Paper: arXiv 2602.07066

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard machine learning classifiers (L1-regularized logistic regression, random forests, gradient boosting) rather than deep theoretical mathematical derivations. However, it excels in empirical rigor by establishing a strict, real-time expanding-window out-of-sample framework using CRSP data, focusing heavily on probability calibration, discrimination, and practical financial econometrics.

Research Flowchart

  flowchart TD
    A[Goal: Measure & Forecast Near-Term U.S. Equity Market Stress] --> B[(CRSP Daily Stock Data)]
    B --> C[Compute Cross-Sectional Fragility Signals]
    C --> D[L1-Regularized Logistic Regression<br>Real-Time Expanding Window]
    D --> E[Construct Market Stress Probability Index - MSPI]
    E --> F[Key Outcomes: High Out-of-Sample Accuracy,<br>Outperforms VIX/Volatility Benchmarks,<br>Calibrated 1-Month Ahead Stress Probabilities]

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0 of 56 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.