Estimating Stock Market Betas via Machine Learning

被引：1

作者：

Drobetz, Wolfgang ^{[1
]}

Hollstein, Fabian ^{[2
]}

Otto, Tizian ^{[1
]}

Prokopczuk, Marcel ^{[3
]}

机构：

[1] Univ Hamburg, Fac Business Adm, Hamburg, Germany

[2] Saarland Univ, Sch Human & Business Sci, Saarbrucken, Germany

[3] Leibniz Univ Hannover, Hannover Sch Econ & Management, Hannover, Germany

来源：

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS | 2024年

关键词：

CROSS-SECTION; RISK; MODEL; EQUILIBRIUM; MOMENTUM; INFORMATION; PRICES; TESTS;

D O I：

10.1017/S0022109024000036

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

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页数：37

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