrell [1981], and Simon [1989],
only initial public security offerings will be examined. Thus, new
seasoned issues will be excluded. Also, this study will only examine
the long-term mispricing of IPOs initiated since 1980. With the
exception of Ritter's [1989] 1975-1984 sample period, previous studies
have not utilized post-1970 IPOs.


CHAPTER 4
EMPIRICAL METHODOLOGY
Introduction
The empirical methodology implemented in the testing of the three
hypotheses is described in this chapter. First, the three variables
utilized as empirical proxies for the accounting risk theoretical
construct are discussed. The measurement of abnormal risk-adjusted
aftermarket returns (i.e., long-term mispricing) is then illustrated.
Next, both univariate and multivariate tests of the three hypotheses are
detailed. Finally, a summary of the empirical methodology is provided.
Three Measures of Accounting Risk
Overview of the Three Accounting Risk Surrogates
The accounting risk theoretical construct is a multifaceted
concept which is difficult to empirically capture with any one proxy
variable. Thus, this dissertation utilizes three surrogates for
accounting risk, each of which is intended to capture a different aspect
of accounting riskiness. The three unique components of accounting risk
captured by this study's proxy variables are (1) the level of
credibility associated with the reported accounting numbers, (2) the
level of bias inherent in those numbers, and (3) the quantity of
accounting disclosures supplied to potential IPO investors. Auditor
33


10
first day of public trading and earn a one day return (before
transactions costs) in excess of 15%.2 Thus, conclusive empirical
evidence exists concerning the short-term underpricing of initial offers
by underwriters. Table 2-1 summarizes the results of a number of
studies in this area.
Proposed Explanations of Short-Term Underpricing
Various analytical models have been proposed as explanations of
the short-term IPO underpricing phenomenon. Rock's [1986] model is
based on an asymmetry of information between "informed" and "uninformed"
investors. The informed investors have purchased perfect information
concerning the true value of an IPO. A major result of Rock's analysis
is that underpricing is required to entice the uninformed investors to
enter the IPO market. In the model, the informationally advantaged
investors only submit purchase orders for IPOs which are underpriced
(i.e., those in which the IPO price is less than the known true price).
By contrast, uninformed investors are unable to differentiate
underpriced and overpriced IPOs and must either not participate in the
IPO market or submit purchase orders for both underpriced and overpriced
issues. Thus, assuming uninformed demand is sufficient to fully
subscribe a new issue, uninformed investors are allocated 100% of the
shares in an overpriced issue and only a fraction of the shares in an
(oversubscribed) underpriced issue. This rationing of underpriced
2
It is important to note that on average IPOs are underpriced and
that any one particular initial offer could be overpriced. In fact,
evidence in Tinic [1988] and Miller and Reilly [1987] indicates that
approximately 30-40% of IPOs are not underpriced.


46
the estimation period.12,13 Specifically, Dlt is coded 1 during the
first 12 months following the IPO and 0 otherwise. Similarly > Dzt
equals 1 only for months 13-24, while D3t is coded 1 for months 25-36
and 0 otherwise. Thus, the parameters on the dummy variables (i.e., 9U
to 03i) represent an IPO firm's abnormal returns over the first, second,
and third year of public trading, respectively.
Equation (12) is estimated for each firm on a time-series basis
over the first 36 months of public trading. Cumulative abnormal
performance over the entire 36 month estimation period may be computed
by simply adding the parameter estimates on the three time period
specific dummy variables. In other words,
3
CAR36i = 2 9ji., (14)
where CAR36i = firm i's 36 month cumulative abnormal return.14 This
12 In a typical event study, the error terms (i.e., the eit terms)
represent risk-adjusted abnormal returns. However, when the model's
parameters must be estimated over the event period, the sum of the error
terms is constrained to equal zero by the OLS regression procedure and
thus the errors cannot be utilized as measures of abnormal performance.
13 See Schipper and Thompson [1983] for an example of the
utilization of time period specific dummy variables to measure abnormal
performance surrounding different events.
14The return generating process described by Equation (12) does
not include an intercept term. Instead, it includes a time period
specific dummy variable for each of the first three years of public
trading. Alternatively, Equation (12) could be estimated by utilizing
an intercept term along with dummy variables relating only to the first
two years of public trading. The model would then be written as
follows:
Rit RFt = or, + ^(Rfit RFt) + JV9JiDJt
j=l
+ i
where ai is an intercept term and all other terms are as previously
defined. In this alternative specification, aq picks up abnormal


115
price as set by the underwriter. Thus, UPt measures the return (before
transaction costs) that an investor could have earned by purchasing an
IPO from the underwriter at the initial offering price and then selling
it at the end of the first day of public trading.
Equation (22) expresses short-term underpricing as a function of
accounting risk and business risk variables. Thus, asstuning that Beatty
and Ritter's [1986] ex ante uncertainty construct can be decomposed into
accounting risk and business risk components, Equation (22) follows the
predictions of Beatty and Ritter's model. In other words, short-term
underpricing should be a positive function of both accounting risk and
business risk, since it is a positive function of ex ante uncertainty.
Hence, each of the variables in Equation (22) is predicted to be
positive except for the size variable which is predicted to be negative
(since SIZE is inversely related to business risk).
Table A-l reports the results of estimating Equation (22).
White's [1980] test for the presence of heteroscedasticity revealed that
the null hypothesis of homoscedasticity could be rejected at the .0001
level. Therefore, Table A-l reports test statistics based (1) on the
typically utilized t statistics and (2) on heteroscedasticity adjusted
X2 statistics based on White's [1980] correction. Unless otherwise
noted, only the statistical tests based on the heteroscedasticity
adjusted test statistics will be discussed in this appendix.
Consistent with the predictions of the model, the table reveals
that firm size and the accounting procedure choice variable are
significant in the explanation of short-term underpricing.


25
where FSRt is firm specific risk, BUSRISKi equals firms i's business
risk, and ACCTRISKi denotes accounting risk.
The first component of the preceding definition describes the cash
flow uncertainty associated with the firm's production, investment, and
financing activities, and will hereafter be referred to as business
risk. In other words, business risk represents the underlying
uncertainty related to the overall business activities (both operating
and financial) of the firm. The second component in the above
characterization of an IPO's firm-specific risk (hereafter referred to
as accounting risk) reflects the influence of accounting quality on IPO
firm uncertainty. According to Equation (lb), the total level of
uncertainty surrounding an IPO equals the firm's business risk (i.e.,
the lower bound) only when there is no uncertainty surrounding the
quality of the accounting numbers supplied to potential investors.
However, when uncertainty surrounding accounting reliability exists, the
total uncertainty surrounding the security issue exceeds the IPO's
business risk, and increases as accounting risk increases.3
3 Equation (lb) characterizes the total risk of an initial offering
as a linear and additive function of business risk and accounting risk.
Thus, the equation assumes the independence of the two concepts. This
assumption appears to be reasonable, since there is no a priori reason
to postulate the absence of any of the four possible combinations of
business and accounting risk. Specifically, one could reasonably expect
to find IPO firms in each of the following categories: (1) high
business risk and a high level of accounting reliability and accuracy,
(2) high business risk and a low level of accounting reliability, (3)
low business risk and a high level of accounting reliability, and (4)
low business risk and a low level of accounting reliability. Thus, it
seems reasonable to assume that the level of uncertainty surrounding an
IPO firm's production, investment, and financing activities (i.e., its
inherent cash flow uncertainty) is independent of the precision of its
accounting valuations.


81
Results of Univariate Tests of Hypothesis 2
Hypothesis 2 predicts that the mean abnormal return variance
(MVAR) of high accounting risk firms will exceed that of low accounting
risk firms in each of the time horizons examined. Panels A, B, and C of
Table 5-6 report the results of examining Hypothesis 2 using the
AUDITOR, FORM, and APCHOICE accounting risk proxy variables,
respectively.9
Panel A reveals that when AUDITOR is utilized in the
classification of firms into high and low accounting risk categories,
the mean abnormal return variance of the high accounting risk group
exceeds that of the low risk group in each of the first three years.
The difference is significant at the .05 level only for year 2.
However, Hypothesis 2 is marginally supported in years 1 and 3.
Specifically, the differences in mean abnormal return variances are
significant at the .06 and .13 levels in years 1 and 3, respectively.
Thus, Hypothesis 2 is supported in year 2 and marginally supported in
years 1 and 3 when auditor quality is utilized as a surrogate for
accounting risk.
Panel B demonstrates that Hypothesis 2 is supported in years 1 and
2, as well as over the cumulative 36 month period, when the FORM
accounting risk proxy variable is utilized to classify firms into high
9 It is important to note that in Table 5-6 a given portfolio's
cumulative 36 month MVAR does not equal the sum of year 1 through year 3
MVARs. As indicated by Equation (16), abnormal return variances over a
given time period are calculated as the abnormal return over that same
time period squared. Therefore, the cumulative abnormal return variance
equals the cumulative abnormal return squared. Hence, for each firm
(and portfolio) VAR36 = CAR362 rather than AR12 + AR22 + AR32.


Table 5-9
Regression Approach to Testing Hypothesis 1
89
Panel A
Model: ARlt = a +
/SjAUDITORi +
/32FORMi +
y93APCHOICEi
+
yiSIZEi + yzLNRISKSi +
73UE1i + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted \2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
.067
.020
3.275**
5.277#
AUDITOR
-.001
.006
-0.139
0.017
FORM
.008
.007
1.092
0.882
APCHOICE
-.001
.005
-0.243
0.054
SIZE
- .005
.002
-2.613**
3.414
LNRISKS
-.008
.003
-3.030**
8.760##
UE1
.187
.029
6.458**
16.456##
Test of the Model Goodness of Fit: F-Value=8.535
P-Value=.0001
White's [1980] Homoscedasticity Test: \2
Model Adjusted R2 .1141 N-352
Panel B
-Value183
.59 P-Value.0001
Model: AR2a = a +
^AUDITORi +
^2F0RMt +
^APCHOICEi
+
yiSIZEi + yzLNRISKSi + 73UE2i + ei
Heteroscedasticity
Parameter
Standard
t
Adjusted x2
Variable
Estimate
Error
Statistic
Test Statistic
INTERCEPT
-.011
.021
-0.539
0.321
AUDITOR
.010
.006
1.565
2.078
FORM
.007
.007
0.981
0.632
APCHOICE
- .003
.006
-0.600
0.511
SIZE
.001
.002
0.577
0.364
LNRISKS
.004
.003
1.366
2.041
UE2
.150
.022
6.936**
33.596##
Test of the Model Goodness of Fit: F-Value=9.441 P-Value=.0001
White's [1980] Homoscedasticity Test: x2-Value=-281.08 P-Value=.0001
Model Adjusted R2 =
.1277 N=347


47
cumulative abnormal return, as well as the individual abnormal returns
pertaining to the first three years of public trading, is utilized in a
cross-sectional format to test Hypothesis 1. Further, the variances of
these abnormal return measures are used to cross-sectionally test
Hypotheses 2 and 3.
Univariate Tests
Univariate Tests of Hypothesis 1
Hypothesis 1 states that market participants do not misprice IPOs,
on average, regardless of the level of accounting risk they face. In
other words, average aftermarket abnormal returns are predicted to equal
zero (i.e., they are unbiased) and are hypothesized not to be influenced
by accounting risk.
Univariate tests of Hypothesis 1 consist of constructing
portfolios of IPOs based on the firms' levels of the three previously
described accounting risk measures and then comparing the average
abnormal returns of each portfolio over various holding periods (1) to
the hypothesized value of zero and (2) to the average abnormal returns
performance over the period of estimation not accounted for by the dummy
variables (i.e., year 3) and the parameter estimates on the two dummy
variables measure differences between the abnormal performance of years
1 and 2, respectively, and year 3. Thus, the abnormal returns relating
to year 1 would be calculated by adding the dummy variable parameter
estimate pertaining to year 1 (i.e., 9U) to ai. The abnormal returns
of year 2 would be calculated in a similar manner. The cumulative 36
month return would then be calculated by adding the measured abnormal
returns over years 1 through 3. See Maddala [1977, Chapter 9] for a
more detailed discussion of the interpretation of the estimated dummy
variable coefficients in instances when an intercept term (1) is and (2)
is not included in the model.


118
it is possible that auditor reputation loses its explanatory power once
other measures of accounting risk, as well as business risk variables,
are included in the analysis.
Possibly the most interesting result of this exploratory analysis
is the result pertaining to the APCHOICE variable. In particular, Table
A-l demonstrates a significant positive association between the degree
that income increasing (i.e., "liberal") accounting methods are utilized
for inventory and depreciation and the level of short-term underpricing
experienced by IPO firms. Thus, these results indicate that an
entrepreneur of a firm contemplating an initial offering may be able to
influence the short-term underpricing of the issue by his accounting
procedure choices pertaining to depreciation and inventory.
In summary, the results reported in this appendix demonstrate that
accounting choices other than the reputation of the auditor employed in
the IPO process are significantly associated with the level of short
term underpricing experienced by IPO firms. In particular, this
appendix shows that the degree of conservatism inherent in a firm's
depreciation and inventory accounting methods is related to short-term
IPO mispricing.