Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/319309 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Business Finance & Accounting [ISSN:] 1468-5957 [Volume:] 52 [Issue:] 2 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 1095-1115
Verlag: 
Wiley, Hoboken, NJ
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
natural language processing
stock price crash risk
textual disclosures
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