Forecasting Stock Market Crashes via Machine Learning

63 Pages Posted: 11 May 2021 Last revised: 6 Sep 2022

See all articles by Hubert Dichtl

Hubert Dichtl

dichtl research & consulting GmbH; University of Hamburg

Wolfgang Drobetz

University of Hamburg

Tizian Otto

University of Hamburg

Date Written: May 10, 2021

Abstract

This paper uses a comprehensive set of 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 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, aiming at safeguarding financial and macroeconomic stability.

Keywords: extreme event prediction, stock market crashes, machine learning, active trading strategy

JEL Classification: G11, G12, G14, G17

Suggested Citation

Dichtl, Hubert and Drobetz, Wolfgang and Otto, Tizian, Forecasting Stock Market Crashes via Machine Learning (May 10, 2021). Available at SSRN: https://ssrn.com/abstract=3843319 or http://dx.doi.org/10.2139/ssrn.3843319

Hubert Dichtl

dichtl research & consulting GmbH ( email )

Am Bahnhof 7
65812 Bad Soden am Taunus
Germany

HOME PAGE: http://www.dichtl-research-consulting.de

University of Hamburg ( email )

Moorweidenstr. 18
Hamburg, 20148
Germany

Wolfgang Drobetz

University of Hamburg ( email )

Moorweidenstrasse 18
Hamburg, 20148
Germany

Tizian Otto (Contact Author)

University of Hamburg ( email )

MoorweidenstraƟe 18
Hamburg, 20148
Germany

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