The Usefulness of Credit Ratings for Accounting Fraud Prediction
研究发现标准普尔（发行人付费评级机构）的负面评级行动能显著预测会计欺诈，优于欺诈预测模型和其他市场参与者；而投资者付费的EJR评级机构预测能力较弱。
ABSTRACT 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. JEL classifications: G24; K22; M41.