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Industry classification misfits: identification, consequences and guidance

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  • Baptiste Colas

    (Universidad Carlos III de Madrid (UC3M))

  • Carl Brousseau

    (Laval University)

Abstract

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.

Suggested Citation

  • Baptiste Colas & Carl Brousseau, 2025. "Industry classification misfits: identification, consequences and guidance," Review of Accounting Studies, Springer, vol. 30(4), pages 3295-3343, December.
    • Handle: RePEc:spr:reaccs:v:30:y:2025:i:4:d:10.1007_s11142-025-09886-4
    DOI: 10.1007/s11142-025-09886-4
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