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Structural Drivers of Credit Rating Uncertainty: An Examination of the Changes Imposed by Dodd-Frank

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Abstract

We examine credit rating disagreements subsequent to the implementation of the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank). We find that both the rate and magnitude of credit rating disagreements increase following the implementation of Dodd-Frank. Long-term issuer (new bond issue) ratings for non-financial firms in the Dodd-Frank era are 8.7% (12.3%) more likely to exhibit rating disagreement relative to the Regulation Fair Disclosure (Reg FD) era controlling for other factors. Additionally, the unconditional magnitude of disagreement increases 29.8% and 18.1% for issuer and issue ratings relative to those of the Reg FD era, respectively. Finally, we find that disagreements for firms that are more reliant on selective disclosure pre-Dodd-Frank are most pronounced following its passage. Changes in the protections governing the selective disclosure of material, non-public information to CRAs under Dodd-Frank may have unintentionally increased credit rating uncertainty.

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Data Availability

This research is based on commercially available data. Researchers can access these data on a commercial basis through Bloomberg Data Services, Compustat, and the Center for Research in Security Prices (CRSP).

Notes

  1. Dodd-Frank Wall Street Reform and Consumer Protection Act Sect. 939(b).

  2. Although evidence in support of the conformity hypothesis would support Dimitrov et al. (2015), evidence in contrast to the conformity hypothesis does not necessarily refute their results as this study examines structural drivers of credit rating uncertainty and not changes in the incentives of CRAs themselves.

  3. The reputation hypothesis of Dimitrov et al. (2015) argues, roughly, that Dodd-Frank makes optimistic ratings costlier for CRAs because optimistic ratings are more likely to be perceived as optimistically biased, thus inviting legal and regulatory scrutiny. To protect their reputation, CRAs respond by lowering their assessment of credit risk beyond a level justified by firm fundamentals.

  4. See, for example, Ederington (1985), Boot et al. (2006), Odders-White and Ready (2006), He et al. (2010), Jory et al. (2016), McBrayer (2019), among others.

  5. See, for example, Blume et al. (1998), Cheng and Neamtiu (2009), Alp (2013), Baghai et al. (2014), or Dimitrov et al. (2015) among others.

  6. Dodd-Frank Wall Street Reform and Consumer Protection Act Sect. 933.

  7. Dodd-Frank Wall Street Reform and Consumer Protection Act Sect. 932.

  8. The Bloomberg data contain observations on all credit rated firms over the sample period. As such, the data are free of the problems associated with survivorship bias.

  9. We omit bank firms from our sample to lessen the likelihood that the other provisions in Dodd-Frank, specifically those pertinent to banks, may be driving our results. In unreported results, we perform a version our empirical testing where we include banks and find that the results are more pronounced for these firms.

  10. In unreported results, we perform a version of all subsequent empirical testing where we include the observations occurring in the crisis period (i.e., 2008 and 2009) in our sample. Our results are qualitatively unchanged over the expanded sample.

  11. In unreported results, we perform a version of the regression testing presented in Panel A of Table 4 where we restrict the sample to only those firms that existed in both the pre- and post-Dodd-Frank periods to avoid the concern that newly rated firms are potentially affecting our results. Our results are qualitatively similar despite the exclusion.

  12. In unreported results, we perform a version of the regression testing presented in Table 4 where we restrict the sample to balance the time periods around the passage of Dodd-Frank. Specifically, we examine the specification over the fiscal years 2003–2007 and 2011–2015. We exclude the financial crisis years consistent with our primary methodology and exclude the year the regulation passed Congress as the year is split unequally between the two time periods. Our results are qualitatively similar over the time balanced sample.

  13. We are unable to replicate this analysis over our sample of new issues due to data availability.

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Acknowledgements

We gratefully acknowledge Jon Fulkerson and Tim Yeager, as well as the seminar participants at the Eastern Finance Association and Southwest Finance Association annual meetings for their invaluable comments and suggestions. All errors remain our own.

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Correspondence to Garrett A. McBrayer.

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Appendices

Appendix 1

Table 7

Table 7 Conversion matrix used to move from alphanumerical ratings to numerical codes. Ratings coded 2–22 are issued ex-ante and represent predictions of default probabilities. Ratings coded 1 are issued ex-post and represent an issue/issuer currently in default

Appendix 2

Table 8

Table 8 Variable definitions

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Duanmu, J., McBrayer, G.A. Structural Drivers of Credit Rating Uncertainty: An Examination of the Changes Imposed by Dodd-Frank. J Financ Serv Res (2023). https://doi.org/10.1007/s10693-023-00399-2

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