Using machine learning to detect misstatements

Jeremy Bertomeu (), Edwige Cheynel (), Eric Floyd () and Wenqiang Pan ()
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Jeremy Bertomeu: Washington University
Edwige Cheynel: Washington University
Eric Floyd: University of California San Diego
Wenqiang Pan: Columbia University

Review of Accounting Studies, 2021, vol. 26, issue 2, No 2, 468-519

Abstract: 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.

Keywords: Restatement; Manipulation; Earnings management; Machine learning; Data analytics; Regression tree; Misstatement; Irregularity; Fraud; Prediction; SEC; Enforcement; Gradient boosted regression tree; Data mining; Accounting; Detection; AAERs (search for similar items in EconPapers)
JEL-codes: C63 D83 G38 K22 K42 M41 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (23)

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DOI: 10.1007/s11142-020-09563-8

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