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Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning

Author

Listed:
  • Alexandre Momparler

    (Universitat de València)

  • Pedro Carmona

    (Universitat de València)

  • Francisco Climent

    (Universitat de València)

Abstract

In today’s dynamic financial landscape, the integration of environmental, social, and governance (ESG) principles into investment strategies has gained great significance. Investors and financial advisors are increasingly confronted with the crucial question of whether their dedication to ESG values enhances or hampers their pursuit of financial performance. Addressing this crucial issue, our research delves into the impact of ESG ratings on financial performance, exploring a cutting-edge machine learning approach powered by the Extreme Gradient algorithm. Our study centers on US-registered equity funds with a global investment scope, and performs a cross-sectional data analysis for annualized fund returns for a five-year period (2017–2021). To fortify our analysis, we synergistically amalgamate data from three prominent mutual fund databases, thereby bolstering data completeness, accuracy, and consistency. Through thorough examination, our findings substantiate the positive correlation between ESG ratings and fund performance. In fact, our investigation identifies ESG score as one of the dominant variables, ranking among the top five with the highest predictive capacity for mutual fund performance. As sustainable investing continues to ascend as a central force within financial markets, our study underscores the pivotal role that ESG factors play in shaping investment outcomes. Our research provides socially responsible investors and financial advisors with valuable insights, empowering them to make informed decisions that align their financial objectives with their commitment to ESG values.

Suggested Citation

  • Alexandre Momparler & Pedro Carmona & Francisco Climent, 2025. "Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1617-1642, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10618-0
    DOI: 10.1007/s10614-024-10618-0
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    References listed on IDEAS

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