Prior studies employ a two-period empirical model and interpret the negative association between accruals in period one and returns in period two as evidence that investors misprice the information contained in accruals. In contrast to prior studies, I employ a three-period log-linear model decomposed from a firm’s book-to-market ratio and show that investors do not misprice the information contained in accruals. My study shows that in the four-year period prior to accrual recognition, equity prices tend to be driven disproportionately by intangible returns, or returns not explained by accounting measures. Accordingly, the relation between prior period intangible returns and future period returns subsumes the relation between current period accruals and future returns. In addition, I link the accrual anomaly and the value/growth anomaly to a common economic mechanism (intangible returns) and show that a strong negative relation between external financing activities and future returns is not subsumed by the accrual anomaly.

We investigate the impact of the mixed attribute GAAP measurement model on accrual quality inferences. GAAP rules vary from an income statement “matching” focus to a balance sheet “fair-value” focus. Accrual properties are also affected by the business activity being measured and the activity's recurrence. Furthermore, accrual measurement is affected by managerial estimation error/manipulation. As a consequence, accruals are heterogeneous with predictably different statistical properties. We construct a dataset of more than 100,000 accrual items that Compustat aggregates into data item FOPO. We classify these accruals into 32 types over a 21-year span. We show that variation in persistence for the 32 accrual types is consistent with our predictions concerning GAAP rules. We provide a model of accruals and show that it is implausible for estimation error of reasonable magnitudes to explain this variation. Finally, we develop a measure of reporting consistency and show its impact on accrual quality inferences.