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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.
The information content of credit ratings: New evidence from the Dutch Stock market
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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
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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
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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
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§ 1.2 Research framework
§ 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
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§ 1.2 Research framework
§ 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”
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§ 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.
§ 1.3 Corporate credit ratings
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
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§ 1.3 Corporate credit ratings
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
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§ 1.3 Corporate 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)
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§ 1.3 Corporate credit ratings
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.
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§ 1.3 Corporate credit ratings
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
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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
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Chapter 2: Literature review
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)
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Chapter 2: Literature review
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.
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Chapter 2: Literature review
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.
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Chapter 2: Literature review
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.
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Chapter 2: Literature review
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.
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Chapter 2: Literature review
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.
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Chapter 2: Literature review
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
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Chapter 2: Literature review
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)
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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
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§ 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
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§ 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))
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§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)
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§ 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
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§ 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
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§ 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.
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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
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-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.
§ 4.2 AR and CAR for total sample
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
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§ 4.2 AR and CAR 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
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-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
§ 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
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§ 4.3 AR and CAR 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
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§ 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.
§ 4.4 Regression analysis for total sample
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)
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§ 4.4 Regression analysis for total sample
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%.
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§ 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
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§ 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.
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§ 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.
§ 4.7 Empirical findings of most relevant studies
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)
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§ 4.7 Empirical findings of most relevant studies
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)
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§ 4.7 Empirical findings of most relevant studies
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)
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§ 4.7 Empirical findings of most relevant studies
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.
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§ 4.7 Empirical findings of most relevant studies
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.
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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.
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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.
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§ 4.7 Empirical findings of most relevant studies
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)
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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
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Chapter 5: Conclusion
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.
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Chapter 5: Conclusion
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.
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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
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Chapter 6: Literature
• 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.
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Chapter 6: Literature
• 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.
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Chapter 6: Literature
• 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
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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
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Appendixes
APPENDIX A: COMPARISON OF AGENCIES CREDIT RATINGS
(source: Jorion and Zang, 2006)
(Source: Jorion and Zang, 2006)
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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
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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
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-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
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-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
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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 -9 -0.58%*** -8.57 -0.22% -0.57
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 -2 -0.71%*** -7.45 0.27% 0.44
average abnormal return day -1 -0.49%*** -4.77 0.48% 1.12
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 5 0.49%*** 4.36 -0.34%* -1.17
average abnormal return day 6 0.24%*** 3.20 -0.28% -0.39
average abnormal return day 7 0.11%*** 3.28 -0.99%* -1.20
average abnormal return day 8 1.11%*** 11.32 -0.66%** -2.07
average abnormal return day 9 0.83%*** 19.74 -0.38% -0.84
average abnormal return day 10 0.21%*** 2.61 -0.45% -0.59
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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 -8 -1.33%*** -23.08 0.36% 0.53
average abnormal return day -7 -1.35%*** -13.79 -0.38% -0.94
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 5 0.37%*** 2.71 -0.38% -1.01
average abnormal return day 6 0.16% 1.43 -0.36% -0.40
average abnormal return day 7 0.34%*** 9.51 -1.28%* -1.22
average abnormal return day 8 1.31%*** 10.03 -0.85%** -2.31
average abnormal return day 9 0.82%*** 32.85 -0.39% -0.66
average abnormal return day 10 -0.09% -0.84 -0.52% -0.53
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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
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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)
Method: Least Squares
Downgrades Upgrades
Independent Variables Coefficient
(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
S.E. of regression 0.04 0.03
Sum squared residuals 0.03 0.01
Number of
observations 27 14
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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
Method: Least Squares
Downgrades Upgrades
Independent Variables Coefficient
(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
S.E. of regression 0.04 0.03
Sum squared residuals 0.03 0.01
Number of observations 16 11
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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
Method: Least Squares
Downgrades upgrades
Independent Variables Coefficient
(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
S.E. of regression 0.04 0.02
Sum squared residuals 0.02 0.01
Number of observations 16 11
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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)
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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
5-4-2007 Upgrade S&P
Akzo Nobel 28-6-2000 Downgrade S&P
19-3-2003 Downgrade Moody's
ASML 24-12-2002 Downgrade Moody's
28-6-2004 Upgrade Moody's
25-4-2006 Upgrade Moody's
CORPORATE EXPRESS 11-4-2001 Upgrade S&P
4-12-2002 Downgrade S&P
28-11-2003 Upgrade 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
8-10-2002 Downgrade Moody's
10-11-2003 Downgrade Moody's
2-8-2006 Downgrade Moody's
ING 16-8-1999 Downgrade Fitch
8-4-2003 Downgrade Moody's
24-8-2005 Upgrade S&P
23-8-2006 Upgrade Fitch
KPN 1-9-2000 Downgrade S&P
6-9-2001 Downgrade Moody's
5-12-2002 Upgrade S&P
29-1-2004 Upgrade S&P
7-2-2006 Downgrade S&P
PHILIPS 16-7-2003 Downgrade S&P
11-5-2005 Upgrade S&P
17-11-2006 Upgrade Fitch
REED ELSEVIER 18-8-2000 Downgrade S&P
12-7-2001 Downgrade S&P
SHELL 4-2-2005 Downgrade S&P
TNT 18-6-2002 Downgrade Moody's
6-12-2005 Downgrade Moody's
4-9-2007 Downgrade S&P
WOLTERS KLUWER 16-8-2001 Downgrade S&P
12-11-2003 Downgrade S&P
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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
5-4-2007 Upgrade S&P
17-1-2003 Downgrade Moody's
8-3-2000 Downgrade S&P
27-9-2007 Downgrade Moody's
19-5-2006 Upgrade Fitch
2-8-2006 Downgrade Moody's
7-2-2006 Downgrade S&P
11-5-2005 Upgrade S&P
18-8-2000 Downgrade S&P
12-7-2001 Downgrade S&P
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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
6-12-2005 Downgrade Moody's
4-9-2007 Downgrade S&P
16-8-2001 Downgrade S&P