Using machine learning to detect misstatements

doi:10.1007/s11142-020-09563-8

Using machine learning to detect misstatements

2020-10-02

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DOI：10.1007/s11142-020-09563-8

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Abstract

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Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limiteda prioriknowledge 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

Using machine learning to detect misstatements

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