Unraveling the relationship between sustainability and returns: a multi-attribute utility analysis

Marcos Escobar-Anel and Yiyao Jiao
Authors registered in the RePEc Author Service: Marcos Escobar Anel ()

China Finance Review International, 2024, vol. 14, issue 4, 719-758

Abstract: Purpose - This study aims to establish an analytical framework to help investors accommodate their environmental, social, and corporate governance (ESG) preferences. The analytical solutions were complemented by empirical analyses to shed light on their benefits and tractability. Design/methodology/approach - This study proposes an expected multi-attribute utility analysis for ESG investors in which stocks can be treated as more green or less green (brown) than the market, represented by an index, all modeled in a one-factor structure. The solution is found via the Hamilton-Jacobi-Bellman (HJB) equation with proper treatment of various sources of risk. For the empirical analysis, we use the RepRisk Rating of US stocks from 2010 to 2020 to select companies that are representative of various ESG ratings. Findings - This study finds closed-form solutions for optimal allocations, wealth and value functions. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors, even as the overall level of pecuniary satisfaction remains unchanged. Originality/value - This study broadens the existing analytical framework by introducing a market portfolio along with green and brown stocks. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction to increase green investment. Second, we propose a parameterization to capture investors' preferences for green assets over brown or market assets, independent of asset performance.

Keywords: ESG modeling; Expected utility; CAPM model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:cfripp:cfri-09-2023-0241

DOI: 10.1108/CFRI-09-2023-0241

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