A. Credit Rating Agencies and Regulation
There are currently three major CRAs in the U.S. market: S&P, Moody's, and Fitch. In addition to these big three, seven smaller CRAs issue credit ratings that qualify for meeting regulatory standards. While the purpose of a credit rating is to reflect the creditworthiness of an issue or issuer, the rating agencies have some discretion in the philosophy underlying their rating system and are not required to make their rating methodology public.5
CRAs are licensed as Nationally Recognized Statistical Rating Organizations (NRSRO) by the Securities and Exchange Commission (SEC). This official designation has a number of effects. First, CRAs are exempt from Regulation FD, allowing corporations to share value-relevant information with the rating agency without disclosing it publicly. Second, the SEC designation allows credit ratings to be used to meet regulatory requirements that call for a minimum or an average rating value. For example, the SEC requires that money market mutual funds hold instruments with a credit rating in one of the two short-term higher credit rating categories.6 This effectively provides a “safe harbor” for money market mutual funds with respect to litigation over fund failures. Kisgen (2006) discusses the strong link between short- and long-term debt ratings and access to the commercial paper market. He concludes that, in order to have access to the commercial paper market, a rating of BBB is typically required.
U.S. insurance companies explicitly rely on NRSRO ratings in determining risk-based capital. In particular, bonds held by insurance companies are assigned capital charges based upon their credit ratings. For example, a U.S. life insurance company needs to hold over three times as much reserve capital for a BB-rated bond compared to a BBB-rated bond. At the time of writing, European insurance companies will soon be subject to comparable regulations with the implementation of “Solvency II.” Banking regulations enacted under the so-called Basel II Accords impose very similar risk-based capital requirements.7 Many pension funds and mutual funds are restricted from investing in HY corporate bonds by their charter. Although there is much discussion about treating bank and insurance assets in the context of their total portfolio, taking into account covariance rather than security-specific risk, as of mid-2010 a large portion of U.S. institutional portfolios are still subject to rules and regulations tied to ratings by a relatively small number of NRSROs. The impact of such rules and regulations on the functioning of the corporate bond market, and in particular in determining supply and demand, is almost certainly nontrivial since a vast majority of this market is dominated by institutions that are subject to rating-related restrictions, either through explicit rules and regulations or through restrictions stated in their charters.8
In June 2008, the SEC proposed eliminating language in many regulations pertaining to NRSROs, and instead allowing an alternative decision-making function, perhaps recognizing that reliance on credit ratings agencies had the potential to distort the information-gathering and investment decision-making process. In addition, several other regulations were implemented to try to make rating agencies more accountable and to increase the transparency of the rating process. The motivation for these (proposed) changes stemmed from the subprime mortgage crisis that began in 2007, and from concerns that the top three CRAs may represent an oligopoly enabled by government regulation. Among other things, critics argue that this oligopoly might not be the optimal mechanism for revealing information related to the risk of fixed income securities, and instead might be used as an artificial safe-harbor to excuse investment managers from exercising business judgment. As such, it could allow CRAs to extract rents from corporations by virtue of serving as “gate-keepers” to the IG rating, especially as the CRAs are paid by the corporations whose bonds are rated. Moreover, competition among CRAs could lead to a so-called “race to the bottom,” with CRAs decreasing standards to attract more customers. This concern is raised about the structured finance market in the context of the (subprime) mortgage crisis.
While all of the aforementioned issues are of a regulatory nature, the wider financial industry has also grown increasingly dependent on CRAs. Financial institutions center self-regulation around credit ratings; for example, some mutual funds state in their charter that they can only invest in IG quality fixed income securities. Further, trading and internal risk management models often take credit ratings as primary inputs or use them for calibration. Many corporate credit arrangements, like collateral requirements and haircuts, are also driven by credit ratings. Moreover, ratings are an important factor in determining whether a bond qualifies for inclusion in prominent corporate bond indices like the Barclays Capital (formerly Lehman Brothers) U.S. Corporate IG Index.9 Inclusion in such an index may greatly improve the liquidity of an issue, since, for example, index tracking institutions will trade in them more. Several papers show that higher liquidity leads in turn to lower credit spreads (see, e.g., Chen, Lesmond, and Wei (2007)). Typically, these procedures incorporate all (multiple) rating information available, extending possible certification effects well beyond those resulting from financial regulation.
B. Why Multiple Ratings Matter
In this subsection, we consider three different explanations for why firms would solicit and pay for multiple ratings. We base these hypotheses on empirical evidence provided by previous literature, as summarized in the next subsection. Specifically, the three hypotheses we consider are (i) the “information production” hypothesis, (ii) the “rating shopping” hypothesis, and (iii) the “regulatory certification” (or clientele, or regulation) hypothesis. Below, we give a short description of each and discuss its testable empirical predictions. As these hypotheses are generally not mutually exclusive, we discuss how they are related as well as differences in empirical predictions that allow us to distinguish which hypothesis may dominate empirically. These empirical predictions are summarized in Table I.
Table I. Empirical Predictions
|The various empirical predictions of the three hypotheses we consider are summarized in the table below, where “−” indicates that the implication is not supported and “+” means that it is supported.|
|Reason for Multiple Ratings||Information Production||Rating Shopping||Regulatory Certification|
|(i) Additional agreeing rating lowers credit spreads||+||−||−|
|(ii) Additional relatively optimistic rating lowers credit spreads (also away from HY–IG boundary)||+||+||Only at HY–IG boundary|
|(iii) Uncertainty uniformly increases # of ratings||+||+||possible|
|(iv) Additional rating more likely if that could push the issue into IG classification||possible||possible||+|
|(v) Additional rating more optimistic (especially around HY–IG boundary)||−||+||Only for strategic CRA|
|(vi) Additional rating associated with higher expected time variation in ratings||+||−||+|
First, a firm might apply for multiple ratings due to a need for increased information production. More ratings can reduce uncertainty about the credit quality of the rated bonds. In a setting in which each CRA relies on different kinds of information to rate bonds, multiple perspectives reduce uncertainty about default probability. CRAs may specialize in evaluating particular drivers of default and thus each may have some advantage that justifies its continued existence in the marketplace. Thus, one would expect issuers with greater ex ante uncertainty to be more likely to apply for extra ratings, since the potential reduction of uncertainty is largest for these issuers. Moreover, under the information production hypothesis, an extra rating in agreement with existing ratings would reduce credit quality uncertainty and thereby lower credit spreads.
Second, the rating shopping effect can arise in a setting in which CRAs do not perfectly agree or there is considerable uncertainty about credit quality, while issuers may have better information about their own credit quality. In this case, issuers may seek to maximize their average rating by soliciting multiple bids or following a stopping rule that chooses the first rating agency whose rating equals or exceeds the firm's own assessment of quality. Applying for private ratings and making these public only if favorable, or deciding which CRA to use based on advice from an investment bank that has knowledge about each CRA's precise rating algorithms (gaming), would lead to similar patterns. The rating shopping hypothesis thus predicts that issuers will apply for an additional rating only if they think it will be an improvement. Therefore, additional ratings are on better average. Further, if the issuer applies for an additional rating and this additional rating is an improvement, credit spreads should go down. This can be because the additional rating is actually closer to the firm's true credit quality or because it is not, but the market mistakenly takes the new rating at face value. In the latter case, if the market is not fooled, there would be no incentive to engage in rating shopping.
The third explanation for multiple ratings is regulatory certification. Financial regulation has traditionally relied heavily on credit ratings to determine the suitability and riskiness of fixed income investments. For instance, bond ratings are used to score the quality of bonds in the investment portfolios of insurance companies and banks, with regulatory capital reserve requirements determined by this score. Ratings are also important in the structured finance market, the commercial paper market, and the overnight repo market. They are further used to determine “haircuts” at the discount window of the central bank and to determine whether projects qualify for government assistance (see, e.g., the Basel Committee on Banking Supervision (2000)). They may also be the basis for financial contracting between private parties, as the world witnessed in the case of AIG's rating downgrade that triggered a need for increased collateral in its counterparty arrangements. This event underscores the enormous potential impact of certification.
The most prominent distinction made in financial regulation as it pertains to credit ratings is whether an issue, issuer, or structured product is rated IG or HY. In particular, the most prevalent institutional rule used in classifying rated bonds stipulates that, if an issue has two ratings, only the worse rating is used to classify the issue into IG or HY. However, if an issue has three ratings, the middle rating is used (see, e.g., the NAIC guidelines or the Basel II Accord).10,11 Therefore, if S&P and Moody's ratings are on opposite sides of the HY–IG boundary, the Fitch rating (assuming it is third rater) will decide into which class the issue falls. This classification creates strong incentives for issuers trying to achieve an IG rating. Thus, the HY–IG boundary is associated with a clear discontinuity in institutional demand. Assuming a downward sloping demand curve, the lower demand for HY bonds significantly increases the cost of borrowing for those issuers (see Ellul et al. (2010) and Kisgen and Strahan (2009)).12
Under the regulatory certification hypothesis, the principal value of a CRA that systematically gives better ratings (i.e., in our data, Fitch) than the other CRAs (i.e., Moody's and S&P) is simply that it helps satisfy the bright-line requirements of financial regulation. A rating from this CRA could be requested by issuers for which the extra rating might make them qualify for an IG classification. In addition, issuers that consider themselves likely to experience a future downgrade from IG to HY could seek an extra rating due to precautionary motives. This could lead to adverse selection effects, as relatively weaker firms with higher credit spreads would then be more likely to apply for a Fitch rating. Therefore, under the regulatory certification hypothesis, split ratings at the HY–IG boundary by Moody's and S&P should give an issuer significant incentives to get an additional rating from Fitch. Moreover, an additional rating may provide a hedge against the regulatory and rule-based effects of possible future rating downgrades, while also increasing the probability of reaping regulatory benefits from upgrades. This effect should be more pronounced for issuers expecting to have more volatile ratings over time.
Gorton and Pennacchi (1990) and Boot and Thakor (1993) show that the information and regulatory certification hypotheses can be inherently related in a setting with two types of investors, in which issues with a lower credit quality carry more uncertainty. Type I investors have a time-varying natural demand for bonds and high research costs, and type II investors are without the natural demand but have low research costs.13 Since type I investors are at an informational disadvantage relative to type II investors, they will only invest in high credit quality securities for which the informational gain of type II investors is small, that is, in informationally insensitive assets, to avoid losses due to trading with informed investors (see Gorton and Pennacchi (1990)). Typically, type II investors provide liquidity to this market to accommodate aggregate demand shocks. On the other end of the credit quality spectrum, it is worthwhile for type II investors to generate the information needed.14 The region in the middle could suffer from a market breakdown if type II investors only make money if they profit from informed trading with type I investors (as in Boot and Thakor (1993)).15
The importance of regulatory certification could be in preventing a market breakdown for intermediate quality bonds. In this setting, credit ratings can restore trading by reducing the uncertainty about the value of information. Ratings will yield information not only about credit quality, but also about the profitability of research. If the conclusion is “no substantial information benefit,” then type I investors would invest and type II would not bother to research. If the conclusion is “significant information benefit,” then type I investors would not invest and type II investors would invest to hold the security. The HY–IG boundary is the prime candidate for the location on the credit quality spectrum where the unconditionally expected gains from informational trading offset the costs for acquiring information. This setting thus explains how a certification effect could arise in equilibrium.
The regulatory certification and rating shopping hypotheses also may have similar features. In particular, while a rating shopping effect could be observed across the entire rating scale, rating shopping incentives are likely strongest around the HY–IG boundary. Therefore, the distinction between these two hypotheses merits discussion. The central prediction of rating shopping is that additional ratings are, on average, optimistic relative to existing ratings. Thus, if rating shopping is most important around the HY–IG boundary, the positive bias of the marginal rating should also be largest at this boundary. In contrast, certification would give no reason to expect the additional CRA to be more positive at this boundary as compared to other parts of the rating scale. Specifically, certification predicts that, if Moody's and S&P ratings are on opposite sides of the HY–IG boundary, the issuer is significantly more likely to pay for the (assumed marginal) Fitch rating. However, in contrast to the rating shopping hypothesis, regulatory certification does not imply that this Fitch rating would be relatively more positive (compared to Moody's and S&P ratings) than Fitch ratings at other parts of the rating scale.
Second, the expectation of future rating changes decreases incentives for rating shopping but increases the importance of regulatory certification. Rating shopping is more worthwhile if investors expect that ratings will remain relatively stable, as, in that case, credit rating improvements are less likely to be undone or become redundant. Under the regulatory certification hypothesis, (future expected) rating volatility creates a strong precautionary motive, motivating issuers to get an additional rating to hedge against a possible future downgrade below IG.16 For this reason, additional ratings may be associated with adverse selection, as issuers expecting more negative credit news may be more likely to apply for such precautionary, additional ratings.
Each of the three explanations of multiple ratings (information production, rating shopping, and regulatory certification) thus has distinct empirical predictions, though different explanations can coexist. In particular, there are potential differences in whether we would expect (i) credit spread effects of agreeing ratings, (ii) credit spread effects of relatively optimistic ratings across the rating spectrum, (iii) more uncertainty leading to an increase in the number of ratings, (iv) extra ratings to be more likely when these could push an issue into the IG category, (v) greater optimism of the additional rating around the HY–IG boundary, and (vi) an association between additional ratings and the likelihood of future rating changes (see Table I for a summary).
Under information production, an additional rating that is in agreement with the prior ratings will reduce uncertainty and thereby lower credit spreads, while more uncertainty will make an additional rating more worthwhile and therefore lead to more ratings.
Under rating shopping, more uncertainty will again lead to more ratings since initial ratings will err more often. Additional ratings are likely to be better, and better ratings should lower the credit spread.17 However, time variation in ratings makes shopping less worthwhile since the preferred outcome will be less stable.
Under regulatory certification, a better extra rating will only lead to a lower spread at the boundary but unconditionally an additional rating could reflect adverse selection (only weaker issuers take an extra rating) and consequently lead to higher credit spreads. Higher time variation in ratings will give rise to a rating-hedging incentive and hence increase the probability of having an extra rating even for issues that are not at the boundary.
C. Related Research
As asset pricing relies fundamentally on the production and dissemination of information, and this process is endogenously determined, the related literature is vast. CRAs are only one type of research and information provider to the securities markets. Much of the academic literature on the role of research and information providers has focused on equity analysts rather than CRAs rating corporate debt. Studies on the equity markets address a broad range of questions about research providers, ranging from whether analysts' opinions convey value-relevant information to whether conflicts of interest and personal, strategic considerations influence the nature of the information provided. CRAs present a different institutional structure for analysis. While the same basic principles regarding information production apply, CRAs have become integral to regulation pertaining to the credit market (see also the discussion above).
Research on the role of CRAs is more limited. Theoretical work has asked what role CRAs play in the equilibrium pricing process. Boot, Milbourn, and Schmeits (2006) highlight CRAs as a valuable coordination device whereby CRAs provide little value-relevant information at the HY–IG boundary other than regulatory certification but provide useful valuation information about riskier issues. Carlson and Hale (2005) point out that, when each investor's optimal strategy is dependent on the strategy followed by other investors, the public rating provided by the rating agency can serve to coordinate investor actions. Bannier and Tyrell (2006) introduce reputation and competition among rating agencies. Under certain conditions, these features will stimulate investors to search for private information and thus will not only restore a unique equilibrium, but could even lead to a more efficient one.
Each of the three potential explanations for multiple ratings finds support in existing academic literature. On the subject of information production, a number of papers look at the effects of rating changes on asset prices. For example, Kliger and Sarig (2000) use a refinement in the Moody's ratings system to show that rating changes channel information to the market that changes the value of debt. However, their results also suggest that this information leaves the company's value intact and thus only influences the value of debt relative to the value of equity. Güntay and Hackbarth (2010) investigate the effect of analyst dispersion on credit spreads. They find that higher analyst dispersion is associated with higher credit spreads and conclude that this is probably due to cash flow uncertainty.
Jewell and Livingston (1999) investigate whether ratings differ systematically across rating agencies. They find that the average Fitch rating is much more positive than Moody's and S&P ratings, but that this effect disappears once they restrict their sample to bonds rated by all three CRAs. They also investigate whether rating shopping takes place, but find no supportive evidence. Covitz and Harrison (2003) look at the trade-off that rating agencies face between income resulting from giving out favorable ratings and expected future fees from customers resulting from reputation. They argue that reputation concerns dominate and prevent CRAs from being “bribed” by customers. Bannier, Behr, and Güttler (2010), like Poon (2003) and Poon and Firth (2005), investigate possible adverse selection and holdup in the context of CRA and issuer incentives when CRAs issue ratings on an unsolicited basis.18
Inspired by the financial crisis and the critiques aimed at CRAs, several recent theoretical papers put forward models to motivate rating shopping. Skreta and Veldkamp (2009) develop a model in which incentives for rating shopping increase as product complexity increases. Bolton, Freixas, and Shapiro (2009) show that naive investors in the market may give CRAs incentives to inflate their ratings and that, in a duopoly, this gives extra incentives for rating shopping, which in turn aggravates the problem. Sangiorgi et al. (2009) develop a theoretical model of rating shopping and explore biases in ratings conditional upon heterogeneity across issuers in the extent to which different raters agree.
In research most closely related to this paper, Cantor and Packer (1997) also look for evidence of the information effect, the shopping effect, and the certification effect. They use issuer-level ratings data for the year 1994 to understand the motivation for using a third rating agency, but do not use bond price and yield data to evaluate the market effects and price implications of the third rating. Like our paper, they find that the third CRA rating is systematically more optimistic. However, they fail to find evidence that the use of a third CRA is motivated by information, rating shopping, or certification effects. Since the time of their study, bond price data have become more widely available for research. This allows us to conduct more powerful tests of the market response to an additional rating, and to understand in greater detail how market participants interpret and use ratings.
Another closely related paper is Becker and Milbourn (2009), who consider the impact of the major growth in market share of Fitch since 1989. They find that more “competition leads to lower quality in the ratings market: the incumbent agencies produce more issuer-friendly and less informative ratings when competition is stronger.” They explain this result by applying the reputation model of Klein and Leffler (1981), who consider CRA incentives to invest in information production in order to improve their reputation. First, such incentives would be weaker if future rents are lower as a result of increased competition. Second, if demand is more elastic with greater competition, this may force CRAs to spend less on expensive information production or tempt them to be more responsive to issuer demands, potentially inducing rating shopping.
Brister et al. (1994) find evidence of a “superpremium” in yields of junk bonds due to regulation around the HY–IG boundary. Based on only S&P rating data, they find that yields increase disproportionally from a BBB to BB rating relative to the increase in default risk. Moreover, in a recent paper, Kisgen and Strahan (2009) find that credit spreads change in the direction of a Dominion bond rating after the accreditation of Dominion as an NRSRO. They also find that this effect is much stronger around the HY–IG boundary, indicating the importance of regulatory certification. Finally, Kisgen (2006, 2009) investigates whether discrete rating boundaries influence capital structure decisions before and after rating changes. Kisgen (2006) finds evidence of reduced debt issuance when ratings are close to an up- or downgrade, suggesting that credit ratings directly affect capital structure decisions in a way not captured by traditional capital structure theories. Moreover, this effect is especially pronounced around the HY–IG boundary. Kisgen (2009) finds that managers lower leverage after a rating downgrade, suggesting that managers target credit ratings rather than debt levels or leverage ratios. This effect is again more pronounced around the HY–IG boundary.
With respect to the nature of the certification effect that we find, our research relates to earlier work on security design. Gorton and Penacchi (1990) consider a model in which uninformed investors are incentivized to transform risky assets into information-sensitive and information-insensitive parts, where for the latter category they can avoid losses due to trading with informed investors. Boot and Thakor (1993), on the other hand, develop a model in which security issuers lower funding costs by making informed trading more profitable. Our setup motivating the exploration of the regulatory certification hypothesis uses key insights of both papers. In particular, the nontrading region in our setup is a result of the absence of the uninformed investor, whereas the uninformed investor is needed to make trading profitable for the informed investor.