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Historically, the economic recession often came abruptly and disastrously.For instance, during the 2008 financial crisis, the SP 500 fell 46 percent fromOctober 2007 to March 2009. If we could detect the signals of the crisisearlier, we could have taken preventive measures. Therefore, driven by suchmotivation, we use advanced machine learning techniques, including RandomForest and Extreme Gradient Boosting, to predict any potential market crashesmainly in the US market. Also, we would like to compare the performance ofthese methods and examine which model is better for forecasting US stock marketcrashes. We apply our models on the daily financial market data, which tend tobe more responsive with higher reporting frequencies. We consider 75explanatory variables, including general US stock market indexes, SP 500 sectorindexes, as well as market indicators that can be used for the purpose ofcrisis prediction. Finally, we conclude, with selected classification metrics,that the Extreme Gradient Boosting method performs the best in predicting USstock market crisis events.

CRISIS ALERT:Forecasting Stock Market Crisis Events Using Machine Learning Methods