Out‐of‐sample predictability of firm‐specific stock price crashes: A machine learning approach
Year: 2024
Type: article
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.
DOI: 10.1111/jbfa.12831
Authors: Devrimi Kaya, Doron Reichmann, Milan Reichmann
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,620
OpenAlex ID: W4402568053