算法监控：利用机器学习衡量市场压力
Algorithmic Monitoring: Measuring Market Stress with Machine Learning
AI总结：
本文提出通过机器学习构建市场压力概率指数，利用股票横截面数据预测股市压力，提升压力预测的准确性和经济意义。
摘要：
我构建了一个市场压力概率指数（MSPI），该指数利用个股横截面信息来估计美国股市一个月后出现高压力的概率。使用CRSP每日数据，每个月被总结为一组可解释的横截面脆弱信号，并通过实时扩展窗口设计中的L1正则化逻辑回归映射到前瞻性压力概率。在样本外测试中，MSPI跟踪了主要压力事件，并相对于基于滞后市场收益率和实现波动率的简洁基准模型，在判别力和准确性上有所提升，提供了在经济上有意义的校准压力概率。进一步，我说明了如何将MSPI作为金融计量经济学中的基于概率的测量对象。所得到的指数提供了一个透明且易于更新的近期内股市压力风险衡量指标。
英文摘要：
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.
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