Black-Litterman and ESG Portfolio Optimization

ArXiv ID: 2511.21850 “View on arXiv”

Authors: Aviv Alpern, Svetlozar Rachev

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.

Keywords: Black-Litterman, ESG, Stein Shrinkage, Conditional Value at Risk (CVaR), Multivariate Affine Normal-Inverse Gaussian, Portfolio

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced mathematical modeling with multivariate affine generalized hyperbolic distributions, CVaR optimization, and Bayesian Black-Litterman framework, indicating high complexity. It is grounded in empirical backtesting over a 4.7-year period with daily rebalancing, showing strong implementation and data usage.
  flowchart TD
    A["Research Goal<br>Develop a portfolio optimization strategy integrating ESG<br>data with the Black-Litterman framework"] --> B["Methodology: Data & Inputs<br>1. Historical Asset Returns<br>2. ESG Scores<br>3. Daily Rebalancing Data (4.7 Years)"]
    B --> C["Step 1: Stein Shrinkage Estimation<br>Use ESG scores as bias for Stein shrinkage<br>estimation of equilibrium risk premiums"]
    C --> D["Step 2: Black-Litterman Views<br>Integrate ESG-biased premiums into the<br>Black-Litterman model"]
    D --> E["Step 3: Optimization<br>Optimize portfolio weights using CVaR (Conditional Value at Risk)<br>as the risk measure<br>Model: Multivariate Affine Normal-Inverse Gaussian"]
    E --> F["Step 4: Execution<br>Rebalance portfolio daily using the optimized weights"]
    F --> G["Key Findings & Outcomes<br>• High Annual Returns: 40-45%<br>• Success Factor: Soft turnover constraint<br>• Effectiveness: Exceptionally successful despite simplicity"]