Can Artificial Intelligence Detect Financial Market Stress? Evidence from Machine Learning Models

This paper examines whether machine learning models can serve as effective frameworks for detecting financial market stress. Using daily data on the S&P 500, the CBOE Volatility Index (VIX), oil, gold, and the US Dollar Index from January 2006 to September 2026, we construct a binary financial stress indicator and evaluate three classifiers—logistic regression, random forest, and XGBoost—trained on the 2006–2022 sample and tested out-of-sample on 2023–2026. Random forest and XGBoost identify stress days with near-perfect classification performance (AUC-ROC above 0.99), while logistic regression achieves a lower but still meaningful AUC-ROC of 0.980. The results show that volatility-based indicators dominate the detection signal and that tree-based ensembles capture nonlinear stress–volatility relationships more effectively than the linear benchmark. We discuss the practical implications and limitations of these findings for market surveillance and financial risk monitoring. The evidence suggests that artificial intelligence provides a powerful framework for real-time stress detection, although its performance should be interpreted as market-state identification rather than long-horizon crisis forecasting.​