Forecasting Stock Market Crashes via Machine Learning
56 Pages Posted: 11 May 2021 Last revised: 17 Aug 2021
Date Written: May 10, 2021
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 predicting stock market crashes. The statistical predictive performance of a support vector machine-based stock market crash prediction model is significantly different from zero and among the best-performing univariate benchmarks, while still being truly out-of-sample. The ability to forecast subsequent stock market crashes out-of-sample translates into value-added to investors under realistic trading assumptions (net of transaction costs). Incorporating nonlinear and interactive effects is both imperative and foundation for the predictive performance of support vector machines. This adds an economic component to the advantageousness of machine learning-based multivariate crash prediction models over their univariate counterparts. It helps identify and explain the complex relationships in the underlying economic conditions (key economic drivers) that precede substantial stock market downturns.
Keywords: extreme event prediction, stock market crashes, machine learning, active trading strategy
JEL Classification: G11, G12, G14, G17
Suggested Citation: Suggested Citation