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Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning

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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)

  • Damien Challet

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay)

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.

Suggested Citation

  • Jérémi Assael & Laurent Carlier & Damien Challet, 2023. "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning," Post-Print hal-03791538, HAL.
    • Handle: RePEc:hal:journl:hal-03791538
    DOI: 10.3390/jrfm16030159
    Note: View the original document on HAL open archive server: https://hal.science/hal-03791538v3
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    References listed on IDEAS

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    Cited by:

    1. Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning," Working Papers hal-03905325, HAL.
    2. Trotta, Annarita & Rania, Francesco & Strano, Eugenia, 2024. "Exploring the linkages between FinTech and ESG: A bibliometric perspective," Research in International Business and Finance, Elsevier, vol. 69(C).
    3. Jérémi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning," Sustainability, MDPI, vol. 15(4), pages 1-28, February.
    4. Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & Franc{c}ois Soup'e, 2022. "Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning," Papers 2212.10844, arXiv.org.
    5. Michele Costa, 2023. "The evaluation of the effects of ESG scores on financial markets," Working Papers wp1189, Dipartimento Scienze Economiche, Universita' di Bologna.

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    Keywords

    ESG features; sustainable investing; interpretable machine learning; model selection; asset management; equity returns; ESG data;
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