  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.
