I examine a dataset of both quantitative (hard) adjustments to firms' reported U.S. GAAP financial statement numbers and qualitative (soft) adjustments to firms' credit ratings that Moody's develops and uses in its credit rating process. I first document differences between firms' reported and Moody's adjusted numbers that are both large and frequent across firms. For example, primarily because of upward adjustments to interest expense and debt attributable to firms' off-balance sheet debt, on average, adjusted coverage (cash flow-to-debt) ratios are 27 percent (8 percent) lower and adjusted leverage ratios are 70 percent higher than the corresponding U.S. GAAP ratios. I then find that Moody's hard and soft rating adjustments are associated with significantly higher credit spreads and flatter credit spread term structures. Overall, the results indicate that Moody's quantitative adjustments to financial statement numbers and qualitative adjustments to credit ratings enable it to better capture default risk, consistent with it effectively processing both hard and soft information.

This study examines whether and when credit ratings are useful for accounting fraud prediction. We find that negative rating actions by Standard & Poor’s (S&P), an issuer-paid credit rating agency (CRA), have predictive ability for fraud incremental to fraud prediction models (e.g., F-score) and other market participants. In contrast, rating actions by Egan-Jones Rating Company (EJR), an investor-paid CRA relying on public information, have less predictive ability, which is subsumed by S&P and other market participants. Our results are robust to including firms not covered by EJR, using only rating downgrades, controlling for firm characteristics, and using alternative benchmarks. We also find that the ability of negative S&P rating actions to predict fraud becomes stronger after the 2008–2009 financial crisis. Last, compared with EJR, S&P is quicker to take negative rating actions against fraud firms. In sum, our results suggest that issuer-paid CRAs’ information advantage helps predict accounting fraud. Data Availability: Data are available from the public sources cited in the text.