[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117123-en":3,"doc-seo-117123-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117123,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Estimating Stock Market Betas via Machine Learning","Machine learning-based stock market beta estimators provide superior performance to established benchmark models in both statistical fit and economic outcomes. Using U.S. stocks to analyze the predictability of time-varying market betas, the study shows that machine learning methods deliver the lowest forecast and hedging errors. The results also enable stronger market-neutral anomaly strategies and improved minimum variance portfolios, with random forests performing best overall. Model complexity varies substantially over time, while historical betas, turnover, and size are key predictors.","Estimating stock market betas via machine learning  \nArticle  \nPublished Version  \nCreative Commons: Attribution 4.0 (CC-BY)  \nOpen Access  \nDrobetz, W. , Hollstein, F. , Otto, T. and Prokopczuk, M. (2024) Estimating stock market betas via machine learning. Journal of Financial and Quantitative Analysis. ISSN 1756-6916 doi: [https://doi.org/10.1017/S0022109024000036 Available](https://doi.org/10.1017/S0022109024000036 Available) at  \n[https://centaur. reading.ac. uk/1](https://centaur. reading.ac. uk/1) 17477/  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1017/S0022109024000036](http://dx.doi.org/10.1017/S0022109024000036)  \n[Publisher: Cambridge University Press](Publisher: Cambridge University Press)  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nJOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS  \n© The Author(s), 2024 . Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. doi:10.1017/S0022109024000036  \nEstimating Stock Market Betas via Machine Learning  \nWolfgang Drobetz  \nUniversity of Hamburg, Faculty of Business Administration [wolfgang.drobetz@uni-hamburg.de](wolfgang.drobetz@uni-hamburg.de) (corresponding author)  \nFabian Hollstein   \nSaarland University, School of Human and Business Sciences  \n[fabian.hollstein@uni-saarland.de](fabian.hollstein@uni-saarland.de)  \nTizian Otto  \nUniversity of Hamburg, Faculty of Business Administration  \n[tizian.otto@uni-hamburg.de](tizian.otto@uni-hamburg.de)  \nMarcel Prokopczuk  \nLeibniz University, Hannover School of Economics and Management  \n[prokopczuk@fcm.uni-hannover.de](prokopczuk@fcm.uni-hannover.de)  \nAbstract  \nMachine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas ofU.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.  \nI. Introduction  \nIn single-factor asset pricing models, such as the capital asset pricing model (CAPM) introduced by Sharpe (1964), Lintner (1965), and Mossin (1966), the expected return of a stock in equilibrium is determined solely by its sensitivity to market risk. While multifactor models that include additional factors can explain the cross-sectional variation in expected returns somewhat better than the CAPM (see, e.g., Fama and French (2008), Harvey, Liu, and Zhu (2016), for extensive evidence), it explains the time series variation in returns well. Moreover, as shown  \nWe thank two anonymous referees, Yakov Amihud, Michael Bauer, Wolfgang Bessler, Axel Cabrol, Mikhail Chernov, Hubert Dichtl, Thierry Foucault (the editor), Kay Giesecke, Lisa Goldberg, Valentin Haddad, Barney Hartman-Glaser, Bernard ","cbCaifjwzQIJ0kOL","https://ap.wps.com/l/cbCaifjwzQIJ0kOL","pdf",1138394,1,38,"English","en",105,"# Introduction\n## Motivation for estimating time-varying betas\n## Goal of comparing machine learning with benchmark models","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To test whether machine learning-based models outperform established approaches in estimating time-varying market betas and to understand why they do so.\"},{\"question\":\"How do machine learning estimators perform compared with benchmark models?\",\"answer\":\"They outperform benchmark approaches statistically and economically, producing the lowest forecast and hedging errors in the analyzed U.S. stock universe.\"},{\"question\":\"Which machine learning technique performs best and which predictors matter most?\",\"answer\":\"Random forests perform best overall, while historical stock market betas, turnover, and size are identified as the most important predictors.\"}]","Estimating Stock Market Betas via Machine Learning | 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is the main objective of the study?","Question",{"text":75,"@type":76},"To test whether machine learning-based models outperform established approaches in estimating time-varying market betas and to understand why they do so.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do machine learning estimators perform compared with benchmark models?",{"text":80,"@type":76},"They outperform benchmark approaches statistically and economically, producing the lowest forecast and hedging errors in the analyzed U.S. stock universe.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning technique performs best and which predictors matter most?",{"text":84,"@type":76},"Random forests perform best overall, while historical stock market betas, turnover, and size are identified as the most important 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