ESG-driven optimal portfolio selection for separated environmental, social, and governance preferences

Abstract We propose and examine the optimal portfolio selection problem when the investor has different preferences in each of the portfolio’s environmental, social, and governance (ESG) average scores. We provide an explicit formula for the optimal weights in the case of the mean-variance model subject to the E, S, and G constraints and show that the same formula also holds in the case of other models that minimize a risk measure of the portfolio, with focusing on the tail-value-at-risk measure. We show that such models that go beyond the mean-variance model have the same formula for the optimal weight but with an effective risk aversion parameter that depends on the E, S, and G preferences of the investor, unlike in the original mean-variance model where the risk aversion is an external parameter. We then provide some numerical illustrations based on ten stocks from the NASDAQ, which offers clear guidance for allocating the portfolio between the different stocks and shows how each stock is sensitive to changes in the E, S, and G constraints.

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Publication Details

Journal
Operational Research
Published
2025-03-13
DOI
https://doi.org/10.1007/s12351-025-00907-3
Citations
1
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
2.11

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article

ESG-driven optimal portfolio selection for separated environmental, social, and governance preferences

Tomer Shushi
1 citations
Operational Research
Risk and Portfolio Optimization
2.11
article

ESG-driven optimal portfolio selection for separated environmental, social, and governance preferences

Tomer Shushi
article en
1 citations

Abstract

Abstract We propose and examine the optimal portfolio selection problem when the investor has different preferences in each of the portfolio’s environmental, social, and governance (ESG) average scores. We provide an explicit formula for the optimal weights in the case of the mean-variance model subject to the E, S, and G constraints and show that the same formula also holds in the case of other models that minimize a risk measure of the portfolio, with focusing on the tail-value-at-risk measure. We show that such models that go beyond the mean-variance model have the same formula for the optimal weight but with an effective risk aversion parameter that depends on the E, S, and G preferences of the investor, unlike in the original mean-variance model where the risk aversion is an external parameter. We then provide some numerical illustrations based on ten stocks from the NASDAQ, which offers clear guidance for allocating the portfolio between the different stocks and shows how each stock is sensitive to changes in the E, S, and G constraints.

Operational ResearchVol. 25(2)
Ben-Gurion University of the Negev (IL)
Ben-Gurion University of the Negev
Openalex Percentile: Top 16%
Risk and Portfolio Optimization
2.11
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ESG-driven optimal portfolio selection for separated environmental, social, and governance preferences — Tomer Shushi · Operational Research (2025) | TGRS Research Map | TGRS