The Bond Market Effects of Reputational Shocks to Credit Rating Agencies

Kirti Sinha*

Kellogg School of Management, Northwestern University

Linda Vincent

Kellogg School of Management, Northwestern University

March 2018

Abstract

Credit rating agencies (CRAs) play a unique role in capital markets, subject to neither the discipline of a

competitive market nor the incentives of an unregulated market. As a result, the expected effects of a

reputational shock to CRAs’ credibility are difficult to predict. We examine whether investors decrease

their reliance on credit ratings after two reputational shocks, the Enron and WorldCom bankruptcies in

2001-2 and the 2008 financial crisis. For new bond issues, we find that the association between ratings

and the bond spread decreases after each reputational shock. For bond rating changes, we find statistically

lower bond market reactions to downgrades and upgrades after each reputational shock compared to

before the shock. Overall, our findings suggest that investors place less reliance on ratings after a CRA

has been hit by a reputational shock.

JEL classification: G12, G18, G24

Keywords: Credit rating agencies, bond spread, reputation

* Corresponding author. Kellogg School of Management, Northwestern University, 2001 Sheridan Road

Acknowledgements: We thank David Dranove, Michael Fishman, Benjamin Iverson, Nayab Khan, Nicola

Persico, James Schummer, and Rajkamal Vasu for their helpful comments and suggestions. Additional

thanks to all the faculty and PhD students at Kellogg School of Management, Northwestern University.

1 INTRODUCTION

Credit rating agencies (CRAs) play a unique role in capital markets. If other information

intermediaries such as sell side analysts, investment advisors, mutual funds, or asset managers provide

low quality or inaccurate recommendations, or make inappropriate investment decisions, investors may

terminate the relationship with the person or institution and substitute another. Loss of business thus

serves as a disciplining mechanism for these information intermediaries and the threat of the loss of

income provides incentives for credible performance. Unlike other information intermediaries, if a CRA

provides inappropriate ratings, investors do not have the option to terminate the relationship with the

CRA or to stop paying for its services. Furthermore, the CRAs are virtually assured of future business

and corresponding income by the governmental requirements for debt ratings under many circumstances

and the entrenched market expectation for debt ratings for virtually all debt issues. Under these

circumstances, what disciplines CRAs to provide quality, accurate bond ratings and what ramifications, if

any, are there in the face of inappropriate ratings?

The important role of credit rating agencies (CRAs) in the capital markets is well-established in

both academic (e.g., Holthausen and Leftwich (1986), Hand et al. (1992), and Dichev and Piotroski

(2001)) and practitioner literature ( e.g., Partnoy (2009)). The incentives faced by the CRAs and the

related reliability of credit ratings have been the subjects of significant debate for many reasons. One of

the main concerns is that the issuer generally pays the CRAs for the rating resulting in the potential for

conflict of interest. After the establishment of the NRSRO requirement in 1975, the debt ratings market

became a government sanctioned oligopoly, potentially precluding incentive for improving performance.

Two recent events increased scrutiny of the CRAs: the Enron/WorldCom bankruptcies in

2001/2002 and the 2008 financial crisis. Both Enron and WorldCom had investment grade debt ratings

until just prior to their respective bankruptcy filings. The structured finance instruments, including

mortgage backed securities and collateralized debt obligations, that arguably contributed to the financial

crisis of 2008-2009, were frequently rated not only investment grade, but given the highest AAA rating.

2

These two events drew attention not just to the conflict of interest but also to the potentially “excessive”

reliance of investors on ratings.1

Academic research in the aftermath of these two events has focused primarily on how the

conflicts of interest inherent in the CRA’s issuer-pay model and the fallacies of the efficacy of existing

regulatory mechanisms contributed to these events (e.g., Benmelech and Dlugosz (2010), Efing and Hau

(2015), Bolton et al. (2012)). Other studies examine how regulatory changes after these events have

affected the quality of credit ratings (e.g., Jorion et al. (2005), Cheng and Neamtiu (2009), Dimitrov et al.

(2015)). There is little evidence on whether CRA’s loss of reputation as a result of these events has

affected investors’ reliance on credit ratings. This is the research question that we explore in this paper.

Specifically, we study whether reputational shocks to CRAs alter the association between

ratings and returns in the bond markets. Because there are arguments on both sides of this question, it

becomes an empirical issue to resolve. One hypothesis is that investors decrease their reliance on credit

ratings due to their perception that CRAs failed to exercise sufficient care and professional judgment in

developing ratings for the securities at issue and/or inflated their ratings in order to gain business under

the issuer-pay model. The then existing regulatory framework did not hold CRAs accountable for their

actions; that is, there were no regulatory costs imposed for ratings subsequently found to be inappropriate

and/or inaccurate. At the same time, regulations effectively required all bond market securities to be

rated by NRSRO-designated CRAs so there were no obvious reputational costs given there were only

three NRSROs during much of that period.2 This lack of reputational and regulatory costs raises the

question of whether CRAs have any incentives to modify their behavior after what would likely be

perceived as a reputational shock. If investors believe that CRAs will not improve on their rating

1 Bolton et al. (2012) discuss that the combination of CRA reliance on fees from issuers, investors who were too

trusting and issuers looking to benefit from mispricing of their issues could have led to substantial rating inflation

that contributed to the 2008 financial crisis. 2 As Hunt (2009) notes: “The dominant view of rating quality in the legal literature and among policymakers comes

from the “reputational capital” model of rating agencies, which holds that a well-functioning reputation mechanism

will give rating agencies optimum incentives for producing high quality ratings. The underlying idea is that if

investors determine that CRAs’ ratings are of low quality, they will stop crediting the ratings and the agency’s

business will lose value. At the same time, it has been recognized that real world characteristics of the rating market

may cause the reputation mechanism not to function well.” (p 113)

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practices after such a shock, their only recourse might be to place less weight on the ratings relative to the

weight placed prior to the shock and relative to other publicly available information on the credit

instruments. In support of this hypothesis, Sethuraman (2016) finds that after a reputational shock bond

issuers increase their voluntary disclosures as investors start relying on information sources other than

credit ratings. On the other side of the argument, if investors believe that CRAs conducted unbiased

investigations and exercised appropriate due diligence and professional judgement in developing their

ratings prior to the reputational shock, then investors might continue to view the ratings with the same

confidence as before the shock. Relatedly, investors might believe in the disciplining force of the

reputational shock resulting in higher quality ratings and continue to view the ratings with the same or

increased confidence. Our null hypothesis is that the reputational shock does not affect investors’ reliance

on credit ratings.

To explore this hypothesis, we focus on the two reputational shocks noted above.3 Enron filed for

bankruptcy in November 2001 and WorldCom filed for bankruptcy in July 2002. Rating agencies were

widely criticized following these events because Enron’s bonds were rated investment grade four days

prior to the bankruptcy filing and WorldCom’s bonds were investment grade three months before

WorldCom filed for bankruptcy. These two bankruptcies constitute our “Reputational Shock 1” and the

period of the shock is November 2001 to July 2002. “Reputational Shock 2” is the period of the financial

crisis between September 2008 and August 2009.

We analyze individual issue ratings (bond-level approach) instead of issuer ratings (firm-level

approach) for corporate bonds. For the two reputational shocks, we analyze both the ratings at-issue and

subsequent rating changes to assess investor reliance on credit ratings. In addition to analyzing the total

sample, we also partition the bonds by investment grade (IG) and non-investment grade (NIG) or junk

3 One might question why there would be a second reputational shock if investors had already decreased their

reliance on ratings after the first shock. This brings us to an important issue. The goals of the regulatory reforms

such as the Credit Rating Agency Reform Act of 2006 (CRARA 2006) after the Enron/WorldCom scandals, were to

increase the quality of the ratings and investor confidence in them. Sethuraman (2016) uses the introduction of

CRARA 2006 as an event that would restore CRAs reputation in the financial market. But the 2008 financial crisis

provided evidence on shortcomings of CRARA 2006. Even though it increased investors’ confidence in ratings, it

could not enhance rating quality or discipline rating agencies through enhanced competition.

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based on evidence markets’ perceptions differ between the two categories. A common criticism of the

Enron and WorldCom ratings was not just that they were too high but rather that they were investment

grade. Benmelech and Dlugosz (2010) find evidence that the 2008 financial crisis was partly caused by

IG designated securities. Based on conversations with fixed income traders at Nomura Securities in New

York, financial market analysts consider ratings more important for IG bonds than for NIG bonds.

Therefore, we expect to find that investors’ reliance on ratings for IG bonds will decrease more than their

reliance on ratings for NIG bonds.

For ratings at-issue, we examine whether the association between credit ratings and the bond

spread over the appropriate treasury (matched on maturity) differs for bonds issued after each shock

compared to bonds issued prior to each shock. Any deviation in the association between actual spread

and ratings is consistent with a change in investor reliance on the ratings.

We find that, for both reputational shocks, the association between credit ratings and bond interest

rate spread at issue decreases significantly after the respective reputational shock compared to the

association before the shock. As hypothesized, this change is more significant for IG bonds than for NIG

bonds. These results are consistent with investors decreasing their reliance on ratings after CRAs suffer a

reputational shock. While this result is consistent with our hypothesis, we recognize that there are other

influences on bond spreads, including levels and changes in macroeconomic conditions. During

economic expansions (recessions), investors become more (less) trusting of CRAs and the bond spreads

tend to be lower (higher) than in “normal” periods. In such periods, the correlation between bond ratings

and default rates, changes, suggesting that other factors such as recovery rates and risk premia also affect

the movement in spreads (Chen (2010)). While our specifications of the reputation shock windows are

chosen to minimize the effects of such economic periods (particularly for the 2008 financial crisis, for

which we remove the entire period of September 2008 to August 2009 because macroeconomic

conditions might have had an impact on the bond spreads), the results could still be affected by risk

premia. To confirm that the results are not driven by macroeconomic factors, we also examine market

reactions to credit rating changes.

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Our alternate hypothesis is that investors rely less on credit ratings during the period after each

shock compared to the period before. To measure market reaction to credit rating changes, we employ

standard event study methodology. We compute abnormal bond returns around rating change

announcements in periods before and after each shock. Based on our predictions of a lower level of

reliance on credit ratings, we expect rating changes, both downgrades and upgrades, to result in lower

magnitude abnormal returns, on average, after a CRA has experienced a reputational loss. We find results

generally consistent with our hypotheses for both downgrades and upgrades.

We contribute to the accounting and finance literature in at least two ways. First, with respect to the

literature on the information content of credit ratings, we provide evidence that investors’ reliance on

credit ratings changes as a result of a reputational shock to the CRAs. Second, we add to the literature on

the effectiveness of the posited reputational mechanism in the context of CRAs. Decreasing investor

reliance on ratings is one of the biggest motives of regulatory authorities after the 2008 financial crisis

(Partnoy (2009)).

Our work is related to four other papers. deHaan (2016) shows that rating performance improves

after the financial crisis. He argues that this result is consistent with the rating agencies positively

responding to public criticism and regulatory pressures. Using loan-level data, he also shows that debt

participants reduce their reliance on credit ratings after the financial crisis. Jaballah (2015) also studies the

impact of the 2008 financial crisis on the reputation of CRAs by measuring the stock market reactions to

changes in credit ratings before and during the crisis. He documents significantly negative stock market

reactions for downgrades before the crisis and less significant reactions after the crisis. Bedendo et al.

(2013) analyze the credit default swaps (CDS) market immediately following the 2008 financial crisis and

conclude that corporate credit ratings are viewed as less credible during the crisis compared to the period

immediately preceding the crisis. However, the same authors, in a recent working paper, Bedendo et al.

(2016), find contrasting results when they look at the stock market reactions to issuer rating

announcements after a reputational shock to CRAs. They argue that the latter results are consistent with

6

the scenario where investors believe that rating agencies will self-discipline and rebuild their reputation

by increasing rating quality.

Our paper differs from these three papers in several respects. First, we focus on bond markets

rather than equity or private debt markets, and specifically on individual bond ratings instead of issuer

ratings in order to increase the power of our results. Second, we provide evidence that investors discount

ratings after a reputation shock, which contrasts with the result in Bedendo et al. (2016). Third, in

addition to studying the impact of ratings on bond spreads at the time of new bond issues, we find

corroborating results by analyzing investor reactions to rating change announcements for bonds. Fourth,

we provide consistent results for two reputational shocks, questioning the regulatory frameworks ability

to discipline CRAs and their goal of decreased investor reliance through regulatory changes.

This paper proceeds as follows. Section 2 describes the institutional background, discusses related

research, and develops the hypotheses. Section 3 describes the methodology and data and Section 4

provides the main results and robustness checks. Section 5 summarizes and concludes.

2 BACKGROUND, RELATED LITERATURE, AND HYPOTHESES DEVELOPMENT

2.1 Institutional Background

Credit ratings gained importance in capital markets after the great depression of the 1930s. With

the establishment of the Securities and Exchange Commission (SEC) in 1934, certain regulated industries

were permitted to invest only in bonds having satisfactory credit ratings. At that time, credit rating

agencies (CRAs) followed an investor-pay model; that is, investors paid fees for ratings provided by the

CRAs.

Two important changes occurred in the 1970s. In 1975, the SEC created the Nationally

Recognized Statistical Rating Organization (NRSRO) designation for CRAs. Pension funds and money

market mutual funds, for example, can invest only in NRSRO rated investment grade (IG) bonds.

NRSRO credit ratings became part of the regulator’s determination of the reserves required to be held by

7

banks and insurance companies. An IG rating is required for an SEC Rule 415 shelf registration. Credit

ratings became widely used for commercial purposes such as long term leases and requirements for letters

of credit. In other words, NRSRO credit ratings are well-entrenched in the operation of capital markets.

Secondly, most CRAs changed in the 1970s from an investor-pay model to an issuer-pay model

following the invention of the photocopy machine, citing loss of revenue due to photocopying of rating

reports by investors as one of the main reasons for the change. This latter change created a potential

conflict of interest because the largest source of income for CRAs is rating fees. CRAs needed to win

business from other CRAs in order to gain revenue, potentially leading to biased ratings. An issuer can

approach multiple rating agencies to get a specific issue rated prior to issue and choose one or more CRAs

based on a comparison of the ratings, an action known as “rating shopping.” Since the rating agencies

follow an issuer-pay model and issuers can engage in “rating shopping”, the rating agencies have

incentives to inflate ratings at the cost of investors.4

At the time of the Enron and WorldCom bankruptcies in 2001/2002, the SEC had granted NRSRO

status to only S&P, Moody’s and Fitch. There was a lack of transparency in the NRSRO certification

process by the SEC and the process served as an effective barrier to entry. The “Sarbanes-Oxley Act

(SOX)”, passed by the U S Congress in July 2002, requires the SEC to provide details on the

determination procedure for designating NRSROs. The SEC’s January 2003 report, as required by SOX,

resulted in extensive Congressional hearings on the NRSRO process culminating in the Credit Rating

Agency Reform Act of 2006 (CRARA)” (Cheng and Neamtiu (2009)). CRARA established the criteria for

NRSRO certification and imposed a strict timetable on the SEC for granting NRSRO recognition with the

goal of increasing competition among the CRAs so they would have incentives to put more effort into the

ratings generating process and thus decrease the conflicts of interest inherent in the issuer-pay model.5

Prior to CRARA, there were seven NRSROs and three more CRAs were subsequently designated.6

4 He et al. (2015) study rating shopping on the MBS market. They show that single rated tranches have been

“shopped”, and pessimistic ratings never reach the market. 5 See Hunt (2009). Applicants for registration must provide credit ratings performance measurement statistics over

short-term, mid-term, and long-term periods, 15 U.S.C. 78o-7(a)(1)(B)(i), describe the procedures and

8

In addition to passage of SOX after the Enron bankruptcy, the SEC adopted Regulation Fair

Disclosure (Reg FD) in 2002 to prevent selective disclosure of information by publicly traded firms to

analysts and institutional investors. However, Rule 100(b)(2)(iii) of Reg FD provided an exemption for

disclosures made to CRAs which meant that issuers could continue to disclose private information to

CRAs.

The claimed goal of the regulatory changes (SOX and CRARA) was to improve investor

confidence in ratings provided by the CRAs (Sethuraman, 2016). But the 2008 financial crisis suggests

that these new regulatory provisions were inadequate and did not solve quality and conflict of interest

problems associated with CRAs.

In response to the 2008 financial crisis, Congress passed the “Dodd-Frank Act” in 2010, which

included two important provisions relating to CRAs. First, Dodd-Frank Act removed the exemption of

CRAs from the provisions of Reg FD, effective on October 4, 2010. However, Reg FD applies to

“covered persons” and at the time of the introduction of Reg FD, CRAs were registered as investment

advisors and thus qualified as covered persons but were specifically exempted. 7 But the CRARA of 2006

amended Section 2(a)(11)(F) of the Investment Advisors Act of 1940 so that NRSROs were specifically

excluded from the definition of “investment advisor”. Thus, none of the NRSROs are registered as

investment advisors, implying that the Reg FD amendment by the Dodd-Frank Act had no effect on

NRSROs. Interestingly, if a credit rating agency is not an NRSRO it can remain exempt as before because

Rule 100(b)(2)(ii) allows companies to enter a confidentiality agreement with the CRA.

methodologies that the applicant uses in determining credit ratings, 15 U.S.C. 78o-7(a)(1)(B)(ii), and provide

certifications from at least 10 unaffiliated qualified institutional buyers, with each certification indicating that the

buyer has used the credit ratings of the applicant for at least the 3 years immediately preceding the date of the

certification. Applications that contain the prescribed information are to be granted unless the Commission

determines that (a) the applicant does not have adequate and managerial resources to consistently produce credit

ratings with integrity and to materially comply with the rating procedures it says it follows or (b) the applicant or

person controlling the applicant has been convicted of a felony or has been punished for committing certain

securities violations. 15 U.S.C. 78o-7(d) 6 The list of NRSROs is available at http://www.sec.gov/ocr/ocr-current-nrsros.html 7 Under Rule 100(b)(1) of Reg FD, the list of covered persons includes:

• Broker-dealers and their associated persons,

• Investment companies, hedge funds and their affiliated persons, or

• Any security holder or person for whom it is reasonably foreseeable that such person would buy or sell

securities on the basis of information disclosed.

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Second, the Dodd-Frank Act repealed Rule 436(g) of the Securities Act of 1933. Rule 436(g)

exempted rating agencies from “expert” liability when they issue credit ratings. However, The Asset-

Backed Market Stabilization Act of 2011 (H.R. 1539), passed six months after Dodd-Frank, restored Rule

436(g). H.R. 1539 came in six months after the passage of Dodd-Frank Act, which implies that the

provisions of Dodd-Frank, related to lawsuits against CRAs, were no longer valid after 6 months.

In summary, the Dodd-Frank Act 2010 did not have any provisions that would arguably result in

greater reliability of credit ratings after the financial crisis.

2.2 Prior Research

Much of the earlier academic research focuses on the information content of credit ratings.

Holthausen and Leftwich (1986), Hand et al. (1992), and Dichev and Piotroski (2001) all find that equity

investors react to bond rating announcements and the reaction is greater in magnitude for rating

downgrades than for upgrades. Kao and Wu (1990) and May (2010) provide evidence that both ratings

levels and changes, respectively, have predictive ability for subsequent firm performance and credit risk.

Some recent research examines whether credit ratings have decreased in relevance to capital

markets over time. Chava et al. (2012) show that CDS spreads have greater explanatory power for the

cross sectional variation in bond yields in both the primary and secondary bond markets than do credit

ratings. On the other hand, Benmelech and Dlugosz (2010) find that rating inflation, due to rating

shopping, was one of the major drivers of the 2008 financial crisis. Relatedly, Efing and Hau (2015)

provide evidence that CRAs provide higher ratings for issuers that provide the CRAs with more bilateral

securitization business. The authors document that the size of the rating favors is positively related to the

complexity of the structured debt deals and to the activity level in the credit market in terms of bond

issuances.

Within the extensive conflict of interest literature, we focus on two streams of research most

relevant for our study. The first analyzes whether increased regulation has succeeded in improving the

quality of bond ratings by CRAs. Jorion et al. (2005) find that the information content of both credit

rating downgrades and upgrades is greater following the passage of Reg FD (2000). Similarly, Cheng and

10

Neamtiu (2009) find that CRAs issue more timely downgrades and more accurate ratings following the

passage of SOX in 2002. These two papers suggest that the regulatory mechanisms have been successful

but Dimitrov et al. (2015) finds no evidence that the Dodd-Frank Act (2010) disciplines CRAs to provide

more accurate ratings. Instead, they find that CRAs issue lower ratings, give more false warnings, and

issue more but less informative downgrades after Dodd-Frank. They suggest that these results are more

consistent with the reputation model of Morris (2001), according to which CRAs became more concerned

about their reputation following Dodd-Frank because of the new provision that made the rating agencies

liable for the ratings they provided and subject to lawsuits for damages.8

The second strand of literature relates to conflicts of interest and the reputational capital of CRAs.

Theoretical literature in this area focuses on whether reputational concerns, in equilibrium, result in truth-

telling by the rating agencies. Mathis et al. (2009) finds that reputation cycles may exist during which a

CRA first builds up its reputation by relaying information accurately but subsequently exploits this

reputation by collecting fees for inflated ratings. The authors demonstrate that truth telling incentives are

weaker when CRAs are rating complex debt products such as structured securities (e.g., asset backed

securities (ABS) and mortgage backed securities (MBS)). Bolton et al. (2012) model an equilibrium in

which CRAs inflate credit ratings with both endogenous and exogenous reputation costs, suggesting that

reputational concerns are not strong enough or sufficient to discipline CRAs. In the microeconomics

literature, an information intermediary is modeled as engaging in acquiring and certifying information and

committing to it through disclosures. Also, the reputational costs are usually modeled as no business in

the later periods if the intermediary lies in the first period. But in the case of CRAs, these models do not

apply, as documented by Mathis et al. (2009), and Bolton et al. (2012). The results of these two papers tie

well with the institutional setting in which CRAs work. The requirement of being an NRSRO to be able to

8 Dimitrov et al. (2015) refers to the repeal of 436(g) by the Dodd-Frank Act, which made credit rating agencies

liable for increased lawsuit exposure. But, this result seems rather puzzling particularly because, as mentioned

earlier, the Asset-Backed Market Stabilization Act of 2011 (H.R. 1539), restored rule 436(g) of The Securities Act

of 1933, which exempted rating agencies from “expert” liability when they issue credit ratings. H.R. 1539 came in

six months after the passage of Dodd-Frank Act, implying that the provisions of Dodd-Frank, related to lawsuits

against CRAs, were no longer valid after 6 months

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rate securities and the mandatory requirement of getting a bond rated by a CRA, make it difficult for

reputational concerns to work as a disciplining device, unlike traditional microeconomics reputation

models.

This brings us to our main research question: in the absence of disclosure rules, legal liability, and

rights to terminate (due to regulatory requirements to get the bonds rated by an NRSRO), do investors

decrease reliance on ratings after a CRA has been hit by a reputational shock?

We identify three related papers that explore empirically the effectiveness of the reputational

effects and regulatory mechanisms discussed above; however, the results are mixed. Jaballah (2015)

studies the impact of the Subprime crisis on the reputation of CRAs by measuring the stock market

reactions to changes in credit ratings before and during the crisis. Using data for European and American

stock markets for the period of 2005-2009, he documents negative and statistically significant stock

market reaction to rating downgrades before the crisis. However, during the crisis, he only finds negative

and significant reaction for the European stock markets. He argues that these results suggest that U.S.

market participants ignored rating changes during the crisis, suggesting that they found the ratings

unreliable. Bedendo et al. (2013) analyze the credit default swaps (CDS) of 205 issuers immediately

following the 2008 financial crisis and find that corporate credit ratings became less credible following

the 2008 crisis as reflected in the diminished price impact of ratings changes compared to before the

crisis. However, the same authors, in a more recent working paper, Bedendo et al. (2016), find

contrasting results with respect to the stock market reactions to issuer rating announcements following

three blows to CRAs’ reputation: the Enron/WorldCom bankruptcies; the mass structured product

downgrade by Moody’s in 2007; and the federal government’s lawsuit against S&P in 2013. They find a

stronger response by equity investors to ratings downgrades following these three reputational shocks

compared to before. The authors explain these results as consistent with investors’ beliefs that the CRAs

choose to rebuild their reputation by increasing rating quality.

Our paper differs from these three papers in several respects. First, we focus on corporate bond

markets and specifically, on individual corporate bond ratings. In the United States, the corporate bond

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market comprises 21% of the $41 trillion total bonds outstanding as of September, 2016.9 CRAs have

played an important role in bond markets since at least 1909 with the founding of Moody’s. Although the

importance of ratings to equity investors is well-documented, the ratings effect is indirectly manifested in

the firm’s cost of debt, a component of the cost of capital used in equity valuation. Thus we focus on the

direct information content of credit ratings for individual bonds. Second, we provide evidence that the

investors discount the credibility of ratings after a reputation shock in contrast to Bedendo et al. (2016).

Our results from new bond issues as well as for rating changes are consistent with investors discounting

ratings due to the reputational loss suffered by CRAs. Third, in addition to bond rating change

announcements, we also look at how investors perceive ratings at the time of new bond issues.

2.3 Hypothesis Development

Under the assumptions that CRAs incorporate private information in determining their ratings and

that investors rely on these ratings, a reputation loss for the CRAs would adversely affect investors’

beliefs about the quality of the credit ratings, and in turn, about the credit quality of the bond issue.10 We

thus predict that investors reduce their reliance on a credit rating after a reputation shock to CRAs. We

analyze new bond issues as well as bond rating changes to develop specific hypotheses:

2.3.1 Bond Ratings and Bond Spreads at Issue

Before we discuss our hypotheses, we first note the relationship between bond spreads and bond

ratings. The bond spread is the difference between two bonds with the same maturity but different credit

ratings; often the difference between a corporate bond and a risk-free treasury bond. Bond spreads

depend on several risk factors, including credit risk, prepayment risk, liquidity risk, legal risk, maturity

risk, and complexity risk.11 Credit risk refers to a bond’s inability to repay all its principal and interest on

time as promised. Credit ratings typically address this loss risk. Loss risk incorporates the probability of

9 http://www.sifma.org/research/statistics.aspx US Bond Market Issuance and Outstanding (xls) - annual, quarterly,

or monthly issuance to December 2016 (issuance) and from 1980 to 2016 Q3 (outstanding) 10 Zeibar and Reiter (1992) shows that bond ratings affect bond yields (i.e., investors consider bond ratings when

pricing bonds). 11 https://www.moodys.com/sites/products/DefaultResearch/2004600000426733.pdf

13

default (PD) and the expected loss given default (LGD).12 In other words, expected loss or credit loss risk

as a percentage of exposure at default is given by:

Expected Loss = Probability of default (PD) × Loss given default (LGD) (1)

A higher bond spread implies a higher credit risk and higher rating implies lower credit risk. In other

words, bond spread decreases as the rating improves.

CRAs provide bond ratings based on their assessment of PD and LGD or their assessment of bond

spread.13 The ratings-based spread is the E[Bond Spread̃ |Rating]. Investors rely on ratings along with

other publicly available information to assess the riskiness of the bonds. The observed bond spread is the

realized or actual spread (i.e. realized value of Bond Spread̃ = Bond Spread). The impact of credit ratings

on the realized bond spread can be estimated using the following model:

Log(Bond Spreadi) = β

0+ β

1*Rating

i+ Controlsi+ εi (2)

The log transformation allows for a non-linear relationship between the two variables and

mitigates the heteroscedasticity in the residuals. The coefficient 𝛽1 measures the association between

credit ratings and actual bond spreads. An economy wide shock, unrelated to individual ratings, would

move these spreads either up or down, but the slope 𝛽1 should continue to be the same if the association

between the credit ratings and bond spreads remains unchanged. A change in the slope, on the other hand,

implies a deviation between the ratings-based spread and actual spread.

For ratings at-issue, we test whether the association between the at-issue rating and the realized

bond spread decreases for bonds issued in the period following each of the reputational shocks (the Post-

Period) compared to bonds issued in the period prior to the reputational shock (the Pre-Period). Our null

and alternative hypotheses are:

Hypothesis 10: Ceteris paribus, the association between ratings and realized bond spreads is the

same in the Post-Period and the Pre-Period.

12 PD – probability of default for a specific security, LGD – the loss given default as a percentage of exposure at

default 13 https://www.moodys.com/sites/products/DefaultResearch/2006600000441444.pdf

14

Hypothesis 1A: Ceteris paribus, the association between ratings and realized bond spreads is

lower in the Post-Period relative to that in the Pre-Period.

As noted above, macroeconomic conditions can also affect bond spreads. During economic

expansion (recession), investors rely more (less) on bond ratings and the bond spread can be lower

(higher) than in more normal times. While we specified the reputation shock window to reduce the effect

of such economic periods (for the 2008 financial crisis, we exclude the entire period of September 2008 to

August 2009 when macroeconomic conditions likely affected bond spreads), the results could still be

affected by risk premia. To validate that the results are not driven by macroeconomic factors, we also

study bond market reactions to credit rating changes.

2.3.2 Market Reaction to Bond Rating Changes

We investigate the impact of CRAs’ reputational shock on investors’ reliance on ratings by

studying the bond market reaction to credit rating changes. Prior literature suggests that any rating change

results in abnormal bond returns (e.g., Holthausen and Leftwich (1986), Hand et al. (1992), Dichev and

Piotroski (2001), and May (2010)). Most of these studies find significant results for rating downgrades

but mixed evidence on upgrades. Specifically, rating downgrades result in negative abnormal returns

whereas rating upgrades result in zero or positive abnormal returns following a rating announcement. If a

reputational shock to CRAs affects investors’ perception about the information content of credit ratings

adversely, then we should expect a weaker market reaction to rating change events.

Based on our predictions, we hypothesize that a rating downgrade results in a lesser response by

investors as reflected in a smaller magnitude of negative abnormal returns, on average, after a reputational

shock to CRAs. Similarly, for a rating upgrade, we expect a lesser investor response and thus a lower

magnitude abnormal return, on average, after CRAs’ reputational shock. The null hypothesis in both cases

predicts no significant difference in bond market reaction before and after the reputational shock.

Hypothesis 2a: Ceteris paribus, the magnitude of the bond market reaction to rating downgrades

will be smaller, on average, in the Post-Period than in the Pre-Period.

15

Hypothesis 2b: Ceteris paribus, the magnitude of the bond market reaction to rating upgrades will

be smaller, on average, in the Post-Period than in the Pre-Period.

3 RESEARCH DESIGN AND DATA

3.1 Research Design

To explore our research question, we exploit two reputational shocks - the bankruptcies of Enron

and WorldCom in 2001-2002 (Reputational Shock 1), and the 2008 financial crisis (Reputational Shock

2).14 Enron filed for bankruptcy protection on November 29, 2001 and WorldCom filed for bankruptcy

protection on July 19, 2002. We define the period between November 1, 2001 and July 31, 2002 as

Reputational Shock 1. Our first data sample (Reputational Shock 1 sample) includes firm data between

November 1, 2000 and July 31, 2003. The 12-month period between November 1, 2000 and October 31,

2001 is the Pre-Period and the 12-month period between August 1, 2002 and July 31, 2003 is the Post-

Period. The Post-Period corresponds to the period when the greatest fall-out, if any, from the reputational

shock should occur.

Reputational Shock 2, corresponding to the 2008 financial crisis, is the period between September

2008 and August 2009. Lehman Brothers’ bankruptcy on September 15, 2008 is the start of the shock

period because CRAs rated Lehman Brothers AA days before it went bankrupt. Similarly, the end period

of the shock is defined as the last major event during the crisis when Fannie Mae reported a loss of $14.8

Billion and requested $10.7 Billion from the US Treasury Department.15 The second sample

(Reputational Shock 2 sample) comprises firm data between September 1, 2007 and August 31, 2010. The

12-month period between September 1, 2007 and August 31, 2008 is defined as the Pre-Period and 12-

month period between September 1, 2009 and August 31, 2010 is defined as the Post-Period.

14 These events are not the first or only reputational shocks experienced by the CRAs in the U S but the most recent.

Prior shocks, determined by the negative financial press and/or regulatory action, include: the Penn Central

bankruptcy in 1970; New York financial crisis in 1975; Washington State Public Power Commission default in

1982; and the Orange County California bankruptcy in 1994. In all of these examples, the CRAs did not anticipate

the financial status or implications of rated entity. 15 https://www.stlouisfed.org/financial-crisis/full-timeline

16

Fixed income managers primarily use two metrics to evaluate bonds – bond yield and bond spread.

Bond spread is the yield of a corporate bond adjusted for the yield of a treasury bond with the same

maturity. We focus on bond spread in studying the effect of ratings on the default risk of bonds,

consistent with prior literature.

Ratings can be either for the issuer or for the specific bond issue. An issuer rating is based on the

assessed creditworthiness of the borrower’s overall financial condition whereas an issue rating is based on

the assessed probability of default and expected loss given default for the given instrument, as discussed

above. We examine issue ratings for corporate bonds, not for the issuer, because the greater number and

greater variance across issues increase the power of the tests. In addition, although bond issue ratings are

“sticky”, issuer ratings are even stickier.16 In addition, the sample has almost as many distinct issuers as

distinct issues; that is, most of the bonds in our sample are being issued by different issuers.17

Ratings are broadly divided into “Investment Grade” (IG), (i.e., bonds rated BBB- and above by

S&P and Fitch and Baa3 and above by Moody’s), and “Speculative Grade”/“Non-Investment

Grade”/”junk” (NIG) for all other bonds. Institutional investors such as pension funds are prohibited from

investing in NIG grade bonds and a downgrade from IG to NIG requires them to sell the affected bonds.

Therefore, we analyze the entire sample, the IG sample, and the NIG sample separately.18

16 Moody’s Rating Symbols and Definitions (December 2016, p42): “KRAs (Key Rating Assumptions) are, by their

nature, relatively stable inputs to the analytical process, and because they seek to bring a degree of stability,

consistency and transparency to something that may in practice be uncertain, they are intended to be reasonably

resilient to change. They may change over time in response to long-term structural changes or as more is learned

about long-run relationships between risk factors, but they would be very unlikely to change as a result of a short-

run change in economic or financial market conditions.” 17 Maul and Schiereck (2016) discuss the advantages and disadvantages of using bond-level and firm-level data.

Bond-level ratings provide a larger number of observations, thus increasing the power of the tests. The choice of

issues rather than issuer could result in clustering of observations by issuer in large samples, but because we study

the 12-month period before and after each shock, firm clustering is not an issue as our sample has almost as many

distinct issuers as distinct issues. As a precaution against the potential effects of firm-level clustering, we rerun our

tests with clustering standard errors by firm; our results remain unchanged. 18 It is important to distinguish between an issuer-paid rating agency (e.g. S&P, Moody’s, Fitch) and investor-paid

or a subscriber-paid rating agency (e.g. Egan-Jones Rating Company). Companies provide material non-public

information to rating agencies that are issuer paid. In other words, issuer-paid rating agencies claim to have an

advantage over subscriber-paid rating agencies because their ratings are based on private information rather than

only on publicly available information. Therefore, in our study, we focus on issuer-paid ratings, specifically S&P

Ratings. Another important fact in this regard, as discussed above, is that issuer-paid rating NRSROs still have an

17

To test our first hypothesis, we estimate the following regression model:

Log(Bond Spreadi) = β

0+ β

1*Rating

i+ β

2*Shock + β

3*Rating

i×Shock + Controlsi+ εi (3)

Where:

Log(Bond Spread) = the natural logarithm of the bond spread of the new bond issue

Rating = the initial rating of the bond at the time of issue

Shock = an indicator variable taking a value of 1 if the observation belongs to

the Post-Period and 0 otherwise

Controls = include issue specific risks, other than credit risk, that determine the

spread such as maturity, size of the issue, callable, put-able, and

sinking fund.

If investors change their reliance on ratings after a reputational shock, then we expect the coefficient 𝛽3 to

be significant, indicating a deviation between ratings-based spreads and actual spreads.

For our next two hypotheses, we use event study methodology to estimate market reaction to

bond rating changes. Consistent with prior literature, we measure investors’ reliance on credit ratings as

the bond price reaction to a rating change announcement. To study whether investors discount

information in bond rating changes, we compare bond rating reactions in the Pre-Period to those in the

Post-Period for both reputational shocks.

To calculate abnormal bond returns for the event studies, we first estimate normal bond returns.

There are several alternatives for specifying bond event studies.19 We use daily bond returns and form

matching portfolios with treasury securities, consistent with Bessembinder et al. (2009).20 Treasury

securities yield curves are available for seven major maturity categories (1, 2, 3, 5, 7, 10, 20, and 30

access to all non-material public information even after Dodd-Frank Act 2010 (Section 939B) as they do not belong

to the category of “covered persons”.

19 Bessembinder et al. (2009) and Maul and Schiereck (2016) review bond event studies used in the literature.

Bessembinder et al. (2009) analyze various event study methods and provide recommendations on which ones are

more appropriate in terms of power of the test. They find that the use of the daily bond returns significantly

increases the power of the test. The authors provide evidence that calculating a bonds’ excess return against a

matched firm’s or matched portfolio’s bond return is superior to using mean adjusted returns. 20 https://fred.stlouisfed.org/tags/series?t=bofaml

18

years) on the St. Louis Fed website. We interpolate the yield curve whenever a bond maturity does not

match the maturity in the category.

The corporate bond return for the event study is calculated as follows:

RETt = Pt - Pt-1

Pt-1

(4)

The subscript t denotes the Post-Period and t-1 denotes the Pre-Period. The price relative is thus

measured as the first trading price subsequent to the ratings change less the last trading price prior to the

ratings change announcement (i.e., the event window). These prices exclude accrued interest and are

referred to “clean prices.”

The abnormal return (ABRET) for bond i is calculated as the difference between the observed

return (RET) and the expected return (E(RET)). The expected return is computed as the matched (on

maturity) treasury bond return over the event window.

ABRETi = RETi - E(RETi ) (5)

We also estimate the multivariate regression of ABRET on our indicators of shock and on the set of

controls identified in prior literature as significant determinants of abnormal returns (e.g. Holthausen and

Leftwich (1986), Hand et al. (1992), Dichev and Piotroski (2001), and May (2010)):

ABRETi = β0 + β

1Shock + Controlsi + εi (6)

Shock is an indicator variable with a value of 1 if the observation belongs to the Post-Period and 0

otherwise. Controls here include the change in level of ratings (notches) and an indicator variable that

denotes whether the downgrade or upgrade was from IG to NIG or from NIG to IG respectively.

3.2 Data

During the period of our study, there are three major NRSRO designated CRAs – S&P, Moody’s,

and Fitch with either S&P and Moody’s, and often both, rating most of the corporate bond issues.

Although we execute our analyses using both S&P and Moody’s ratings separately, we report only S&P

results in the interests of economy unless the results for Moody’s differ from those for S&P.

19

We use data from several sources: the National Association of Insurance Commissioners (NAIC)

database; the Trade Reporting and Compliance Engine (TRACE) database; and Mergent’s Fixed

Investment Securities Database (FISD).

3.2.1 Bond Spreads and Initial Ratings

Data on bond ratings was gathered from the Mergent FISD database. The bond ratings dataset

consists of issue details for over 150,000 issuers, US Agencies and US treasury debt securities. The raw

dataset includes 2,614,166 rating changes and initial ratings issued for bonds between 1950 and 2015. We

delete observations whenever the rating is not between AAA and D.21 Table 1 provides the mapping from

the CRAs ratings to the cardinal scaled used in our analyses. For our analysis, we report results using

S&P ratings because most of the issues are rated by both the rating agencies and there is no significant

variation. There are 45,163 observations of new issues of corporate fixed coupon bonds rated by S&P for

the sample period between 1950 to 2015.

We further partition this sample for our two reputational shocks. Table 3 provides summary

statistics for these two samples. Reputational Shock 1 contains 869 bond issues in the Pre-Period and

671 bond issues in the Post-Period. Reputational Shock 2 contains 257 bond issues in the Pre-Period and

750 bond issues in the Post-Period.

3.2.2 Bond Market Reaction and Rating Changes

For the event studies, we require bond transaction data for both the Pre-Period and Post-Period for

both reputational shocks. The preferred database for bond transactions data is the Trade Reporting and

Compliance Engine (TRACE) but it is only available from 2002, whereas Reputational Shock 1 requires

data for the 12-month period before November 2001 and 12-month period after July 2002. 22 To execute

21 We follow prior literature and map the rating codes to the cardinal scale (Table 1). Moody’s uses code from Aaa

down to C to rate bonds whereas Fitch and S&P rate bonds from AAA down to D. We transformed the credit ratings

for S&P, Moody’s and Fitch into a cardinal scale starting with 1 as AAA/Aaa, 2 as AA+/Aa1, and so on until 23

(DDD/DD/D) as the default category. Following Jorion et al. (2005), we chose 23 instead of 22 for the default

category because Fitch provides three ratings (DDD/DD/D) for default, so 23 represents the average of the default

DD rating. 22 TRACE database contains price, time and size of transactions for all publicly traded over the counter (OTC)

corporate bonds. Even though TRACE was introduced in July 2002, it started covering all publicly traded bonds

20

the event study for Reputational Shock 1, we combine the TRACE dataset with National Association of

Insurance Commissioners (NAIC) to create a panel dataset of bond trades from 1994 to 2015. NAIC

contains bond transactions for insurance companies starting in 1994. 23

The NAIC and TRACE databases contain bond transaction data for corporate bonds. The TRACE

dataset is more comprehensive and covers transactions of all publicly traded corporate bonds, beginning

in July 2002. For bonds traded multiple times on a given day, TRACE covers all transactions with

individual time stamps. We calculate closing bond price for each bond on a specific transaction date using

the last trade price approach. That is, we extract the last traded price of the day for each bond on a

transaction date. To create unique bond-day transactions data, we create a trade weighted price using

volume for the trades whenever there are multiple trades on the same time stamp.

Consistent with prior literature, we consolidate the two databases to create a long span transaction

data sample (Lin et al. (2011)). We keep transaction records reported by TRACE only if transactions of

same bond are included in both NAIC and TRACE databases after July 2002. Our final sample includes

corporate bond transactions from January 1994 to December 2015. We combine CUSIP and trade data

from NAIC and TRACE datasets to get unique bond-date combinations. We then add price data from

NAIC and TRACE to this dataset.

While we report results only for S&P ratings, Table 2 provides ratings upgrades and downgrades

by all rating agencies for corporate bonds. We have a total of 117,631 rating changes for S&P, 117,002

rating changes for Moody’s, and 55,967 rating changes for Fitch. As mentioned earlier, Moody’s and

S&P are the two primary players in the corporate bond markets, with more than twice the rating changes

as Fitch. Focusing on S&P ratings, we see that number of downgrades increase substantially after both

reputational shocks (highlighted in grey).

only from October 2004 onwards. Currently, TRACE covers 100 percent of OTC activity representing 99 percent of

total US corporate bonds market activity in over 30,000 securities. 23 NAIC consists of all transactions of publicly traded corporate bonds beginning January 1994 by life insurance

companies, property and casualty insurance companies, and health maintenance organizations (HMOs).

21

In order to ensure that the event window captures the rating event of S&P without any

contamination from other concurrent rating changes, we identify the closest rating changes by Moody’s

and Fitch for any S&P rating change. If the S&P rating change event had either Moody’s or Fitch rating

change event for the same bond within 10 days, then we expand our event window from the day of rating

change by S&P to the event start date as the first rating change by either of the CRAs and determine the

event end date as the last rating change by either of the CRAs. We only keep rating events for which we

have rating consensus among all rating agencies. This leaves us with 117,091 rating change events by

S&P with 48,751 distinct issues and 6,617 distinct issuers.

Corporate bonds are generally illiquid and our sample includes bonds for which the first transaction

after a rating change event occurs as long as several weeks after the rating change. To capture the effect

of the rating change event, we exclude observations for which the difference between the last transaction

date before (Last Trans Date) the rating change and the first transaction date after (Next Trans Date) the

rating change is more than 20 days.24 The Reputational Shock 1 sample contains 3,833 downgrades and

625 upgrades. Reputational Shock 2 sample contains 5,185 downgrades and 2,350 upgrades.

4 EMPIRICAL RESULTS

In this section, we test whether the data are consistent with our hypotheses. Section 4.1.1 examines

investors’ reliance on ratings at bond issue in the periods before and after the two reputation shocks.

Section 4.1.2 examines the bond market reactions to rating changes in the periods before and after the two

reputation shocks. We discuss the falsification test in Section 4.2.

4.1 Main Results

4.1.1 Bond Ratings and Bond Spreads at Issue

Table 3 provides descriptive statistics for the variables used in the empirical analysis of ratings at-

issue, for the entire sample as well as separately for the IG and NIG sub-samples. The summary statistics

24 As a robustness check, we specified various windows ranging from 1 day to 20 days. The results were

approximately the same although the sample sizes decreased significantly for windows shorter than 20 days.

22

are divided into Pre-Period and Post-Period. Panel A provides summary statistics for Reputational Shock

1 and Panel B provides the same statistics for Reputational Shock 2.

Relative to the Pre-Period, the bond ratings for the entire sample on average, and for each of the

IG and NIG subsamples, have decreased for both reputational shocks in the Post-Period. (Recall that

higher rating numbers correspond to lower rating quality as illustrated in Table 1.) With respect to bond

spreads, there is an asymmetry between the IG and NIG subsamples. While IG bonds have lower spreads,

in the Post-Period, NIG bonds have higher spreads compared to the Pre-Period. This is not surprising

because after the shock, IG bonds are in greater demand compared to NIG bonds, driving IG prices higher

and thus decreasing the bond spreads.

While the summary statistics provide results on an aggregate level for all ratings, Figure 1 depicts

graphically the association between Log(Spread) and rating across the rating categories for the Pre-

Period and the Post-Period in Panels A and B. Figure 1 illustrates that the slope of the graph between

market implied treasury spread and ratings levels is different in the Post-Period compared to the Pre-

Period, for both reputational shocks, consistent with a change in investors’ reliance on the credit ratings

after each shock.

The main results for our first hypothesis are in Table 4. We estimate equation (3) for the two

reputational shocks separately and provide regression estimates including and excluding the control

variables for completeness as some observations are lost when the controls are added. For the

Reputational Shock 1 sample (see columns (1) and (2)), we find that a one unit increase in rating (where

an increase implies that a rating is getting worse) would result in a 13% ((exp(0.125)-1)*100) increase in

the level of the bond spread before the reputational shock. The main coefficient of interest is

Rating × Shock, which measures the change in the association of ratings with bond spreads. We find that

the coefficient on Rating × Shock (0.062) is significant at the 1% level. In the Post-Period, a one unit

increase in rating results in a 20% ((exp(0.062 + 0.125)-1)*100) increase in bond spread. Similarly, for

Reputation Shock 2, the coefficient on Rating x Shock (0.053) is significant at the 1% level after

controlling for all other variables, which suggests that one unit change in ratings results in a 19%

23

((exp(0.125+0.053)-1)*100) increase in the bond spread. These results are consistent with the alternate

hypothesis that the impact of credit ratings on bond spreads is significantly different in the Post-Period

from the Pre-Period. This result suggests that investors doubt that CRAs have incentives to modify their

behavior because of the reputation shock and, therefore, the investors decrease their reliance on credit

ratings, substituting other salient information.

We also estimate the same regression for each reputation shock for separate subsamples of IG and

NIG bond issues because of the fundamental differences in which ratings affect the two categories of

bonds. Results for IG bonds in Table 4 Panel B indicate that ratings are more important for investment

grade bonds than for non-investment bonds (Panel C). We find that the coefficient on Rating × Shock

(0.079) is significant at the 1% level. In the Post-Period, a one unit increase in rating results in a 19%

((exp(0.079 + 0.097)-1)*100) increase in bond spread. Similarly, for Reputation Shock 2, the coefficient

(0.092) is significant at 1% level after controlling for all other variables, and a one unit change in ratings

results in an 18% ((exp(0.092+0.077)-1)*100) increase in the bond spread. The results for NIG bonds are

listed in Table 4 Panel C and the impact of rating on bond spread is not significantly different in the Post-

Period from the Pre-Period.

To ensure that the results from our first hypothesis are not a result of changes in risk premia

related to macroeconomic events, we also analyze rating changes.

4.1.2 Market Reaction to Bond Rating Changes

Table 5 presents summary statistics for rating changes. There are 482 downgrades in the Pre-

Period and 3,351 in the Post-Period for Reputational Shock 1. For upgrades, there are 91 upgrade events

in the Pre-Period and 534 in the Post-Period. The average downgrade decreased from 2.5 notches in the

Pre-Period to 1.6 notches in the Post-Period. Similarly, the number of notches decreased from 1.8 to 1.3

for upgrades.

For Reputational Shock 2, rating downgrades decreased from 4,017 in the Pre-Period to 1,168 in

the Post-Period and downgrade notches were similar at 2.3 and 2.1 respectively. The greater number of

24

downgrades in the Pre-Period is due to the large number of downgrades initiated in the first seven months

of 2008 prior to the Lehman bankruptcy (and the beginning of our Pre-Period) but subsequent to March

2008 events that included the rescue of Bear Sterns by J P Morgan Chase, the initiation and then

expansion of the Fed’s Term Auction Facility Program, and the lowering of the Fed’s discount rate by 75

basis points. Other major events in the 2008 portion of our Pre-Period included the failure of IndyMac

Bank and the decision to bail out Fannie and Freddie, both in July. Upgrades were similar at 1,123 and

1,227 in the pre and post periods as were the notches at 1.6 and 1.8 respectively.

We investigate the bond price reaction to rating downgrades and upgrades separately, consistent

with prior evidence that markets react more strongly to the announcement of downgrades than to

upgrades. We first present the univariate results and then provide multivariate results with controls for

other determinants of bond price changes.

Table 6 reports mean abnormal returns (ABRET) around rating downgrades and upgrades for the

Pre-Period and the Post-Period. Panel A reports ABRET for the entire sample for both reputational

shocks. Panel B reports ABRET for IG bonds and Panel C reports ABRET for NIG bonds.

Our findings are consistent with prior literature that announcements of rating downgrades usually

result in negative abnormal returns around the announcement date. The results on upgrades are also

generally consistent with prior literature because most of the upgrade abnormal returns are not

significantly different from zero. The magnitude of ABRET after each shock is generally smaller in

magnitude relative to before the shock, consistent with our hypothesis. For Reputational Shock 1

downgrade events, we find that ABRET is -0.038 in the Pre-Period and decreases significantly (at the 1%

level) to -0.023 in the Post-Period for the entire sample. ABRET is insignificant for upgrades in both the

Pre- and Post-Periods for Reputation Shock 1 in the total sample and positive for upgrades both before

and after Reputational Shock 2, with a significantly smaller coefficient in the Post-Period. The abnormal

returns for downgrade announcements for the total sample for Reputation Shock 2 are a puzzle as they are

strongly negative in the Pre-Period but unexpectedly positive, although smaller in magnitude, in the Post-

Period.

25

Although we provide results for the entire sample in Panel A, we are more interested in the IG

bond subsample because, as discussed earlier, we expect IG bonds to be more affected than NIG bonds

after a CRA has been hit by a reputational shock. Results for the subsample of IG bonds indicate that the

magnitude of the response to both downgrades and upgrades for both reputational shocks is consistently

smaller in the Post-Period than in the Pre-Period and there is a consistently weaker reaction to bond

downgrades than to upgrades. Specifically, we find that the mean ABRET decreases from -0.051 to -

0.013 for downgrades for Reputational Shock 1 and from -0.157 to -0.005 for downgrades for

Reputational Shock 2. We find similar results for bond upgrades for both reputational shocks. These

results are consistent with our hypothesis that bond market investors decrease their reliance on credit

ratings after a reputational shock.

Results for NIG bonds in Panel C of Table 6 are less consistent. ABRETS are greater in magnitude

for downgrades in both Post-Periods but inconsistent in sign for Reputational Shock 2. ABRETs for

upgrades are insignificant both before and after Reputational Shock 1 and although insignificant prior to

Reputational Shock 2, they are significantly positive in the Post-Period for Reputational Shock 2, contrary

to our expectations.

We also estimate multivariate regressions for IG bonds with the results in Table 7. We find results

consistent with the univariate results after controlling for the notch changes and for whether the

downgrade moved the bond from IG to NIG. Our results for bond downgrade shows that after the shock,

ABRET is lower by 0.017 for Reputational Shock 1 bond downgrades and lower by 0.075 for

Reputational Shock 2. We find consistent results for bond upgrades for Reputational Shock 2.

Overall, our univariate and multivariate results suggest that investors decrease their reliance on

rating changes in the Post-Period compared to the Pre-Period.

4.2 Falsification Test

In Table 8, we report results from a falsification analysis. We randomly assume a reputational

shock in May 2013 and look at the effect on bond ratings in the 12-months preceding the “false” shock

26

and 12-month after the “false” shock. We find that the coefficient on Rating x Shock is not significant in

any regressions, confirming that there is no statistically significant difference between the Pre-Period and

Post-Period for our false shock. These results are also reported graphically in Figure 1, Panel C.

5 SUMMARY AND CONCLUSIONS

This paper investigates the bond market effects of two recent reputational shocks to the major U.S.

credit rating agencies (CRAs). Because the three main CRAs operate in an effectively oligopolistic

market under government auspices, they are not subject to the typical discipline of a competitive market.

In addition, because capital markets require, either de jure or de facto, that most corporate debt issuances

be rated by an NSRSO, the CRAs have fewer incentives to differentiate and improve their services than

they would in an unregulated market. As a result, the effect on bond investors and therefore on bond

prices, of a reputational shock to the CRAs is not straight forward to predict.

We analyze whether bond investors decrease their reliance on ratings after CRAs have been hit by

two separate reputational shocks, namely the Enron and WorldCom bankruptcies and the 2008 financial

crisis. We find that investors discount the information content of ratings for new bond issues after each

reputational shock compared to before the shock. We also look at the rating changes and find weaker

bond price reaction to rating downgrades and upgrades, on average, after the shocks, particularly for

downgrades. Our results are generally more pronounced for investment grade bonds than for non-

investment grade bonds, consistent with our expectations.

27

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Sethuraman, M. 2016. The Effect of Reputation Shocks to Rating Agencies on Corporate Disclosures.

29

Appendix: Variable Definitions

Variable Definition (Data Source)

Log(Spread) The natural logarithm of basis point spread (100 basis points = 1 percent) between yield

to maturity of the sample bond and the comparable treasury bond (Mergent FISD)

Rating The S&P bond rating converted to an ordinal scale from 1 to 22 - Described in detail in

Table 1 (Mergent FISD)

Shock A dichotomous variable indicating 12-month period after a reputational shock if 1 and

12-month period before a reputational shock if 0

Log(Issuance) The natural logarithm of the original amount ($ Million) of the bond issue (Mergent

FISD)

Maturity The time to maturity, in years (Mergent FISD)

Notches Absolute value of change in ratings after a downgrade or an upgrade (Mergent FISD)

NIG_IG A dichotomous variable indicating if the downgrade moved a specific bond from IG to

NIG or upgrade that moved a specific bond from NIG to IG (Mergent FISD)

Callable A dichotomous variable indicating if the bond is callable (Mergent FISD)

Sinking Fund A dichotomous variable indicating if the bond has a sinking fund (Mergent FISD)

ABRET Difference between the return of corporate bond and return of matched treasury security

around rating change event (Bond Return – NAIC, TRACE; Treasury Return – St. Louis

Federal Reserve Website)

30

Figure 1: Log(Spread) vs. Rating for New Bond Issues

The figure below shows the scatter plot of means of Log(Spread) vs. rating levels. Panel A provides these results for

Reputational Shock 1, Panel B for Reputational Shock 2, and Panel 3 for the falsification test.

Panel A: Reputational Shock 1

Panel B: Reputational Shock 2

31

Panel C: Falsification Test

32

Table 1: Rating Classifications by Rating Agencies

The following table provides mapping of ratings issued by Credit Rating Agencies (CRAs) to a cardinal scale. The

rating scale used by S&P and Fitch is same. Their rating scale goes down from AAA to D (in case of Fitch DDD).

Moody's rating scale goes down from Aaa to C. We transform the rating scale. In line with prior literature, we use

cardinal scale such that rating 1 implies the highest rating AAA/Aaa and rating 23 implies D/DD/DDD. As Fitch

provides three ratings for default, we use 23 as an average rating for D/DD/DDD instead of using 22 for D, 23 for

DD and 24 for DDD. For our analysis we utilize S&P ratings.

Classification SPR Moody's Fitch Scale

Investment Grade

Highest Grade AAA Aaa AAA 1

AA+ Aa1 AA+ 2

High Grade AA Aa2 AA 3

AA- Aa3 AA- 4

A+ A1 A+ 5

Upper Medium Grade A A2 A 6

A- A3 A- 7

BBB+ Baa1 BBB+ 8

BBB Baa2 BBB 9

BBB- Baa3 BBB- 10

Non-Investment Grade

Lower Medium Grade BB+ Ba1 BB+ 11

BB Ba2 BB 12

BB- Ba3 BB- 13

Speculative B+ B1 B+ 14

B B2 B 15

B- B3 B- 16

Poor Standing CCC+ Caa CCC+ 17

CCC Caa1 CCC 18

CCC- Caa2 CCC- 19

Highly Speculative CC Ca CC 20

Lowest Quality C C C 21

Default D N/A D/DD/DDD 23

33

Table 2: Upgrades and Downgrades over the years

This table provides the number of downgrades and upgrades for corporate bonds by Credit Rating Agencies from

1996 to 2016. We have highlighted the years used to analyze the effect of rating upgrades and downgrades around

two reputation shocks – Enron/WorldCom bankruptcy (November 2001 – July 2002) and the 2008 financial crisis

(September 2008 – August 2009). These upgrades and downgrades can be common across rating agencies, and

therefore, does not suggest a cumulative total of rating upgrades and downgrades in those years. We utilize all the

data to identify S&P rating change events, adjusting for any simultaneous rating change announcements by Moody’s

or Fitch.

S&P Moody's Fitch

Year Upgrades Downgrades Upgrades Downgrades Upgrades Downgrades

1996 452 32 232 98 66 8

1997 2,151 1,275 1,338 1,219 986 161

1998 1,559 1,476 1,712 1,473 511 338

1999 1,096 1,490 1,483 1,650 850 206

2000 1,542 3,947 1,832 3,002 961 1,939

2001 919 5,276 1,763 4,407 404 2,979

2002 538 7,115 508 5,622 531 4,473

2003 1,159 3,984 1,499 3,688 698 2,818

2004 1,802 2,689 1,298 2,609 1,256 2,185

2005 2,189 3,860 2,311 3,902 586 4,025

2006 5,326 3,136 2,663 3,606 1,569 1,676

2007 4,268 2,412 5,431 4,798 2,261 1,183

2008 2,012 11,609 925 13,189 715 5,673

2009 1,620 10,430 1,918 15,566 561 4,546

2010 2,241 3,452 2,150 1,961 1,561 742

2011 2,484 4,151 1,406 2,025 1,206 1,723

2012 1,499 1,811 1,517 5,200 676 1,490

2013 2,573 2,149 1,400 2,236 442 449

2014 1,941 1,658 2,211 1,284 908 519

2015 1,436 5,208 2,446 1,733 534 786

2016 634 1,030 370 1,321 216 550

Total 39,441 78,190 36,413 80,589 17,498 38,469

34

Table 3: Summary Statistics (New Bond Issues)

This table provides summary statistics for new bond issues before and after both reputational shocks. Panel A

summarizes the bond characteristics at the time of an issue for Reputational Shock 1 (Enron/WorldCom

bankruptcy). Reputational Shock 1 is defined as the period between November 2001 (Enron bankruptcy) and July

2002 (WorldCom bankruptcy). Pre-Period is defined as one year before November 2001 (November 2000 - October

2001) and Post-Period is defined as one year after June 2002 (July 2002 - June 2003). Similarly, Reputational Shock

2 is defined as the 2008 financial crisis period between Lehman Brothers' bankruptcy (September 2008) and the last

significant event of the crisis (loss reported by Fannie Mae in August 2009) in Panel B. Pre-Period is defined as one

year before September 2008 (September 2007-August 2008) and Post-Period is defined as one year after August

2009 (September 2009 - August 2010). Log(Spread) denotes the natural logarithm of the bond spread, defined as the

bond yield minus the yield on a similar maturity U S Treasury security. Rating is the assigned rating at the time of

bond issuance by the CRA. Log(Issuance) denotes the natural log of original amount of bond issued in $ million.

Maturity denotes bond maturity in number of years. Callable is a dichotomous variable indicating if the bond is

callable. Sinking Fund is a dichotomous variable indicating if the bond has a sinking fund.

Panel A: Reputational Shock 1 (Enron/WorldCom Scandal)

Pre-Period Post-Period

N Mean SD p25 p50 p75 N Mean SD p25 p50 p75

Entire Sample

Log(Spread) 869 5.4 0.5 5.1 5.4 5.8 671 5.3 0.8 4.7 5.2 6.0

Rating 869 8.5 3.7 6.0 8.0 10.0 671 9.1 3.8 6.0 9.0 12.0

Log(Issuance) 869 12.9 0.8 12.4 12.9 13.5 671 12.6 0.7 12.2 12.6 13.1

Maturity 869 9.6 6.5 5.0 10.0 10.0 671 10.4 6.8 6.0 10.0 10.0

Callable 611 0.0 0.0 0.0 0.0 0.0 515 0.0 0.1 0.0 0.0 0.0

Sinking Fund 611 0.0 0.2 0.0 0.0 0.0 515 0.0 0.1 0.0 0.0 0.0

Investment Grade (IG)

Log(Spread) 665 5.2 0.4 5.0 5.2 5.5 480 4.9 0.5 4.6 4.9 5.3

Rating 665 6.9 2.4 6.0 7.0 9.0 480 7.1 2.3 6.0 7.5 9.0

Log(Issuance) 665 13.1 0.8 12.6 13.1 13.5 480 12.7 0.7 12.2 12.6 13.1

Maturity 665 10.0 7.4 5.0 10.0 10.0 480 11.2 7.8 5.0 10.0 11.0

Callable 429 0.0 0.0 0.0 0.0 0.0 333 0.0 0.1 0.0 0.0 0.0

Sinking Fund 429 0.1 0.2 0.0 0.0 0.0 333 0.0 0.1 0.0 0.0 0.0

Non-Investment Grade (NIG)

Log(Spread) 204 6.2 0.3 5.9 6.1 6.4 191 6.3 0.4 6.0 6.3 6.6

Rating 204 13.7 1.8 12.0 14.0 15.0 191 14.1 1.7 13.0 14.0 15.0

Log(Issuance) 204 12.5 0.6 12.2 12.4 12.8 191 12.4 0.6 11.9 12.3 12.7

Maturity 204 8.5 2.1 7.0 8.0 10.0 191 8.3 1.9 7.0 8.0 10.0

Callable 182 0.0 0.0 0.0 0.0 0.0 182 0.0 0.1 0.0 0.0 0.0

Sinking Fund 182 0.0 0.0 0.0 0.0 0.0 182 0.0 0.0 0.0 0.0 0.0

35

Panel B: Reputational Shock 2 (2008 Financial Crisis)

Pre-Period Post-Period

N Mean SD p25 p50 p75 N Mean SD p25 p50 p75

Entire Sample

Log(Spread) 257 5.6 0.5 5.1 5.5 5.8 750 5.8 0.7 5.3 6.0 6.4

Rating 257 8.6 3.7 6.0 8.0 11.0 750 11.2 3.7 9.0 11.0 14.0

Log(Issuance) 257 13.0 0.8 12.6 13.0 13.5 750 13.0 0.6 12.6 13.0 13.5

Maturity 257 11.4 8.0 7.0 10.0 10.0 750 10.0 6.8 6.0 8.0 10.0

Callable 232 0.0 0.0 0.0 0.0 0.0 702 0.0 0.0 0.0 0.0 0.0

Sinking Fund 232 0.0 0.1 0.0 0.0 0.0 702 0.0 0.0 0.0 0.0 0.0

Investment Grade (IG)

Log(Spread) 192 5.3 0.3 5.1 5.3 5.6 361 5.2 0.5 4.9 5.2 5.5

Rating 192 6.8 2.2 6.0 7.0 8.0 361 8.0 2.1 7.0 9.0 10.0

Log(Issuance) 192 13.1 0.7 12.6 13.1 13.5 361 13.2 0.6 12.8 13.1 13.7

Maturity 192 12.6 8.9 6.0 10.0 10.0 361 12.3 9.1 5.0 10.0 11.0

Callable 171 0.0 0.0 0.0 0.0 0.0 322 0.0 0.0 0.0 0.0 0.0

Sinking Fund 171 0.0 0.1 0.0 0.0 0.0 322 0.0 0.0 0.0 0.0 0.0

Non-Investment Grade (NIG)

Log(Spread) 65 6.3 0.4 6.0 6.3 6.6 389 6.4 0.3 6.2 6.4 6.6

Rating 65 13.8 1.9 12.0 14.0 15.0 389 14.2 1.8 13.0 14.0 15.0

Log(Issuance) 65 12.7 1.2 12.4 12.8 13.1 389 12.8 0.6 12.4 12.8 13.2

Maturity 65 7.9 2.0 7.0 8.0 10.0 389 7.8 1.8 7.0 8.0 10.0

Callable 61 0.0 0.0 0.0 0.0 0.0 380 0.0 0.0 0.0 0.0 0.0

Sinking Fund 61 0.0 0.0 0.0 0.0 0.0 380 0.0 0.1 0.0 0.0 0.0

36

Table 4: Treasury Spread and Bond Ratings at Issuance

This table reports the results from regression estimation (3) for both reputational shocks. The outcome variable

Log(Spread), measures the natural log of bond spread for a new bond issue. Shock is an indicator variable where 0

denotes the 12-month period prior the shock and 1 denotes 12-month period after the shock. Rating is the S&P bond

rating converted to an ordinal from 1 to 23. Log(Issuance) denotes the natural log of original amount of bond issued

in $ million. Maturity denotes bond maturity in number of years. Callable is a dichotomous variable indicating if the

bond is callable. Sinking Fund is a dichotomous variable indicating if the bond has a sinking fund. Putable is a

dichotomous variable indicating if the bond is putable (omitted in following regressions as none of the bonds in the

sample were putable). Panel A reports the results for the entire sample. Panel B and Panel C report results for

subsample of Investment Grade and Non-Investment Grade bonds respectively. T-statistics, included in brackets, are

computed using robust standard errors. Two tailed p-values are reported. *** p<0.01, ** p<0.05, *p<0.10.

Log(Bond Spreadi) = β

0+ β

1*Rating

i+ β

2*Shock + β

3*Rating

i×Shock + Controlsi+ εi (3)

Panel A: Entire Sample

Reputation Shock 1 Reputation Shock 2

Log(Spread) Log(Spread) Log(Spread) Log(Spread)

(1) (2) (3) (4)

Rating 0.125*** 0.125*** 0.124*** 0.124***

(41.59) (35.06) (22.81) (20.16)

Shock -0.682*** -0.771*** -0.643*** -0.650***

(-13.03) (-12.10) (-9.17) (-8.57)

Rating x Shock 0.053*** 0.062*** 0.051*** 0.053***

(10.58) (10.97) (7.38) (7.20)

Log(Issuance) 0.009 -0.058***

(0.61) (-3.28)

Maturity 0.001 0.003

(0.57) (1.61)

Callable 0.139 -

(1.07)

Sinking Fund 0.397*** 0.345***

(5.68) (4.57)

Constant 4.370*** 4.242*** 4.494*** 5.192***

(148.25) (20.26) (94.60) (21.05)

Observations 1,540 1,126 1,007 934

R-squared 0.7213 0.7395 0.7624 0.7669

37

Panel B: Investment Grade (IG)

Reputation Shock 1 Reputation Shock 2

Log(Spread) Log(Spread) Log(Spread) Log(Spread)

VARIABLES (1) (2) (3) (4)

Rating 0.105*** 0.097*** 0.085*** 0.077***

(18.17) (13.69) (8.70) (7.54)

Shock -0.618*** -0.886*** -0.838*** -0.947***

(-7.85) (-8.45) (-6.84) (-7.36)

Rating x Shock 0.043*** 0.079*** 0.078*** 0.092***

(4.08) (5.86) (5.11) (5.85)

Log(Issuance) 0.037** -0.056**

(2.08) (-2.06)

YearsTM 0.004** 0.007***

(2.41) (4.13)

Callable 0.07 -

(1.11)

Sinking Fund 0.370*** -0.056

(5.99) (-0.86)

Constant 4.487*** 4.039*** 4.730*** 5.408***

(103.20) (17.00) (67.57) (14.67)

Observations 1,145 762 553 493

R-squared 0.4439 0.4509 0.4234 0.4324

38

Panel C: Non-Investment Grade (NIG)

Reputation Shock 1 Reputation Shock 2

Log(Spread) Log(Spread) Log(Spread) Log(Spread)

(1) (2) (3) (4)

Rating 0.083*** 0.088*** 0.108*** 0.087***

(8.35) (7.41) (6.82) (5.55)

Shock -0.163 0.06 0.302 0.34

(-0.65) -0.24 -1.18 -1.35

Rating x Shock 0.019 0.003 -0.018 -0.02

(1.09) (0.16) (-0.96) (-1.07)

Log(Issuance) -0.013 -0.02

(-0.54) (-1.28)

YearsTM -0.043*** -0.078***

(-4.76) (-8.09)

Callable 0.547*** -

(9.95)

Sinking Fund - 0.400***

(8.99)

Constant 5.024*** 5.479*** 4.795*** 5.946***

(36.73) (15.13) (22.04) (17.69)

Observations 395 364 454 441

R-squared 0.2821 0.3536 0.2708 0.4599

39

Table 5: Summary Statistics for Rating Changes

This table report summary statistics for rating changes that take place before and after the reputational shocks. The

rating actions include upgrades and downgrades by S&P, adjusted for any concurrent rating change announcements

for Fitch and Moody’s. Reputational Shock 1 is defined as the period between November 2001 (Enron bankruptcy)

and July 2002 (WorldCom bankruptcy). Pre-Period is defined as one year before November 2001 (November 2000

- October 2001) and Post-Period is defined as one year after June 2002 (i.e. July 2002 - June 2003). Similarly,

Reputational Shock 2 is defined as the 2008 financial crisis period between Lehman Brothers' bankruptcy

(September 2008) and the last significant event of the crisis (loss reported by Fannie Mae in August 2009). Pre-

Period is defined as one year before September 2008 (September 2007 - August 2008) and Post-Period is defined as

one year after August 2009 (September 2009 - August 2010).

Event Downgrades Upgrades

Pre-Period Post-Period Pre-Period Post-Period

Reputation Shock 1

Number 482 3351 91 534

Notches 2.5 1.6 1.8 1.3

% Investment Grade 0.7 0.7 0.8 0.8

Reputation Shock 2

Number 4017 1168 1123 1227

Notches 2.3 2.1 1.6 1.8

% Investment Grade 0.5 0.5 0.5 0.2

40

Table 6: Bond Market Reaction to Rating Changes

This table shows mean abnormal returns around credit rating changes before and after the reputational shock.

Abnormal returns are bond returns, adjusted for comparable treasury security return, over event window, calculated

around a rating change. The rating change includes upgrades and downgrades of bond issues, issued by S&P

(adjusted for concurrent Fitch and Moody's rating announcement). Panel A reports the abnormal returns for the

entire sample. Panel B reports the abnormal returns for investment grade issues and Panel C reports the abnormal

returns for non-investment grade issues. The Difference is a two-sample t-test for difference in means. ***, ** and *

indicate statistical significance at 1%, 5% and 10% levels, respectively.

Panel A: Total Sample

Event

Downgrades Upgrades

Pre-Period Post-

Period Difference

Pre-

Period

Post-

Period Difference

Reputation Shock 1

Mean ABRET -0.038*** -0.023*** -0.015** 0.001 -0.001 0.001

t-stat -4.70 -10.13 -2.20 0.25 -0.46 0.35

# Observations 482 3351 91 534

Reputation Shock 2

Mean ABRET -0.093*** 0.023*** -0.116*** 0.012*** 0.007*** 0.006***

t-stat -28.25 11.83 -18.71 5.23 4.66 2.20

# Observations 4017 1168 1123 1227

Panel B: Investment Grade (IG)

Event

Downgrades Upgrades

Pre-Period Post-

Period Difference

Pre-

Period

Post-

Period Difference

Reputation Shock 1

Mean ABRET -0.051*** -0.013*** -0.036*** -0.002 -0.001 -0.001

t-stat -6.20 -7.92 -6.62 -0.68 -0.48 -0.41

# Observations 343 2422 73 408

Reputation Shock 2

Mean ABRET -0.157*** -0.005*** -0.151*** 0.024*** -0.001 0.025***

t-stat -28.02 -4.53 -13.90 5.84 -0.46 3.81

# Observations 2096 555 598 247

Panel C: Non-Investment Grade (NIG)

Event

Downgrades Upgrades

Pre-Period Post-

Period Difference

Pre-

Period

Post-

Period Difference

Reputation Shock 1

Mean ABRET -0.008 -0.048*** 0.039 0.012* -0.001 0.012

t-stat -0.44 -7.00 2.03 1.80 -0.11 1.02

# Observations 139 929 18 126

Reputation Shock 2

Mean ABRET -0.024*** 0.048*** -0.074*** -0.001 0.008*** -0.009***

t-stat -10.07 15.16 -15.58 -0.93 4.80 -3.78

# Observations 1921 613 525 980

41

Table 7: Multivariate Analysis of Bond Rating Changes for IG Bonds

This table reports results from estimating equation (6) for rating changes for Investment Grade bonds. Notches

denotes the number of notches corresponding to a rating upgrade or downgrade. NIG_IG denotes a dummy variable

that denotes if the bond was downgraded from Investment Grade status to Non-Investment Grade. Shock is defined

as previously for both reputational shocks. T-statistics, included in brackets, are computed using robust standard

errors. Two tailed p-values are reported. *** p<0.01, ** p<0.05, *p<0.10.

ABRETi = β0 + β

1Shock + Controlsi + εi (6)

Reputation Shock 1 Reputation Shock 2

DNG UPG DNG UPG

(1) (2) (3) (4)

Shock 0.017** -0.002 0.075*** -0.009**

(2.48) (-0.58) (20.50) (-2.10)

Notches -0.017*** -0.006** -0.066*** 0.032***

(-4.38) (-2.36) (-48.36) (9.18)

NIG_IG -0.014 0.034***

(-1.27) (3.92)

Constant -0.007 0.008 0.013*** -0.034***

(-0.82) (1.56) (4.08) (-7.26)

Observations 2,765 481 2,651 845

R-squared 0.0798 0.0252 0.7213 0.3446

42

Table 8: Falsification

This table denotes results for falsification test related to our first hypothesis. We define a false shock on May 2013

and the Pre-Period is defined as the 12-month preceding May 2013 (May 2012 – April 2013) and Post-Period is

defined as 12-month after May 2013 (June 2013 – May 2014). T-statistics, included in brackets, are computed using

robust standard errors. Two tailed p-values are reported. *** p<0.01, ** p<0.05, *p<0.10.

Log(Bond Spreadi) = β

0+ β

1*Rating

i+ β

2*Shock + β

3*Rating

i×Shock + Controlsi+ εi (3)

Entire Sample Investment Grade Non-Investment Grade

Log(Spread) Log(Spread) Log(Spread) Log(Spread) Log(Spread) Log(Spread)

(1) (2) (3) (4) (5) (6)

Rating 0.194*** 0.201*** 0.206*** 0.208*** 0.133*** 0.124***

(52.77) (50.18) (17.22) (16.74) (18.92) (16.26)

Shock -0.245** -0.228** -0.175 -0.123 -0.006 0.074

(-2.44) (-2.29) (-0.89) (-0.63) (-0.02) -0.27

Rating x Shock 0.009 0.007 0.001 -0.005 -0.01 -0.016

(0.93) (0.77) (0.03) (-0.20) (-0.54) (-0.82)

Log(Issuance) -0.006 0.058* -0.087***

(-0.24) (1.77) (-3.40)

YearsTM 0.013*** 0.015*** -0.027***

(8.35) (9.85) (-2.84)

Callable - - -

Sinking Fund - - -

Constant 3.433*** 3.298*** 3.322*** 2.337*** 4.329*** 5.817***

(79.98) (10.27) (34.85) (4.99) (41.81) (15.37)

Observations 1,166 1,105 694 641 472 464

R-squared 0.7632 0.7743 0.4529 0.505 0.3552 0.392