Out-of-sample predictability of firm-specific stock price crashes: A machine learning approach
Kaya D, Reichmann D, Reichmann M (2024)
Publication Type: Journal article
Publication year: 2024
Journal
DOI: 10.1111/jbfa.12831
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.
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Germany (DE)How to cite
APA:
Kaya, D., Reichmann, D., & Reichmann, M. (2024). Out-of-sample predictability of firm-specific stock price crashes: A machine learning approach. Journal of Business Finance & Accounting. https://doi.org/10.1111/jbfa.12831
MLA:
Kaya, Devrimi, Doron Reichmann, and Milan Reichmann. "Out-of-sample predictability of firm-specific stock price crashes: A machine learning approach." Journal of Business Finance & Accounting (2024).
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