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

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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.

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Data availability

The data that supports the findings of this study are available from the corresponding author, upon reasonable request.

Notes

  1. More detailed information can be found at http://english.sse.com.cn/news/newsrelease/c/4986736.shtml

  2. We use the 2010–2020 period for our investigation of corporate violations because 2009 was an important year for the A-share market due to the introduction of a number of policies (e.g., strengthening market regulations, promoting new stock issuance, and reducing stamp duty), and companies may have taken some time to adapt to these policies.

  3. CEOs with business degrees tend to exhibit more business ethics and are less likely to commit violations (Troy et al., 2011). Thus, we separate the possession of an MBA from education in general to investigate the impact of CEO MBA on corporate violations.

  4. Using the logarithm to compress the original data into a smaller range has been widely employed to analyze CEO characteristics because the data are easier to process and the impact of noise and outliers on the results is reduced (e.g., Chidambaran and Prabhala, 2003; Gao and Li, 2015).

  5. CEO pay in our study does not include equity incentives because the equity incentive plan for Chinese listed companies starts late, while the proportion of equity incentives implemented by listed companies and the proportion of equity incentive shares granted by equity incentives are low.

  6. According to the database guide, CEO shareholdings do not include unexercised stock options.

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Acknowledgements

We gratefully acknowledge insightful suggestions from the editors and the anonymous reviewers, which substantively improved this article. We also thank Mingjie Fang, Caixia Liu, Simon Shufeng Xiao, Gil Coombe, and Zongli Dai for their comments on earlier versions of this paper. We thank the members of Star-lights Research Team for research assistance.

Funding

This work was supported by the Humanities and Social Sciences Foundation of the Ministry of Education of China [Grant No. 21YJC630076].

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  1. Business School, Shandong University, Weihai, China

    Ruijie Sun, Feng Liu, Yinan Li & Rongping Wang

  2. School of Mathematics and Statistics, Shandong University, Weihai, China

    Jing Luo

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Sun, R., Liu, F., Li, Y. et al. Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?. J Bus Ethics (2024). https://doi.org/10.1007/s10551-024-05685-0

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