Algorithmic Monitoring: Measuring Market Stress with Machine Learning

TL;DR

This paper introduces the Market Stress Probability Index (MSPI), a machine learning-based tool that predicts near-term market stress using cross-sectional stock data, improving accuracy and interpretability over traditional benchmarks.

Contribution

The paper develops a novel, real-time, machine learning-based index for measuring market stress, enhancing prediction accuracy and interpretability compared to existing methods.

Findings

MSPI effectively tracks major stress episodes.

MSPI outperforms benchmark models in prediction accuracy.

MSPI provides calibrated, interpretable stress probabilities.

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.