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Estimating the Impact of ESG on Financial Forecast Predictability Using Machine Learning Models

Author

Listed:
  • Marius Sorin Dincă

    (Department of Finance, Accounting and Economic Theory, Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Vlad Ciotlăuși

    (Department of Finance, Accounting and Economic Theory, Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Frank Akomeah

    (Department of Finance, Accounting and Economic Theory, Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov, 500036 Brasov, Romania)

Abstract

This study examines whether the integration of Environmental, Social, and Governance (ESG) factors enhances the accuracy of financial forecasts. Using a dataset of 2548 publicly listed companies from 98 countries, we evaluate a range of machine learning models—from ARIMA to XGBoost—by comparing the forecast performance of firms with high and low ESG scores (based on the sample median). Model accuracy is assessed through MAE, RMSE, MSE, MAPE, and R 2 , complemented by statistical significance tests. Results show no consistent improvement in predictive performance for high-ESG firms, with only the Business Services sector displaying a marginal effect. These findings challenge the assumption that ESG integration inherently reduces forecast uncertainty, suggesting instead that ESG scores contribute little to predictive accuracy under long-term investment conditions. The study highlights the importance of model choice, careful control of exogenous variables, and rigorous testing, while underscoring the broader need for standardized ESG metrics in financial research.

Suggested Citation

  • Marius Sorin Dincă & Vlad Ciotlăuși & Frank Akomeah, 2025. "Estimating the Impact of ESG on Financial Forecast Predictability Using Machine Learning Models," IJFS, MDPI, vol. 13(3), pages 1-20, September.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:3:p:166-:d:1741768
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    References listed on IDEAS

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    1. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2021. "Fundamental ratios as predictors of ESG scores: a machine learning approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1087-1110, December.
    2. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    3. Jun Xu, 2024. "AI in ESG for Financial Institutions: An Industrial Survey," Papers 2403.05541, arXiv.org.
    4. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    5. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    6. Agliardi, Elettra & Alexopoulos, Thomas & Karvelas, Kleanthis, 2023. "The environmental pillar of ESG and financial performance: A portfolio analysis," Energy Economics, Elsevier, vol. 120(C).
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