Machine learning approach to stock price crash risk

TL;DR

This paper introduces a new machine learning method using covariance determinants to measure and predict stock price crash risk, incorporating investor sentiment data to enhance accuracy.

Contribution

It presents a novel covariance-based measure for crash risk prediction and demonstrates its effectiveness with a new sentiment index, improving existing methodologies.

Findings

The proposed method effectively captures stock crash risk.

Higher investor sentiment correlates with increased crash risk.

Results are robust across firm sizes and sentiment index versions.

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.