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Outperforming ESG stocks portfolio: A machine learning ranking model with catboots regressor

Author

Listed:
  • Carlei, Vittorio
  • Furia, Donatella
  • Ceccarelli, Alessandro
  • Cascioli, Piera

Abstract

The paper investigates whether outperforming ESG (Environmental, Social, and Governance) stocks can generate alpha over the S&P 500 through a machine learning (ML)-driven simulation. Leveraging advanced ML techniques, particularly the CatBoostRegressor model, the study explores the relationship between ESG factors and financial performance to construct a high-performing, ESG-compliant portfolio.

Suggested Citation

  • Carlei, Vittorio & Furia, Donatella & Ceccarelli, Alessandro & Cascioli, Piera, 2025. "Outperforming ESG stocks portfolio: A machine learning ranking model with catboots regressor," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:ecofin:v:80:y:2025:i:c:s1062940825001573
    DOI: 10.1016/j.najef.2025.102517
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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