Black-Litterman and ESG Portfolio Optimization
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909928142143488 |
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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 |