【讲座时间】2023年3月13日  9：30

【讲座地点】BV韦德108室

【讲座主题】The Usefulness of Credit Ratings for Accounting Fraud Prediction

【嘉宾介绍】Allen Huang，香港科技大学工商管理学院副教授、副经理。

   Allen Huang is an associate Professor  of accounting at HKUST. He received his Ph.D. in accounting from Duke University. Dr. Huang’s research focuses primarily on  earnings management, financial analysts, and disclosure.  Dr. Huang has published extensively in top accounting，finance and management  journals, including  JAR, JAE, TAR, CAR, RAST, JF,MS.

【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.