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Using machine learning to detect misstatements

Author

Listed:
  • Jeremy Bertomeu

    (Washington University)

  • Edwige Cheynel

    (Washington University)

  • Eric Floyd

    (University of California San Diego)

  • Wenqiang Pan

    (Columbia University)

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.

Suggested Citation

  • Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    • Handle: RePEc:spr:reaccs:v:26:y:2021:i:2:d:10.1007_s11142-020-09563-8
    DOI: 10.1007/s11142-020-09563-8
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    References listed on IDEAS

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    1. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    2. Mark L. DeFond & K. Raghunandan & K.R. Subramanyam, 2002. "Do Non–Audit Service Fees Impair Auditor Independence? Evidence from Going Concern Audit Opinions," Journal of Accounting Research, Wiley Blackwell, vol. 40(4), pages 1247-1274, September.
    3. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    4. Kasznik, R, 1999. "On the association between voluntary disclosure and earnings management," Journal of Accounting Research, Wiley Blackwell, vol. 37(1), pages 57-81.
    5. Bertomeu, Jeremy & Marinovic, Ivan, 2015. "A Theory of Hard and Soft Information," Research Papers 3318, Stanford University, Graduate School of Business.
    6. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    7. E. Johnson & Inder K. Khurana & J. Kenneth Reynolds, 2002. "Audit†Firm Tenure and the Quality of Financial Reports," Contemporary Accounting Research, John Wiley & Sons, vol. 19(4), pages 637-660, December.
    8. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    9. Avramov, Doron & Chordia, Tarun & Jostova, Gergana & Philipov, Alexander, 2009. "Credit ratings and the cross-section of stock returns," Journal of Financial Markets, Elsevier, vol. 12(3), pages 469-499, August.
    10. Jon A. Garfinkel, 2009. "Measuring Investors' Opinion Divergence," Journal of Accounting Research, Wiley Blackwell, vol. 47(5), pages 1317-1348, December.
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    Cited by:

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    7. Yunchuan Sun & Xiaoping Zeng & Ying Xu & Hong Yue & Xipu Yu, 2024. "An intelligent detecting model for financial frauds in Chinese A‐share market," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1110-1136, July.
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    11. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
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    13. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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