Can Machine Learning Explain Alpha Generated by ESG Factors?

Vittorio Carlei (), Piera Cascioli (), Alessandro Ceccarelli () and Donatella Furia ()
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Vittorio Carlei: University of Chieti-Pescara
Piera Cascioli: University of Chieti-Pescara
Alessandro Ceccarelli: University of Chieti-Pescara
Donatella Furia: University of Chieti-Pescara

Computational Economics, 2025, vol. 65, issue 3, No 12, 1457-1477

Abstract: Abstract This research explores the use of machine learning to predict alpha in constructing portfolios, leveraging a broad array of environmental, social, and governance (ESG) factors within the S&P 500 index. Existing literature bases analyses on synthetic indicators, this work proposes an analytical deep dive based on a dataset containing the sub-indicators that give rise to the aforementioned synthetic indices. Since such dimensionality of variables requires specific processing, we deemed it necessary to use a machine learning algorithm, allowing us to study, with strong specificity, two types of relationships: the interaction between individual ESG variables and their effect on corporate performance.The results clearly show that ESG factors have a significant relationship with company performance. These findings emphasise the importance of integrating ESG indicators into quantitative investment strategies using Machine Learning methodologies.

Keywords: Sustainability; Machine learning; Portfolio management (search for similar items in EconPapers)
JEL-codes: G1 G11 G12 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10602-8

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