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The role of third rating agencies in the market for public debt

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THE ROLE OF THIRD RATING AGENCIES IN THE MARKET FOR PUBLIC
DEBT: IS FITCH DIFFERENT FROM MOODY'S AND STANDARD & POOR'S?










By

JEFFREY JAY JEWELL


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


1997














ACKNOWLEDGMENTS


This paper would not have been possible without the advice and guidance of

many people. Among those who deserve a special thanks are Professors Miles

Livingston, Christopher James, and Mark Flannery. I also thank Bart Danielsen,

Richard Warr, and Hui Yang for the many helpful suggestions they have made over the

course of this project. In addition, I thank my other committee members, Professors

Joel Houston, Roy Crum, and Bill Bomberger for their patience and cooperation.

Finally, I thank my wife, Dana, and our children, Ali, Maddie, and J.C. for their love

and understanding throughout the term of this enterprise.














TABLE OF CONTENTS
Page

ACKNOWLEDGMENTS ....................................... ii

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

CHAPTERS

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

2 BACKGROUND ON BOND RATINGS .......................... 10
The Bond Rating Agencies ................................ 10
The Bond Rating Process ................................ 13
Recent Fitch History ................................... 15

3 REVIEW OF THE LITERATURE ............................ 18
Literature Comparing Ratings of Fitch, Moody's and S&P ........... 18
Literature on Rating Changes ............................. 22
Literature on Split Ratings .............................. 26

4 DESCRIPTION OF THE DATA ............................. 35
Full Sample ......................................... 36
Matched Sample ...................................... 36
March Sample ........................................ 37
Fitch March Sample ................................... 38

5 SUMMARY STATISTICS ................................... 40

6 COMPARING RATING LEVELS ............................ 45
Comparing Mean Ratings ................................. 45
Comparing Split Ratings ................................ 48

7 COMPARING RATING CHANGES .......................... 61
Frequency of Rating Changes ............................ 61
Comparing Magnitude of Rating Changes ...................... 63
Tests of Causality in Rating Changes: Who Changes First? ........... 70
Market Reaction Around Rating Changes ...................... 74








8 SPLIT RATINGS AND TREASURY SPREADS .................. 101

Split Rating Regression ................................. 101
Fitch Split Rating Regression ............................ 106

9 CONCLUSION ......................................... 115

REFERENCES ............................................ 117

BIOGRAPHICAL SKETCH ................................... 120














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

THE ROLE OF THIRD RATING AGENCIES IN THE MARKET FOR PUBLIC
DEBT: IS FITCH DIFFERENT FROM MOODY'S AND STANDARD AND
POOR'S?

By

Jeffrey Jay Jewell

August 1997


Chairperson: Professor Miles Livingston
Major Department: Finance, Insurance, and Real Estate

Recently, the academic literature has debated the role that 'third' rating agencies

such as Fitch and Duff & Phelps play in the public debt markets. The vast majority of

public debt issues receive ratings from both Moody's and Standard and Poor's.

Therefore, it is not clear why additional ratings are required by some firmnns. The one

known fact about Fitch ratings is that on average they are higher than ratings from

Moody's and Standard and Poor's. This has lead to speculation that the third agencies

have more lenient rating standards than Moody's and Standard and Poor's, and should

thus be regulated.

The purpose of this dissertation is to perform a comprehensive comparison of

the ratings of Fitch, Moody's, and Standard and Poor's over the period 1991 to 1995 in








order to determine if Fitch ratings convey any incremental information about default

risk to the market. The ratings of the three agencies are compared in the following

areas: mean ratings, frequency and magnitude of rating changes, timing and market

reaction to rating changes, and market pricing of ratings. Several interesting results

were found.

First, Fitch is shown to have a higher mean rating than the other two rating

agencies. However, the magnitude of difference is fairly small. In fact, Fitch is

shown to agree with Moody's and/or S&P at the letter rating level approximately 90%

of the time. Second, Fitch changes its ratings far less frequently than the major

agencies. Moody's and S&P are both found to change their ratings almost twice as

frequently as Fitch. This could be a compelling reason for frequent debt issuers to

obtain a Fitch rating. Ratings stability should lead to a more predictable cost of

capital. Finally, Fitch ratings are found to add significant explanatory power to

regression models of the Treasury spread against bond characteristics. This indicates

that the market does indeed find incremental information in the Fitch ratings.

Therefore, it is likely that, if regulations to insure uniformity in rating techniques are

enacted, the end result would do more harm than good. Uniformity in rating

techniques would likely destroy the marginal information that is contained in the Fitch

ratings.














CHAPTER 1
INTRODUCTION


Bond ratings have long been considered an important part of the credit

certification process by government regulators, firms, and the general public in the

issuance of public corporate debt. The academic literature has vigorously debated the

importance of bond ratings. However, a consensus appears to have been reached that

ratings do convey important information to the market above and beyond that conveyed

by financial information alone.'

Recent developments in the credit rating industry have raised new questions

about the role of the bond rating, particularly when multiple ratings are obtained for the

same debt issue. Cantor and Packer (1995) point out that there has been a recent

increase in the number of agencies rating public debt. There are currently four full

service rating agencies that rate a wide variety of debt issues: Moody's, Standard and

Poor's (S&P), Fitch, and Duff & Phelps. In addition, they have been joined in the

industry recently by several specialized rating agencies.2 According to Cantor and




'See Hand, Holthausen, and Leftwich (1992), Reiter and Zeibart (1991),
Ederington, Yawitz, and Roberts (1987), Liu and Thakor (1984) among others.
2 For example, Thompson Bankwatch and IBCA, both started in the early
1990s, rate financial institution debt exclusively. A.M. Best rates insurance
companies' ability to pay claims exclusively.

1










Packer, the SEC currently designates six agencies as nationally recognized statistical

rating organizations (NRSROs), and several more agencies have applications pending

with the SEC. It is clear that firms seeking to issue public debt have more alternatives

than ever before in obtaining a rating. In addition, it appears that more firms are

seeking third and even fourth ratings for debt issues.

While the number of agencies rating debt has increased recently, our

understanding of the role these agencies play has not. In fact, until recently only the

ratings provided by Moody's and Standard and Poor's had been studied by academics.

Little is known about ratings from Fitch or Duff & Phelps except that on average their

ratings appear to be higher than those issued by Moody's and S&P.3 Due to

differences in market share, reputation, and operating procedures between Moody's and

S&P on the one hand and Fitch, Duff & Phelps, and other rating agencies on the other

hand, it is not clear that results from research done on ratings from Moody's and S&P

should generalize to ratings from the other agencies.

Moody's and S&P both maintain a policy of rating most SEC registered, U.S.

corporate debt securities, thus ensuring that these issues typically have at least two

ratings. These ratings are issued regardless of whether the firm requests a rating.

However, firms willing to pay a rating fee4 gain the benefit of participating in the


3 Cantor and Packer (1995, 1996) document this fact. They also show that
part of this difference (but not all) can be explained by differences in the firms rated by
Duff & Phelps and Fitch.

4 According to Cantor and Packer, typical fees on new long-term corporate debt
range from 2 to 3 basis points of the principal for each year the rating is maintained.










rating process, which allows them to put their best case before the agencies (Cantor

and Packer, 1995). According to Ederington and Yawitz (1987), less than 2% of

domestic issuers receiving a rating from S&P fail to pay the rating fee.

Other rating agencies follow very different policies from Moody's and S&P in

rating debt. For example, Fitch and Duff & Phelps only rate debt issues upon request

from the issuing firm. Both of these agencies charge fees comparable to Moody's and

S&P for their services. This raises the question of why firms are willing to pay for

these additional ratings on their debt issues, given that they have probably already

received ratings from Moody's and S&P.

The trend towards obtaining more than two ratings has the potential to add an

additional layer of complexity to assessing a firm's 'true' credit risk. Even when given

access to the same information and hearing the firm's 'best case', Moody's and S&P do

not always reach the same conclusion about the creditworthiness of a debt issue.

Several studies in the literature document that approximately 13 %-17% of U.S.

corporate debt issues receive different letter ratings from Moody's and S&P (a split

rating).5 The number of issues receiving split ratings at the notch level approaches

50%. As the number of rating agencies increases, it is logical to assume that the

number of debt issues receiving split ratings will increase as well. Thus, more

information is available when there are more than two agencies, but the information is

not necessarily easy to interpret.


5 See Billingsley, Lamy, Marr, and Thompson (1985) and Jewell and Livingston
(1997) among others.










One possible explanation offered by Cantor and Packer for the increase in the

use of third raters is regulatory in nature. Many financial institutions have limits,

either self imposed or imposed by government regulators, on the amounts of debt they

can hold of certain ratings. Traditionally the cutoff rating of interest was that between

investment and noninvestment grade securities (Baa and Ba on the Moody's scale).

However, recent regulations have established other important cutoffs at the Aa and

even A ratings.6 As most of these regulations only require that the highest or second

highest rating be above the cutoff point, the firm's chances of meeting the standard

increase if a third or fourth rating is obtained. Therefore, firms could have a strong

incentive to obtain multiple ratings in order to make it possible to sell their debt to

7
these regulated institutions.

Adding to the desirability of obtaining a third or fourth rating is that once a

rating has been requested from Fitch or Duff & Phelps, it is only made public if the

firm is satisfied with it. Thus, requesting a rating from Fitch or Duff & Phelps is

similar to buying an option on a rating. This has the effect of insuring that lower than



6 For example, Congress has established the AA rating as the cutoff in
determining the eligibility of mortgage-related securities and foreign bonds as collateral
for margin lending. In addition the National Association of Insurance Commissioners
has adopted capital rules that give the most favorable capital charge to bonds rated A or
above. (Cantor and Packer, 1995)

7 Livingston, Pratt, and Mann (1995) document that investment grade bonds
have far lower underwriter fees than high yield bonds. One likely explanation for this
is that investment grade bonds are easier (cheaper) to sell due to the large number of
regulated institutions which may participate in this market. The authors do not
examine the effect of split ratings on underwriter spreads.








5
expected ratings from these agencies are rarely, if ever, made public. Thus the average

'observed' rating from Fitch and Duff & Phelps is likely significantly higher than the

'true' average rating from the two agencies.

Another possibility is that obtaining a better bond rating from the third or

fourth rater may convey information to the market that reduces the cost of borrowing

for the firm. Several academic papers have investigated the effect of split bond ratings

from Moody's and S&P on bond yields. These papers have failed to reach a consensus

on how the market prices bonds with split ratings. Billingsley et al. (1988), Liu and

Moore (1987) and Perry, Liu, and Evans (1988) all find that the market prices bonds

with split ratings as if only the lower of the two ratings conveys information. Thus the

higher of the two ratings gives no interest cost reduction to the firm. However, Hsueh

and Kidwell (1988) and Reiter and Zeibart (1991) find that the market prices the bonds

as if only the higher of the two ratings conveys information. These different results

may be attributable to differences in the samples used by the various papers. Most

recently Jewell and Livingston (1997) show that when firms receive a split rating from

Moody's and S&P the Treasury (default) spread on the bond is an average of the

typical spreads on bonds with the higher of the ratings and the typical spreads on bonds

with the lower of the ratings. This suggests that the market uses an average of the two

ratings when determining default spreads for the bond. Thus the market places some

value on both bond ratings. To date, no research has been done on the impact of Fitch

or Duff & Phelps ratings on bond default spreads. If ratings beyond those from










Moody's and S&P have the ability to reduce interest costs this could be a powerful

incentive to seek additional ratings.

Cantor and Packer (1996) lean toward endorsing the regulatory theory. They

suggest that the difference in average ratings between Moody's and S&P versus Fitch

and Duff & Phelps is due to the latter group having lax rating standards. They go on

to suggest that there may be a need for government regulators to impose uniform

standards on all rating agencies. This would prevent firms from obtaining artificially

high ratings merely for the purpose of meeting the above mentioned regulatory hurdles

on debt ratings. If this view of the role of Fitch and Duff & Phelps were correct, we

would expect to see only firms with debt ratings near one of the regulatorily important

levels be interested in their ratings. In addition the market would realize that their

ratings were artificially high, therefore, it would ignore these ratings for the purposes

of determining borrowing costs. In short, if ratings from Fitch or Duff & Phelps are

artificially high the market would view them as uninformative and would not value

them as sources of information.

The purpose of this dissertation is to compare the ratings of three of the major

bond rating agencies (Moody's, S&P, and Fitch)8 in an attempt to ascertain whether or

not the third rating provides the market with any incremental information. If there is

incremental information in the ratings, then regulation to insure uniformity in rating



8 Duff & Phelps is excluded from the analysis due to difficulty in obtaining
complete data for the sample period. This study may be expanded in the future to
include Duff & Phelps ratings as well.










processes is likely to destroy this incremental value to investors, thus impairing the

market's ability to accurately assess the credit risk of firms. The issue of whether or

not Fitch ratings provide any incremental information can be addressed through

answering five questions.

First, do all three agencies appear to have the same policies on how to grade

default risk? This will primarily impact the mean rating level of each agency. Fitch

ratings are found to be significantly higher than those of Moody's and S&P, even after

attempting to correct for the selection bias present in the Fitch ratings. However, the

magnitude of the difference in ratings is small in absolute and relative terms. In 90%

of the observed cases Fitch gives the same letter rating to an issue as either Moody's or

S&P (or both).

Second, do all three agencies appear to have the same policies on when to

change ratings? This will impact both the frequency of rating changes and the

magnitude of the change when a change occurs. Fitch is found to change its ratings far

less frequently than either Moody's or S&P. However, this is somewhat offset by

larger magnitudes of rating changes for Fitch. This is consistent with a policy of

focusing on long-term default risk, which Fitch professes to follow.9

Third, do Fitch rating changes consistently lag those of Moody's and/or S&P?

If Fitch changes are found to follow those of the other agencies, this would indicate


9 Fitch analysts use the phrase'rating through the business cycle' to describe
their rating philosophy. This implies that one rating should be sufficient to capture the
firm's long term default risk, regardless of short term financial variations due to
economic or industry cycles.










that Fitch does little monitoring of firm credit risk, but merely mimics rating the

changes of the other agencies. Regression analysis shows that Fitch ratings do not

consistently follow those of the other agencies. Thus, Fitch appears to make its rating

change decisions independently of the actions of Moody's and S&P.

Fourth, are there changes in bond Treasury spreads around the time of changes

in Fitch ratings? Other studies'0 have found significant abnormal returns surrounding

bond rating changes by both Moody's and S&P. If similar results are found for Fitch

changes, this would indicate that Fitch ratings are playing a similar role in the market

to Moody's and S&P. The interpretation of these abnormal returns is unclear. Some

would argue that the rating changes cause the abnormal returns, while others would

argue that the abnormal returns are merely a reaction to the underlying forces which

caused the rating change. For purposes of this study, it is unimportant what causes the

abnormal returns. Rather, this study is merely concerned with comparing the changes

for the three agencies to see if there are significant differences among them. For

comparison purposes the idea of 'timeliness' of rating changes is adopted. Changes are

considered to be timely if the rating change is followed by a significant change of the

expected sign in the Treasury spread. Results in this area were somewhat mixed for all

three agencies with none of them having very timely rating changes. However, Fitch

rating changes were certainly found to be no less timely than those of Moody's and

S&P.


10 See Hand, Holthausen, and Leftwich (1992) and Hite and Warga (1997)
among others.










Fifth and finally, do Treasury spreads reflect the current level of the Fitch

rating for bonds rated by Fitch? This question addresses in a direct manner whether the

market finds any information content in Fitch ratings. If Fitch ratings contain

incremental information there should be a statistically significant correlation between

the ratings and Treasury spreads. Regression analysis shows that this is the case. Fitch

ratings are found to provide additional information over and above that provided by

Moody's and S&P.

In sum, the evidence presented by answering the five questions should be

sufficient to show that Fitch is not simply 'Moody's lite'. Fitch appears to follow

somewhat different policies on evaluating credit risk and on changing ratings than its

larger competitors. However, different policies are not necessarily wrong policies.

The assumption that Cantor and Packer make seems to be 'if the raters are using

different standards then they should be regulated.' A more appropriate decision rule

may be 'if the raters are using a standard which brings no information to the market

then they should be regulated.' The market does find some incremental information in

the Fitch ratings. Therefore, government regulation that would insure uniform

standards for all rating agencies would appear to be a mistake.














CHAPTER 2
BACKGROUND ON BOND RATINGS


The Bond Rating Agencies

There is a long history of credit evaluation services in the United States. These

services were first applied to public debt issuances in 1909, when John Moody began to

rate the default risk of railroad bonds. Shortly thereafter, Moody expanded his ratings

to include bonds of utility and industrial issuers. Other rating agencies were quickly

formed to compete with Moody's. Fitch Investor's Service, L.P. began operating in

1913. In 1922, Fitch introduced its famous rating symbols that graded default risk on a

scale of 'AAA' to 'D'. These symbols were later licensed to S&P, which continues to

use them to this day. Poor's Publishing Company issued its first ratings in 1916, while

Standard Statistics began issuing ratings in 1922. These two firms merged to form

Standard and Poor's in 1941. Since that time the only major new full-service entrant to

the rating industry has been Chicago based Duff & Phelps11, the only one of the four

full-service agencies not headquartered in New York. Duff & Phelps has been in the

rating industry since the 1930s, but only began to make its ratings available for public

consumption in 1980.



McCarthy, Crisanti, and Maffei, another full service rating agency, was
formed in 1975, but merged with Duff & Phelps in 1991.

10










The four full-service rating agencies mentioned above also face competition

from several niche raters, such as Thomson BankWatch and IBCA, which both

specialize in rating financial institutions. In addition, as the debt markets have become

more global in nature, the rating agencies have both expanded their coverage to include

international issues, and faced competition from rating agencies headquartered in

Europe, Canada, and Japan.

The purpose of bond ratings has always been to provide the public, and

government regulators, an estimate of the default risk associated with particular bond

issues. The bond rating agencies typically assigned a single letter grade to each bond

issue, with most issues by the same firm having the same letter grade. This grade was

intended to summarize the creditworthiness of the issue. So, a AAA-rated bond, for

example, was assumed to have virtually no default risk for the foreseeable future, while

a B-rated bond posed considerably greater default risk.'2 A summary of the

interpretations of the various letter grades is available in Table 1. The bond agencies

made their revenue by selling their rating books, containing all of their ratings, to bond

market investors who desired the information. The industry functioned in this same

basic way with very few innovations for over 60 years.





12 Many studies over the years have documented the fact that bond ratings do an
excellent job at rank ordering the default risk of debt issues. For example, AA-rated
bonds have lower default probabilities over any time horizon than A rated bonds, which
in turn have lower default probabilities than BBB-rated bonds, etc. See Hickman
(1958), Cantor and Packer (1995) and Carty and Fons (1994) among others.










However, from 1969 to 1973 two major innovations occurred in the market.

First, in 1969, Moody's instituted the use of 'user fees' on issuing firms. The original

justification for these fees was that the additional revenue would allow Moody's to

invest more time and effort into its credit evaluation, thus resulting in more accurate

ratings. It is important to note that payment of the fees was completely optional for the

firm, since the vast majority of public debt would be rated by Moody's anyway.

However, most firms quickly agreed to pay the fees, perhaps out of fear that a failure

to do so would result in lower than expected ratings. Fitch followed Moody's example

by instituting its own user fees in 1970, while S&P followed several years after.

The structure of these fees varies somewhat among the agencies and even

among the firms rated by the agency. The most common fee charged by Moody's and

S&P for firms that already have outstanding debt is thought to be two to three basis

points of the par value of the bond issue for each year that the rating is maintained,

though this could be subject to modifications based on issue complexity. This would

result in a fee of $20,000 to $30,000 per year on a $100 million dollar issue. First

time issuers are subject to higher fees due to the additional time and effort involved in a

new rating. Fitch also sells ratings for individual bond issues, at prices ranging from

$10,000 to $100,000 plus a smaller annual maintenance fee, depending on the size and

complexity of the issue. However, Fitch encourages issuing firms to pay an annual

'relationship fee' that will cover the cost of rating all preferred stock, bond, and

commercial paper issues over the course of the year. These fees range in size from

$10,000 to over $1,000,000 depending on the expected market activity of the issuing








13

firm. According to Ederington (1987), user fees now constitute approximately 80% of

the revenue of the rating agencies.

The second major innovation in the rating industry was the introduction by Fitch

of the modified rating system in 1973. This system, also known as the subrating

system, was intended to provide finer gradation of assigned ratings. Fitch implemented

this by adding a '+' or '-' to its letter grades for issues that had significantly lower or

higher default risk than an average bond of the same letter grade. For example, a

'BBB+' rated bond was assumed to have somewhat lower default risk than a 'BBB'

rated bond or a 'BBB-' rated bond, but somewhat higher default risk than a 'A-' rated

bond. S&P adopted Fitch's modified ratings in 1974, while Moody's followed with its

own modified ratings in 1982.



The Bond Rating Process

Though there are some variations in the details of the rating process among the

various agencies, all of the full-service agencies follow the same basic sequence of

events when analyzing firms and assigning ratings. The rating agencies are typically

approached by the firm's underwriters for a preliminary analysis of the firm before

ever registering the debt issue with the SEC. For first time debt issuers, the decision to

issue debt may hinge on the expected rating communicated at this preliminary meeting.

Frequent debt issuers are primarily concerned with finding out whether an additional

bond issue will cause the firm's existing ratings to change.










After this preliminary meeting the firm must decide whether or not to proceed

with the debt issuance and rating process. If the firm proceeds, the rating agency will

assign a team of analysts and support personnel to the project. The firm typically

provides this team with five years of financial statements, forecasts of key financial

performance measures, and capital spending and financing plans. Analysts are also

sometimes provided with inside information, such as internal reports created for the use

of senior officers and the board. After considering all of this information, the team

will form a preliminary rating opinion.

Once this preliminary opinion has been formed, the team will meet with the

senior officers of the firm, typically including the CEO, CFO, and Treasurer. The

rating team presents its analysis of the firm's credit position and answers questions

posed by the firm's representatives. Following this meeting the rating agency holds a

meeting of its rating committee. This committee typically consists of the lead analyst

from the rating team, along with other analysts familiar with the industry of the issuing

firm, and several senior officers from the rating agency. The final rating is decided by

a majority vote of this committee.

An important difference in the ratings process of Moody's and S&P versus

Fitch and Duff & Phelps is that the former group rates almost all issues of sufficient

size, while the latter group rates issues only on request. Since the ratings of Fitch and

Duff and Phelps are completely optional for the issuing firm, there are several

opportunities for the firm to decide against publicizing the rating once the process has

begun. For Fitch, the issuing firm can decide against proceeding with the rating at any








15

point prior to the meeting of the rating committee. Once the rating committee decides

a final rating, Fitch is committed to making it public. However, the firm can estimate

its likely rating with a very high degree of accuracy following the meeting of the rating

team with the issuing firm's management. Therefore, the rating process can easily be

halted in time if the expected rating is below the desired level. When this situation

occurs, the issuing firm must pay Fitch for expenses it has incurred up to that point,

but no rating will be made public. For Duff & Phelps, the issuing firm may decide

whether or not to publicize the rating after the final rating has been decided. Once the

firm knows the final rating, it must inform Duff & Phelps whether or not to make the

rating public. If the firm wants the rating to be made public, it must pay an additional

fee to Duff & Phelps over and above the fees paid to that point. This option-like

feature to the purchasing of a bond rating from Fitch or Duff & Phelps is the source of

much of the selection bias in the ratings.



Recent Fitch History

Fitch was purchased and recapitalized by a group of investors in April, 1989.

This management team made several important changes in the operating policies at

Fitch. First, many new analysts were hired and a full review of all outstanding Fitch

ratings was initiated. Second, Fitch increased its use of new rating analytics, such as

increasing the focus on the cash flow of issuing firms rather than focusing on traditional

earnings based ratios. These new analytics garnered much praise for Fitch in the

popular financial press. Third, the new management made a strategic decision to have










their ratings reflect long-term default risk and to change their ratings less frequently

than those of the other rating agencies. Fourth, the management team decided to

position Fitch as more flexible, accessible, and customer service oriented than its larger

competitors, Moody's and S&P. Finally, a decision was made to aggressively pursue

market share in the rapidly growing areas of structured finance and mortgage backed

securities.

Whether or not Fitch was successful in all of the above areas is difficult to

know. However, the fact that the firm has grown dramatically over the last six years

speaks to some measure of success. Specifically, Fitch has grown from a 43 employee

operation with total revenue of $3 million in 1989 to a 300+ employee firm with over

$60 million in revenues in 1996. In addition, Fitch's market share in several rapidly

growing areas has increased dramatically. Fitch now has market share of over 70% in

residential mortgage-backed securities, 55 % in public asset-backed and commercial

mortgage-backed securities, and 50% in general obligation municipal bonds. In fact,

these areas represent a larger portion of Fitch's revenue than industrial and utility

bonds, where Fitch has market share of approximately 35 % 13 and 50% respectively.

Finally, some estimate of the acceptance of Fitch ratings may be obtained by

considering the organizations that consider Fitch ratings valid estimates of credit

quality. Fitch is one of six Nationally Recognized Statistical Rating Organizations

(NRSROs) whose ratings are fully recognized by the SEC, Federal and state banking


13 The 35 % represents Fitch's market share in industrial bonds of the top 200
industrial issuers. Fitch's market share of all corporate bonds is approximately 17%.








17

and insurance regulators, and other U.S. oversight bodies. In addition, Fitch ratings

are recognized by the Japanese Ministry of Finance, the Bank of England, and various

government agencies in Australia.














CHAPTER 3
REVIEW OF THE LITERATURE


Literature Comparing Ratings of Fitch, Moody's and S&P

One of the first acknowledgments of potential differences among the various

rating agencies was from Cantor and Packer (1995). The authors used a large sample

of bond ratings from the end of 1990 to perform various tests. The sample contains

1398 bonds jointly rated by Moody's and S&P, 524 bonds rated jointly by Moody's

and Duff & Phelps, and 295 bonds rated jointly by Moody's and Fitch. Moody's

ratings were used as the base case since Moody's had the most ratings in the sample. A

comparison of the mean rating levels of these jointly rated bonds revealed that S&P's

mean rating was .05 notches higher than Moody's, while Duff & Phelps was .38

notches higher and Fitch was .29 notches higher. Similar comparisons were also done

for original issue junk bonds over the period 1989 to 1993. Again Moody's and S&P

had virtually identical mean ratings, while Duff & Phelps was .97 notches higher than

Moody's and Fitch was almost 1.4 notches higher than Moody's. The authors interpret

these differences as evidence that Fitch and Duff & Phelps have more lenient rating

scales than Moody's and S&P.

The authors next attempt to find out what types of firms are more likely to seek

out a third (or fourth) bond rating. They find that 46% of the firms in their sample










with one investment grade rating and one noninvestment grade rating from the two

major agencies seek a third rating. Of these firms, approximately 85 % (29 of 34)

receive an investment grade rating from the third agency. As the firms' ratings from

Moody's and S&P became further from the investment grade cutoff, fewer third ratings

were sought. The authors conclude that it appears third ratings are more likely the

closer the firm is to an investment grade rating. Combined with the above results on

differences in mean rating levels, this was very suggestive of 'rating shopping' on the

part of some firms. Even more compelling was that it appeared that Fitch and Duff &

Phelps were cooperating with firms engaged in rating shopping by giving them the

higher ratings that they sought.

Cantor and Packer (1996) revisit the issue of rating shopping by firms. More

specifically the authors test two theories on the existence of third ratings. The first

theory is that third ratings are more likely when there is great uncertainty about the

default risk of the firm. If this is the case, the third rating could provide valuable

incremental information to the market about the default risk of the firm. There are

several factors that would support this theory. First, third ratings would be more

common for firms that have split ratings from Moody's and S&P. Second, the

likelihood of a third rating should increase as the difference (in rating notches) between

Moody's and S&P grows. Finally, the authors believe that default risk should be

inherently more uncertain for small firms and firms with high leverage. Interestingly,

probit regressions revealed that none of the above factors increased the likelihood of a








20

third rating. In fact, many of the above factors significantly decreased the likelihood of

a third rating.

The second theory the authors investigate is that third ratings are more likely

when the debt-issuing firm is 'shopping' for a better rating. According to this theory, a

third rating should be more likely when the existing ratings of the firm are close to

important regulatory cutoff ratings, such as the investment grade cutoff. However,

regression analysis revealed that this also was not true. Therefore, rating shopping

does not appear to explain the existence of third ratings.

The authors finally turn themselves to attempting to explain the difference in

mean rating levels between the 'third' rating agencies and the two major rating

agencies. In a sample of year-end 1993 ratings, the mean Fitch rating was .74 notches

higher than the mean Moody's rating and .56 notches higher than the mean S&P rating,

while the mean Duff & Phelps rating was .57 notches higher than Moody's and .36

notches higher than S&P. Heckman's two stage approach was used to determine how

much of the difference in mean ratings was due to sample selection bias caused by

differences in the firms rated by the agencies. These tests show that .31 of the .74

notch observed difference between Fitch and Moody's can be explained by sample

selection bias; however, selection bias can account for none of the observed .56 notch

difference between Fitch and S&P. Selection bias also accounts for .33 of the .57

notch difference between the mean ratings of Duff & Phelps and Moody's, and. 16 of

the observed .36 difference between Duff & Phelps and S&P. In sum, the authors find

that selection bias can account for about 40% 50% of the observed difference in








21

ratings between the major agencies and the third agencies. The remaining difference is

presumably due to more lenient rating standards used by Fitch and Duff & Phelps. The

authors conclude that reputational concerns do not prevent Fitch and Duff & Phelps

from giving artificially high ratings on average. The implication is that one of two

actions should be taken. Either financial regulations should be redesigned to insure

equivalent rating scales (equivalent average ratings) on the part of all rating agencies;

or ratings from Fitch and Duff & Phelps should not be considered for purposes of

meeting regulatory rating requirements.

The two studies by Cantor and Packer make several useful contributions to the

literature on rating agencies. They document the higher average ratings of the 'third'

rating agencies compared to the two major agencies. In addition, they find no evidence

for the theories that only firms with greater default risk uncertainty or firms engaged in

'ratings shopping' are interested in obtaining third ratings. However, there are several

questions the authors leave unanswered. First, if ratings shopping and default risk

uncertainty are not the motivation for obtaining third ratings, what is? Second, how do

the 'third' rating agencies compare to Moody's and S&P in areas beside mean ratings,

such as frequency of rating changes? Third, does the market value the ratings of the

'third' rating agencies, and if so, why should they be forced to conform to the

standards of Moody's and S&P?










Literature on Rating Changes

The earliest modem study of the relationship between bond rating changes and

bond yield changes was in Katz (1974). The author examined yield changes in an

eighteen-month window around rating changes in electric utility bonds over the time

period 1966 1972. The sample contained 115 rating changes, of which 95 were

downgrades and 20 were upgrades. Regression models were developed to predict the

appropriate yield to maturity for each bond in each month of the sample. Excess

returns were calculated by subtracting these predicted yield from the actual yields. The

analysis found that there was very little change in yields for the 12 months preceding a

rating change, but that significant changes in yields did occur after the changes. Katz

concluded from this that very little institutional research was being performed on bond

credit quality. It appeared that bond investors relied on the rating agencies to

communicate changes in credit quality.

Grier and Katz (1976) also study rating changes over the 1966 1972 time

period. However, unlike Katz (1974), the authors employed a matching sample of

bonds with similar ratings, but with no rating changes. This refinement allowed them

to more precisely separate changes in yields caused by rating changes from changes in

yields caused by general market movements. The sample included 56 public utility

bonds and 40 industrial bonds. Only bonds experiencing rating downgrades over the

measurement period were included in the sample. Each downgraded bond was matched

with a bond of similar rating and maturity. Excess returns were calculated by

comparing yields of the changed and unchanged bonds. This analysis found that rating








23

changes were anticipated to some extent for the industrial bonds, but not for the utility

bonds. However, yield changes for both types of bonds persisted for several months

after the rating change. The authors concluded from this that the market takes several

months to fully digest the information contained in the rating change. This could be

partly due to the extremely thin secondary markets for most corporate debt issues.

Weinstein (1977) found dramatically different results from the earlier works in

this area. The sample consisted of 100 rating change announcements for 132 bonds

over the period 1962 to 1974. Abnormal returns were examined for the eighteen

months preceding and six months following a rating change. The author constructed

portfolios of bonds based on rating for every month in the sample. Abnormal returns

were then calculated by subtracting the holding period return of the appropriate

portfolio from the return of the bond in question. The analysis showed that the market

fully adjusted to the information contained in the rating change well before the change

was announced. In fact, there were no significant excess returns for the six months

preceding the rating change. In addition, there was no significant price reaction

following the rating change as earlier studies had claimed. Weinstein concluded that

bond rating changes conveyed no new information to the bond market.

Hand, Holthausen, and Leftwich (1992) took a dramatically different approach

from other studies by using daily, not monthly, bond returns. The authors examined

daily bond returns from 11 days before to 60 days after a rating change for a sample of

1133 rating changes from 1977 to 1982. Of the 1133 rating changes, 841 were

downgrades and 292 were upgrades. Excess returns were calculated by subtracting the










return on a long term Treasury bond from the return on the bond in question. When

examining all rating changes, significant excess returns were found for both bond

upgrades and downgrades around the rating change. Excess returns for downgrades

were larger in magnitude, with a mean of -1.27%, than those for upgrades, which had

a mean of .35 %. The authors also examined a subset of rating changes which were

confirmed to be 'uncontaminated'. These changes were made at times when no other

important information was released by the firm. For this subset of rating changes,

significant excess returns existed around the upgrades, but not the downgrades. The

authors also found excess stock returns around rating downgrades for both the full and

the uncontaminated samples. No significant stock excess returns were found around

rating upgrades. Finally, the authors examined abnormal bond returns around additions

to the S&P Credit Watch List. The authors found no significant abnormal returns

overall for bonds added to the Credit Watch List. However, when they examined only

those additions classified as 'unexpected' by their model, they found significant returns

of negative 1.39% for potential downgrades and 2.25 % for potential upgrades. The

authors concluded from these findings that rating changes do convey important new

information to both the stock and bond markets.

Hite and Warga (1997) examined abnormal returns around a large sample of

rating changes for both Moody's and S&P over the period 1985 to 1995. Abnormal

returns were calculated by subtracting the Lehman Brothers index return for the

appropriate rating class from the bond return. The authors focus on abnormal returns

in a twelve-month period preceding and following three types of rating changes:








25

changes from one investment grade rating to another investment grade rating, changes

that cross the investment grade line, and changes from one noninvestment grade rating

to another noninvestment grade rating. Evidence of significant abnormal returns was

found in the month of the change and up to six months preceding the change for both

upgrades and downgrades. Abnormal returns were generally larger for downgrades and

generally larger when noninvestment grade ratings were involved. In addition, some

marginally significant abnormal returns were found to persist for several months

following the rating change.

Although the authors present some evidence that the marginal impact of a

Moody's rating change is greater than that of an S&P rating change, the sample size is

too small to make this finding conclusive. The authors therefore conclude that rating

changes convey important information to the market, and that the changes of both

Moody's and S&P appear to play a similar role in this information conveyance. These

results are are extremely similar to those of Grier and Katz (1976). However, the

much more comprehensive nature of the Hite and Warga findings makes the results

even more compelling.

In summary, the weight of evidence about bond market reactions to rating

changes indicates that there are significant abnormal returns around the rating changes.

Hand, Holthausen, and Leftwich (1992) document this fact in the days immediately

surrounding the rating change for a very large sample of changes. Hite and Warga

(1997) document these changes in the months surrounding the rating change for a very

large sample of changes. The one primary shortcoming in this area of the literature is










the omission of rating changes from the 'third' rating agencies. The literature shows

that rating changes of Moody's and S&P are correlated with very similar changes in

bond yields. However, it remains unknown whether the market reacts to rating

changes from Fitch or Duff & Phelps in the same way.

The interpretation of the abnormal returns around rating changes is still unclear.

Hite and Warga, among others, seem to argue that the rating changes are the source of

new information. According to this view, the abnormal returns in the months

preceding the rating change are due to the market anticipating a rating change.

Weinstein, on the other hand, argues that a rating change cannot possibly be a source

of new information, as the rating change is itself usually a reaction to some event or

sequence of events that change corporate credit quality. According to this view the

abnormal returns are in response to the event or events that precipitate the rating

change, not in response to the rating change itself. Regardless of the cause of the

abnormal returns, it is possible to evaluate potential similarities or differences among

the various rating agencies by comparing the abnormal returns associated with each of

their rating changes.



Literature on Split Ratings

The first real acknowledgement that split ratings might impact bond yields and

underwriter spreads was from Sorensen (1979). In a study devoted primarily to

comparing interest costs of bonds sold by competitive bids versus those sold by

negotiation, Sorensen used control variables that identified issues with split ratings.








27

Using a data set of 716 newly issued industrial and utility bonds issued between January

1974 and April 1978, the author attempted to find the determinants of the "true"

interest cost14, bond yield, and underwriter spread. The independent variables

included dummies representing each of the Moody's ratings, two dummies indicating

whether the S&P rating was higher or lower than the Moody's rating15 (with the case

where Moody's and S&P issued the same rating being omitted), a dummy indicating

whether the issue was sold by competitive bid or negotiation, and other control

variables.

The test showed that when the S&P rating was higher than the Moody's rating,

yield and true interest cost both fell by about 16 basis points, while underwriter spread

increased by 1.7 basis points. When the S&P rating was lower than Moody's rating,

true interest cost and yield both rose by about 12 basis points, while underwriter spread

increased by about 5 basis points. Sorensen concluded from this that a second rating

would lower the cost of borrowing if it were more favorable than the first rating.

Conversely, a second rating would raise the cost of borrowing if it were less favorable

than the first rating. Thus the incentives to obtain a second rating were not clear. It is






14 True interest cost (TIC) is essentially a weighted average of bond yield and
underwriter spread used to reflect the total cost to the company of issuing public debt.
It can be shown that dTIC/dyield > 0 and dTIC/dunderwriter spread > 0.

15 The author did not reveal what percentage of bonds in his sample received
split ratings or what percentage of the split ratings received higher ratings from S&P
than from Moody's.










interesting to note that the underwriter spread always increased if a split rating

occurred, regardless of whether the second rating was favorable or not.

Ederington (1986) explored three possible reasons why Moody's and S&P might

disagree about the ratings on new debt issues. These reasons would also be possible

explanations for any differences between Fitch and the two major rating agencies. The

first possible reason is that the two agencies agree on the probability of default for the

bond, but have different standards for assigning particular ratings. The second

possibility is that there may be systematic differences in the rating procedures used by

the two agencies which lead to different estimates of the probability of default for

certain issues. The third hypothesis is that there are no systematic differences in the

agencies' standards for particular ratings or in their rating procedures. According to

this third hypothesis, split ratings would occur because of inherently unpredictable

changes in the opinions of rating analysts that occur from issue to issue and from day to

day. This would cause a particular problem for issues whose "true" rating lies close to

the cutoff point between adjacent ratings.

Ederington used a sample of 494 industrial bonds, 67 of which had split ratings,

to test the three hypotheses. Using an ordered probit model to predict both Moody's

and S&P ratings based on publicly available financial information, he found no

consistent differences in the standards for particular ratings between the two agencies

(thus rejecting the first hypothesis). In addition, Ederington found no evidence that the

two agencies place different levels of importance on the various financial information

included in the tests (thus rejecting the second hypothesis). Ederington therefore










accepted the third hypothesis and concluded that split ratings must be the result of

random differences in raters' judgments about the creditworthiness of particular issues.

This implies that firms would have an incentive to seek additional ratings (beyond the

first) if they believed that an error in judgment caused them to receive an inaccurately

low rating, or conversely that an error in judgment could cause them to receive an

inaccurately high rating from the next rater.

Billingsley, Lamy, Marr, and Thompson (1985) henceforth BLMT, attempted

to empirically test whether or not the market prices split ratings as if they are caused by

random differences in judgement. The authors examined a sample of 258 industrial

nonshelf bonds rated Ba and above, 33 of which received split ratings, issued between

January 1977 and June 1983. The authors assigned a bond a rating of Aaa, Aa, A, or

Baa (indicated by four dummy variables) only if the equivalent rating was received

from both rating agencies. In addition, four different dummy variables were used to

indicate split ratings. For example, a bond which received a Aaa rating from Moody's

and a AA rating from S&P would have been assigned the rating SPLIT 1. Likewise a

bond receiving a AA rating from S&P and an A rating from Moody's would have been

assigned the rating SPLIT2.

The authors then regressed the yield off-Treasury16 against the eight rating

dummy variables (bonds assigned a Ba from both agencies were omitted) and several



16 Yield off-Treasury is defined as the bond's reoffering yield minus Moody's
index of long-term Treasury bonds on the date of issue as reported by Moody's Bond
Survey.










control variables. The regression results showed an inverse relationship between the

yield off-Treasury and the better ratings, as expected. In addition, each of the eight

rating categories except for SPLIT4 were found to have coefficients significantly

different from zero. However, when the authors tested the adjacent coefficients for

significant differences an interesting pattern emerged. The coefficients on the split

ratings were found to be significantly different from the coefficients on the higher of

the adjacent ratings, but not significantly different from those on the lower of the

adjacent ratings. The authors concluded from this that the market prices bonds with

split ratings as if only the lower of the two ratings conveys information. In addition,

the authors argued that split ratings are not merely a result of random differences in

judgment, but that they do in fact represent a significant difference of opinion

concerning the true default risk of particular issues. The market notes this divergent

opinion, but chooses to value only the lower (more conservative) opinion when pricing

the bond.

Liu and Moore (1987) and Perry, Liu, and Evans (1988) use very different

techniques 17 and different samples'8 to find essentially the same result as BLMT. In


17 The basic technique used in both of these papers involves comparing the
average default yield premiums of split rated issues to those of adjacent synonymously
rated issues. Since this is a nonregression procedure, it does not control for other
factors which could be contributing to differences in the default yield premiums.

's Liu and Moore (1987) used a sample of 282 corporate bonds listed in the June
1984 issue of Moody's Bond Record. All of the bonds were nonconvertible, senior
claims which were rated as investment grade by both agencies. Perry, Liu, and Evans
(1988) used a sample of 269 nonfinancial corporation bonds obtained from two separate
periods in 1982.










both cases the authors conclude that the market only considers the lower of the two

ratings when determining bond yields. According to the results of these three studies

there is no evidence of a cost based incentive for firms to seek additional ratings.

However, two other studies find strikingly different results which could indicate

a powerful cost based incentive to seek additional ratings. Hsueh and Kidwell (1988)

used a sample of 1512 general obligation bonds issued in the state of Texas between

1976 and 1983 to test the hypothesis that there is no benefit to seeking a second rating

once the first rating has been obtained. Of the 1512 bonds in the sample, 560 (41 %) of

them had two identical bond ratings, 135 (9%) had split ratings, and 817 (59%) had

only one rating. In order to account for the possibility that the decision to obtain more

than one rating is not random, the authors used a switching regression to estimate how

a second rating affected the interest cost of the debt issues. They found that having two

identical ratings lowered the cost of borrowing by approximately five basis points over

the cost with only one rating. In addition, the authors found that split ratings lowered

the cost of borrowing over the lower of the two ratings by 16 to 21 basis points. F

tests of the coefficients on the ratings confirm that in general there is no significant

difference between the coefficients on the split ratings and those on the adjacent higher

synonymous rating. This is the opposite of the BLMT result. The only exception to

this finding was in the split rating category between A and Baa. For this category, the

F tests showed that its coefficient was significantly different from the coefficients on

both the A and Baa rating. In other words the market priced this particular split

category as a unique rating, between the A and Baa categories.










Reiter and Zeibart (1991) examined a sample of 320 public utility issues sold

between February 1981 and February 1984, 53 of which (16.56%) have split ratings,

to test several hypotheses. The authors used a simultaneous equations model to show

that bond ratings provide incremental ability to explain bond yields over and above that

provided by firm financial information. In addition, they showed that when split ratings

occur, on average bond yields reflect the higher of the two ratings. This result should

be interpreted with caution, however, as their sample contained systematic differences

between Moody's and S&P ratings. Over 50% of the split ratings in the sample were

rated A by Moody's and BBB by S&P, a systematic difference not present in the rest of

the literature.

Cantor, Packer, and Cole (1996) examined a sample of 4399 straight public bond

offerings over the period 1983 to 1993. Of these, over 2000 had split ratings between

Moody's and S&P at the notch level. The authors tested various specifications of a

bond pricing model to determine whether the best model included only Moody's

ratings, only S&P ratings, only the higher rating, only the lower rating, or an average

of both ratings. They find that models using only Moody's or only S&P ratings

produced unbiased, but highly inefficient forecasts of bond prices. The models which

included only the higher or the lower rating (but not both), had forecasts that were

more biased and had insignificant gains in efficiency compared to the models including

the ratings of only one agency. However, the models using an average of the Moody's

and S&P ratings produced the best results in terms of bias and forecast accuracy. This










result held across several different subsamples. The authors conclude that both bond

ratings have a significant effect on bond yields.

Jewell and Livingston (1997) use a very large sample of industrial bonds issued

from 1980 to 1992 to re-examine the impact of split ratings on Treasury spreads and

underwriter spreads. The authors performed similar regressions to those of BLMT,

with the expectation that their larger sample size would yield more powerful and

accurate results. The regression analysis showed that when a split rating between

Moody's and S&P occurred, the Treasury spread19 on the bond was an average of the

spread typically found on bonds with two of the higher or two of the lower ratings.

Thus, the market was placing some value on both ratings, not systematically ignoring

either the higher rating or the lower rating as had previously been argued in the

literature. The authors also showed, in separate tests, that ratings of both Moody's and

S&P added significant explanatory power to the Treasury spread model, but neither

agency's ratings were found to be more important than those of the other.

As with the other areas of the literature reviewed above, the primary

shortcoming of the split rating literature is that it has focused exclusively on ratings

from Moody's and S&P. Studies by Cantor, Packer, and Cole (1996) and Jewell and

Livingston (1997) each showed that ratings from both Moody's and S&P impact the

Treasury spread in the case of a split rating. The next logical question is whether

ratings from the third rating agencies can also impact the spread. If ratings from other


19 The Treasury Spread is defined as the yield to maturity of the bond minus the
yield to maturity of the same maturity Treasury security.








34

agencies are shown to impact the spread, this would be compelling evidence that these

ratings 'matter' to the market.














CHAPTER 4
DESCRIPTION OF THE DATA


The Warga Fixed Income Database is the primary source of data for this study.

This database is based on data collected by Lehman Brothers. To be included in the

database a bond must be over one million dollars in face value and have had an

investment grade rating at some point in its life. Thus, one shortcoming of the

database is that many original issue junk bonds are omitted. The database only includes

original issue noninvestment grade bonds that are traded by Lehman Brothers. In total,

the database contains monthly observations on thousands of publicly traded bonds.

Data items taken from this source include Moody's and S&P ratings, bond yields,

maturities, and various indenture provisions. Fitch ratings were provided by Fitch

Investors Service, L.P. Treasury rates were obtained from the Federal Reserve

Bulletin.

Ratings in the database are coded on a scale of 1 to 23, with 1 representing

AAA+, 2 representing AAA, and so on. The letter rating to numerical code

conversion is shown in Table 1. Higher numbers correspond to lower perceived credit

quality. The worst possible rating, D, corresponds to a code of 23. Each number from

1 to 23 represents a rating 'notch'.










Several different groups of data were used in the tests in the paper. The

following is a brief description of each data set.



Full Sample

The full sample contains information on publicly traded corporate straight debt

over the period January 1991 through March 1995. This sample is limited to one

senior bond and one subordinated bond per firm. This is to reduce 'double counting'

of events such as rating changes that likely affect all bonds of a firm. When one bond

from a large number of issues of a firm must be selected, the bond with the longest

time to maturity is selected. If more than one bond from an issuer has the same time to

maturity, then the bond from that group with the largest issue size is selected. There is

no restriction on the length of time a bond may be in the sample. Therefore, some

bonds may appear in the sample for only a few months, while others may be present

for the full 51 month sample period. The total number of bonds in the full sample is

1475 at the beginning of the sample period, and 1766 at the end of the sample period.

Of these, 1177 were rated by both Moody's and S&P at the beginning of the sample

period, and 1555 were rated by both at the end of the period. More details of the

coverage of the full sample are available in Tables 2 and 3.



Matched Sample

The matched sample is a subset of the full sample. The purpose of the matched

sample is to minimize the selection bias present in the firms with Fitch ratings. This










selection bias arises from the fact that Fitch does not give unsolicited ratings.

Therefore, firms must make a conscious decision to retain the rating services of Fitch.

This is in contrast to the policies of Moody's and S&P, which rate almost all SEC

registered public debt, whether the issuer wants the rating or not. A further bias arises

because even firms that express an interest in obtaining a Fitch rating can decline to

make the rating public if they are not satisfied with it. Thus the only Fitch ratings we

observe are those from firms that were ultimately satisfied with the rating they

received. This bias can be reduced, but not eliminated, by directly comparing the

ratings from all three agencies for issues with a public Fitch rating. Thus, the matched

sample contains only bonds rated by all three rating agencies. Further, bonds must be

rated by all three agencies for at least 12 consecutive months to appear in the sample.

The matched sample contains 235 bonds at the beginning of the sample period and 267

bonds at the end of the sample period. The full sample and the matched sample are the

basis for all tests involving comparisons of mean ratings and comparisons of rating

changes.



March Sample

The March sample contains information on publicly traded corporate straight

debt in March of 1991, 1992, 1993, 1994, and 1995. Firms may be represented by

multiple bonds in this sample. This results in a total of 24,886 observations. This

sample is used exclusively for regression analysis of the determinants of bond Treasury

spreads. Permitting multiple bonds for each firm should not be a problem in these










tests. Though the ratings of the firm's bonds are highly correlated, there is a low

degree of correlation amongst the other independent variables in the regressions.



Fitch March Sample

The Fitch March Sample is a subset of the March Sample. It includes only

bonds rated by all three rating agencies. Thus, the sample contains 8,359 observations,

roughly one-third the number of observations in the full sample. This sample is used to

examine the impact of Fitch ratings on market yields without the impact of selection

bias. More details of the composition of the Fitch and March Fitch samples are

available in Table 32.










Table 1
Bond Rating Letters to Numerical Code Conversions


Code Mood's Fitch and S&P InterDretation

1 Aaa+ AAA+ Highest Quality

2 Aaa AAA

3 Aal AA+

4 Aa2 AA High Quality

5 Aa3 AA-

6 Al A+ Strong Payment

7 A2 A Capacity

8 A3 A-

9 Baal BBB+ Adequate Payment

10 Baa2 BBB Capacity

11 Baa3 BBB-

12 Bal BB+ Likely to fulfill

13 Ba2 BB obligations; ongoing

14 Ba3 BB- uncertainty

15 Bl B+

16 B2 B High Risk Obligations

17 B3 B-

18 CCC+ Current vulnerability

19 Caa CCC to default

20 CCC-

21 Ca CC In bankruptcy or

22 C C default or other

23 D D marked shortcomings














CHAPTER 5
SUMMARY STATISTICS


Empirical results presented in the tables are arranged to follow the logical

sequence of the five questions discussed in the introduction. The first three tables

simply provide the letter rating to numerical code conversion and summary statistics,

respectively. Tables 4 9 cover the issue of whether Fitch grades default the same way

as Moody's and S&P. This involves a detailed comparison of mean rating levels and

the number of split ratings. Tables 10 15 examine the frequency of rating changes for

each agency. Tables 14 21 investigate differences in mean rating changes among the

three agencies. Tables 22 and 23 summarize the regressions of Granger causality

among the rating changes. This technique allows us to discover whether one of the

rating agencies consistently changes its ratings before the others. Tables 24 31 show

changes in Treasury spreads for the 13 month window around the rating changes of

each agency. These tables allow us to draw some inferences about the timeliness of the

rating changes in relation to changes in market yields. Finally, tables 32 36 display

the results of the split rating regressions and the tests on the significance of adjacent

rating categories. These tests allow us to see if there is any incremental information

conveyed by a Fitch rating when there are already Moody's and S&P ratings in place.










Table 1 presents the conversion methodology used in the paper for changing

letter bond ratings to numerical codes, along with the interpretations of the various

ratings. Table 2 shows a summary of the number of bonds rated by each agency in the

full sample at the beginning and end of the sample period. It is interesting to note that

approximately three to four percent of the bonds are unrated by any of the major

agencies. These issues may be rated by Duff & Phelps or one of the more specialized

rating agencies. For purposes of this study, however, they are considered unrated, and

thus excluded from any further analysis. Also of interest is the surprisingly large

number of bonds with only one rating, particularly in 1991. Approximately 16% of the

sample in 1991 and 7.5% of the sample in 1995 have only one rating. Most of these

issues choose Moody's as their sole rater. It is not clear why such a relatively large

number of firms would have one or fewer bond ratings, particularly in light of the

stated policies of Moody's and S&P to rate virtually all SEC registered public debt.

Table 3 summarizes ratings, issue size, and other issue characteristics by agency

for the each bond in the full sample. These statistics show the bonds rated by Moody's

and S&P to be virtually identical. However, the smaller mean and median issue size

for Moody's indicates that the approximately 150 bonds rated by Moody's and not

rated by S&P must be far smaller issues on average than the bonds rated by both

agencies.

When Fitch rated bonds are compared to those of Moody's and S&P, there are

several findings of note. First, Fitch has a far larger percentage of AA, A, and BBB

rated bonds than the other two agencies, and a far smaller percentage of AAA and










noninvestment grade bonds. The small number of junk bonds is expected due to the

selection bias present in the Fitch ratings, however, the small number of AAA bonds is

somewhat surprising. If Fitch consistently gave more lenient ratings, we should expect

it to have at least the same percentage of AAA ratings as the other two agencies.

Second, a very high percentage of the firms Fitch rates are utilities. The Moody's and

S&P samples are both approximately 20% utilities, while the rate for Fitch is three

times as high. Third, the mean issue size for Fitch is relatively close to that of

Moody's and S&P, but the median issue size is only about 20 25 % the medians of the

other two agencies. This indicates Fitch rates many more small issues in a relative

sense. Finally, the Fitch issues appear to have a slightly smaller time to maturity than

those of the other agencies.










Table 2
Summary Statistics at the Beginning and End of the Sample Period


January 1991 March 1995


Total # Bonds 1475 1766

# Unrated Bonds 46 3.12% 73 4.13%

Total # Bonds Rated By:

Fitch 270 18.30% 284 16.08%

S&P 1214 82.31% 1564 88.56%

Moody's 1368 92.75% 1638 92.75%

# Bonds Rated By:

Fitch Only 24 1.63% 11 0.62%

S&P Only 32 2.17% 22 1.25%

Moody's Only 185 12.54% 93 5.27%

# Bonds Rated By:

Fitch and S&P Only 5 0.34% 2 0.11%

Fitch and Moody's Only 6 0.41% 10 0.57%

S&P and Moody's Only 1177 79.80% 1555 88.05%










Table 3
Summary Statistics by Rating Agency for the Beginning of the Sample Period



Moody's S&P Fitch

# % # % # %
# observations 1368 1214 270

AAA 105 7.68% 98 8.07% 7 2.59%

AA 143 10.45% 168 13.84% 74 27.41%

A 345 25.22% 339 27.92% 104 38.52%

BBB 240 17.54% 238 19.60% 71 26.30%

BB 109 7.97% 82 6.75% 6 2.22%

Band below 406 29.68% 289 23.81% 8 2.96%

Callable 995 72.73% 855 70.43% 210 77.77%

Putable 46 3.36% 46 3.79% 10 3.70%

Subordinated 28 2.05% 28 2.31% 2 0.74%

Sinking Fund 622 45.47% 505 41.60% 93 34.44%

Utilities 270 19.74% 263 21.66% 168 62.22%


Mean Issue Size (millions) 120.0 126.5 111.3
Median Issue Size 88.1 100.0 25.0

Mean Years to Maturity 8.73 8.90 8.07
MNedian Years to Maturity 7.00 _____ 7.00 _____ 6.00_____














CHAPTER 6
COMPARING RATING LEVELS


Comparing Mean Ratings

Table 4 compares the mean ratings of the three rating agencies at the beginning

and end of the sample period for both the full and matched samples. In the full

sample, there is a dramatic difference in the mean rating between Fitch and the other

two agencies. At the beginning of the sample Fitch's mean of 7.26 corresponds most

closely to a letter rating of 'A'. S&P and Moody's mean ratings of 9.28 and 10.04

correspond most closely to letter ratings of BBB+ and BBB respectively. Therefore,

there is almost a full letter difference in the mean ratings at the beginning of the

sample. A similar pattern holds at the end of sample period as well.

In the matched sample, Fitch still has a higher mean rating, but the difference is

not nearly as dramatic as in the full sample. Rather than a difference of 2 to 2.5 rating

notches as in the full sample, the difference in means is approximately .3 notches at the

beginning of the sample period. By the end of the sample this difference rises to over

.5 notches; but this is still a far smaller difference than in the full sample.

Table 5 tests the differences in mean ratings for statistical significance. In the

full sample, every single difference among the three rating agencies is statistically

significant at the .01 level. However, in the matched sample there is no significant








46

difference between the mean S&P and the mean Moody's ratings at either the beginning

or the end of the period. Fitch's mean rating is still significantly higher than both S&P

and Moody's at the .01 level.

Table 6 addresses the same issue of comparing mean ratings from the agencies

in a slightly different way. It presents mean ratings at the beginning and end of the

sample period, but only for those bonds which are present in the matched sample for at

least 48 months. This technique not only looks at mean ratings at two different points

in time, but also holds the bonds analyzed constant across the two time periods. Thus,

it probably gives the best method of comparing the ratings' relationship to each other

over time.

Tables 6 and 7 show that Fitch still has the highest mean ratings throughout the

sample period. However, the differences are slightly smaller for this group of bonds

than for the matched sample as a whole. Mean ratings at the beginning of the sample

period are 7.16 for Fitch, 7.36 for S&P and 7.48 for Moody's. The differences

between Fitch and S&P and Fitch and Moody's are statistically significant, while the

difference between S&P and Moody's is not. This same pattern holds for the end of

the sample period.

Table 7 also shows clearly that the differences in mean ratings between Fitch

and the other two agencies diverge slightly over the sample period. The difference

between Fitch and S&P increases from .2 notches to .47 notches, while the difference

between Fitch and Moody's increases from .32 notches to .39 notches. The difference










between Moody's and S&P ratings actually narrows slightly from .12 notches to .08

notches.

Tables 6 and 7 confirm the basic findings from Tables 4 and 5. Fitch had

higher mean ratings in every sample throughout the entire sample period. In addition,

the difference in mean ratings increases slightly over the course of the sample period.

The reason for the discrepancy in the magnitude of differences in the means

between the full and matched samples in Table 4 lies in the fact that a relatively small

number of firms choose to have Fitch ratings. Only 267 firms at the beginning of the

full sample have a Fitch rating, as opposed to 1185 with S&P ratings and 1340 with

Moody's ratings. The 267 firms which chose Fitch have better average credit quality

than the average firm rated by Moody's and S&P. This is confirmed by the results for

the matched sample. When comparing ratings for firms rated by all three agencies, the

average rating is much closer. In fact, for the beginning period the difference in mean

ratings drops from approximately two notches to approximately .3 notches. Thus,

about 85 % of the difference in mean ratings is caused by differences in the credit

quality of the average firms the agencies rate. This .3 notch difference is slightly

smaller than the difference of .43 notches that Cantor and Packer (1996) claim cannot

be explained by selection bias.

Explaining the remaining 15% difference in mean ratings (the .3 notch

difference) is considerably more difficult. Several possible explanations exist. First,

the difference could be due to some short-term phenomenon. However, the fact that

the difference widens from .3 notches to .5 notches over time appears to contradict this










hypothesis. Second, Fitch could have a different set of criteria for evaluating credit

quality. For example, Fitch could take a view of credit quality which is longer term in

nature than either S&P or Moody's. This would result in differences in mean ratings,

particularly when the economy is entering or leaving a recession. Third, Fitch could

use the same set of criteria for evaluating credit quality as S&P and Moody's, but could

apply them more 'leniently'.

Fitch's management and analysts claim that the second explanation is the most

correct. They maintain that Fitch takes a very long-term view of credit quality and

attempts to rate through the business cycle. If true, this would result in fewer rating

changes for Fitch than for Moody's and S&P, who follow a more conventional policy

of changing ratings based on short-term financial performance.

Cantor and Packer seem to endorse the third of the above explanations. They

believe that Fitch's higher average rating is due to lax standards adopted by Fitch in

order to encourage firms needing a higher rating to pay for Fitch's services. This is the

basis for their call for regulation of rating agencies. This paper will attempt to infer

the correct explanation from an analysis of the frequency of rating changes, the size of

rating changes when changes do occur, and the market reaction to both the level of

rating and changes in ratings.



Comparing Split Ratings

Tables 4 through 7 attempt to give us a basic feel for the rating criteria of the

three rating agencies by comparing the mean rating levels of each. Perhaps a more








49

traditional way of discerning the rating criteria of the different agencies is to compare

the number of ratings they agree on and the number on which they split. This issue is

examined in Tables 8 and 9.

Table 8 focuses on a comparison of Moody's and S&P at the beginning and end

of the sample period. In the full sample at the beginning of the sample period, the two

agencies gave different ratings at the notch level 49.70% of the time. By the end of the

sample period this had increased to 55.31 % of the time. The percentage of issues with

split ratings at the letter level begins as 15.12 %, but also increases to 18.14% by the

end of the sample period. In the matched sample, a similar pattern holds. The

percentage of bonds with split ratings at the notch level begins at 43.40% and increases

to 49.63%, while the percentage split at the letter level begins at 12.77% and increases

to 16.18%. These percentages of split ratings are all consistent with those reported

previously in the literature.20

A discussion of the number of split ratings is fairly meaningless without an

accompanying discussion of which agency gives the higher rating when a split occurs.

If split ratings are caused by random errors on the part of one of the rating agencies,

and if each agency is equally likely to commit one of these random errors, then we

would expect each agency to have the higher rating approximately 50% of the time

when a split rating occurs. However, if one of the agencies consistently gives the

higher rating when a split occurs, it is more likely that there is a significant difference


20 See Billingsley, Lamy, Marr, and Thompson (1985); Ederington (1986); and
Jewell and Livingston (1997) among others.










between the agencies in either the evaluation of credit risk or the timing of rating

changes.21 If this is the case, there may be important information contained in the split

rating above and the beyond the information a random error would provide. This is

exactly the situation we observe in Table 8.

At the beginning of the full sample, S&P gives the higher rating 49.57% of the

time when a split occurs at the notch level. This percentage increases to 55.47%,

which is statistically different from 50%, by the end of the sample period. When a

split occurs at the letter level S&P gives the higher rating a surprising 69.10% of the

time at the beginning of the period. This declines to 62.06% by the end of the sample

period. Both of these percentages are statistically different from 50%. In the matched

sample a similar pattern holds, with S&P giving the higher rating every time there is a

significant difference from the 50% mark. These results confirm the findings from

Tables 4 and 5 that S&P has higher mean ratings than Moody's, though not all the

differences were statistically significant in the earlier tables.

Table 9 focuses on the matched sample so Fitch may be included in the analysis.

The addition of a third agency adds a layer of complexity to the comparisons. The

technique employed is to examine three separate cases. The first case is when Moody's

and S&P have identical ratings. In this situation Fitch may be compared to both



21 For example, assume that both agencies have identical criteria for determining
the amount of credit risk present in a debt issue. However, one agency consistently
upgrades more quickly than the other and downgrades more slowly. That agency
would have the higher rating when a split occurs a large proportion of the time even
though the only difference between the agencies is in the timing of rating changes.










Moody's and S&P simultaneously with no loss of precision. The second case is a

comparison of Fitch and Moody's when Moody's and S&P have disagreed on the

rating. The third case is a comparison of Fitch and S&P when Moody's and S&P have

disagreed on the rating.

Since there is very little difference between the results for the beginning and end

of the sample period, the discussion will primarily focus on results for the beginning of

the period. We will begin by examining the case where Moody's and S&P agree.

When Moody's and S&P have identical ratings at the notch level at the beginning of the

sample period, Fitch agrees with them 58.65% of the time. However, when Moody's

and S&P have identical ratings at the notch level, but Fitch disagrees with them, Fitch

gives the higher rating 70.91 % of the time. When Moody's and S&P have the same

letter rating, Fitch agrees with them over 87% of the time. However, when Moody's

and S&P agree at the letter level, but Fitch disagrees with them, Fitch gives the higher

rating 73.08% of the time. This pattern holds identically for the end of the period.

Thus, when Moody's and S&P agree with each other at the notch or letter level, we

find that Fitch also agrees with them the majority of the time. However, when Fitch

disagrees with the other two agencies, its assigned rating is higher than those of the

other agencies a great percentage of the time.

The next case to examine is a comparison of Fitch with Moody's and S&P when

those two agencies have split on their assigned rating. When S&P and Moody's

disagree at the notch level, Fitch agrees with Moody's 33.33% of the time and with

S&P 38.25% of the time. Thus Fitch agrees with either Moody's or S&P over 71% of










the time when Moody's and S&P split at the notch level. It is possible that in these

cases the market may view the Fitch rating as a tie-breaker between the different

ratings of Moody's and S&P. The remaining 29 % of the time Fitch has a different

rating than either Moody's or S&P, creating a three way split. As in the earlier case,

when Fitch disagrees with the other agencies, it gives the higher rating a statistically

significant percentage of the time. Specifically, when Fitch disagrees with Moody's at

the notch level, Fitch is higher 60.29% of the time. When Fitch disagrees with S&P,

Fitch is higher 69.84% of the time.

When Moody's and S&P disagree at the letter level, Fitch agrees with Moody's

40% of the time and with S&P 50% of the time. Thus, Fitch agrees with either

Moody's or S&P 90% of the time when a split rating occurs between the two major

agencies. When comparing differences at the letter level, we still find that Fitch

consistently gives the higher rating when it disagrees with the other agencies. When

Fitch and Moody's disagree at the letter level, Fitch gives the higher rating 72.22% of

the time. When Fitch disagrees with S&P at the letter level, Fitch gives the higher

rating 60.00% of the time. Perhaps the most interesting fact about Table 8 is that Fitch

gives the higher rating when a split occurs a majority of the time in every single set of

comparisons, both at the beginning and the end of the sample period. This confirms

the evidence from Tables 4 7 that Fitch gives higher ratings on average than Moody's

or S&P.

The patterns are very similar for the end of the sample period. There are,

however, a few differences worth mentioning. First, when Moody's and S&P agree








53

with each other, Fitch agrees with them less frequently at the end of the period than at

the beginning for both the notch and the letter ratings. Second, when Moody's and

S&P split at the letter level Fitch agrees with one or the other of them over 97% of the

time at the end of the period, up from 90% at the beginning. Third, when Fitch

disagrees with the other agencies, it gives the higher rating an even greater percentage

of the time at the end of the period than at he beginning in every single comparison.

This confirms the findings from Table 7 that the differences between Fitch and the

other two agencies widened slightly over the course of the sample period.

Also shown in Table 9 are comparisons of the mean ratings of the agencies for

each of the three cases. The differences are calculated as Fitch rating Other Agency

rating, therefore negative values indicate that Fitch has the higher rating. As expected,

we find that Fitch has the higher mean rating in all six comparisons shown in the table.

In this instance, however, we are more interested in whether the difference in mean

ratings is larger when Moody's and S&P have a split rating (at the notch level) versus

when they agree. Agreement between Moody's and S&P on a rating at the notch level

indicates that there is little uncertainty about the bond's default risk. We might

therefore expect to see Fitch's mean rating be closer to those of Moody's and S&P

when the latter two agencies are in agreement. Likewise, when Moody's and S&P

have a split rating, we might expect to see Fitch's mean rating be farther from those of

the other agencies due to the greater uncertainty about the bond's default risk. This

pattern does not hold true in the data, however. Fitch does have the higher mean rating

in every case, but there is no significant relationship between the size of the difference








54

in mean rating and a Moody's/S&P split. In addition, due to the reduced sample size

from splitting the sample into two cases (split and nonsplit ratings), the mean rating for

Fitch in each case is not even significantly different from the mean ratings of the other

agencies. Therefore, this particular test does not reveal any significant relationship

between mean Fitch ratings and split ratings between Moody's and S&P.

Taken together, Tables 8 and 9 provide further evidence that there may be

important differences among the three rating agencies. Though the agencies agree with

each other a large percentage of the time, both at the notch and the letter level, two

specific patterns occur when they do not agree. First Table 8 shows us that S&P

consistently gives higher ratings than Moody's when they disagree. Second, Table 9

shows us that Fitch consistently gives higher ratings than either Moody's or S&P when

there are disagreements. These consistent differences are large enough to suggest that

more than simple random errors in the ratings process are at work. Further, these

consistent differences suggest that split ratings can give important insight into both the

criteria the agencies use to assign ratings and the relative value that the bond market

places on the ratings of each agency.










Table 4
Comparison of Mean Ratings at the Beginning and End of the Sample Period

Number Minimum Maximum
Sample Aony of Bonds Mean Rin Rating Ratin

Fitch 270 7.27 2 21

Full Sample Sp 1214 9.45 2 21

Jan. 1991 Moody 1368 9.86 2 19


Full Sample Fitch 284 7.93 2 22

March 1995 SP 1564 10.04 2 22

Moody 1638 10.46 2 22


Fitch 235 7.21 2 21

Matched Sample Sp 235 7.52 2 21

Jan. 1991 Moody 235 7.56 2 19


Matched Sample Fitch 267 7.78 2 22

March 1995 SP 267 8.29 2 22

______ Moody 267 8.35 2 21

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Bonds in default are excluded from this comparison. Ratings are
converted to numbers using the system presented in Table 1. In this system a 1
represents a AAA+ rating while a 23 represents a D rating. Therefore lower
numerical ratings represent higher perceived credit quality.











Tests of Differences in Mean


Table 5
Ratings at Beginning and End of Sample Period


Difference in Mean
Sample Comparison Ratings T statistic

Fitch-SP -2.18 -9.55**
Full Sample Fitch-Moody -2.59 -11.77***
Jan. 1991 SP-Moody -0.41 -2.17**________


Full Sample Fitch-SP -2.10 -9.31"**

March 1995 Fitch-Moody -2.52 -11.13***
_______SP-Moody -0.42 -2.60**________


Fitch-SP -0.31 -3.68***

Matched Sample Fitch-Moody -0.35 -4.02***
Jan. 1991 SP-Moody -0.04 -0.60


Matched Sample Fitch-SP -0.51 -5.85"*

March 1995 Fitch-Moody -0.57 -6.87***

______ SP-Moody -0.06 -1.11

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Bonds in default are excluded from this comparison. Comparisons
are calculated by comparing mean ratings from Table 4. 1 represents a AAA+ rating
while 23 represents a D rating. Therefore lower numerical ratings represent higher
perceived credit quality. Negative differences in mean ratings indicate the first firm in
the comparison had the lower numerical rating (higher perceived credit quality).










Table 6
Mean Ratings at Beginning and End of Sample Period for Bonds in the Matched
Sample with 48 or More Monthly Observations

Number Minimum Maximum
Samole Aoeny of Bonds Mean Rating Rating Rating

Fitch 165 7.16 2 16

Beginning Sp 165 7.36 2 15

_____Moody 165 7.48 2 15


Fitch 165 7.09 2 16
End SP 165 7.56 2 16

_____Moody 165 7.48 2 14

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. 1 represents a AAA+ rating while
23 represents a D rating. Therefore lower numerical ratings represent higher perceived
credit quality.










Table 7
Tests of Differences in Mean Ratings at Beginning and End of Sample Period for
Bonds in Matched Sample with 48 or More Monthly Observations

Difference in
Sample Comparison Me Ratin T Statistic

Fitch SP -.20 -2.46**

Beginning of Period Fitch Moody -.32 -3.73***

_______ SP Moody -.12 -1.60


Fitch SP -.47 -5.33"*

End of Period Fitch Moody -.39 -4.76**

_____I SP Moody -.08 -1.22


End Fitch Begin Fitch -007 -0.91

Across Periods End SP Begin SP 0.20 2.02**

________ End Moody Begin Moody 0.00 0.00
*** denotes significance at the .01 level
** denotes significance at the .05 level
denotes significance at the .10 level

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. Differences in Mean Ratings are
calculated by comparing values from Table 6. 1 represents a AAA+ rating while 23
represents a D rating. Therefore lower numerical ratings represent higher perceived
credit quality. Negative differences in mean ratings indicate the first firm in the
comparison had the lower numerical rating (higher perceived credit quality).










Table 8
Comparison of Split Ratings Between Moody's and S&P
at the Beginning and End of the Sample Period


January 1991 March 1995
amo Descrition (% of 1177) (% of 1555)
% of bonds split at the notch level 49.70% 55.31%

% of splits at notch level with S&P higher 49.57% 55.47 %*

Full

% of bonds split at the letter grade level 15.12% 18.14%

% of splits at the letter level with S&P 69.10 % *** 62.06 % ***
higher_______ ____ __


% of bonds split at the notch level 43.40% 49.63 %

% of splits at notch level with S&P higher 49.02% 48.89%*

Matched

% of bonds split at the letter grade level 12.77% 16.18%

% of splits at the letter level with S&P 66.67%*** 54.55%***
_____ higher___ ___
** denotes the reported value is significantly different from 50% at the .01 level
* denotes the reported value is significantly different from 50% at the .05 level
denotes the reported value is significantly different from 50% at the. 10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year.











Comparison


Table 9
of Split Ratings Between Fitch, Moody's, and S&P in the Matched Sample


Fitch vs. Both Fitch vs. Fitch vs.
Time Description (Moodv's=S&P) Moody's =uP

Fitch same at notch level 58.65% 33.33% 38.25%

Fitch higher at notch 70.91%* 60.29%** 69.84%*
level (when different)

Jan. 1991 Difference in Mean -0.29 -0.43 -0.34
Ratings (notches)


Fitch same at letter level 87.32% 40.00% 50.00%

Fitch higher at level letter 73.08%*% 72.22%**" 60.00%***
____~__(when different) _________________


Fitch same at notch level 50.36% 31.85% 31.11%

Fitch higher at notch 83.82%*** 69.57%*** 78.49%**
level (when different)

March Difference in Mean -0.47 -0.52 -0.49
1995 Ratings (notches)


Fitch same at letter level 80.26% 50.00% 47.73%

Fitch higher at level letter 82.22%* 77.27%*s 73.91%*
______(when different) ______I
"*denotes the reported value is significantly different from 50% at the .01 level
Sdenotes the reported value is significantly different from 50% at the .05 level
denotes the reported value is significantly different from 50% at the. 10 level

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. Fitch vs. Both compares Fitch to
both Moody's and S&P when Moody's and S&P have the same rating at the indicated
level (notch or letter grade). Fitch vs. Moody's compares Fitch to Moody's in the case
where Moody's and S&P have different ratings. Likewise Fitch vs. S&P compares
those two agencies when Moody's and S&P have different ratings.














CHAPTER 7
COMPARING RATING CHANGES


Frequency of Rating Changes

Tables 10 through 15 all address the issue of how frequently the rating agencies

change their ratings. It is difficult to say with great certainty whether a policy of very

frequent changes in ratings or less frequent changes is optimal for the consumers of

bond ratings. Frequent changes would appear to keep consumers of bond ratings most

up to date on the current financial health of the firm, but may rely too heavily on

extremely short-term fluctuations in credit quality. Infrequent changes may indicate a

policy of focusing on long-term default risk, but may fail to keep rating consumers

appraised of relevant changes in short-term credit quality. Further, it is also difficult to

say what policy debt issuing firms would prefer. It is almost certain that firms

expecting a rating upgrade would prefer a policy of frequent changes, but firms

expecting a rating downgrade would prefer infrequent changes. What is certain is that

Fitch professes a policy of focusing on long-term default risk and rating through the

business cycle. Therefore, we would expect to see fewer changes in Fitch ratings than

in S&P and Moody's ratings (assuming they are not following this same long-term

policy).










It is possible that some firms unambiguously prefer a policy of fewer rating

changes. Firms that are dependent on frequent debt issuances for corporate funding

and want to know their expected rating with a high degree of certainty might fit this

description (a utility for example). This type of firm might be willing to pay for a

Fitch rating if Fitch truly does follow a policy of rating through the business cycle.

Table 10 reports the number of bonds in the full and matched samples that have

no rating changes from each agency over the period they are in the sample. In the Full

sample, 78.38% of the Fitch rated bonds never experience a rating change, compared

to 52.97% for S&P and 48.62% for Moody's. In the matched sample, 72.41 % of the

Fitch rated bonds never experience a rating change, compared to 54.43 % for S&P and

47.85 % for Moody's. Therefore, Fitch has by far the highest percentage of bonds

with no rating changes in both samples, while Moody's has the lowest percentage of

bonds with no rating changes in each sample. Table 11 shows that these differences in

percentage of firms with no rating changes are all statistically significant.

Table 12 looks at this issue in a slightly different way. It reports the probability

of a rating change per bond per year for each rating agency. This technique does not

simply check whether a bond's rating was changed over the sample period, as Table 10

did. Rather, it considers all rating changes by each agency for each bond and the

number of observations for each bond. It is obvious from the results that there is a far

higher probability in both the full and matched sample of S&P or Moody's changing

their ratings than of Fitch changing. In fact, the probability of Fitch changing is

approximately 1/3 that of the other two agencies in the full sample and about 1/2 of the










other two changing in the matched sample. Table 13 confirms that all of the

differences in probabilities are statistically significant in both samples.

A third way of addressing the issue of the frequency with which ratings are

changed is the rating change ratio, found in Table 14. This is simply the number of

rating changes by the agency over the sample period divided by the number of bonds

rated over the sample period. This gives the average number of rating changes per

bond over the entire sample period. Again, it is clear from the results that Fitch

changes its ratings far less frequently than either S&P or Moody's in both samples.

For example, in the matched sample Fitch has a rating change ratio of .357, compared

to .673 for S&P and .813 for Moody's. Table 15 confirms that the differences in

rating change ratio are all significant at the .01 level.

These tables show convincingly that Fitch changes its ratings far less frequently

than either S&P or Moody's. This appears to be consistent with Fitch following a

policy of focusing on long term credit quality. This ratings dependability could be an

important factor in choosing Fitch as a rating agency for firms that are frequent issuers

in the debt markets. In addition, Moody's is found to change its ratings significantly

more frequently than S&P. This suggests that Moody's may focus more heavily than

S&P on short-term changes in credit quality.



Comparing Magnitude of Rating Changes

So far, we have seen that Fitch's mean rating is significantly higher than the

mean ratings of S&P and Moody's. We have also seen that Fitch changes its ratings








64

far less frequently than either S&P or Moody's. The next issue is comparing the mean

rating change when a change does occur.

It is important to remember when examining these tables that negative mean

changes indicate the average change is an upgrade, while positive mean changes

indicate the average change is a downgrade. This is due to the fact that the letter

ratings are converted to a numerical scale of 1 to 23, with lower numbers representing

higher ratings (better credit quality). Thus a change in rating from BBB (10) to A- (8)

is represented by a rating change of -2 notches. An exact breakdown of the letter

ratings to numerical code conversions is shown in Table 1.

Tables 14 and 15 compare the mean rating changes of each agency for all rating

changes in the two samples. Mean rating changes are somewhat difficult to interpret,

as they are a function of both the number of changes and the size (number of notches)

of the changes. However, mean rating changes can still provide important insights

into potential differences among the three rating agencies. In the full sample there

appears to be a discernible difference between Fitch and the other two agencies. Fitch

is the only agency with a mean change less than zero, although the value of -.084 is

very close to zero. In contrast, S&P and Moody's have mean changes of .295 and

.315 respectively, Likewise the median change for Fitch is negative one, indicating the

median change is an upgrade of one notch. Moody's and S&P both have median

changes of one, indicating the median change is a one notch downgrade. Table 15

confirms that the differences in mean change between Fitch and the other two agencies

are significant, while the difference between S&P and Moody's is not.










The picture changes somewhat when examining the matched sample. Both

Fitch and S&P have mean changes fairly close to zero, while Moody's has a mean

change of almost a half notch upgrade. Moody's and Fitch both have median changes

of negative one compared to a median change of one for S&P. Table 15 shows that in

the matched sample there is no significant difference in mean change between Fitch and

the other two agencies. The only significant difference is between S&P and Moody's.

In the interest of brevity the discussion of Tables 16 18 will focus primarily on

results for the matched sample. Table 16 centers on the mean change in rating for

upgrades only. The first interesting point in this table is the number of upgrades by

each agency over the sample period. Fitch has the fewest upgrades in the matched

sample with 73, followed by S&P with 119 and Moody's with 192. These upgrades

translate into 51.77% of all rating changes for Fitch, 44.74% for S&P and 59.81% for

Moody's. Fitch's value of 51.77% is not statistically different from 50%, but the

value for S&P is significantly lower than 50% while the value for Moody's is

significantly greater than 50%. Thus S&P gives significantly fewer of its changes as

upgrades rather than downgrades, while Moody's gives significantly more of it changes

as upgrades than downgrades. Fitch appears to split its rating changes almost evenly

between upgrades and downgrades. Note that this pattern does not hold in the full

sample, where both Moody's and S&P have significantly fewer upgrades than

downgrades. The fact that only 39.14% of Moody's changes are upgrades in the full

sample, but 59.81 % of the changes are upgrades in the matched sample is very

interesting, but somewhat difficult to interpret. S&P also has more upgrades in the










matched sample than the full sample, but the difference is less dramatic than that of

Moody's. It could be that some firms who hire Fitch believe their ratings from the

other agencies are too low. Once Fitch is hired this could cause Moody's and S&P to

re-evaluate their own ratings, frequently leading to an upgrade.

All three agencies have a median upgrade of one rating notch. However, there

is some difference in the mean upgrade. Fitch has a mean upgrade of -2.44, compared

to-2.03 for S&P and -1.90 for Moody's. Thus, it is clear that Fitch has far fewer

upgrades than the other two agencies, but it appears that this is somewhat compensated

for by the larger magnitude of upgrade for Fitch when a change does occur.

Table 17 focuses on the mean change in rating for downgrades only. Again it is

interesting to note that Fitch has far fewer downgrades, with 68 in the matched sample,

than either of the other agencies. However, S&P has more downgrades than Moody's

with 147 and 129 respectively. This is quite different than the upgrades, where

Moody's had far more than S&P. As with the upgrades, the magnitude of change for

Fitch is higher on average than S&P and Moody's, with mean changes of 2.43, 1.65,

and 1.74 respectively. Further evidence that Fitch has larger downgrades is found in

the median change values, where Fitch registers a two notch median compared to one

notch medians for the other two agencies. Thus the downgrades show the same general

pattern as the upgrades. Fitch has far fewer rating changes, but appears to somewhat

compensate for this with larger changes on average.

Table 18 compares the differences in mean upgrades and downgrades from

Tables 16 and 17 and tests the differences for statistical significance. The pattern of








67

Fitch having larger mean changes is apparent here. However, the differences are only

large enough to be statistically significant for the downgrades. Taken together Tables

16 18 provide additional evidence that Fitch may take a longer-term view of credit

quality and default risk. If Fitch is truly 'rating through the business cycle' it makes

sense that it would both change ratings fewer times and have larger mean changes when

a change does occur. If Fitch waits longer to change ratings, then it is likely that more

has happened to warrant a rating change over the intervening period, thus resulting in

larger mean upgrades and downgrades than the other agencies.

Table 19 examines rating reversals for each agency. A rating reversal is defined

as any rating change which results in the rating assuming a value it previously held in

the recent past. Thus, the current rating change 'reverses' an earlier change. In this

case, the reversal period has been limited to include the three most recent rating

changes. Rating reversals may indicate a focus by the rating agency on short term

credit quality. For example, consider a firm that receives a downgrade from A to BBB

during a recession, but an upgrade to A during the subsequent economic recovery.

Some would argue that these changes are due to short term fluctuations in financial

performance, not to any change in the true long term default risk of the firm.

Table 19 shows that S&P with 24, and Moody's, with 25, had almost triple

the number of rating reversals in the matched sample as Fitch, which had 9. However,

one would expect S&P and Moody's to have more reversals since they change their

ratings far more frequently than Fitch. When taken as a percentage of the number of

rating changes, the difference in reversals does not appear so great, with 6.38% for








68

Fitch compared to 9.02% for S&P and 7.79% for Moody's. Table 19 also shows that

it takes an average of 27.33 months for Fitch to reverse itself when a reversal occurs,

compared to 20.41 months for S&P and 23.72 months for Moody's.

Thus Table 19 provides more evidence, though not overwhelming, that Fitch

takes a longer-term view of credit quality than S&P and Moody's. This is displayed by

two facts. First, Fitch has fewer rating reversals than the other two agencies. Second,

when a reversal does occur Fitch takes longer to reverse itself.

Table 20 examines net upgrades and net downgrades over the period of the

matched sample. This table is of particular importance in determining if the differences

between Fitch and the other two agencies are important over time. We have seen

above that Fitch changes its ratings and reverses its rating changes far less frequently

than S&P or Moody's, but that when Fitch does change, the change tends to be of

larger magnitude than the changes of S&P or Moody's. If these countervailing

tendencies cancel each other out completely, we would expect to see the same number

of net upgrades and net downgrades for all three agencies over time. In addition, we

should expect to see the same net change in rating, but we would expect Fitch to

accomplish that net change in a smaller number of steps (fewer changes). If all of

these expectations were met, then we could conclude that over time the differences in

timing and magnitude of rating changes do not matter.

However, reality is somewhat different from our expectations in this case.

There are several points of interest in Table 20. First, counter to our expectations

above, Fitch has fewer net upgrades over the sample period than the other two








69

agencies. Specifically, Fitch has 60 net upgrades, compared to 75 for S&P and 121 for

Moody's. These numbers translate into 15.19%, 18.99% and 30.63% of the sample

respectively. Second, the net change in rating for those bonds receiving net upgrades

are very close to each other, with Fitch having a mean net upgrade of -2.50 compared

to -2.67 for S&P and -2.51 for Moody's. Third, it takes Fitch fewer rating changes to

achieve this net change than it takes S&P or Moody's. Specifically, Fitch has a mean

number of rating changes for the bonds with net upgrades of 1.30, compared to 1.47

for S&P and 1.48 for Moody's.

The pattern for the net downgrades is virtually identical to that of the net

upgrades. Fitch, with 47, has fewer net downgrades than S&P and Moody's, with 94

and 70 respectively. The mean net changes for these bonds are fairly close, with 2.72

notches for Fitch, compared to 2.45 for S&P and 2.30 for Moody's. Finally, Fitch

takes fewer changes to achieve it net change, with 1.21 changes compared to 1.38 for

S&P and 1.50 for Moody's.

Thus, two of the three expectations mentioned above were met. First, Fitch has

approximately the same mean net change as the other two agencies for bonds

experiencing a net change. Second, Fitch achieves its net change in fewer steps than

the other two agencies. However, perhaps the most important of the three expectations

was not met. Fitch has significantly fewer net upgrades and net downgrades over the

sample period than the other two agencies. Thus, it is safe to say that the differences in

timing and frequency of rating changes do not cancel out completely. However, as

stated earlier, this may be viewed as a positive feature of Fitch ratings by many issuing










firms. Particularly, firms that make frequent trips to the public debt markets may be

willing to trade a somewhat lower probability of receiving an upgrade for a lower

probability of receiving a downgrade. Stability of ratings may lead to a more

predictable cost of capital for issuing firms.



Tests of Causality in Rating Changes: Who Changes First?

Tables 22 and 23 explore the issue of whether one rating agency consistently

changes its ratings before the other agencies. This is an important question for several

reasons. First, if one agency consistently changes its ratings before the other agencies,

it may be true that one or more of the agencies is not effectively monitoring the credit

quality of its rated firms. Rather, one or more of the agencies could simply be

mimicking the rating changes of the first mover. Second, to the extent that recently

changed ratings are more accurate (since they presumably reflect recent changes in firm

credit quality), if one agency consistently changes its ratings before the other agencies

it may be assumed to have more accurate ratings. Third, if rating changes have any

correlation to contemporary or future changes in bond yields, the agency which

changes its ratings first will provide more useful information to bond market investors

who wish to re-weight their portfolios.

The technique used to examine this issue is the Granger causality regression.

This test is able to indicate whether a rating change by one agency can be said to have

been caused statistically by a similar change by another rating agency in an earlier time

period. A six month window was arbitrarily selected as the time period for the










regressions in Tables 22 and 23. The dependent variable in the regression is the

number of rating notches of the change in question. For example, a value of -2 would

indicate that the current change is a two notch upgrade. The independent variables

include any other rating changes (denoted by both sign and number of notches) by the

same agency in the previous six months as well as any other rating changes of the same

direction by the other rating agency in the previous six months. The following is an

example of one of the Granger causality regressions.



S&P upgrade in month 0 = f (S&P changes in previous six months, Moody's
upgrades in previous six months, Fitch upgrades
in previous six months)


Similar regressions would be specified for S&P downgrades and for upgrades

and downgrades from Moody's and Fitch. This structure allows the test to tell whether

a current upgrade was 'caused' by an earlier upgrade from another agency or likewise

whether a current downgrade was 'caused' by an earlier downgrade. The critical test in

the above specification is whether the coefficients on the Moody's and/or Fitch

independent variables are statistically significant as a group. This is found by

calculating an F statistic of the appropriate degrees of freedom. The independent

variables for S&P are only included as control variables. Thus, their coefficients and

significance levels are fairly unimportant.

In order to ascertain whether one agency changes its ratings before the other, at

least two regressions must be run. For example, to determine if Moody's consistently










upgrades before S&P, we would have to run the regression discussed above to see if

Moody's upgrades 'cause' S&P upgrades. Next we would run a similar regression to

see if S&P upgrades 'cause' Moody's upgrades. The results of the two regressions

together will indicate whether one of the agencies consistently upgrades before the

other. So, if Moody's upgrades are found to 'cause' S&P upgrades, but the converse is

not true, then Moody's must upgrade first on average. If both agencies' upgrades are

found to 'cause' the upgrades of the other, then neither agency consistently upgrades

first.

Table 22 presents summary results for the regressions on the full sample. Fitch

ratings are omitted from the full sample regressions to maximize the number of

observations included. Four regressions were run on this sample. For each regression,

the number of observations, the adjusted R squared, F tests and accompanying P

values for the independent variables of interest are reported. The results show that in

the full sample, Moody's upgrades are highly significant in 'causing' S&P upgrades,

while S&P upgrades are marginally significant in 'causing' Moody's upgrades. Thus,

neither agency can be said to consistently upgrade before the other. However, S&P

downgrades are found to 'cause' Moody's downgrades. Since the converse is not true,

we can conclude that on average S&P downgrades before Moody's.

Table 23 applies the same methodology to the matched sample. Since all of the

bonds in the sample have ratings from all three agencies, the regressions are expanded

to include Fitch variables. Results from the matched sample are quite different from

the full sample. For the upgrades, the only causality relationship appears to be that








73

S&P upgrades 'cause' Moody's upgrades. Therefore, we can assume that on average

S&P upgrades before Moody's. For downgrades the situation is somewhat more

complex. Fitch is found to 'cause' the downgrades of both Moody's and S&P, but

downgrades by Moody's and S&P are also found to 'cause' Fitch downgrades. There

are no significant relationships between the downgrades of Moody's and S&P.

Therefore it appears that none of the agencies consistently downgrades before the

others.

In sum, Tables 22 and 23 fail to provide much evidence that one agency

consistently changes its ratings before the other agencies. S&P appears to downgrade

before Moody's in the full sample, but this result does not carry over to the matched

sample. In the matched sample S&P appears to upgrade consistently earlier than

Moody's, a curious result given that Moody's has far more upgrades than S&P in the

matched sample.22 Fitch does not appear to change its ratings consistently earlier or

consistently later than either of the other agencies. Therefore, we can conclude that

Fitch does as good a job as Moody's and S&P at monitoring the credit quality of its

firms.







22 It must be noted that there is no evidence to suggest that a six month
measurement window is the optimal specification for this type of regression. While,
these results are robust to somewhat shorter measurement windows, limitations in the
sample prevent the use of longer measurement periods (too many observations are lost
by requiring longer windows).










Market Reaction Around Rating Changes

It has now been established that Fitch appears to follow different policies than

Moody's and S&P on the frequency of rating changes. A related issue concerns the

timeliness of rating changes. Some estimate of the timeliness of rating changes may be

obtained by examining the changes in Treasury spreads in the time around the rating

changes. If the rating change follows large changes in the spread, then the market has

the ability to anticipate rating change (or alternatively, the agency may be reacting to

the changes in spread). If the rating change precedes large changes in the spread, then

the rating change may be predicting the changes in spread. To use terminology from

the preceding section of the paper, the rating change may have caused the change in

spread in a Granger sense.

Table 24 is the first of a series of tables which examine this issue. The table

shows mean Treasury spreads for a 13 month window around rating upgrades (six

months prior to the change, the month of change, and six months post change). The

sample used is a subset of the full sample for which Treasury spreads were available for

every month in the 13 month window. Since rating upgrades should accompany a

decrease in credit risk, we expect to see the mean spreads decline over the course of the

measurement period. This pattern is followed exactly over the first seven months of

the measurement period, when the mean spreads fall monotonically from 3.404% to

2.997% for S&P and from 3.280% to 2.839% for Moody's.

Table 25 shows differences in mean spreads between several key months from

Table 24. Since spreads should be declining over the sample period we expect the










differences between spreads to be negative. Several interesting points are evident in

this table. First, there is very little change in Treasury spreads in the month of the

change. Neither Moody's nor S&P have any significant movement in spreads in the

months immediately preceding and following the rating change. Second, both agencies

show large declines in Treasury spreads over the six month period preceding the rating

change. Spreads declined a total of 40.7 basis points in the six months prior to a

change by S&P and 43.7 basis points in the six months prior to a Moody's change.

Both of these numbers are significant at the 0.01 level. This pattern is consistent with

both agencies upgrades reacting to changes in Treasury spreads. Third, Moody's

appears to show some ability to predict further declines in spreads. Treasury spreads

decreased an average of 14.4 basis points following an upgrade by Moody's, compared

to decreases of only 3.7 basis points following S&P upgrades.

Table 26 shows mean Treasury spreads around rating upgrades for a subset of

the matched sample. Again, the spreads appear to follow a general pattern of falling

both prior to and after the rating change. Exact details of these differences along with

T statistics are shown in Table 27. Several interesting points are apparent in the table.

First, there appears to be much more movement in the Treasury spreads in the months

closely surrounding the rating upgrades. For example, all four months closest to the

Fitch rating changes show significant changes in spreads, though one of the changes is

in the opposite of the expected direction. In addition, there are significant changes of

the expected sign in the two months directly around the S&P rating upgrades. Second,

both Fitch and S&P have significant changes in spreads in the six months preceding the










upgrades, indicating that their upgrades may be reactions to decreases in Treasury

spreads. Third, there are large, statistically significant decreases in Treasury spreads

following the upgrades of both Fitch and Moody's, showing these agencies may have

some ability to predict changes in spreads.

Table 28 shows mean Treasury spreads around rating downgrades for a subset

of the full sample. Since rating downgrades reflect a decline in credit quality, we

expect to see increases in Treasury spreads around the downgrades. This pattern

appears to hold for both S&P and Moody's. Table 29 shows differences in mean

spreads between key months. Since spreads should be increasing over the measurement

period, we expect these differences to be positive. As with the upgrades, both

Moody's and S&P downgrades are preceded by large, significant changes in Treasury

spreads, indicating that the downgrades may be reactions to the changes in spreads.

Moody's downgrades are also followed by significant declines in spreads, while S&P

downgrades are not. This is also consistent with the pattern found in the upgrades.

Table 30 and 31 show Treasury spreads around downgrades for a subset of the

matched sample. It is quite apparent from an examination of Table 30 that the pattern

of spreads does not meet our expectations. For Fitch, the mean spread six months

before the downgrade is 2.166%. This falls to 2.037% in the month of the downgrade

and continues falling to 1.896% six months after the downgrade. A similar pattern

exists for the S&P downgrades. This narrowing of the spread is the opposite of what

we expect to see around a rating downgrade. No simple explanation exists for this










counter-intuitive behavior. Moody's is the only agency whose spreads do not display

dramatic decreases over the 13 month measurement period.

Table 31 offers little further insight into this mystery. First, every single

difference in means for Fitch is in the wrong direction, with two of them being slightly

significant. Second, only Moody's shows the pattern of Treasury spreads changing the

expected direction over the six months prior to the rating change. This pattern was

observed for most agencies in Tables 25, 27, and 29. Third, there is a large,

significant decline in Treasury spreads following downgrades by Moody's and S&P.

This is the opposite of the expected pattern. This is consistent with a downgrade being

a positive signal to the market, which makes no sense. A second explanation is that the

downgrades are on average so grossly mistimed that the firm's credit quality has

already begun to improve dramatically by the time the downgrade is issued. A third

possible explanation is that the declines in spreads are a statistical illusion created by a

survivorship bias in the test. By forcing the bonds to have 13 months of valid yield

data around the rating change, all bonds which subsequently defaulted may have been

eliminated. This could conceivably account for the mysterious pattern present in the

spreads above.

In sum, Tables 24 29 provide interesting evidence on the timeliness of rating

changes. The rating changes of all three rating agencies appear to be reactions to

changes in Treasury spreads to some extent. In addition, Fitch and particularly

Moody's appear to have some ability to predict changes in spreads since their rating

changes precede significant changes in spreads. S&P changes do not seem to precede








78

significant changes in spreads. Most importantly, Fitch rating changes elicit essentially

the same reaction in Treasury spreads as changes from Moody's and S&P. Therefore,

it appears that Fitch rating changes serve a very similar role to the market as changes of

the two major agencies.










Table 10
Percentage of Bonds with Unchanged Ratings Over the Sample Period

Samle Aenc # Bonds # Unchanged % Unchanged

Fitch 555 435 78.38%

Full Sample SP 2388 1265 52.97%

_______Moody 2501 1216 48.62%


Fitch 395 286 72.41%

Matched Sample SP 395 215 54.43%

_______ Moody 395 189 47.85%

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year.










Table 11
Proportions Tests of Differences Between Percentage of Firms with Unchanged Ratings
Over the Sample Period

Samle Comparison Differences Z tatistic
Fitch SP 25.41% 10.49**"
Full Sample Fitch Moody 29.76% 12.20"***

______ SP Moody 4.35% 2.17"*


Fitch SP 17.98% 5.34**
Matched Sample Fitch Moody 24.56% 7.28**
______ SP Moody 6.58% 1.85*
denotes significance at the .01 level
Sdenotes significance at the .05 level
denotes significance at the. 10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Differences are calculated by comparing Percentages of Unchanged
Ratings from Table 10. Positive differences indicate the percentage of firms with
unchanged ratings is higher for the first agency in the comparison. Z Statistics are
calculated using the normal approximation of the binomial distribution.











Probability of a Rating


,ble 12
Change per Bond per Year


Samnle Aeencv Probability

Fitch .0929

Full Sample SP .3035

__________Moody .3438


Fitch .1226

Matched Sample SP .2174

__________Moody .2844

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Probabilities are calculated by dividing the total number of rating
changes for each agency for each bond by the total number of observation years for
each bond and then averaging across the bonds (an observation year is equal to twelve
monthly observations).










Table 1
Tests of Differences in Probabilities of a


Rating Changes per Bond per Year


aple Comparison Difference Z Stistic
Fitch SP -.2106 -13.58"*
Full Sample Fitch Moody -.2509 -16.13***

______ [SP Moody -.0403 -3.01"*


Fitch SP -.0948 -7.07**
Matched Sample Fitch Moody -.1618 -10.26**

_________ SP Moody -.0670 -4.42*
*** denotes significance at the .01 level
"* denotes significance at the .05 level
denotes significance at the .10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Differences are calculated by comparing Probabilities of a Rating
Change from Table 12. Negative differences indicate the probability of a rating change
is lower for the first agency in the comparison.










Table 14
Summary of All Rating Changes by Agency


Number Rating Mean Median
Number of Rating Change Rating Rating
Sample Aenc of Bonds Chanees ti Change Chane

Fitch 555 155 0.279 -.084 -1

Full Sp 2388 1941 0.813 .295 1

Moody 2501 2284 0.913 .315 1


Fitch 395 141 0.357 -.092 -1

Matched SP 395 266 0.673 .004 1

____ Moody 395 321 0.813 -.439 -1

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. The Rating Change Ratio is defined as the number of rating changes
by the agency divided by the number of bonds rated by the agency over the sample
period. Mean and median rating changes are expressed in terms of rating notches.
Changes are based on mapping the rating notches to a 1 to 23 scale. 1 represents a
AAA+ rating while 23 represents a D rating. Therefore negative changes represent
upgrades and positive changes represent downgrades.











Tests of Differences


Table 15
in Mean Rating Changes and Rating Change Ratios


_______ Rating Change Ratio Mean Rating

Samle Comoarison Difference Z statistic Difference Z Statistic

Fitch SP -0.534 -25.87*** -0.379 -2.24"*

Full' Fitch -0.634 -31.93*** -0.399 -2.35 **
Moody

_____ SP Moody -0.100 -10.28*** -0.021 -0.20


Fitch SP -0.316 -9.37s* -0.096 -0.26

Matched Fitch -0.456 -14.67"*** 0.347 0.97
Moody

_____ SP Moody -0.140 -4.61"* 0.443 1.90*
*** denotes significance at the .01 level
** denotes significance at the .05 level
denotes significance at the. 10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Differences are calculated by comparing Mean Rating Changes and
Rating Change Ratios from Table 14. The Rating Change Ratio is defined as the
number of rating changes divided by the number of rated bonds over the sample period.
Negative differences in rating change ratio indicate the first agency in the comparison
had a lower rating change ratio. Negative differences in mean rating change indicate
the first agency in the comparison had a lower mean rating change. Lower mean rating
changes indicates that more changes lead to 'better' ratings on average for the first
agency in the comparison.










Table 16
Summary of Rating Upgrades by Agency


# of # of % of Mean Median
Sml Aency Bonds Uorades Changes Change Change

Fitch 555 79 50.97% -2.37 -1
Full Sample Sp 2388 763 39.31%*" -2.53 -1

_____Moody 2501 894 39.14%** -2.76 -1


Fitch 395 73 51.77% -2.44 -1
Matched Sample SP 395 119 44.74%* -2.03 -1

______Moody 395 192 59.81%*** -1.90 -1
*** denotes the reported value is significantly different from 50% at the .01 level
denotes the reported value is significantly different from 50% at the .05 level
Sdenotes the reported value is significantly different from 50% at the .10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Mean and median rating changes are expressed in terms of rating
notches. Changes are based on mapping the rating notches to a 1 to 23 scale. 1
represents a AAA+ rating while 23 represents a D rating. Therefore negative changes
represent upgrades and positive changes represent downgrades.










Table 17
Summary of Rating Downgrades by Agency


# of Number of % of Mean Median
Sample Aencv Bonds Downgrades Changes Change Change

Fitch 555 76 49.03% 2.29 2
Full Sample Sp 2388 1177 60.64 % 2.13 1

_____Moody 2501 1389 60.81%*** 2.29 2


Fitch 395 68 48.23% 2.43 2

Matched Sample SP 395 147 55.26%* 1.65 1

_____Moody 395 129 40.19 % 1.74 1
*** denotes the reported value is significantly different from 50% at the .01 level
**denotes the reported value is significantly different from 50% at the .05 level
denotes the reported value is significantly different from 50% at the. 10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Mean and median rating changes are expressed in terms of rating
notches. Changes are based on mapping the rating notches to a 1 to 23 scale. 1
represents a AAA+ rating while 23 represents a D rating. Therefore negative changes
represent upgrades and positive changes represent downgrades.










Table 18
Tests of Differences in Mean Upgrades and Downgrades


Mean Upgrades Mean Downgrades
Sample Comyarison Difference Z atisi Qirn" Z statistic
Fitch SP 0.16 0.43 0.16 0.61
Full Fitch Moody 0.39 1.04 0.00 0.00

SP Moody 0.23 1.49 -0.16 -1.82*


Fitch SP -0.41 -0.90 0.78 2.17**

Matched Fitch Moody -0.51 -1.23 0.69 1.88*

______ SP Moody -0.13 -0.47 -0.09 -0.38
*** denotes significance at the .01 level
** denotes significance at the .05 level
denotes significance at the. 10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. Differences are calculated by comparing mean upgrades and mean
downgrades from Tables 16 and 17. Differences are in terms of rating notches.
Positive differences indicate that the first agency in the comparison upgraded less
(downgraded more) than the second agency. Negative differences indicate that the first
agency upgraded more (downgraded less) than the second agency in the comparison.










Table 19
Summary of Rating Reversals by Agency


Number % of Changes Mean Median
Sample Agency of Resulting in Months to Months
Reversals a Reversal Reversal to Reversal

Fitch 9 5.81% 27.33 30

Full Sample Sp 181 9.33% 16.43 15

_____Moody 184 8.06% 15.61 13


Fitch 9 6.38% 27.33 30

Matched Sample SP 24 9.02% 20.41 20

_____Moody 25 7.79% 23.72 26

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. The matched sample is a
subset of the full sample composed of bonds rated by all three agencies for a period of
at least one year. A rating reversal is defined as a rating change that results in the
rating returning to a value it held previously. Reversals may be evidence of a rating
agency focusing heavily on short term fluctuations in credit quality as opposed to
focusing on long term default risk.










Table 20
Summary of Net Upgrades and Net Downgrades in the Matched Sample

# % of Bonds in Mean Net Mean#
Sale Aenc Bonds Grou (from 395) Change Changes

Fitch 60 15.19% -2.50 1.30

Net Upgrades SP 75 18.99% -2.67 1.47
_____Moody 121 30.63% -2.51 1.48


Fitch 47 11.90% 2.72 1.21
Net Downgrades SP 94 23.80% 2.45 1.38

_____Moody 70 17.72% 2.30 1.50

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. Changes are based on mapping the
rating notches to a 1 to 23 scale. 1 represents a AAA+ rating while 23 represents a D
rating. Therefore negative changes represent upgrades and positive changes represent
downgrades. Net Upgrades are bonds which had a net decrease in rating notches over
the sample period. Net Downgrades are bonds which had a net increase in rating over
the sample period.










Table 21
Tests of Differences in Net Upgrades and Net Downgrades in the Matched Sample

Mean Changes % of Bonds in Group

Samle Comoarison Differences Z Statistic Differences Z Statistic
Fitch SP 0.17 0.31 -3.80% -1.42

Upgrades Fitch Moody 0.01 0.02 -15.44% -5.25**
_____SP Moody -0.16 -0.43 -11.64% -3.82"*


Fitch-SP 0.27 0.50 -11.90% -4.42**

Downgrades Fitch Moody 0.42 0.91 -5.82% -2.31"**
_____SP Moody 0.15 0.36 6.08% 2.11"
denotes significance at the .01 level
"* denotes significance at the .05 level
denotes significance at the. 10 level

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. Differences in Mean Rating
Changes are calculated by comparing Mean Rating Changes from Table 20.
Differences in % of Bonds in Group are also calculated by comparing values from
Table 20. Negative differences mean that the first agency in the comparison upgraded
(downgraded) less frequently than the second agency.










Table 22
Summary of Tests of Granger Causality in Rating Changes for the Full Sample


Dependent Variable
= Changes in S&P


Dependent Variable =
Changes in Moody's


# Obs 665 714

Adjusted R2 .1904 .1517

Upgrades Moody's Changes 10.51*"
(0.0000)

S&P Changes 2.50*
__________________ ______(0.0826)


# Obs 735 775

AdjustedR2 .1921 .1148

Downgrades Moody's Changes 1.09
__________ (0.3505)

S&P Changes 8.50**
__________________ ______(0.0002)
Sdenotes significance at the .01 level
** denotes significance at the .05 level
Sdenotes significance at the .10 level

Note: The full sample limits each firm to one senior bond and one subordinated bond.
Therefore most firms will be represented by a single bond. Reported values for
independent variables are F tests of the joint significance of any rating changes from
the previous six months in 'causing' the rating change of the current month. P values
are reported in parentheses.










Table 23
Summary of Tests of Granger Causality in Rating


Changes for the Matched Sample


Changes in Changes in Changes in
__________________ S&P Moody's Fitch

# Obs 105 173 66
Adjusted R2 .0603 .0610 .0307
Upgrades Moody's 0.56 0.89
Changes (0.4566) (0.4748)

S&P Changes 73.73*** 0.05
_________ _______ (0.0000) (0.9860)
Fitch Changes 0.88 1.91
_________________ (0.3501) (0.1514)______


# Obs 122 96 53
Adjusted R2 .1505 .0303 .0468
Downgrades Moody's 0.58 56.67***
Changes (0.6749) (0.0000)

S&P Changes 2.02 6.17***
______ ______(0.1184) (0.0016)
Fitch Changes 54.18*** 9.96***
__________________ (0.0000) (0.0001)_____1
*** denotes significance at the .01 level
** denotes significance at the .05 level
denotes significance at the. 10 level

Note: The matched sample is a subset of the full sample composed of bonds rated by
all three agencies for a period of at least one year. Reported values for independent
variables are F tests of the joint significance of any rating changes from the previous
six months in 'causing' the rating change of the current month. P values are reported
in parentheses.










Table 24
Mean Treasury Spreads Surrounding Rating Upgrades from the Full Sample

________ &P Moody's

# obs 416 426

change 6 months 3.404 3.280

change 5 months 3.331 3.219

change 4 months 3.308 3.200

change 3 months 3.218 3.138

change 2 months 3.106 2.991

change 1 month 3.086 2.906

month of change 2.997 2.839

change + 1 month 2.961 2.849

change + 2 months 2.968 2.849

change + 3 months 2.946 2.789

change + 4 months 2.948 2.770

change + 5 months 2.973 2.664

change + 6 months 2.960 2.703

Note: A subset of the full sample was used to construct this table. Bonds were
required to have observations for the six months pre and post rating change. In
addition only bonds for which Treasury spread data was available were used. Each
firm is limited to one senior bond and one subordinated bond for purposes of this
sample. Therefore most firms will be represented by a single bond. Data for Mean,
Min, and Max upgrades are given in # of rating notches. Decreases in Treasury
Spreads are Expected around Rating Upgrades.










Table 25
Differences in Mean Treasury Spreads Surrounding Rating Upgrades
from the Full Sample


S&P Moody's
Differences Diff t stats Diff t ss

month-1 month-2 -0.021 -0.84 -0.086 -2.18**

month of change month-1 -0.089 -1.06 -0.066 -0.66

month+1 month of change -0.035 -1.57 0.002 0.02

month+2 month+1 0.007 0.21 0.000 0.00


month of change month-6 -0.407 -3.54"* -0.437 -3.60**

month+6 month of change -0.037 -0.64 -0.144 -2.17**

month+6 month-6 -0.444 -3.28** -0.577 -3.26***
*** denotes significance at the .01 level
** denotes significance at the .05 level
denotes significance at the .10 level

Note: Differences are calculated by comparing values from Table 24. Differences are
shown between selected periods that best illustrate the changes in spreads over time.
When comparing mean spreads around rating upgrades we expect lower spreads as time
progresses. Therefore we expect the differences in the table below to be positive in
sign.




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7DEOH 3HUFHQWDJH RI %RQGV ZLWK 8QFKDQJHG 5DWLQJV 2YHU WKH 6DPSOH 3HULRG 6DPOH $JHQF\ IW %RQGV 8QFKDQJHG b 8QFKDQJHG )XOO 6DPSOH )LWFK b 63 b 0RRG\ b 0DWFKHG 6DPSOH )LWFK b 63 b 0RRG\ b 0ROH 7KH IXOO VDPSOH OLPLWV HDFK ILUP WR RQH VHQLRU ERQG DQG RQH VXERUGLQDWHG ERQG 7KHUHIRUH PRVW ILUPV ZLOO EH UHSUHVHQWHG E\ D VLQJOH ERQG 7KH PDWFKHG VDPSOH LV D VXEVHW RI WKH IXOO VDPSOH FRPSRVHG RI ERQGV UDWHG E\ DOO WKUHH DJHQFLHV IRU D SHULRG RI DW OHDVW RQH \HDU

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7DEOH 3URSRUWLRQV 7HVWV RI 'LIIHUHQFHV %HWZHHQ 3HUFHQWDJH RI )LUPV ZLWK 8QFKDQJHG 5DWLQJV 2YHU WKH 6DPSOH 3HULRG 6DPOH &RP'DULVRQ 'LIIHUHQFHV = 6WDWLVWLF )XOO 6DPSOH )LWFK 63 b rrr )LWFK 0RRG\ b frr 63 0RRG\ b rf 0DWFKHG 6DPSOH )LWFK 63 b rrr )LWFK 0RRG\ b rrr 63 0RRG\ b r fr GHQRWHV VLJQLILFDQFH DW WKH OHYHO rr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7DEOH 3UREDELOLW\ RI D 5DWLQJ &KDQJH SHU %RQG SHU
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7DEOH 7HVWV RI 'LIIHUHQFHV LQ 3UREDELOLWLHV RI D 5DWLQJ &KDQJHV SHU %RQG SHU
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7DEOH 7HVWV RI 'LIIHUHQFHV LQ 0HDQ 5DWLQJ &KDQJHV DQG 5DWLQJ &KDQJH 5DWLRV 5DWLQJ &KDQJH 5DWLR 0HDQ 5DWLQJ 6DPOH &RP'DULVRQ 'LIIHUHQFH = VWDWLVWLF 'LIIHUHQFH )XOO )LWFK 63 rrr rr )LWFK 0RRG\ rrr rf 63 0RRG\ rrr 0DWFKHG )LWFK 63 rrr )LWFK 0RRG\ rrr 63 0RRG\ rrr r GHQRWHV VLJQLILFDQFH DW WKH OHYHO rr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fEHWWHUf UDWLQJV RQ DYHUDJH IRU WKH ILUVW DJHQF\ LQ WKH FRPSDULVRQ

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7DEOH 6XPPDU\ RI 5DWLQJ 8SJUDGHV E\ $JHQF\ 6DPOH $HHQFY WL RI %RQGV RI 8'HUDGHV b RI &KDQHHV 0HDQ &KDQHH 0HGLDQ &KDQHH )XOO 6DPSOH )LWFK b 6S brrr 0RRG\ brrr 0DWFKHG 6DPSOH )LWFK b 63 br 0RRG\ brrr rrr GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b DW WKH OHYHO rr GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b DW WKH OHYHO GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b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7DEOH 6XPPDU\ RI 5DWLQJ 'RZQJUDGHV E\ $JHQF\ 6DPOH $HHQFY RI %RQGV 1XPEHU RI 'RZQJUDGHV b RI &KDQJHV 0HDQ &KDQJH 0HGLDQ &KDQJH )XOO 6DPSOH )LWFK b 6S br 0RRG\ br 0DWFKHG 6DPSOH )LWFK b 63 bf 0RRG\ brrr GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b DW WKH OHYHO rr GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b DW WKH OHYHO GHQRWHV WKH UHSRUWHG YDOXH LV VLJQLILFDQWO\ GLIIHUHQW IURP b DW WKH OHYHO 1RWH 7KH IXOO VDPSOH OLPLWV HDFK ILUP WR RQH VHQLRU ERQG DQG RQH VXERUGLQDWHG ERQG 7KHUHIRUH PRVW ILUPV ZLOO EH UHSUHVHQWHG E\ D VLQJOH ERQG 7KH PDWFKHG VDPSOH LV D VXEVHW RI WKH IXOO VDPSOH FRPSRVHG RI ERQGV UDWHG E\ DOO WKUHH DJHQFLHV IRU D SHULRG RI DW OHDVW RQH \HDU 0HDQ DQG PHGLDQ UDWLQJ FKDQJHV DUH H[SUHVVHG LQ WHUPV RI UDWLQJ QRWFKHV &KDQJHV DUH EDVHG RQ PDSSLQJ WKH UDWLQJ QRWFKHV WR D WR VFDOH UHSUHVHQWV D $$$ UDWLQJ ZKLOH UHSUHVHQWV D UDWLQJ 7KHUHIRUH QHJDWLYH FKDQJHV UHSUHVHQW XSJUDGHV DQG SRVLWLYH FKDQJHV UHSUHVHQW GRZQJUDGHV

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7DEOH 7HVWV RI 'LIIHUHQFHV LQ 0HDQ 8SJUDGHV DQG 'RZQJUDGHV 0HDQ 8 AJUDGHV 0HDQ 'RZQJUDGHV 6DPOH &RP'DULVRQ 'LIIHUHQFH = VWDWLVWLF 'LIIHUHQFH = VWDWLVWLF )XOO )LWFK 63 )LWFK 0RRG\ 63 0RRG\ r 0DWFKHG )LWFK 63 f )LWFK 0RRG\ r 63 0RRG\ GHQRWHV VLJQLILFDQFH DW WKH OHYHO rr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f WKDQ WKH VHFRQG DJHQF\ 1HJDWLYH GLIIHUHQFHV LQGLFDWH WKDW WKH ILUVW DJHQF\ XSJUDGHG PRUH GRZQJUDGHG OHVVf WKDQ WKH VHFRQG DJHQF\ LQ WKH FRPSDULVRQ

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7DEOH 6XPPDU\ RI 5DWLQJ 5HYHUVDOV E\ $JHQF\ 6DPSOH $JHQF\ 1XPEHU RI 5HYHUVDOV b RI &KDQJHV 5HVXOWLQJ LQ D 5HYHUVDO 0HDQ 0RQWKV WR 5HYHUVDO 0HGLDQ 0RQWKV WR 5HYHUVDO )XOO 6DPSOH )LWFK b 6S b 0RRG\ b 0DWFKHG 6DPSOH )LWFK b 63 b 0RRG\ b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7DEOH 6XPPDU\ RI 1HW 8SJUDGHV DQG 1HW 'RZQJUDGHV LQ WKH 0DWFKHG 6DPSOH 6DPOH $HHQFY IW %RQGV b RI %RQGV LQ *URX' IURP f 0HDQ 1HW &KDQHH 0HDQ IW &KDQHHV 1HW 8SJUDGHV )LWFK b 63 b 0RRG\ b 1HW 'RZQJUDGHV )LWFK b 63 b 0RRG\ b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7DEOH 7HVWV RI 'LIIHUHQFHV LQ 1HW 8SJUDGHV DQG 1HW 'RZQJUDGHV LQ WKH 0DWFKHG 6DPSOH 0HDQ &KDQJHV b RI %RQGV LQ *URXS 6DPSOH &RP'DULVRQ 'LIIHUHQFHV = 6WDWLVWLF 'LIIHUHQFHV = 6WDWLVWLF 8SJUDGHV )LWFK 63 b )LWFK 0RRG\ b rrr 63 0RRG\ b rrr 'RZQJUDGHV )LWFK 63 b rf )LWFK 0RRG\ b f 63 0RRG\ b f rrr GHQRWHV VLJQLILFDQFH DW WKH OHYHO rr GHQRWHV VLJQLILFDQFH DW WKH OHYHO GHQRWHV VLJQLILFDQFH DW WKH OHYHO 1RWH 7KH PDWFKHG VDPSOH LV D VXEVHW RI WKH IXOO VDPSOH FRPSRVHG RI ERQGV UDWHG E\ DOO WKUHH DJHQFLHV IRU D SHULRG RI DW OHDVW RQH \HDU 'LIIHUHQFHV LQ 0HDQ 5DWLQJ &KDQJHV DUH FDOFXODWHG E\ FRPSDULQJ 0HDQ 5DWLQJ &KDQJHV IURP 7DEOH 'LIIHUHQFHV LQ b RI %RQGV LQ *URXS DUH DOVR FDOFXODWHG E\ FRPSDULQJ YDOXHV IURP 7DEOH 1HJDWLYH GLIIHUHQFHV PHDQ WKDW WKH ILUVW DJHQF\ LQ WKH FRPSDULVRQ XSJUDGHG GRZQJUDGHGf OHVV IUHTXHQWO\ WKDQ WKH VHFRQG DJHQF\

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7DEOH 6XPPDU\ RI 7HVWV RI *UDQJHU &DXVDOLW\ LQ 5DWLQJ &KDQJHV IRU WKH )XOO 6DPSOH 'HSHQGHQW 9DULDEOH &KDQJHV LQ 6t3 'HSHQGHQW 9DULDEOH &KDQJHV LQ 0RRG\fV 8SJUDGHV 2EV $GMXVWHG 5 0RRG\fV &KDQJHV frr f 6t3 &KDQJHV r f 'RZQJUDGHV 2EV $GMXVWHG 5 0RRG\fV &KDQJHV f 6t3 &KDQJHV rrr f GHQRWHV VLJQLILFDQFH DW WKH OHYHO rr GHQRWHV VLJQLILFDQFH DW WKH OHYHO GHQRWHV VLJQLILFDQFH DW WKH OHYHO 1RWH 7KH IXOO VDPSOH OLPLWV HDFK ILUP WR RQH VHQLRU ERQG DQG RQH VXERUGLQDWHG ERQG 7KHUHIRUH PRVW ILUPV ZLOO EH UHSUHVHQWHG E\ D VLQJOH ERQG 5HSRUWHG YDOXHV IRU LQGHSHQGHQW YDULDEOHV DUH ) WHVWV RI WKH MRLQW VLJQLILFDQFH RI DQ\ UDWLQJ FKDQJHV IURP WKH SUHYLRXV VL[ PRQWKV LQ fFDXVLQJf WKH UDWLQJ FKDQJH RI WKH FXUUHQW PRQWK 3 YDOXHV DUH UHSRUWHG LQ SDUHQWKHVHV

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