Dissecting the Explanatory Power of ESG Features on Equity Returns by Sector, Capitalization, and Year with Interpretable Machine Learning

Jérémi Assael (), Laurent Carlier and Damien Challet
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Jérémi Assael: MICS Laboratory, CentraleSupélec, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France
Laurent Carlier: BNP Paribas Corporate & Institutional Banking, Global Markets Data & Artificial Intelligence Lab, 75009 Paris, France

JRFM, 2023, vol. 16, issue 3, 1-22

Abstract: We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market. Using interpretable machine learning, we examine whether ESG scores can explain the part of price returns not accounted for by classic equity factors, especially the market one. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Gradient boosting models successfully explain the part of annual price returns not accounted for by the market factor. We check with benchmark features that ESG data explain significantly better price returns than basic fundamental features alone. The most relevant ESG score encodes controversies. Finally, we find the opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter and reversely for the former.

Keywords: ESG features; ESG data; sustainable investing; interpretable machine learning; model selection; asset management; equity returns (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning (2023)
Working Paper: Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning (2023)
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