Predictability of future returns using ex ante information (e.g., analyst forecasts) violates market efficiency. We show that predictability can be due to non-random data deletion, especially in skewed distributions of long-horizon security returns. Passive deletion arises because some firms do not survive the post-event long horizon. Active deletion arises when extreme observations are truncated by the researcher. Simulations demonstrate that data deletion induces a negative relation between future returns and ex ante information variables. Analysis of actual data suggests a 30–50% bias in the estimated relations. We recommend specific robustness checks when testing return predictability using ex ante information.

We examine the specification and power of tests based on performance-matched discretionary accruals, and make comparisons with tests using traditional discretionary accrual measures (e.g., Jones and modified-Jones models). Performance matching on return on assets controls for the effect of performance on measured discretionary accruals. The results suggest that performance-matched discretionary accrual measures enhance the reliability of inferences from earnings management research when the hypothesis being tested does not imply that earnings management will vary with performance, or where the control firms are not expected to have engaged in earnings management.