Best Face Forward: Does Rating Shopping Distort Observed Bond Ratings?

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1 Best Face Forward: Does Rating Shopping Distort Observed Bond Ratings? Mathias Kronlund The University of Chicago January 26, 2011 Abstract I empirically analyze the impact of rating shopping on observed corporate bond ratings. I establish that firms are more likely to obtain ratings for new issues from an agency that rated their past issues relatively highly. Consistent with recent theoretical models, rating shopping is more prevalent among junior and longer maturity (more complex) bonds. Rating shopping is also more prevalent when the Aaa Baa spread is high, which is when issuers particularly benefit from higher ratings. Shopped ratings are biased in a predictable way: defaults are significantly higher for issues that are rated by an agency that rated the issuer relatively highly in the past. However, investors at least partly account for the expected rating inflation, as yields are also higher for issues that are more likely to have biased ratings. Since investors appear to recognize shopping, regulation to limit rating shopping may not be necessary to protect investors, but may nevertheless be warranted to prevent regulatory arbitrage. Booth School of Business, The University of Chicago S. Woodlawn Ave, Chicago, IL mkronlun@chicagobooth.edu. I am grateful to my advisors Steven Kaplan, Tobias Moskowitz, Raghuram Rajan, and Amir Sufi, as well as to Eugene Fama, Juhani Linnainmaa, Anil Kashyap, Roni Kisin, Pepa Kraft, Gregor Matvos, Adair Morse, Alexi Savov, and Amit Seru for helpful comments and suggestions. I gratefully acknowledge financial support from the Finnish Cultural Foundation and the Foundation for Economic Education (Liikesivistysrahasto). Electronic copy available at:

2 1 Introduction Researchers and lawmakers are increasingly concerned about a potential flaw in the incentive structure in the credit rating industry namely that rating agencies are solicited and paid by the same issuers they rate. 1 One concern with issuers choosing which agencies that rate their bonds is rating shopping : the practice whereby issuers only solicit ratings from the agency or agencies that is most favorable to them. If issuers engage in this behavior, the ratings we observe may not reflect the true distribution of opinion across agencies, but rather the highest-order statistics of these opinions (Skreta and Veldkamp, 2009). Both Congress and the SEC have recognized this threat to the informativeness of credit ratings, and have recently proposed several new rules to limit rating shopping. 2 A number of recent papers incorporate features of rating shopping in theoretical models (Bolton, Freixas, and Shapiro, 2010; Sangiorgi, Sokobin, and Spatt, 2009; Skreta and Veldkamp, 2009). In these models, shopping can serve the issuer either by fooling investors who take ratings at face value or because inflated ratings reduce the cost for regulated financial institutions to carry a bond on their books. However, there is an apparent disconnect between lawmakers attention towards rating shopping on the one hand versus current empirical evidence on the other. Evidence on rating shopping has been scarce or inconsistent with the idea that issuers solicit ratings based on how highly they expect the agencies to rate (Cantor and Packer, 1997; Bongaerts, Cremers, and Goetzmann, 2010). Furthermore, even if issuers shop for ratings, it is an empirical question whether investors recognize this behavior. In this paper, I study the extent to which corporate bond issuers shop for ratings, building on the predictions of the recent theoretical models. I also examine whether this behavior affects the informativeness of observed ratings and whether bond prices account for the resulting rating bias. I first establish that each of the three major credit rating agencies Fitch, Moody s, and S&P systematically tends to rate certain issuers higher or lower relative to each other. I then find, consistent with rating shopping, that firms are more likely to obtain ratings for new issues from an agency that rated their previous bonds relatively highly. Specifically, if an agency rated an issuer s bonds one notch higher than the other agencies last year, the issuer is over ten percent more likely to solicit that agency to rate a new issue with a single rating. To further support a causal interpretation of this association, I test the direct theoretical 1 The model whereby issuers solicit and pay rating agencies is called the issuer-pays model. Most rating agencies, including the three largest agencies Fitch, Moody s, and Standard&Poor s (S&P) operate under this model. SEC (2003); Partnoy (2006); Coffee (2008); IOSCO (2008); SEC (2008); White (2010) discuss some of the concerns with the issuer-pays model. 2 The Wall Street Journal, May , Ratings Shopping Lives as Congress Debates a Fix. 2 Electronic copy available at:

3 prediction in Skreta and Veldkamp s (2009) model that rating shopping should be more prevalent for complex issues, as agencies are more likely to disagree on the rating for these issues. 3 Consistent with this prediction, I find that issuers are particularly likely to solicit an agency that rated them highly in the past for new issues that are more complex, as proxied by lower seniority and longer time-to-maturity. Issuers are also more likely to solicit an agency that rated relatively generously in the previous year to rate new issues in times when higher ratings are associated with a larger yield benefit, as proxied by the Aaa Baa yield spread (also called the default spread ). Further, ratings immediately after bond issuance are significantly lower than ratings before issuance, which suggests issuers seek to delay lower ratings until after a bond is sold. The difference between preand post-issuance ratings is also greater for issues that are more complex and in times when the default spread is large. To test whether shopping is more prevalent around a regulatory threshold, I exploit the rating requirements in Rule 2a 7 for money market mutual funds, and examine whether issuers choose agencies more selectively for issues that marginally qualify under this rule. This rule requires that issues held by money market mutual funds have at least one rating of A ; but the rule penalizes any lower ratings, as the second-highest rating counts for issues that have more than one rating. Consistent with an incentive to meet this regulatory threshold, I find that short-term issues with the minimum rating to qualify under this rule are more likely than issues with higher ratings to have a single rating at issuance. No such difference exists for longer-term issues which are unaffected by demand from money-market funds. In the final set of results, I find that shopped ratings are biased in a predictable way: a bond is more likely to default, controlling for its rating, when that rating is from an agency that gave the issuer relatively higher ratings last year. However, investors appear to account for this bias by charging higher yields for bonds that are rated by an agency that was more generous to the issuer in the past. The yield adjustment is quantitatively consistent with the difference in default probability, although we cannot determine whether investors perfectly account for the rating bias without making additional assumptions about expected recoveries. Nevertheless, investors ability to adjust for the expected rating bias from shopping (at least partly), contrasts with the assumption of models (e.g., Bolton, Freixas, and Shapiro, 2010; Skreta and Veldkamp, 2009) where investors are unaware of this bias. 3 Also Bolton, Freixas, and Shapiro (2010) and Sangiorgi, Sokobin, and Spatt (2009) have similar predictions regarding the relationship between shopping and the precision of the rating agencies models. However, this prediction is not central to these two models, so I mainly focus on the mechanism in Skreta and Veldkamp (2009). 3

4 This paper seeks to bridge the gap between existing theoretical models and empirical evidence by first showing that issuers shop for ratings, and that they do so especially in circumstances that are consistent with the predictions of recent models. The paper also seeks to examine which of several potential reasons for shopping that incentives issuers to engage in this behavior. Because investors appear to recognize shopping, one principal reason is likely to be arbitrage of rating-based regulation. Consistent with this rationale, I find strong evidence of shopping around a threshold the minimum rating requirement for money market mutual funds where a small difference in ratings can determine the ability of a large group of regulated buyers to purchase a bond. New legislation to limit rating shopping may therefore not be necessary to protect investors, but may be warranted to prevent such regulatory arbitrage. A few papers have examined rating shopping among corporate bond issuers, but the evidence has generally not been consistent with shopping. Cantor and Packer (1997) find that ratings by Fitch and Duff&Phelps are typically higher than those by Moody s and S&P. But they argue that this difference is not the result of a selection bias whereby issuers solicit ratings from Fitch and Duff&Phelps only when their ratings are higher, but rather it reflects different rating scales across the agencies. 4 Bongaerts, Cremers, and Goetzmann (2010) find that adding a Fitch rating that is higher than existing ratings by Moody s and S&P is not associated with lower yields, and argue that the lack of benefit from shopping for a higher Fitch rating undermines issuers rationale to shop. 5 Becker and Milbourn (2010) suggest that the scope for shopping in the corporate bond market is limited particularly between the three major agencies as the differences between these agencies are small and because Moody s and S&P rate most of these bonds. Due to the traditionally high market shares of Moody s and S&P, previous papers have examined one particular type of shopping: the choice whether to solicit a third rating from Fitch, conditional on having ratings from Moody s and S&P. However, the share of issues rated by Moody s and S&P has fallen over the last decade from around 90% each to 75%, whereas the share rated by Fitch, the third-largest agency, has increased from 10% to almost 40% (Figure I, top graph). Over the same period, the share of corporate bonds 4 Fitch and Duff & Phelps merged in 2000, making the Moody s, S&P, and the newly merged Fitch the only remaining nationally recognized credit rating agencies. 5 Nevertheless, in the structured securities market, Benmelech and Dlugosz (2010) find that CDO (collateralized debt obligation) tranches with only one rating are more likely to be downgraded than tranches with two or three ratings, which they interpret as support for rating shopping for these securities; Ashcraft, Goldsmith-Pinkham, and Vickery (2010) find the same result for mortgage-backed securities (MBSs). There is also anecdotal evidence of shopping in the structured securities market. For example, Cantor and Packer (1995) note that Moody s had lower enhancement requirements for certain types of mortgage-backed securities in the 1980s, and that Moody s gained market share for these securities. The SEC (2008) also reports that internal discussions have taken place within certain agencies about the effect on the agency s market share if the agency were to change its rating model/methodology. 4

5 with only one rating at issuance has increased from around 5% to over 30% (Figure II). These trends may have increased the prevalence of rating shopping among corporate bonds, as issuers in more industries become freer to select from among these three established agencies. 6 Therefore, I examine the choice of whether or not to solicit ratings from each of these three agencies and in particular the choice of agency when issuers solicit a single rating. Rating shopping has been a recent concern for lawmakers, and this paper has implications for the newly proposed and enacted regulation that seeks to limit shopping. For example, the financial reform bill that passed the U.S. Senate in May 2010 imposed that the SEC would determine which agencies will rate structured securities to limit issuers discretion to choose their own raters for these types of issues (although this provision was dropped in reconciliation for the final bill). 7 Furthermore, the Obama administration s legislative draft for the financial reform bill featured a rule that issuers must disclose all preliminary ratings they seek from an agency, including those that are not reported. 8 The intended purpose was to enable investors to observe all shadow ratings and not merely those from the agencies that issuers eventually solicit. In a similar vein, the Cuomo settlement in June 2008 required issuers to pay rating agencies for initial assessments of loan pools for Residential Mortgage-Backed Securities (RMBSs), regardless if an agency is ultimately selected to rate the RMBS. 9 However, making preliminary ratings costlier to obtain may not prevent shopping, given my finding that rating differences across agencies are highly persistent over time for an issuer. Rather than asking all agencies for preliminary assessments, an issuer needs only to examine how relatively high the agencies rated its previous bonds to engage effectively in shopping. Furthermore, while structured bond ratings have been the exclusive focus for several of these proposed rules, this paper also shows that rating shopping is a concern among corporate bonds. The impact of rating shopping is likely to increase as the number of Nationally Recognized Statistical Rating Organizations (NRSROs) grows. Since 2003, the SEC has 6 Becker and Milbourn (2010) discuss the pattern that Fitch has tended to enter new industries at different times, thus giving issuers in these industries an additional choice. 7 As part of this bill, the SEC is currently investigating what steps to take to address the incentive problems in the credit rating industry, in particular for structured issues. See The Wall Street Journal, May , Ratings Shopping Lives as Congress Debates a Fix. 8 See The Investor Protection Act of 2009, and Shorter and Seitzinger (2009). Also the SEC has proposed a similar rule that issuers must disclose any preliminary ratings they obtain; see SEC October 2009 Release No Credit Ratings Disclosure. 9 Agencies would also have to disclose which loan pools were ever submitted to them for review. See Attorney General Cuomo Announces Landmark Reform Agreements with the Nation s Principal Credit Rating Agencies, Release from the Office of the Attorney General, State of New York, 5 June Also The Wall Street Journal, June 4, 2008, Bond-Rating Shifts Loom in Settlement, and Financial Times, 5 June 2008, Ratings agencies mortgage probe deal. 5

6 designated seven new credit ratings agencies as NRSROs, which implies that their ratings are valid for regulatory purposes. 10 But as the number of rating agencies grows, so does the opportunity for an issuer to find at least one agency that rates it abnormally highly. This in turn has the potential to increase the bias for observed ratings. 11 Regardless of whether investors correctly account for this bias, if shopping becomes more prevalent as the number of agencies grows, regulated financial institutions may thereby be able to increase the risk of their bond portfolios above the intent of current rating-based regulation. Although previous papers have examined rating shopping in the corporate bond market as well as in other markets, one of the contributions of this paper is to document a significant extent of shopping among corporate bonds. The paper also examines when and for what types of bonds shopping is more prevalent, and finds evidence consistent with recent theoretical models. Furthermore, I find that the measure of the bias from shopping how highly the rating agency for the current bond rated the issuer relative to the other agencies last year predicts default, but that investors appear to account for this bias when pricing bonds. Because investors at least partly account for the expected rating bias due to shopping, the issuers incentive to engage in rating shopping is smaller than if investors were naïve. A primary reason why issuers engage in this behavior is therefore likely to be arbitrage of rating-based regulation (however, these reasons are not mutually exclusive). This evidence is important for better understanding the extent to which new legislation that seeks to prevent rating shopping could be effective or helpful to investors. 2 Institutional background and theoretical framework In theory, rating shopping has at least a couple of distinct benefits that may explain why corporate issuers would engage in this practice. First, shopping could fool investors who take ratings at face value into thinking a security is safer than it really is, as in the models by Bolton, Freixas, and Shapiro (2010) and Skreta and Veldkamp (2009), and issuers could thus obtain higher prices for their bonds. Even if investors rationally expect rating shopping to occur but cannot perfectly distinguish between situations when the benefit from shopping is small or large, issuers may still have an incentive to shop regardless of whether the expected rating bias is canceled out in the yields. The incentive to inflate ratings in such a rat race equilibrium would be similar to that in the model for reporting 10 The large increase in credit rating agencies that the SEC has recognized is deliberate, and follows the Credit Rating Agency Reform Act of 2006 which formalized the application procedure to become an NRSRO to foster competition in the credit rating agency industry. 11 Skreta and Veldkamp s (2009) model describe this effect of competition. Also Bolton, Freixas, and Shapiro (2010) show that increased competition may be detrimental as a duopoly leads to lower efficiency in the rating industry than a monopoly due to issuer shopping. 6

7 bias, i.e. earnings manipulation, in Fischer and Verrecchia (2000). A second, distinct benefit from shopping is due to arbitrage of rating-based regulation of financial institutions. Reporting only the highest rating could make the difference whether an issue is classified above a regulatory rating threshold, which would allow certain regulated market participants such as banks, pension funds, insurers, and broker-dealers to carry the bond on their books. 12 A higher rating for a bond can also result in lower capital costs for regulated buyers because the regulatory capital that various financial institutions, e.g. insurers, need to hold against a bond depends on its rating. If shopping achieves inflated reported ratings for an issue, its yield can thus be lower not because investors are fooled and expect a lower default risk, but because the cost of capital to carry the bond for these regulated institutions is lower. Sangiorgi, Sokobin, and Spatt (2009) motivate their model, which incorporates rating shopping, with such regulatory benefits. 13 Other reasons why issuers may seek to engage in rating shopping include agency (multitasking) conflicts or preferences over ratings. Even if a firm finds that shopping does not affect the yield of its bonds, the firm may still seek inflated ratings as a seal of high firm quality or because the CFO (who typically interacts with the rating agencies) is partly evaluated based on the firm s bond ratings. A firm may also want a higher rating to signal to, for example, product-market buyers that the firm is financially sound and will honor its warranties, assuming at least some of these other stakeholders take ratings at face value. Theoretically, any of these reasons could motivate issuers to shop for ratings, although the circumstances in which we would expect shopping to be more prevalent would vary. For rating shopping to cause observed ratings to be biased, three conditions must hold. First, the agencies must disagree on what rating a particular security should receive. Without disagreement, any choice among the agencies would obviously not affect average observed ratings. Second, the issuer must be at least partly aware of these differences, so the issuer is able to consistently solicit ratings from only the most generous agencies. If issuers solicit ratings at random or always solicit ratings from all agencies, disagreement across agencies will not bias average observed ratings relative to the agencies opinions. Third, the issuer must be at least partially free to choose which agency or agencies will rate an issue. That is, the agency choice cannot be fully constrained by regulation or lack of alternatives. 12 One example of such a rating threshold is between BB+ versus BBB, i.e. the investment grade threshold, although there exists also other important rating thresholds that are important for use in financial regulation, e.g. around A and AAA. Calomiris and Mason (2009) discuss some of these benefits from higher ratings. Coffee (2008) refers to this function of ratings as providing regulatory licenses. Cantor and Packer (1995) have a more detailed discussion about rating-based regulation. 13 Also Opp, Opp, and Harris (2010) model the extent to which the regulatory benefit of a higher rating influences whether rating agencies report their opinion truthfully. 7

8 These three conditions appear to be satisfied in the corporate bond market. We often observe disagreement across agencies. Of issues that have two or three ratings at issuance, approximately 60% have split ratings. Table I describes how often different combinations of agencies rate new issues and the frequency of disagreement. Furthermore, if issuers shop for ratings, the observed frequency of split ratings may understate the true underlying level of disagreement. Issuers can learn about the differences across agencies in at least two ways. Issuers can observe ratings for their previous issues or for similar issuers, and reverse-engineer the agencies formulae to estimate how the agencies would rate a new issue relative to each other. Alternatively, issuers can directly approach the agencies to obtain proposed or preliminary ratings. Rating shopping is explicit if an issuer knows the exact rating each agency would give an issue and implicit if the issuer merely has a good idea how each agency would rate. 14 Explicit shopping is typically costly as agencies charge cancellation fees and initial confidential rating fees for providing preliminary ratings (Partnoy, 2006; Coffee, 2008). Implicit shopping is costless, but can be less precise. Implicit shopping nevertheless becomes easier if the issuer has been rated often by different agencies in the past, or if the methodologies the agencies use to determine ratings are public. 15 Issuers are also free to choose from which agency or agencies they solicit ratings. However, in practice, issuers do not shop perfectly. That is, they do not solicit ratings from only the most generous agency if there are countervailing factors that compel them to seek other ratings, even if some of these ratings are lower. For example, large bond buyers often require ratings from specific agencies for their internal risk management practices or to satisfy contractual requirements with their clients, which in turn can compel the issuer to acquire ratings from those specific agencies to cater to such buyers. 16 Financial 14 Sangiorgi, Sokobin, and Spatt (2009) discuss the distinction between explicit and implicit rating shopping. 15 For structured issues, the exact formulae the agencies use has largely been known (New York Times, 23 April 2010, Rating Agency Data Aided Wall Street in Deals ; also illustrated by the rating replications performed by Griffin and Tang (2010)), while it is somewhat more difficult to quantitatively predict corporate bond ratings (the agencies themselves also claim that corporate bond ratings involve a greater degree of judgment ). Therefore, the call by regulators for greater transparency in the rating process may perversely cause implicit rating shopping to become easier for corporate bonds (SEC, Amendments to Rules for Nationally Recognized Statistical Rating Organizations, February 2, President s Working Group on Financial Markets, Policy Statement on Financial Market Developments, 12 March 2008, European Commission, Approval of new Regulation will raise standards for the issuance of credit ratings used in the Community, 23 April 2009, For example, the SEC now requires rating agencies to document their methodologies and assumptions behind their ratings (see Federal Register, February , pp and Federal Register, December , pp ). However, if methodologies are more transparent, investors can also more easily deduce when an issuer has shopped for a rating. 16 Haight, Engler, and Smith (2006) survey the use of ratings in the investment policies of college 8

9 regulation of broker-dealers also requires bonds held by these institutions to have at least two ratings for determining their regulatory capital. Issuers may therefore be compelled to solicit multiple ratings to enable broker-dealers to carry their bonds with less capital. Further, bond buyers often have a tacit requirement that issues have at least two and sometimes three ratings (IOSCO (2008), at 28), or may simply want to see more ratings to improve the precision of their assessment of the bond s credit quality. As a result, only around 15% of issues have a single rating at issuance. Taking existing rating shopping models to data is complicated by the following fact: we do not observe all shadow ratings, that is, how an agency that did not rate a bond would have rated if it had been asked to do so. For example, in Skreta and Veldkamp s model, all ratings are unbiased on average but have i.i.d. noise, so we cannot predict which agency is likely to rate a new issue higher or lower. In the model, the issuer nevertheless gets to sample proposed ratings from an agency, and the issuer can choose whether to publicize that rating if it is sufficiently high or resample. 17 Empirically, however, agencies do not disagree in an i.i.d. fashion across issues. Rather, agencies disagree systematically about new issues from the same issuer year-to-year. Relatively high ratings by an agency for an issuer in year t-1 are strongly correlated with relatively high ratings for new issues by the same issuer in year t. Table II reports how often each of the agencies rate higher or lower relative to the other agencies (on average across all new issues for an issuer in a year), and the year-to-year correlation for how highly the agencies rate new issues by an issuer relative to each other. The time-series correlation is around 75% 80% and is highly significant. The strong year-to-year correlation of how highly each of the agencies rates a given firm s new issues relative to each other suggests the observed rating differences are largely due to persistent model differences between the agencies, and not random. Because these models do not change often and the inputs to the models are highly persistent over time for an issuer (e.g., cash flows, leverage ratios, assessments of competition and management quality, and other financials and firm characteristics relevant to ratings), the relative rating endowment funds, and Cantor, Gwilym, and Thomas (2007) study the contractual use of ratings between pension plan sponsors and investment managers in the U.S. and Europe. 17 Although Skreta and Veldkamp (2009) mainly discuss their model s applications to structured securities, their model appears more suitable for corporate bonds. In the model, an agency merely rates an issue that an issuer has given it to rate a relationship between issuer and agency that Holmström (2008) refers to as ex post rating. By contrast, for structured securities, the relationship between issuers and agencies is more intricate since the agencies are often involved already in the design stage of the security itself (Gorton, 2008; Ashcraft, Goldsmith-Pinkham, and Vickery, 2010; Benmelech and Dlugosz, 2009). Bolton, Freixas, and Shapiro (2010) model such a back-and-forth structuring process between issuer and agency. One feature of Skreta and Veldkamp s model which nevertheless corresponds better with structured securities than corporate bonds is that of sampling proposed ratings or initial assessments, which is uncommon for corporate bonds (except around large corporate restructurings, such as M&As) 9

10 differences across agencies remain persistent as well. 18 Although Skreta and Veldkamp (2009) provide the main theoretical framework for examining rating shopping, I exploit this persistence in issuer-level differences across agencies to predict which agencies are likely to rate an issuer generously or conservatively in the future. To measure relative issuer-level rating differences across agencies, the empirical approach in this paper requires that issuers do not always shop perfectly by soliciting only the highest rating(s) for an issue. The reason is simple: if we only observe a single rating per issue, or if all reported ratings are the same for an issue, we cannot hope to estimate any differences about how the agencies are likely to rate new issues relative to each other. However, given that we often observe split ratings implies that issuers often do not or cannot shop perfectly or they need more than one rating to sell a bond. As Table I illustrates, around 85% of issues have more than one rating at issuance, and the majority of these issues have split ratings. These co-rated issues in turn provide the econometrician an opportunity to measure issuer-specific differences of opinion across the agencies. 3 Data and methodology The sample of issues I study consists of all U.S.-domiciled and dollar-denominated industrial and financial bonds in the FISD Mergent database that were offered between 1995 and These issues are mainly medium term notes and debentures, but also include some convertible bonds and preferred issues. I start the sample in 1995, as this is the first year for which FISD has comprehensive data on ratings from the three major rating agencies. I obtain financial data from Compustat, which I link to FISD based on Cusip/Year. Data on treasury yields and average corporate bond yields are from the Federal Reserve Board. I also obtain additional data specific to S&P ratings from Standard&Poor s RatingsXpress. I translate letter ratings to a numerical scale as 1 for C to 21 for AAA. I match letter ratings by Moody s, which uses a different lettering scheme, to comparable ratings by Fitch and S&P using the standard convention (e.g., Moody s Baa1 is comparable to S&P BBB+). In the first tests for rating shopping, I predict whether an agency rates a new issue based on the agency s relative rating for the issuer in the previous year which is a measure of how highly the agency rated the issuer s bonds last year relative to the other agencies. The observations are at the issue-agency level, and the main dependent variable is a dummy for whether an agency rates a new issue before or up until the bond settlement date. I restrict the sample to ratings before settlement because ratings are most important in primary issuance, so if issuers solicit and pay for a rating, they get the greatest benefit 18 Kraft (2010) provides an example of the criteria (both hard quantitative/hard and subjective/soft measures) with corresponding weights that go into a Moody s rating for a large manufacturing firm. 10

11 from ratings that are reported before the bond is sold. Unsolicited ratings, which are ratings that agencies report even though the issuer has not asked them to do so, is a potential confounder when measuring whether the issuer solicited an agency to rate a bond. To the extent that ratings (up until settlement) are unsolicited, my measure of whether the issuer has solicited the agency to provide a rating will be measured with error. However, although data on unsolicited ratings are scarce, unsolicited ratings appear uncommon in practice (SEC, 2003). Among the three major agencies, only S&P reports publicly whether its ratings are solicited (in the RatingsXpress database), and across 30,701 S&P-rated issues I can link to my sample, only seven (or 0.02%) have an unsolicited S&P rating. 19 The main explanatory variable, an agency s relative rating, is a measure of how highly the agency rated the issuer s bonds last year compared to the other agencies. For each issuer and year, I can compute up to three relative ratings, one for each agency that co-rated at least one bond that the issuer offered in that year. To construct this measure, I examine all ratings (up until each issue s settlement date) for each issuer s new bonds in a year. 20 I then estimate issuer-year-level differences across agencies as follows: the relative rating, ri,t a, by agency a for issuer i in year t is defined as the average difference of the agency s ratings from each bond s mean rating across all of the issuer s bonds that year. This measure averages to zero across all agencies for an issuer-year. The measure can alternatively be defined in a regression as ratingj a = µ j + ri,t a + ea j, where j indexes issues for issuer i and year t by agency a. By including issue effects µ j, I can only estimate the relative ratings across agencies using issues that at least two agencies rated. The benefit of this approach is that by holding the issue constant (which also captures all issuer characteristics, time of offering, etc.), I do not need to take a stance on what is the correct rating for an issue to assess whether some agencies on average rate an issuer generously or conservatively. Comparing ratings within-issue also ensures that the measured differences across agencies are not driven by the fact that different agencies on average may rate issues with different underlying credit quality. If an agency does not co-rate any issues with other agencies for an issuer in a given 19 Moody s and Fitch have separately reported that fewer than 1% and 5% of their ratings worldwide are unsolicited; see Partnoy (2006) and Washington Post, 24 November 2004, Credit Raters Power Leads to Abuses, Some Borrowers Say. Unsolicited ratings are even less common in the United States: Poon and Firth (2005) examine unsolicited and solicited Fitch ratings for banks, and find that all unsolicited ratings in their sample are for non-u.s. issuers even though over 30% of their sample of solicited ratings are for U.S.-based issuers. Furthermore, unsolicited ratings have become even less common after 2003, when such ratings were subject to scrutiny by the SEC as a potentially abusive practice (Coffee, 2008). Limiting the sample to ratings before the settlement date also limits instances where I may mistake an unsolicited rating for a solicited rating out of seven unsolicited S&P ratings in my sample, only one is reported before the bond settlement (and for this particular issue, S&P was the only agency that ever rated it). 20 If the same agency rates an issue multiple times before settlement, I retain the last of these ratings. 11

12 year, the data for how relatively highly this agency rates the issuer is missing and not included in the tests. Therefore, this methodology may induce a selection bias in the following sense: if an issuer never solicits a rating for any of its bonds from an agency that the issuer expects would rate it particularly poorly, I do not capture these instances of perfect rating shopping. The estimated prevalence of shopping would thus be a lower bound for the true level of rating shopping. 4 The prevalence of rating shopping This section describes evidence on the prevalence of rating shopping. First, I examine the extent to which the rating differences across agencies for the issuer last year predict which agency or agencies an issuer solicits to rate new issues. I then analyze whether the strength of this association varies with bond complexity and the yield spread across ratings. Next, I test whether ratings immediately after bond issuance are lower than those immediately before issuance, and whether this difference varies with the bond s complexity and the default spread. In Section 5, I exploit Rule 2a 7 for money market mutual funds and test whether issuers choose agencies more selectively around the rating threshold that determines the eligibility of short-term bonds for money market funds. 4.1 Past relative ratings and agency choice I first test whether the issuer s agency choice is associated with how highly each agency rated the issuer last year relative to the other agencies, by regressing a dummy for whether an agency rates an issue on the agency s relative rating for the issuer in year t-1 in a simple linear probability model. The observations are at the issue-agency level. I control for agency effects throughout to account for the possibility that some agencies have a higher or lower rating market share across all issuers and/or tend to rate higher or more conservatively on average (i.e., what Cantor and Packer (1997) refer to as different rating scales ). Without this control, the fact that Moody s and S&P traditionally have both rated issues more conservatively and have had higher (unconditional) market shares than Fitch would influence the association between how generously an agency rated an issuer in the past and whether the issuer solicits this agency to rate its new issues. With agency effects, the underlying question becomes more suitable for addressing rating shopping: When an agency rates an issuer s bonds more generously than it does for other issuers, is that issuer more likely to solicit this agency for new bonds compared to how often other issuers solicit the agency? For the same reason, I also control for agency/year effects to account for possible changes in the average ratings and the market shares for the agencies 12

13 over time (I plot the share of issues rated by each agency and the agencies average relative ratings over time in Figure I). The standard errors are clustered by issuer since we can expect the agency choice to be correlated across agencies for the same issue and because the relative ratings are highly correlated across years for the same agency issuer pair, as reported in Table II. Table III, Panel A presents results. I find that across all issues and agencies, a one-notch higher relative rating by an agency for an issuer last year is associated with a 3 percentage-point increase in the probability that that the issuer solicits the agency to rate a new issue this year. However, this coefficient is attenuated by issues that have no ratings (9% of all issues) or that have ratings by all three agencies (29% of issues), since these issues have no variation in the outcome variable across agencies. It is only when an issuer chooses a subset of agencies to rate an issue that relative rating differences across the agencies could have any explanatory power for the agency choice. Thus, in the next columns of Panel A (Table III), columns 3 4, I predict the agency choice based on past relative ratings for the subsample of issues which are rated by exactly one agency at issuance, and in columns 5 6, I predict the agency choice for issues rated by exactly two agencies. I find that past relative ratings predict the agency choice only for issues rated by a single agency. Among these issues, a one-notch higher relative rating by an agency last year is associated with an 18 percentage-point increase in the probability that the issuer solicits that agency for a new issue (controlling for agency effects), and 11 percentage points when controlling for agency-year effects. Conversely, among issues with two ratings, the choice of agencies is not associated with how highly these agencies rated the issuer in the past. Because issuers have more freedom to select only the most generous agency when they solicit a single rating for an issue, we would expect the association between agencies past relative ratings and the issuer s agency choice to be stronger among issues that have one rating than among issues with two ratings. The contrast between issues that have one versus two ratings is nevertheless unexpectedly large, since I find no evidence of shopping at all among the issues with two ratings. One possible explanation for this result is that tradition or demand conditions constrain many issues with two ratings to be rated by Moody s and S&P, and that any shopping incentives that exist do not measurably affect this particular choice of agencies. Next, I examine the extent to which the association between the agency choice and past relative ratings varies across different subsamples of issues with one rating. In Panel B of Table III, I first split the sample based on industrial versus financial issuers (columns 2 and 3). I find that this association is positive for both industrial and financial issuers, but is larger and only significant for the financial issuers. This result is consistent Morgan s (2002) finding that financial firms are more opaque and their bonds therefore are associated 13

14 with more disagreement, which theoretically should result in stronger incentives for rating shopping (Skreta and Veldkamp, 2009). 21 I further split the sample by agency, and separately examine for each agency whether its past relative rating for an issuer predicts whether the issuer is more likely to solicit that agency for new issues. I find that the past relative ratings of Fitch and S&P strongly predict whether issuers solicit either of these agencies to rate their new issues. However, whether Moody s rated an issuer generously or conservatively in the past does not predict how likely the issuer is to solicit a rating from Moody s for its new issues. This result suggests issuers selectively solicit ratings from Fitch and S&P when they expect these ratings to be higher, but that Moody s ratings on average are not subject to this bias. The finding that the expected shopping bias is strongest for corporate bonds that are rated by S&P alone also corresponds with Benmelech and Dlugosz s (2010) finding in the structured securities market where CDOs with only an S&P rating are more likely to be downgraded. A potential alternative explanation for the finding that new bonds are more likely to be rated by an agency that rated the issuer s previous bonds relatively high is due to reverse causality: agencies give higher ratings to issuers that tend to solicit the agency often. This explanation would be an example of a different incentive concern in the credit rating industry, namely that the agencies may be dishonest when determining their ratings. Theoretically, an agency may have an incentive to misreport and inflate its ratings to attract the issuer s business if the agency has competition (Bolton, Freixas, and Shapiro, 2010) or to capture higher fees from the issuer if the agency is monopolistic (Mathis, McAndrews, and Rochet, 2009; Opp, Opp, and Harris, 2010). In a recent empirical paper, Becker and Milbourn (2010) find evidence consistent with such rating inflation on the part of the agencies. In particular, they argue that when Fitch entered new industries, the incumbents Moody s and S&P faced weaker incentives to maintain their reputations for rating honestly, which caused these agencies to inflate their ratings as competition increased. In a similar vein, Kraft (2010) finds that rating changes are more favorable to issuers with contracts that depend on these ratings, which she interprets as catering by the rating agencies towards these issuers. Ashcraft, Goldsmith-Pinkham, and Vickery (2010) as well as Griffin and Tang (2010) have also found evidence consistent with such rating inflation on part of the rating agencies in the structured securities market. However, models of dishonest agencies do not predict that different agencies would have an incentive to distort ratings in a way that produces split ratings differences across agencies for the same issue or issuer that drive the results in this paper. Bolton, Freixas, 21 Another possible explanation is that the rating differences across agencies are better measured for the financial issuers due to their larger number of issues: although industrial issuers represent a larger number of unique firms, the financials issuers as a group issue significantly more bonds. 14

15 and Shapiro (2010) model truth-telling by agencies when there is competition (in their case, a duopoly). They predict that agencies will distort ratings more for larger issuers and in times when such distortion is more difficult to detect but that both agencies will distort ratings equally for a specific issue. 22 It is nevertheless possible that feedback effects exist between rating shopping and equilibrium inflation by the agencies, such that if issuers shop for ratings, agencies in turn have a stronger incentive to inflate their ratings to capture more business. 23 In that case, the rating shopping tests in Table III will pick up how the equilibrium rating differences across agencies predict the agency choice for new issues, even though the total bias for observed ratings will be even higher than that from shopping alone if all agencies provide already-biased shadow ratings. However, rating distortion by the agencies is not necessary for rating shopping: even if agencies rate truthfully when solicited, rating shopping may still cause the solicited and therefore observed ratings to be biased on average. 4.2 Robustness: Alternative specifications Because the agencies relative rating differences for a given issuer are highly correlated from year to year, the results do not depend much on the period over which I measure the relative ratings. In Panel C of Table III, columns 1 and 2 compare results when the agencies relative ratings for an issuer are measured in year t-1 versus year t-2. The coefficients and significance levels are similar, although the number of observations is slightly lower when using relative ratings from t-2 (mainly because the sample of issues with two-year lagged data starts one year later). If the past relative ratings consist of both part-systematic (e.g. model-driven) differences and part-random disagreement, the coefficient on past relative ratings may be biased downward due to classical error-in-variables (CEV). In column 3, I instrument each agency s relative rating in year t-1 with their relative ratings in year t-2. To the extent that the errors in the relative ratings are uncorrelated from year to year, this multiple-indicator solution produces estimates free from the attenuation bias due to CEV (Wooldridge, 2002). The coefficient increases somewhat from 10.9% when using non-instrumented relative ratings in t-1, to 19.2% when instrumenting with the relative ratings in t-2 which suggests the true correlation between systematic rating differences and the issuer s choice of agencies may be higher than that measured in the simple OLS regression. 22 Consistent with rating distortion for large issuers, He, Qian, and Strahan (2010) find that the prices of mortgage-backed security tranches dropped more for tranches sold by large issuers than tranches sold by small issuers. 23 Introducing heterogeneity across agencies or additional constraints in a model of dishonest agencies may also deliver predictions about split ratings, although such an extension appears far from trivial. 15

16 These results are also similar if estimated with independent or conditional/multinomial logit or probit models instead of OLS. I nevertheless prefer the linear probability model due to the inclusion of multiple fixed effects, which can cause biased and inconsistent estimates in non-linear models. Furthermore, the regressions include only one non-saturated explanatory variable, so predicted probabilities are rarely outside the 0 to 1 interval. Finally, the linear probability model is also more suitable in light of the interaction terms that I include in the cross-sectional tests in the next section, as interaction terms can be difficult to interpret in non-linear models (Ai and Norton, 2003). Nevertheless, for comparison, Panel C (columns 4 6) reports results using independent logit and independent probit regressions, as well as using the multinomial probit model for the agency choice. The results from these models are similar to those from the linear probability model. The coefficient on past relative ratings when predicting agency choice (controlling for agency effects) in the linear probability model was 18%, whereas the marginal effects for the independent logit and probit models are 17.9% and 17.6% respectively. The marginal effects from the multinomial probit are 8% (t-stat 2.55) for Fitch, 5% (1.75), for Moody s, and 12.9% (3.31) for S&P (only the coefficient is tabulated). 4.3 Bond complexity and rating shopping Skreta and Veldkamp (2009) predict that issuers incentives to shop for ratings is stronger for more complex issues, as complexity increases the likelihood of disagreement among the agencies. To test this hypothesis, I employ two simple proxies for asset complexity: lower seniority and longer time-to-maturity. 24 One reason why we would expect junior and longer maturity bonds to be more complex to rate follows from the Merton model. A claim with lower seniority and/or longer maturity is associated with more uncertainty about its ultimate value, as it is more sensitive to model parameters and assumptions such as expected growth rate and volatility. First, I test whether issuers are more likely to shop for ratings for junior bonds by interacting the agency s past relative rating with the issue s seniority in the regression for predicting whether the issuer solicits the agency to rate a new issue. I classify the sample of issues into four seniority categories: senior secured, senior unsecured, junior, and undefined. I define junior issues as those with a security level lower than senior unsecured these include securities denoted subordinated, junior, senior subordinated, and junior subordinated in FISD. I omit results for issues with an undefined security level ( none 24 These proxies are similar to those that Sangiorgi, Sokobin, and Spatt (2009) suggest as proxies for the complexity to rate. Specifically, they suggest using longer maturities and lower-rated tranches of structured securities as proxies for the rating precision, which plays a role in their model similar to bond complexity in Skreta and Veldkamp (2009). 16

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