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.

We examine how the market power of credit rating agencies (CRAs) affects their rating standards. Using a global sample across 26 countries from 1994 to 2019, we find that greater market power of global CRAs, measured by their country-level market shares, is associated with stricter corporate ratings. In addition, the increase in global CRAs' market shares contributes to the tightening trend in their credit ratings worldwide. Exploiting the NRSRO designation of local CRAs in Japan, we find that global CRAs issue more inflated ratings following a decline in their market power. Further, global CRAs' greater market power is associated with timelier ratings, fewer missed defaults, but more false warnings. Collectively, our findings suggest that global rating agencies' market power leads to stricter rating standards and timelier ratings by strengthening the agencies’ reputation concerns, but at the expense of increased false warnings.

Using time stamps of Standard & Poor's rating changes, we examine the timing of rating changes in an intraday setting. Our evidence shows that although most rating changes occur during trading hours, the proportion of downgrades announced after regular trading hours is higher than that of upgrades. In addition, unexpected after-hour downgrades are associated with more negative stock returns and lower trading volume in comparison to those announced during trading hours. We also find that Standard & Poor's is more likely to announce downgrades after hours when downgrades are released on busy days with many concurrent rating announcements, when they concern financial firms, and when they are unexpected. In addition, Egan-Jones Ratings (EJR), an investor-paid credit rating agency, demonstrates a similar tendency in announcing downgrades after trading hours. This is the first study to document systematic differences in the timing of credit rating changes announced before and after the market closes. Our findings suggest that Standard & Poor's announces downgrades after trading hours to better disseminate information, and thus have important policy implications.

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.