How much new information does a

credit rating announcement convey to

the financial markets?

-A comparison before and after the 2008 global financial crisis

Master thesis

Author: Simon Otterberg & August Zetterberg

Supervisor: Håkan Locking

Examiner: Anderas Jansson

Co-Examiner: Magnus Willesson

Term: VT20

Subject: Finance

Level: Master

Course code: 4FE17E

Abstract

Master Thesis in Business Administration, School of Business and

Economics, Linnaeus University, 4FE17E, 2020.

Authors: Simon Otterberg and August Zetterberg

Supervisor: Håkan Locking

Examiner: Anderas Jansson

Title: How much new information does a credit rating announcement convey

to the financial markets? -A comparison before and after the 2008 global

financial crisis

Background: The credit rating agencies have been heavily contested and

criticized. In addition to this, other informational sources may potentially

deliver the information that the CRA is intended to provide. This may have

changed their role in reducing information asymmetry in the financial

market.

Purpose: This thesis will investigate (i) whether changes

(upgrade/downgrade) in credit ratings lead to abnormal returns in share

value, and thereby provide useful information to potential and current

investors. The thesis will also (ii) examine whether there are significant

differences between the periods before and after the GFC in 2008.

Method: Regression based event study using a dummy-variable approach.

Conclusions: No strong evidence that credit ratings have a significant effect

on stock prices in the European stock market. Small indications that the

market is responding more strongly to a rating change announcement during

the period 2000-2008 compared to 2009-2019.

Key words

Event study, finance, credit rating, credit rating agency, information content,

stock market.

Acknowledgments

We would like to start by thanking our supervisor Håkan Locking who has been a

great support throughout the work. Without your mathematical and statistical

knowledge, this study would not have been possible. Also, greetings to doctoral

student Maziar Sahamkhadam for taking the time to help us. Finally, we would also

like to thank our opponent group, led by Magnus Willesson, for insightful

comments and opinions.

Simon Otterberg and August Zetterberg

Växjö, Sweden

2020-05-24

Table of contents

1 Introduction ....................................................................................................... 1 1.1 Background ................................................................................................. 1 1.2 Problem discussion ..................................................................................... 3 1.3 Purpose ....................................................................................................... 8 1.4 Outline......................................................................................................... 8

2 Credit ratings .................................................................................................... 9 2.1 Literature review ......................................................................................... 9 2.2 The credit rating agencies ........................................................................ 12 2.3 The credit rating process .......................................................................... 15 2.4 Criticism of the CRAs................................................................................ 20

3 Theoretical Framework .................................................................................. 22 3.1 Efficient markets ....................................................................................... 22 3.2 Information content hypothesis ................................................................. 24 3.3 Hypothesis development............................................................................ 25

4 Methodology .................................................................................................... 27 4.1 Choice of method ...................................................................................... 28 4.2 Event and window definitions ................................................................... 29 4.3 Data selection ........................................................................................... 30 4.4 Method issues and potential bias .............................................................. 34 4.5 Normal and abnormal returns .................................................................. 35 4.6 Testing procedure ..................................................................................... 36

5 Empirical Results ............................................................................................ 39 5.1 Development of abnormal returns ............................................................ 39 5.2 Abnormal returns before the financial crisis (2000-2008) ....................... 42 5.3 Abnormal returns after the financial crisis (2009-2019) .......................... 44 5.4 Comparison between periods .................................................................... 47

6 Analysis ............................................................................................................ 49

7 Conclusions ...................................................................................................... 55

8 Suggestions for Future Research ................................................................... 56

9 References ........................................................................................................ 57 9.1 Literature .................................................................................................. 57 9.2 Electronic references ................................................................................ 63

Appendices ............................................................................................................... 65

Figure 1. Outline of the thesis. .................................................................................... 8

Figure 2. The credit rating process (Standard & Poor’s Global Ratings, 2020) ....... 16

Figure 3. Estimation and event window in days. ...................................................... 30

Figure 4. Abnormal returns for the 2000-2008 period with confidence intervals. ... 44

Figure 5. Abnormal returns for the 2009-2019 period with confidence intervals. ... 46

Figure 6. Abnormal results for both periods. ............................................................ 47

Table 1. Summary of previous research. .................................................................. 11

Table 2. Ratings classification (Standard & Poor Global Ratings, 2020) ................ 17

Table 3. Companies included in the sample sorted by country. ............................... 31

Table 4. Abnormal returns for the period 2000-2019 ............................................... 40

Table 5. Abnormal returns in the pre-financial crisis period (2000-2008) ............... 42

Table 6. Abnormal returns in the post-financial crisis period (2009-2019) ............. 45

Equation 1. Actual return. ......................................................................................... 35

Equation 2. Market model......................................................................................... 35

Equation 3. Abnormal return. ................................................................................... 36

Equation 4. Regression equation............................................................................... 37

Equation 5. Equation for F-test. ................................................................................ 41

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1 Introduction This initial part intends to give an introduction to the essay. It is introduced with a

background of the development of the Credit rating agencies. Then follows a problem discussion that highlights issues surrounding the information content of

ratings. Furthermore, a concrete problem formulation is designed and the purpose

and contribution of the study is presented. The section is then concluded with the outline of the study.

______________________________________________________________

1.1 Background

The first signs of the emergence of the credit rating agency (hereinafter

referred to as CRA) industry appeared during the nineteenth century when

markets evolved, and it became evident that there were economies of scale in

gathering and circulating credit information in an organized way. The first

initiative to sell bond ratings can be found in the early 1900s. John Moody, a

Wall Street analyst, adopted reports containing elaborate statistics and

financial data from the railroad industry who required external financing. By

transforming non-perspicuous data into single rating symbols, he started

making fortunes out of selling the ratings to public investors. From this event

and forth, the demand for third party judgment of borrower's creditworthiness

increased along with the emerging concept of credit ratings (Partnoy, 1999).

Several decades later, the importance of the CRA:s rose to higher levels. In

the 1970s, the financial markets in the U.S. faced globalization with high

volumes of international investments as a result. Furthermore, the bond

markets began to partially replace commercial bank lending as a source of

credit for major firms in the U.S., and the demand for assessment of bonds

led to the emergence of the CRA industry (Scalet & Kelly, 2012). It was

during this period of time a critical change in the CRA business model where

revenues now were obtained from the companies issuing the bond. Firms

with interests in being rated from the CRA:s paid for the credit rating and not

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the public investors, as was the previous case of generating income (Frost,

2007). In 2003, 90 percent of the CRA revenue was brought from the issuer

of the bond (SEC, 2003), indicating that CRA:s have conflicts of interest in

that there might exist incentives for inflating the grades in order to satisfy

their customers (Partnoy, 2006).

Following the global financial crisis in 2008 (hereinafter referred to as GFC),

credit rating agencies were subject to criticism for failing in identifying risks

linked to financial instruments. Essentially, agencies gave high ratings to debt

securities signalling that they were safe to invest in when, in reality, they

turned out to be high-risk investments (White, 2009). The consequences of the

crisis tell us that misjudgments of asset values may lead to devastating

aftereffects, and the role of rating agencies has lately endured a lot of

questioning (Scalet & Kelly, 2012). The crisis also shows that credit ratings

have a significant impact on guiding investments in the financial markets.

Credit ratings are assigned to reflect the creditworthiness of both firms and

governments, thus providing information about their ability to repay debt and

the probability of default (Frost, 2007; Dilly & Mählmann, 2010). The ratings

are supposed to reflect the firm’s financial statements, franchise value,

management quality, and consider its competitive position under different

possible economic scenarios to form their judgment. They are assigned and

announced from professional credit rating agencies who, according to

protocol, are independent actors mediating between a firm’s top management

and its creditors and investors. These stakeholders are presumed to have

different levels of information about the repay abilities of firms, creating an

asymmetry of information. This asymmetry is expected to decrease with the

credit information provided by ratings. (White, 2001)

The underlying assumption is that the issuance of credit ratings has

informational value to the market, which would not exist without the CRA:s

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judgment. This implies that the rated firm would be affected by this newly

launched information and in turn influence the value of the firm. However, it

is possible that this information could already be anticipated by the market if

other information sources provide similar content on a firm’s creditworthiness.

Some studies suggest that the information content assumption is misleading

since none or insignificant effects in stock and bond prices was found after

changes in credit ratings (Weinstein, 1977; Pinches & Singleton, 1978; Li et

al., 2004). These results indicate that the information provided from CRA:s

were not relevant, or at least to a large extent, anticipated by the market.

Meanwhile, numerous reports were published that contradict these results. The

collective findings in credit rating studies suggest that downgrading changes

in ratings have a negative impact on stock prices while increasing changes

have no corresponding effect (Griffin & Sanvicente, 1982; Holthausen &

Leftwich, 1986; Dichev & Piotroski, 2001). Moreover, other studies show that

both downgrades and upgrades in ratings have significant effects on stock

prices (Barron, 1997; Poornima, 2015).

In this thesis, the information content of credit ratings is investigated by

examination of the stock price impact of rating changes. The issue of

information content of the ratings for the market highlights the critical question

of the relevance of the agencies for reducing information asymmetry. The

informational value of credit ratings is a subject of continuing debate (Elayan

et al. 2003)

1.2 Problem discussion

Credit ratings are opinions that are claimed to be based on company-specific

fundamentals (Langohr & Langohr, 2008). This means that every company is

evaluated independently from others, which would make the rating absolute

and anchored to the reality in which the assessor interprets the fundamentals.

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In reverse, the ratings could be established concerning the relative position of

the company where the level of the rating reflects the specific company’s

standpoint against other companies. For instance, some qualitative attributes

of the company are considered during the rating process (Crouhy, 2001),

which indicates that the ratings are partially based on parameters that only can

be measured in comparison to similar parameters found elsewhere. This would

imply that the information content of ratings cannot entirely reflect the actual

state of creditworthiness since the dependency of relative aspects may

influence the assessment of the credit risks of firms.

Another issue is whether or not all information relevant to pricing is integrated

into the share price in conjunction with the announcement of rating changes.

This touches on the implications of whether markets are effective or not. The

efficient market hypothesis (EMH) states that financial markets are effective

where the strict definition of market efficiency assumes that all information,

public as well as private, is reflected in market prices. It is further described

that the ideal market is where investors can choose among securities under the

assumption that prices always fully reflect all available information (Fama et

al. 1970). This would imply that even investors with precise inside information

will be unable to beat the market (Damodaran, 2012). Based on this

assumption, a change in credit rating should affect the value for the company

in question, given that the grades contain new information. Another

interpretation is that since share prices already reflect all information available

in the market, the CRA:s do not provide new information to the market, which

some studies indicate (Weinstein, 1977; Pinches & Singleton, 1978; Li et al.,

2004). However, this study does not intend to test the accuracy of market

efficiency. It is though a vital theory to discuss, since this study revolves

around how fast the market absorbs the information content of ratings. Also,

some elements in the methodology assumes an efficient market.

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The issue of whether a rating change should also convey news about the

valuation of corporate equity depends on the nature of the news that is

contained in the rating change (Richards & Deddouche, 1999). However, there

is a demand for credit ratings or otherwise they wouldn't have existed. Some

literature suggests that EMH is not fully complied with reality since the

exchange of information between market actors is not perfect (Jensen, 1978).

For this reason, actors like the CRA can help the market to increase

information transparency.

The expressed primary purpose of CRA:s is to mitigate the information

asymmetry between inside corporate managers and outside stakeholders by

announcing independent opinions on credit risks (Langohr & Langohr, 2008).

This asymmetry will presumably never be erased since insiders have direct

insights into the company and will always have a more precise perception of

the credit risks than outsiders. However, there are good reasons for reducing

this asymmetry which credit ratings are intended to do. If no credit ratings had

existed, it would have been likely that investors and creditors had made more

ineffective investment decisions or at least based their decisions on

information that is not validated by an independent third party. Furthermore,

the information content of ratings may differ depending on the direction of the

rating change. Verrecchia (2001) stated that if the manager’s primary objective

is to maximize shareholder value in the firm, then beneficial information

enhancing the market capitalization will be disclosed straight away while

unfavourable information will be revealed more slowly. The interpretation is

that information favouring the firm is more commonly known to the market.

In contrast, negative information has an involuntary nature of disclosing that

is, to an extent, unexpected to the market. This information thus has a

surprising effect that is uncovered from the CRA:s during the announcements.

In that sense, good news is linked with greater disclosure and reduced

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information asymmetry, while bad news is related to reduce disclosure and

greater information asymmetry.

Another important aspect is that other actors in the financial market develop

and apply their own prediction models for both loan defaults as well as

upcoming credit ratings. These are for example banks, risk management

divisions in companies and private investors, who will make their own

forecasting models of how the rating agencies will act (Altman & Rijken,

2004). These actors may use the rating to calibrate their expectations and for

validating their own predictions. This means that a part of the information that

should be released on the announcement day could already be known to the

public.

How much the change in credit rating itself affects the share price may have

changed over time. Brown et al. (1988) found that bad news affects prices

more strongly than good news does after a dramatic financial event, and

concluded that markets reacted to uncertain information efficiently. With the

financial crisis of 2008 in mind, where CRA:s in effect endured a lot of

criticism for their failure of assessing various financial instruments (Scalet &

Kelly, 2012), there is a possibility that investors will associate the former

inaccurate ratings with a higher level of uncertain information. Therefore,

there is a possibility to react stronger to bad news. This would imply that news

about decreased ratings is considered to have a more substantial impact on the

market after the financial crisis compared to the period before.

On the other hand, the development of information structure has made it easier

for investors to analyze potential investments. The increasing demand for

accessible information for investors has evolved from a significant increase in

the quantity of Internet-based information, for example investor news,

company websites, and social media platforms (Kelton & Pennington, 2016).

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Based on this, it should not be difficult for a private investor to utilize the same

information given by credit ratings. By analyzing a company's financial

statements, it is possible to draw its own conclusions about the financial status

and creditworthiness. In conjunction with increased access to information,

there is a higher probability that investors may already have anticipated that a

change in credit rating will occur. In that case, the rating would be reactive

rather than proactive. This means that the market anticipates companies’

creditworthiness on its own and that the rating merely provides confirmatory

evidence to that evaluation. Based on this, a credit rating announcement today

may not contain as much new information to the market as it used to do. Also,

the creation of extensive regulation frameworks prohibiting CRA:s from

selective disclosures of major corporate events (Utzig, 2010) supports the

argument that ratings are restricted in providing new information to the

market.

There are several possible explanations for the results of previous event studies

differing from one another. In previous event studies, abnormal returns were

measured for several months around the publishing date of the announcement

of a changed credit rating (Weinstein 1977; Pinches & Singleton, 1978). There

is therefore a possibility for problems regarding precision in measurements,

and several other factors besides the actual rating announcement may have

affected the result. In recent years, the availability of improved methodologies

have enabled researchers to exclude observations that disrupts the isolation of

the event effects (Altman & Saunders, 1998). Another explanation for the

contradictory results is that markets have different grades of information

access. Emerging markets are likely to have less access to other information

sources than the rating announcements from CRA:s. Therefore stakeholders

would have to rely on credit ratings to a larger extent.

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Summarized, the credit rating agencies have been heavily criticized (Partnoy,

2006; Frost, 2006; Evans, 2011). In addition to this, other information sources

may potentially deliver the information that the CRA is intended to provide.

This may have changed their role in reducing information asymmetry in the

financial market. Several studies have investigated whether credit ratings

affect bond and stock prices. However, these studies show contradictory

results where the majority of these focus on the U.S. market and a few on other

markets. Therefore, we find it scientifically relevant to further investigate this

subject in a European context. Furthermore, the research on how stock returns

act from credit changes after the GFC in 2008 is insufficient. Based on that,

this study can provide additional value to the previous research made, and we

undertake further analysis to determine whether the market reacts more

strongly to credit rating announcements after the GFC compared to before.

1.3 Purpose

This thesis will investigate (i) whether changes (upgrade/downgrade) in credit

ratings lead to abnormal returns in share value, and thereby provide useful

information to potential and current investors. The thesis will also (ii) examine

whether there are significant differences between the periods before and after

the GFC in 2008.

1.4 Outline

Figure 1. Outline of the thesis.

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2 Credit ratings

A selection of the most relevant studies and their empirical results is presented

below. At the end of the literature review, a summary of all previous empirical

findings can be found. A more detailed description of credit rating agencies

and their environment will be presented as well as some of the criticism that

has been directed towards them.

______________________________________________________________

2.1 Literature review

The information content of ratings has been widely examined across the world,

where most of them revolve around the U.S. market (Dale & Thomas, 1991).

Table 1 reviews the most important studies during the last four decades. Some

of them indicate that credit ratings provide almost no information to the capital

market. An early research on the subject was made by Weinstein (1977) who

analyzed the return on the bond market and found no evidence that a change

in credit rating would have any significant impact on bond prices. Pinches and

Singleton (1978) studied two hundred bond rating changes. By using a market

model with monthly stock prices, they concluded that the rating changes

generated information of little or no value. These results mainly reflected the

fact that rating actions were in line with publicly known events. During the

same period, numerous reports were published that contradicted these results.

Griffin and Sanvicente (1982) studied U.S. stock prices eleven months prior

to a bond rating change and during the rating announcement month. They

found that bond downgrading had a negative stock impact and conveyed new

information to the financial market actors.

Recent studies provide mixed results, but many are consistent with this notion

of asymmetric impact on stocks; a lot of the reviewed studies show that

significant abnormal returns are more frequently occurring in downgrading

actions than upgrading. For instance, Dichev & Piotriski (2001) found

evidence for this asymmetric pattern and further suggested that the small

returns emerged from an underreaction to the announcement of downgrades,

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rather than a change in systematic risk. These studies revolve around the U.S.

market.

Some studies are focusing on the European market. Barron et al. (1997)

examine the impact of both new ratings and credit rating changes on U.K.

common stock returns. The results suggest that there are significant excess

stock returns associated with bond rating downgrades and that rating agencies

provide information to the U.K. capital market. Likewise, Linciano et al.

(2004) examined Italian stock price reactions after 299 rating changes and

found indications of information content solely in downgrade ratings.

Calderoni et al. (2009) used a more comprehensive dataset containing 17

European countries and found the same asymmetric relationship. Moreover,

this asymmetry is described as less occurring in the financial sector, which is

referred to be characterized by stricter disclosure rules and extensive analysts’

coverage. Vassalou & Xiang (2005) emanates from this asymmetry and found

that it is primarily due to the methodology of computing abnormal returns.

When equity returns are calculated with respect to the variations of default risk

around the date of an announcement, this asymmetry is claimed to mostly

disappear.

To our knowledge, few studies have investigated whether investors have taken

greater note of rating changes before or after the GFC of 2008. Reddy et al.

(2019) belong to the exceptions where they divided the sample into these three

subperiods and used t-tests in order to find any differences between them. The

results were significant and suggested that investors reacted stronger to credit

changes after the GFC compared to before. Pacheco (2011) verified this

notion, where he found that the Portuguese stock market strongly reacted to

the issuance of credit ratings after the GFC of 2008. These findings indicate

that markets are more vulnerable to ratings after financial turmoil.

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Table 1. Summary of previous research.

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2.2 The credit rating agencies

The credit rating agencies provide the market with an independent evaluation

of borrower creditworthiness, based on company fundamentals, making it easy

for investors to compare potential investments (Langohr & Langohr, 2008).

CRA:s assess financial information that mostly are publicly available but time-

consuming and costly to interpret for the individual firm or investor.

Moreover, the analysis is not based solely on public information, but also on

private information which companies agree to share with the CRA:s (Micu et

al. 2004; Matthies 2013). Based on this information analysis, the agencies

express their opinion on credit risks by assigning a rating, which is made

public during the rating announcements. The role of CRA:s is thus to achieve

information economies of scale and use it to make an independent third-party

judgment of the firm’s repayment ability and thereby make markets more

effective in allocating good investments (Gonzales et al. 2004).

CRA:s have a vital role in the financial market. Despite this, it is one of the

most understudied actors of modern corporate finance (Pettit, 2004). It is of

great importance to understand what environment the CRA operates within to

fully capture the impact of credit rating agencies. The CRA:s role in the market

can be understood from different perspectives. The extent to which a company

is affected by the rating agencies depends entirely on the company in question,

as some are more dependent than others. Certain companies receive substantial

value through the publication of independent ratings that give them access to

public debt markets (OECD, 2010), while others do not need or do not want

to be rated. For example, it is described that some issuers believe that their

financial performance is not satisfactory and wish to avoid any chance of

receiving an unfavourable rating (Poon, 2003).

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The impact of the cost of capital is one relevant aspect to understand the

importance of credit ratings for companies. The cost of capital consists of the

cost of equity and cost of debt and represents the cost of different components

of financing (Damodaran, 2012). A favourable credit rating can reduce the cost

of debt by signalling to the credit market that the company in question has

good abilities to repay its debts. Hence, the lower cost of the company's debt

capital leads to a lower cost of capital, thus increasing the value of the

company (Damodaran, 2012). Kligr and Sarig (2000) stated that firms with

high leverage show a stronger reaction to rating announcements, which may

indicate that CRA:s have different effects on firms depending on the debt

structure. Also, the research of Atiase (1985) suggested that information

asymmetry is negatively related to firm capitalization. Smaller companies with

low debt that are not dependent on the bond market may not be affected by

credit ratings at all. The reason for this is based on the fact that the advantage

of debt is reduced for smaller companies (McConnell & Pettit, 1980; Pettit &

Singer, 1985).

Investors use rating information to enhance the perception of firm values by

obtaining information about the credit risks associated with the firms. An

investor can further ensure that their own analyzes are consistent with the

CRA:s view of companies creditworthiness. Even a rating change that is not

related to any major change in credit risk could still send a signal to the market.

In this sense, CRAs could deliver confirming information to the investor about

the financial status of a company and in turn, base his or her investment

decisions with greater certainty (White, 2001). Altman & Rijken (2004) mean

that there is an issue of two conflicting goals - rating timeliness and rating

stability. The rating agency works from a long time perspective and places less

weight on short-term indicators of credit quality. The ratings are aimed at

ignoring temporary shocks and are therefore less likely to be reversed within

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a short period of time. Contradictory, the investor has no requirement for rating

stability and are extensively short-term oriented. From this point of view, the

ratings are slow in responding to changes in corporate credit quality, and it is

further described that the slowness in rating adjustments is well recognized by

investors (Altman & Rijken, 2004). This indicates that some of the rating

information could already be anticipated by the market a period before the

announcement.

Other market actors, such as intermediaries, risk management in businesses

and corporations, benefit from the information of ratings. Since these types of

actors do advanced risk analysis on their own, they most likely have an opinion

of the risk exposure of different investments. Thus, the CRA rating might not

add so much new information to these actors. However, the rating could be

used to validate their own expectations. For example, the level of equity capital

in banks in the U.S. is based upon the credit risks of their assets which by

extension is determined by CRA credit ratings. Likewise, pension funds base

their investment criteria on bond ratings to facilitate investment decisions

(Langohr & Langohr, 2008).

CRA's role in the market can also be understood from a legal perspective.

Fitch, Moody's and S&P are the largest CRAs in the world (Kedia et al. 2017).

Partnoy (2006) means that these credit rating agencies have benefited from an

oligopoly market structure. A central factor in this dominance is that the

Securities and Exchange Commission (SEC) limits new entry and competition.

The reason is that the government both mandates demand rating agency

services and severely restricts supply (Pollock, 2005). The nationally

recognized statistical ratings organizations (NRSRO), where S&P, Moody’s,

Fitch and a few other agencies are included, is certified by the Securities and

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Exchange Commission (SEC, 2013). Various proposals have been put forth to

reform the CRAs. For instance, in 2014, SEC adopted new requirements to

strengthen the overall quality of credit ratings, increase the transparency of

credit rating agencies and increase their responsibility. This would result in

increased protection for investors and markets against a recurrence of behavior

and practices that were central to the GFC in 2008 (SEC, 2014). Moreover,

U.S. financial regulators and lawmakers increasingly have been using credit

ratings-based criteria (Frost, 2007). For example, Rule 2a-7 under the

Investment Company Act (1940) limits money market funds to investing

solely in high-quality short-term instruments, where the minimum quality

investment standards are based on ratings published by the NRSROs (SEC,

1940).

2.3 The credit rating process

The process of assigning a rating is divided into multiple steps. Firstly, the

company, often referred to as the issuer, requests a rating from an agency. The

agency makes a firs assessment and then meets with the issuers’ management.

The agency analyzes the company, which is later provided to a rating

committee that reviews the analysis and assesses its accuracy. If it is

considered accurate, the new rating is then notified to the issuer and at a later

stage, a public announcement is made. After the rating is published, there is

surveillance of rated issuers and issues (Standard & Poor’s Global Ratings,

2020).

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Figure 2. The credit rating process (Standard & Poor’s Global Ratings, 2020)

By designating alphabetical ratings of debt, the CRAs communicate their

opinion of the company's creditworthiness. Each agency applies its own

methodology in measuring creditworthiness and uses a specific rating scale to

publish its ratings opinions (Standard & Poor, 2018). However, a widely

recognized scale is the one used by Standard & Poor’s and some other rating

agencies: AAA, AA, A, BBB, BB e.g., with pluses and minuses as well

(White, 2001). The different ratings are illustrated in Table 2.

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Table 2. Ratings classification (Standard & Poor Global Ratings, 2020)

The rating agency must take into consideration many attributes of a firm

while analyzing the creditworthiness;

• Qualitative (quality of management) and quantitative (financial

analysis)

• Earnings and cash flows

• Quality of company assets

• Liquidity

• Industry and country risk

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Similarly, the credit analyst would also want to analyze the degree to which

the firm has access to the capital markets the capability to borrow money

(Crouhy, 2001).

“A model without sufficient validation is only a hypothesis.” - Stein, 2007

Given that there has been an increase in the number of bankruptcies, more

competitive margins on loans and rapid growth of off-balance sheet

instruments, credit risk measurement has evolved dramatically over the last

two decades. Forty years ago, most financial institutions exclusively relied on

subjective analysis or so-called banker expert. These experts used information

on various borrower characteristics such as, capital (leverage), borrower

character (reputation), collateral and capacity (volatility of earnings) (Altman

& Saunders, 1998).

Today, the credit risk assessments are more based on measurable objective

aspects, i.e. mathematical and statistical models predicting probabilities of

default and rating decisions. Since the GFC 2008, CRAs have been

experiencing pressure from juridical directives which have increased the

transparency in decision-making processes of the CRA methodologies (IMF

2010; European Council 2009, 2011). The leading CRAs have used different

types of models. There have been several new approaches that have been

proposed as alternatives to traditional credit-scoring and bankruptcy prediction

models. For example, the mortality rate model and the ageing approach have

been popularized in the last decades. These models follow underpinnings

found in the insurance company risk analyzes where decisions are based on

data on previous bond defaults. In general, newer models take into account the

credit concentration risk rather than analyzing the risks of individual loans,

and these are well implemented in the CRA industry (Altman & Saunders,

1998).

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Even though the financial institutions have increasingly moved away from

subjective systems over the past two decades towards more objectively based

systems (Altman & Saunders, 1998), there are both quantitative and qualitative

components that the rating agency includes in its rating process (Standard &

Poor, 2020). Consequently, since there is a subjective aspect in the rating

process, there is a risk for emotional bias and thus a potential risk of

misclassifying in creditworthiness. For example, Sommerville & Taffler

(1995) mean that the allocation of credit to less-developed countries depends

upon lenders' judgments. Bankers assess country credit-risk using a range of

techniques, from formal statistical models to informal judgmental methods.

These assessments are a crucial part of the process of credit allocation. Their

empirical results show that bankers are overly pessimistic about the

creditworthiness of less-developed countries, which results in lenders missing

a profitable lending opportunity. This is something that the authors call a type

II error. Contrariwise, when a company is incorrectly rated as creditworthy

(referred as a type I error), it will negatively affect the creditor’s cash flows

and the value of their assets (Sommerville & Taffler, 1995).

In a CRA context, there is a possibility that the rating agents are personally

connected to the managers within the rated company, or in some way, have

more than a professional relationship with the managers. For example, in such

a way that judgment that is made has no connection whatsoever to the

manager’s professional knowledge or leadership abilities. There is a risk for

lower credit ratings than what reflects reality. The consequences of this will

be that the company will be unable to enter the credit markets and have

difficulties to access external capital.

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2.4 Criticism of the CRAs

Several authors highlight issues of conflict of interest and mean that it is

problematic that the CRA charges the issuers for credit ratings (Frost, 2007;

Partnoy, 2006). Some critics argue that a CRA’s dependence on fees from

issuers might encourage the CRA to issue more favorable ratings and to be

more likely to avoid negative information (SEC [2003a, 2005bl). The issuers

receive substantial value through the publication of independent ratings, which

gives them access to public debt markets and improves the cost of capital.

Simultaneously, the rating agencies need their revenues to be able to sustain

the costs of their activity. Rating institutes and the issuers are interdependent,

and the justification for charging issuers is two-fold (OECD, 2010).

Enron's highly publicized failure in December 2001 occasioned the sharpest

and most pointed criticism of CRAs. The giant accounting and auditing

scandals of 2000 to 2002, and Enron in particular, led many to question the

CRAs competence and the value of their grades (Frost, 2007). The most

significant criticism was the fact that the CRAs lowered Enron's credit rating

only a few days before its financial collapse. Frost (2007) states that the CRAs

failed to ask critical questions to the management of the company and that they

did not use the private and confidential information that they had access to.

This meant that they failed to convey relevant and necessary information to

the financial market in good time.

We have previously discussed the timeliness of the ratings, and potential

dilemmas and problems that could potentially arise when the CRA and the

investor have different time horizons. In conjunction with the Enron

bankruptcy, the media and Congress observed that S&P, Moody’s and Fitch

had kept “investment grade” in rating on Enron’s bonds until five days before

the company bankruptcy (White, 2009). This could be one of the reasons why

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the timeliness of agency ratings has come under closer scrutiny and criticism

(Altman & Rijken, 2004). One of the rating criteria from Standard & Poor's

(2003) is that ratings are meant to be forward-looking; that is, their time

horizon extends as far as is analytically foreseeable. They further mean that

ratings should never be a snapshot of the present situation. Altman & Rijken

(2004) also argues that the critique of rating agencies focuses mainly on the

timeliness of agency ratings, and not on the accuracy of agency ratings. The

author refers to a survey where 83% of investors believe that CRAs, most

often, accurately reflect the issuer's creditworthiness.

“Never waste a good crisis.” Andrew Wolstenholme, 2009

The CRAs was also a subject of criticism during and after the 2008 GFC. From

the middle of 2007 to early 2009, more than twenty-five per cent of U.S.

household net worth evaporated. A combination of irresponsible risk-taking

and debt-fueled speculation led to the near-collapse of the U.S.financial

system (Evans, 2011). The three major CRA:s assigned favourable ratings on

subprime mortgage securities and other debt obligations. The sales of these

bonds were an essential part of the eruption of the price-rise bubble in the U.S.

housing market (White, 2009).

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3 Theoretical Framework

This chapter gives a presentation of the theory about the subject. These terms

and theories will frequently be used in this study and is therefore of great

importance for the reader.

______________________________________________________________

3.1 Efficient markets

The efficient market theory suggests that assets are priced in accordance with

all relevant information in a market at any point in time. This idea was founded

and developed by Eugene F. Fama (1970), who later formed the efficient

market hypothesis. The hypothesis expresses that the current price of any asset

is an accurate reflection of all publicly available information associated with

the asset. In the stock market, this would mean that investors cannot anticipate

stock prices based on public information, as it is assumed that the prices

already incorporate all accessible information. If new information is exposed

that indicates that a particular stock is mispriced, the market will react to it so

that the price is effectively adjusted to reflect the new information (Bodie et

al. 2008). For the Efficient market hypothesis to be true, Fama (1970) states

three underlying assumptions: investors are rational, meaning that the

decision-making is based on the optimization of economic utility. The

investors are further assumed to have homogeneous expectations, and

transaction costs are non-existent. Several academics have tested the

efficiency hypothesis, and many have produced empirical results suggesting

that the theory should hold (Shleifer, 2000). There are various levels of market

efficiency in the hypothesis that is determined by the amount of information

reflected in the stock.

• The strong form efficiency assumes that the price is determined by all

information affecting the company, including a few individuals’

private information about the company, often referred to as insiders.

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• The semi-strong efficient market assumes that the stock price, in

addition to historical information, also includes all public information

available about the underlying company.

• The weak form suggests that stock prices reflect the historical

information associated with the stocks, for instance, historical prices

and trade volume.

The degree to which the markets are effective is relevant to discuss in terms of

how and when the rating change affects stock prices. If the market were

assumed to be strong form efficient, the information content of the rating

announcements would already be reflected in the stock prices. In such a case,

the private information that CRA is said to have access to should already be

known to the public. Thus, according to this degree of efficiency, no

information asymmetry exists in the market and CRA:s would likely not exist.

On the other hand, if the market instead was assumed to be weak-form

efficient, the CRA:s would not fulfil their function since they are characterized

by using information about the borrowers that are more or less hidden from

the public (Micu et al. 2004). The most reasonable starting point to investigate

whether the rating has an impact on the stock price is therefore to consider the

market as semi-efficient.

However, over the past two decades, independently of the levels of the EMH,

both the theoretical foundations and the empirical evidence supporting the

hypothesis have been challenged. Incohesive evidence is arising that seems to

be inconsistent with the theory (Jensen, 1978; Ball, 1978; Watts, 1978).

Among others, proponents of behavioural finance state that it is difficult to

sustain the case that individuals are entirely rational (Shleifer, 2000) where

they, for instance, tend to over- and underreact to specific events (De Bondt &

Thaler, 1987). People have a tendency to overweight recently published

information and underweight data, which generates deviations on the stock

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markets (Watts, 1978). The overreaction in the share price is contrary to the

effective market as the stock prices may temporarily deviate from their

underlying core values. This results in an imbalance in the pricing of the assets

(De Bondt & Thaler, 1987). Other critics, with regard to fundamental analysis,

claim that initial dividend yield and price-to-earnings multiples have

predictive traits in calculating future stock prices (Burton, 2003). These

challenges of the credibility of the EMH have given rise to alternative

hypotheses, such as the information content hypothesis.

3.2 Information content hypothesis

“Companies are not cubic feet of lumber, barrels of oil, or pork bellies whose

substance and quality one can readily inspect and measure. They are

extremely complex, continuously adapting organisms whose competitive

advantages are based on unique knowledge and proprietary information that

cannot, and should not, be communicated to outsiders. The information gap

can never be fully bridged”. - Langohr & Langohr, 2008

In the stock market, it is presumed that the actors possess different levels of

information and this difference is denoted as an information asymmetry.

Information asymmetry arises when one party has more information than the

other in a transaction (Nel et al. 2018). Akerlof (1970) explains asymmetric

information by metaphorically telling a story about a car. When the car is

brand new, the first owner does not know whether the new vehicle is in good

or bad shape. After owning the car for a length of time, the car owner can form

a good idea of the quality of it. An asymmetry in available information has

developed since the seller now has more knowledge about the quality of a car

than the potential buyers. The author argues that the seller often has an

information advantage in a car transaction compared to the buyer, which

makes it difficult for the buyer to see any difference between a bad car and a

good car. The same principle regarding asymmetric information occurs in the

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financial markets, where corporate insiders possess public as well as private

information about their companies while the market only can rely on public

information.

This asymmetry of information possession among the actors can be reduced

through signals to the market. It is described from the incentive-signalling

approach that markets evaluate the information received to make valuations of

companies (Ross, 1977). In terms of CRA:s, this means that the agencies

express opinions about firms’ creditworthiness and even though they do not

make recommendations of investment actions in the market, they still are

perceived valuable for the investors. A credit rating should send signals to the

market about a company’s financial status.

3.3 Hypothesis development

Based on previous findings and existing theories, two different hypotheses are

considered for enlightening how the European stock market responds upon

credit rating changes.

Hypothesis 1: Credit rating announcements cause abnormal returns

The CRA analysis is not exclusively based on public information but also

private information (Micu et al. 2004; Matthies 2013). Under the assumption

that the market is semi efficient, the rating change announcements should

bring new and valuable information to the market, which gives rise to

abnormal returns.

Hypothesis 2: The stock price of companies where credit rating changes

occurred prior to the 2008 financial crisis is more strongly impacted

compared to the companies receiving a rating post the 2008 financial crisis

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The need for CRA:s has been heavily questioned since the eruption of the GFC

in 2008 (Scalet & Kelly, 2012; Evans, 2011). This, in turn, may have changed

the view of CRA:s capability of reducing information asymmetry in the

financial market. In addition, the availability of internet-based information

sources has increased (Pennington & Kelton, 2012) that has the potential of

replacing the information content of ratings. With the time distinction of GFC

2008, we therefore hypothesize that investors were more dependent on ratings

before the crisis than after the crisis.

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4 Methodology

With this chapter, we want to give the reader an opportunity to gain insight into how

we have worked to reach our conclusions and thus give the reader an opportunity to

critically review the results of the study.

___________________________________________________________________

In order to answer the research question, an appropriate research strategy must

be chosen. Which method that should be applied is very dependent on the type

of examination to be done, and what the research question is. Researchers who

are using quantitative research employ experimental methods and quantitative

measures to test hypothetical generalizations (Hoepfl, 1997). Quantitative

research allows the researcher to familiarize themself with the problem or

concept to be studied and generate hypotheses to be tested (Golafshani, 2003).

To test the hypotheses in this thesis, a large amount of data needs to be

collected and later on, statistically analyzed. Accordingly, the quantitative

research methodology is more fitting than the qualitative approach.

Ejvegård (2003) means that for the survey method, the test, or the measure to

be usable, it is required that it is reliable and valid. Reliability could be

explained as the accuracy of an instrument (Twycross, Heale, 2015). Joppe

(2000) means that the instrument is considered to be reliable when results are

consistent over time, holds an accurate representation of the total population

and if the results can be reproduced under a similar methodology. Meanwhile,

Bell (2000) describes validity as measurements producing the same result at

different times, where there are similar conditions as in the first measurement.

Joppe (2000) means that the study should be considered valid when the

research truly measures what it was intended to measure or how accurate the

research results are. If the two requirements reliability and validity are not met,

the research result does not have any scientific value. By clearly presenting the

various steps in how this study should be conducted, we allow the reader to

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critically review the results, and consequently increase the reliability and

validity of the study.

4.1 Choice of method

In order to test the two hypotheses that constitute our targeted relationship

between rating announcements and stock price changes, an event study will be

conducted. The method is commonly used among economists analyzing how

a specific event is influencing the value of a firm. This study assumes that the

impact of a credit rating announcement may be represented as the event and

the effect is captured by using the event study methodology. The method

means to compare the firm's value (i.e. stock price) before and after the event

of rating has been announced and based on these values, evaluate the

information content from credit ratings. Under the assumption of market

efficiency and rationality, it is implicitly that the event immediately affects the

stock prices and that there exist no other expectations about the event

(Mackinlay, 1997). Our study is to some extent inspired by Ekstedt &

Hammarstrand (2019). The authors makes a comparison between the

European and U.S. market concerning ratings impact on stock price. However,

the purpose, data selection and methodology of our study is different.

In this approach, it is crucial to identify the event date accurately (Campbell et

al., 1997) along with creations of event windows to make the rating effects

visible and measurable. The methodology used in this study is to a large extent

based on the approach described by Mackinlay (1997). According to this

author, the event study could be conducted in several ways but follows a

general pattern of analysis. The procedure is divided into six steps; a) event

and window definitions, b) data selection c) estimations of normal and

abnormal returns, d) testing procedure, e) empirical results, and f) analysis.

However, refinements and developments from this classical event study

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approach have been made throughout the last few decades. Binder (1998)

means that in early studies there were problems with heteroscedasticity and

time-series dependence, but that several authors have worked out solutions to

this problem. Based on this, we perform an event study with a slightly different

approach that takes these difficulties into consideration. Each step of this

procedure will be further described in the following sections. We apply this

methodology for the analysis, both prior and post GFC.

4.2 Event and window definitions

The event investigated in this thesis is the day a new rating is revealed from

the CRA and thereby exposed to the market, making the company experience

the effects of this new-published information.

The event window is used to capture the surrounding effects of the rating

announcements. Earlier research suggests different lengths of the event

window where monthly data is used during six months prior to the event and

three years after the event (Dichev & Piotroski, 2001). Others use daily data

and event windows consisting of a couple of weeks before and after the rating

announcements (Calderoni, 2009; Reddy et al. 2019; Linciano et al. 2004).

Our ambition is to isolate the effects of the rating announcement and prevent

other information to interact on the effect in the event window. Therefore, we

consider that the event window should be big enough to capture eventual

abnormal returns. At the same time, it has to be short enough to avoid external

impacts such as disclosures from competing CRA:s that might affect the stock

performance in connection to the announcement (Reddy et al. 2019). The event

window for this thesis is set to 13 days, 6 days prior to the event, the actual

event day, and 6 days after the event.

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Figure 3. Estimation and event window in days.

According to Mackinlay et al. (1997), the estimation window and event

window should not overlap when using daily data to prevent the event from

affecting the normal stock performance estimates. The author recommends an

estimation window of 120 days (around 79-80 trading days depending on

market specifics) prior to the event in an event study, which in accordance will

be used in our research.

4.3 Data selection

The process of data collection is of great importance for the methodological

framework used in this study. By including companies from 16 countries in

the study, it is possible to control for market-specific variables. For example,

the fact that companies in different European countries have a tradition of

financing themselves in different ways, both through bonds and more

traditional bank loans. Also, it is solely companies of considerable size

included. Because of this, we reduce the possibility that there are systematic

differences between the companies. The national sectioning of firms is

illustrated in table 3.

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Table 3. Companies included in the sample sorted by country.

The credit rating announcements and daily stock prices data is collected

through Thomson Reuters Eikon and includes observations from the period

from January 2000 to December 2019. This period was chosen as it should be

considered sufficient time both before and after the 2008 financial crisis.

The selection criteria intend to determine the inclusion of firms for the study.

The criteria restrict the data and specify the sample characteristics. At this

stage, it is important to note any potential biases in the selection which has

been taken into consideration when stating our criteria (Mackinlay, 1997). To

execute the event study in a proper way, all included companies in the sample

have to fulfil the following requirements:

• The company has received at least one rating change by Standard &

Poor’s, Moody’s or Fitch from January 2000 to December 2019.

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• The company needs to be included on the S&P Euro 350 index during

the time of the rating change.

• The company needs to have daily stock prices accessible at the time

of the rating change.

• The company must have been provided a rating prior to the new

rating, so that the new rating is an actual change.

Furthermore, rating changes occurring between September 1, 2008 to October

31, 2008 are excluded. This is due to the massive decline in the European stock

market during this period (Thomson Reuters, 2020). If these observations

would be included, there is a risk for biased estimates since substantial

downgrades were evident during the two months for many of the companies

included in the sample.

The selection is based on the S&P Euro 350 index, which consists of 350

leading blue-chip companies drawn from 16 developed European markets

(S&P, 2020). For a company to be included in the index, they must meet

specific criteria regarding market capitalization (company size must belong in

the top 95th percentile). Consequently, the chosen index can be used as a

suitable indicator of European large companies' stock performance. The

number of companies included in the index is ideal for this particular study

based on the number of observations that is needed.

When collecting the risk-free rate for calculations of market risk premium, we

use an interbank rate for Germany, which is an interest rate charged on short-

term loans between private banks. This rate should be considered as low for

the whole period except during the months where the GFC hit the European

markets. The interest rate was obtained on a monthly basis but was converted

to a daily basis in order to be matched with the share prices.

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As described in section 2.1, previous studies state that there may be differences

in how much of an impact ratings have on the share price, depending on

whether it is a downgrade or upgrade. The weight between upgrades and

downgrades included in the sample can, in that way, affect the outcome. We

choose to study the general effect of both different types of changes. Thus,

both negative changes (downgrade in rating) and positive (upgrade in rating)

will be included in the sample. This makes it hard for this study to conclude

whether downgrading or upgrading has a more significant impact than the

other. However, this part is nothing that this study intends to reflect.

Using the S&P Euro 350 index as a starting point, 362 companies were

included in the preliminary sample. The majority of companies in the sample

have received more than one rating change during the period. Consequently,

several rating changes for specific companies are included. In the sample, 151

companies did not meet the data selection criteria which resulted in 380

different rating changes related to 211 different companies. Out of these rating

changes, 195 were upgrades and 185 were downgrades. 164 rating changes

occur before the GFC and 216 post the GFC. The ratings included are the

common Long-term Issuer Ratings from the three global rating agencies S&P,

Moody's and Fitch. Unsolicited ratings, which are neither requested nor paid

for by the rated companies (Behr, Guttler, 2008), are not included.

In line with Altman & Rijken (2004) who conducted a similar study, we treat

our dataset as a panel dataset. Panel data (e.g. data that contain observations

on multiple firms in multiple years), is a dataset in which the behaviour of

entities is observed across time (Torres, 2007). Panels are attractive since they

often contain far more information than single cross-sections and thus allow

for increased precision in estimation (Hoechle, 2007). On the other hand, in

these data sets, the residuals could be correlated between firms or over time,

and OLS standard errors might be biased (Petersen, 2007).

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4.4 Method issues and potential bias

This study revolves around listed companies, which may be assumed to be

under greater supervision than smaller, non-listed companies. This is based on

the fact that they have to adapt to more regulations and that there are

significantly more stakeholders who have interests in the company. We

presume that the companies included are complying with generally accepted

accounting principles, and are following the norms and rules that apply to

accounting standards. It is thereby assumable that the financial numbers and

figures presented are correct and give an accurate and fair view of the financial

status regarding the companies included.

Given the data for the exact period of January 2000 to December 2019, this

study is valid for this time only. If future studies wish to make use of other

periods, it is likely that different results may be obtained. For example, the

companies included in the S&P Euro 350 index is continuously changing. If a

company no longer meets the market capitalization criteria, it may be replaced

by another company. This means that future studies using data from the same

index may have other companies included in comparison with this study.

Although this study revolves around the 2008 global financial crisis, other

events and crises may have affected the stock market during the study period.

These events may produce misleading results on individual observations and

in turn, affect the subsequent results.

Given that there might be a considerable time between the ratings for one

specific company, it is conceivable that companies may have undergone major

changes. It could revolve around both financial and operational conditions. We

therefore avoid the argument that the rating effect should be the same because

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it is the same company. Based on this, we will not be able to draw any certain

conclusions about the impact of ratings on individual companies. Moreover,

every event included in the dataset is treated independently from each other.

4.5 Normal and abnormal returns

In order to retrieve abnormal returns, both the actual and normal returns must

be calculated. The actual return is given by Equation 1.

Equation 1. Actual return.

where 𝑃𝑖,𝑡 is the stock price at time t and 𝑃𝑖,𝑡-1 is the stock price one day

prior to t.

According to Mackinlay (1997), the normal return is the return the market

would have expected from a security if the event would not have taken place.

In this research, the normal returns are estimated by the market model, which

is the most common way of calculating expected returns (Choy et al. 2006;

Joo & Pruitt, 2006; Li et al. 2003). The following equation (2) states the market

model used in this study:

Equation 2. Market model.

where 𝑅𝑖𝑡 is the return of the security i at the time t and 𝑅𝑚𝑡 is the market

return at the time t.∈ represents the zero mean disturbance term and is

assumed to have an expected value of 0.

This is a model that relates the return of any given security return to the market

portfolio return. The expected return assumes a linear relationship between the

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return of the market and the return of the security (Mackinlay, 1997). This

single index model is used to control for the market premium, and is based on

the assumption that markets are efficient (Fama & French, 2004). The

parameters are estimated by using ordinary least squares (OLS) regression

analysis, which is a consistent estimation procedure for the market model

(Campbell et al. 1997).

In order to achieve the abnormal returns for each observation in the event

window, it is usually conducted by subtracting the expected return from the

actual return, given by Equation 3.

Equation 3. Abnormal return.

4.6 Testing procedure

To execute the event study, we will use the classic approach from Mackinlay

(1997) with one important modification. Instead of calculating the abnormal

return related to the normal return on a particular stock in the conventional

way, we alternatively use regression analysis based on a dummy-variables

approach that captures the event variables which are of our interest. This

means that the impact of ratings is assumed to be non-linear. The method

facilitates and improves the estimates for the standard errors, which is the

primary strength of the method in comparison to the more traditional approach.

It is also a convenient and quick way to calculate the general effect of rating

change when having a broad set of observations. The approach is based on

Pynnönen (2005), who uses dummy variable regressions over the combined

sample and event windows. This means that the event and estimation window

is captured with dummy-variables (where a dummy variable is equal to 1

within the event day and 0 otherwise). The estimated regression coefficients

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of the dummy-variable provide indications on the abnormal return (Pynnönen,

2005). The regression model is given by Equation 4, and is used for all periods

in the study.

Equation 4. Regression equation.

The equation is to some extent based on Abad & Robles (2014) work, who

also performed an event study dummy approach. The regression is a fixed-

effect regression model with Driscoll and Kraay (1998) standard errors for

linear panel models. The fixed-effect model captures the influence in which

companies are different from each other and it is assumed that these

differences are constant (Borenstein et al. 2010). The regressions will be

performed in STATA, which is a suitable program for this type of study. We

create lagged and forward variables, which is derived from the ARIMA model

mathematics, and is a convenient way to structure the variables. The lagged

variables will capture potential effects before the announcements and reflect

how much the market anticipates the information given by the ratings. For

example, l2 indicates the rating announcement effect on the stock price two

days before the event (t-2). The forward variables will contrariwise capture the

effects after the announcements have taken place and represent the impact of

the rating information to the market when announced. For example, f3

indicates the effect three days after the event (t+3). In the traditional approach,

it is common to select several different event windows, for example (t-3 to

t+3), (t-4 to t+2). However, in this study we choose to analyze one event

window.

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Given that companies are differently volatile and are separately correlated with

the market risk, it requires an individual market risk beta for each event.

Additional dummy variables will therefore be created to differentiate each

company risk from each other. This is also based on the fact that one specific

company could potentially be included multiple times on different occasions.

In other words, we assume that the beta value is not constant over time for the

companies. The alpha value will be included in the fixed effect. In (Eq. 3), the

dummy variables are multiplied by the market risk premium (market return -

risk-free rate).

According to Petersen (2007), many published articles fail to adjust the

standard errors appropriately and by that, their research is in many cases

incorrect. By that, we assess that it is of great importance to adjust the standard

errors correctly. Heteroskedasticity consistent or “White" standard errors

(Hoechle, 2007) will be used in the regression by choosing option vce(robust).

By using robust standard errors, the regression model corrects for potential

autocorrelation and considers different variances between observations in the

dataset. Relying on robust standard errors is a common way to ensure valid

statistical inference when some of the underlying regression model's

assumptions are violated (Hoechle, 2007). It is arguable that the inclusion of a

GARCH-model in the study would have been useful, but this is compensated

for by having robust variance estimates. By estimating the parameters with

fixed effect, STATA includes a constant for each specific company. By using

a vce (variance-covariance matrix), STATA clusters the panel variable

automatically and takes into account that the variance for each company may

be different. The clustered standard errors account for data dependency in a

panel data set and generate unbiased estimates (Petersen, 2007).

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5 Empirical Results

In the following chapter we are presenting the main results and findings from the

empirical study. At first, we present the regression model for all of the announcements

happening between 2000 and 2019, i.e. the whole time period. This is done in order

to fulfill our first purpose of this study which is investigating whether or not the rating

changes bring informational value to the stock market. We then divide the dataset

into two periods representing pre and post-financial crisis to fulfill the purpose of

testing whether the market reacts stronger to rating changes before the crisis

compared to the subsequent period.

___________________________________________________________________

5.1 Development of abnormal returns

To investigate if rating announcements cause abnormal returns on the

European stock market, we performed our first regression model that

represents the whole period between 2000 and 2019. Thus, this section intends

to test the first hypothesis of our study, which states that credit rating changes

cause abnormal returns. The number of observations included in this model is

380. A summary of the main statistical parameters from the regression is

presented in table 4 and let us analyze each day separately. What is of most

considerable interest is the value of the coefficients, since it describes the level

of abnormal return for the specific day within the event window. Table 4

shows the average abnormal return for the individual days along with the

corresponding standard errors, t-statistics and P-values. The standard error

estimates are measures of uncertainty associated with the coefficient value. It

indicates the deviation of the sample distribution from the real population

(Salinger, 1992). The table values are obtained using the individual stock’s

market risk Beta values as control variables.

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Table 4. The estimation of abnormal returns for the period 2000-2019

At first, we look at the goodness-of-fit measures of the model to see if the

model is useful for analyzing. The coefficient of determination (R2) reveals

that approximately 29 % of the variability in the stock returns are explained

by the independent variables used in this regression model. The interpretation

of our relatively low standard errors is that our sample is quite representative

of the overall population.

The estimates indicate that there are some announcement effects. Even though

the abnormal return estimates are small, we find that the estimations of the

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prior-variables T-6 and T-3 expose significant coefficient values, meaning that

they are significantly different from 0. These values report a small increase in

the returns six days before the announcement and an equivalent decrease three

days prior to the event. The P-values for the other days in the window are too

high for confidently interpret the AAR:s as correct. However, these coefficient

values reveal small negative effects of abnormal returns three days prior to the

event and last until one day after the event where the market recovers and some

positive AAR:s are present. The estimates do not indicate any considerable

event effect (T0) or after the event (T+1 to T+6).

By calculating the cumulative effect (CAAR), we obtain the total effect for all

days in the event window. The aggregated effect helps us make general

interpretations of the impact of credit rating announcements. This

measurement is achieved by summing the estimated abnormal returns

(Salinger, 1992). The CAAR in the combined sample from both periods is -

0,00487 and not statistically significant, which obviously must be interpreted

as a low impact.

Even though the window variables individually did not produce any significant

results, we further several F-tests to combine the variables in different ways to

see if they gave new usable information. By this, we take into consideration

that the calculated effects are correlated with each other. The following

equation was used:

Equation 5. Equation for F-test.

where 𝑙𝑖 is the lag and forward variables

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However, it was found that combinations of the different lag and forward

variables did not produce any results of statistical significance. In total, we

reject the hypothesis stating that ratings have an abnormal average effect on

stock prices.

5.2 Abnormal returns before the financial crisis (2000-2008)

To analyze our results more profoundly, this section concentrates on the rating

announcements occurring before the financial crisis. The time restriction is set

between the beginning of the year 2000 until the eruption of GFC in 2008. The

statistical properties of the regression model surrounding the coefficient values

of each day in the event window are described in table 5.

Table 5. The estimation of abnormal returns in the pre-financial crisis period

(2000-2008)

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First, the coefficient of determination (R2) of 30 % indicate that the

independent variables have some explanatory value to our model. The table

further shows that none of the coefficient values are significantly different

from zero on a 5% level, whereas t-2 and t-3 have significant values on a 10

% level. As can be shown from the table, there are signs of negative abnormal

returns during the days prior to the event, especially in T-2 and T-3. However,

the coefficient values of 0,0032 (T-2) and 0,0028 (T-3) show that the negative

AAR:s of approximately 0,3% are low. This means that the coefficient values

representing the AAR:s for these days should be carefully interpreted before

proclaiming abnormal effects. When the rating is announced, the market

slightly recovers from the rating announcements and displays some positive

AAR:s, primarily on the third and fourth day after the announcement. What is

also observable is that the announcement day (T0) shows the lowest AAR of -

0.51%, indicating that the event has some surprising properties to the

market. The cumulative effect of the 2000-2008 period is -0,01017. Since it

can be seen that the null hypothesis can not be rejected on a 5% level, a fair

conclusion is that there is not enough evidence that rating changes have an

effect on stock returns in the pre-financial crisis period. An F-test was done

where we combined the variables, and we did not find any results of value or

interest.

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Figure 4. Abnormal returns for the 2000-2008 period with confidence

intervals.

In figure 4, we plotted the relationship between our coefficient values and the

corresponding limit values of their confidence interval. The confidence

intervals, represented by the dotted lines, show small fluctuations around our

estimated values, especially on the third and fourth day prior to T0 and on the

first and second day after T0. The figure displays that all hypothetical values

transcending our limit values would be rejected with 95% certainty. Since all

possible estimate values are accommodated between the limits, we find that

the true AAR effect of ratings revolves around zero. This strengthens the

suggestion that ratings changes have negligible effects on stock returns.

5.3 Abnormal returns after the financial crisis (2009-2019)

Next, we focused on the ratings assigned in the post-financial crisis period.

The results presented here will give us the reference to compare between the

two periods as some researchers have found that the market reacts differently

before and after the GFC in 2008 (Pacheco, 2011). A summary of the main

parameters from the regression output is described in table 6.

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Table 6. The estimation of abnormal returns in the post-financial crisis period

(2009-2019)

Similarly to the pre-financial crisis period, we found just a few statistically

significant coefficient values (T+1, T+2, T+6) on a 10% alpha level, which is

taken into consideration when interpreting the estimated values. The

coefficient values in the event window are considered low with a peak

negative value of -0,00199 in T+6, indicating that on average, the returns are

0,2% abnormally negative six days after the rating announcements. The

market is somewhat pessimistic to the disclosure of rating changes. One

interpretation is that the collision between CRA long-term strategy of ratings

and investors short-term search for profit cause investor disappointment

when awareness of the credit information is reached. However, it should

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again be stressed that the AAR:s are generally low, which must be

considered before making drastic inferences from the result. As was the case

in the pre-financial crisis period, we do not reject the null hypothesis,

meaning that we cannot prove that the rating effect is significantly different

from zero.

The coefficient of determination reports an overall value of 0,2783, which

implies that our regressed variables are adequate for explaining the variability

of the stock returns. The cumulative effect of the 2009-2019 period is

-0,00092. Thus, the cumulative effect for this period is lower compared to

2000-2008. In the following F-test, where we combined the window variables,

we did not find any results of value or interest. As was the case in the pre-

financial crisis period, we conclude that evidence for rating announcements

causing abnormal returns are insufficient for the post-financial crisis period.

Figure 5. Abnormal returns for the 2009-2019 period with confidence

intervals.

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Finally, we plotted the relationship between our estimates and their confidence

intervals, as shown in figure 5. We previously described the strength of our

estimated values in the pre-financial crisis section, where the limits of the

intervals were quite neighbored to our estimates. In this period, we find even

stronger proof of the negligible effects of rating changes. As visualized, we

see that no possible effect exceeding 0,5% is present for any of the days in the

event window.

5.4 Comparison between periods

After presenting the separate statistical outcomes for both periods, we may

observe their traits when comprising them to make an overall interpretation.

In figure 6, the average abnormal returns (AAR:s) across the two investigated

periods are displayed over the 13-day event window. This figure visualizes the

effects of the rating announcements in the event windows and what magnitude

they might have. It is also possible to detect similarities and differences

between the two periods.

Figure 6. Abnormal results for both periods.

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When analyzing the figure, the overall impression is that no distinct relation is

recognized between the pre-financial crisis period and the post-financial crisis

period. In the pre-financial crisis period, investors tend to overreact negatively

on the days before the event whereas in the subsequent period, investors are

seemingly unaffected. Interestingly, what most strongly separates the periods

is that the market declines to the news of rating changes in the previous period

when the opposite reaction is found in the subsequent period. However, when

the market starts to recover after the news of rating changes in the previous

period, the latter period indicate no sustainable reaction to the announcement.

To clarify, the line representing the post-financial crisis exhibits no powerful

in the event window, where meager positive and negative effects are found

without any noticeable trend. This is compared to the previous period where a

tendency of negative AAR:s is recognized in the days before the event, and a

tendency of positive AAR:s are present as a response to the rating

announcements. The overall interpretation of this comparison is that the

market exhibits a more clear reaction to rating changes in the pre-financial

crisis period. This notion supports the argument that markets are less

dependent on the information given by CRA:s after the financial crisis

compared to previously.

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6 Analysis

In the following part the empirical results will be analyzed. Results and hypotheses

will be explained with support of the theories previously presented to provide a deeper

knowledge on the obtained results.

____________________________________________________________

One of the purposes of the thesis is to investigate whether there are differences

in the impact on the stock market between the pre-financial crisis period and

the post-financial crisis period. To find potential differences, we initially tested

if credit ratings in general affected stock prices to understand the information

content of credit ratings. Our general finding is that credit ratings are poor in

providing new information to the market. The empirical analysis does not

suggest that the rating announcement has any impact on the share price since

it displays poor or solely insignificant effects in stock prices after rating

changes. This result suggests that we reject the first hypothesis stating that

credit ratings cause abnormal returns. Our results are consistent with those of

Weinstein, 1977; Pinches & Singleton, 1978; Li et al., 2004.

The basis from where rating changes should bring new information and

therefore affect the stock prices were derived from two different

explanations. First, the credit ratings contribute new information about the

rated company. This should be probable given that CRA:s claim having

access to private information unapproachable to the market, and

consequently, the information given by rating changes should affect firm

value. The other explanation states that even if the credit ratings do not

contain any new information, the publication of a rating has a validation

value on already known information. Our results are in line with the latter

explanation, since limited abnormal returns were found due to rating changes

but sufficiently large to argue that rating changes potentially bring some

confirmation value to the market. The theoretical interpretation of our results

is, however, that the CRA communicates information that essentially was

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already known to the market. Even though the results indicate that the credit

rating agencies have a minor impact on the share price, we do not argue for

the CRAs to have a negligible role in the market. They play an essential role

as independent actors for ensuring that investors and companies do not make

mistakes, e.g. regarding loans, acquisitions and stability in the markets

overall (White, 2001).

Regardless of the information content of credit ratings, it is worth analyzing

the pace in which the market absorbs information. First of all, the extent to

which markets are effective or not is one of the most debated subjects, and we

do not intend to draw certain conclusions about market efficiency in this thesis.

However, our results suggest that relevant information for investors are to

some extent incorporated in the stock price, since no significant under- or

overreaction was found at the occasion where the market could observe rating

changes. Thus, we notice that before the announcement day, the market has

perceived that a change is to take place. This finding contradicts the CRA’s

claimed use of private information (Ogden et al., 2002) because such

information would have a surprising effect on a rational market when

announced. Based on our results, and under the assumption that credit ratings

to some extent contain useful information, one could oppose the semi-efficient

market view in favour of the strong-efficient view since private information

appears to be included in the stock price. When resting the information content

assumption, we do not exclude the opportunity that credit ratings have

negligible or no informational value to investors.

It is also relevant to discuss the results concerning a company's perspective

and possible explanations out of a valuation context. As mentioned earlier, the

credit rating is of considerable importance for a company's cost of debt and

thus their ability to access external capital (Damodaran, 2012) under the

assumption that the company determines its financial policy to be able to

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minimize the cost of capital (Auerbach, 1980). Furthermore, a changed cost is

reflected in earnings and consequently in the share price. In the derivation of

our results, the weak impact of a changed rating can be attributed to two

different explanations. Firstly, the rating change does not affect the cost of

debt. This would indicate that the rating itself is not a good investment for the

company, as it has no significant impact on the cost of debt and will not

improve the company value. Secondly, it does have a positive impact on the

cost of capital, but the market is incapable of pricing a change in grade in an

entirely correct way.

As mentioned, the expressed primary purpose of CRA:s is to reduce

information asymmetry between inside corporate managers and outside

stakeholders. This information gap is assumed to establish ineffective

investment decisions and obstruct adequate credit lending. First, we cannot

prove whether the information gap exists or not. If it exists, the logical

interpretation of our results is that CRA:s are poor in reducing it. This is

because stock market participants do not react upon the information given by

credit ratings. If it does not exist, our results imply that no new information is

valuable on markets because everyone possesses all relevant information at all

times. For the latter interpretation to be correct, it requires several

assumptions, such as homogeneous expectations, full rationality and equal

availability of information sources. We hold such an argument as unlikely

because, in practice, market participants possess different kinds and levels of

information.

The second part of the study tests the hypothesis of whether the effect of credit

ratings have changed since the breakout of the GFC in 2008. We argued that

CRA's influence on the market might have changed over time because of the

potential for private investors to quickly and easily gather information about a

firm's creditworthiness. This arises from the improved information technology

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or/and less disclosure. Thus, we argued that CRA's role would have

diminished, as investors find other sources providing the same information as

the content of credit ratings. Our second central argument stated that CRAs

had been severely criticized in connection to the financial crisis, which may

affect the public's view and the level of trust for the agencies. We reasoned

that due to the economic uncertainty under the financial crisis, investors might

still associate the financial crisis with inaccurate credit ratings. As our results

suggest, the preceding arguments may both have had an impact on the

evolution of credit ratings.

By observing our results, it should be stressed that no period exposes

abnormalities of large values and no sharp difference of the periods was found.

However, the pre-financial crisis period shows some interesting evidence

where negative AAR:s was initiated from T-5 until T0. Subsequently, the

market began to partially recover and positive AAR:s were evident on the

fourth and fifth day after the rating announcement. No significant market

reaction was found on the day of the publication. The interpretation is that

investors tend to overreact negatively before the rating announcement with no

regard to the direction of the rating (upgrades/downgrades). There are potential

explanations that could be provided to capture this effect. Among them, it is

reasonable to believe that investors experience uncertainty when knowing that

a rating change will occur and sell off their holdings to prevent further

potential losses.

In comparison, the post-financial period reveals poor effects of AAR:s, where

a minor positive AAR of 0,0019347 at T+2 showed significance at a 5% level.

Also, no pattern of returns was present for this period, where both positive and

negative AAR:s were distributed in the days prior and the days after the

announcement. The interpretation made for this result is that ratings assigned

after the GFC in 2008 have no powerful impact on European stock returns.

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This result is consistent with the second hypothesis of our study, which stated

that the ratings should have less impact after the crisis compared to prior.

Likewise, when analyzing the aggregated effect for both periods, we find that

the cumulative effect is more significant in the earlier period (0,01017)

compared to the latter (-0,00092), which also is indicative for the significance

of ratings to investors between the periods.

The explanations for the non-effect in the latter period are speculative and

many. First, CRA:s use long-term horizons when issuing ratings and do not

take into account temporary market shocks (Standard & Poor's, 2003). The

slowly adjusted ratings would imply that the information given by ratings is

already observed by investors, which makes the returns somehow unaffected

by the announcements. This is related to the findings of Altman & Rijken

(2004), who claimed that investors are aware of CRAs slowness of adjusting

the credit ratings. Second, as previously discussed, the content of ratings is

poor in providing useful information to investors. This is reasonable

considering the increased availability of credit information provided by

alternative sources. Third, leakage of CRA information may provide answers

to the stagnant stock markets after rating announcements. Fourth, the private

information that CRA is said to use may not be as valuable to the market, and

thus we see no greater effect when the private information becomes public.

This is a plausible explanation given that certain information tends to reach

the market before the official announcement takes place (Verrecchia, 2001).

Fifth, regulatory interventions applied after the GFC, for example, the

implemented requirements from SEC in 2014 may restrict CRA:s from making

deviating assessments of creditworthiness of firms.

In section 2.1, we highlighted two earlier studies investigating the relation

between rating changes and asset markets. Both of them found solid evidence

of a larger market sensitivity of credit ratings after the GFC. Reddy et al.

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(2019) examined the impact of rating changes on 449 of the S&P 500 firms

using 10 year daily data. They concluded, similar to Pacheco (2011) who

studied the Portuguese stock market, that the market is more sensitive to the

announcements after the crisis compared to before. The results obtained, he

argues, was not surprising given the considerable influence of rating agencies

and the greater market sensitivity. Contrariwise, our study shows a more

significant impact before the GFC than afterwards, suggesting that the

markets are less sensitive to rating changes in the latter period. However, this

comparison should be interpreted with great caution given that our study

revolves around different markets. This is based on the fact that markets are

not similarly efficient, and that larger companies are characterized by a lower

degree of information asymmetry and higher transparency than small

companies. Thus, it is not surprising that studies based on different markets

show different empirical results.

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7 Conclusions

In this concluding chapter we will retell and summarize the most important and

relevant findings. We further provide suggestions for future research in the area.

___________________________________________________________________

The purpose of this study has been to investigate how the European stock

market reacts to credit rating announcements. The sample is partitioned into

two sub-periods to analyze the effect of changes in credit rating before and

after the GFC. This in order to be able to investigate whether CRA's impact on

the share price, e.g., their role in reducing information asymmetry, has

changed.

Our study finds no strong evidence that credit ratings have a significant effect

on stock prices in the European stock market. However, we see small

indications that the market is responding more strongly to a rating change

announcement during the period 2000-2008 compared to 2009-2019. This is

based on a greater cumulative effect during the pre-crisis period. This would

indicate that the information that CRA:s announces has a less informative

value today than in the past, perhaps because alternative sources of

information fulfil the same function that the CRA is intended to publish. The

financial crisis may have influenced the characteristics of the European stock

market as it behaves differently between pre and post-financial crisis. Another

explanation is that the CRA is too slow in changing the ratings, so that the

market has already expected a rating change to happen.

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8 Suggestions for Future Research

______________________________________________________________

For future research, several undertakings are suggested. Firstly, it would have

been scientifically interesting to study what effect different types of grading

changes have. For example, a shift from AA to AAA may have a more

substantial (or smaller) effect compared to a downgrade from BB to B.

Secondly, it would be relevant to compare specific companies over time, i.e.

whether the particular company has the same or different impact in a change

of rating on several different occasions in time. Thirdly, a study of whether

companies in some types of industries are more affected by credit ratings than

others.

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Appendices

1. Companies included in the study.

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2. Credit ratings included (both upgrades and downgrades).

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3. Datastream Request table for obtaining stock prices

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4. Selection of control variables in the 2000-2008 period

5. Selection of control variables in 2009-2019

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6. Regression output 2000-2019.

7. Regression output 2009-2019

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8. Regression output 2000-2008

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9. Calculations of confidence intervals.

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10. Output F-test, forward variables.

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11. Output F-test, lag variables.

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12. Risk free rate

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