This paper examines the implications of using the absolute value of discretionary accruals when testing for earnings management. First, we analytically develop the mean and variance of the distribution of absolute discretionary accruals, and show that the expected value is an increasing function of the variance in the underlying error term from the first‐stage discretionary accrual estimation model. Second, we highlight several firm characteristics that are related to the error variance in discretionary accrual estimation models. Using simulations, we show that correlation between the earnings management partitioning variable and these firm characteristics leads to an overrejection of the null hypothesis of no earnings management. Third, we provide research design suggestions to help researchers mitigate the potential bias arising from the use of unsigned measures of earnings management. Using these suggestions, we replicate a recent study, and demonstrate that the inferences change after controlling for operating volatility.

This study examines the extent to which parsimonious and general cross‐sectional valuation models, restricted to include only publicly available historical accounting information, explain share prices in the cross section, identify periods when market mispricing may be more pervasive, and also identify which shares within those cross sections are more likely to be mispriced. Our model simply includes historical book value, earnings, dividends, and growth, but it explains on average over 60 percent of the cross‐sectional variation in share prices in annual estimations across 1975–2011. We also examine the extent to which the residuals indicate mispricing. The quintile of stocks picked by our model as most likely underpriced outperform the quintile of stocks picked as most likely overpriced by an average of 9.9 percent over the following 12 months, after controlling for size. We also predict and find that value residuals are better predictors of future abnormal returns: (i) among firms that are not covered by analysts; (ii) among firms that face fewer accounting measurement challenges; and (iii) when we estimate value model parameters by industry/year. We also predict and find our approach works better in periods when the mapping of fundamentals into prices is weaker. This study contributes a novel and straightforward approach to map accounting fundamentals into share prices in order to identify mispricing in time‐series and in the cross section.