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

This research proposes new estimations of the Fama-French three- and five-factor models via a machine learning approach. Speci fically, it uses a Bayesian optimization-support vector regression (BSVR) approach to obtain predictions of portfolio returns. On data from fi ve industries' portfolio returns in the United States over the period July 1926 to January 2019, the BSVR models perform well. 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)