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....

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...

We introduce a new approach to predicting market returns using the cross-section of earnings and book values to explain current stock prices and extract aggregate expected returns. The proposed measure is countercyclical; it portends a significant fraction of the time-series variation in stock...

We examine why, as a summary statistic, earnings is better than cash flows at explaining contemporaneous returns despite being a worse predictor of future operating cash flows. Several studies compare the ability of earnings and operating cash flows to predict valuation-related outcome variables...

We examine how informed trading activities affect the market reaction to accounting restatements. We find significantly less negative reactions to accounting estatements when managers are net purchasers of stock before the restatement, and significantly more negative market reactions when...

We document that the accrual anomaly is substantially mitigated for firms followed by more experienced analysts. We then examine research and monitoring as possible channels through which experience mitigates mispricing. For firms followed by more experienced analysts, we find that forecast...