Author(s)

Zihao Chen

Corresponding Author

Zihao Chen

Abstract

This study delves into the prediction of the S&P 500 ESG Index, a critical indicator reflecting the Environmental, Social, and Governance (ESG) performance of publicly traded companies. We employ two distinct methodologies to forecast this index: Long Short-Term Memory (LSTM), representing advanced deep learning techniques, and AutoRegressive Integrated Moving Average (ARIMA), a more conventional statistical approach used in time series analysis. Through rigorous experimentation and data analysis, our results indicate a clear superiority of LSTM over ARIMA. This is evidenced by lower prediction errors, specifically in terms of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), establishing LSTM as a more accurate and reliable method for forecasting the S&P 500 ESG Index. The implications of our findings are significant, suggesting that deep learning approaches, particularly LSTM, hold substantial promise in enhancing stock market predictions, especially when considering complex and increasingly relevant ESG factors. This research not only contributes to the academic discourse on financial forecasting but also offers practical insights for investors and policymakers interested in integrating ESG considerations into investment strategies and economic planning.

Keywords

LSTM, ARIMA, Prediction, ESG, Deep Learning