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02-10-2020 | Issue 2/2021

Review of Accounting Studies 2/2021

Using machine learning to detect misstatements

Journal:
Review of Accounting Studies > Issue 2/2021
Authors:
Jeremy Bertomeu, Edwige Cheynel, Eric Floyd, Wenqiang Pan
Important notes
We gratefully thank B. Cadman, P. Dechow, C. Lennox, S.X. Li, D. Macciocchi, M. Plumlee, X. Peng, and seminar participants at LSE, University of Utah, the USC-UCLA-UCSD-UCI conference, MIT, and the CMU Accounting Mini Conference for valuable feedback. We also thank J. Engelberg for the many suggestions that were central in seeding the project.

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Abstract

Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year- and two-year-ahead predictions and interpret groups at greater risk of misstatements.

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