Discussion of The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition

摘要

Understanding the accrual process and developing good models that accurately capture the behavior of accruals is a key issue in a broad range of accounting research. Ball and Shivakumar incorporate conditional conservatism, the asymmetric timeliness with which accruals recognize economic losses, into the existing accruals models. The authors' findings are important and consistent with existing literature on the asymmetry with which earnings recognize positive and negative news, and should result in better-specified models of the accrual process. At the same time, the authors' analysis highlights several issues for future research. First, the modeling of when and to what extent accruals recognize economic gains is left as an unanswered question. The timely recognition of economic gains is expected to improve contracting efficiency, and even though loss recognition is more timely than gain recognition, it would be helpful to know more about the timeliness of gains and how it should be modeled. Ball and Shivakumar are not able to interpret their regression coefficients on economic gains due to econometric issues, and so leave this question for future research. Second, the modeling of working capital accruals has received the most attention in the literature and, as a result, working capital accruals are better understood than long-term accruals. More accurate models of long-term accruals will likely improve the overall modeling of the accrual process. Finally, Ball and Shivakumar's results suggest improvements that can be made to models of the discretionary accrual component of earnings. Recent work by KLW argues that performance-matched discretionary accruals measures are necessary because accruals expectation models do not adequately control for the relation between accruals and firm performance. As models of expected accruals become more complete (e.g., by incorporating asymmetries in the recognition of economic gains and losses), performance matching may be less necessary or may require a performance-matching variable that is different from the return on assets measure advocated by KLW.