When Smart Beta Meets Machine Learning and Portfolio Optimization

Abstract

Smart beta products using common factors like value, low volatility, quality, and small cap experienced an underwhelming performance from 2005–2022. On average, long-only factor portfolios built from a wider set of global factors identified in the finance literature generated significantly positive excess returns across countries, suggesting diversifying across many factors is more prudent than selecting a handful that have performed the best. Moreover, long-only portfolios built from expected returns fit to these 87 factors using linear ridge and nonlinear machine learning models like gradient boosting generated larger and more statistically significant excess returns in nearly all countries. A long-only portfolio optimized to maximize return given an aversion to tracking error delivered yet higher excess returns and information ratios across countries. Taken together, these results provide strong evidence against the claim that most of the documented factors are datamined and without investment merit.