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Volume 12
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10.3390/systems12050143
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Article

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

by
Yan Li
,
Huiyuan Xue
,
Shiyu Wei
,
Rongping Wang
and
Feng Liu
*
Business School, Shandong University, Weihai 264209, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(5), 143; https://doi.org/10.3390/systems12050143
Submission received: 5 March 2024 / Revised: 7 April 2024 / Accepted: 17 April 2024 / Published: 23 April 2024
(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)
Versions Notes

Abstract

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally.
Keywords: stock price crash risk; firm characteristics; CEO characteristics; machine learning stock price crash risk; firm characteristics; CEO characteristics; machine learning

Share and Cite

MDPI and ACS Style

Li, Y.; Xue, H.; Wei, S.; Wang, R.; Liu, F. A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics. Systems 2024, 12, 143. https://doi.org/10.3390/systems12050143

AMA Style

Li Y, Xue H, Wei S, Wang R, Liu F. A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics. Systems. 2024; 12(5):143. https://doi.org/10.3390/systems12050143

Chicago/Turabian Style

Li, Yan, Huiyuan Xue, Shiyu Wei, Rongping Wang, and Feng Liu. 2024. "A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics" Systems 12, no. 5: 143. https://doi.org/10.3390/systems12050143

APA Style

Li, Y., Xue, H., Wei, S., Wang, R., & Liu, F. (2024). A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics. Systems, 12(5), 143. https://doi.org/10.3390/systems12050143

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