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

Vittorio Carlei, Donatella Furia, Alessandro Ceccarelli and Piera Cascioli

The North American Journal of Economics and Finance, 2025, vol. 80, issue C

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.

Keywords: ESG investing; Alpha generation; Machine learning; Cat boots regressor (search for similar items in EconPapers)
JEL-codes: G11 G17 Q56 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:80:y:2025:i:c:s1062940825001573

DOI: 10.1016/j.najef.2025.102517

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