The paper provides a cost-based explanation for decision makers' reluctance to use fraud prediction models, particularly as these models have nearly doubled their success at identifying fraud (true positive rates) when compared to the initial models in Beneish (1997, 1999). We estimate the costs...

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...