Factor Investing and ESG Integration in Regime-switching Models- An Empirical Study on ESG Factor Integration Using Infinite Hidden Markov Models

Abstract

ESG investing is an active area of interest, both for the investment and academic communities. However, research is inconclusive on the financial benefits of integrating ESG factors in portfolio construction. In this thesis, we propose a novel approach to examining the informational content in ESG data using an infinite Hidden Markov framework to capture market regimes. Our objective is to find if ESG factors can increase a portfolio’s risk-return characteristics by capturing additional effects that other factors do not. We build a baseline model with the factors Value, Quality, Growth, Momentum, and Risk. Next, we add layers of ESG data to the baseline model and analyze the effect on portfolio performance. Our findings show that the infinite Hidden Markov Model portfolios consistently outperform the market index EURO STOXX 50. However, we do not observe value added by ESG scores in our regime-switching factor investing framework.