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Algorithmic Monitoring: Measuring Market Stress with Machine Learning

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  • Marc Schmitt

Abstract

I construct a Market Stress Probability Index (MSPI) that estimates the probability of high stress in the U.S. equity market one month ahead using information from the cross-section of individual stocks. Using CRSP daily data, each month is summarized by a set of interpretable cross-sectional fragility signals and mapped into a forward-looking stress probability via an L1-regularized logistic regression in a real-time expanding-window design. Out of sample, MSPI tracks major stress episodes and improves discrimination and accuracy relative to a parsimonious benchmark based on lagged market return and realized volatility, delivering calibrated stress probabilities on an economically meaningful scale. Further, I illustrate how MSPI can be used as a probability-based measurement object in financial econometrics. The resulting index provides a transparent and easily updated measure of near-term equity-market stress risk.

Suggested Citation

  • Marc Schmitt, 2026. "Algorithmic Monitoring: Measuring Market Stress with Machine Learning," Papers 2602.07066, arXiv.org.
    • Handle: RePEc:arx:papers:2602.07066
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