Forecasting Stock Market Crashes via Machine Learning

Hubert Dichtl, Wolfgang Drobetz and Tizian Otto

Journal of Financial Stability, 2023, vol. 65, issue C

Abstract: This paper uses a comprehensive set of predictor variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in forecasting stock market crashes. In terms of statistical predictive performance, a support vector machine-based crash prediction model outperforms a random classifier and is superior to the average univariate benchmark as well as a multivariate logistic regression model. Incorporating nonlinear and interactive effects is both imperative and foundation for the outperformance of support vector machines. Their ability to forecast stock market crashes out-of-sample translates into substantial value-added to active investors. From a policy perspective, the use of machine learning-based crash prediction models can help activate macroprudential tools in time.

Keywords: Extreme event prediction; Stock market crashes; Machine learning; Active trading strategy (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:65:y:2023:i:c:s1572308922001206

DOI: 10.1016/j.jfs.2022.101099

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