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

Ruijie Sun (), Feng Liu (), Yinan Li (), Rongping Wang () and Jing Luo ()
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Ruijie Sun: Shandong University
Feng Liu: Shandong University
Yinan Li: Shandong University
Rongping Wang: Shandong University
Jing Luo: Shandong University

Journal of Business Ethics, 2024, vol. 195, issue 1, No 8, 166 pages

Abstract: 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.

Keywords: Corporate violations; CEO characteristics; CEO tenure; Violation severity; Intentional violations; Machine learning; China (search for similar items in EconPapers)
JEL-codes: G30 G38 K22 M48 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10551-024-05685-0

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