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

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.

Editorial on the Volume “ESG Investing and ESG Finance”

“…One of the standout papers, "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning" (Assael et al 2023), employs advanced technological methodologies to draw insights. This is juxtaposed with a grounded analysis of the "Impact of Environmental, Social, and Governance Activities on the Financial Performance of Indian Health Care Sector Firms" (Agarwal et al 2023) using competition as a lens of scrutiny.…”