Predicting Financial Market Stress with Machine Learning
Using newly constructed market conditions indicators (MCIs) for three pivotal markets centered around the US dollar (Treasury, foreign exchange, and money markets), we show that tree-based machine learning (ML) models significantly outperform traditional time-series approaches in predicting the full distribution of future market stress. Through quantile regressions, we document that the random forest method achieves up to 27% lower quantile loss than autoregressive benchmarks, particularly at longer horizons (up to 12 months). Shapley value analysis reveals that variables related to macro expectations and uncertainty—especially about the monetary policy stance—are important predictors of future tail realizations of market conditions. For individual market segments, the state of the global financial cycle, as well as liquidity conditions, also play important roles. These results highlight the value of ML in forecasting tail risks and identifying systemic vulnerabilities in real time, bridging the gap between high-frequency data and macroeconomic stability frameworks.
