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Using Cognitive Load Theory to Explain the Accrual Anomaly
Max Hewitt, Indiana University
ABSTRACT. This paper 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 investigate how task decomposition and disclosure format combine to enable 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 investors are only more accurate when investors attend to the earnings components and this information is disclosed in a format that minimizes their information processing costs.
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