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

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