The Information Content Of Credit Ratings

The information content of credit ratings:

New evidence from the Dutch Stock market

Author: Vincent van Meeuwen

Eur study number: 287650

Thesis Supervisor: Andrey Lizyayev

Finish Date: 12-08-08

The information content of credit ratings: New evidence from the Dutch Stock market

Vincent van Meeuwen, 287650, [email protected] Page 1 of 67

Preface

Preface

I would like to thank Dr. Henk Tuin from Nachenius Tjeenk, BNP PARIBAS Private

banking for providing access to Bloomberg. Without this access I could not have done

this research. In addition I would like to thank Dr. Andrey Lizyayev for his help and

support with this thesis.



Abstract

Abstract

This study examines the information content, measured by stock price effects, of credit

rating changes for Dutch listed firms. If Credit rating agencies (CRAs) reveal new

information to the market, stock prices should react accordingly. In line with most

previous research, price effects for Dutch listed firms are only associated with

downgrades but not with upgrades. Furthermore, this study confirms the findings of

Jorion and Zang (2006) that the prior rating is an important factor in explaining stock

price effects. The empirical results show that low rated companies exert much stronger

stock price effects compared to high rated firms.

Keywords: Credit rating agencies, prior rating, credit rating changes,

stock price reaction, event study



Table of contents

Table of Contents

1. Introduction .............................................................................................................. 4 § 1.1 Objective of the Paper ................................................................................. 4

§ 1.2 Research framework ................................................................................... 5

§ 1.2.1 Corporate credit ratings ................................................................... 6

§ 1.2.2 Study of existing literature .............................................................. 6

§ 1.2.3 Information content of downgrades/upgrades .................................. 7

§ 1.2.4 Draw conclusions and suggestions for further research .................... 7

§ 1.3 Corporate credit ratings ................................................................................ 7

2. Literature review .................................................................................................. 12

3. Data and Methodology .......................................................................................... 21

§ 3.1 Data ............................................................................................................ 21

§ 3.2 Methodology .............................................................................................. 22

§ 3.3 Variables .................................................................................................... 25

§ 3.4 Limitations ................................................................................................. 27

4. Empirical Analysis ................................................................................................ 28 § 4.1 Descriptive statistics ................................................................................... 28

§ 4.2 AR and CAR for total sample...................................................................... 29

§ 4.3 AR and CAR for uncontaminated sample .................................................... 31

§ 4.4 Regression analysis for total sample ............................................................ 33

§ 4.5 Regression analysis for uncontaminated sample .......................................... 35

§ 4.6 Testing of hypothesis .................................................................................. 36

§ 4.7 Empirical findings of most relevant studies ................................................. 37

5. Conclusion ............................................................................................................. 45

6. Literature .............................................................................................................. 48

7. List of Appendixes, figures and tables .................................................................. 52

8. Appendixes ............................................................................................................ 53



Chapter 1: Introduction

Introduction

In this section the importance of corporate credit ratings and the role of credit rating

agencies (CRAs) in financial markets will be explained.

§ 1.1 Objective of this Study

The objective of this paper is to investigate the information content of credit rating

changes for Dutch firms listed on the AEX index. If credit rating changes are informative,

stock price should react significantly. In order to examine the information content of

credit ratings the event study approach is used. Cumulative abnormal returns (CARs) are

calculated for the period [-10,+10] for each event. Here day 0 is the day of the rating

change. This is extended with cross sectional regression analysis to control for other

factors, including the prior rating.

The contribution of this paper is twofold. First, it extends the scarce empirical literature

of credit ratings outside the United States. Second, the prior rating is taken into account

in the cross sectional analysis, which is ignored in most previous research.

1



§ 1.2 Research framework


Corporate credit ratings Information content of Downgrades/Upgrades

Study of existing literature Draw conclusions and suggestions for further research

Figure 1.1: Research framework

Each sub paragraph addresses one of the following parts of the framework:

• § 1.2.1 Corporate credit ratings

• § 1.2.2 Study of existing literature

• § 1.2.3 Information content of downgrades/upgrades

• § 1.2.4 Draw conclusions and suggestion for further research

Corporate Credit

ratings definitions

and use

Credit rating

agencies and rating

changes

Theory on the

information content of

credit ratings

Information content

hypothesis

Redistribution

Hypothesis

Differential

information hypothesis

Price pressure

hypothesis

Downgrades

Upgrades

Analysis of

results

Conclusions

Analysis of

results

Time




§ 1.2.1 Corporate credit ratings

The first part of this thesis is devoted to an analysis of the meaning of credit ratings and

the role of Credit rating agencies (CRAs) in financial markets in order to give the reader

insight into the theoretical framework related to this topic.

§ 1.2.2 Study of existing literature

A study of the existing literature has been conducted in order to provide insight into the

theoretical topics related to the information content of credit ratings.

The following hypotheses are tested:

• Information content hypothesis

• Redistribution hypothesis

• Differential Information hypothesis

• Price pressure Hypothesis

For finding literature, the following sources have been used:

• Books, journals and working papers available at the University Library of the

Erasmus University and other Dutch universities

• Books and articles available using Google and Google Scholar

• Digital sources available at the Erasmus University

• Digital sources (Bloomberg) available at “Nachenius Tjeenk”



§ 1.3 Corporate credit ratings

§ 1.2.3 Information content of downgrades/upgrades

The results of the literature study are used to set up an empirical framework to test the

information content for both downgrades and upgrades. For each rating event the

abnormal return and cumulative abnormal return are calculated for the pre announcement

window (-10,-2), the announcement window (-1,+1) and the post announcement window.

(+2,+10). Thereafter the factors that determine the CAR are investigated using cross

sectional regression analysis.

§ 1.2.4 Draw conclusions and suggestion for further research

The results of the empirical study are used to test the information content of credit rating

changes and investigate which factors determine the information content of credit rating

changes. Thereafter implications for further research are discussed.


Rating agencies give their opinion about the creditworthiness of firms and debt

instruments. Credit rating agencies (CRAs) provide credit ratings to debt instruments but

also to the issuers of these debt instruments. In contrast to CRAs, credit bureaus provide

credit scores to individuals.

In the United States, seven CRAs are assigned as

Nationally Recognized Statistical Rating Organizations

(NRSRO) and they are regulated by the SEC. Among

these companies Standard & Poor's (S&P), Moody's,

and Fitch are the largest CRAs and they dominate the

market with a world market share of almost 95 percent.

(SEC, 2008)

Figure 1.1 Credit Rating Agencies




Rating agencies provide ratings which range between triple A (AAA), representing the

highest credit quality and C or D for the lowest credit quality. In most cases the letter D

stands for Default. S&P and Fitch triple A (AAA) rating is comparable to Moody’s

highest rating of Aaa. (Appendix A).

Empirical studies considering rating trends show an apparent correlation between credit

ratings and the probability of a following default. Higher initial ratings reflect a lower

probability of default and vice versa. The cumulative percentage of defaults for

companies rated by S&P with an “AAA” rating for the period 1986-2001 was only 0.52

per cent. The probability of default for companies rated by S&P with a”CCC” was 54.38

per cent for this period. (John et al, 2001)

(Source: Moody’s)

Figure 1.2 Definition of credit ratings




Rating agencies further make a distinction between investment grade and speculative

grade companies. Investment grade means that the credit quality ranges from very high to

adequate payment capacity. Speculative grade is assigned to companies for which the

payment capacity becomes vulnerable to adverse conditions. Ratings below “BBB” from

S&P and Fitch and “Baa3” for Moody’s are classified as speculative grade.

Very many institutions and companies are restricted to invest only in companies which

are considered as investment grade. Therefore falling below the investment grade barrier

can have large implications for a firm.

The ratings in the investment grade class are very similar for the different rating agencies

and have comparable levels of risk. However, below the investment grade threshold it is

difficult to compare the ratings of the different CRAs. S&P and Fitch’s lowest ratings

only reflect default risk while Moody’s lowest ratings reflect both default risk and

expected recovery values. (Moody’s)

Credit ratings are intended to reflect the long term creditworthiness of the underlying

issuer and are not influenced by short term fluctuations. Therefore credit ratings agencies

rate “through the cycle”. (Micu et al, 2006) This means that short term fluctuations, such

as a temporary downturn in the economy almost have no impact on credit ratings.

While credit ratings are very stable, debt and equity prices change more often and

therefore are more timely indicators of changes in credit quality but also more volatile.

To provide the market with more timely indicators than credit ratings but less volatile

than stock prices, CRAs introduced since 1980 two other types of rating instruments:

outlooks and reviews. These instruments give a prediction of an issuer’s credit quality

over the medium term. (Micu et al, 2006)




Reviews give a more distinct indication of a future change in ratings compared to rating

outlooks. A review can take the form of an addition to “CreditWatch” by S&P, or to

“Watchlist” by Moody’s. This indicates that there is a very high probability that the

issuer will be downgraded or upgraded. (Micu et al, 2006)

While ratings and rating reports are costly, almost all issuers pay for the rating and very

many investors purchase these reports. Rating information is considered as valuable

because issuers provide inside information to raters without fully disclosing the specific

underlying details to the public. This makes the interaction between firms and the

financial markets more predictable and lead to more stability in financial markets.

Graham and Harvey (2001) held an investigation under chief financial officers (CFOs).

The outcome of their investigation was that 57.1 percent of the CFOs regarded the credit

rating as the most important factor in the decision to issue more debt.

While credit ratings are an important factor in the decision of firms to issue more debt,

the performance of rating agencies has been under discussion recently. After the

collapses of Enron and WorldCom, both rated as high profile companies before they

collapsed, the value of rating agencies has been questioned. Some researchers suggest

that credit ratings contain no information beyond what is already publicly available. To

test whether rating information is in fact price relevant, extensive empirical research has

been conducted. The most applied method to test the information content of credit rating

changes is the event study approach where the impact of rating changes on security prices

is examined. A lot of studies investigated the information content for the US stock market

but there is almost no empirical research for stock markets outside the US.




Moreover the empirical studies provide mixed results. The different methodologies used

to study this subject and the lack of a clear assessment explains for a large part the mixed

results. In addition, it is very difficult to study the impact of rating changes on security

prices in isolation of other factors, because rating changes are often triggered by

economic events. Though, a similarity in the most recent studies is the asymmetrical

response of stock prices on downgrades and upgrades. Most studies document an

economically large and statistically significant effect for downgrades on daily stock

prices but almost no impact of upgrades.1

While the most empirical studies on this subject have investigated the market for the US,

there are only a few comparable studies outside the US. Exceptions for this are Barron et

al. (1997) who studied the market for the United Kingdom, Matolcsy and Lianto (1995)

and Chan et al. (2004) who investigated the Australian stock market and Romero and

Fernandez (2006) who studied the Spanish stock market.

For the Dutch stock market there is almost no empirical research done about this subject.

This thesis contributes therefore to the existing empirical literature by investigating a new

stock market outside the US and thereby extending the scarce empirical research for

markets outside the US. Moreover this thesis incorporates insights from previous research

and in addition takes the prior rating into account as suggested by Jorion and Zang (2006)

which was not considered in previous studies.

This paper will continue as follows. Section two provides an overview of the relevant

literature considering the information content of rating changes. The third section

describes the methodology and data used for this analysis. The fourth section is devoted

to the empirical results followed by the main conclusions and suggestions for further

research.

1 Holthausen and Leftwith (1986), Cornell et al. (1989) and Hand et al. (1992) find evidence of a negative

response in the markets to downgrades in debt ratings, whereas no reaction is found for upgrades



Chapter 2: Literature review

Literature Review

This section provides an overview of the most relevant empirical studies concerning the

information content of credit ratings.

In the academic literature with respect to credit ratings there is an ongoing debate about

whether rating changes reveal new, price relevant information to the market: “The

Information content hypothesis”. According to the information content hypothesis, rating

changes reveal new information to the market; therefore stock prices should react after a

rating event. If this hypothesis does not hold stock prices should not react to rating

events, because credit ratings only reflect information that is already public available.

There are two different views with respect to the information content of credit rating

changes. One view suggests that rating agencies provide only a summary of public

information about the creditworthiness of a firm.2 If the market is semi-strong efficient,

credit rating changes should provide no information, because share prices already reflect

the information on which the change was based. An alternative view is that rating

agencies do add additional information to the market. Credit rating analysts have entrance

to internal sources of a firm; because they have meetings with the management of the

firm and therefore have access to private information. Moreover, in the United States

rating agencies are exempted from the Securities and Exchange Commission’s fair

disclosure regulation which was introduced in 2000.

2 See for example Wakeman (1990)

2




This regulation forbids firms to make selective non-public disclosures to market

participants but allows them to share non-public information with rating agencies. (BIS,

2008)

The Redistribution Hypothesis relates the default risk to the redistribution of wealth

between stockholders and bondholders. During rating changes there exist a conflict of

interest between bondholders and stockholders. A downgrade reduces bond value, which

shifts wealth from bondholders to stockholders, this leads to an increase in the share

price. A rating upgrade shifts the wealth in the reverse direction. 3

Goh and Ederington (1993) also studied the redistribution hypothesis. They find that

downgrades are not per se bad news for shareholders. Downgrades which are driven by a

change in the financial leverage of a firm indicate a transfer of wealth from bondholders

to shareholders. In later work they also find much stronger negative stock price effects

for downgrades within the speculative grade category compared to the investment grade

category. 4 Also larger negative stock price effects are observed for firms which faced

negative pre-downgrade abnormal returns. A downgrade after a period of negative

abnormal returns should be less surprising as a downgrade after a period of positive

abnormal returns. Therefore they argue that this larger stock price reaction must be due to

the information that is viewed by investors as more important. (Goh and Ederington,

1999)

The size of a firm also influences the information content of credit ratings. Atiase (1985)

pointed out the following hypothesis: “The production and dissemination of private

information is an increasing function of the size of a firm.” “the differential information

hypothesis.”

3 See for example Zaima and McCarthy (1988)

4 Goh and Ederington, (1999)




Beard and Sias (1997), show that the market capitalization of a firm is highly correlated

with the number of analysts following a firm. Information from rating agencies about

small-cap firms is therefore more valuable than information about large-cap firms.

Consequently, the price effect of rating changes should be larger for small-cap firms

than for large-cap firms. (Micu et al, 2006)

According to most empirical evidence, downgrades in lower classes of the rating scale

lead to larger negative stock price effects.

Moreover, according to the “price pressure hypothesis”, a downgrade from investment

grade to speculative grade should have a larger price impact than other downgrades. Even

if rating changes would not have any information content, downgrades from investment

grade to speculative grade would still affect stock prices. The reasoning behind this

hypothesis is the following: especially Institutions but also portfolio managers of

companies are obliged to sell securities that are downgraded to below investment grade.

(Micu et al, 2006)

There are several studies which find evidence for the “price pressure hypothesis”.

For example Steiner and Heinke (2001), find a larger widening of credit spreads for

downgrades from investment grade to speculative grade. Hand et al. (1992), find

evidence for a much stronger price reaction of investment-grade bonds to rating

downgrades compared to speculative-grade bonds. Chan et al. (2004), investigated the

Australian stock market. Their findings are inline with Hand et al. (1992). Their results

indicate that most of the negative abnormal returns found in the pre-announcement

periods are contributed to downgrades in the speculative grade category.

On the other hand, Jorion and Zang (2006) show that the statistically significant effect of

the investment grade variable in their regression model disappears when the prior rating

is taken into account.




They further argue that not taking the prior rating into account can lead to misleading

results, especially if the prior rating is correlated with the variable of interest which is the

case with the variable “investment grade”.

Jorion and Zang (2006) argue that the rating prior to the announcement should be taken

into account in studies which test the information content of credit ratings. They find

much stronger information effects for low rated companies compared to high rated

companies. The authors further argue that taking into account the prior rating explains for

a large part the empirical puzzle that stock price effects are only associated with

downgrades but not with upgrades. They also show that the distribution of prior ratings is

not identical for downgrades and upgrades. A downgrade is most of the time associated

with a much larger change in the credit rating than an upgrade. (Jorion and Zang, 2006)

According to Jorion and Zang, the prior rating explains a large part of the empirical

puzzle that stock price effects are only associated with downgrades but not with

upgrades. This asymmetrical response to rating changes is a puzzle because it is

inconsistent with the investment behavior of rational economic agents. Rational

economic agents would actually require higher returns to hold riskier stocks. Therefore

after a downgrade it should be expected that returns would increase instead of decreasing.

Jorion and Zang show that the prior rating explains a large part of this observed empirical

puzzle. However, Vassalou and Xing (2003) argue that this empirical regularity can for a

large part be explained by the ignorance of the large variations in default risk around the

date of the announcement of the downgrade. Vassalou and Xing (2003) use default

likelihood indicators (DLIs) as a measure of default risk.




After adjusting the returns for DLI, as well as Book to Market ratio and size, they show

that the negative abnormal returns largely disappear. They show that the development of

DLIs follows an inverted V shape. They first increase significantly in the period prior to

the downgrade and reach their peak at the time of the downgrade announcement. After

this announcement DLIs start to decrease. Moreover, the variation in DLI around

upgrades is minimal. According to the authors this pattern explains the asymmetric

reaction of equity returns to upgrades and downgrades.

Based on their results they conclude that default risk varies too much over time for credit

ratings to provide useful information about the future default risk of a firm. However they

argue that credit ratings and downgrades do have a disciplinary effect on the management

of the company. (Vassalou and Xing, 2003)

The research thus far which studied whether credit rating information is valuable was not

able to examine the effect of rating changes in isolation. In most of the cases rating

changes are triggered by economic events. Therefore it is not clear how much of the price

reaction to rating changes is explained by the rating announcement itself and how much

is attributable to the underlying economic event. Kliger and Sarig (2000) have proposed a

new approach to examine the information content of credit ratings. They study price

reactions to rating changes which only reflect rating information. They do this by

examining stock and bond prices for the month April 1982. In this month Moody’s

started to report ratings with a finer rating classification. This refinement was not driven

by any fundamental change in the risks of the issuers. Therefore, the authors argue that

this refinement can be used to examine the information content of bond ratings in

isolation of other price relevant information. Their results show that rating information is

valuable. After Moody’s implemented their finer rating system, both bond and stock

prices reacted significantly to this new information.




For high-leverage firms (typically rated speculative grade) stock prices reacted much

stronger than low-leverage firms (typically rated investment grade). This last finding is

inline with the price pressure hypothesis. (Kliger and Sarig, 2000)

Another factor which influences the impact of rating changes on security prices is

whether the changes are already expected by the market. Hand et al. (1992) examines this

by excluding rating changes which were already expected by the market. There results

show that unexpected downgrades have a negative effect on the firm’s returns.

Creighton et al. (2004) shows that stock prices have a tendency to fall before a

downgrade. This suggests that markets react in anticipation of a rating event. These

results are in line with Steiner and Heinke, (2001) and Hull et al. (2004). In addition,

stock price reactions are larger for small firms, when the rating drops below investment

grade and also if agencies have not indicated that the rating is under review. (Creighton et

al., 2004)

Covitz and Harrison (2003) also advocate that markets react in anticipation of a rating

event. They estimated that almost 75 percent of the change in credit rating spreads occurs

in the six months prior to a downgrade. However the empirical evidence is relatively

mixed: Katz (1974), Griffin and Sanvicente (1982) and Holthausen and Leftwich (1986)

find evidence that markets do not anticipate rating changes but react directly after rating

changes because they reveal new information.

The early empirical studies advocate that markets do not anticipate rating changes, while

the evidence for the last years proposes that markets do react in anticipation of a rating

event. A possible explanation for this might be the availability of public information to

investors. Nowadays investors have much easier access to information sources than in the

past.




Therefore it could be that the information content of credit rating changes has decreased

over time. Another plausible explanation is the difference in methodology and model

specifications used to investigate this question.

While most studies only investigate the information content of rating changes, the

number of studies which provide information concerning the possible impact of the credit

raring to watch procedure is very limited. Holthausen and Leftwich (1986) and Hand, et

al. (1992) are exceptions of this. Both studies compare the impact of an addition to the

S&P credit Watch list with the final change in the credit rating. They find statistically

significant negative stock price effects following a downgrade. The addition to the credit

watch list results to an even more significant change in the stock price. These findings

suggest that the addition to a credit watch list is more informative than the effective

change in the credit rating. Though, it should be noticed that an addition to the credit

watch list is often accompanied by negative news about a company.

Outside the United States, there are only a few comparable studies concerning this

subject. A possible explanation for this could be that in the United States the role for

rating agencies is more important because in these countries there is more financing with

debt. Moreover the three largest rating agencies have their headquarters in the United

States. However, the United Kingdom, the Australian and the Spanish Stock market are

already investigated.

Barron et al. (1997) have studied the stock market for the United Kingdom. They find

similar to most studies in the US a statistically significant negative stock prices effect for

downgrades but no statistically significant effect for upgrades.




Matolcsy and Lianto (1995) investigated the information content of credit ratings for the

Australian stock market. They find that both downgrades and upgrades have significant

information content.

Chan et al. (2004) investigated publicly listed Australian firms which are subjected to

Moody’s credit rating changes in the period between September 1986 and June 2004.

They find that there is no evidence of excess long run stock returns after Moody’s credit

rating revisions. Therefore they conclude that both credit upgrades and credit downgrades

are lagging indicators. These results differ from recent studies in the US.

Most recent studies in the US report an economically large and statistically significant

effect for downgrades on daily stock prices. However, the impact of upgrades on daily

stock prices is very modest and not statistically significant.5 (Chan et al, 2004)

While most of the literature about rating changes studies the impact on stock returns,

there are only a few studies which also consider systematic risk around rating changes.

Impson et al. (1992), examines the relationship between bond re-ratings and changes in

systematic risk, using both time series and cross-sectional regressions. They find that

downgrades are associated with an increase in beta. This increase in beta is positively

correlated with firm size. On the other hand, for rating upgrades they find no effect on

systematic risk. (Impson et al., 1992)

Romero and Fernandez (2006) analyze the effect of corporate bond rating changes on

stock prices for the Spanish stock market. They explore the effects on excess returns and

systematic risk. They find evidence of negative effects on systematic risk for both

downgrades and upgrades.

5 Holthausen and Leftwith (1986 , Cornell et al. (1989) and Hand et al. (1992) find evidence of a negative

response in the markets to downgrades in debt ratings, whereas no reaction is found for upgrades




They argue that rating changes result in a higher level of uncertainty of the firm and also

to a change in beta risk. They further find a rebalancing effect on total risk of re-rated

Spanish firms. The different empirical findings for the Spanish stock market compared to

the US might be related to the difference in the characteristics of these two stock markets.

The markets differ with respect to size, liquidity and intensity. For example, the total

market capitalization and share trading of Spanish stocks in January 2002 represented

respectively 5.1 percent and 6.3 percent of the total on the New York stock exchange.

(Romero and Fernandez, 2006)

Summarizing, the empirical literature on the effect of rating changes on stock prices is

mixed. Some studies show that credit rating changes are leading indicators of share

market prices.6 However other studies find evidence for the opposite, namely that credit

ratings are lagging indicators.7 In addition, most recent studies report an asymmetrical

response of stock prices for downgrades and upgrades. However, Jorion and Zang (2006)

show that this asymmetrical response almost disappears when the prior rating is correctly

taken into account which is ignored in previous research. Vassalou and Xing (2003)

further argue that this asymmetrical response can be explained by the ignorance of the

large variations in default risk around the date of the announcement of the downgrade.

6 See for example Katz (1974), Griffin and Sanvicente (1982) and Holthausen and Leftwich (1986)

7 See for example Creighton et al. (2004) Steiner and Heinke, (2001), Hull et al., (2004) and

Covitz and Harrison (2003)



Chapter 3: Data and Methodology

Data and Methodology

This section describes the sample of rating changes for all Dutch listed firms rated by

Moody’s and S&P from January 1996 till April 2008. In addition, the methodology

used to test the information content of credit ratings is described.

§ 3.1 Data

The sample of rating changes is gathered from the Bloomberg Database and incorporates

rating changes from the raring agencies S&P, Moody’s and Fitch. These rating agencies

provide only ratings for the firms listed on the AEX. These ratings are matched with daily

stock data from the AEX gathered from DataStream. The final sample consists of rating

changes for the following companies: Aegon, Ahold, Akzo Nobel, ASML, Corporate

Express, DSM, Fortis, Getronics, ING, KPN, Philips, Reed Elsevier, Shell, TNT and

Wolters Kluwer. For these companies, daily stock returns are gathered for the period

1/1/1996 till 30/4/2008. (Appendix N)

Before the rating data could be used for analysis the ratings must be expressed into

numerical values and range from 14 to 36 (Appendix B). The enumeration is motivated

by the research of Odders-White and Ready (2006).

3



§ 3.2 Methodology

§ 3.2 Methodology

The final Sample consists of rated firms which meet to the following criteria:

• Firms should be listed on the AEX index for a least 1 year before the first rating

change occurs

• The rating announcement in the event windows must be uncontaminated with

other informative corporate news8

• In order to calculate abnormal returns daily stock returns must be available for

both the event window and the estimation window

• Downgrades to default are not taken into account; in this case the rating change

brings no supplementary information to the market

• The estimation window must exclude return data from earlier rating events for the

same company, therefore minimal 260 business days must lay between two

subsequent rating events for one company

To determine the effect of rating changes on stock returns, the cumulative abnormal

return in percentage returns (CAR) is measured for the announcement window. Day 0 is

the day of the announcement of the rating change. To examine the effect before and after

an announcement have taken place, CARs are calculated for three event periods. The first

event period is the pre announcement window and is measured for the [-10,-2] window.

The second event period is the [-1,+1] announcement window. The third period is the

post announcement window and is measured for the period [+2,+10] .

The time frame of the pre and post announcement windows is measured for a relative

short period. This is motivated by previous research from Steiner and Heinke (2001).

They show that the periods right before and right after a rating change provide the most

information. The market model is used to measure normal performance. This model

relates the return of any given security to the return of the market portfolio.

8 The “Financieel Dagblad” is used to search for news on the specific event windows



§ 3.2 Methodology

A critical assumption of this model is the joint normality of asset returns. However the

usefulness of the estimated alpha and beta obtained from the market model depends

critically on the goodness of fit of the underlying regression. Therefore the estimated

alpha and beta must be statistically significant at least at 10 percent. If not, these

estimations are replaced with historical beta‘s and alpha’s for this specific event window.

For each security i the normal return is estimated with use of the market model:

Rit and Rmt are the period-t returns for security i and the market portfolio, εit is the zero

mean residual. αi, βi, and σ2εi are the parameters of the market model

. (I) (Mackinlay,1997)

For the market portfolio the AEX total return index is used. For all events the alpha and

beta are estimated using linear regression analysis for the period (-250,-50). These

estimated alpha and beta are used to calculate the normal return Rit.A major benefit of the

market model is that it removes the fraction of the return that is contributed to the

variation in the market’s return. This reduces the variance of the abnormal return.

According to Mackinlay (1997), the market model is therefore preferable over the

constant mean return model where the normal Rit is simply the average return over the

estimation window. The market model is estimated using ordinary least squares (OLS).

For each firm i, Rit and Rmt are the return in event period t for security i and the market. L1

is the length of the estimation window. (L1= T1-T0 +1)

((IIII)) ((IIVV))

((IIIIII))

((VV))

((MMaacckkiinnllaayy 11999977))



§3.2 Methodology

The normal return is calculated with the market model and can be used to calculate the

abnormal return. The abnormal return is the difference between the actual return on the

specific event data and the estimated normal return. (VI)

. The average abnormal return is

obtained by aggregation of the abnormal return for all events divided by the number of

events. (VII)

((VVII)) ((VVIIII))

The significance of the average abnormal returns is tested using the standard Brown and

Warner (1985) test statistic. The underlying assumption of this test statistic is cross-

sectional independence. The abnormal returns for each stock are standardized by its

standard deviation calculated from the estimation period from day -250 till day -50. This

is a parametric test which is based on assumptions about the probability distribution of

returns. ((VVIIIIII))

((VVIIIIII))

This test is based on the assumption that the sample is independent and identically

distributed (i.i.d.). However this assumption does not hold for this study. When different

events occur in the same calendar period, it is likely that ARi and ARj of stock i and j (i ≠

j) are collerated. In this case the independent assumption does not hold anymore. Brown

and Warner (1980) solve this problem with the crude dependence adjustment. The

standard deviation σt is replaced with the estimator s ((IIXX)).. The test statistic becomes:

((IIXX)) (Brown and Warner, 1985)



§ 3.2 Methodology

The sample average abnormal return can then be aggregated over the event window to get

the cumulative abnormal return. (CAR) X

(X) ((XXII))

The obtained cumulative abnormal return (CAR) is then used to test the information

content of credit rating changes with the following cross section;

CARi = δδδδ0 + δδδδ1 ∆∆∆∆RATi + δδδδ2 PRTi+ δδδδ3 P/Bi +εεεεi

§ 3.3 Variables

The regression model consists of two kinds of variables: discrete random variables and

continuous random variables. A discrete random variable can take only a finite number of

values. A dummy variable is a discrete variable which can take on the values 0 or 1. This

indicates the absence or presence of the related variable. In this study a dummy variable

is used to separate among different rating classes. The dummy variable prior rating is

transformed into 4 classes which present the prior rating. To avoid the trap of exact

collinearity one of the variables has to be omitted. This variable taking the value of zero

is the highest rating class, which serves as benchmark. (ratings above A)

The CAR is the dependent variable in the regression analysis whereas the prior rating

(PRT), rating change (∆RAT) and Price to book value (P/B) are the independent

variables. The variable PRT is a measure for the prior rating. Which is a cardinal value

ranging from 36-149. The variable ∆RAT measures the absolute level of the rating

change. The P/B ratio measures the market value of the firm relative to the book value.

9 Appendix B expresses the Credit ratings in numerical values



§ 3.3 Variables

.

According to Jorion and Zang (2006) the prior rating is an important variable which

cannot be ignored. However the variable IGRADE is statistically insignificant in their

study. Therefore the variable IGRADE is not included in this study. Moreover the

number of rating changes which passes or falls below the Investment grade threshold is

very small in this sample.

Table 3.1 : Definition of variables

10

the P/B ratio at the day of the announcement is gathered from DataStream

Variable Definition

Dependent Variable

Cumulative Abnormal Return

(CAR)

Measure for the abnormal

performance cumulated over the

event period

Independent Variables

Prior rating

(PRT)

The rating prior to the rating event

expressed in a numerical value

Rating Change

(∆RAT)

Absolute difference between prior

rating and new rating after an rating

event

Price to Book Value

(P/B)10

Market value divided by book value

for each firm on the day of the rating

event. Market price-year end *

Common shares /common equity at

year-end

Dummy variable Prior rating

(DPR)

Categorical variable dummy for

different rating classes

Dpr1: PRT = (29-27) A- BBB+

Dpr2: PRT = (26-24) BBB- BB+

Dpr3::PRT = (23-21) BB- B+

Dpr4: PRT = ( <21 ) < B



§ 3.4 Limitations

§ 3.4 Limitations

First and foremost this research concerns the Dutch Stock market and as such limits

generalization for other countries or on an International level. Furthermore the available

data were limited. Historical credit rating changes are not available via the electronic

sources of the Erasmus University. Therefore the data were finally gathered from the

Bloomberg database of the company “Nachenius Tjeenk”, which is a private bank.

On the other hand, only data for companies listed on the AEX after 1998 were available.

Another Limitation is the correlation between economic events and rating changes. In

order to investigate the stock price effect of rating changes there must be no other

informative news on the same day. However, rating changes are often triggered by

economic events. Therefore the uncontaminated sample is much smaller than the total

sample. This makes the results of this sample less reliable.



Down

grades

27

66%

Upgrades

14

34%

Chapter 4: Empirical results

Empirical Results

This section is devoted to the empirical analysis. It starts with descriptive statistics of

the data set and the analysis of the AR and CAR for the different event windows. This

section is followed by a cross sectional analysis with the CAR as the dependent

variable. The last part is devoted to the empirical findings of most relevant studies.

§ 4.1 Descriptive statistics

The total sample consists of 41 rating actions, 27 downgrades and 14 upgrades for the

period 1st January 1999– 30th April 2008 by Fitch, Moody’s and S&P. (figure I)

The rating data were gathered from the Bloomberg database combined with information

gathered from DataStream.

Figure 4.1: Distribution of rating changes

4



-2,00%

-1,50%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

AR

in

%

Downgrades Upgrades

§ 4.2 AR and CAR for total sample

The division of the rating actions by year shows no clear trend. The frequency of the

rating actions increases from 1999-2003. In 2004 and 2005 the number of rating actions

decreases and rises again in 2007. (Appendix C). There seems to be virtually no clustering

effects in the data set because the rating events are quite dispersed over time. Moreover

rating agencies rate “through the cycle” which means that short term fluctuations in the

economy should have no effect on ratings. If credit ratings would be strongly affected by

short term fluctuations like a slowdown in the economy, clustering would be more likely to

occur. The number of rating actions by rating agency differs considerably. (Appendix D)

The share of events driven by rating changes from S&P and Moody’s is much larger then

those performed by Fitch.


Part A of appendix E shows the daily average abnormal Return (AR) of the total sample for

downgrades and upgrades for the event window (-10,+10). For downgrades the AR for day

0 is positive but not statistically significant. (AR0 = 0.34%) The AR for day 1 is negative

and highly statistically significant. (AR1 = -0.72%, T-statistic -4. 19). For downgrades

almost all the days the results are statistically significant at 1 percent. For upgrades the AR

for day 0 and 1 are both positive and statistically significant at 10 percent. (AR0 = 0.44%,

T- statistic 1.58, AR1 = 0. 49%, T-statistic 1.67). For upgrades, most of the days the AR is

not statistically significant.

Figure 4.2: Abnormal returns (-10,+10) for total Sample




Figure 4.2 shows the AR from part A of appendix E for downgrades most ARs in the

period before the rating events are negative while the AR is positive in the period after

the rating change has taken place. For upgrades most of the ARs are negative. But in the

period (-2,+2) ARs are positive.

Table 4.111

shows the CAR for the total sample for downgrades and upgrades. CARs are

calculated for the windows (-10,-2), (-1,+1), (+2,+10). The CARs for the three windows

are -3.06 %, -0.86% and 4.12 % respectively. For downgrades only the CAR (-10,-2) is

statistically significant at 10 percent (T-statistic -1. 79)

Table 4.1: CAR for the subsequent event windows (Total sample n= 41)

For upgrades the CAR for the three windows are -0.30%, 1.42% and -3.46 %

respectively. For upgrades only the CAR (+2,+10) is statistically significant at 1 percent

(T-statistic 14. 50). It is remarkable that the CAR (+2,+10) following a downgrade is

negative while the CAR after an upgrade is positive for the same window. Moreover the

CAR for the period (-1,+1) is not statistically significant for both downgrades and

upgrades. The statistically significant negative abnormal return prior to a downgrade is

inline with studies that argue that stock prices react in anticipation of a downgrade. Still it

is remarkable that the CAR (+2,+10) following an upgrade is negative and highly

statistically significant. However the sample of upgrades is too small to draw general

conclusions from this.

11

Note: CAR is the cumulative abnormal return in percentage for the different event windows, where day 0

is the day of the announcement of the rating change. CARd ,CARu represents the cumulative abnormal

return for downgrades and upgrades and TCARd TCARu correspond to the t values for CARd and CARu

respectively. The Abnormal returns are calculated using a market model estimated over the period (-250,-

50) ***, **, * denotes statistical significance at the 1%, 5 %, 10% levels correspondingly.

Downgrades/Upgrades CARd T1CARd CARu T1CARu

CAR (-10,-2) 3.06%* -1.79 -0.30% -0.09

CAR (-1,+1) -0.86% -0.53 1.42% 0.72

CAR (+2,+10) 4.12% 1.42 -3.46%*** 14.50



-2,00%

-1,50%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

AR

in

%

Downgrades Upgrades

§ 4.3 AR and CAR for uncontaminated sample


When there is other price relevant news during the event window of a rating change, this

event is classified as contaminated. In the total sample, 14 rating actions are

contaminated. Including the contaminated ratings might lead to a biased estimation of the

effect of rating changes on stock prices. However, by definition rating actions are almost

always to some extent contaminated with price relevant news because rating actions are

often triggered by economic events.

Part B of appendix E shows the daily average abnormal return (AR) for the

uncontaminated sample for the event window (-10,+10). The AR for day 0 following a

downgrade is positive and highly statistically significant. (AR0 = 1.14, T- statistic 30.03).

While the AR for day 1 is also highly statistically significant but negative. (AR1 = -1.42,

T- statistic -18.19). For downgrades the AR for most days are statistically significant. The

AR after an upgrade is slightly positive and also statistically significant. (AR0 = 0. 54%,

T- statistic 2.55), (AR1 = 0.64 %, T- statistic 1.64). Though for upgrades the AR is not

statistically significant for most of the days.

Figure 4.3: Abnormal Returns for Days (-10,+10) for uncontaminated sample




Figure 4.2 shows the AR from part B of appendix E for the uncontaminated sample. The

pattern for downgrade is quite similar. However it is remarkable to see that the AR for

downgrades at day 0 is very large and positive, while at day 1 the AR is largely negative.

This could mean that stock prices react not immediately to the rating change.

Table 4.2 shows the CAR for the uncontaminated sample for downgrades and upgrades.

CARs are calculated for the windows (-10,-2), (-1,+1), (+2,+10). The CAR for the three

windows are – 4.17 %, -0.31% and 2.47% respectively. Only the CAR (-10,-2) is

statistically significant. (T-statistic -2.28) For upgrades the CAR for the three windows is

-0.68%, 1.36% and -4.47%. However none of these CARs is statistically significant.

Table 4.2: CAR for the subsequent event windows (Uncontaminated sample n= 27)

Compared to the results obtained from the total sample, the CAR (-10,-2) is more

negative for both downgrades and upgrades .The CAR (-1,+1) is less negative for

downgrades and less positive for upgrades. The CAR (+2,+10) is also lower for the

uncontaminated sample for both downgrades and upgrades. For both the total and

uncontaminated sample the CAR in the event window (-1,+1) for downgrades/upgrades is

negative/positive (figure2). However in the post event window this trend reverses. The

CAR for downgrades becomes positive whereas the CAR for upgrades is negative in the

post event window.

The empirical results for the pre announcement window(-10,-2) show in line with

Creighton et al. (2004), Steiner and Heinke, (2001) and Hull et al., (2004) that stock

prices have a tendency to fall before a downgrade. For both the total and the

uncontaminated sample CAR (-10,-2) are negative and statistically significant. This

finding suggests that markets react in anticipation of a rating event.

Downgrades/Upgrades CARd T1CARd CARu T1CARu

CAR (-10,-2) -4.17%** -2.28 -0.68% -0.22

CAR (-1,+1) -0.31% -0.32 1.36% 0.64

CAR (+2,+10) 2.47% 1.15 -4.47% -0.60



§ 4.4 Regression analysis for total sample

For upgrades only the CAR (+2,+10) for the total sample is statistically significant. This

result contradicts the results of previous studies, e.g. Holthausen and Leftwith (1986)

,Cornell et al. (1989) and Hand et al. (1992), which document no statistically significant

reaction to upgrades For the uncontaminated sample none of the upgrades is statistically

significant.


To test the information content of credit rating changes the following cross section is

used motivated by Jorion and Zang (2006).

CARi = δδδδ0 + δδδδ1 ∆∆∆∆RATi + δδδδ2 PRTi+ δδδδ3 P/Bi +εεεεi

The first variable measures the magnitude of the rating change (∆RAT). It is the absolute

difference between the new rating and the prior rating. For downgrades, this coefficient is

expected to be negative and conversely for upgrades. This means that a multiple step

downgrade results in a stronger price reaction than a single step downgrade. The second

variable is the prior rating (PRT). The prior rating is a numerical value ranging between

36 and 14. The number 36 correspondents to the highest rating whereas the value 14

correspondents to the lowest rating in the sample. For downgrades the coefficient is

expected to be positive and conversely for upgrades. This implies that downgrades from

lower ratings classes have stronger (negative) stock price effects.The last variable of

interest is the price to book ratio (P/B). In the literature it is argued that rating changes of

smaller companies are more informative than those of larger companies. For downgrades

the coefficient is therefore expected to be positive and conversely for upgrades, this

implies that lower P/B firms have stronger (negative) stock price effects. Furthermore the

variable size and turnover were included but those variables were both statistically

insignificant. Appendix F shows the results for the regression above. The coefficient for

the rating change is negative and highly statistically significant. (-0.200, T-statistic -3.83)




The coefficient of this variable can be intrepreted as followed: a two notch downgrade

(drop of two rating classes) has a 20 percent12

larger price effect as a one notch

downgrade. The coefficient for the prior rating is slightly positive but highly statistically

significant (0.013,T-statistic 3.59). The coefficient of this variable can be interpreted as

followed: a 100 percent decrease in the prior rating has a 1.3 percent larger (negative)

stock price effect. The price to book ratio is also positive but not statistically significant.

However this variable leads to more significant results and increases the R2

of the

regression. For upgrades none of the coefficients is statistically significant. The R2

of the

regression for downgrades is 0.42 and 0.03 for upgrades. These results are in line with

previous research which documents a statistically significant stock price effect after

downgrades but almost no effect following upgrades.

In line with the results of Jorion and Zang for downgrades the coefficient for the Prior

rating and the rating change are both statistically significant. However the results differ

for upgrades. For upgrades none of the coefficients is statistically significant whereas

Jorion and Zang show that the prior rating is slightly positive (0.11) and statistically

significant at 5 percent. A possible explanation for this could be the difference in sample

size or the difference in the characteristics of these two stock markets. The Dutch stock

market differs in size, liquidity and intensity from the US stock market. Another

explanation for this asymmetrical reaction to upgrades and downgrades is the idea that

firms release favourable news more timely. Therefore positive news is already reflected

in the price. Firms dislike releasing negative news, they try to avoid that this information

leaks to the public. Therefore a downgrade often reveals new information to the market,

which explains the significant stock price reaction.

Appendix G augments appendix E with a finer classification of the pior rating. The prior

rating is divided into 5 classes. To avoid the dummy trap 4 (5 -1) dummy variables are

created.

12

A one notch rating change is measured by 1 while a 2 notch rating change is 2, Therefore the change from 1

notch to 2 notches is 100%.



§ 4.5 Regression analysis for uncontaminated sample

The highest prior ratings (36-30)13

are the base case whereas the lowest credit ratings

correspond to the highest dummy variable for the prior rating (DVPR). This means that

the higher the DVPR the lower is the prior rating. DVPR2, DVPR4 and the rating change

are statistically significant. (-0.172, T-statistic -3.56), (-0.400, T-statistic -9.71), (0.020,

T-statistic 2.25). The higher the dummy variable for the prior rating the larger (negative)

is the stock price reaction except for DVPR3 which is also not statistically significant.

The coefficient for the variable rating change is much smaller(less negative) as in the

previous regression (0.200 compared to 0. 0020). For upgrades the considered variables

are not statistically significant. Moreover the dummy variable for the prior rating

increases the R2

of the regression. For downgrades the R

2 increases from 0.42 to 0.83 and

for upgrades from 0.03 to 0.13. However this does not mean that the explanatory power

of the second regression is twice that of the first regression, because including additional

variables to a regression results in a larger R2.

§ 4.5 Regression analysis for uncontaminated sample

Appendix H shows the regression results for the first regression of the last section for the

uncontaminated sample. Both the coefficient for the price to book ratio and the prior

rating are statistically significant. (0.211, T-statistic 2.03), (0.135, T-statistic 1.88). It is

interesting to note that the coefficient for the P/B is statistically significant for the

uncontaminated sample. The Prior rating is also statistically significant and the

coefficient is much larger than the coefficient for the total sample. (0.135 vs. 0.013). For

upgrades none of the variables is significant. The R

2 for downgrades is 0.49 whereas it is

0.13 for upgrades. Compared to the total sample, the R2

increased slightly from 0.43 to

049 and for upgrades from 0.03 to 0.11.

Appendix I augments appendix G with a finer classification of the pior rating. Only the

DVPR3 and the rating change are statistically significant. (-0.432, T-statistic -1.80),

(0.142, T-statistic 1.97).

13

See Appendix B for the credit ratings expressed in numerical values



§ 4.6 Testing of hypotheses

For downgrades the coeficient for DVPR3 can be intrepreted as followed: compared to

the highest rating class (the base case) downgrades in rating class 3 exert a 43.2 percent

larger(more negative) stock price effect. The coefficient for DVPR4 = 0 because in the

uncontaminated sample there are no observations included for the DVPR4.The rating

change is also statistically significant and much higher as the coefficient for the total

sample. (0.020 vs. 0.142). For upgrades the significance of the variables has increased

slightly. However, still none of the variables is statistically significant. For downgrades

the R

2 is 0.75 and for upgrades 0.56. Compared to the total sample the R

2 for downgrades

decreased from 0.83 to 0.75 and for upgrades it increased from 0.13 to 0.56. Appendix K

shows the standardized residual plots for the regressions discussed above. The points in

the plot fluctuate randomly around zero in an un-patterned fashion. This plot does not

suggest violations of the assumptions of zero means and constant variance of the random

errors. However the third plot shows that the thirteenth observation is an outlier.

Therefore this outlier is removed from the data set.

§ 4.6 Testing of hypotheses

In the literature study four hypotheses are proposed. In this section this hypothesis are

examined using the empirical results obtained from this study.

• The Information content hypothesis:

o If credit ratings reveal new information to the market, stock prices should

react after a rating event. The empirical results show that stock prices react

significantly after downgrades but not after upgrades, therefore only

downgrades are informative.

• The Redistribution hypothesis:

o According to redistribution hypothesis, after downgrades wealth shifts from

bond holders to shareholders. This reduces bond value and increases share

value. A rating upgrade shifts the wealth in the reverse direction. The results

confirm this hypothesis. In the post event window CARs are positive after

downgrades and negative following an upgrade.



§ 4.7 Empirical findings of most relevant studies

• The Differential Information hypothesis:

o The empirical results show that the size is not statistically significant in this

sample. This indicates that for Dutch listed firms on the AEX index size has

no effect onthe information content of credit ratings. Both the size and the

turnover variable are not significant. A possible explanation for this could be

that the size of firms listed on the AEX index is not very different.

• The price pressure hypothesis:

o According to the price pressure hypothesis, downgrades from investment

grade to speculative grade should have a larger price impact than other

downgrades. This hypothesis is not tested because in this sample there were

no downgrades from investment grade to speculative grade.


In the different studies that test the information content of credit ratings, results are often

quit different. The main reason for this is the different methodologies used to study this

subject. In this chapter the results of this study are compared and extended with the

results of the most relevant research of other studies about this topic.

Chan et al. (2004)

While this study only has investigated the short term effect of credit rating changes, other

studies also consider the long term effect. Chan et al. (2004) study the long term effect by

calculating buy and hold investments returns for Australian listed firms.

• BHARkτ : buy-and-hold abnormal returns for k sets

• ERit : buy-and-hold investment return for the event firm i at day t

• CRit : buy-and-hold investment return for the control firm j at day t

(Chan et al, 2004)




For downgrades, the cumulative BHAR decreases (-0.107 to -0.249) for the period -24 to

-6 months prior to the rating change. The event windows for the post announcement

windows are not statistically significant.

In contrary to this research, Chan et al, (2004) find a positive and significant effect after

upgrades. For eight months after an upgrade, BHARs are positive and significant. None

of the event windows for the pre-announcement periods are significant. Therefore they

conclude that rating changes are lagging indicators. (Chan et al, 2004)

In contrary to Chan et al. (2004) this study finds abnormal returns in the pre-

announcement periods. Therefore the conclusion of this study is that rating changes are

leading indicators while Chan et al. conclude that rating changes are lagging indicators.

Moreover Chan et al. also found positive abnormal return after an upgrade while this

study shows no significant effect after upgrades. A possible explanation for these

differences is the length of the estimated periods. While Chan et al. investigated the long

term effect, this study only investigated the short term effect.

Barron et al. (1997)

Barron et al. studied the effect of bond rating changes for the UK stock market with the

following cross section:

Here Ri is the absolute value of the excess return, α0 is a constant, CL is a dummy

variable for the rating class, GRD is the number of grades changed, CW shows whether a

rating change is the result of an addition to credit watch and DIR represents the direction

of the rating change. Their results show that a change between rating classes has a larger

stock price effect than a rating change within a class. However the other variables are

statistically insignificant. Furthermore they find abnormal returns after downgrades but

also after positive Credit watch additions. (Barron et al, 1997)




Barron et al. showed in line with this study that multiple step rating change exerts a

stronger stock price effect than a single step rating change. However, Barron et al. find no

significant result for the rating class (CL), this variable is comparable to the prior rating

in this study.

Vassalou and Xing (2002)

Vassalou and Xing adjust returns for the variation in default risk. Instead of using

downgrades as measure for an increase in default risk they use increases in default

likelihood indicators (DLIs) calculated with Merton’s model (1974). The advantage of

DLIs is that they are more timely indicators because they are updated every month

whereas credit ratings change mostly only once a year or less. There results show that

increases in default risk lead to increased stock returns. These results are the opposite of

the results found in these study and most previous research. (Vassalou and Xing, 2002)

Figure 4.4 obtained from the paper of Vassalou and Xing shows the DLI around

downgrades. The horizontal axis is the time in months whereas the vertical axis is the

average default likelihood indicator (ADLI). It has an inverted V shape. DLI increases

from 36 months before the rating change up to the date of the rating change. The DLI

reaches its highest value at the day of the rating change and decreases after the rating

change. Figure 4.5 shows the DLI around upgrades. The graph shows that DLI do not

vary much around upgrades. (Vassalou and Xing, 2002)

Figure 4.4: Average DLI around downgrades (Vassalou and Xing, 2002)




Figure 4.5: Average DLI around Upgrades (Vassalou and Xing, 2002)

Vassalou and Xing conclude that this pattern of DLI explains for a large part the

abnormal return after downgrades. After adjusting for the DLI the stock price effect for

downgrades largely disappear. This explains for a large part the puzzle.

While this study uses credit rating changes as a measure for a change in default risk,

Vassalou and Xing use increases in default likelihood indicators (DLIs). This study

confirms the empirical puzzle that stock prices react significantly after downgrade but not

after upgrades. Though, this study gives no structural explanation for this puzzle.

Vassalou and Xing give an explanation for this puzzle.

Choy et al. (2006)

Choy et al. studied the effect of rating changes for the Australian stock market rated by

Moody’s and S&P for the period 1989–2003. They calculate the AR and CAR with the

market-adjusted returns model for the windows (-10,+10), (-5,+5), (-1+1).

For downgrades they find evidence of significant negative abnormal returns. The ARs for

the two days before the downgrades are -1.89 percent and -1.65 percent.

On the day of the rating change the AR is -1.65 percent and the day after the rating

change the AR is -1.39 percent. All the ARs are statistically significant at 1 percent.




Choy et al, found no evidence of a statistically significant stock price reaction after an

upgrade. The mean CAR is 1.35 percent but statistically insignificant. None of the ARs

for the days in the event windows are statistically significant at 10 percent.

(Choy et al. 2006)

Figure 4.6: Stock market reaction to rating change announcements

Choy et al. also test whether multiple downgrades (e.g. AAA to A) exert stronger stock

price effects as a single rating change (e.g. BBB+ to BBB). Not surprisingly; they find

that stock price reactions for multiple step downgrades are much larger than a single step

downgrade (Figure 4.6). Furthermore they found in contrast of their expectations that the

stock price reaction for anticipated downgrades is much larger then for unanticipated

downgrades (AR =2.38 % vs. 0.11 %). (Choy et al. 2006)

Choy et al. showed inline with this research, negative abnormal returns after downgrades

but no significant effect after upgrades. Furthermore they find that multiple step

downgrades exert stronger stock price effect than a single step downgrade. These results

are inline with the research of this study.



4.7 Empirical findings of most relevant studies

Avramov et al, (2006)

Avramov et al, (2006) also test the information content of credit rating changes. They

regress stock returns on credit rating and control for firm characteristics wit the following

cross section:

The variable RATING reflects the numerical value attached to the rating of the firm. The

second component of the regression Cmjt reflects the firm specific characteristics. These

are firm size, measured as the market value of equity, book to market value of equity and

turnover. The coefficient of the lagged credit rating variable Ratingjt-1 is -0.07 ( T-statistic

= −2.01). Their overall results show that companies with higher credit ratings realize

higher returns than lower rated companies. (Avramov et al, 2006)

Avramov et al. also take the prior rating into account. They show in line with this study

that the prior rating is an important variable which cannot be ignored. Low rated

companies exert larger negative stock price effects after downgrades and lower positive

abnormal returns after upgrades.

Romero and Fernandez (2004)

Romero and Fernandez (2004) investigate the effect of rating changes on market

returns and systematic risk for firms listed on the Spanish stock exchange. They use the

following cross section:

Here tRmt = the return of the market at time t, Ds,t is a dummy variable which is one for

the days in the event window s and zero otherwise, γ s,i = Cumulative Abnormal Return

(CAR) and λs,i = Cumulative Change in Beta (CCB). In contrast to most previous

research they find no statistically significant stock price reaction for downgrades. While

for upgrades they find statistically significant abnormal returns (CAR = -0.2 percent) in

the post-event window for both the total sample as the uncontaminated sample for the

(-15, +15) and (-5,+5) window.



4.7 Empirical findings of most relevant studies

For downgrades the CCB is also negative and statistically significant for the windows

(−5,+5), (0,+15) and (−15,+15). The median and mean CCB are negative, this means that

downgrades reduces the level of Beta risk. (Romero and Fernandez, 2004)

Romero and Fernandez also investigated the effect of credit rating changes on systematic

risk. For upgrades the CCB are negative and statistically significant for the post-event

windows. (CCB = -0.19 for the total sample and -0.17 for the uncontaminated sample).

Therefore they conclude that upgrades lead to a reduction in systematic risk. (Romero

and Fernandez, 2004)

The results of Romero and Fernandez are the contrary of the results of this study. This

study documents a significant effect after downgrades but no significant effect after

upgrades. Romero and Fernandez show no significant effect after downgrades but a

significant effect after an upgrade. A possible explanation for these differences is the

differences in methodology and investigated market. Romero and Fernandez investigated

the Spanish stock market while this study investigated the Dutch stock market. Moreover

Romero and Fernandez did not take the prior rating into account.

Jorion and Zang (2005)

Jorion and Zang (2005) are the first researchers which take the prior rating into account.

They use the following cross section:

CARj = α0 + α 1 PRTj + α 2 RCHGj+ α 3 IGRADEj +εεεεj

The first variable is the prior rating. The second variable is the rating change and the third

variable IGRADE is a dummy variable which is 1 if the rating crosses the investment to

speculative grade barrier and zero otherwise. For downgrades the coefficient for the prior

rating is negative and highly statistically significant.(-0.77, T-statistic -7.62). The

coefficient for the Rating change is also negative and highly statistically significant

(-2.79, T-statistic -5.35). The variable IGRADE is not statistically significant.




For upgrades the coefficient for the prior rating is positive and statistically significant at 5

percent. (0.11,T-statistic 2.23). The variables RCHG and IGRADE are not statistically

significant. (Jorion and Zang, 2005)

The main difference between the findings of Jorion and Zang and this study is the

significance of the prior rating for upgrades. Jorion and Zang show that this variable is

statistically significant while this variable is not statistically significant in this study. A

possible explanation for this result could be the difference in sample size. This study

incorporates only the Dutch Stock market, whereas Jorion and Zang study the stock

market in the United States. (Jorion and Zang, 2005)



Chapter 5: Conclusion

CCoonncclluussiioonn

This chapter briefly summarizes and concludes, also giving

suggestions for further research

Although ratings and rating reports are very expensive, almost all companies pay rating

agencies to be rated. The underlying rationale for this is that companies provide insider

information to rating agencies. Rating agencies reflect this information through their

ratings without showing the specific underlying details to the public. This makes rating

information valuable. To test whether rating changes contain price relevant information,

this study examined the stock price effects following a rating change for Dutch listed

firms for the period 1/1/1996 till 30/4/2008. To study the effect of rating changes in

isolation of other price relevant news, contaminated ratings are distinguished from

uncontaminated ratings.

For downgrades the empirical results for both the total and the uncontaminated sample

show a rising trend of the CAR with a negative CAR for the pre announcement window

(-10,-2) and the announcement window (-1,+1) and a positive CAR for the days

(+2,+10). Almost all CARs in the event window are statistically significant. The results

are in line with Creighton et al. (2004), Steiner and Heinke (2001) and Hull et al. (2004)

and suggest that markets react in anticipation of a rating event.

For upgrades the CAR for both the total and the uncontaminated sample are slightly

negative for the pre announcement window, however they become positive for the

announcement window. In the post announcement window the CAR is negative again.

Though, for most of the days the CARs are statistically insignificant. After downgrades

in the post event window the CAR is positive whereas after upgrades the CAR in the post

event window is negative.

5




These results confirm the “redistribution hypothesis”. This hypothesis states that after

downgrades wealth shifts from bond holders to shareholders. This reduces bond value

and increases share value. A rating upgrade shifts the wealth in the reverse direction. In

the post event window the CAR is positive after downgrades and negative following an

upgrade. Another possible explanation for these results is that the market overreacts to

rating changes.

While most previous research has ignored the prior rating as explanatory variable, this

study shows that the prior rating is an important factor which can not be ignored in the

cross sectional analysis. In the cross sectional analysis the results show that for

downgrades the prior rating is statistically significant at 1 percent. For upgrades none of

the results are statistically significant. Lower prior ratings exert much stronger stock price

effects for downgrades. Furthermore the empirical results show that the magnitude of the

rating change after controlling for the prior rating and the price to book ratio is

statistically significant in the total sample but not in the uncontaminated sample.

In line with previous research for the United States, this study documents statistically

significant stock price effects for downgrades but virtually no effects for upgrades. The

lack of impact of an upgrade could be explained by the idea that firms release favourable

news more timely. Therefore, this positive news is already reflected in the price. Firms

dislike releasing negative news, they try to avoid that this information leaks to the public.

Downgrades often reveal new information to the market, which explains the statistically

significant stock price reaction.

Still, the question remains whether the information content of rating changes can be

measured solely by stock price effects. Boot et al. (2006) argues that credit ratings serve

as coordination mechanisms in financial markets. Credit ratings make the interaction

between firms and shareholders more predictable. Therefore credit ratings amplify the

stability of financial markets. This means that the information content measured solely by

stock price effects underestimates the real value of credit ratings.




Further Research

This study shows that the prior rating cannot be ignored in research which examines the

information content of credit ratings. Future research in this field should therefore

incorporate the prior rating as explanatory variable in the cross sectional analysis.

Moreover future research could examine the stock price reaction controlling for both the

prior rating as suggested by Jorion and Zang (2006) and also for Default likelihood

indicators (DLIs) as suggested by Vassalou and Xing (2003).

While most studies investigate the information content of credit ratings for a specific

stock market, there has been virtually no research done about the behaviour of the

information content over time. It would be interesting to examine whether the

information content is stable or whether it increases or decreases over time. In addition, it

would be interesting to examine whether accounting indicators such as earning

announcements and Economic value added (EVA) are comparable to rating changes.

Another direction of further research is to examine the role of credit rating as

coordination mechanisms in financial markets. Boot el al. (2006) suggests that credit

ratings make the interaction between firms and shareholders more predictable; however

this theory is not empirically tested. Therefore it would be interesting to test this

hypothesis empirically.



Chapter 6: Literature

LLiitteerraattuurree

• Atiase, R. (1985) "Predisclosure Information, Firm Capitalization and Security

Price Behavior around Earnings Announcements," Journal of Accounting

Research, (spring), 21-36.

• Avramov, D., Chordia T., Jostova G., and Philipov A. (2006) “Credit ratings and

the cross-section of stock returns.” Preliminary version.

• Barron, M. J., Clare, A. D. and Thomas, S. H. (1997) “The Effect of Bond Rating

Changes and New Ratings on UK Stock Returns.” Journal of Business Finance &

Accounting, 24(3) & (4), 497-509.

• Basel Committee on banking supervision. (2000) “Credit Ratings and

Complementary Sources of Credit Quality Information.” Working papers no. 3,

August.

• Beard, C., and Sias R. (1997) “Is there a neglected-firm effect?” Financial

Analysts Journal, September/October, 19-23.

• Boot, W.A., Milbourn T., Schmeits A. (2006) “Credit ratings as coordination

mechanisms.” Review of financial Studies, volume 19, number 1.

• Brown, S., and Warner J. (1980) “Measuring Security Price Performance,”

Journal of Financial Economics 8, pp. 205-258.

• Brown, S., and Warner, J. (1985) “Using Daily Stock Returns: The Case of Event

Studies,” Journal of Financial Economics 14, pp. 3-31.

6




• Chan, P. T. , Edwards V. and Walter T. (2004) “The Information Content of

Australian Credit Ratings: A Comparison between Subscription and Non-

subscription based Credit Rating Agencies.” research discussion paper School of

Banking and Finance, The University of New South Wales 1-32.

• Cornell, B., Landsman W. and Shapiro A. (1989) “Cross-sectional Regularities in

the Response of Stock Prices to Bond Rating Changes”, Journal of Accounting,

Auditing and Finance, Vol. 4, pp. 460–79.

• Creighton, A., Gower L., and Richards, A. (2004) “The Impact of Rating Changes

in Australian Financial Markets” research discussion paper Reserve Bank of

Australia, Sydney, NSW.

• Goh, J. C. and Ederington, L. H. (1999) “Cross-sectional Variation in the Stock

Market Reaction to Bond Rating Changes.” The Quarterly Review of Economics

and Finance, 39, 1, 101-112.

• Goh, J. C. and Ederington, L. H. (1993) “Is a Bond Rating Downgrade Bad News,

Good News, or No News for Stockholders?” The Journal of Finance, 5, 2008.

• Graham, J. R., and Harvey C. (2001) “The theory and practice of corporate

finance: evidence from the field” Journal of Financial Economics 60, 187–243.

• Griffin, P. A. and Sanvicente, A. Z. (1982) “Common Stock Returns and Rating

Changes: A Methodological Comparison.” The journal of Finance, 37, 103-119.

• Grossman, S. J. and Stiglitz, J. E. (1976) “Information and Competitive Price

Systems.” The American Economic Review, 66, 2, 246-253.

• Grossman, S. J. and Stiglitz, J. E. (1980) “On the Impossibility of Informationally

Efficient Markets.” The American Economic Review, 70, 3, 393-408.




• Hand, J. M., Holthausen, R. W. and Leftwich R. W. (1992) “The Effect of Bond

Rating Agency Announcements on Bond and Stock Prices.” The Journal of

Finance, 47, 733-752.

• Holthausen, R. W. and Leftwich, R. W. (1986) “The Effect of Bond Rating

Changes on Common Stock Prices.” Journal of Financial Economics 17, 57-89.

• Hull, J., Predescu, M., White, A., (2004) “The relationship between credit default

swap spreads, bond yields, and credit rating announcements.” Journal of Banking

and Finance.

• Impson, C. M., Karafiath, I. and Glascock, J. (1992) “Testing Beta Stationarity

Across Bond Rating Changes.” The Financial Review, 27, 4, 607-618.

• John, K., A. Lynch, and M. Puri, (2001) “Credit ratings, collateral and loan

characteristics: implications for yield.” Journal of Business 76, 371–409.

• Katz,S., (1974) “The price adjustment process of bonds to rating reclassifications:

A test of bond market efficiency.” Journal of Finance 29, 551–559.

• Kliger, D. and Sarig, O. (2000) “The Information Value of Bond Ratings.” The

Journal of Finance, 55, 6, 2879-2902.

• Matolcsy, Z. P. and Lianto, T. (1995) “The Incremental Information Content of

Bond Rating Revisions: The Australian Evidence.” Journal of Banking and

Finance, 19, 891-902.

• Micu, M. Remolona, E. and Wooldridge, P. (2006) “The price impact of rating

announcements: which announcements matter?” BIS Working Papers No 207.




• Odders-White, R. E., and Ready, J.M., (2006) “Credit Ratings and Stock

Liquidity.” Published by Oxford university press on behalf of the society for

financial studies.

• Romero, A. and Fernandez, M.D. (2006) “Risk and Return around Bond Rating

Changes: New Evidence from the Spanish Stock Market” Journal of Business

Finance & Accounting, 33(5) & (6), 885–908.

• Steiner, M. and Heinke, V.G., (2001) “Event study concerning international bond

price effects on credit rating actions. “ International Journal of Finance and

Economics 6, 139–157.

• Vassalou, M. and Xing, Y. H. (2003) “Equity Returns Following Changes in

Default Risk: New Insights into the Informational Content of Credit Ratings.”

EFA 2003 Annual Conference Paper, Working paper no. 326.

• Wakeman, L. M. (1990) “The Real Function of Bond Rating Agencies” The

Modern Theory of Corporate Finance, 2nd ed., McGraw Hill, New York.

• Zaima, J. K. and McCarthy J. (1988) “The Impact of Bond Rating Changes on

Common Stocks and Bonds: Tests of the Wealth Redistribution Hypothesis.” The

Financial Review, 23, 483-498.

Websites

• The Moody’s Investors Service - http://www.moodys.com

• The Standard & Poor’s - http://www.standardandpoors.com

• The Fitch ratings service- http://www.fitchratings.com

• The Securities and Exchange Commission- http://www.sec.gov

• The Bank of International Settlements- http://www.bis.org



List of appendixes, figures and tables

List of appendixes

Appendix A: comparison of agencies credit rating ........................................................................ 53

Appendix B: credit ratings expressed in numerical values ............................................................. 54

Appendix C: rating actions by year ................................................................................................ 55

Appendix D: rating action by agency ............................................................................................. 55

Appendix E: Graph of the AR for day(-1,+1) ................................................................................ 56

Appendix F: Graph of the CAR for the subsequent event windows .............................................. 57

Appendix G: AR for day (-10,+10) ................................................................................................ 58

Appendix H: The sensitivity of stock prices to rating changes (Total sample) .............................. 60

Appendix I: The sensitivity of stock prices to rating changes:

Conditioned by rating class (total sample) ................................................................ 61

Appendix J: The sensitivity of stock prices to rating changes (Uncontaminated sample) ............. 62

Appendix K: The sensitivity of stock prices to rating changes:

Conditioned by rating class (uncontaminated sample) ............................................. 63

Appendix L: Standardized residual plots ....................................................................................... 64

Appendix M: All rating actions for total sample ............................................................................ 65

Appendix N: News with respect to the contaminated rating changes ............................................ 66

Appendix O: List of Abbreviations ................................................................................................ 67

List of figures

Figure 1.1: Research framework ...................................................................................................... 5

Figure 1.2: Credit Rating Agencies .................................................................................................. 7

Figure 1.3: Definition of credit ratings ............................................................................................. 8

Figure 4.1: Distribution of rating changes ...................................................................................... 28

Figure 4.2: Abnormal returns (-10,+10) for total Sample .............................................................. 29

Figure 4.3: Abnormal Returns for Days (-10+10) for uncontaminated sample ............................. 31

Figure 4.4: Average DLI around downgrades ................................................................................ 39

Figure 4.5: Average DLI around upgrades ..................................................................................... 40

Figure 4.6: Stock market reaction to rating change announcements .............................................. 41

List of tables

Table 3.1: Definition of variables ................................................................................................... 26

Table 4.1: CAR for the subsequent event windows (Total sample n= 41) ..................................... 30

Table 4.2: CAR for the subsequent event windows (Uncontaminated sample n= 27) ................... 32



Appendixes

APPENDIX A: COMPARISON OF AGENCIES CREDIT RATINGS

(source: Jorion and Zang, 2006)

(Source: Jorion and Zang, 2006)



Appendixes

APPENDDIX B: CREDIT RATINGS EXPRESSED IN NUMERICAL VALUES

Numerical value Moody’s S&P and

Fitch

36 Aaa1 AAA+

35 Aaa2 AAA

34 Aaa3 AAA-

33 Aa1 AA+

32 Aa2 AA

31 Aa3 AA-

30 A1 A+

29 A2 A

28 A3 A-

27 Baa1 BBB+

26 Baa2 BBB

25 Baa3 BBB-

24 Ba1 BB+

23 Ba2 BB

22 Ba3 BB-

21 B1 B+

20 B2 B

19 B3 B-

18 Caa1 CCC+

17 Caa2 CCC

16 Caa3 CCC-

15 Ca1 CC+

14 Ca2 CC



Appendixes

APPENDIX C: RATING ACTIONS BY YEAR

(1 ST JANUARY 1999 – 31 ST DECEMBER 2007)

Year Downgrades Upgrades Total

1999 1 0 1

2000 4 0 4

2001 4 1 5

2002 5 1 6

2003 7 1 8

2004 0 3 3

2005 2 2 4

2006 2 5 7

2007 2 1 3

Total 27 14 41

APPENDIX D: RATING ACTIONS BY AGENCY

(1 ST JANUARY 1996 – 31 ST DECEMBER 2007)

Agency Downgrades Upgrades Total

Fitch 1 3 4

Moody’s 14 3 17

S&P 12 8 20

Total 27 14 41



-2,00%

-1,50%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

-1 0 1

AR

in

%

total uncontaminated

0,00%

0,10%

0,20%

0,30%

0,40%

0,50%

0,60%

0,70%

-1 0 1

AR

in

%

Total uncontaminated

Appendixes

APPENDIX E: GRAPH OF THE AR FOR DAY (-1,+1)

A. Downgrades

B. Upgrades



-5,00%

-4,00%

-3,00%

-2,00%

-1,00%

0,00%

1,00%

2,00%

CAR (-2,-10) CAR (-1,+1) CAR (+2,+10)

CA

R in %

Total Uncontaminated

-5,00%

-4,00%

-3,00%

-2,00%

-1,00%

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

CAR (-2,-10) CAR (-1,+1) CAR (+2,+10)

CA

R i

n %

Total Uncontaminated

Appendixes

APPENDIX F: GRAPH OF THE CAR FOR THE SUBSEQUENT EVENT

WINDOWS

A. Downgrades

B. Upgrades



Appendixes

APPENDIX G: AR FOR DAY (-10,+10)

A: TOTAL SAMPLE (N=41)

Note: AR is the Average abnormal return in percentage for the different event windows, where day 0 is the

day of the announcement of the rating change. ARd ,ARu represents the Abnormal return for downgrades

and upgrades and T1ARd T1ARu correspond to the t values for ARd and ARu respectively. The Abnormal

returns are calculated using a market model estimated over the period (-250,-50)

***, **, * denotes statistical significance at the 1%, 5 %, 10% levels correspondingly.

ARd TARd ARu TARu

average abnormal return day -10 -0.38%*** -5.20 -0.20% -0.58


average abnormal return day -8 -0.57%*** -8.29 0.44% 0.80

average abnormal return day -7 0.01% 0.06 -0.35% -1.12

average abnormal return day -6 0.67%*** 7.64 0.98%*** 3.23

average abnormal return day -5 0.03% 0.62 -0.41% -1.44

average abnormal return day -4 -1.47%*** -7.01 -0.71%* -1.80

average abnormal return day -3 -0.06% -1.18 -0.08% -0.30



average abnormal return day 0 0.34% 1.09 0.44%* 1.64

average abnormal return day 1 -0.72%*** -4.19 0.49%* 1.58

average abnormal return day 2 0.46%*** 4.96 0.20% 0.47

average abnormal return day 3 0.09%*** 2.49 -0.21% -0.71

average abnormal return day 4 0.59%*** 2.61 -0.35%* -1.26




average abnormal return day 8 1.11%*** 11.32 -0.66%** -2.07





Appendixes

B: UNCONTAMINATED SAMPLE (N=27)

Note: See part a of appendix E for the specifications of the model

ARd TARd ARu TARu

average abnormal return day -10 -0.01% -0.09 -0.26% -0.62

average abnormal return day -9 -1.02%*** -10.18 -0.54%* -1.37



average abnormal return day -6 0.62%*** 7.35 1.20%*** 3.38

average abnormal return day -5 -0.23%*** -3.87 -0.61%** -1.81

average abnormal return day -4 -1.01%*** -8.50 -0.85%** -1.73

average abnormal return day -3 0.16%*** 2.50 -0.11% -0.32

average abnormal return day -2 0.00% 0.01 0.50% 0.68

average abnormal return day -1 -0.03% -0.59 0.17% 0.37

average abnormal return day 0 1.14%*** 30.03 0.54%*** 2.55

average abnormal return day 1 -1.42%*** -18.19 0.64% 1.64

average abnormal return day 2 0.02% 0.34 0.03% 0.06

average abnormal return day 3 -0.39%*** -23.43 -0.36% -1.03

average abnormal return day 4 -0.06%* -1.37 -0.36% -1.03


average abnormal return day 6 0.16% 1.43 -0.36% -0.40


average abnormal return day 8 1.31%*** 10.03 -0.85%** -2.31


average abnormal return day 10 -0.09% -0.84 -0.52% -0.53



Appendixes

APPENDIX H: THE SENSITIVITY OF STOCK PRICES TO RATING

CHANGES (TOTAL SAMPLE N=41)

Note: CAR is the cumulative abnormal return in percentage for the (-1, +1 )event window,,

where day 0 is the day of the announcement of the rating change. The Abnormal returns are

calculated using a market model estimated over the period (-250,-50). RCHG is the absolute

size of the rating change. the bond ratings are transformed into a cardinal variable estimated

on a point 14 scale., P/B is the price to book value and PRT is the prior rating.

***, **, * denotes statistical significance at the 1%, 5 %, 10% levels correspondingly.

Dependent Variable: CAR(-1,+1)

Method: Least Squares

Downgrades Upgrades

Independent Variables Coefficient

(T-statistic)

Coefficient

(T-statistic)

∆∆∆∆RAT -0.200*** -0.020

(-3.83) (-0,33)

P/B 0.005 -0.001

(0.80) (-0,04)

PRT 0.013*** 0.001

(3.59) (0.52)

R -squared 0.42 0.03

Adjusted R-squared 0.38 -0.15

S.E. of regression 0.07 0.03

Sum squared residuals 0.13 0.01

Number of observations 27 14



Appendixes

APPENDIX I: THE SENSITIVITY OF STOCK PRICES TO RATING

CHANGES: CONDITIONED BY RATING CLASS

(TOTAL SAMPLE N= 41 )

Note: See appendix G.A for variable definitions, Here DVPRj is a dummy variable for the

prior rating. The dummy variable equals 1 if the prior rating is in the rating class j and zero

otherwise. j=1, if prt = 29, 28, 27, j=2, if prt = 26,25,24,, j=3,if prt = 23,22,21, and j=4,if prt

< 21. ***, **, * denotes statistical significance at the 1%, 5 %, 10% levels correspondingly

Dependent Variable: CAR(-1,+1)


Downgrades Upgrades


(T-statistic)

Coefficient

(T-statistic)

DVPR1 -0.020 -0.011

(-1.19) (-0.51)

DVPR2 -0.172** -0.008

(-3.56) (-0.43)

DVPR3 -0.054 0.011

(-2.10) (0.52)

DVPR4 -0.400*** -

(-9.71) -

∆∆∆∆RAT 0.020** 0.014

(2.25) (0.69)

P/B -0.005 -0.001

(-1.07) (-0.02)

R-squared 0.83 0.13

Adjusted R-squared 0.79 -0.25



Number of

observations 27 14



Appendixes

APPENDIX J: THE SENSITIVITY OF STOCK PRICES TO RATING CHANGES

(UNCONTAMINATED SAMPLE N= 27)

Note: See Appendix G.A for the definition of variables

Dependent Variable: CAR2


Downgrades Upgrades


(T-statistic)

Coefficient

(T-statistic)

∆∆∆∆RAT 0.005 0.018

(0,47) (-0,35)

P/B 0.211** 0.003

(2,03) (0,47)

PRT 0.135* 0.001

(1,88) (0.50)

R-squared 0.49 0.11

Adjusted R-squared 0.28 0,06






Appendixes

APPENDIX K: THE SENSITIVITY OF STOCK PRICES TO RATING

CHANGES: CONDITIONED BY RATING CLASS

(UNCONTAMINATED SAMPLE N=27)

Note: See appendix G +H for definition of variables

Dependent Variable: CAR2


Downgrades upgrades


(T-statistic)

Coefficient

(T-statistic)

DVPR1 -0.033 -0.034

(-1.26) (-0.67)

DVPR2 -0.166 -0.071

(-2.56) (-0.98)

DVPR3 -0.432* -0.153

(-1.80) (1.42)

DVPR4 - -

- -

∆∆∆∆RAT 0.142** 0.51

(1,97) (1.45)

P/B -0.002 -0.002

(0,30) (-0.33)

R-squared 0.75 0.56

Adjusted R-squared 0.45 0.12






Appendixes

APPENDIX L: STANDARDIZED RESIDUAL PLOTS

SENSITIVITY OF STOCK PRICES TO RATING CHANGES: CONDITIONED

BY RATING CLASS

1. CAR (-1, +1) Residuals (Total) 2. Residuals (Uncontaminated)

THE SENSITIVITY OF STOCK PRICES TO RATING CHANGES

3. CAR(-1,+1) Residuals (total ) 4. Residuals (Uncontaminated)



Appendixes

APPENDIX M: ALL RATING ACTIONS FOR TOTAL SAMPLE

company date rating action Rating

agency

AEGON 12-12-2002 Downgrade Moody's

AHOLD 8-3-2000 Downgrade S&P

17-1-2003 Downgrade Moody's

20-9-2004 Upgrade Moody's

16-1-2006 Upgrade S&P


Akzo Nobel 28-6-2000 Downgrade S&P


ASML 24-12-2002 Downgrade Moody's



CORPORATE EXPRESS 11-4-2001 Upgrade S&P

4-12-2002 Downgrade S&P


DSM 27-9-2007 Downgrade Moody's

FORTIS 2-5-2003 Downgrade Moody's

19-5-2006 Upgrade Fitch

Getronics 26-3-2001 Downgrade Moody's




ING 16-8-1999 Downgrade Fitch




KPN 1-9-2000 Downgrade S&P





PHILIPS 16-7-2003 Downgrade S&P



REED ELSEVIER 18-8-2000 Downgrade S&P


SHELL 4-2-2005 Downgrade S&P

TNT 18-6-2002 Downgrade Moody's



WOLTERS KLUWER 16-8-2001 Downgrade S&P




Appendixes

APPENDIX N: NEWS WITH RESPECT TO THE CONTAMINATED

RATING CHANGES

• April 5, 2007 : Ahold wil Hema overnemen

• January 16, 2003: Ahold en Aegon: niet samen

• March 08, 2000,: Ahold doet opnieuw grote aankoop in Amerika

• September 27, 2007: DSM start nieuw aandeleninkoopprogramma en

verhoogt dividend

• May 18, 2006 : Winstgroei Fortis overtreft verwachtingen

• August 3, 2006: Getronics meld telleurstellende kwartaalcijfers

• February 7, 2006: Winst KPN gedaald

• May 11, 2005: Philips sluit software-overeenkomst met Microsoft

• August 18, 2000: Reed Elsevier-dochter Cahners gaat twee onderdelen

verkopen

• July 12, 2001: Reed Elsevier: overname Harcourt officieel afgerond













Appendixes

• December 6, 2005: stockwatch TNT-advies verhoogd naar 'buy' door

Petercam vanwege een 'strategie-update'

• September 4, 2007: TNT Post neemt belang in klein Duits postbedrijf

• August 15, 2001: Wolters Kluwer stoot grote uitgeverij af

(source: Financieel dagblad)

APPENDIX O: LIST OF ABBREVATIONS

ADLI Average Default Likilihood Indicator

AEX Euronext Amsterdam

AR Abnormal return

BHAR buy-and-hold abnormal return

CAR Cummulative Abnormal Return

CCB Cumulative Change in Beta

CFO Chief Financial Officer

CRA Credit Rating Agency

DLI Default Likelihood Indicator

NRSRO Nationally Recognized Statistical Rating Organization

P/B Price to book ratio

PRT Prior rating

SEC Securities and Exhange commision

S&P Standard & Poors




The Information Content Of Credit Ratings

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