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Predicting financial market stress with machine learning

Author

Listed:
  • Inaki Aldasoro
  • Peter Hördahl
  • Andreas Schrimpf
  • Sonya Zhu

Abstract

Using newly constructed market condition indicators (MCIs) for three pivotal US markets (Treasury, foreign exchange, and money markets), we demonstrate that tree-based machine learning (ML) models significantly outperform traditional timeseries approaches in predicting the full distribution of future market stress. Through quantile regression, we show that random forests achieve up to 27% lower quantile loss than autoregressive benchmarks, particularly at longer horizons (3–12 months). Shapley value analysis reveals that funding liquidity, investor overextension and the global financial cycle are important predictors of future tail realizations of market conditions. The MCIs themselves play a prominent role as well, both in the same market (self-reinforcing dynamics within markets) and across markets (spillovers across markets). These results highlight the value of ML in forecasting tail risks and identifying systemic vulnerabilities in real time, bridging the gap between highfrequency data and macroeconomic stability frameworks.

Suggested Citation

  • Inaki Aldasoro & Peter Hördahl & Andreas Schrimpf & Sonya Zhu, 2025. "Predicting financial market stress with machine learning," BIS Working Papers 1250, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:1250
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    More about this item

    Keywords

    machine learning; financial stress; quantile regressions; forecasting; Shapley value;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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