Machine learning approach to stock price crash risk

Karasan A., Alp O. S., Weber G.

Annals of Operations Research, vol.350, no.3, pp.1053-1074, 2025 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 350 Issue: 3
  • Publication Date: 2025
  • Doi Number: 10.1007/s10479-025-06596-7
  • Journal Name: Annals of Operations Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1053-1074
  • Keywords: Finance, Investor sentiment, Machine learning, Stock price crash risk, Time series
  • Middle East Technical University Affiliated: Yes

Abstract

In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.