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: BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab, MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay
Laurent Carlier: BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab

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Abstract: We systematically investigate the links between price returns and Environment, Social and Governance (ESG) features in the European market. 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. Boosted trees successfully explain a part of annual price returns not accounted by the market factor. We check with benchmark features that ESG features do contain significantly more information than basic fundamental features alone. The most relevant sub-ESG feature encodes controversies. Finally, we find 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; sustainable investing; interpretable machine learning; model selection; asset management; equity returns; ESG data (search for similar items in EconPapers)
Date: 2023-03-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-env and nep-fmk
Note: View the original document on HAL open archive server: https://hal.science/hal-03791538v3
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Citations: View citations in EconPapers (2)

Published in Journal of Risk and Financial Management, 2023, 16 (3), pp.159. ⟨10.3390/jrfm16030159⟩

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Related works:
Journal Article: 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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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03791538

DOI: 10.3390/jrfm16030159

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