Industry classification misfits: identification, consequences and guidance

• 成果类型: Article; Early Access
• 署名作者: Colas, Baptiste; Brousseau, Carl
• 署名单位: Universidad Carlos III de Madrid; Laval University
• 刊物名称: REVIEW OF ACCOUNTING STUDIES
• ISSN/ISSBN: 1380-6653
• DOI: 10.1007/s11142-025-09886-4
• 发表日期: 2025
• 关键词: earnings; accruals; MARKET; INFORMATION; analysts; QUALITY; schemes; common

摘要

We exploit differences in two industry classification schemes to distinguish between industry classification misfits and industry core firms. We posit that misfits differ from their industry peers, and we document consequences of this heterogeneity. Misfits have larger absolute abnormal accruals, firms in industries with a greater proportion of misfits have larger absolute abnormal accruals, and contemporaneous abnormal accruals are associated with future restatements for industry core firms but not for misfits. We attribute these results to measurement error generated by the inclusion of misfits in the estimation of accrual models. We then provide guidance to alleviate this issue. For both misfits and industry core firms, using fixed peer groups based on the largest firms in a given industry significantly outperforms other peer selection methods in detecting abnormal accruals. In additional analyses, we highlight other economic consequences of industry classification misfits such as higher information processing costs.