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

ArXiv ID: 2401.06172 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Extreme Gradient Boosting (XGBoost), Market Crash Prediction, Random Forest, Classification Metrics, Financial Crisis Prediction, Equities

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies standard machine learning models (Random Forest and XGBoost) with basic feature engineering, lacking advanced mathematical derivations or novel theory, but demonstrates strong empirical rigor through detailed data processing, cross-validation, and backtest-ready implementation on real financial data.

  flowchart TD
    A["Research Goal: Forecast US Stock Market Crises"] --> B["Data: 75 Explanatory Variables<br>Market Indexes, Sector Indexes, Indicators"]
    B --> C["Methodology: Machine Learning Models"]
    C --> D["Random Forest"]
    C --> E["Extreme Gradient Boosting<br>XGBoost"]
    D --> F["Model Evaluation<br>Classification Metrics"]
    E --> F
    F --> G["Outcome: XGBoost Performs Best<br>for Crisis Prediction"]