Deep Statistical Fusion of LSTM and ARIMA for ESG BasedFinancial Risk and Volatility Forecasting

In the era of sustainable finance, Environmental, Social, and Governance (ESG) factors have emerged as key indicators influencing market behavior and investor sentiment. This study presents a novel hybrid framework that integrates Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA) models to forecast financial market volatility and risk while incorporating ESG signals. The proposed deep statistical fusion model leverages the strengths of ARIMA in capturing linear temporal dependencies and LSTM’s ability to model complex nonlinear patterns from sequential data. ESG scores, along with historical price movements and macroeconomic indicators, are used as primary inputs to enhance model sensitivity to sustainability-related risk. Experiments were conducted using real-world datasets from global stock indices (e.g., NSE, S&P 500) and third-party ESG rating providers. The model's performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and volatility clustering evaluation. The hybrid LSTM-ARIMA model achieved an RMSE of 1.92 and MAPE of 3.85%, outperforming standalone ARIMA (RMSE: 3.14, MAPE: 6.42%) and LSTM (RMSE: 2.41, MAPE: 5.12%). Additionally, the proposed model demonstrated better risk sensitivity by accurately flagging high-volatility periods linked to ESG controversies and macroeconomic disruptions. These results confirm that incorporating ESG factors within a deep statistical fusion framework enhances forecasting precision, offering a robust tool for financial institutions and ESG-conscious investors in proactive risk management and strategic decisionmaking.