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Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?

Author

Listed:
  • Ruijie Sun

    (Shandong University)

  • Feng Liu

    (Shandong University)

  • Yinan Li

    (Shandong University)

  • Rongping Wang

    (Shandong University)

  • Jing Luo

    (Shandong University)

Abstract

Based on upper echelon theory, we employ machine learning to explore how CEO characteristics influence corporate violations using a large-scale dataset of listed firms in China for the period 2010–2020. Comparing ten machine learning methods, we find that eXtreme Gradient Boosting (XGBoost) outperforms the other models in predicting corporate violations. An interpretable model combining XGBoost and SHapley Additive exPlanations (SHAP) indicates that CEO characteristics play a central role in predicting corporate violations. Tenure has the strongest predictive power and is negatively associated with corporate violations, followed by marketing experience, education, duality (i.e., simultaneously holding the position of chairperson), and research and development experience. In contrast, shareholdings, age, and pay are positively related to corporate violations. We also analyze violation severity and violation type, confirming the role of tenure in predicting more severe and intentional violations. Overall, our findings contribute to preventing corporate violations, improving corporate governance, and maintaining order in the financial market.

Suggested Citation

  • Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    • Handle: RePEc:kap:jbuset:v:195:y:2024:i:1:d:10.1007_s10551-024-05685-0
    DOI: 10.1007/s10551-024-05685-0
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    More about this item

    Keywords

    Corporate violations; CEO characteristics; CEO tenure; Violation severity; Intentional violations; Machine learning; China;
    All these keywords.

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law

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