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

Jérémi Assael (1, 2) , Laurent Carlier (1) , Damien Challet (2)

1 BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab
2 MICS - Mathématiques et Informatique pour la Complexité et les Systèmes

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.

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

Domaines: Machine Learning [stat.ML]; Ingénierie, finance et science [cs.CE]; Applications [stat.AP]; Finance [q-fin.GN]; Gestion de portefeuilles [q-fin.PM]; Gestion des risques [q-fin.RM]; Econométrie de la finance [q-fin.ST]

Jérémi Assael, Laurent Carlier, Damien Challet. Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning. Journal of Risk and Financial Management, 2023, 16 (3), pp.159. ⟨10.3390/jrfm16030159⟩. ⟨hal-03791538v3⟩