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Cross-sectional explanatory power of ESG featuresProf. Damien Challet - Mathematics & Computer Science, University of Paris-Saclay
Date:
Friday, Oct. 7, 2022
12:00 PM—1:00 PMLocation:
online
Friday, Oct. 7, 2022
12:00 PM—1:00 PMLocation:
online
Cross-sectional explanatory power of ESG featuresProf. Damien Challet - Mathematics & Computer Science, University of Paris-Saclay
We systematically investigate the links between price returns and ESG features. 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 single bit of annual price returns not accounted for in the traditional 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.
Department of Mathematics & Statistics
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Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 -
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math.dept@ttu.edu