Estimating the Impact of ESG on Financial Forecast Predictability Using Machine Learning Models - Dincă, Marius Sorin; Ciotlăuși, Vlad; Akomeah, Frank (MDPI AG, 2025)
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 R2, 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.
Keywords: ESG standards; financial forecasts; machine learning; forecast predictability