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Using machine learning to detect misstatements
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DOI：10.1007/s11142-020-09563-8.png)
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摘要
En 中文
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.
Keyword:
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
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期刊
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5.8
论文数:
1.1K
被引数:
6.4K
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