An accounting-based earnings manipulation detection model has strong out-of-sample power to predict cross-sectional returns. Companies with a higher probability of manipulation (M-score) earn lower returns on every decile portfolio sorted by size, book-to-market, momentum, accruals, and short...

An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probability of manipulation (MSCORE) earn lower returns in every decile portfolio sorted by: Size, Book-to-Market, Momentum, Accruals, and...

An earnings manipulation detection model based on forensic accounting principles (Beneish 1999) has substantial out-of-sample ability to predict cross-sectional returns. We show that the model correctly identified, ahead of time, 12 of the 17 highest profile fraud cases in the period 1998-2002....