Economic determinants of earnings persistence

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Economic determinants of earnings persistence
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Thiagarajan, Sundararaman, 1957-
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Thesis:
Thesis (Ph. D.)--University of Florida, 1989.
Bibliography:
Includes bibliographical references (leaves 92-95).
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by Sundararaman Thigarajan.
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Typescript.
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Vita.

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ECONOMIC DETERMINANTS OF EARNINGS PERSISTENCE


By

SUNDARARAMAN THIAGARAJAN





















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


1989































Copyright 1989

by

Sundararaman Thiagaraj an














ACKNOWLEDGEMENTS

It is hard to list the names of the numerous friends of mine who

have helped me in this marathon effort. I would like to thank Professor

Robert Freeman, my chairman, and Professor Prakash Loungani for getting

me interested in the area of persistence and for their constant guidance

especially in the formative stages of the project. My co-chairman,

Professor Bipin Ajinkya, provided detailed comments on my work and also

provided constant moral encouragement for which I am ever grateful.

Professor Senyo Tse asked penetrating questions which made me return to

the drawing board several times. I ground my teeth at that time but am

extremely thankful now for his insight and incisive comments. Professor

Linda Bamber went through my dissertation with a fine-toothed comb. She

made me infuse common intuition into all the concepts and made me

rewrite large portions of the paper. It was painful to do so at that

time, but the dissertation improved so much that I am eternally grateful

that she agreed to be on my committee. Professor Miles Livingston made

me think and work efficiently. He encouraged me to work hard and finish

the dissertation with his sound advice on several aspects. Professor

Steve Cosslett provided a lot of help in the formative stages of the

project which helped write efficient computer programs. All of my

committee members put up with my tardiness but were not tardy in their

responses.







The most difficult stage of the dissertation is the formative

stage and the help obtained at this stage is the most valuable. Sarmila

Banerjee, Charlie Chi, Steve Kachelmeier, John Neill, and K. Ramesh

served as bouncing boards for several of my ideas, and once the idea was

formalized, provided plenty of help in the tests. I am extremely

grateful to them.

Perhaps the most important was the moral support and research help

provided by my wife, Bhama, without whose patience, constant

encouragement, and faith in my abilities this dissertation would not

have seen the light of day.

Finally my family back home provided a lot of encouragement to

finish the dissertation and the faith in the Lord provided me with the

spiritual energy to carry me through all the difficult stages of the

project.















TABLE OF CONTENTS


page
ACKNOWLEDGEMENTS.............................................. iii

ABSTRACT. ..................................................... vii

CHAPTERS

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

2 MEANING OF PERSISTENCE... ....................... ... .. 5

Early Studies...................................... 5
Miller And Rock Framework........................... 6
Kormendi And Lipe Framework........................ 8
A Comment Regarding KL Specification ............... 9
Cochrane's Specification............................. 12
Comparison With The KL Specification ................ 14
Estimation Of Cochrane's Measure
Of Persistence................................... 16
Estimates Of Persistence Based
On Analysts' Forecasts ........................... 17

3 MOTIVATION AND POSSIBLE
ECONOMIC DETERMINANTS.................................. 19

Introduction........................................ 19
Motivation.......................................... 19
Economic Determiants Of
Persistence--The Framework....................... 22
Persistence Of Industry Earnings................... 24
Firm Specific Factors.............................. 26
Effects Of Financing Decisions..................... 29
Effects Of Accounting Techniques................... 30
Conclusion......................................... 34

4 MODELS, MEASUREMENT OF VARIABLES AND
SAMPLE CHARACTERISTICS........................... 35

Basic Model....................................... 35
Measurement Of Variables... ....................... 37
Kormendi And Lipe Model............................ 40
Tests For The Effects Of
Accounting Methods................................ 42









CHAPTERS page

5 SAMPLE SELECTION, DESCRIPTIVE STATISTICS
AND RESULTS OF MODEL ESTIMATION................. 44

Sample Selection.................................. 44
Matters Concerning Empirical
Specification.................................... 47
Descriptive Statistics............................. 51
Results Of Estimation Of Basic Model............... 56
Robustness Checks For The Basic Model.............. 57
Kormendi And Lipe Model........................... 62
Effects Of Accounting Techniques .................. 65
Effects Of Differential Accounting
For R&D Variable................................. 69
Generalization Of The Results..................... 70

6 SUMMARY AND CONCLUSIONS................... ........... 77

APPENDICES

1 RELATIONSHIP BETWEEN KL AND COCHRANE
SPECIFICATIONS OF PERSISTENCE.................. 79

2 ASSOCIATION OF ISP WITH ECONOMIC VARIABLES............ 87

REFERENCES................................................... 92

BIOGRAPHICAL SKETCH.......................................... 96















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

ECONOMIC DETERMINANTS OF EARNINGS PERSISTENCE

By

Sundararaman Thiagarajan

August 1989

Chairman: Robert N. Freeman
Major Department: Fisher School of Accounting


This dissertation examines the association of earnings persistence

with investment factors internal to the firm. The purpose of this

examination is to better understand an important attribute of accounting

earnings numbers. This attribute has been shown to have valuation

implications, but it is not clear what factors internal to the firm

contribute to this attribute. The results of the investigation suggest

that earnings persistence is positively associated with the investment

decisions of the management. Specifically, earnings persistence is

found to be associated with the investments in capital expenditure, R&D

and advertising. In addition, it is shown that the positive

association between earnings persistence and the firm-specific

investment factors is robust with respect to the measurement of earnings

persistence and the investment factors chosen as determinants. Apart

from enabling us to understand some of the components which comprise


vii







earnings persistence, the results confirm that persistence is a

meaningful construct.


viii















CHAPTER 1
INTRODUCTION


The relationship between earnings and security prices has been the

focus of intensive research effort since the publication of the well-

known studies of Ball and Brown (1968) and Beaver (1968). The general

conclusion of these studies and follow-up studies [e.g., Brown and

Kennelly (1972) and May (1971)] is that earnings announcements are

informative for pricing of stocks. This conclusion is "robust across

statistical methodologies, time periods and stock exchanges" [Lev and

Ohlson (1982, p. 261)]. Furthermore, any given firm's accounting

numbers depend on factors such as the firm's production, investment,

and financing decisions, the general economic environment, and the

firm's accounting techniques [Foster (1986, p. 134)]. Since these firm-

specific factors can vary cross-sectionally, stock prices may react to

earnings announcements in a manner that reflects these differences.

Cross-sectional differences in the information content of earnings

have been a matter of theoretical, econometric and empirical

investigations for a long time [Choi (1984), Burgstahler (1981), Beaver,

Clark and Wright (1979)]. Recently, Miller and Rock (1985) [MR] used a

simple two-period model of firm valuation to suggest that the magnitude

of return reaction to an earnings innovation, is related to the

"persistence of earnings" (hereafter, "persistence"). They define










persistence as the impact of an earnings innovation (or earnings

surprise) on the expectations of future earnings.1 Thus, one possible

reason for cross-sectional differences in the information content of

earnings, as measured by the stock reaction to an earnings innovation,

is differential persistence. Kormendi and Lipe (1987) [KL] tested MR's

proposition in a cross-sectional setting and found that the magnitude of

the return reaction is positively associated with persistence.

This dissertation focuses on the association of earnings

persistence with firm specific variables. The variables of interest are

the firm-specific investment variables.

Earnings persistence, which is a measure of the long-term behavior

of the earnings series, is shown to have a valuation impact both

theoretically (Miller and Rock) and empirically (Kormendi and Lipe).

However, it is not clear what firm-specific factors contribute to the

difference in such long-term behavior of earnings. The motivation for

this examination arises from the desire to understand the economic

reasons underlying the long-term behavior of accounting earnings, as

measured by persistence. Since the long-term behavior of earnings, as

captured by persistence, is seen as a valuation construct by the market,

it is important to understand the firm-specific factors which influence

this long-term behavior of earnings. This dissertation explores the

relationship between earnings persistence and certain economic





1The terms "earnings surprise," "earnings innovation" and "shock
in earnings" are all used synonymously in this dissertation. Such
earnings innovation is observed at the time of the earnings
announcements.










characteristics of the firm. Specifically, it examines the association

of persistence with the investment decisions of the firm. The research

question is whether firm-specific investment decisions explain cross-

sectional differences in persistence. Documenting an association

between persistence and the selected economic factors confirms that

persistence is a meaningful construct. This, in turn, should enable us

to examine whether earnings persistence is a reduced form

characterization of certain economic factors internal to the firm. This

examination should further our general understanding of the determinants

of earnings behavior of firms with differences in estimated persistence.

The association between persistence and the selected economic factors is

examined using two different measures of persistence. The two different

measures are a parametric measure of persistence suggested by KL and a

non-parametric measure suggested by Cochrane (1988).2 An appendix to

the dissertation (appendix 1) examines the relationship between

Cochrane's measure and KL's measure.

The structure of the dissertation is as follows. The next chapter

briefly explains the meaning of persistence as used in the different

studies and compares the two measures of persistence employed in this

dissertation. Chapter 3 motivates the research question and discusses

possible economic determinants of persistence. Chapter 4 specifies the




2 The term "nonparametric" is not used in the sense of statistical
analysis based on ranks. As explained later, this measure of earnings
persistence is nonparametric in the sense that the specification of the
measure does not depend on the underlying process that describes
earnings; it is flexible enough to allow for a variety of earnings
processes. In other words, no assumptions need to be made about the
process that describes earnings.







4

models to be estimated and the basis for the measurement of the

variables. Chapter 5 describes the sample characteristics and discusses

the results of the models estimated. The sixth and final chapter

concludes summarizes the results and provides some suggestions for

future research.















CHAPTER 2
MEANING OF PERSISTENCE


This chapter briefly explains the different measures of

persistence and compares the two measures of persistence used in this

study. Though the notion of persistence has attracted a lot of research

attention in recent years, the concept has been used in industrial

organization studies for more than two decades.

2.1 Early Studies

Persistence refers to the quality of being persistent which, in

turn, is understood as "continuing for a long time into the future."1

This general meaning of persistence was used in economic studies by

Stigler (1963), Brozen (1970) and others in examining departures from

perfectly competitive conditions [see Scherer (1980) and Clarkson and

Miller (1982) for a review of the relevant literature]. These studies

hypothesized that non-competitive conditions (i.e., monopolies) are

characterized by persistence of high rates of return (i.e., continuance

of high rates of return).2



'Webster's dictionary defines persistence as "existing for a long
time or longer than usual time or continuously" [Webster's New
Collegiate Dictionary, 1977, p.855].

2Even though the preponderance of evidence suggests that non-
competitive conditions are characterized by persistence of high rates,
there is controversy regarding the measurement of the relevant variables
[see Scherer(1980) for details]. Since the notion of persistence is the
focus here, the controversy is not discussed.

5









Persistence was defined in these studies as the correlation of

rates of return at two distinct points in time such as t and t+l

see Stigler (1963, table 18, p. 71)]. High correlation implied high

persistence. Stigler showed that high rates of return were persistent

for non-competitive industries.

2.2 Miller And Rock Framework

The recent MR study has sparked considerable interest in the

notion of persistence. In the MR model, the firm exists for only two

periods (Periods 1 and 2) and is liquidated at the end of the second

period. At the beginning of period 1 and at the beginning of period 2,

investments I1 and 12 are made respectively.3 The earnings from

investments I1, made at the beginning of period 1, are denoted as xl and

the earnings from investments 12, made at the beginning of period 2, are

denoted as x2 (a tilde over xt indicates a random variable; xt without a

tilde indicates the realized value of xt). Expectations are formed at

the beginning of period 1 for the earnings of periods 1 and 2. These

expectations are denoted as E1(xi) and El(i2) respectively. At the end

of period 1, the actual earnings for period 1, xl, are revealed. As

soon as the actual earnings are revealed, two events occur:

(1) The surprise in earnings for period 1 is calculated. This surprise

in earnings, denoted el, is the difference between the actual earnings

for period 1 (xl) and the expectation of such earnings at the beginning

of period 1 [El(x1)]. In other words, el = x, El(xi).

(2) The expectation for the earnings of time period 2 is revised.



3 The notation has been slightly altered from the original MR model
to help exposition.










In the absence of any information, at the beginning of period 1,

the expected values of the earnings surprises for the two periods (el,

e2) are zero--i.e., El(ei) = El(e2) = 0. On the release of information

x, and the calculation of el, at the end of period 1, the expected

earnings for the next period are revised. MR denote this revision as

Tre, where r is called the coefficient of persistence--i.e., E2(e2el) =

re1. This coefficient measures the extent to which the current earnings

innovation (el) is incorporated in the expectations of earnings for next

period.

If r=0, then the expected earnings for the next period are not

revised and the earnings surprise for the current time period is not

expected to continue in the future--i.e., it is not persistent. In this

case, the earnings surprise, el, is "transitory."

If, on the other hand, r=l then the expected earnings for time

period 2 are revised by the entire amount of the current period's

earnings surprise. Thus, the entire current period shock is expected to

continue in the future--i.e., the current shock is persistent. In this

case, the earnings surprise is "permanent." Typically, r takes the

value between 0 and 1.4

In summary, the MR model defines persistence as the extent to

which the current period earnings surprise is expected to continue in








4 Conceivably, the r coefficient can take a value greater than 1
which would imply that a $1 innovation in the current period results in
more than $1 revision in the expected value of earnings in the future.









the future.5 The future in the MR model is restricted to one period

(period 2). The analytical extension of the MR model to several periods

is likely to require certain restrictive assumptions.

2.3 Kormendi And Lipe Framework

As indicated earlier, KL tested the MR proposition that the cross-

sectional difference in the information content of earnings

announcements (captured by the earnings response coefficient) can be

explained by differential persistence. Kormendi and Lipe needed a

measure of the revision in the expectation of future earnings induced by

the current period earnings innovation. To derive this, KL assumed that

the earnings expectations of each firm in their sample could be

approximated by the following second order autoregressive process:

(1) AX, = b + caAXt-1 + C2AXt-2 + et,

where

AXt is the difference earnings at time t--i.e.,

AXt = Xt-Xt-. 6

b is the intercept, and

e, is the earnings surprise.



5 The discussion above suggests that each earnings shock of a firm
could have different persistence. In terms of the MR model, the r could
be different for each shock. Hence, in a multi period framework,r would
carry a time subscript. Operationally, however, it is not possible to
calculate the persistence of individual earnings shocks when the time-
series properties of earnings are used to estimate persistence.
Consequently, persistence estimators typically measure the average
persistence of all the earnings surprises during the period of
estimation. Such average persistence of all the earnings shocks is
called the persistence of the firm's earnings.


6 The earnings series were difference by KL to obtain a
stationary series.









Conditional on the assumption that the expectations of future

earnings can be approximated by equation 1, simple techniques in time-

series analysis enable the calculation of the effect of a current

earnings innovation (et) on the revisions in the expectations of future

earnings (see appendix 1 for details). The present value of these

revisions (denoted as PVR), calculated using an assumed discount rate,

is the KL measure of persistence. Firms with high PVRs are interpreted

as having high persistence, and vice versa. Kormendi and Lipe's notion

of persistence, therefore, empirically extends the MR notion to a multi-

period framework, and estimates the revisions in expectations for all

the future periods.7

2.4 A Comment Regarding KL Specification

The proper estimation of persistence under the KL specification

requires the identification and estimation of the underlying process

for each sample firm.8 A popular way of estimating persistence is to

avoid the problem of identification of the underlying process for each

sample firm by assuming that the earnings process of all the firms can



7 Present value of revisions (PVR) is not the same as the measure
of persistence employed in the earlier studies. To illustrate this,
consider two firms, both of which have an identical $10 innovation in
earnings at time t. For firm 1, this innovation results in a $8
revision in expectation in time t+l and no revisions in any subsequent
period. For firm 2, this innovation results in $.25 revisions for each
of the future time periods up to the sixth period. According to the K-L
measure firm 1 would have a higher persistence, whereas according to the
definition used in earlier studies, firm two, which experiences the
effect of the first period shock for as long as the sixth period, has
higher persistence.

8 The process of identification and estimation is likely to involve
errors in judgement which are directly reflected in the estimates of
persistence. Such estimation errors are independent of the errors in
the specification.







10

be described by a certain process such as an autoregressive process of a

specified order [See Lipe (1986) and Kormendi and Lipe (1987)]. This

creates a problem since the underlying process may be quite different

from the one assumed. If the underlying model is some other ARMA

process, such as a moving average process instead of the autoregressive

process assumed by KL, the magnitude of estimated persistence would be

altered [see Collins and Kothari (1987, figure 1)]. To illustrate this,

figure 1 shows the effect of calculating the KL persistence measure for

different orders of the autoregressive process for six firms based on

time-series data from 1950-1985. The X-axis indicates the assumed order

of the autoregressive process and the Y-axis the magnitude of

persistence of net income (before extraordinary items and discontinued

operations). Both the magnitude of the estimated persistence and the

rank-ordering of firms with respect to persistence change when the

underlying assumption about the expectation model is altered.9 Both of

these errors arise out of the need to specify an expectation model so

that the innovation can be estimated. While it is not clear that

specification errors resulting from these sources systematically biased

KL's results, it is quite likely that attempts to reduce these errors

could result in better estimates of persistence.




9In this example, the assumption is that all the underlying firms
can be described by an autoregressive process. Based on this
assumption, the effect of altering the order of the process was revealed
in figure 1. In reality, each firm could be described by a different
process. Firm 1 may follow an Arma (1,3) process while firm 2 may
follow an arma (3,2) process. In such a case, the assumption of an
autoregressive process, in addition to the assumption of the order of
the process would be in error.









11














28

26

24

22


4)
U 20





14


8
10

a




2 4 6 7


Order of the autoregressive process








Kormendi And Lipe Measure Of Persistence
For Different Lags


Figure 1









2.5 Cochrane's Specification

This specification is based on the premise that any series can be

modeled as the sum of two components, a permanent component and a

temporary component. An estimate of the permanent component is the

suggested measure of persistence. An intuition behind this measure can

be obtained by examining two extreme processes illustrated in the

following two cases.

Case 1

Consider the following model:

(2) xt = xt_- + ut.

In this case, the earnings at time t (xt) depend only on the past

earnings (xtl) and a white noise error term (ut). In this case, all the

innovations are permanent and are incorporated in the expectations of

future earnings. To see this, we can write out the equation for

xt1--i.e.,

(3) Xt-1 = Xt-2 + Ut-1l

Substituting for xt- in equation 2 we have

xt = ,t-2 + ut-1 + ut. By recursive substitution it can be shown

that

(4) ix = Ut-k + ut-k-1 + ... + Ut-i + Ut.

Thus, all the past innovations are imbedded in the expectations of

earnings at time t. All the innovations are permanent and the series

itself has only a permanent component since no part of the earnings is

transitory. This is the familiar random walk model.

Case 2

If all the innovations are transitory, then the current earnings









innovation does not cause a revision in the expected future earnings.

This can be illustrated with the following model:

(5) xt ut

In this case, earnings are modeled as white noise. Consequently, the

current earnings innovation, ut, does not affect the expectations of

earnings in the future. The expectation of earnings at any point is

measured as E(ut) which is zero. In case 2 all the innovations are

transitory and the earnings do not have a permanent component.10

These two cases illustrate that if the current earnings innovation

does not result in any revisions in the expectations of future earnings,

then such innovation is transitory (case 2). If it results in some

revision of expected future earnings, it has a degree of permanence

(case 1). Persistence measures the extent to which the innovations are

permanent. This was identified as the r coefficient in the MR model.

These two cases further illustrate that if all the innovations are

permanent, then the series itself would have only a permanent component

and no transitory component (Case 1). On the other hand, if all the

innovations are transitory, then the series would have only a transitory

component and no permanent component (Case 2). Most earnings series



10 Case 2 is clearly a hypothetical process and is used for
illustration only. It is difficult to conceive of any earnings process
which is pure white noise. To illustrate the idea that current earnings
innovation does not affect future expectations, we can construct a
similar case where earnings are described by the following process xt =
a + ut. In this case, the current innovation, ut, does not affect the
expectation of xt+k which is a. The permanent component is an
intertemporal constant, a. Innovations in earnings do not affect the
permanent component (a) unlike the random walk model where all the
innovations are incorporated in the permanent component. The extreme
white noise process was chosen to highlight the effect of the temporary
component.









fall between these two extremes.11 Thus, earnings surprises typically

have both a temporary component and a permanent component. By

inference, therefore, the earnings series also has both a permanent

component and a temporary component. The Cochrane specification

directly estimates the permanent component of the series. The estimated

magnitude of the permanent component is Cochrane's suggested measure of

persistence.

2.6 Comparison With The KL Specification

In the KL framework, persistence is defined as the present value

of future revisions in expected earnings. The revisions themselves are

estimated from a time series model such as an autoregressive model which

is assumed to be inter-temporally constant. Thus, the KL specification

measures the cumulative effect of a shock on the level of a series. On

the other hand, the Cochrane specification adopts the notion that if the

permanent component of a series is high, then the shocks or innovations

are highly persistent and vice versa. This is clear from the two cases

illustrated above. In case 1, all the shocks are persistent and the

series has only a permanent component. In case 2, however, none of the

shocks is persistent and this series has no permanent component. Thus,

measuring the magnitude of the permanent component of the series should



11 This assertion is not without basis. If most of the earnings
series fell into either of the two extremes, then there would be little
variability in any estimate of persistence. The results of this
research (reported in chapter 5) and those in KL (Table 1 of KL) suggest
significant variability in persistence. In addition, the results in a
companion paper with Loungani, applying recent tests, (Thiagarajan and
Loungani [1989]) suggest that the random walk hypothesis can be rejected
for annual earnings. This suggests that earnings are a mixture of these
two extreme processes.







15

reveal the persistence of the shocks in the series without the need for

modeling the series and estimating the unobservable innovations. This

is the intuition behind the Cochrane specification wherein the magnitude

of the permanent component of the observed series is measured directly.

The Cochrane specification focuses on measuring the permanent component

of the observed series. As explained in the appendix 1, the permanent

component can be estimated from the autocorrelations of the observed

series and does not require the estimation of the unobserved innovations

or the unobserved revisions arising from such unobserved innovations.

Since the innovations need not be estimated, there is no need for an

expectations model in the Cochrane specification. Thus, the problems

resulting from estimation error arising from the need to parameterize an

expectations model are eliminated. Some of these problems are

associated with the KL specification and were discussed in section 2.4.

Furthermore, the Cochrane specification provides more information about

the revisions than the KL specification. In particular, the Cochrane

specification measures both the sum of the future revisions and the

variance associated with such future revisions whereas the KL

specification measures only the sum of future revisions, appropriately

discounted. This relationship is explained further in appendix 1. The

Kormendi and Lipe specification, however, has the advantage of

conforming to the intuitive notion of persistence as the present value

of the future revisions in the series, for a given innovation.

In summary, the Cochrane measure captures the long term effect of

an innovation as does the KL specification but avoids the need to have

an expectation model and thereby avoids some of the problems of







16

estimation inherent in the KL specification. In addition, the Cochrane

specification provides additional information about the nature of

revisions in the series. Both the Cochrane and the KL specifications

are used for estimating persistence in the first essay, which examines

the economic determinants of earnings persistence. They are also used

in the second essay wherein the association between persistence and the

earnings response coefficient is examined.

2.7 Estimation Of Cochrane's Measure Of Persistence

To provide an intuitive description of the estimation procedure of

the Cochrane's measure of persistence, we can express the earnings at

time t, Xt, as the sum of two components: (1) a permanent component Zt

and (2) a transitory component Ct. Thus,

(6) Xt = Zt + Ct.

We can rewrite this equation in first differences as

(7) AX, = AZt + ACt.12

Cochrane suggests measuring persistence with the ratio

Var(AZt)/Var(AXt). This ratio measures the fraction of the variance of

the difference earnings series that is accounted for by the variance of

the permanent component. This ratio is directly related to the size of

the permanent component of the series. This definition of persistence

provides an appealing measure of the relative strength of the permanent

component.




12 The series is difference for two reasons: (1) to make it
stationary and (2) to get finite bounds on the measure of persistence.
Since first differencing may not make all the series stationary, in
empirical tests all the series that were not stationary on first
differencing were eliminated. The details are in Chapter 5.










Cochrane shows that in finite samples this ratio can be

consistently estimated by taking a linear combination of the sample

autocorrelations of the difference series. This can be expressed in

the following equation:


Var(AZt) k k-j
(8) = 1 + 2 Z r
Var(AXt) j=1 k

where

rj is the jth autocorrelation of the difference series AXt and k is

the number of lags up to which the autocorrelations are estimated. This

is an appealing measure of persistence, because the correlation between

AXt and AX,+j, captured by Fr, measures the extent to which the

innovation at time t is felt at time t+j. By including the appropriate

number of lags one can obtain the relative strength of the permanent

component.13

2.8 Estimates Of Persistence Based On Analysts' Forecasts14

Kormendi and Lipe define persistence as the discounted sum of the

revisions made in the expectations of future earnings arising from the




13 Monte Carlo simulations by Lo and Mackinlay (1987) and by
Campbell and Mankiw (1987) indicate inclusion of too many lags may
result in unreliable estimates of persistence. It is difficult to
define "too many" since it depends on the series. As a rule of thumb,
one could adopt the suggestion of Lo and Mackinlay, in their
specification test for random walk, to restrict the lag length to one-
eighth of the sample size.


14 Lipe (1986) proposes another measure of persistence which is
very similar to the KL measure, except that the expectation model used
to estimate the innovations is a first-order autoregressive model
instead of the second-order specification used in KL. This is not
discussed because it is a simplified version of the KL model.







18

current earnings innovation. Under this definition, analysts' forecast

revisions can be used to measure persistence directly, as long as their

revisions are available for several periods in the future. However,

such revisions are available only for very short periods into the

future.15 This requires an assumption that the short term revision is

sufficient for capturing the effects on all the future periods. This

assumption might be problematic especially in light of the Brown et.

al., (1985) finding that the capital market appears to employ a multi-

year forecast horizon. If the market adopts a multi-year perspective,

then the market agents may use the information in the current earnings

innovation to revise expectations for a long period into the future.

Consequently, calculating persistence based on revisions over the short

horizon might result in estimates of persistence very different from

those estimated by market agents which is implicit in security prices.16

Analysts' forecasts have been employed in recent studies to estimate

persistence [e.g., Easton and Zmijewski (1986) and Regier (1986)]. This

approach is not used here because of potential differences in the

magnitude of revisions of expectations for short versus long horizons.










15The revisions are available for about four quarters ahead from
the Value Line Investment Survey.

16 It is tempting to conclude that the use of the short-term
revisions of analysts' forecasts results in systematically downward
biased estimates of persistence. This may not be true in all the cases
as trend reversions can cause an innovation to have alternating effects
on expectations over the long horizon.















CHAPTER 3
MOTIVATION AND POSSIBLE ECONOMIC DETERMINANTS


This chapter provides the motivation for the examination of the

economic determinants of earnings persistence and outlines the

determinants chosen for analysis in the first essay.

3.1 Introduction

This essay examines some potential determinants of earnings

persistence. In their overview of the research issues in time-series

analysis, Ball and Foster (1982) indicate "at present our knowledge as

to why certain statistical properties are found for the annual earnings

series of firms is very meager indeed" (page 212). The proposed

research investigates the reasons for cross-sectional differences in one

statistical property of annual earnings--persistence. Specifically, the

objective is to address the following research question: "What is the

role of industry factors and more importantly firm-specific factors in

explaining cross-sectional differences in persistence?"

3.2 Motivation

Prior research has examined the relationship between persistence

and security prices in general and earnings response coefficients in

particular [KL and Easton and Zmijewski (1986)]. Market efficiency

suggests that investors use the information about the resource stocks

and flows of the firm to form their expectations about the future.









By positing a positive association between persistence and earnings

response coefficients, KL assume that the estimated measure of

persistence captures, in a meaningful way, the economic factors that

influence the market's assessments of future earnings/cash flows. The

observed positive association between earnings response coefficients and

persistence suggests that persistence has valuation implications.1

This, in turn, implies that persistence should be related to the

underlying firm-specific variables which generate firm value. This link

is illustrated in figure 2. To be meaningful for valuation, therefore,

persistence must reflect information about the real resource stocks and

flows induced by the firm's decisions and other factors, such as

industry factors, which describe the firm's economic environment. It

is, therefore, important to examine the extent to which persistence

reflects the economic factors which influence the resource flows of the

firm. Explicit documentation of the association between persistence and

the firm-specific variables would confirm that persistence is a

meaningful construct. In addition, documentation of the association

between persistence and firm-specific variables could provide evidence

of the information content of extant financial disclosures. Such

explicit documentation is one of the goals of the research in this

essay.

A question of significant interest to accounting research is

whether different accounting techniques contribute to cross-sectional



1 The tests of KL are joint tests of the validity of the underlying
model and the robustness of their measures. This research investigates
whether estimates of persistence are related to decisions within the
firm which affect the value of the firm.



















EFFICIENT MARKET
HYPOTHESIS


EXISTING RESEARCH
Kormendi and Lipe (1987)
Easton and
Zmijewski (1986)


PROPOSED RESEARCH


ECONOMIC STUDIES
Scherer (1980)


Framework For The Research Question


Figure 2









differences in persistence of accounting income. This is of interest

because persistence has been documented as having valuation implication.

If accounting techniques are found to be meaningfully associated with

persistence, then it could be inferred that accounting techniques have

economic consequences.2 Examination of the association between firm-

specific factors and persistence can help address this question.

If the determinants of earnings persistence are not affected by the

choice of different accounting techniques, then we may be able to infer

that accounting techniques have either no effect or only a second order

effect on persistence. Identifying the firm-specific factors is the

first step in this process.

3.3 Economic Determinants Of Persistence--The Framework

Income arises from past firm-specific decisions (e.g., investment

and operating decisions) and is influenced by a variety of factors at

the industry level such as labor conditions and barriers to entry.3

From an economic perspective, income has a fundamental role--that of a

"driving force for change" [Lev (1983), p. 33]. This perspective

implies that income levels and income trends signal the course of future



2The inference that accounting techniques may have economic
consequences arises from the valuation implication of persistence. If
the component of persistence attributable to accounting techniques can
be isolated, a direct test of the economic consequences can be
performed. Isolation of such a component, a formidable task, is outside
the scope of this dissertation.

3 A barrier to entry is a condition which prevents potential rivals
from entering the market and competing away the advantage enjoyed by the
incumbent firms. This can take several forms such as absolute cost
advantage enjoyed by the incumbent firms in the form of scale economies,
or economies in purchasing, capital raising or promotion not attainable
by the rivals. [See Scherer(1980), Chapter 8 for a discussion of the
forms of barriers to entry.]







23

action both at the industry level (such as entry into and exit from the

industry) and at the firm level (such as change in the level of research

and development expenditures, investment in new products).4 Since

earnings result from past firm-specific decisions and also provide the

guidelines for future decisions, such as investment decisions, an

association can be expected between these firm-specific decisions and

the stochastic properties of the earnings series.5 In this essay, an

association is posited between some of the these firm-specific decisions

and one property of the earnings series--persistence.

Earnings persistence is a characteristic of the earnings process

which reflects the extent to which an innovation (or earnings surprise)

results in revisions of expected levels of future earnings. It can also

be viewed as a measure of the magnitude of the permanent component of

the earnings series (see appendix 1). In an efficient market economy,

where the flow of resources to activities earning excess profits is

relatively quick, profits above the equilibrium level should quickly



4Both historical income and expected future income provide
guidelines for future action. Expectations for future income are formed
from trends of past historical income.


5This relationship is essentially dynamic. Past decisions affect
current income and current income and income trends affect future
decisions and so on. Thus, over time, the stochastic properties of
accounting income should be related to the general pattern of firm-
specific decisions.

6 It is natural to think that since the effects of all these firm
specific decisions eventually flow through the income statement, there
should be relationship by construction. It needs to be noted that
persistence is a well defined specific property of the series. It is
not at all clear there would be a systematic relationship between this
property of earnings and an accounting policy such as expensing of the
research and development expenses.









disappear. This implies that persistence of profits in any industry

will attract new capital to the industry and persistence of losses will

detract capital away from the industry. If this process is quick and

efficient, the firms in any industry would earn only the long-run or

equilibrium rate of return and cross-sectional differences in

persistence across industries would be minimal. Consequently, existence

of different levels of persistence across industries should reflect

certain characteristics at the industry level (such as entry barriers)

which may contribute to this disequilibrium. Similarly, existence of

different levels of persistence across firms within an industry should

reflect certain firm specific actions (e.g., product differentiation).

Persistence can, therefore, be characterized as a function of both

industry factors and firm-specific factors as illustrated in figure 3.

The proposed examination of the determinants of persistence is

restricted to the chosen firm-specific factors. Consequently, the

proposed examination recognizes the industry-level characteristics and

examines certain firm-specific actions which are hypothesized to result

in differential persistence.

3.4 Persistence Of Industry Earnings

Several industry-specific factors influence a firm's earnings

series--e.g., barriers to entry, demand variability, product type [see

Lev(1983)]. For example, an industry may have several barriers to

entry, such as the scale of operations required to enter the industry

and legal restrictions in the form of licenses.















FACTORS INFLUENCING PERSISTENCE


Industry Factors


Firm Specific Factors


Nature of the product
Demand variability
Labor conditions
Barriers to entry


Investment Decisions Other Decisions



Capital expenditures
R&D
Advertising


Classification Of Factors Influencing Persistence

Figure 3


The persistence of profits in such an industry (and hence the

persistence of profits of the firms in that industry) is likely to be

higher than the persistence in an industry which does not have such

barriers to entry. Since the primary objective of the first essay is to

examine the relationship between earnings persistence and selected firm-

specific variables, the industry-specific determinants of persistence







26

must be controlled.7 One way of controlling for industry factors is to

develop appropriate empirical surrogates for all the variables affecting

industry earnings. An alternative is to use the persistence of industry

earnings to capture the effects of all such industry factors on

individual firms' earnings persistence. The second approach is adopted

here. It is expected that the higher the persistence of the industry's

earnings (for whatever reason), the higher the persistence of the

earnings of a firm in that industry. Thus, industry persistence should

be positively related with firm-specific persistence.

3.5 Firm Specific Factors

The firm-specific decisions which could influence earnings include

investment decisions, labor decisions, production decisions and

financing decisions. All these decisions could potentially influence

the firm's earnings persistence. However, for parsimony and given the

difficulty in obtaining data on other decisions (such production

decisions in the form of product quality changes), the proposed

empirical examination is restricted to the role of the firm's investment

decisions in determining persistence. Investment decisions are defined

broadly to include research and development (hereafter, R&D) and

advertising which have potential long-term effects and arise from the

discretionary investment spending of the management. Since management's

investment decisions have a major impact on the stream of future




7The specific industry level determinants of persistence are
interesting in themselves (see appendix 2 for some results in this
regard). However, a majority of these determinants (such as entry
barriers) are outside the firm's control. The scope of this essay are
restricted to controllable firm-specific factors.










earnings, these decisions are expected to be a major determinant of

persistence.

In summary, out of the several factors which influence earnings

persistence at the firm level, this research examines the role of

investment decisions as potential explanators of cross-sectional

differences in earnings persistence. The framework for the choice of

factors and the hypothesized direction of their effects on earnings

persistence are based on the industrial organization literature in

economics [Scherer (1980)].

3.5.1 Investments In Property. Plant And Equipment.

Investment provides evidence that management has identified

positive net present value projects and an earnings innovation confirms

these expectations. A high rate of investment indicates that management

expects sustained positive earnings innovations from their investments.

In addition, increased investment in property, plant and equipment

enables the firm to increase the scale of operations and potentially

reduce the long-term average cost of production. Sustained investment

in profitable projects coupled with the reduction in the average cost

imply that the persistence of earnings for such a firm should be high.

In other words, high rates of investment are expected to result in high

persistence of earnings. This conjecture is supported (at the industry

level) by Stigler's (1963) finding of a positive correlation between

expected profits and investment growth in the non-recession years and by

Wenders (1971) finding of a positive association between profitability










and asset growth. Thus, the expected relationship between rate of

investments and persistence is positive.8

3.5.2 R&D And Advertising

Industrial organization literature suggests that both R&D and

advertising create barriers to entry that enable incumbent firms to

sustain high profits. This is done by discouraging firms seeking to

enter the industry through increased production costs, R&D and

advertising costs that have to be borne by the potential entrant [see

Clarkson and Miller (1982, chapter 16)]. Barriers to entry enable the

firm to sustain high profits by discouraging competition resulting in

persistence of such high profits. Thus, R&D and advertising are

potential determinants of persistence.

Research and development. Investments in research and development

might result in new products and/or cost saving technological change,

resulting in higher profits over a long period of time.9 The higher the

R&D expenditure, the better the position of the firm to discover and

develop new products or lower costs and sustain the profits arising out

of such products/technological changes. Empirical support for this

expectation can be found in Branch (1974) whose results support the

causal links from profitability to R&D efforts and from R&D efforts to

increased profitability. Thus, incremental spending on R&D is expected



8If there is a decline in earnings which is perceived to be
permanent, there is likely to be disinvestment in order to reduce the
persistence of losses. In a growing industry, investment is positively
associated with persistence of profits and in a declining industry,
investment is positively associated with persistence of losses.

9 Registered patents arising out of R&D efforts can be retained for
17 years.










to result in persistence of profits and persistence of high profits is

likely to encourage R&D spending."1 Therefore, the expected

relationship between persistence and the magnitude of R&D is positive.

Advertising. Economic studies [Comanor and Wilson (1977), Weiss

(1969)] indicate that high advertising expenses create a barrier to

entry and result in high persistence of profits. A firm which

advertises heavily, relative to the industry, may be able to create

"brand loyalty" and thus reap sustained high profits. Thus, the more

the firm spends on advertising, the greater their chance to preserve an

earnings increase arising out of the demand so captured. Thus, the

magnitude of advertising is expected to be positively associated with

persistence.

3.6 Effects Of Financing Decisions

Earnings are affected not only by the management's decisions

concerning investments, advertising and R&D, but also by financing

decisions. Different capital structures result in different finance

charges, and hence differences in earnings behavior." Since the

framework for empirical investigation is provided by the investment

decisions of the management, the effects of different financing

decisions are outside the scope of this study. An additional reason for

excluding the effects of different financing decisions is the absence of



0Just as increased profits arising out of R&D is likely to spur
increased R&D spending, increased profits from capital expenditure and
advertising are also likely to encourage further spending on such
investment.

1 Differences in capital structure result in different earnings
levels which could result in different time series properties.









a clear direction of the effects of different choice on persistence.

For example, it is not clear that the choice of debt over equity would

have a systematic effect on persistence. Therefore, this study

abstracts from the effects of financing decisions on persistence by

defining persistence in terms of operating income, rather than net

income. Operating income excludes interest income and interest

expenditure and hence abstracts from the effect of financing decisions.

3.7 Effects Of Accounting Techniques

A question of considerable interest is whether cross-sectional

differences in the choice of accounting methods can explain differential

persistence of reported accounting income. As indicated earlier (in

chapter 2) this is of interest because of the observed valuation

implication of persistence (Kormendi and Lipe). If choice of accounting

methods is a determinant of persistence, then, by inference, accounting

methods have economic consequence. This question arises because

accounting income is not only affected by the firm-specific decisions on

investments, R&D etc., but also by the accounting techniques used to

measure them. Consequently, the estimated persistence of accounting

income is likely to be influenced by the choice of accounting

techniques. The effect on persistence arises due to two sources. The

first source is the independent effect of different choices due timing

differences. To provide an example, the income numbers of a firm using

straight line depreciation will be different from those of a firm using

declining balance methods. The timing difference from this source gets

reflected in the measured persistence. The second source is the

interactive effect. To continue the prior example, the firm using










straight line depreciation could be making systematically different

investment decisions compared to a firm using declining balance methods.

The economic consequences literature posits interdependence between the

choice of accounting methods and the substantive attributes of the

firm's decisions. The choice of accounting methods may affect

management's decisions about production, investment, marketing and

financing (PIMF) because of links arising out of management compensation

and contracting costs [see Gonedes and Dopuch (1979) and Watts and

Zimmerman (1986)]. These effects may be explained as follows:

Accounting Income = f(economic environment;

firm's production,

marketing and finance [PIMF]; and

accounting techniques).

Firm-specific Persistence (FSP) = F(Accounting Income).

Therefore, FSP = G(economic environment, PIMF and

accounting techniques).

Economic consequences theory suggests that

PIMF = h(accounting techniques).

This study captures some of the aspects of the economic

environment through the industry persistence measure. Production,

investment and marketing decisions are captured by the investment,

advertising and R&D variables and abstracts from the effects of

financing decisions.12



12 It is expected that most (although not all) aspects of
production, investment and marketing decisions may not be captured by
the investment, advertising and R&D variables.









In summary, the choice of accounting methods may have both an

independent and an interactive effect on the measured FSP. The research

interest arises primarily because of the interactive effect of

accounting techniques on FSP through management decisions on PIMF. The

independent effect of choice of accounting methods on persistence could

pose a validity threat to the study and confound the effects of the

economic determinants.13 That is, any difference in persistence across

firms could be attributed to the economic determinants when it may have

arisen from different accounting methods.

To provide some insight into the effect of accounting techniques

on persistence, this study employs two procedures: First, the analysis

is replicated using two different dependent variables which are expected

to be less affected by choice of accounting techniques than is operating

income.14 These variables are (1) persistence of sales and (2)

persistence of cash flow from operations (CFO). Differential choice of

accounting techniques affects sales much less than operating income [see

Lev (1983) for a similar treatment]. However, sales may not capture all

the effects of the independent variables. For example, cost savings

arising out of R&D will not be reflected in sales. This implies that

there may be some loss of information in the persistence of sales.




13 The interactive effect poses less of a validity threat. If
accounting techniques have an interactive effect, then it should be
considered another or part of the other economic determinants.

14 An alternative way of examining whether the estimated measure of
earnings persistence, based on accounting income, reflects real resource
flows is to examine the association between firm specific persistence
and non-accounting variables such as production, employment etc. This
is investigated in a related paper.









Consequently, the analysis is also replicated based on the persistence

of CFO as a second proxy for persistence of earnings. The advantage of

this proxy is that it is likely to reflect more of the effects of the

economic determinants; the disadvantage is that it may not be completely

independent of the effects of accounting techniques.15 Replication of

the analysis using these two different dependent variables should enable

us to infer if the choice of accounting techniques affects the

relationship between the hypothesized determinants and firm-specific

persistence. If this procedure indicates that choice of accounting

techniques has an effect on the estimated persistence, then the

inference is that such an effect could be due to either the independent

effect due to the timing differences inherent in the different methods

of accounting or due to economic consequences or a combination of both.

It would be possible to isolate these two effects if we could specify

the direction and/or magnitude of either the timing difference effect or

the economic effect. Unfortunately this is not an easy task for either

the independent effect or the interactive effect.16 Hence, the most




15The reason why this may not be completely independent of the
effects of accounting techniques is because the cash flow variable is
generally derived from the income variable after removing the effects of
all non-cash items such as depreciation and amortization. Consequently,
some of the effects of items such as choice of inventory accounting
method is likely to be reflected in the derived cash-flow variable.

16 Specification of the independent effect involves the delineation
of all combinations of the different accounting choices and the
examination of the effect of each of the combination on persistence.
Since the nature of relationship between accounting choices and
persistence is not well specified, it is not possible to specify the
nature and magnitude of the interactive effect. For example, the effect
of a portfolio of accounting choices such as an income-decreasing
strategy on persistence is not clear.







34

that could be done is to perform overall tests to check if differential

choice of accounting methods makes any overall differences in the

inferences to be drawn.

Analysis of the interactive effects of accounting techniques on

persistence is interesting due to the potential insights on the economic

consequences of the choice of accounting methods. However, a rigorous

analysis is hampered due to two reasons: (a) limited data availability

of detailed information on firm's accounting techniques across time and

(b) inability to postulate an unambiguous direction of the effects of

any particular choice of accounting technique on persistence. The scope

of this study is limited to examining if cross-sectional differences in

choice of accounting techniques have an effect on persistence.

Specification of the source of such an effect, if it exists, is outside

the scope of this dissertation.

3.8 Conclusion

The variables chosen as possible determinants of persistence are

based on economic reasoning supported by empirical findings found in the

industrial organization literature. This study does not claim that

these variables constitute a complete structural model of the

relationship between a systematic property of accounting numbers,

namely persistence, and the attributes of firms' decisions.

It is hoped that the empirical relationships observed in this study will

pave the way for a more rigorous model of the determinants of earnings

persistence.















CHAPTER 4
MODELS, MEASUREMENT OF VARIABLES AND SAMPLE CHARACTERISTICS


4.1 Basic Model

The analysis involves a cross-sectional regression with firm

specific persistence (FSP) as the dependent variable. Since FSP is a

function of both industry factors and firm specific factors, as

explained in chapter 3, the independent variables include industry

variables and firm specific variables. The industry factors are

controlled through the measure of industry specific persistence (ISP).

ISP is used as a covariate in the proposed cross sectional regression.

The other independent variables are the firm specific variables which

include investment, R&D and advertising variables. FSP and ISP are

primarily operationalized through Cochrane's persistence measure which

is calculated as a linear combination of autocorrelation coefficients.1

Since autocorrelation coefficients are scale free, the estimates of FSP

and ISP are also scale free. The other independent variables, the firm

specific economic factors, are normalized by sales to make them










1 To test for robustness of the association between the independent
and the dependent variables, the basic model is replicated using the KL
measure of persistence. This is explained further in the chapter.

35









independent of the units of measurement.2 This process makes the

definition of all the variables consistent. All the firm specific

variables are defined as averages calculated over the sample period.

This definition makes the independent variables compatible with the FSP

and ISP which are based on the average magnitude of the permanent

component of all the shocks in the earnings series of the firm and the

industry, respectively. Based on this reasoning the following cross-

sectional model will be estimated:

(9) FSPij = a + Po ISPj + fi Average investment(n)ij

+ 02 Average R&D(n)ij

+ P3 Average advertising(n)ij + eij,

where

FSP is firm-specific persistence

ISP is industry-specific persistence

n denotes the respective variables being normalized by sales

i = 1,...,p is the firm subscript

j = 1,...,q is the industry subscript.

Since each of the economic determinants identified in sections 3.4

and 3.5 are expected to be positively associated with FSP, the expected

sign on all the P's is positive. Conceptually, the beta coefficients in

this model measure the extent to which a dollar increase in normalized

investment impacts on the permanent component of operating earnings.




2 Normalizing by sales also helps control for size effects in the
independent variable. For example, the magnitude of advertising, one of
the selected independent variables, may be influenced by size but
persistence would not be so influenced, because autocorrelations are
impervious to scale.









4.2 Measurement Of Variables

FSP. Firm-specific persistence (FSP) is calculated for each firm

using Cochrane's specification. As indicated earlier, Cochrane suggests

the limit of the ratio of variances as a measure of persistence. This

ratio is defined in the following way:

1 Var (xt+k xt)

k Var (xt+l xt)

where

x, denotes the value of the difference operating income at

time t and k denotes the lag length.

It is important to choose the appropriate lag length relative to

the sample size. Campbell and Mankiw (1987) caution that including too

few lags may obscure the trend reversion manifested in higher

autocorrelations. On the other hand, including too many lags, relative

to the sample size, results in a downward bias in the estimates of

persistence.3 Moreover, Lo and Mackinlay (1987) [LM] find that lag

lengths in excess of one-eighth of the sample size, result in unreliable

inferences.4 Using this rule of thumb to estimate persistence, this

study limits the lag length to one-eighth of the available time-series

observations for each firm in the sample. Since the minimum number of



3 Campbell and Mankiw (1987) find it difficult to distinguish
between a random walk and an AR(2) process in their Monte Carlo
simulations for large lags (relative to the sample size) due to the
downward bias in the persistence measures calculated using large lags
[See Campbell and Mankiw, p. 113, Table 1]. It is important to note
that sample size here indicates the number of time series observations.

4 The suggestion of LM was made in the context of a specification
test for random walk. However, since the specification test is based on
variance ratios, this benchmark may be applied here.







38

time-series observations (on earnings) available for each sample firm is

35 (the maximum is 36), FSP is calculated for each firm using a lag

length (k) of five.5

To improve the small-sample behavior of the estimates of

persistence, this study adopts both the suggestions of Cochrane(1988).

In the first place, unbiased estimates of variances are used by

calculating s2 instead of a2. Secondly, the sample mean of the first

differences is used as an estimate of the drift term in the process that

describes the permanent component instead of estimating a different

drift term for each k.6 Without these corrections, there is a downward

bias in the estimates of persistence for finite sample as the lag length

is increased (relative to the sample size).

To control for non-stationarities arising out of price level

changes, the operating income is adjusted for price level changes using

the GNP deflator.

In summary, FSP is measured by the ratio of the unbiased variance

of the fifth lag of the difference real operating income series to the






5 One fifth of 35 is 4.38. Since the lag length cannot be in
fractions it is rounded to the next highest number. Preliminary
investigation of my sample indicates that the rank correlation between
estimates of persistence using lag length of four and lag length of five
is .953 (product moment correlation is .98). This suggests that
inferences based on persistence estimates based on lag length of five
would not be very different from those based on lag length of four.

6 While the intuition behind the use of s2 is clear, the intuition
behind the estimation of the drift term is not that obvious. This
correction is required to prevent the variance of k differences from
declining toward zero as k, the lag length, tends toward t, the sample
size (see Cochrane[1988] for details).







39

unbiased variance of the first lag.7 The operating income series is

obtained from Compustat.8

ISP. Industry specific persistence (ISP) is calculated in the same

manner as FSP using the aggregation of sample firms' earnings in the

different industries.9

Average investments(n). The normalized investment for each firm

i, for each year t is defined as

Investment in property, plant and equipment

Salesit.

The average of such ratios calculated over the sample period forms the

average investment(n) for each firm in the sample. Investment in

property, plant and equipment of firm i at time t is obtained from

Compustat item thirty. Additions to property, plant and equipment due




7 Cochrane's measure of persistence can be calculated from the
estimated sample autocorrelations. In this study, FSP is estimated
directly as a ratio of variances. There is no difference in these two
methods of estimating persistence.

8 The operating income is obtained from Compustat items thirteen
(operating income before depreciation) and fourteen (depreciation).

9 Industry-wide aggregate earnings are not available on the
Compustat tape for the early years of the sample. In order to have a
consistent definition of industry earnings, over time, an equally
weighted index of industry earnings was created from the sample firms.
This aggregation of earnings was made out of the firms in the sample at
the two-digit SIC industry code level. To provide an example, if there
were two firms one with the SIC industry code of 2800 and the second
with a code of 2834, both of these firms had the same industry
persistence as a control for industry factors. Such industry
persistence was measured on a series of industry earnings formed from
the aggregation of the earnings of all the firms with the 2 digit SIC
code of 28. The limitations of this definition are discussed in chapter









to merger activity are included in the definition of investment. The

reason for this is that operating income, which provides the basis for

the measurement of the dependent variable, is likely to be influenced by

such additions. The sales for firm i at time t is obtained from

Compustat item twelve.

Average R&D(n). The normalized value of R&D is calculated for

each firm 1, for each year t, as the ratio of R&D to sales. The average

of such ratio calculated over the sample period forms average R&D(n) for

each firm. The R&D for each year is obtained from Compustat item forty-

six and the sales from item twelve.

Average advertising(n). The average magnitude of advertising is

calculated in a manner analogous to the calculation of the average

R&D(n). Advertising for each firm in each year is obtained from

Compustat item forty-five and the sales from item twelve.10

4.3 Kormendi And Lipe Model

This study compares the results of the basic model (equation 1)

across both the definitions of persistence discussed in Chapter 3,

namely the nonparametric measure suggested by Cochrane and the

parametric measure employed by Kormendi and Lipe. If the nature and the

strength of the relationship is similar across the two definitions, then

Cochrane's nonparametric approach provides only a computational



10 It will be noticed that the dependent variable, FSP and one of
the independent variables ISP are adjusted for price-level changes using
the GNP deflator, whereas none of the other independent variables are so
adjusted. The reason for this is that all the other independent
variables are normalized by sales and any adjustments for price-level
changes has to be done both to the numerator and the denominator. Such
adjustments both to the numerator and the denominator will be canceled
out making them equivalent to the unadjusted ratios.









advantage with respect to this sample. If the relationship is

different, then the nature and the magnitude of the relationship

specified in the basic model (equation 1) is sensitive the measurement

of persistence.1 In order to examine this, the basic model is

reestimated using the KL measure of persistence.

The Kormendi and Lipe specification is independent of the units of

measurement analogous to the Cochrane specification. Consequently, the

definition of the variables in this model are analogous to the basic

model (equation 1). To keep the measurement of FSP and ISP consistent,

both of them are measured using the KL specification and denoted FSP(KL)

and ISP(KL) respectively. The KL model may be stated as follows:

(10) FSP(KL), = a** + **oISP(KL)j

+ 8**i Average investment(n)ij

+ P**2 Average R&D(n)ij

+ P** Average advertising(n)ij

+ eij

As in the basic model, all the P** 's are expected to be positive in the

KL model.

In summary, the KL model is estimated to assess the sensitivity of

the relationship postulated in equation 1 to the measurement of

persistence. Conceptually, the beta coefficients in the KL model




11 Clearly, the relevant question here is which specification is
better. The appendix explains that the Cochrane specification provides
more information about the series than the KL specification.
If the results of the two specifications are similar, then such
additional information does not affect the determinants specified in
this study. If the results are different, it can be argued that the
such additional information does affect the determinants.









measure the extent to which a dollar increase in the normalized

investment impacts on the present value of the revisions of future

operating earnings. As suggested earlier, the KL model is conceptually

more appealing than the Cochrane model, but the purpose of the

estimation here is to assess the sensitivity of the relationship to the

nature of measurement of persistence.

4.4 Tests For The Effects Of Accounting Methods

Overall Tests. To provide some insight into the effects of cross-

sectional differences in the choice of accounting methods on

persistence, equation (1) is re-estimated using persistence of sales

[FSP(S)] and persistence of cash flow from operations [FSP(CFO)] as the

dependent variables. The models are:

(11) FSP(S)i = a' + P'o ISP(S)j

+ P3 Average investment(n)ij

+ 80'2 Average R&D(n)ij

+ 3 Average advertising(n)ij + e'ij

(12) FSP(CFO),j = a + "o ISP(CFO)j +

+ /3" Average investment(n)ij

+ /"2 Average R&D(n)ij

+ pf Average advertising(n)ij + e ij.

Both sales and CFO are obtained from Compustat.12 A priori, cross-


12In order that CFO reflect the effects of total accruals, both
current and noncurrent, but not the effects of financing decisions it is
defined as follows [adapted from Wilson(1987)]:

CFOt = OIBDt + (CL, DCLt) (CLt DCIt1)

(CAt CASH,) + (CAtg_ CASHt,1)


where









sectional differences in accounting techniques are not expected to

significantly affect the substantive relationship between the economic

determinants and FSP. Consequently, the same relationships should hold

for the persistence of sales and the persistence of CFO as for the

persistence of operating income. Thus, P coefficients in equations 11

and 12 are expected to be positive.























OIBD is the operating income before depreciation and
amortization at the end of year t (13)

CL is the current liabilities at the end of year t (5)

DCL, is the current portion of long term debt included in
current liabilities at the end of year t (34)

CAt is the current assets at the end of year t (4)

CASHt is the cash and short term investments at the end of
year t (1).

Numbers in parenthesis refer to Compustat data items. OIBD adjusts the
operating income for noncurrent accruals such as depreciation,
amortization, minority interest, deferred taxes etc., and the other
items represent the adjustment for current accruals (i.e., the net
changes in all the working capital accounts except for changes in cash,
marketable securities, and short term debt).















CHAPTER 5
SAMPLE SELECTION,DESCRIPTIVE STATISTICS
AND RESULTS OF MODEL ESTIMATION


This chapter outlines the basis for the choice of the

sample, discusses the issues concerning the operational definition of

the variables and provides the relevant descriptive statistics. This

chapter also discusses the results of the estimation of the different

models, summarizes the inferences to be drawn from the results.

5.1 Sample Selection

The sample was selected by combining data from two Compustat

tapes, the first carrying data from 1950 through 1969 and the second

carrying data from 1966 through 1985. There were several restrictions

for a firm to be included in the sample. These restrictions can be

classified as follows:

(a) restrictions concerning the measurement of persistence,

(b) restrictions concerning the formation of the industry index to

calculate industry persistence and,

(c) restrictions concerning the measurement of the firm specific

variables. Each of these restrictions is discussed in the following

paragraphs.

Restrictions concerning measurement of persistence. The

calculation of persistence requires the availability of observations on

a continuous basis. The primary restriction was the availability of










data on a continuous basis on operating and net income.1 This step

yielded 403 firms. The calculation of both the Cochrane measure of

persistence and the KL measure of persistence requires that the

difference earnings be stationary.2 Stationarity was checked by

examining the autocorrelation series of the difference real operating

income to ensure that they dampen out at higher lags. The

autocorrelation coefficients were examined upto 24 lags to ensure that

there were no nonstationary series. This procedure resulted in the

elimination of 51 firms.

Restriction required to calculate industry persistence.

Persistence of industry earnings was calculated from an index of

industry earnings formed from the earnings of sample firms.3 Hence, it

was important to ensure that the selected firms were defined to be in

the same major industry group during the sample period. The definition

of industry, in this study, is based on the 2 digit SIC code.4 The

industry affiliation of the sample firms throughout the sample period




1 The need to ensure that data on net income were available on a
continuous basis was required to enable the calculation industry
persistence on net income.

2 Stationary is required in the Cochrane specification to get
bounds on the persistence measure. In the KL specification, if the
series is non-stationary, the series would not be invertible.

3 This problem arises because aggregate data on industry earnings
were not available on the Compustat tape for the early years.

4 As explained in footnote 9 of Chapter 4, the earnings of the
sample firms were accumulated to form an index of industry earnings.
This accumulation was done at the 2 digit industry code level and not at
the 4 digit code level because there was not a sufficient number of
observations at the 4 digit level to form any reasonable estimate of
industry earnings.









was checked with the CRSP tape and the firms which had changed

affiliation at the two digit industry level during the sample period

were eliminated. This data restriction resulted in the elimination of

26 firms.

Since the earnings of the sample firms were accumulated to form an

index of industry earnings, it was important to ensure that there were

adequate number of firms in each industry to get a representative index

of industry earnings. To illustrate, in the automotive service and

repair industry (SIC code 7500) there is only one firm in the sample. To

use the earnings of a single firm to form an index of industry earnings,

would not be representative. Firms were included in the sample only if

they represented at least 20% of all firms in the industry.5 The index

of industry earnings formed using this truncation rule represented, on

an average, 36% of the earnings of each industry at the level of 2 digit

SIC code.6 This step resulted in the elimination of 29 firms.

Restrictions concerning measurement of firm specific variables.

As indicated in Chapter 4, the independent variables are capital

expenditures, R&D and advertising. While information on most firms'

capital expenditures is available on the Compustat tape, information is

not always available on the other firm specific variables, R&D and

advertising. In the utility industry, for example, there are 81 sample



5 Industry affiliation was determined by checking the Compustat
files.

6 Preliminary analysis showed that a higher truncation rule (30%)
made little difference in the estimates of industry specific
persistence. Since a higher truncation rule results in loss of firms,
all the analyses were based on industry earnings formed using the 20%
truncation rule.







47

firms with no information on R&D and advertising. Only firms which had

information available on advertising and R&D for at least 5 years during

the sample period were included.7 This procedure resulted in the

elimination of 111 firms, including 81 firms in the utility industry.

The average number of years of data availability was 12 years for

advertising and 17 years for R&D.

All the above steps resulted in a final sample of 186 firms as

summarized in table 1.


Table 1
Sample Selection

Total number of firms from two Compustat files 403
Removed because of non-stationarity 51
Removed due to change of industry 26
Removed to enable the calculation of
industry specific persistence 29
Removed due to non availability of
information on R&D and advertising 111

Final Sample 186



The next section discusses the issues concerning the empirical

specification of the variables in the different models.

5.2 Matters Concerning Empirical Specification

The basic model to be estimated,as specified in chapter 4, is as

follows:

(1) FSPij = a + po ISPj + pl Average investment(n)ij

+ P2 Average R&D(n),i

+ P3 Average advertising(n)ij + eij



7 Using a higher truncation rule, while restricting the number of
firms did not change any of the results.









The estimation plan for each of the variables was discussed in

chapter 4. However, the operational definition of some of the variables

gives rise to certain measurement issues. These are discussed in the

following paragraphs.

Specification of ISP. There are three issues concerning the

specification of ISP. The first relates to the appropriateness of the

choice of industry persistence to surrogate for the industry factors.

As indicated earlier, industry specific persistence is measured by using

the Cochrane specification of persistence on an index of industry

earnings. One way of controlling for industry-level determinants is to

develop appropriate surrogates for each determinant. An alternative

adopted in the dissertation is to use industry specific persistence. In

order to ensure that industry specific persistence does indeed reflect

some of these industry level determinants of persistence, the

association between ISP and some of these determinants was examined.

The details of this analysis is provided in appendix 2. The results

suggest that the estimated ISP reflects the major determinants of

industry persistence. Thus, the choice of ISP to surrogate for the

industry level determinants seems appropriate.

The second and the third issues relate to the actual measurement

of ISP. The second issue is the definition of income for estimating ISP

and the third issue is the definition of the sample to construct the

industry earnings index. Since ISP is used to surrogate industry level

determinants of earnings persistence, it is important that the choice of

income for aggregation to measure ISP be such that the industry level

determinants are well represented. Income could be defined as operating







49

income or net income (before extraordinary items). Net income includes

items such as cost of idle capacity, foreign exchange adjustments and

finance charges. Industrial organization literature suggests that

excess capacity could be important deterrent to entry by potential

rivals [see Wenders (1971) and Spence (1977)]. Foreign exchange

adjustments are likely to reveal characteristics about the industry such

as the nature of markets in which they operate. Finance charges are

likely to reveal information about the absolute cost advantages enjoyed

by the firms in the industry.8 Thus, in terms of being able to reflect

industry characteristics, net income seems more appropriate than

operating income.9 The analyses reported in this chapter are all based

on the definition of ISP based on net income.

ISP was measured on an index of earnings constructed from the

sample firms. The index was created by aggregating sample firms'

income. Even though the final sample is 186 firms, the aggregation of

industry earnings was based on all of the 403 firms excluding the 29

firms which were removed because of they did not satisfy the 20% cut off







8It is reasonable to argue that use of net income while reflecting
certain characteristics of the industry might introduce some noise due
to differential choice of accounting methods for items such as pension
costs or investment tax credit. This is less of a concern for industry
earnings than the earnings of the firm, due to the aggregation process.

9ISP was calculated using both these definitions of income. The
product moment correlation between these two measures is .88 and the-
Spearman correlation is .68. This suggests that the choice of the
definition of income is less of a concern with respect to the estimated
measure of industry persistence. All the analyses were repeated using
the alternative definition of ISP and produced no change in the results.









rule (see table 1). This provides a large number of firms and hence a

more representative index of industry earnings.10

Specification of firm specific variables. The firm specific

variables, outlined in chapter 4, require the calculation of the average

over the sample period. In addition, all the three chosen variables

need to be standardized by a size variable. This gives rise to certain

measurement issues. The size variable chosen for standardization could

either be sales or the book value of assets.1 The average calculated

over time could be either the median or the mean.

Each of the independent variables, R&D, advertising and capital

expenditures were standardized by both sales and the book value of

assets. The correlation between the two versions of the operational

definition is very high. The Spearman correlation between versions

ranges from .78 (for capital expenditure) to .98 (for both R&D and





10 The rationale for adding back the firms is that the reasons for
excluding the firms from the final sample such as (a) nonstationarities
of firms' earnings or (b) nonavailability of information on investment,
R&D etc and (c) change of industry should not affect the aggregation of
industry earnings. Nonstationarities of firm's earnings does not imply
nonstationarity of the industry earnings. Since ISP is measured on the
aggregated earnings, the first difference of the index so formed should
be stationary. This was checked and the indices for all the industries
were stationary. Nonavailability of information on the independent
variables should not affect the aggregation of industry earnings.
Finally, if a firm changes industry at the two digit level during the
sample period, the income of such firm should be included in the
appropriate industry to get a representative index.

11 A third alternative for a size variable would be the market
value of the firm at the end of the fiscal year. This alternative was
not considered because the fiscal year endings of the firms in the
sample were not uniform either across the firms in the sample or across
time making it more difficult to obtain the market value of each firm
for each year.







51

advertising).12 Since the correlations are very high (and significant)

the inferences should not be affected by the choice of size variable for

deflation. The independent variables standardized by sales is the

operational definition used for estimation of the different models.13

The rank correlation between the mean and the median of the

different variables is also very high ranging from .93 to .98 (Pearson

correlation ranges from .93 to .99). This suggests that the definition

of the average makes no difference in the inference to be drawn. The

reported results are based on the mean of each variable.14

5.3 Descriptive Statistics

In this section the mean and the correlations of the relevant

variables are discussed. Table 2 shows the mean, median and the

standard deviation of the variables used in the different models. An

examination of the descriptive statistics suggests that the mean of FSP

is fairly high when compared to the theoretical maximum of 1 (the random

walk case) suggesting a high permanent component. This is true with

respect to the KL measure of persistence as well where the maximum is 10

(assuming an interest rate of 10%). However, given the mean, the

standard deviation is large enough to provide a reasonable degree of

variability in the FSP measure.


12The Pearson correlation ranges from .68 (for capital expenditure)
to .96 (for R&D).

13 In the preliminary stages of the analyses, all the key models
were estimated using both the versions of the variables. There was no
differences in any of the inferences.

14 As in the case of choice of size variable, all the key model
were estimated using the median of the respective variables, and none of
the inferences changed.















Table 2
Descriptive Statistics


Median


Standard Deviation


FSP(C)
FSP(KL)
ISPNI
INDNI(KL)
RDSMN
ADVSMN
CEMSMN
MNREALRD*
MNREALADV*
MNREALCEM*
MNNOMRD*
MNNOMADV*
MNNOMCEM*
MNREALSAL*
MNREALASST*
MNNOMSAL*
MNNOMASST*
* indicates that the unit


.81 .69
9.03 8.19
.60 .58
9.87 8.14
.02 .013
.014 .004
.07 .05
.99 .846
.54 .47
2.34 2.03
73.21 12.26
43.61 4.3
146.30 19.04
29.87 26.98
23.7 22.77
1835.2 607.7
1421.98 485.8
of measurement is a million


Legend:


FSP(C) Cochrane's specification of firm specific persistence
FSP(KL) KL specification of firm specific persistence


ISPNI(C) -
ISPNI(KL)-
RDSMN
ADVSMN
CEMSMN
MNREALRD
MNREALADV
MNREALCEM
MNNOMRD
MNNOMADV
MNNOMCEM
MNREALSAL
MNREALASST
MNNOMSAL
MNNOMASST


Cochrane's specification of industry specific persistence
KL specification of industry specific persistence.


Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean


of real R&D expenditures standardized by sales
of real advertising expenditures standardized by sales
of real capital expenditures standardized by sales
of real R&D expenditures
of real advertising expenditures
of real capital expenditure
of nominal R&D
of nominal advertising expense
of nominal capital expenditures
of real sales
of real assets
of nominal sales
of nominal value of assets.


* Mean refers to the cross-sectional mean of the specified variable,
say RDSMN, where the variable itself is defined as the time-series
average of R&D standardized by sales for each firm.


Variable


Mean


.45
3.27
.31
4.27
.02
.022
.07
2.7
1.18
6.8
230.96
106.02
544.64
75.72
55.62
4068.3
2973.6
dollars


*









It is interesting to note that the average of all the measures of

persistence, (FSP, ISPOI and ISPNI) are well within the theoretical

limit of 1 (the random walk case) in the case of the Cochrane

specification and within the limit of 10 in case of the KL

specification. This is encouraging since the number of time series

observations used to calculate the persistence measures is not very

large compared to the use of this measure in other applications such as

GNP or stock prices.15 The averages of the independent variables,

RDSMN, ADVSMN AND CEMSMN, are quite small in magnitude."1 It is

important to note that standardization by sales makes these averages

very small. This is clear from the examination of the unstandardized

averages of real R&D, advertising and capital expenditures, denoted as

MNREALRD, MNREALADV and MNREALCEM respectively and the examination of

their nominal counterparts MNNOMRD, MNNOMADV and MNNOMCEM

respectively. The average of real R&D expenditures across the sample,

for example, is close to a million dollars per year whereas the median

nominal R&D expenditure, unadjusted for price level changes, is about

12.5 million dollars. The general inference is that even though the

absolute magnitude of the independent variables appear small, the







15 Cochrane (1988) uses the measure for GNP and Fama and
French(1987) use the measure in examining the permanent component of
stock prices.


16 RDSMN ranged from 0 to .10 million dollars per year, ADVSMN
ranged from 0 to .14 million dollars per year and CEMSMN ranged from
.009 to .764 million dollars per year.









underlying dollar amounts of the variables are not trivial.17 The

median sales of firms in the sample, adjusted for price level changes is

approximately 24 million dollars and the average value of assets, so

adjusted, is about 27 million dollars. The average size of the firms in

the sample is better revealed by examining the median nominal sales

which is about 608 million dollars, whereas the median nominal value of

the assets is close 490 million dollars. This suggests that the average

size of the firms in the sample is quite large. This is not surprising

since the sample firms includes only those which have been in existence

for at least 35 years.

In summary, the descriptive statistics suggest that both the size

of the firms in the sample and the magnitude of the relevant variables

are large. The need to standardize the chosen independent variables

diminish the underlying magnitude. It is difficult to make inferences

based on the statistics in table 2, because the numbers represent

averages over a long period of time (1950-1985) and across the sample of

186 firms. The next section discusses the correlations among the

different variables used in the basic model and the KL model.

Correlations among the variables used in the different models.

Panel A of table 3 discusses the relationship between the dependent and

the independent variables used in the basic model and panel B discusses

the same relationship for the KL model. The correlations in panel A of

table 3 suggests that firm specific persistence (FSP) is positively and




17 Approximately 3% (.99/29.87) of the price-adjusted revenue was
being spent on R&D, on an average and approximately 1.8% of the price
adjusted revenue was being spent on advertising.










significantly associated with each of the independent variables in a

significant manner. The correlation between FSP and ISPNI (Industry

specific persistence based on net income) is the highest.


Table 3
PANEL A
Pearson Correlations Among
The Dependant And Independent Variables For The Basic Model
ISPNI RDSMN ADVSMN CEMSMN

FSP .27 .18 .13 .16
(.0002) (.014) (.07) (.03)

ISPNI .17 .10 -.07
(.02) (.16) (.30)

RDSMN -.001 .009
(.9037) (.9000)

ADVSMN -.186
(.011)

PANEL B
Pearson Correlations Amongst
The Dependant And Independent Variables For The KL Model
ISPNI(KL) RDSMN ADVSMN CEMSMN
FSP(KL) .22 .123 .14 .117
(.003) (.099) (.06) (.115)

ISPNI(KL) -.12 .28 -100
(.109) (.0001) (.18)

RDSMN -.015 .007
(.8413) (.921)

RADVMEAN -194
(.008)

The numbers in parenthesis are the significance probability of
correlation and denote PROB > IRI where R is the probability.


The examination of the correlations between the independent variables

does not suggest a multicollinearity problem. Even though the

correlation between ISPNI and RDSMN and the correlation between ADVSMN










and CEMSMN appear significant, examination of the tolerance factor

(which measures the strength of the interrelationship between the

regressor variables) does not suggest multicollinearity.18

Panel B of table 3 yields similar inferences with respect to the

KL model. FSP(KL) is positively and significantly associated with all

the independent variables. Even though the Pearson correlation between

ISPNI(KL) and ADVSMN is high, the rank correlation is .13 suggesting the

existence of some extreme observations. Further tests (reported later)

suggest that there is no problem of multicollinearity. The next section

reports the results of estimation of the different models.

5.4 Results Of Estimation Of Basic Model

The results of estimating the basic model are provided in table 4.

As expected, all the variables are positive and significant.


Table 4
Results Of Estimation Of Basic Model
Basic Model: FSP = a + p1ISP + p2RDSMN + f3ADVSMN + 64CEMSMN

Parameter Standard Error T

INTERCEPT .41 .08 5.075**
ISPNI .35 .10 3.5*
RDSMN 2.97 1.51 1.96*
ADVSMN 2.93 1.42 2.06*
CEMSMN 1.38 .47 2.9

** significant at .01 level
* significant at .05 level

Test of the Goodness of Fit for the Model F Value: 7.531
P > jIF .0001

Adjusted R Square of the Model .1237

n = 186.


18 The details of the different tests to examine
possible violations of the OLS model is provided in a later section.








57

The model is significant and the adjusted r square (R2) for the model is

.1237 which seems reasonable considering that the model is cross-

sectional. The coefficients suggest that the higher the expenditure on

R&D, advertising and capital items (property, plant and equipment), for

a given level of sales, the higher is the firm specific persistence.

5.5 Robustness Checks For The Basic Model

Since the statistical procedure used is ordinary least squares,

conformity to assumptions of the linear regression procedure such as

homoscedasticity and orthogonolity of the independent variables needs to

be examined. According to White's (1980) test, there was no evidence of

heteroscedascticity. Table 5 shows the relevant statistics for the

heteroscedasticity and multicollinearity tests.


Table 5
Robustness Tests For The Basic Model

White's test to examine the presence of heteroscedasticity

Chi Square Value 9.95 P Value .77
Null hypothesis of homoscedasticity not rejected.

Test to detect the presence of multicollinearity
Variable Tolerance

ISP .96
RDSMN .97
ADVSMN .96
CEMSMN .96

The tolerance parameter is calculated as 1-R2 that results from the
regression of the other variables in the model on that regressor. If
the variables are orthogonal to each other, Tolerance is 1 for all the
variables. The inference is that there is no evidence of
multicollinearity among the variables.19


"1As a further step to examine the problem of multicollinearity,
the procedure suggested in Besley,Kuh and Welsh (1980) was employed.
This procedure effectively examines if any particular variable
contributes to the variance of two or more estimates. This procedure










The collinearity diagnostics revealed no evidence of multicollinearity

amongst the independent variables. The results in table 5 suggest that

the assumptions of ordinary least squares are satisfied in the case of

the basic model. The next step in checking the robustness of the

results was to examine the presence of outliers which may be distorting

the inference. This was done by examining the studentized residual,

Cook's D, DFFITS an DFBETAS [See Besley, Kuh and Welsh (1980) for a

detailed explanation of these statistics]. Each of these statistics

examines the effect of each observation on the estimated parameters and

the predicted value. Based on the examination of these parameters 13

outliers were removed and the basic model was reestimated.20 The

results of the estimation are provided in Table 6.


TABLE 6
Basic Model After Removing Outliers

Model: FSP = a + fiISP + 02RDSMN + p3ADVSMN + 14CEMSMN

Parameter Standard Error T

INTERCEPT .429 .077 5.55**
ISP .260 .082 3.16**
RDSMN 4.522 1.35 3.34**
ADVSMN 2.965 1.39 2.13**
CEMSMN 1.03 0.64 1.61s

** significant at .01 level Test of Goodness of Fit: F value 8.039
$ significant at .052 level P value 0.0001

Adjusted R Square of the model .141


did not suggest the presence of multicollinearity.

20 The determination of outliers is necessarily somewhat arbitrary.
When outliers are removed based on criteria such as the standardized
residual and Cook's D and the model reestimated, the reestimated model
would still reveal new outliers. The basic model was estimated
iteratively and conservative cut off rules were used determine which
observations should be discarded.







59

The results from the estimation of the basic model after removing

the outliers are essentially the same as those reported in table 4. The

R&D variable becomes more significant and the significance of capital

expenditure declines. While the reason for this change is not entirely

clear, all the variables continue to be significant at conventional

levels.21 The removal of the outliers improves the fit for the model as

measured by the adjusted R2 but not overwhelmingly.

In summary, the results in tables 5 and 6 suggest that the

assumptions of ordinary least squares seem to satisfied and outliers do

not pose a problem for the results of the basic model.

Two additional procedures were employed to test the robustness of

the basic model's results. The first procedure was to test the

stability of the model over a period shorter than the 36 year period

used for the estimation of the basic model reported in table 4.

To check for robustness, the basic model was estimated over a shorter

period, after dropping the first 11 time series observations. Thus, all

the estimates of persistence and the other firm specific variables were








21 The significance of the capital expenditure drops and is
significant only .052 level. The sample selection procedure suggests one
possible reason for this loss of significance. Table 1 suggests that
more than 100 firms had to be dropped from the sample in order to ensure
that information was available for the R&D and advertising variables.
With respect to the capital expenditure variable, firms had been dropped
for no systematic reason. Adding back the firms and reestimating the
model with only capital expenditure suggests that capital expenditure is
very significant. This is not reported here since the results in table
5 suggests that capital expenditure is quite close to significance at
conventional levels.









measured over 25 observations.22 The results of the estimation of this

restricted model are provided in Table 7.


TABLE 7
Results Of Estimation Of Restricted Model
Restricted Model: FSP = a + f1ISP + z2RDSMN + 3sADVSMN + 04CEMSMN

Parameter Standard Error T

INTERCEPT .53 .096 5.54**
ISP .37 .089 4.12**
RDSMN 2.81 1.29 2.17**
ADVSMN 1.98 1.3 1.52s
CEMSMN 1.11 0.424 2.61**
** significant at .01 level
$ significant at .06 level

Test of Goodness of Fit for the Model F Value 7.542
P Value 0.0001
Adjusted R Square of the Model .1239 n = 186


The results of the restricted model are generally consistent with

the results of the basic model.23 The advertising variable is not

significant at the conventional level of 5%, but is significance at 6%

level. It is important to realize, however, that estimating persistence

with just 25 observations may not be entirely reliable. The mean of

estimated persistence based on 36 observations was .81 whereas the mean



22 In the estimation of both firm specific persistence and
industry specific persistence, the lag length used was 1/8th of the
number of observations. This rule was preserved in this restricted
model and persistence was measured using a lag length of 3 instead of 5
used in the estimation of the basic model.

23 All the diagnostic checks such as the conformity with
assumptions of regression, effects of outliers etc were checked for this
and the other models. The reported results give effect to the required
correctional procedures. For example, if outliers are detected on
examining the studentized residual, it is removed and results are
reported after such removal. If homoscedasciticity is violated, a
heteroscedastic consistent covariance matrix is estimated and test
statistics are reestimated based on such matrix.










based on 25 observations was 1.008 and a paired t test to compare the

means of the two measures of persistence had a value of 9.69 (p value

.0001). This test strongly rejects the null of equality of the two

means. This is not very surprising since the negative autocorrelations

at the higher lags have been ignored by restricting the measure of

persistence to just 3 lags when it was based on only 25 observations.

Thus, even though it is encouraging to note that the results of the

basic model are robust when estimated over a shorter horizon, the

results need to interpreted with caution.

The second procedure to check the robustness of the results of the

estimation of the basic model was to perform a regression on ranks. The

diagnostic checks applied to the OLS regressions reported in table 5

suggested that a linear regression was appropriate. However, if the

relationship between the independent variables and persistence was

nonlinear in nature, then fitting a parametric linear regression may not

be most appropriate. Puri and Sen (1985) suggest that a rank regression

may be appropriate in such cases. In addition performing a regression

on ranks requires no distributional assumptions with respect to the

residuals or the independent variables. Table 8 reports the results of

a regression on the ranks of the dependent and the independent

variables. The results of the rank regression mirror the results of the

parametric linear regression. Capital expenditure is significant at .08

level, but is not significant at the conventional levels.










Table 8
Rank Regression Of The Basic Model
Model: FSPRANK = a + 81ISPRANK + f2RDRANK
+ p3ADVRANK + f4CEMRANK

Parameter Standard Error


INTERCEPT 20.44
ISPRANK 0.367
RDRANK 0.1464
ADVRANK 0.148
CEMRANK .103

** significant at .01 level
* significant at .05 level
$ significant at .054 level
# significant at .08 level

Test of goodness of fit for the model:

Adjusted R square of the Model .183


12.68 1
0.07 5
0.069 2
0.0746 1
0.072 1


F Value
P Value
n = 173


10.641
.0001


In summary, both parametric and non-parametric tests of the basic

model suggest that firm specific investment variables are significant in

explaining the cross-sectional variation of persistence.

5.6 Kormendi And Lipe Model

As indicated in Chapter 4, KL suggest an alternative measure of

persistence which measures the present value of revisions in

expectations of the levels of future earnings to a dollar innovation in

current earnings. Appendix 1 explains the technical details of this

measure. The Kormendi and Lipe model uses this alternative measure of

persistence as the dependent variable. The rate of discount used to

discount the revisions in the expectations of future earnings is 10%.24


24The use of different discount rates alters the magnitude of
persistence, but does not alter the empirical rank ordering of the firms
with respect to persistence. The choice of 10% is essentially arbitrary
and is chosen to make it consistent with the choice of KL. The rate of
10% was applied uniformly to all the firms. It is very likely that the
appropriate rates of discount would be different for different firms and


T

. 612s
.21 **
.092*
.988*
..42 #









Analogous to the Cochrane specification of persistence, the KL

specification is devoid of the units of measurement. Consequently, the

independent variables are the same ones used in the basic model and

measure the standardized averages of real R&D, real advertising and real

capital expenditure. Table 9 presents the results of the regression of

the KL model.


Table 9
Results Of KL Model
KL Model: FSP(KL) = a + i1ISPNI(KL) + P2RDSMN + 93ADVSMN + f4CEMSMN

Parameter Standard Error T

INTERCEPT 6.58 .468 14.054
ISPNI(KL) 0.096 .0375 2.55**
RDSMN 24.69 8.22 3.003**
ADVSMN 11.67 8.62 1.35$
CEMSMN 7.008 2.54 2.763**

** implies significant at .01 level
$ implies significant at .09 level
Test of Goodness of Fit for the Model F Value 6.43
P Value 0.0001
Adjusted R square of the model .112

n=173


The results of this model are generally consistent with the

results of basic model. The advertising variable is significant at .09

level, whereas all the other variables are significant at .01 level.

The collinearity and heteroscedasticity diagnostics were examined and

the residuals were plotted to check for normality. None of the

assumptions of the classical linear regression model were violated in



may undergo changes for the same firm over time. Since the choice of
different rates for different firms would be arbitrary, this improvement
is not explored at this stage.










the estimation of the KL model. As indicated in footnote 25, the

magnitude of the KL specification is dependant on the assumption of the

interest rate. To enhance comparability with the Cochrane specification

requires an estimation strategy of the KL model which is less influenced

by the assumption about the interest rate. A rank regression of the KL

model would accomplish this purpose. Table 10 presents the results of a

rank regression of the KL model.


Table 10
Rank Regression Of The KL Model

Model: FSP(KLRANK) = a + PISP(KLRANK) + 02RDRANK
+ f3ADVRANK + 4CEMRANK

Parameter Standard Error T

INTERCEPT 33.782 14.245 2.371
ISP(KLRANK) 0.1926 0.074 2.603**
RDRANK 0.1232 0.0748 1.647*
ADVRANK 0.1186 0.077 1.539$
CEMRANK .1936 0.076 2.526**
** significant at .01 level
* significant at .05 level
$ significant at .07 level

Test of Goodness of Fit for the Model F Value 3.949
P Value 0.0095
Adjusted R square of the model .08
n = 173


The results of the rank regression are consistent with the parametric

estimation which suggests that the relationship posited in the KL model

is not dependent on the assumption about the interest rate. Thus, the

results reported in tables 9 and 10 together suggest that the results of

estimating the basic model do not change when the basis of measurement

of persistence is changed. Both the Cochrane and KL specifications

provide information about the duration of the memory of a shock in a










series. Tables 9 and 10 suggest that the operating decisions of the

firm which determine such duration is not sensitive to how such duration

is measured.25 This is encouraging as it conforms to the intuition that

the economic decisions of a firm that determine the persistence of the

resultant earnings series should not be sensitive to the technique

employed to measure such persistence.

5.7 Effects Of Accounting Techniques In order to assess the effect of

accounting techniques on estimated persistence, firm specific

persistence was estimated on (1) sales (2) cash flows after adjusting

for noncurrent accruals26 (3) cash flows after adjusting for current and

noncurrent accruals.27 If the results from the estimation of these

models were similar to those of the basic model (table 4), then it could

be inferred that choice of accounting techniques does not have either an

independent effect or an interactive effect. If the results are

different, then additional structure (and/or assumptions)

is required in the model is required to disentangle the two effects.

Sales. Table 11 reports the results of estimation of the basic

model using persistence of sales as the dependent variable.



25 This also suggests that the additional information provided in
the Cochrane specification does not change the rank ordering of the
firms on the persistence dimension as determined by the operating
decisions of the firm.

26As explained later, two specifications of cash flow were used to
assess that the results of estimation are robust to the measurement of
cash flow

27 To estimate persistence of sales, 4 firms were omitted because
of non availability of sales data. In the estimation of cash flow after
adjustment of noncurrent and current accruals, 39 firms were omitted
because of non availability of data about items such as current portion
of long term debt, cash, current asset or current liability.










Table 11
Model Estimation Based On Persistence Of Sales28

Model: FSP(S) = a + 01ISP + 02RDSMN + f3ADVSMN + 04CEMSMN

Parameter Standard Error T

INTERCEPT .84 .114 7.35**
ISP .32 .142 2.24**
RDSMN 4.697 2.14 2.459**
ADVSMN 4.92 2.00 2.459**
CEMSMN 0.66 0.67 0.983

** significant at .01 level

Test of Goodness of Fit for the Model F Value 4.795
P Value 0.0011

Adjusted R Square for the Model .08
N=182


The results reported in Table 11 are similar to the results

reported for the basic model in table 9. All the variables are

significant except capital expenditure. As suggested in footnote 21,

one possible reason for this is the loss of firms due to missing data on

R&D and advertising variables. This reduces the power of the test with

respect to the capital expenditure variable. Although this test is not

a conclusive test of the effects of the choice of accounting methods, it

provides limited evidence that accounting methods are not important in

explaining the variation in persistence.

Cash Flows Adjusted For Current And Non Current Accruals fCF1. The

procedure for estimating cash flows after adjustments for current and

non-current accruals was rendered difficult as a result missing data for




28 This model was reestimated using ISP based on Sales instead of
net income and the results were identical. Hence the results of this
model are not reported here.







67

some items such as long-term debt included in current liabilities. This

resulted in a loss of 39 firms and the estimation was based on only 147

firms. The results of the estimation of this model are provided in

table 12.29


Table 12
Model Estimation Based On Cash Flow (Definition 1)
Model: FSP(CF) = a + fIISP + f2RDSMN + f3ADVSMN + f4CEMSMN

Parameter Standard Error T
INTERCEPT .286 .046 6.22**
ISP .07 .05 1.28s
RDSMN 1.04 .878 1.19
ADVSMN -1.10 .78 -1.4s
CEMSMN 1.52 .378 4.009**

** significant at .01 level
* significant at .10 level

Test of goodness of fit for
the model: F Value 6.411
P Value 0.0001
n = 147

Adjusted R Square for the model .13


The results of the estimation of this model are not consistent with the

results of the basic model estimated earlier. Only the capital

expenditure variable is significant at conventional levels. It is

interesting to note that even the industry persistence variable is not

significant at conventional levels, while advertising approaches

significance in a direction opposite to the one expected. One possible

reason is the reduction in the power of the test due to the reduction in

the sample size. A more important reason could be that the cash flow



29 There were not enough observations to enable the calculation of
industry persistence based on cash flows. Thus, industry persistence is
based on net income as in the case of the basic model.









68

variable, after possibly removing the effect of differential accounting

methods might introduce noise as a result of differential cash

management techniques across firms. Thus, differences in credit and

cash management policies could be reflected in this specification of

cash flow variable. Such differences in cash management policies are

not purported to be explained by the chosen firm specific variables. In

order to investigate this possibility the analysis is repeated using a

definition of cash flow based non current accruals.

Cash Flow Variable Adjusted For Non Current Accruals [CFNC1. This

variable is measured by adding back depreciation and amortization to the

operating income variable. Since the desire is to examine the effects

of differential accounting techniques in the operating income variable,

the amount of depreciation and amortization are added back and the cash

flow variable is defined as operating income before depreciation and

amortization.30 Based on this measure of cash flow, the basic model was

reestimated wherein the dependent variable is persistence based on this

measure of cash flow. Table 13 provides the results of the estimation

of this model.31








30 A direct way to get this variable to get the working capital
from cash flow (Compustat item # 110). Information is not available for
this variable for the early years.

31 The model estimated here uses a measure of industry persistence
based on net income. This model was estimated again using a measure of
industry persistence based on cash flows (after adjustment for non-
current accruals). The results were very similar.









Table 13
Model Estimation Based On Cash Flow (Definition 2)
Model: FSP(CFNC) = a + f1ISP + #2RDSMN + J3ADVSMN + f4CEMSMN

Parameter Standard Error T

INTERCEPT .41 .081 5.08**
ISP .35 .1007 3.50**
RDSMN 2.971 1.513 1.96*
ADVSMN 2.94 1.422 2.065*
CEMSMN 1.383 .476 2.91**

*significant at .01 level
significant at .05 level

Test of Goodness of Fit for the Model F Value 7.531
P Value 0.0001
Adjusted R Square for the Model .1237
n = 186


The results of this model are very similar to the basic model

reported in table 7. All the firm specific variables are significant

and the adjusted R square is comparable to that of the basic model.

In summary, the combination of evidence provided in tables 11

(sales) and 13 (cash flow adjusted for noncurrent accruals) suggests

that differential choice of accounting techniques is not likely to have

either an independent or interactive effect on estimated persistence for

this sample.

5.8 Effects Of Differential Accounting For R&D Variable

Firms could differ with respect to the treatment of items such as

depreciation, inventory and R&D. The analysis presented in tables 11

through 13 examine the overall effects of cross sectional difference in

the choice of accounting methods. One of the variables chosen for our

analysis is R&D. Before 1974 firms had the option to either capitalize

or expense R&D. The sample period spans a period of 36 years from 1950









through 1985. More than half of this period is before 1974. The

decision to capitalize or expense has an effect on operating income and

the persistence of operating income. Hence, the choice of accounting

method for R&D (before 1974) could have an effect on estimated

persistence. This poses an internal validity threat with respect to the

inferences for firms which had capitalized R&D before 1974. In order to

check this, data on the method of accounting were obtained for about 324

firms from the data base discussed in Shehata (1988).32 However, there

is an overlap of only 40 firms between the sample of Shehata (1988) and

the sample used in this study. Out of this small sample of firms there

are only 2 firms which capitalized R&D expenditures. Thus, these 2

firms were the only ones which had different accounting methods for R&D

before and after 1974. This sample is too small to detect any effects

of the choice of accounting method for R&D.33

5.9 Generalization Of The Results

Persistence, however measured, is independent of any sign or units

of measurement. Since the firms in the sample could have either

positive or negative income, the invariance of persistence to the sign







32 Shehata (1988) included all the NYSE and AMEX firms for which
data were available during the sample period discussed in that study.

33 Nevertheless, the basic model was run without these two firms
and produced no change. This is not surprising since two firms is too
small a sample size for any type of statistical procedure to reveal any
systematic pattern even if it exists. As an additional step the basic
model was estimated with just these 40 firms and in this regression,
choice of accounting method with respect to treatment of R&D expenditure
did not seem to have an effect.










of income gives rise to the following issues:34

(a) is the model generalizable across firms with both positive and

negative innovations?

(b) If the model is not generalizable, does the presence of negative

observations in the sample affect the results in this study?

The Issue. Persistence describes the duration of memory of the

innovations in the series. In the extreme case of a random walk, the

memory of the innovation is the highest and in the case of a stationary

series, it is the lowest. Since the phenomenon described is the

duration of the memory, it is clearly devoid of any sign. In other

words, persistence describes the duration of the memory of an innovation

irrespective of whether such innovation itself is positive or negative.

In fact, in the extreme case where there are two series of realizations

with the only difference between them being the sign on the

realizations, the estimated persistence would be identical. Thus, for a

series with the innovations 100,250,445... and another with the same

numbers but opposite sign -100,-250,-445... persistence would be the

same under both the KL and Cochrane specifications of persistence. In

terms of the research question in this essay, this gives rise to an

interesting issue as to investment behavior of firms wherein the

realizations from investment (and consequently the innovations) are




34The dependent variable in the estimated models is the permanent
component of the income series. The permanent component of the income
series is related to the permanent component of the innovations as
described in chapter 2. Irrespective of the nature of the underlying
series, income or income innovations, persistence is independent of the
sign on the observations of the series.










primarily negative as opposed to a firm wherein the such realizations

are primarily positive.

Genaralizability of the model. The model proposed in this study

suggests that persistence is positively associated with investment. In

other words, firms which have high investment are likely to have high

persistence, and vice versa. The model does not, however, suggest

anything about the sign of the resulting realizations from such

investment. It is possible that firms with either income consistently

declining over time and/or primarily negative realizations would make

adjustments in either the nature and/or magnitude of investments in

order to reduce the persistence of losses.35 The model proposed in this

essay is not a dynamic model which can characterize the changes in the

investment program of a firm as a result of negative realizations from

investments. The basic premise of the model is that investment is

positively associated with persistence. That is, by committing to a

high investment program, ceteris paribus, the management is committing

itself into a high persistence profile in the income series. The

dynamics of the adjustment in the investment profile arising from

negative realizations is outside the scope of this model. The sample

selection procedure used in this study diminishes the magnitude (and

possibly the existence) of this potential concern. A firm was included

in the sample only if it survived in the same industry for a minimum of




35 Such adjustments are likely to happen only if the losses from
investments are considered permanent due to reasons such as permanent
changes in demand. Temporary shifts in demand are likely to be handled
by altering the variable factors of production such as labor or raw
material.







73

35 years. Survivorship arguments suggest that a firm would not survive

unless the realizations from the investments are non-negative on

average.

Possible effects of negative realizations. Firms that have

primarily negative income and/or declining income, could be making more

frequent adjustments in its investment program in order to reduce the

persistence of losses. Consequently, the variance of the investment,

over time, of such firms is likely to be higher than firms which do not

have negative or declining income.36 If data were available for a

reasonable number of firms with such declining income and/or negative

income such a hypothesis could be tested. However, as suggested by the

data in table 14, there are very few firms which have either negative or

declining income. This is not surprising given the sample selection

criterion which required that the firm be in the same industry over the

sample period of 35 years. Table 14 suggests that more than 90% of the

firms in the sample had positive income for about 90% of sample period

(32 years or more). A comparison of the real operating income and the

absolute value of real operating income suggests that there is little

difference between them, confirming the relatively small number of

negative observations in the sample. A regression of real operating

income on time suggests that only 20 firms had income declining over

time (table 14). An limited examination of the behavior of variance of

investments for such firms in this sample was conducted. As table 14




36 It is not clear that the mean of investment of such firms would
be lower because the firms shift the production to other related
products or shift investment to other related areas within the industry.










suggests there are 20 firms with income declining over time and there

are 26 firms with negative income during at least 10% of the sample

period ( 4 years or more).


Table 14
Descriptive Statistics On Firms With Negative Numbers

Number of years of Number of firms Cumulative
negative 01 Percentage

0 114 62
1 23 75
2 12 80
3 11 86
4 10 91
5 5 94
6 2 95
7 2 96
8 4 98
10 2 99.5
15 1 100

186

Number of firms for which average real operating
income was negative 0

Mean Median
(1) Real operating income (in million $) 2.94 .83
(2) Absolute value of real
operating income (in million $) 3.06 .85

Pearson Spearman
Correlation between (1) and (2) .98 .97

Model: Real operating income = a + fTime.
Coefficient on 3 Number of firms Average T value
Positive 132 8.83
Negative 20 -3.59
Not Significant 34


There is an overlap of 14 firms between these two sets of firms

resulting in 32 firms with negative/declining income. The variances of

real capital expenditure (RCE), real advertising (RADV) and real R&D










(RRD) of these 32 firms were compared with their respective counterparts

of the rest of sample (154 firms).37 Table 15 reports the results of

this analysis. A folded form of F statistic, F', was used to test the

equality of the variances of these two groups.38


Table 15
Test Of Hypothesized Effects On Variance Of Firms With
Negative/Declining Income

RCE RRD RADV

Variance for 32 firms 142.08 25.32 2.27
Variance for 154 firms 25.53 3.63 1.22
F' Statistic 5.57 6.98 1.86


The results in table 15 suggest that the variance of investment

decisions of firms with negative and/or declining income was higher than

the other firms and they were all statistically significant at .01

level. The inference to be drawn from this result is that there is

larger variation in the amounts of investment in the firms which had not

been as successful in obtaining positive realizations from investments







37 The decision to include only firms which had negative income
during at least 10% of the sample period is essentially arbitrary. It
is implicitly assumed that negative income during a shorter period, 3
years or less, may not trigger changes in investment behavior. Exclusion
of other firms biases the test against finding an effect and is hence
conservative. Since the choice of this 10% rule is arbitrary, the
analysis in table 14 was repeated with just the 20 firms which had
income declining over time and the results were unchanged.

38 Refer to Steel and Torie (1980) for details. A folded form of F
statistic is expressed as F' = (larger of s12,s22)/(smaller of s12,s22)
This is a two tailed F distribution since we do not specify which s2 is
larger. The P value gives the probability of a greater F under the null
of a12 = '22








76

compared to other firms which had been more successful.39 The necessary

caveat, however, is that the sample size for this analysis is very small

and inferences, therefore, may not be unambiguous.

In summary, persistence, however measured, provides information

about the duration of the memory of a shock without regard to whether

such shock is positive or negative. Thus a firm with nothing but

negative income would still have positive persistence. In terms of the

research question in this essay, it gives rise to an interesting issue

as to the possible differences in the investment behavior of firms which

have negative and/or declining income. Such investigation is outside

the scope of the model proposed in this essay. This is because the

model is not dynamic and does not address the response to negative

realizations from investment. However, it is conjectured that the

variance of the investments would be higher in firms with such

declining/negative income due to the adjustments that need to be made to

reduce the persistence of losses. A sub-sample of 32 firms with such

declining/negative income exhibits significantly higher variance when

compared with the rest of the sample.






39 Comparison of the variances of the firms with negative numbers
might be better when inter-industry differences in variances are
controlled. Thus it might be better to compare the variances of such
firms with those of other firms in the same industry. This was
difficult because the 32 firms were spread over 16 industries at the two
digit SIC code level. Thus there were very few industries where such
firms could be considered a distinct group within an industry. Only in
two industries, [blast furnaces and steel (SIC code 33) and motor
vehicles, parts and accessories (SIC code 37)] was this possible. In
both these industries, the variances of the chosen firms were higher
than the industry average.















CHAPTER 6
SUMMARY AND CONCLUSIONS


The evidence presented in this dissertation strongly suggests that

firm specific investment variables, namely capital expenditure, R&D and

advertising explain a significant amount of the cross sectional

differences in estimated persistence after controlling for industry

factors.1 This result is robust across different statistical procedures

and different persistence measures. Choice of accounting techniques did

not seem to play a major role in explaining cross sectional variation in

estimated persistence. This inference arises because the significance

of the chosen variables was unchanged when the analysis was repeated

using two different instrumental variables. The contribution of the

research in this dissertation is the explicit documentation of the

association between economic attributes of the firm and persistence.

This confirms that persistence is a meaningful construct. This in turn

provides one underlying reason for the positive association between

persistence and earnings response coefficient observed in prior

studies (KL).





'Capital expenditure is not significant at conventional levels in
certain analyses. As suggested in footnote 21 of chapter 5, one
possible reason is the reduction in the number of firms due to non
availability of observations on the other firm specific variables.









The major limitation of the analysis presented here is that lack

of a rigorous theory to guide the choice of the variables. This study's

choice of variables and the reasoning behind the expected direction of

the variables' effects on persistence is somewhat heuristic and arises

from economic arguments rather than from a rigorous economic model. In

the absence of a rigorous theory to guide the choice of variables and

the expected direction of effects, the tests conducted herein are joint

tests of the validity of the hypothesized effects and the validity of

the estimates of variables used. Said differently, specification errors

and estimation errors are indistinguishable in the absence of a theory

giving rise to the joint nature of the tests. Future research should

focus on trying to build a rigorous model to guide the choice of

variables which determine the magnitude of the permanent component of

earnings. While such a task may be quite formidable, it is important to

consider other variables associated with the financial and operating

decisions of the management which are likely to affect the magnitude of

persistence. Other possible avenues of research involve the examination

of the association of estimated earnings persistence with other

characteristics of the firm such as production, employment and

management compensation. The primary interest in the construct of

persistence arises because of the observed valuation implication of

persistence. The association between earnings persistence and firm-

specific returns could be reexamined to evaluate the sensitivity of the

association to measurement errors in the estimation of both earnings

response coefficients and persistence.

















APPENDIX 1
RELATIONSHIP BETWEEN KL AND COCHRANE SPECIFICATIONS OF PERSISTENCE


This appendix explains the relationship between KL and Cochrane

measures of persistence. Before explaining the relationship between the

two measures, it is important to understand how these measures are

calculated. Sections 1 and 2 of this appendix explain the KL

specification and the Cochrane specification respectively. Section 3

examines the relationship between the two and section 4 provides some

concluding remarks.

Section 1: Kormendi and Lipe specification

Kormendi and Lipe assume that earnings, Xt, can be reasonably

well approximated by an AR(2) process in the first differences.

Specifically,

(13) AX, = b + cAX t-1 + c2AX-2 + et,

where

AXt is the first difference earnings at time t and

u, is a white noise error term.

Suppose a forecaster or the market agent knows all the parameters and

knows all the u's and the X's through time t-l, then his optimal

forecast of X, at time t-1 would be

(14) Et_-(AXt) b + clAX t-1 + c AXt-2.









(15) AXt Et_1(AXt) = ut.

Persistence can be measured by finding out by how much the

forecaster would revise his forecast of earnings for a

future period t+n, i.e., Xt+n, given the surprise information at time t,

i.e., ut. To see this, it is convenient to rewrite equation 15 in a

moving average form in the levels using standard causality conditions.

Specifically,

(16) Xt = 6(L)ut

where

8(L)ut = ut + elUt_1 + 82t2 + 3Ut-3 + ... c and

ej is the jth moving average coefficient.

To obtain the revisions in forecasts for all the future periods we

would need

(17) [Et(Xt+1) Et_(Xt+i)] + [Et(Xt+2) Et-(X+2)] + **

It can be shown that

(18) Et(Xt+) Et1(Xt+) = 8nut.

Using equation 18, we can get an expression for equation 17. This is

shown hereunder.

[Et(Xt+1) Et_1(Xt+1)] + [Et(Xt+2) Et-1(Xt+2)] + *

= 81Ut + e2Ut + 83Ut + 84Ut

= u, [8e + E2 + 83 + ...]

(19) = u, [A(1)]

where

A(1) denotes the sum of the moving average coefficients

e8 +82 + ...

Equation 19 can be rewritten as follows:










(20) A(l) =Zkek k = 1 to .

Equation 20 provides the basis for KL's measure of persistence. They

needed a measure of the present value of the revisions in the expected

future earnings. Hence, their measure was the sum of the moving average

coefficients appropriately discounted.

Their measure was

(21) Zk6 (k) k = 1 to c

where

S = l/(l+i) and i is the discount rate.1

In summary, the KL measure of persistence is the discounted sum of

moving average coefficients arising out of an estimated time-series

expectation model. This implies that the KL specification compares the

persistence of two series based on the discounted sum of the moving

average coefficients and a series with a higher discounted sum is ranked

higher in persistence. Assuming the same discount rate, however, it

should be possible to compare two different series on the basis of the

sum of the undiscounted moving average coefficients, i.e., A(1). The

undiscounted sum, A(1), will be used compare the KL and Cochrane

specifications in Section-3.

Section 2: Cochrane Specification

Cochrane (1988) suggests measuring persistence via an estimate of

the relative magnitude of the permanent component of a series. To

understand this measure, we can write the earnings series, xt, in terms



1 KL's reported results are based on a 10% discount rate. Altering
the discount rate affected the magnitude of persistence but did not
alter the rank-ordering of the firms with respect to persistence both in
their analysis and in the analysis done in this study.










of permanent and stationary components. Specifically,

(22) Xt = Zt + ct ,

where

z, is the permanent (i.e., random walk or trend) component and ct is the

temporary or stationary component.2

As equation 22 is non-stationary in the levels, assuming that

first differencing makes it stationary it can be written in the form of

a first differences as follows:

(23) Axt = Azt + Act

Cochrane suggests the calculation of the ratio a2Az/o2AX as a

measure of persistence. This measures the ratio of variance of the

permanent component to the variance of the first differences, and

answers the question "what portion of the variance of the change in

income is accounted for by the variance of the permanent component?"

This gives a convenient measure of the relative strength of the

permanent component of a series. If this ratio is high, the variance of

the permanent component accounts for a high proportion of the variance

of the difference series. This suggests that the series has a high

permanent component, which implies that a change in income will have a

large long-run or permanent effect.

Estimation of Cochrane ratio. Since the random walk component is

unobserved, estimation of this component becomes important. Cochrane




2 Writing the earnings equation in this form does not imply any
restrictions on the series other than the series having to be stationary
in the first differences. This is a standard assumption. Cochrane
(1988) shows that all first difference stationary processes can be
represented as a sum of stationary and random walk components.










proves that the variance of the random walk component can be estimated

for any series by calculating the limit of the variance of the kth

differences. Cochrane shows that

1
(24) Lim Var(xt+k xt) = a2(AZ).
k- c k

From equation 24, we can get an expression for the suggested variance

ratio a2AZ/a2Ax. Rewriting a2(Ax) as Var(xt_- xt) we have

a2Az 1 Var(xt+k xt)
(25) = Lim
Sk- k Var(xt+i xt).


Cochrane also shows that

1
(26) Lim Var(xt+k xt) = (1 + 2 Zj [(k-j)/k j]} a2Ax ,
k- k k

where

rj is the jth autocorrelation coefficient of the difference series xt.

Equation 26 suggests that the variance of the permanent component of a

series, a2(Az), is a function of a linear combination of the

autocorrelation coefficients of the difference series.

From Equations 25 and 26 we have

a2AZ 1 Var(xt+k xt)
(27) = Lim
Sk- k Var(xt+i xt)

(28) = 1 + 2 Ej [(k-j)/k Fj].


Cochrane suggests equation 27 as a measure of persistence. This ratio

can be interpreted as an estimate of the magnitude of the permanent

component of the series. Equation 28 tells us that the magnitude of the

permanent component of the series can be estimated as a linear









combination of the sample autocorrelation coefficients of the

difference series. Since the variance ratio measures the magnitude of

the permanent component, it is a natural measure of persistence as it

estimates the long run effect of the shocks in the series (This was

illustrated in chapter 2 of the dissertation).

Section 3: Relationship Between The Two Measures:

Recall that the KL measure is the discounted sum of the moving

average coefficients, which, for convenience, can be referenced by

A(1)3. As indicated earlier,

(29) A(1) = Ekek k = 1 to

Cochrane expresses the relationship between the variance ratio and the

A(1) representation in the following equation:

a2Az (8ek)2
(30) = k = 1 to .
a2Ax (e82')


Using rules of algebra, we can rewrite 8e 2k as follows:

(31) ke82k = nVar(ek) + n(m )2

where

8m = mean of k4

Also (32) k, Ok = nem

Substituting equations 31 and 32 in equation 30, we have


3 For the purposes of exposition, the K-L measure is referenced by
the A(1) notation which is the sum of the undiscounted moving average
coefficients. Discounting does not change the inferences in any
substantial manner.


4 The use of "n" arises from the assumption that the moving average
coefficients are finite. This is a reasonable assumption, since for
most stationary series the moving average coefficients tend to zero
after a reasonable number.










(zek )2 (ne)2
(33) =
(X82k,) nVar(8k) + n(B.)2

Equation 32 is the undiscounted sum of moving average (MA)

coefficients, A(1), and forms the basis for the KL measure of

persistence which is the discounted sum of MA coefficients. Equation 33

is the variance ratio. The difference between the two is that the

variance ratio standardizes the KL type measure by the variance of the

MA coefficients 8k. Thus, the KL specification captures only one of the

moments of the MA coefficients, the mean, whereas the Cochrane

specification incorporates the second moment as well, namely the

variance of the MA coefficients.5

Section 4: Conclusion

In summary, both the specifications provide measures of the long

run effect of a shock to a series. The difference between the measures

lies in the fact that the Cochrane specification incorporates the second

moment of the distribution of MA coefficients, namely the variance

whereas the KL specification is restricted to the first moment the

mean. The variance ratio can be estimated as a linear combination of

the sample autocorrelations of the difference values of the observed

series; it does not require estimation of the unobserved innovation.

This reduces possible estimation error arising out of the assumption

that a particular time series model describes annual earnings. As




5 Under conditions of uncertainty and heterogeneity of liquidity
preferences and endowments on the part of market agents, it may be
argued that more information about the distribution of the earnings
series, i.e., more information about the moving average coefficients is
desirable.







86

indicated in chapter 2 of the dissertation, these errors relate to (1)

the use of a parsimonious model to capture expectations, and (2) the use

of univariate models to measure innovation giving rise to the

possibility of correlated omitted variables.















APPENDIX 2
ASSOCIATION OF ISP WITH ECONOMIC VARIABLES


This appendix explains the association between ISP, the variable

chosen to surrogate the industry level determinants of earnings

persistence, and certain economic factors which have been identified as

determinants of industry-level persistence in the industrial

organization literature.

Section 1: Introduction

As suggested in chapter 4, industry level persistence (ISP) was

chosen to surrogate industry level determinants because of the

difficulty in obtaining data for all the industry-level determinants

over the entire sample period. The purpose of the analysis presented in

the appendix is to validate that the chosen surrogate is appropriate by

examining the association between ISP and certain major industry-level

determinants. The determinants of industry persistence chosen for

examination are the same as chosen by Lev (1983), namely barriers to

entry and durability of the product. Lev examined the association

between the first and the second order autocorrelation of earnings and

factors such as barriers to entry and durability of the product, firm

size etc. Factors such as barriers to entry and durability of the

product influence the correlation structure of the earnings (and hence,

the persistence) through industry level persistence. The reason for









this is that such industry characteristics are generally out of the

control of the firm. The next section provides the conceptual link

between the chosen factors and industry persistence. Section 3 provides

the details regarding the data and also provides the results of

estimation of the different models. Section 3 also provides some

concluding comments.

Section 2: Conceptual Link

The chosen factors are barriers to entry and durability of the

product. The association between these factors and industry level

persistence is explained in this section.

Barriers to entry. The higher the barriers to entry in an industry,

the easier it is for the incumbent firms in the industry to preserve an

earnings increase for a longer period of time. Hence, the higher the

barriers to entry, higher the persistence of industry earnings. This

suggests that the expected sign for the coefficient on barriers to entry

is positive.

Durability of the product. Friedman's 'permanent income theory'

implies that the consumption of nondurables and services is a function

of permanent income, whereas the consumption of durables is a function

of transitory income. This suggests that the consumption series for

nondurables and services would be more durable over time when compared

to the consumption series of durables.1 This, in turn, suggests that

the income of industries producing durable commodities would be more




1 This is confirmed by the fact that the prediction errors for the
consumption of durable commodities are much larger for durables. [See
Zarnowitz (1972)].









volatile when compared to the income of industries engaged in

nondurables and services. Thus, a random walk series is likely to

describe the earnings process of a durable industry better than any

other model. It has been shown in Chapter 2 that a random walk model is

the one with the highest magnitude of persistence. Persistence of

earnings is expected to increase with durability. Thus the expected

association between persistence and durability of the product is

positive.

Based on the arguments stated above, industry level persistence

(ISP) can be viewed as a function of two factors: barriers to entry and

durability of the product. The empirical model to be estimated may be

states as follows:

ISP, = a + fi Barriers to entry + P2 Durability of product + ei

Section 3: Data. Measurement Of Variables And Results.

Data and measurement of variables: Data on durability of

products of different industries were obtained from the Survey Of

Current Business published by the US Department Of Commerce.2

Industries with durable products were coded 1 and the other industries

coded 0. Industry is defined at the 4-digit SIC code level for the

purpose of analysis in the Lev model. Data on the barriers to entry

were obtained for the industries in the sample from a study by Palmer

(1973). Palmer's classification is based on the four digit industry



2The Survey of current business is published monthly by the
Department of Commerce. The monthly issue of January 1985 was used to
measure of the durability of the product of the industry. Even though
ISP is measured over 36 years, the independent variable is chosen at a
point in time. This is a less of a concern because it is unlikely that
the industries would be classified differently over time.









code and is compiled from four sources: Bain (1965), Mann(1970),

Shepherd (1970) and a 1967 Federal Trade Commission study.3 Firms with

high barriers to entry were coded as one and those with low barriers to

entry were coded 0.4 Persistence was measured using the Cochrane

specification. The results of estimation are provided in table 16.


Table 16
Panel A
Association Of ISP (net income based) With Economic Variables
MODEL: ISPNI = a + PfBarriers to Entry + )2Durability of Product
Parameter Standard Error T

INTERCEPT .47 .055 8.48***
HBE .094 .059 1.60s
DUR .104 .046 2.26**

Adjusted R Square of the Model .04

Panel B
Association of ISP (operating income based)
With Economic Variables
MODEL: ISPOI = a + fiBarriers to Entry + i2Durability of Product

Parameter Standard Error T

INTERCEPT .55 .05 9.48**
HBE .102 .059 1.739s
DUR .166 .046 3.59**

*** significant at .01 level Legend: HBE High barriers to entry
** significant at .05 level DUR Durability of product.
$ significant at .06 level.
Adjusted R Square of model .08.




3 While barriers to entry may change over time, Palmer's
classification was used for three reasons: (1) it is a combination of
the information from four different sources (2) the choice is consistent
with a previous study and hence useful to make comparisons and (3) the
period of Palmer's study coincides approximately with the middle of the
sample period in this study (1950-1985).

4 Palmer's study has three levels of barriers to entry high,
substantial and low. For the purpose of this analysis, industries
classified as having substantial barriers to entry were classified as
high.







91

Panel A of table 16 provides the results of the estimation of the

model using the Cochrane specification on aggregate industry income

based on net income. In Panel B, persistence is based on operating

income. The tables in both panel A and B suggest that the chosen

independent variables are significantly and positively associated with

persistence as expected. In both the models, the adjusted R square is

small suggesting that there is substantial amount of unexplained

variation in estimated persistence measure. However, it is encouraging

to note that the effects of two major economic determinants of industry

persistence are reflected in the estimated measure of ISP.

In summary, industry persistence is a function of several factors,

chief among which are barriers to entry and durability. The variable

chosen to surrogate all the industry factors in this dissertation is

industry specific persistence. The results presented in this appendix

validate the choice of this surrogate. It is shown that industry

specific persistence is associated with these key factors in the

expected direction in a significant way. All the variability in

industry specific persistence is not explained by these two factors (the

adjusted R square is low). This could be due to reasons: (1) noise in

the measurement of ISP due to the aggregation process and more likely

(2) omitted variables in the model.















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