Deep Learning for Sustainable Finance: Robust ESG Index Forecasting in an Emerging Market Context

Abstract

Sustainable finance increasingly relies on Environmental, Social, and Governance (ESG) data, yet forecasting ESG-based stock indices remains challenging in an emerging-market context. Using Thailand as a representative case due to limited historical information, this study constructs a realistic simulated SET ESG Index using free-float-adjusted market capitalization and semiannual rebalancing rules that reflect the methodology of the Stock Exchange of Thailand. Using this index as the forecasting target, this study compares traditional statistical time series models (ARIMA, SARIMA, SARIMAX) with seven deep learning architectures (RNN, GRU, LSTM, DF-RNN, DeepAR, DSSM, Deep Renewal) to evaluate performance in multi-step (36-day) prediction. Results reveal that deep learning models significantly outperform statistical approaches, with GRU delivering the highest accuracy and the most consistent robustness across reduced-data scenarios. These findings highlight the ability of advanced AI techniques to capture nonlinear ESG market dynamics better. This study provides a replicable modeling pipeline for ESG index forecasting in data-constrained contexts, with practical implications for sustainable investment decision-making, risk management, and market resilience in emerging economies.