Determinants and consequences of credit ratings actions ... · 2 Determinants and consequences of credit ratings actions during bull vs. bear markets Abstract: From a large sample

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Determinants and consequences of credit ratings actions during bull vs. bear markets

Luc Paugam ESSEC Business School, CREAR

Pierre Astolfi Université Paris-Est Créteil

Hervé Stolowy HEC Paris

This version: January 31, 2014. Preliminary draft, please do not quote or cite without permission. Acknowledgments. The authors would like to thank Walid Alissa, Andrei Filip, Thomas Jeanjean and Cheng Lai for helpful comments. We gratefully acknowledge helpful comments from participants at the ESSEC workshop (October 2013), Université of Paris Est-Créteil workshop (December 2013), Université de Lille 2 – SKEMA workshop (January 2014). Luc Paugam is a member of CREAR – Research Center on Risk. Hervé Stolowy is a member of the GREGHEC, CNRS Unit, UMR 2959.

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Determinants and consequences of credit ratings actions during bull vs. bear markets

Abstract: From a large sample of U.S. firms rated by Standard and Poor’s from 1988 to 2012, we analyze the effect of equity market cycles on credit rating changes, after controlling for changes in firm-specific risk characteristics and changes in the macroeconomic environment. We also examine stock market response to credit ratings actions in bull vs. bear markets. We document that credit rating agencies (CRAs) tighten credit rating standards during bear markets and have more relaxed rating standards during bull markets. We also document stronger stock reaction for downgrades, negative credit watch and downgrade below the investment grade threshold during bear markets than during bull markets, which is consistent with greater quality for rating actions initiated during bear markets. Our results confirm predictions by analytical literature that explains varying credit rating quality over business cycles by endogenous reputation risk. Reputation risk that disciplines CRAs is greater during poor economic conditions than during good economic conditions because probabilities of default that could damage CRAs’ reputation are higher.

Keywords: Credit rating agencies, rating quality, bull vs. bear markets, event studies.

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1. Introduction

Credit rating agencies (CRAs) play two main roles in capital markets. First, they convey

information useful for valuation purposes to market participants, provided that information is

both timely and accurately delivered. Second, ratings are used to facilitate private contracting

between economic agents. Loan agreement and bond covenants often refer to rating letters,

providing “efficient quality benchmark” (Frost 2007, 474) to U.S. financial regulators and

lawmakers. On the basis of a survey of 392 CFOs, Graham and Harvey (2001) find that firms

are concerned about credit ratings when issuing debt.

The role of CRAs has increased since the beginning of the 2000s: Jorion et al. (2005)

explained the extent to which ratings represent a credible alternative channel of information

for equity analysts, after the implementation of Regulation Fair Disclosure (FD) in the U.S. on

October 23, 2000. Indeed, Regulation FD aimed at reducing selective disclosure to a few

privileged interested parties, and resulted in a change of disclosures practices, consisting of

relatively more non-public disclosures to public institutional parties, among them CRAs.

Consequently, CRAs have access to confidential information that is not available anymore to

equity analysts. Jorion et al. (2005) demonstrate that the implementation of Regulation FD

resulted in a more significant effect of rating changes on stock prices for both downgrades and

upgrades. Thereby, “rating agencies remain the main conduits of selective disclosure after

FD” (Jorion et al. 2005, 313).

Yet, after the two significant market events we have gone through since the beginning of

2000, some authors support that the dominant role of CRAs is likely to be challenged. First,

regarding auditing scandals and the internet bubble burst, which occurred during the 2000-

2002 period, the first main market event, Frost (2007, 470) notes that “the massive accounting

and auditing scandals of 2000–02, in particular the highly publicized failure of Enron in

December 2001, led many to question their competence and the value of their ratings”.

Similarly, the reputation of CRAs is likely to have been impaired during the most recent

financial crisis (2007’s summer – beginning of 2009), the second main market event.

Eijffinger (2012)’s work also questions the relevance of the weight given to CRAs.1 Eijffinger

(2012, 912) supports that “rating agencies lag behind markets” and “that the lack of

competition renders the big three CRAs with too strong market position.” Opp et al. (2013,

1 This study considers critically the effect of credit ratings on interest rates of sovereign and corporate bonds. We extend the question to the analysis of the effects of credit rating on stock prices.

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46) even states that “massive downgrading and defaults during the 2008/2009 financial crisis

have led politicians, regulators, and the popular press to conclude that the rating agencies’

business-model is fundamentally flawed.” Cheng and Neamtiu (2009) also recall that in recent

years, credit rating agencies have faced increased regulatory pressure and investor criticism

for their ratings’ lack of timeliness. As a result, “the reliance on CRAs should be reduced by

attaching less importance to them” (Eijffinger 2012, 912). In other words, rating quality

seems to have suffered since the beginning of the 2000s.

In this context, several authors have investigated the possible relationship between credit

rating and earnings management (Alissa et al. 2013), earnings smoothing (Jung et al. 2013),

earnings quality (Ayers et al. 2010), management of cash from operations (Lee 2012),

earnings benchmark (Jiang 2008), and corporate governance (Ashbaugh-Skaife et al. 2006).

In this paper, we focus on the quality of ratings over market cycles. Credit rating should

not be conditional on short-term economic or financial market cycles because ratings intend to

inform market participants about the riskiness of investments in the long term (Altman and

Kao 1992; Basel Committee on Banking Supervision 2000; Altman and Rijken 2004; Altman

and Rijken 2005). Standard & Poor’s (2003) explains that “the ideal is to rate ‘through the

cycle’. There is no point in assigning high ratings to a company enjoying peak prosperity if

that performance is expected to be only temporary.” CRAs’ credit ratings are forward looking

indicators that should already factor in cyclical fluctuations (Standard & Poor's 2003). Recent

literature has studied the claim by CRAs that they were rating “through the cycles”, i.e.,

independently from borrower current short-term conditions that are expected to reverse

(Amato and Furfine 2004; Löffler 2004). Amato and Furfine (2004) present empirical

evidence that ratings are not overly dependent on macroeconomic cycles.

However, Bolton et al. (2012) analytically point out that ratings are more likely to be

inflated during financial booms when there are more investors trusting CRAs and less

reputation risk than during financial crises. According to White (2010)2, in case of

reputational incident impacting a CRA, investors may withdraw their business from CRA-

rated products and, consequently, dry up the market of the CRA concerned. As a result, CRAs

may support high financial losses. This risk seems to be less prevalent in a boom period than

in a recession period. Indeed, during boom periods, CRAs may be less sensitive to

reputational concerns and, as a result, to credit rating quality delivered by their analysts. Bar-

Isaac and Shapiro (2013) also argue that reputation risk fluctuates over business cycles which 2 Quoted by Bar-Isaac and Shapiro (2013).

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could potentially lead to varying credit rating quality. For CRAs, issuers are less likely to

default during boom periods, which creates an incentive for CRAs to decrease the quality

control of ratings. Regulation may also be more relaxed during booms, because economic and

financial environment itself is less demanding for rules and specific CRA credit watch

mechanism. Finally, during boom periods, CRAs have the opportunity to earn more money,

because there are more market operations, and specifically, more complex products subject to

ratings. Mathis et al. (2009) demonstrate that truth telling incentives are weaker when the

CRAs have more business from complex products. CRAs book also additional sales related to

consulting services during boom market (for instance, pre-rating assessments) (see Bar-Isaac

and Shapiro 2013). As a result, effects of rating quality’s incident leading to a default of an

issue highly rated, are diluted and less damaging during boom markets, in terms of reputation

and, consequently, in terms of financial consequences. These arguments tend to support that

reputational risk seems to be less prevalent during booms than during recessions. In addition,

Finnerty et al. (2013) document that rating changes had stronger impact on credit default swap

spreads during the 2001 and 2008-2009 recessions.

In this paper, we examine two related research questions. First, we investigate the relation

between stock market cycles (boom vs. bear markets) and credit rating changes3, after

controlling for firm-specific characteristics capturing changes in credit risk and changes in the

macroeconomic environment4. This research question focuses on the argument that, because

of changing reputation concerns over stock market cycles, the credit rating decision is

affected by bull vs. bear markets. As a result credit rating changes are affected by borrower

short-term conditions, although CRAs’ objective is to inform investors about riskiness of

investments in the long term.

Second, as credit rating actions convey information to market participants, we investigate

whether equity investors respond differently to credit rating actions during bull vs. bear

markets. Can investors see through rating processes and understand that the credibility of the

information content delivered by rating actions is different during bull vs. bear markets?

We use a sample of 21,308 U.S. firm-year observations rated by Standard & Poor’s from

1988 to 2012 to examine the determinants of credit rating changes. Since 1988, the U.S.

3 “Credit rating changes” refer to downgrades and upgrades, which are types of “credit rating actions” that also include reviews for credit watch and past credit watch that did not lead to a rating change. 4 We use change in ROA, change in leverage, change free cash flow, change in capital intensity, change in firm size, change in interest coverage ratio, and past stock returns to proxy for financial and business risks. We use the change in the US GDP growth rate to proxy for change in the macroeconomic environment.

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equity market has exhibited two periods of bull markets and two periods of severe bear

markets, i.e., the collapse of the internet bubble from 2001 to the end of 2002 and the

financial crisis from the summer of 2007 to the beginning of 2009. We document the

following results. First, we show that the decision to downgrade and upgrade a firm is

affected by stock market cycles, after controlling for changes in credit risk and changes in the

macroeconomic environment. CRAs are more conservative in their decisions to change

ratings during contracting equity markets than during booming equity markets. After

controlling for changes in credit risk and the macroeconomic environment, CRAs are almost

50% more (13% less) likely to downgrade (upgrade) a firm during a stock market contraction

(expansion). In addition, after controlling for changes in credit risk and changes in the

macroeconomic environment, a firm is also 60% more likely to be downgraded below the

investment grade threshold during a bear market than during a bull market. Ratings standards

are more relaxed during bull market than during bear markets. This is consistent with the

prediction of Bolton et al. (2012) and Bar-Isaac and Shapiro (2013) that demonstrate that

CRAs are more prone to provide less accurate ratings (or inflated ratings) during booms when

the risks of failure that could damage CRAs’ reputation are lower.

Second, we use a sample of 4,706 credit rating actions including downgrades, upgrades

and reviews for credit watch initiated by Standard & Poor’s for firms composing the Russel

3000 Index from 1988 to 2012 to study the consequences of credit rating actions. We

document that investors react differently to credit rating actions during bull vs. bear markets.

We show a significant positive price impact for rating upgrades and a significant negative

price impact for rating downgrades. We provide evidence that market participants react more

strongly to negative credit rating actions during bear markets than during bull markets. This is

true for downgrades, downgrades below the investment grade threshold, and negative credit

watch. Our results indicate that credit ratings actions are more credible during bear markets.

The economic magnitude of these different reactions in bull vs. bear markets is rather large

ranging from 2 times to 4 times larger impact over three- to five-day cumulated abnormal

returns.

We make the following contributions to the literature. First, we provide empirical support

to the prediction that because reputation risk varies over market cycles, credit rating actions

quality is also conditional on financial market cycles (Bolton et al. 2012; Bar-Isaac and

Shapiro 2013). Market cycles (bull vs. bear markets) affect credit rating decisions after

controlling for changes in firm-specific credit risk and changes in the macroeconomic

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environment. Second, while Amato and Furfine (2004) study the effect of macroeconomic

cycles on credit ratings and document low pro-cyclicality, we examine the effect of equity

market cycles and provide evidence of a cyclical pattern in rating changes. This result is

relevant to policy makers examining the role of CRAs, as it suggests that lower reputation risk

during bull market may cause less accurate ratings. Therefore, regulation policies would

highly contribute to promote accurate and credible ratings from CRAs by making punishment

credible when quality and accuracy of ratings are poor. Bar-Isaac and Shapiro (2013, 63)

showed that “if reputation losses are higher, there are greater incentives to provide accurate

ratings”. Third, we extend previous studies (e.g., Holthausen and Leftwich 1986; Nayar and

Rozeff 1994; Dichev and Piotroski 2001) by documenting significant market impacts for both

upgrades and downgrades. Fourth, we show that investors react differently to rating changes

in bull vs. bear markets, i.e., they “see through” the rating change process. The

informativeness of ratings tends to increase during financial crises. Fifth, from a

methodological point of view, unlike previous studies (e.g., Amato and Furfine 2004; Altman

and Rijken 2005) that group several ratings into larger categories, we use a more refined scale

to analyze rating changes as we rely on all actual notches used by Standard & Poor’s from

1988 to 2012. Using actual notches employed by CRAs allows to examine the real impact of

actual rating changes initiated by CRAs during the time period.

The remainder of this paper is organized as follows. We develop our hypotheses in section

2. We describe our research design in section 3, our sample selection and result for

determinants of credit rating changes are provided in section 4, our sample selection and

results for consequences of credit rating actions are presented in section 5. We discuss

sensitivity tests in section 6, and conclude in section 7.

2. Hypotheses development

Credit ratings may be addressed regarding initial ratings, as well as regarding rating

changes initiated by CRAs, the latter being the main focus of this article. CRAs maintain that

their ratings intend to inform investors about credit risk through credit cycles because they

measure default risk over long investment horizons and take into account only the permanent

component of credit risk (Cantor 2004; Altman and Rijken 2005). Amato and Furfine (2004)

argue that ratings are not overly dependent on macroeconomic cycles and demonstrate that

rating changes exhibit very little (macroeconomic) cyclicality after controlling for financial

and economic factors affecting ratings.

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Conversely, from an analytical perspective, as mentioned above, Bolton et al. (2012) argue

that “CRAs are more prone to inflate ratings during booms, when there is a larger clientele of

investors in the market who take ratings at face value and when the risks of failure that could

damage CRA reputation are lower.” It could be argued that CRAs adjust their rating change

process when they face increased pressure from outsiders and more reputation risk. Bar-Isaac

and Shapiro (2013) further argue that reputation risk, which ultimately disciplines CRAs, is

not constant over the business cycle. Using a model relying on endogenous reputation and a

time-varying economic environment, they demonstrate that rating quality is countercyclical,

i.e., ratings are less accurate during booming markets than during contracting markets.

If the rating change process of CRAs is independent from financial market cycles the

association between changes of firm-specific proxies for credit risk and credit rating changes

should not depend on current stock market conditions. However reputation risk potentially

varies over market cycles which could lead CRAs to change their rating process during bull

and bear markets. Our first hypothesis (stated in signed alternative form) relates to stock

market cycles and the decision to change credit ratings.

H1: CRAs are more (less) likely to downgrade (upgrade) firms during bear markets

than during bull markets after controlling for changes in firm-specific credit risk and

changes in the macroeconomic environment.

The investment grade threshold is critical for firms since it determines access to important

capital sources, in particular from portfolio managers or large institutional investors with

restriction to invest in debt or equity securities of non-investment grade firms.5 The decision

to downgrade a firm below or upgrade a firm above the investment grade threshold is also

critical for CRAs for which reputation concerns are highest around this rating level, all else

equal. CRAs may be more reluctant (willing) to downgrade (upgrade) firms below (above) the

investment grade threshold during bull market whereas during bear market rising reputation

risk may increase (decrease) CRAs’ willingness to downgrade (upgrade) firms below (above)

the investment grade status. Our second hypothesis (stated in signed alternative form)

therefore relate to the decision to change credit ratings below or above the investment grade

threshold.

H2: CRAs are more (less) likely to downgrade (upgrade) firms below (above)

investment grade threshold during bear markets than during bull markets.

5 See Table 1.

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The issue of credit rating changes impact on stock prices was a controversial topic in the

1970’s and in the 1980’s (see, e.g., Pinches and Singleton 1978). Most recent studies

generally document that credit rating changes convey valuable information content to

investors.6 Ederington and Goh (1998) confirm this argument, showing that analysts tend to

revise earnings forecasts sharply downward following downgrades, suggesting that credit

ratings reveal information that is not due to earlier negative information about the firm or

current earnings.

Nayar and Rozeff (1994) document that, while rating upgrades have no identifiable effect

on stock returns, rating downgrades are significantly associated with negative abnormal

returns. These findings lie within the framework of previous studies investigating the effect of

credit rating changes on stock prices (Holthausen and Leftwich 1986; Hand et al. 1992).

Yet, the effect of credit rating changes remains a controversial topic. For instance, Goh and

Ederington (1993) suggest that some downgrades may lead to a positive response in stock

prices. Indeed, a downgrade resulting from an increase in leverage can be associated with a

transfer in the company’s wealth from debtors to shareholders.

Dichev and Piotroski (2001) study the long-run stock returns (within a time window

ranging from three months to three years) following a change in credit ratings. They examine

buy and hold and abnormal return and document no significant abnormal returns for credit

rating upgrades over a long-term window. They find significant negative abnormal returns

following downgrades. Underperformance essentially occurs during the first year following

announcement of the credit rating change. According to Dichev and Piotroski (2001), these

results suggest that “stock prices possibly underreact to the information content [of

downgrade] (…). Thus, it seems that the market does not fully anticipate the negative

implications of downgrades for future profitability” (p. 175). Consequently, credit rating

downgrades are “strong predictors of future deteriorations in earnings” (p. 202).

In a similar vein, He et al. (2011, 104) underline the extent to which credit rating changes

also heavily impact equity markets: “a better bond rating or a bond rating upgrade signals

good news about a firm’s financial situation, and such good news is usually released quickly,

giving rise to greater disclosure and reduced information asymmetry.” Beyond the sole issue

of information asymmetry, credit rating changes also contribute to reduce earnings forecast

dispersion.

6 Other studies examine the long-term change of credit ratings. Blume et al. (1998) show that CRAs have made their standards evolve to become more stringent from 1978 to 1995.

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Under the EMH, if CRAs change their rating process during bull and bear markets,

investors should see through the rating change process and react differently to downgrades

than to upgrades. Ratings actions could be perceived as more credible and having a stronger

information content during bear markets than during bull markets, leading to a higher absolute

price impact on the stock market. Finnerty et al. (2013) document a stronger effect on credit

default swap spread during the two recent financial crises. We state our third hypothesis in the

signed alternative form.

H3: The information content of credit rating actions is greater during bear markets than

during bull markets.

To empirically test this hypothesis we examine if the absolute value of cumulated

abnormal returns computed following credit rating changes is larger during bear markets than

during bull markets.

3. Research design

3.1. Association between credit rating changes and changes in credit risk

Credit rating changes are a reaction to changes of underlying business and financial

characteristics of rated firms (Amato and Furfine 2004). To test our first and second

hypotheses, we analyze the association between stock market cycles and credit rating changes

after controlling for changes in credit risk and macroeconomic environment.

We define bull and bear markets based on the cycles of the S&P 500 index from January 1,

1988 to December 31, 2012. A bear market is defined from a common market practice as a

drop of the index from a previous high of 20% or more. We base our analysis on long stock

market cycles lasting several quarters that are more likely to affect the credit rating process

rather than short-term cyclical fluctuations lasting only a few months. We identify two

periods of bear markets and two periods of bull markets based on local minimums and

maximums of the S&P 500 index (see Figure 1). Our first period of bear market is from

March 24, 2000 when the S&P 500 closed at 1,527.46 points to October 9, 2002 when the

S&P 500 reached a local minimum of 776.76 points (-49.2%), the so-called “high tech bubble

burst”. Our second period of bear market is from November 9, 2007 when the S&P 500

reached a local maximum of 1,565.15 points to March 9, 2009 when the S&P 500 reached a

local minimum of 676.53 points (-56.8%), the so-called “2008 financial market crisis.” The

three periods of bull markets are from January 4, 1988 to March 23, 2000 (+496.8%), from

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October 10, 2002 to November 8, 2007 (+101.5%) and from March 10, 2009 to December 31,

2012 (+110.8%).

[Insert Figure 1 About Here]

We analyze credit rating changes through two models: the decision to downgrade (or

downgrade below investment grade) or upgrade (or upgrade above investment grade) a firm

with model (1) and the decision to change the number of notches for a firm with model (2).

Model (1) allows to examine potential differences between decisions to downgrade and

decision to upgrade a firm, whereas model (2) allows to study the determinants of the “depth”

of credit rating changes, i.e., the factors that drive the number of notches changed by CRAs.

Based on prior literature (e.g., Ashbaugh-Skaife et al. 2006; Alissa et al. 2013; Jung et al.

2013), we relate these rating changes to seven firm-specific variables capturing changes in

business and financial risks: change in operating performance, change in financial leverage7,

change in free cash flows, change in firm capital intensity, change in firm size, change in the

interest coverage ratio and stock returns.8 We also control for changes of macroeconomic

environment with change in the US GDP growth rate and for the investment grade status of

the firm before the rating action (if any). Indeed, rating decisions could be different for

investment grade vs. non-investment grade firms.9 We examine if stock market cycles affect

credit rating changes after controlling for firm-specific changes in credit risk and

macroeconomic environment. Specifically, model (1a) is a logit model as follows:

Downgradet+1 (Upgradet+1) = b0 + b1*Beart+1 + b2*IGt + b3*D_ROAt + b4*LEVt +

b5*FCFt + b6*PPEt + b7*SIZEt + b8*INT_COVt + b9*Returnt + b10*GDPt +

bi*D_Sector +

(1a)

where:

Downgradet+1 (Upgradet+1) = 1 if the credit rating change involves a downgrade (an

upgrade) in t+1, and 0 otherwise;

Beart+1 = 1 if the rating change (if any) takes place between March 24, 2000 and October

9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1);

7 Molina (2005) analyzes the leverage’s effect on ratings and Kisgen (2009) also studies the link between leverage and rating. 8 Jung et al. (2013) also include change in sales. We do not include change in sales because of concerns over potential colinearity with change in firm size. Other studies analyze the determinants of rating levels or of rating changes (see, e.g., Adams et al. 2003; Chen et al. 2012). 9 Model (1a) does not include IGt with downgrades below (upgrades above) the investment grade threshold as a dependent variable because only firm above (below) the investment grade threshold can be downgraded below (upgraded above).

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IGt = 1 if the firm is rated above BB+ before the rating change (if any), and 0 otherwise;

D_ROAt = 1 if the return on assets increased over fiscal year t, and 0 otherwise (Jung et

al. 2013);

LEVt = change from year t-1 to t in long-term debt and current portion of long-term debt

minus cash divided by lagged total assets (Alissa et al. 2013; Jung et al. 2013);

FCFt = change from year t-1 to t of cash flow from operating activities minus average

capital expenditure over current and past two years divided by lagged total assets (Jung

et al. 2013);

PPEt = change from year t-1 to t in gross PPE divided by lagged total assets (Ashbaugh-

Skaife et al. 2006; Jung et al. 2013);

INT_COVt = change from year t-1 to t of EBIT divided by interest expense (Ashbaugh-

Skaife et al. 2006; Alissa et al. 2013; Jung et al. 2013);

SIZEt = change from year t-1 to t in natural logarithm of total assets (Ashbaugh-Skaife et

al. 2006; Jung et al. 2013);

Returnt = stock returns computed over fiscal year t (Alissa et al. 2013);

GDPt = Change from year t-1 to t in the US GDP growth rate;

D_Sector = Sector dummy based on the Global Industry Classification Standard Sector

level (two digits).

Model (1a) examines the association between the decision to downgrade (upgrade) a given

firm in t+1 and firm-specific credit risk proxies capturing changes from t-1 to t of profitability

(change in ROA, change in free cash flows, change in the interest coverage ratio), business

risk (change in firm size or change in capital intensity), and financial structure (change in the

leverage ratio). Model (1a) also captures the effect of stock returns and macroeconomic

conditions on the decision to downgrade or upgrade a firm.

In model (1a), the main coefficient of interest is b1. According to H1, we expect a positive

(negative) association between Beart+1 and Downgradet+1 (Upgradet+1) since all else equal

downgrades are more likely during down markets if there are increased reputation concerns

for CRAs which potentially correct past inflated ratings.

We expect the following association between control variables and credit rating changes.

The higher the performance, the lower the probability of default, therefore we expect a

negative (positive) relation between D_ROAt, FCFt, INT_COVt, and Downgradet+1

(Upgradet+1). The larger the company, the lower the probability of default, therefore we

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expect a negative (positive) relation between SIZEt and Downgradet+1 (Upgradet+1). Firms

with greater capital intensity present lower risk to debt providers, therefore we expect a

negative (positive) association between PPEt and Downgradet+1 (Upgradet+1). Leverage

captures default risk. The more the firm is leveraged the higher the probability that the firm

will not be able to meet all its financial obligations, therefore we predict a positive (negative)

association between LEVt and Downgradet+1 (Upgradet+1). We expect a negative (positive)

association between past returns and Downgradet+1 (Upgradet+1) since a firm is more likely to

be downgraded (upgraded) if past information about the company negatively (positively)

affects stock prices. Finally, we predict a negative (positive) association between change in

GDPt and Downgradet+1 (Upgradet+1).

Model (1b) focuses on the association between changes in firm-specific credit risk

variables and the number of notches changed by CRAs in t+1:

Notcht+1 = b0 + b1*Beart+1 + b2*IGt + b3*D_ROAt + b4*LEVt + b5*FCFt + b6*PPEt

+ b7*SIZEt + b8*INT_COVt + b9*Returnt + b10*GDPt + bi*D_Sector + (1b)

where:

Notcht+1 = number of notches changed by CRAs in t+1. A positive number indicates an

upgrade and a negative number indicates a downgrade. We used all the notches used by

CRAs over the period (see Table 1). For instance if Notcht+1 = - 2, it means that the

rated firm has been downgraded by two notches during t+1.

The other variables are already defined above. Model (1b) is estimated with OLS.

In model (1b), the coefficient of interest is b1. According to H1, we expect a positive

(negative) association between Beart+1 and Notcht+1 since all else equal reputation concerns

increase CRAs’ incentives to issue more conservative credit ratings.

We predict the following association with control variables. Since a positive number

indicates an upgrade and a negative number indicates a downgrade, we expect a positive

relation between D_ROAt, FCFt, SIZEt, PPEt, INT_COVt, Returnt, GDPt and

Notcht+1 and a negative association between LEVt and Notcht+1.

We examine if stock market cycles affect the decision to change credit ratings below or

above the investment grade threshold (H2). Specifically, model (2) is a logit model as

follows:

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Below_IGt+1 (Above_IGt+1) = b0 + b1*Beart+1 + b2*D_ROAt + b3*LEVt + b4*FCFt +

b5*PPEt + b6*SIZEt + b7*INT_COVt + b8*Returnt + b9*GDPt + bi*D_Sector +

(2)

where:

Below_IGt+1 (Above_IGt+1) = 1 if the credit rating change involves a downgrade below (an

upgrade above) the investment grade threshold in t+1, and 0 otherwise;

The other variables are already defined above.

Following H2, we expect a positive (negative) association between Beart+1 and

downgrades below (upgrades above) the investment grade status since reputation concerns

would increase (decrease) the incentives to downgrade firm below (upgrade firm above) the

investment grade threshold.

We expect the same associations between control variables and Below_IG t+1 (Above_IGt+1)

as in model (1a).

3.2. Market reaction to rating actions

We assess the relative market impact of credit rating actions during bull and bear markets

(our third hypothesis) by examining stock reactions to credit rating actions taken by CRAs.

We rely on the standard event study methodology that has been widely used in the literature

(e.g., Holthausen and Leftwich 1986; Choy et al. 2006; Finnerty et al. 2013). Model (3) is

used to examine the price impact of downgrades, upgrades and other credit rating actions

taken by CRAs conditionally to market cycles:

CAR[t-1; t+j] = b0 + b1*Beart + b2*Downgradet + b3*Upgradet + b4*NegCWt + b5* PosCWt

+ b6*CWt + b7*Below_IGt + b8*Above_IGt + b9*Beart*Downgradet +

b10*Beart*Upgradet + b11*Beart*NegCWt + b12*Beart*PosCWt + b13*Beart*CWt +

b14*Beart*Below_IGt + b15*Beart*Above_IGt + bi*D_Sector +

(3)

where:

ARt = Abnormal return on day t computed with the market model using the CRSP value-

weighted market portfolio. For firm i: ARi,t = Ri,t – i,t*RMt where RM is the market

return. Beta coefficients are obtained from CRSP for every year. Cumulated abnormal

returns are computed on three different windows (j =1, 2, 3), i.e., time windows [t-1;

t+j] where the rating action is taken in t. CAR[t-1; t +j] = sum of abnormal returns (AR)

15

over the event window. For instance, for j = 1 the window [t-1; t+1] we have: CAR[t-

1;t+1] = ARt-1 + ARt + ARt+1.

Downgradet = 1 if the credit rating action involves a downgrade in t, and 0 otherwise;

Upgradet = 1 if the rating action involves an upgrade in t, and 0 otherwise;

NegCWt = 1 if the credit rating action involves placing the rated firm on the negative credit

watch list in t, and 0 otherwise;

PosCWt = 1 if the credit rating action involves putting the rated firm on the positive credit

watch list in t, and 0 otherwise;

CWt = 1 if the credit rating action involves placing the firm on the credit watch list without

mentioning if it is a positive or negative credit watch, and 0 otherwise;

Below_IGt = 1 if the firm loses the “investment grade” status after the rating action in t,

and 0 otherwise;

Above_IGt = 1 if the firm obtains the “investment grade” after the rating action in t+1, and

0 otherwise;

Beart = 1 if the rating action takes place between March 24, 2000 and October 9, 2002 or

between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1).

The other variables are already defined above.

We consider six types of credit rating actions with model (3): downgrades, upgrades,

reviews for negative credit watch, reviews for positive credit watch, reviews for unsigned

credit watch (when there is no indication whether the credit watch is positive or negative), and

previous reviews for credit watch that did not lead to a downgrade or an upgrade (the credit

watch is “cancelled” by CRAs). We do not include a dummy variable in model (3) for this

latest type of credit rating action, the effect is therefore captured by b0. Note that some of

these rating actions occur at the same time. For instance a firm can be downgraded on a given

day but also be placed on a review for further downgrade that same day. Our model allows to

isolate the individual effect of each credit rating action on stock prices. In model (3), we also

analyze the implication of obtaining or losing the investment grade status.

Our main focus is the price impact of downgrades and upgrades that have been mostly

studied in the literature. We extend the analysis to a comparison of market consequences in

bull and bear markets and also examine the price impact of reviews for credit watch. We

expect a negative association between Downgrade and CAR[t-1;t+j] since a downgrade is

usually perceived as a negative pricing signal by market participants. We do not predict a

positive association between Upgrade and CAR[t-1;t+j] because prior literature has shown

16

limited or no impact for upgrades (e.g., Holthausen and Leftwich 1986; Nayar and Rozeff

1994; Dichev and Piotroski 2001). We expect a negative association between credit rating

actions that could be perceived as a negative pricing signals, i.e., reviews for negative credit

watch, and CAR[t-1;t+j]. We do not predict a positive association between reviews for positive

credit watch and CAR[t-1;t+j] for the same reasons as for upgrades. Some credit rating actions

have an ambiguous effect on security prices, as they could be interpreted either as a positive,

neutral or negative signal, i.e., reviews for unsigned credit watch and previous credit watch

that do not lead to a downgrade or an upgrade. We expect a negative association between

Below_IG and CAR[t-1;t+j] because the stock of a firm losing the investment grade status (also

called “fallen angel”) is likely to experience a greater decline than another firm that is simply

downgraded either within the investment grade category or that is already below the

investment grade category. Based on past literature, we do not predict a particular association

for actions involving a firm obtaining the investment grade status (also called “rising star”)

and CAR[t-1;t+j].

The coefficients of interest are b9 to b15 that capture the incremental effect on stock returns,

during bear markets, of credit rating actions (H3). In particular b9, b11 and b14 capture the

incremental informativeness of respectively downgrades, reviews for negative credit watch

and downgrades involving losing the investment grade status, since the literature document a

negative effect on stock prices for negative credit rating actions. According to the EMH,

investors should understand the different information content of rating actions during bear

markets if those actions have a higher quality. If credit rating actions have a greater quality

during bear markets – because CRAs face greater reputation risk – it should lead to more

informative rating actions and larger absolute price impact. CRAs could improve the quality

of rating to respond to increased scrutiny from outsiders, in which case coefficients b9, b11 and

b14 should be significantly negative.

4. Determinants of credit rating changes

4.1. Data and sample

To examine the determinants of credit rating changes we collect credit ratings from the

Compustat Ratings database which provides monthly credit rating levels from Standard &

Poor’s. We collect for the Compustat universe credit rating levels from January 1, 1988 to

December 31, 2012 and match them with financial variables in Compustat Fundamentals

17

Annual. We only consider rating changes issued on “domestic long term issuer credit rating”

which allows to assess the credit worthiness of the issuer on the long run. In order to analyze

the number of notches changed by CRAs, we follow past research (e.g., Becker and Milbourn

2011; Jiang et al. 2012) and create a numerical rating scale ranging from 1 to 22, where 22 is

the best rating used (typically “AAA”) and 1 is the lowest rating used (typically “D” or

“SD”). We present in Table 1 an overview of the ratings levels for Standard & Poor’s and the

numerical value assignments used in our empirical work.10

[Insert Table 1 About Here]

We rely on all actual notches (including “+” and “-“) used by CRAs in order to assess the

real determinants rating changes and impact of actual rating actions initiated by CRAs while

avoiding artificially grouping rating notches into broader categories. It is consistent with

CRAs practice as CRAs change ratings incrementally and usually avoid changes ratings by

more than one notch at a time (Löffler 2004, 712).

We obtain 21,308 firm-year observations from Compustat. Table 2, Panel A presents the

number of observations per rating level. Panel A shows a concentration of firms in the middle

of the ratings’ scale (more than 80% of observations are rated between B+ and A). Panel B

shows the number of observations per industry (Global Industry Classification Standard). The

number of credit rating actions is relatively well distributed across the sectors, although

Consumer Discretionary accounts for 20.8% of all observations and the Telecommunications

Services represents only 4.1% of all observations.


Table 3, Panel A, provides descriptive statistics of the main variables used in models (1a),

(1b) and (2).


Table 3, Panel A shows that from 1988 to 2012, the annual frequency of downgrade is

13.6% and the annual frequency of upgrade is 11.4%. The mean (median) change in the

number of notches is -0.053 (0.000) which ranges from a minimum of -15 to a maximum of

+12 notches. The percentage of investment grade observations is 59.7%. The frequency of

downgrade below the investment grade threshold is 1.8% while the frequency of upgrade

10 The term credit rating “category” refers to a distinct level in a rating scale represented by a unique symbol, number, or score (e.g., AA). If a rating scale has gradations within a category, the category and each gradation would constitute a “notch” in the rating scale. For example, the symbols AA+, AA, and AA- would each represent a notch in the rating scale (U.S. Securities and Exchange Commission 2012).

18

above the investment grade threshold is 1.5%. The frequency of ROA increase is 51.3%, the

mean (median) change in leverage is -0.006 (-0.006), the mean (median) change in free cash

flow is 0.000 (0.000), the mean (median) change in capital intensity is -0.008 (0.005), the

mean (median) change in firm size is 0.090 (0.054), the mean (median) change in interest

coverage ratio is 0.035 (0.119), the mean (median) annual stock return is 0.097 (0.041), and

the mean (median) change in GDP growth rate is -0.001 (-0.005).

Figure 2 shows the percentage of firms downgraded vs. upgraded by year for the sample

period.


Figure 3 shows the average change of notch (Notch) based on our numerical ranking by

year for the sample period.


As can be seen on Figures 2 and 3, credit rating changes seem to be related with market

cycles. There is a larger percentage of downgrades during the years of 2001 and 2002 (high

tech bubble burst) as well as during the years of 2008 and 2009 (financial crisis). Similarly the

average number of notch changed during the years of bear markets is negative, indicating that

CRAs tend to downgrade firms during bear markets. From Figure 3, we also see that the

magnitude of downgrades is higher during bear markets while the magnitude of upgrades is

higher during bull markets. These observations alone are insufficient to conclude that CRAs

rating changes are influenced by financial market cycles because these changes could be

related to fundamental changes in credit risk, also correlated with market cycles. Nonetheless

this is consistent with the cyclical nature of the credit rating industry.

We will study whether market cycles drive part of these results with models (1a), (1b) and

(2), which control for changes in credit risk and change in the macroeconomic environment.

Table 3, Panel B, presents the correlation matrix between the main variables used in

models (1) and (2). Downgrade is, as expected, significantly negatively correlated with

D_ROA, FCF, PPE, SIZE, INT_COV, Return, and GDP (significant at less than 1%,

two-sided). Conversely, Downgrade is significantly positively correlated with LEV

(significant at less than 1%, two-sided). Interestingly, Downgrade is positively correlated with

Bear (significant at less than 1%, two-sided). This relationship could be explained by changes

in firm-specific credit risk and/or the macroeconomic environment. Upgrade exhibits

19

statistically significant correlation with these variables with opposite sign. Upgrade is

negatively correlated with Bear (significant at less than 5%, two-sided). Notch also presents

expected association with underlying variables capturing changes in credit risk.

5.2. Association between changes in credit risk proxies and credit rating changes during bull

and bear markets

To test H1, we examine if firms are more (less) likely to be downgraded (upgraded) during

bear markets after controlling for changes in credit risk and changes in the macroeconomic

environment. To test H2, we examine if the decision to downgrade below (upgrade above) the

investment grade threshold is also affected by stock market cycles. During bear markets,

CRAs could have greater reputation concerns and change their rating process so that their

rating changes would have a greater quality. As a result, credit rating standards could be

looser and quality could decrease during booming markets and increase during bear markets

(Bolton et al. 2012; Bar-Isaac and Shapiro 2013). We use models (1a), and (1b) to examine

hypothesis H1, and we use model (2) to examine H2. We report the estimation results in

Table 4.


In model (1a), coefficients b3 to b10 (coefficients b2 to b9 in model (2)) measure the

association between proxies for changes in credit risk and the macroeconomic environment

and the likelihood of a downgrade (or upgrade) whereas coefficients b1 capture the

incremental effect on the likelihood of a downgrade (or upgrade) during bear markets.

Table 4, Panel A shows that, after controlling for changes in other firm-specific and

macroeconomic factors, the likelihood of a firm being downgraded increases during bear

markets (b1 positive and significant at less than 1%, two-sided). A given firm is 48.7% more

likely to be downgraded during a bear market than during a bull market (see note below Panel

A). This indicates that CRAs become more conservative during contracting markets and it

confirms H1. As expected, positive changes in ROA, free cash flow, firm size, interest

coverage ratio, stock return, and GDP growth rate are negatively associated with the

probability of a downgrade. Positive change in financial leverage is positively associated with

a downgrade (significant at less than 1%, two-sided). The investment grade status or changes

in capital intensity are not statistically associated with downgrades.

20

The association between changes in underlying credit risk and upgrades could be different

from downgrades. Hence we examine separately credit rating upgrades. Panel B shows that

during bear markets, all else equal, the likelihood of an upgrade is lower (b1 significant at less

than 5%, two-sided), which is also consistent with more conservative upgrades during

contracting stock market (H1). A given firm is 12.9% less likely to be upgraded during a bear

market than during a bull market (see note below Panel B). Panel B shows that, as expected,

an increase in ROA, free cash flow, capital intensity, size, interest coverage ratio, stock return

and GDP growth rate is positively and significantly associated with upgrades. Conversely, an

increase in financial leverage in t is negatively associated with the likelihood of an upgrade in

t+1. Firms already rated above investment grade are less likely to be upgraded (significant at

less than 1%, two-sided). Overall, results support a greater degree of conservatism also for

upgrades during bear markets.

Panel C presents the estimation results of model (1b) that examines the association

between stock market cycles and changes of notches in t+1 by CRAs. A positive value for

ΔNotcht+1 indicates an upgrade and a negative value indicates a downgrade. Table 4, Panel C

shows consistent results with Panels A and B. During bear markets, the number of notches

changed is significantly negative, which is consistent with H1 that posits more conservative

ratings during bear markets (coefficient b1 negative and significant at less than 1%, two-

sided). Control variables present the expected signs and are all significant at 10% or less (two-

sided).

Table 4, Panel D reports a similar analysis for model (2) for which Below_IGt+1 is the

dependent variable. CRAs’ reputation risk is likely to be higher for firms close to the

investment grade threshold as crossing the investment grade threshold has severe financial

consequences for rated firms. Therefore to test H2 we also examine if CRAs are more likely

to downgrade firms below the investment grade threshold during down markets after

controlling for changes in firm-specific credit risk and changes in the macroeconomic

environment. Panel D shows that during bear markets the probability to downgrade a firm

below the investment grade threshold significantly increases (significant at less than 1%, two-

sided). A given firm is 60.3% more likely to be downgraded below the investment grade

threshold during a bear market than during a bull market (see note below Panel D). This is

consistent with H2 and indicates stricter ratings during bear markets.

Panel E reports estimation results for model (2) that examines the determinants of upgrades

above the investment grade status. Panel E shows that stock market cycles do not seem to

21

influence the decision to upgrade a firm above the investment grade threshold. The coefficient

on Bear is negative but not significant (see z-stat = -1.18). Stock market cycles appear to be

neutral regarding the decision to upgrade firms above the investment grade threshold.

Overall results from models (1a), (1b) and (2) document that during bear markets, after

controlling for changes in credit risk and changes in the macroeconomic environment, CRAs

are more likely to issue downgrades, to downgrade below the investment grade threshold and

are less likely to issue upgrades than during bull market.

5. Consequences of credit rating actions

5.1. Data and sample

Our second sample is used to study the effect of credit rating actions. We collect ratings

data from Bloomberg, which provides daily credit rating actions,11 initiated by Standard &

Poor’s between January 1, 1988 and December 31, 2012 on companies composing the Russel

3000 index. The Russel 3000 index measures the performance of the 3,000 largest U.S.

companies and represents approximately 98% of the U.S. equity market. We also only

consider rating actions issued on “long term issuer credit rating” (domestic and foreign).

Standard & Poor’s initiated 11,210 individual credit rating actions over the period on those

two rating types as presented in Table 5, Panel A. We consider six types of credit rating

actions: downgrades, upgrades, reviews for negative credit watch, reviews for positive credit

watch, reviews for unsigned credit watch (no indication if the credit watch is positive or

negative), past reviews for credit watch that do not lead to a rating change. Next, we match

credit rating actions with CRSP in order to compute cumulated abnormal returns. We obtain a

sample of 9,568 rating actions. Several credit rating actions can be initiated by CRAs on the

same day: a firm can be upgraded and be considered for a review for further upgrade on the

same day.

Standard & Poor’s usually downgrade on the same day several credit rating types, i.e., both

domestic and foreign long-term issuer credit rating. Therefore we collapse rating actions on

different rating types into unique daily events to examine the price impact of rating actions in

model (3). We obtain a sample of 4,706 unique daily credit rating events for model (3) that

examines stock reaction to those events (see Table 5, Panel A).

11 To compute cumulated abnormal returns, daily credit rating actions (which are not provided in Compustat) are required.

22


Table 5, Panel B provides the number of credit rating actions per industry (GICS). The

number of credit rating actions is distributed across the sectors similarly to our first sample:

Consumer Discretionary accounts for 30.2% of all rating actions and the Telecommunications

Services represents only 2.5% of all rating actions. Table 5, Panel C provides the number of

rating actions for each year. The number of credit rating actions is rather low during the early

beginning of the sample period and CRAs’ activity seems to have mostly increased since the

beginning of 2000.

Table 6 provides descriptive statistics of credit rating actions.


Table 6 shows that 31.9% of credit ratings actions involve downgrades and 29.5% consist

in upgrades. During the period 24.3% of credit rating actions involve a review for negative

credit watch, 7.5% a review for positive credit watch, 1.9% a review for unsigned credit

watch, and 10.4% of credit rating actions are past reviews for credit watch that do not lead to

a rating change. The percentage of credit rating that led a firm to lose the investment grade

status is 3.3% and the percentage of credit rating actions that led a firm to obtain the

investment grade status is 3.3%. In addition, 45.2% of firms in the sample are rated above

investment grade before the rating action. Approximately 26.2% of rating actions occurred

during bear markets.

5.2. Stock market impact of rating actions during bull and bear markets

Under the EMH, if credit ratings actions initiated by CRAs have a different information

content in bull markets than in bear markets, market participants should react differently.

Results from models (1a), (1b) and (2) show that the decision to change credit ratings is

conditional on stock market cycles. Under H3, we hypothesize that the stock reaction is

greater during bear markets than during bull markets if rating quality increases during bear

markets. Table 7 presents descriptive statistics of cumulated abnormal returns (CAR)

computed around three different time windows, i.e., [t-1; t +1], [t -1; t+2], [t-1; t+3] when the

rating action occur in t on our sample of 4,706 unique daily credit rating actions.


Table 7, Panel A shows that average CAR are significantly negative (at less than 1%, two-

sided) following all our credit events. Average (median) CAR ranges between -83 basis points

23

and -84 basis points (-16 and -21 basis points). This is consistent with the composition of our

sample that comprises more negative credit rating actions (from Table 3, Panel A:

approximately 31.9% of downgrades and 24.3% of reviews for negative credit watch) than

positive credit rating actions (approximately 29.5% of upgrades and 7.5% of positive credit

watch). Panel B exhibits CAR computed for downgrades only. The mean (median) CAR

ranges between -215 and -226 basis points (-86 and -100 basis points) and are significantly

negative (at less than 1%, two-sided). Panel C and D show CAR computed respectively

during bear and bull markets. Average CAR are significantly negative in both market

conditions but the price impact is approximately 1.9 to 2.9 times larger for downgrades issued

during bear markets than during bull markets, i.e., the average CAR ranges between -313 and

-399 basis points during bear markets while it ranges only between -137 and -165 basis points

during bull markets. From Panel E, the differences are significant (at less than 1%, two-

sided). The greater market reaction to downgrades during bear markets is consistent with a

higher quality of ratings over declining stock markets and is consistent with our hypothesis

H3.

Table 7, Panel F shows the market impact of upgrades. The average (median) CAR for an

upgrade ranges over the period between +53 and +72 basis points (+35 and +43 basis points)

and is significant (at less than 1%, two-sided). We extend prior literature and show that

upgrades are associated with statistically significant positive abnormal returns, although the

absolute value of CAR is lower for upgrades than for downgrades. Panels G and H compare

CAR computed respectively during bear and bull markets. The average market impact tend to

be larger for upgrades issued during bear markets than during bull markets, i.e., the mean

market impact ranges between +57 and +102 basis points during bear markets while it ranges

between +52 and +67 basis points during bull markets. However, Panel E shows that this

difference is not significant over our three windows.

Overall, Panels A through I indicate that credit rating downgrades appear to be more

credible during bear markets than during bull markets since they trigger larger stock reactions.

However, credit rating upgrades do not seem to generate significantly larger stock reaction

during bear market.

24

Panels J, K, L and M present descriptive statistics for the remaining credit rating actions,12

i.e., respectively reviews for negative credit watch (1,144 events), reviews for positive credit

watch (353 events), reviews for unsigned credit watch (88 events) and past reviews for credit

watch that do not lead to a change of rating (489 events). Reviews for negative credit watch

are associated with significant negative abnormal returns ranging on average from -294 basis

points to -316 basis points (significant at less than 1%, two-sided). Reviews for positive credit

watch are associated with positive but insignificant CAR ranging on average from +55 basis

points to +62 basis points. Reviews for credit watch are not associated with statistically

significant CAR. Past credit watch that did not lead to a rating change are associated with

statistically insignificant positive mean CAR ranging from +27 basis points to +33 basis

points. 13

Next, we estimate model (3) that examines the association between all the credit rating

actions and CAR in booming vs. contracting stock markets. The estimated results are reported

in Table 8.


In model (3) there is no dummy for past credit watch that did not lead to a rating change,

which are captured by the intercept. The other coefficients capture the incremental effect of

specific rating actions (no interaction terms) and the incremental effect in bear markets

(interactions terms).

Table 8, Panel A shows that downgrades and negative credit watch are negatively

associated with cumulated abnormal returns (see coefficients b2 and b4 negative and

significant at less than 1%, two-sided). These credit rating actions trigger larger stock reaction

during bear markets (see coefficients b9 and b12 negative and significant at 5% or less, two-

sided). Losing the investment grade status after a downgrade is also associated with an

additional negative stock market reaction in bear market only (see coefficient on Bear *

Below negative and significant at less than 10%, two-sided in two out of the three windows).

Interestingly reviews for credit watch are associated with statistically positive CAR during

(significant at 10% or less, two-sided).

12 Because we focus mainly on downgrades and upgrades that have been mostly studied in the literature we did not present comparisons between bull and bear markets for those credit rating actions. However the results of model (3) presented in Table 8 show such differences. 13 The majority of past reviews for credit watch were reviews for negative credit watch (83.0%, see note below Panel M). As a result we measure mainly the reaction to reviews for negative credit watch that are “cancelled” which is good news.

25

Table 8, Panel B shows the total market impact of all the rating actions examined and

allows comparisons of their consequences in bull vs. bear market. Downgrades issued during

bear market trigger a -303 basis point stock reaction over a five-day window which is

approximately two times the stock reaction during a bull market (-303/-156) (difference

significant at less than 1%, two-sided). A review for a negative credit watch leads to a stock

reaction of -565 basis points over five days, which is approximately 3.4 times the stock

reaction during bull market (-565/-166) (difference significant at less than 1%, two-sided). A

downgrade below the investment grade status during a bear market triggers a stock reaction of

-500 basis points over a five-day window which is 4.2 times the stock reaction of the same

credit rating actions during bull market (-500/-118) (difference significant at less than 1%,

two-sided).

Overall, results reported in Tables 7 and 8 support that rating actions issued during

contracting markets have a greater market impact on stock prices, in particular negative credit

rating actions. The absolute market impact is larger for downgrades and other negative credit

rating actions such as reviews for negative credit watch and downgrades below the investment

grade threshold during bear markets than during bull market. Based on results from models

(1) and (2), we argue that at least part of this differential impact comes from ratings with

greater quality during bear markets.

6. Sensitivity analysis

One could argue that our results are driven by one of the two bear markets examined in our

sample, either the collapse of the high tech bubble in 2001-2002 or the 2008 financial crisis.

To rule out this potential explanation, we run models (1a), (1b), (2) and (3) excluding data

from the first bear market (collapse of the internet bubble) then excluding data from the

second bear market (2008 financial crisis). Untabulated results14 are qualitatively similar for

either of the two market cycles.

7. Conclusion

CRAs argue that their ratings are not overly influenced by short-term cycles because

ratings intend to inform investors about credit risk in the long term. However, Bar-Isaac and

Shapiro (2013) argue that reputation depends on economic fundamentals and varies over the

14 Available upon request.

26

business cycles. Reputation risk is greater during a contracting economy and lower during a

booming economy. Therefore credit rating quality could also vary with cycles. Bolton et al.

(2012) further argue that ratings are more likely to be inflated during booms when there are

more investors taking ratings at face value and less reputation risk for CRAs.

We examine the association between equity market cycles and credit rating changes after

controlling for changes in firm-specific credit risk proxies and changes in the macroeconomic

environment. Our analysis is based on credit rating changes initiated by Standard & Poor’s,

from 1988 to 2012 on a large sample of U.S. firms. We show that credit ratings changes are

associated with stock market cycles. Downgrades (upgrades) are more (less) likely during

bear markets than during bull markets, after controlling for other factors influencing rating

changes. Ratings appear to be more conservative during bear markets and more relaxed

during bull markets. In addition, the stock market impact for negative credit rating actions,

namely downgrades, downgrades below the investment grade threshold and negative credit

watch is statistically and economically larger during bear markets than during bull markets.

This is consistent with greater rating quality during bear markets.

We contribute to the literature about the effects of credit rating actions on stock markets by

demonstrating that rating changes are conditional on stock market cycles and that investors

react differently to rating actions. We provide empirical support to the prediction that the

rating change process is conditional on financial market/business cycles (Bolton et al. 2012;

Bar-Isaac and Shapiro 2013). Given the importance of CRAs, our results have important

implications for policy makers since it suggests that ratings do not necessarily reflect long-

term credit risk and could lead to inflated rating during bull markets and contribute to

financial cycles.

27

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Figure 1 – S&P 500 index (1988-2012)

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Figure 2 – Percentage of firms downgraded vs. upgraded among companies rated by Standard & Poor’s – by year (1989-2012)

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Figure 3 – Average number of notch changed of ratings among companies rated by Standard & Poor’s – by year (1989-2012)

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Table 1 - Rating scale

The table describes categories for credit ratings, as well as the numerical scale used in the paper. We have transformed original S&P Ratings letters into numbers, with higher number indicating better quality ratings.

Rating agency

Numerical value

assigned

Investment grade (1 = Above investment grade, 0 otherwise)

S&P AAA 22 1AA+ 21 1AA 20 1AA- 19 1A+ 18 1A 17 1A- 16 1BBB+ 15 1BBB 14 1BBB- 13 1BB+ 12 0BB 11 0BB- 10 0B+ 9 0B 8 0B- 7 0CCC+ 6 0CCC 5 0CCC- 4 0CC 3 0C 2 0D 1 0

34

Table 2 – Sample for credit rating determinants

Panel A – Number of observations per rating

Ratings Numerical Value # of observations % AAA 22 264 1.2% AA+ 21 119 0.6% AA 20 456 2.1% AA- 19 576 2.7% A+ 18 1,020 4.8% A 17 1,895 8.9% A- 16 1,648 7.7% BBB+ 15 2,099 9.9% BBB 14 2,623 12.3% BBB- 13 2,025 9.5% BB+ 12 1,352 6.3% BB 11 1,758 8.3% BB- 10 2,033 9.5% B+ 9 1,744 8.2% B 8 920 4.3% B- 7 473 2.2% CCC+ 6 128 0.6% CCC 5 63 0.3% CCC- 4 22 0.1% CC 3 21 0.1% C 2 1 0.0% D 1 68 0.3% Total 21,308 100.0%

Panel B – Number of observations per sector

GICS Sector # of observations % Energy 2,429 11.4% Materials 2,609 12.2% Industrials 3,609 16.9% Consumer Discretionary 4,433 20.8% Consumer Staples 1,797 8.4% Health Care 1,720 8.1% Information Technology 1,719 8.1% Telecommunication Services 865 4.1% Utilities 2,127 10.0% Total 21,308 100.0%

35

Table 3 – Statistics for the determinants model

Panel A – Univariate statistics

N Mean St. Dev Min 1st Q Med 3rd Q Max Downgrade t+1 21,308 0.136 0.342 0 0 0 0 1Upgrade t+1 21,308 0.114 0.317 0 0 0 0 1Notch t+1 21,308 -0.053 0.999 -15 0 0 0 12IGt 21,308 0.597 0.490 0 0 1 1 1Below_IGt+1 21,308 0.018 0.132 0 0 0 0 1Above_IGt+1 21,308 0.015 0.122 0 0 0 0 1Beart+1 21,308 0.197 0.398 0 0 0 0 1ΔD_ROAt 21,308 0.513 0.500 0 0 1 1 1ΔLEVt 21,308 -0.006 0.219 -1.062 -0.063 -0.006 0.055 0.993ΔFCFt 21,308 0.000 0.066 -0.220 -0.030 0.000 0.030 0.217ΔPPEt 21,308 -0.008 0.204 -0.985 -0.048 0.005 0.054 0.711ΔSIZEt 21,308 0.090 0.211 -0.441 -0.011 0.054 0.143 1.078ΔINT_COVt 21,308 0.035 4.520 -16.379 -0.854 0.119 1.158 13.266Returnt 21,308 0.097 0.510 -0.829 -0.198 0.041 0.279 2.588ΔGDPt 21,308 -0.001 0.019 -0.032 -0.009 -0.005 0.010 0.053

Downgradet+1 = 1 if the firm is downgraded in t+1, and 0 otherwise. Upgradet+1 = 1 if the firm is upgraded in t+1, and 0 otherwise. Notcht+1 = number of notches changed in t+1. A positive number indicates an upgrade and a negative number indicates a downgrade. For instance Notcht+1 = - 2 means that the rated firm has been downgraded by two notches in t+1. IGt = 1 if the firm is rated above investment grade, and 0 otherwise. Below_IGt+1 = 1 if the firm is downgraded below investment grade, and 0 otherwise. Above_IGt+1 = 1 if the firm is upgraded above investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

All continuous variables are winsorized at 1%

36

Panel B – Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (1) Downgrade 1.000

(2) Upgrade -0.142 1.000 0.000

(3) DNotch -0.621 0.544 1.000 0.000 0.000

(4) IG -0.102 -0.076 0.047 1.000 0.000 0.000 0.000

(5) Below_IG 0.339 -0.048 -0.339 -0.164 1.000 0.000 0.000 0.000 0.000

(6) Above_IG -0.049 0.347 0.252 0.102 -0.017 1.000 0.000 0.000 0.000 0.000 0.015

(7) Bear 0.059 -0.015 -0.061 -0.034 0.031 -0.006 1.000 0.000 0.029 0.000 0.000 0.000 0.354

(8) ΔROA -0.126 0.120 0.126 0.012 -0.041 0.041 -0.001 1.000 0.000 0.000 0.000 0.072 0.000 0.000 0.860

(9) ΔLEV 0.019 -0.028 -0.025 0.008 0.009 -0.018 -0.008 0.058 1.000 0.005 0.000 0.000 0.249 0.205 0.008 0.241 0.000

(10) ΔFCF -0.065 0.082 0.084 -0.004 -0.022 0.031 -0.010 0.229 0.091 1.000 0.000 0.000 0.000 0.554 0.001 0.000 0.138 0.000 0.000

(11) ΔPPE -0.020 0.021 0.029 0.003 -0.012 0.002 -0.015 0.163 0.563 0.186 1.000 0.004 0.002 0.000 0.699 0.076 0.800 0.024 0.000 0.000 0.000

(12) ΔSIZE -0.071 0.030 0.053 -0.026 -0.022 0.017 0.072 0.052 0.451 0.084 0.318 1.000 0.000 0.000 0.000 0.000 0.001 0.014 0.000 0.000 0.000 0.000 0.000

(13) ΔINT_COV -0.111 0.097 0.113 0.029 -0.046 0.028 -0.041 0.322 -0.070 0.208 0.038 -0.052 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(14) Return -0.158 0.174 0.197 -0.075 -0.060 0.044 -0.009 0.228 0.059 0.151 0.082 0.098 0.131 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.179 0.000 0.000 0.000 0.000 0.000 0.000

(15) ΔGDP -0.051 0.038 0.047 0.007 -0.013 0.004 -0.154 0.132 0.041 0.018 0.048 0.041 0.110 0.062 1.000 0.000 0.000 0.000 0.295 0.058 0.536 0.000 0.000 0.000 0.008 0.000 0.000 0.000 0.000

37

Table 4 – Determinants of credit rating changes

Panel A – Determinants of downgrades - Model (1a)

Downgradet+1 = b0 + b1*Beart+1 + b2*IGt + b3*D_ROAt + b4*LEVt + b5*FCFt + b6*PPEt +

b7*SIZEt + b8*INT_COVt + b9*Returnt + b10*GDPt + bi*D_Sector +

Pred. Coeff. z-stat p-value Bear + 0.397*** 8.18 0.000 IG ? 0.047 1.07 0.285 ΔD_ROA - -0.394*** -8.61 0.000 ΔLEV + 1.049*** 7.07 0.000 ΔFCF - -0.794** -2.30 0.021 ΔPPE - -0.056 -0.40 0.686 ΔSIZE - -1.282*** -9.90 0.000 ΔINT_COV - -0.042*** -9.49 0.000 Return - -1.047*** -15.14 0.000 ΔGDP - -2.217* -1.88 0.060 Sector dummies IncludedConstant -1.866*** -23.77 0.000 Pseudo R² 0.074Chi2 950.714p(Chi2) 0.000N 21,308

*p<.1 (two-sided tests); **p<.05(two-sided tests); ***p<.01 (two-sided tests)

Model (1a) is a logit model. z-statistics are adjusted for heteroskedasticity.

Downgradet+1 = 1 if the firm is downgraded in t+1, and 0 otherwise. IGt = 1 if the firm is rated above investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

The odd ratio for (Bear = 1) = exp(0.397) = 1.487

38

Panel B – Determinants of upgrades - Model (1a)

Upgradet+1 = b0 + b1*Beart+1 + b2*IGt + b3*D_ROAt + b4*LEVt + b5*FCFt + b6*PPEt +


Pred. Coeff. z-stat p-value Bear - -0.138** -2.30 0.021 IG ? -1.023*** -20.81 0.000 ΔD_ROA + 0.476*** 9.28 0.000 ΔLEV - -0.767*** -6.46 0.000 ΔFCF + 1.390*** 4.08 0.000 ΔPPE + 0.224* 1.68 0.094 ΔSIZE + 0.444*** 3.89 0.000 ΔINT_COV + 0.043*** 6.89 0.000 Return + 0.548*** 14.92 0.000 ΔGDP + 2.869** 2.46 0.014 Sector dummies IncludedConstant -2.124*** -26.52 0.000 Pseudo R² 0.093Chi2 1327.59p(Chi2) 0.000N 21,308


Model (1b) is a logit model. z-statistics are adjusted for heteroskedasticity.

Upgrade+1 = 1 if the firm is upgraded in t+1, and 0 otherwise. IGt = 1 if the firm is rated above investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

The odd ratio for (Bear = 1) = exp(-0.472) = 0.8711

39

Panel C – Determinants of changes of Notches – Model (1b)

Notcht+1 = b0 + b1*Beart+1 + b2*IGt + b3*D_ROAt + b4*LEVt + b5*FCFt + b6*PPEt +


Pred. Coeff. t-stat p-value Bear - -0.154*** -8.39 0.000 IG ? -0.192*** -12.36 0.000 ΔD_ROA + 0.116*** 7.92 0.000 ΔLEV - -0.341*** -5.36 0.000 ΔFCF + 0.458*** 3.73 0.000 ΔPPE + 0.098* 1.78 0.075 ΔSIZE + 0.287*** 6.65 0.000 ΔINT_COV + 0.014*** 8.03 0.000 Return + 0.308*** 15.37 0.000 ΔGDP + 0.665** 2.03 0.042 Sector dummies IncludedConstant 0.001 0.03 0.973 R² 0.070Adj. R² 0.069F 55.769p(F) 0.000N 21,308


Model (2) is estimated with OLS. t-statistics are adjusted for heteroskedasticity.

Notcht+1 = number of notches changed in t+1. A positive number indicates an upgrade and a negative number indicates a downgrade. For instance Notcht+1 = - 2 means that the rated firm has been downgraded by two notches in t+1. IGt = 1 if the firm is rated above investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

40

Panel D – Determinants of downgrades below investment grade - Model (2)

Below_IGt+1 = b0 + b1*Beart+1 + b2*D_ROAt + b3*LEVt + b4*FCFt + b5*PPEt + b6*SIZEt

+ b7*INT_COVt + b8*Returnt + b9*GDPt + bi*D_Sector +

Pred. Coeff. z-stat p-value Bear + 0.472*** 4.01 0.000 ΔD_ROA - -0.257** -2.20 0.028 ΔLEV + 1.035*** 3.04 0.002 ΔFCF - -0.330 -0.40 0.693 ΔPPE - -0.499 -1.34 0.179 ΔSIZE - -0.894*** -2.92 0.004 ΔINT_COV - -0.040*** -4.31 0.000 Return - -1.046*** -6.21 0.000 ΔGDP - 1.526 0.50 0.615 Sector dummies IncludedConstant -4.762*** -19.13 0.000 Pseudo R² 0.056Chi2 182.46p(Chi2) 0.000N 21,308


Model (1a) is a logit model. z-statistics are adjusted for heteroskedasticity.

Below_IGt+1 = 1 if the firm is downgraded below investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

The odd ratio for (Bear = 1) = exp(0.472) = 1.603

41

Panel E – Determinants of upgrades above investment grade - Model (2)

Above_IGt+1 = b0 + b1*Beart+1 + b2*D_ROAt + b3*LEVt + b4*FCFt + b5*PPEt + b6*SIZEt

+ b7*INT_COVt + b8*Returnt + b9*GDPt + bi*D_Sector +

Pred. Coeff. z-stat p-value Bear - -0.179 -1.18 0.240 ΔD_ROA + 0.493*** 3.73 0.000 ΔLEV - -1.117*** -4.10 0.000 ΔFCF + 1.916** 2.09 0.037 ΔPPE + 0.163 0.50 0.620 ΔSIZE + 0.856*** 3.37 0.001 ΔINT_COV + 0.018 1.29 0.196 Return + 0.376*** 4.68 0.000 ΔGDP + -0.919 -0.32 0.752 Sector dummies IncludedConstant -4.923*** -23.16 0.000 Pseudo R² 0.034Chi2 145.618p(Chi2) 0.000N 21,308


Model (1b) is a logit model. z-statistics are adjusted for heteroskedasticity.

Above_IGt+1 = 1 if the firm is upgraded above investment grade, and 0 otherwise. Beart+1 = 1 if the rating is between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). ΔD_ROAt = 1 if the return on asset increased during year t, and 0 otherwise. LEVt = change from year t-1 to t in long-term debt divided by lagged total assets. FCFt = change from year t-1 to t of cash flow from operating activities minus average capital expenditure over current and past two years divided by lagged total assets. ΔPPEt = change in gross PPE divided by lagged total assets. ΔSIZEt = change in natural log of total assets. INT_COVt = change from year t-1 to t of EBIT divided by interest expense. Returnt = stock return for year t. ΔGDPt = change from year t-1 to t in GDP growth rate.

The odd ratio for (Bear = 1) = exp(-0.179) = 0.836

42

Table 5 – Sample selection of credit rating actions and composition of the sample

Panel A – Sample selection

# of credit rating

actions % Credit rating actions (1988-2012) 11,210 100.0% Matched with CRSP 9,568 85.4% Unique daily events 4,706 42.0%

Panel B – Credit rating actions per industry

GICS Sector # of credit rating actions % Energy 408 8.7% Materials 490 10.4% Industrials 866 18.4% Consumer Discretionary 1,419 30.2% Consumer Staples 309 6.6% Health Care 320 6.8% Information Technology 441 9.4% Telecommunication Services 120 2.5% Utilities 333 7.1% Total 4,706 100.0%

43

Panel C – Number of credit rating actions per year

Year # of credit rating

actions % 1988 34 0.7%1989 32 0.7%1990 33 0.7%1991 39 0.8%1992 42 0.9%1993 44 0.9%1994 41 0.9%1995 68 1.4%1996 80 1.7%1997 85 1.8%1998 129 2.7%1999 198 4.2%2000 218 4.6%2001 242 5.1%2002 265 5.6%2003 255 5.4%2004 214 4.5%2005 260 5.5%2006 321 6.8%2007 343 7.3%2008 433 9.2%2009 424 9.0%2010 295 6.3%2011 334 7.1%2012 277 5.9%

Total 4,706 100.0%

44

Table 6 – Descriptive statistics of credit rating actions

N Mean St. Dev Min 1st Q Med 3rd Q Max Downgrade 4,706 0.319 0.466 0 0 0 1 1Upgrade 4,706 0.295 0.456 0 0 0 1 1NegCW 4,706 0.243 0.429 0 0 0 0 1PosCW 4,706 0.075 0.263 0 0 0 0 1CW 4,706 0.019 0.135 0 0 0 0 1Cancel 4,706 0.104 0.305 0 0 0 0 1Bear 4,706 0.262 0.440 0 0 0 1 1Below_IG 4,706 0.033 0.178 0 0 0 0 1Above_IG 4,706 0.033 0.178 0 0 0 0 1IG 4,706 0.452 0.498 0 0 0 1 1

Downgradet+1 = 1 if the credit rating action involves a downgrade in t+1, and 0 otherwise. Upgradet+1 = 1 if the rating action involves an upgrade in t+1, and 0 otherwise. NegCWt+1 = 1 if the credit rating action involves placing the rated firm on the negative credit watch list in t+1, and 0 otherwise. PosCWt+1 = 1 if the credit rating action involves putting the rated firm on the positive credit watch list in t+1, and 0 otherwise. CWt+1 = 1 if the credit rating action involves placing the firm on the credit watch list without mentioning if it is a positive or negative credit watch, and 0 otherwise. Cancel = 1 if the CRA cancel a past review for negative or positive credit watch. Below_IGt+1 = 1 if the firm loses the ‘investment grade’ status after the rating action in t+1, and 0 otherwise. Above_IGt+1 = 1 if the firm obtains the ‘investment grade’ after the rating action in t+1, and 0 otherwise. Beart+1 = 1 if the rating action takes place between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1). IGt = 1 if the firm’s previous rating is above investment grade, and 0 otherwise.

45

Table 7 – Descriptive statistics cumulated abnormal returns

Panel A – Cumulated abnormal returns – all rating actions

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 4,706 -0.0083 *** 0.08252 -0.3650 -0.0318 -0.0016 0.0236 0.2357CAR[-1; +2] 4,706 -0.0084 *** 0.088368 -0.3793 -0.0357 -0.0017 0.0271 0.2477CAR[-1; +3] 4,706 -0.0083 *** 0.095261 -0.4119 -0.0392 -0.0021 0.0312 0.2794

Panel B – Cumulated abnormal returns – downgrades

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 1,501 -0.0215 *** 0.0973 -0.3650 -0.0457 -0.0086 0.0205 0.2357CAR[-1; +2] 1,501 -0.0226 *** 0.1041 -0.3793 -0.0539 -0.0100 0.0223 0.2477CAR[-1; +3] 1,501 -0.0225 *** 0.1134 -0.4119 -0.0555 -0.0093 0.0266 0.2794

Panel C – Cumulated abnormal returns – downgrades during bear markets

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 504 -0.0313 *** 0.1191 -0.3650 -0.0701 -0.0176 0.0264 0.2357CAR[-1; +2] 504 -0.0359 *** 0.1300 -0.3793 -0.0808 -0.0178 0.0247 0.2477CAR[-1; +3] 504 -0.0399 *** 0.1432 -0.4119 -0.0857 -0.0200 0.0278 0.2794

Panel D – Cumulated abnormal returns – downgrades during bull markets

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 997 -0.0165 *** 0.0838 -0.3650 -0.0367 -0.0061 0.0185 0.2357CAR[-1; +2] 997 -0.0159 *** 0.0875 -0.3793 -0.0403 -0.0069 0.0207 0.2477CAR[-1; +3] 997 -0.0137 *** 0.0937 -0.4119 -0.0433 -0.0052 0.0260 0.2794

Panel E – Price impact downgrades – bull vs. bear markets

Difference Sig CAR[-1; +1] -0.0148 *** CAR[-1; +2] -0.0201 *** CAR[-1; +3] -0.0262 ***


46

Panel F – Cumulated abnormal returns – upgrades

N Mean Sig sd min p25 p50 p75 max CAR[-1; +1] 1,389 0.0053 *** 0.0430 -0.2269 -0.0165 0.0035 0.0223 0.2357CAR[-1; +2] 1,389 0.0065 *** 0.0487 -0.2266 -0.0174 0.0039 0.0250 0.2477CAR[-1; +3] 1,389 0.0072 *** 0.0552 -0.2539 -0.0198 0.0043 0.0286 0.2794

Panel G – Cumulated abnormal returns – upgrades during bear markets

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 207 0.0057 0.0571 -0.1621 -0.0256 0.0026 0.0234 0.2357CAR[-1; +2] 207 0.0083 * 0.0619 -0.2137 -0.0232 0.0053 0.0314 0.2477CAR[-1; +3] 207 0.0102 ** 0.0659 -0.2539 -0.0233 0.0096 0.0457 0.2794

Panel H – Cumulated abnormal returns – upgrades during bull markets

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 1,182 0.0052 *** 0.0400 -0.2269 -0.0156 0.0039 0.0217 0.2357CAR[-1; +2] 1,182 0.0061 *** 0.0460 -0.2266 -0.0155 0.0038 0.0233 0.2477CAR[-1; +3] 1,182 0.0067 *** 0.0531 -0.2109 -0.0190 0.0031 0.0267 0.2794

Panel I – Price impact upgrades – bull vs. bear markets

Difference Sig CAR[-1; +1] 0.0005CAR[-1; +2] 0.0022CAR[-1; +3] 0.0035

Panel J – Cumulated abnormal returns – Negative credit watch

N mean Sig sd Min p25 p50 p75 max CAR[-1; +1] 1,144 -0.0294 *** 0.1142 -0.3650 -0.0693 -0.0129 0.0254 0.2357CAR[-1; +2] 1,144 -0.0304 *** 0.1205 -0.3793 -0.0732 -0.0140 0.0298 0.2477CAR[-1; +3] 1,144 -0.0316 *** 0.1288 -0.4119 -0.0795 -0.0145 0.0343 0.2794

Panel K – Cumulated abnormal returns – Positive credit watch

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 353 0.0062 0.0750 -0.3650 -0.0272 0.0023 0.0332 0.2357CAR[-1; +2] 353 0.0060 0.0791 -0.3793 -0.0324 0.0033 0.0406 0.2477CAR[-1; +3] 353 0.0055 0.0804 -0.3338 -0.0341 0.0029 0.0436 0.2794

Panel L – Cumulated abnormal returns – credit watch

N mean Sig sd min p25 p50 p75 Max CAR[-1; +1] 88 0.0180 0.1273 -0.3650 -0.0383 0.0045 0.0990 0.2357CAR[-1; +2] 88 0.0175 0.1405 -0.3793 -0.0380 0.0138 0.1243 0.2477CAR[-1; +3] 88 0.0227 0.1438 -0.4119 -0.0398 0.0261 0.1190 0.2794

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Panel M – Cumulated abnormal returns – credit watch that do not lead to a rating in

change1

N mean Sig sd min p25 p50 p75 max CAR[-1; +1] 489 0.0033 0.0583 -0.3597 -0.0199 0.0012 0.0232 0.2357CAR[-1; +2] 489 0.0030 0.0660 -0.3693 -0.0256 0.0010 0.0290 0.2477CAR[-1; +3] 489 0.0027 0.0733 -0.4077 -0.0304 0.0017 0.0324 0.2794


1Among the 489 past reviews for credit watch that do not lead to a change of rating, 405 (83.0%) were reviews for negative credit watch.

CAR = Cumulated abnormal return on day t computed on three different windows, i.e., [-1; +1], [-1; +2], and [-1; +3] centered on the rating action in t.

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Table 8 – Market reaction to credit rating actions

Panel A – Credit rating actions and cumulated abnormal returns

CAR[-1; +t] = b0 + b1*Beart + b2*Downgradet + b3*Upgradet + b4*NegCWt + b5* PosCWt + b6*CWt + b7*Below_IGt + b8*Above_IGt +

b9*Beart*Downgradet + b10*Beart*Upgradet + b12*Beart*NegCWt + b13*Beart*PosCWt + b14*Beart*CWt + b15*Beart*Belowt + b15*Beart*Abovet

+ bi*D_Sector +

CAR[-1; +1] CAR[-1; +2] CAR[-1; +3] Pred. Coeff. t-stat p-value Coeff. t-stat p-value Coeff. t-stat p-value

Bear ? 0.0111* 1.66 0.098 0.0158** 2.20 0.028 0.0128* 1.65 0.098 Downgrade - -0.0198*** -4.59 0.000 -0.0181*** -3.92 0.000 -0.0169*** -3.40 0.001 Upgrade ? 0.0001 0.03 0.979 0.0029 0.60 0.550 0.0021 0.41 0.683 NegCW - -0.0178*** -4.08 0.000 -0.0169*** -3.62 0.000 -0.0179*** -3.55 0.000 PosCW ? 0.0025 0.42 0.675 0.0038 0.58 0.560 0.0008 0.11 0.913 CW ? 0.0186* 1.78 0.076 0.0232** 2.07 0.039 0.0255** 2.11 0.035 Below_IG - 0.0026 0.30 0.761 0.0040 0.44 0.661 0.0021 0.21 0.832 Above_IG + 0.0012 0.16 0.871 -0.0014 -0.18 0.860 -0.0035 -0.41 0.682 Bear * Downgrade - -0.0150** -2.02 0.044 -0.0236*** -2.96 0.003 -0.0275*** -3.19 0.001 Bear * Upgrade ? -0.0109 -1.18 0.236 -0.0135 -1.37 0.172 -0.0101 -0.95 0.344 Bear * NegCW - -0.0459*** -6.12 0.000 -0.0515*** -6.41 0.000 -0.0527*** -6.08 0.000 Bear * PosCW ? -0.0174 -1.41 0.159 -0.0222* -1.67 0.095 -0.0136 -0.95 0.342 Bear * CW ? -0.0030 -0.15 0.878 -0.0207 -0.98 0.330 -0.0113 -0.49 0.621 Bear * Below ? -0.0278* -1.93 0.054 -0.0283* -1.83 0.067 -0.0218 -1.31 0.190 Bear * Above ? 0.0197 1.05 0.292 0.0152 0.76 0.449 0.0221 1.02 0.308 Sector dummies Included Included Included Constant 0.0030 0.55 0.581 0.0023 0.39 0.697 0.0013 0.21 0.831 R² 0.057 0.056 0.054 Adj. R² 0.052 0.052 0.049F 12.318 12.127 11.613p(F) 0.000 0.000 0.000N 4706 4706 4706


49

Model (3) is estimated with OLS. t-statistics are adjusted for heteroskedasticity.

CAR = Cumulated abnormal return on day t computed on three different windows, i.e., [-1; +1], [-1; +2], and [-1; +3] centered on the rating action in t. Downgradet+1 = 1 if the credit rating action involves a downgrade in t+1, and 0 otherwise. Upgradet = 1 if the rating action involves an upgrade in t, and 0 otherwise. NegCWt = 1 if the credit rating action involves placing the rated firm on the negative credit watch list in t, and 0 otherwise. PosCWt = 1 if the credit rating action involves putting the rated firm on the positive credit watch list in t, and 0 otherwise. CWt = 1 if the credit rating action involves placing the firm on the credit watch list without mentioning if it is a positive or negative credit watch, and 0 otherwise. Below_IGt = 1 if the firm loses the ‘investment grade’ status after the rating action in t, and 0 otherwise. Above_IGt = 1 if the firm obtains the ‘investment grade’ after the rating action in t, and 0 otherwise. Beart = 1 if the rating action takes place between March 24, 2000 and October 9, 2002 or between October 9, 2007 and March 9, 2009, and 0 otherwise (see Figure 1).

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Panel B - Effect of credit rating actions on cumulated abnormal returns during bull and bear markets

Credit rating actions Effect during bull market (1) Effect during bear market (2) Change (2) - (1) CAR[-1;+1] CAR[-1;+2] CAR[-1;+3] CAR[-1;+1] CAR[-1;+2] CAR[-1;+3] CAR[-1;+1] CAR[-1;+2] CAR[-1;+3]

Downgrade b0 + b2 b0 + b1 + b2 + b9 b1 + b9 Upgrade b0 + b3 b0 + b1 + b2 + b10 b1 + b10 Negative CW b0 + b4 b0 + b1 + b2 + b11 b1 + b11 Positive CW b0 + b5 b0 + b1 + b2 + b12 b1 + b12 CW b0 + b6 b0 + b1 + b2 + b13 b1 + b13 Downgrade below IG b0 + b2 + b7 b0 + b1 + b2 + b7 + b9 + b15 b1 + b9 + b14 Upgrade above IG b0 + b3 + b8 b0 + b1 + b2 + b8 + b10+ b16 b1 + b10 + b15 Cancel past CW b0 b0 + b1 b1

Credit rating actions Effect during bull market (1) Effect during bear market (2) Change (2) - (1) CAR[-1;+1] CAR[-1;+2] CAR[-1;+3] CAR[-1;+1] CAR[-1;+2] CAR[-1;+3] CAR[-1;+1] CAR[-1;+2] CAR[-1;+3]

Downgrade -0.0168*** -0.0158*** -0.0156*** -0.0207*** -0.0236*** -0.0303*** -0.0039 -0.0078 -0.0147***

Upgrade 0.0031 0.0052 0.0034 -0.0166 0.0075 0.0061 -0.0197 0.0023 0.0027

Negative CW -0.0148*** -0.0146*** -0.0166*** -0.0496*** -0.0503*** -0.0565*** -0.0348*** -0.0357*** -0.0399***

Positive CW 0.0055 0.0061 0.0021 -0.0008 -0.0003 0.0013 -0.0063 -0.0064 -0.0008

CW 0.0216** 0.0255** 0.0268** 0.0297* 0.0206 0.0283 0.0081 -0.0049 0.0015

Downgrade below IG -0.0142 -0.0118 -0.0118 -0.0459*** -0.0479*** -0.0500*** -0.0317** -0.0361** -0.0382**

Upgrade above IG 0.0043 0.0038 -0.0001 0.0242 0.0213 0.0247 0.0199 0.0175 0.0248 Cancel past CW 0.0030 0.0023 0.0013 0.0141** 0.0181** 0.0141* 0.0111* 0.0158** 0.0128*


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