Mean-CVaR portfolio optimization under ESG disagreement

The ESG score of a company is a measure of its commitment to environmental, social and governance investing standards. ESG scores are produced by rating agencies using unique and proprietary methodologies. The complexity of measurement and the lack of widely accepted standards contribute to inconsistencies across agencies. Discrepancies in ratings issued by multiple data providers are particularly relevant in portfolio optimization problems that integrate ESG objectives into the classical risk-reward framework. In this work, we specifically study the impact on portfolio composition by examining Mean-CVaR-ESG optimal portfolios, where the objective function incorporates the portfolio’s ESG score. To address ESG score discrepancies, we introduce a Distributionally Robust Optimization (DRO) reformulation of the Mean-CVaR-ESG model and assess its potential benefits. Our findings reveal a persistent divergence in optimal strategies across the investment horizon when ESG values from different rating agencies are used. We then apply the DRO approach by replacing a single provider’s ESG score with a statistic derived from the scores of five different agencies. Our results show that, in this case, the DRO approach effectively mitigates score discrepancies by significantly reducing optimal portfolio concentration while enhancing the ESG evaluation of optimal portfolios across all rating agencies.