Black-Litterman and ESG Portfolio Optimization

TL;DR

This paper presents a straightforward ESG-integrated portfolio optimization method using the Black-Litterman model, leveraging ESG scores for risk premium estimation, and demonstrates high returns over a multi-year period.

Contribution

It introduces a novel ESG-based bias in the Black-Litterman framework combined with a multivariate affine model and CVaR risk measure, achieving impressive annual returns.

Findings

Achieved approximately 40-45% annual returns.

Effective use of ESG scores as a bias in risk premium estimation.

Simple strategy with a soft turnover constraint performs exceptionally well.

Abstract

We introduce a simple portfolio optimization strategy using ESG data with the Black-Litterman allocation framework. ESG scores are used as a bias for Stein shrinkage estimation of equilibrium risk premiums used in assigning Black-Litterman asset weights. Assets are modeled as multivariate affine normal-inverse Gaussian variables using CVaR as a risk measure. This strategy, though very simple, when employed with a soft turnover constraint is exceptionally successful. Portfolios are reallocated daily over a 4.7 year period, each with a different set of hyperparameters used for optimization. The most successful strategies have returns of approximately 40-45% annually.