Style Investing with Machine Learning

Abstract

This paper applies machine learning techniques to style investing. Support Vector Regression is applied to multi-factor investing based on momentum, dividend, quality, volatility and growth. The results show that Support Vector Regression selects stocks consistently with a higher efficiency ratio than a broad market investment and outperforms linear regression methods. The methods are applied to global stocks in the MSCI World index between 1996 and 2016. The behavior of both models is analyzed for economic sectors and over time. Interestingly, factors like low-volatility and momentum contribute both positively and negatively in some economic sectors and certain time periods.