Automated Machine Learning and Asset Pricing

IF 2 Q2 BUSINESS, FINANCE
Risks Pub Date : 2024-09-14 DOI:10.3390/risks12090148
Jerome V. Healy, Andros Gregoriou, Robert Hudson
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引用次数: 0

Abstract

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.
自动化机器学习和资产定价
与金融和计量经济学文献中普遍使用的基于回归的标准策略相比,我们评估了机器学习方法是否能更好地模拟超额投资组合回报。我们研究了基于预期效用理论和行为金融理论的 17 种基准因子模型规格。我们评估了机器学习是否能识别标准方法未发现的数据生成过程特征,并对表现最佳的算法进行了排名。我们的测试使用了从 1926 年到 2021 年的 95 年 CRSP 数据,涵盖了整个美国股票市场的价格历史。我们的研究结果表明,与基于回归的标准估算方法相比,机器学习方法能根据风险因素提供更准确的股票回报模型。研究结果还表明,如果更适当地考虑相关资产的非线性特性,某些风险因素和风险因素组合可能更具吸引力。
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来源期刊
Risks
Risks Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.80
自引率
22.70%
发文量
205
审稿时长
11 weeks
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