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A Machine Learning Approach to the Fama-French Three- and Five-Factor Models

1. Boubacar Diallo, Qatar University - College of Business and Economics
2. Aliyu Bagudu, AiFi Technologies LLC
3. Qi Zhang, People’s Bank of China

2019

SSRN

Asset pricing model, factor model, machine learning, support vector regression, Bayesian optimization

This research proposes new estimations of the Fama-French three- and five-factor models via a machine learning approach. Specifically, it uses a Bayesian optimization-support vector regression (BSVR) approach to obtain predictions of portfolio returns. On data from five industries’ portfolio returns in the United States over the period July 1926 to January 2019, the BSVR models perform well. Specifically, our new model, called the Fama-French BSVR three-factor model, outperformed the Fama-French BSVR five-factor model. More precisely, the Fama-French BSVR three-factor estimations attain out-of-sample (testing dataset) correlation coefficients of 94% for portfolio returns for the consumption and manufacturing industries. A correlation of 92% between the predicted and experimental values of portfolio returns was found for the high-tech industry; 91% was found for the mining, construction, transportation, hotels, entertainment and finance industries. However, for the Fama-French BSVR five-factor model, the correlation coefficients lie between 48% (health industry) and 89% (high-tech industry).

Diallo, Boubacar, Aliyu Bagudu, and Qi Zhang. “A Machine Learning Approach to the Fama-French Three-and Five-Factor Models.” Available at SSRN 3440840 (2019).

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3440840

1. 论文是关于什么的？[请提供该论文的简要摘要。]

作者利用SVR模型和贝叶斯优化算法（BSVR）对Fama-French三因子模型和五因子模型做了实证分析。结果显示基于BSVR的三因子模型在样本外数据上表现较好，泛化误差低，而基于BSVR的五因子模型则表现较弱。

2. 这篇论文的长处和短处是什么？[请以以下角度评述：（a）创新（研究问题、建模、方法等）；（b）相关性（研究问题、发现等）；（c）严谨性（适当的方法、分析的正确性等）]

这篇论文属于将新方法应用于旧问题，在研究问题上没有创新。作者的创新之处在于在建模时选用SVR算法，SVR算法本身具有低泛化误差，抗数据扰动等优点。此外，作者利用贝叶斯优化理论自动寻找SVR模型的超参数，减少了手动调参的工作量，提高了模型的准确性。

这篇论文使用机器学习的建模研究方法，将样本数据分外training data和testing data两部分，利用training data训练模型，利用testing data验证模型精度，分析方法正确、严谨。

3.如果有的话，潜在改进的主要地方是什么？[如果这些关键要求和建议能够被适当处理，请重点关注能使文章发表的关键要求和建议。如果你看到不可逾越的障碍，请清楚地描述你的担忧。如果能为编辑和作者提供具体有建设性的意见最好不过了，并在可能的情况下，提出可行的建议。同样，应避免含糊不清和/或含糊不清的批评。]

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5.有没有机会做一项新的研究？