Black-Litterman and ESG Portfolio Optimization

Fuente: arXiv
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Auteurs principaux: Alpern, Aviv, Rachev, Svetlozar
Format: Preprint
Publié: 2025
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author Alpern, Aviv
Rachev, Svetlozar
author_facet Alpern, Aviv
Rachev, Svetlozar
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_21850
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Black-Litterman and ESG Portfolio Optimization
Alpern, Aviv
Rachev, Svetlozar
Portfolio Management
Computational Finance
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.
title Black-Litterman and ESG Portfolio Optimization
topic Portfolio Management
Computational Finance
url https://arxiv.org/abs/2511.21850