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Can Machine Learning Explain Alpha Generated by ESG Factors?

Author

Listed:
  • Vittorio Carlei

    (University of Chieti-Pescara)

  • Piera Cascioli

    (University of Chieti-Pescara)

  • Alessandro Ceccarelli

    (University of Chieti-Pescara)

  • Donatella Furia

    (University of Chieti-Pescara)

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.

Suggested Citation

  • Vittorio Carlei & Piera Cascioli & Alessandro Ceccarelli & Donatella Furia, 2025. "Can Machine Learning Explain Alpha Generated by ESG Factors?," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1457-1477, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10602-8
    DOI: 10.1007/s10614-024-10602-8
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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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