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

Author:

Li Yan1ORCID,Xue Huiyuan1,Wei Shiyu1,Wang Rongping1ORCID,Liu Feng1ORCID

Affiliation:

1. Business School, Shandong University, Weihai 264209, China

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.

Funder

Humanities and Social Sciences Foundation of the Ministry of Education of China

Publisher

MDPI AG

Reference33 articles.

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