Identifying Unexpected Accruals: a Comparison of Current Approaches We Received Helpful Comments from Identifying Unexpected Accruals: a Comparison of Current Approaches Identifying Unexpected Accruals: a Comparison of Current Approaches

While prior research, as noted in our paper, often uses various accrual prediction models to detect earnings management, not much is known about the accuracy, both relative and absolute, associated with these models. Our paper investigates the accuracy of six different accrual prediction models, and offers the following findings. Only the Kang-Sivaramakrishnan (1995) model performs moderately well. The remaining five models provide little ability to predict total accruals: they are less accurate than a naïve model which predicts that total accruals equal -5 percent of total assets for all firms and years. Conventional R2 values from a regression of actual accruals on predicted accruals are less than zero for a substantial majority of firms for these five models. These low R2 values in the prediction period contrast sharply with the much higher R2 values that are obtained within the estimation period. Similar performance is observed when predicting current accruals alone. However, the relative rankings of the different models are altered somewhat: the Jones (1991) model is then the only model that exhibits some predictive ability. I d e n t i f y i n g u n e x p e c t e d a c c r u a l s : a c o m p a r i s o n o f c u r r e n t a p p r o a c h e s