Using cognitive load theory to explain the accrual anomaly

Abstract

The accrual anomaly represents the positive abnormal returns generated by a trading strategy that seeks to exploit investors' failure to accurately forecast earnings when the accrual and cash components of earnings (earnings components) are differentially persistent. This dissertation investigates: (i) whether analysts and nonprofessional investors accurately forecast earnings when the earnings components are differentially persistent; and, (ii) a behavioral process that contributes to the accrual anomaly. I find that the earnings forecasts of analysts and nonprofessional investors are less accurate when the earnings components are differentially persistent relative to when the earnings components are equally persistent. Using cognitive load theory as a framework, I consider the effect of two hurdles (i.e., intrinsic and extraneous cognitive load) that investors need to overcome to accurately forecast earnings of firms with differentially persistent earnings components. I investigate how task decomposition and disclosure format combine to enable analysts and nonprofessional investors to overcome the cognitive load hurdles and more accurately forecast earnings when the earnings components are differentially persistent. I predict and find that the earnings forecasts of analysts and nonprofessional investors are only more accurate when analysts and nonprofessional investors attend to the earnings components and this information is disclosed in a format that minimizes their information processing costs.