Out‐of‐sample predictability of firm‐specific stock price crashes: A machine learning approach

Devrimi Kaya, Doron Reichmann and Milan Reichmann

Journal of Business Finance & Accounting, 2025, vol. 52, issue 2, 1095-1115

Abstract: We use machine learning methods to predict firm‐specific stock price crashes and evaluate the out‐of‐sample prediction performance of various methods, compared to traditional regression approaches. Using financial and textual data from 10‐K filings, our results show that a logistic regression with financial data inputs performs reasonably well and sometimes outperforms newer classifiers such as random forests and neural networks. However, we find that a stochastic gradient boosting model systematically outperforms the logistic regression, and forecasts using suitable combinations of financial and textual data inputs yield significantly higher prediction performance. Overall, the evidence suggests that machine learning methods can help predict stock price crashes.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jbfa.12831

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jbfnac:v:52:y:2025:i:2:p:1095-1115

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0306-686X

Access Statistics for this article

Journal of Business Finance & Accounting is currently edited by P. F. Pope, A. W. Stark and M. Walker

More articles in Journal of Business Finance & Accounting from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().