【报告主题】The Usefulness of Credit Ratings for Accounting Fraud Prediction
【主讲嘉宾】Allen Huang，香港科技大学工商管理学院副教授、副院长。
【报告时间】2023年3月13日 9：30
【报告地点】会计学院108室
【内容提要】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 frauds incremental to fraud prediction models (e.g., Fscore) and other market participants. In contrast, rating actions by Egan-Jones Rating Company (EJR), an investor-paid CRA relying on public information, have smaller predictive ability, which is subsumed by S&P and other market participants. We further show that S&P takes more timely rating actions against fraud firms than EJR and that this advantage is especially pronounced for fraud firms with high information uncertainty. Last, we find that S&P’s negative rating actions are informative to the market and associated with a shorter fraud duration, particularly when the accompanying credit rating reports have negative content. Our results suggest that issuer-paid CRAs’ information advantage helps predict accounting fraud.