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