6 citations · 10 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 6 cited
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning
Jeremi Assael, Thibaut Heurtebize, Laurent Carlier +1
As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We pro…
q-fin.PM2022★ 4 cited
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
Jérémi Assael, Laurent Carlier, Damien Challet
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market. Using interpretable machine learnin…