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ESG Investment Analysis and Stock Price Prediction Using FinBERT and LSTM Models

Author(s)

Nan Zheng

Affiliation(s)

International Business School, Xi'an Jiaotong-Liverpool University, Suzhou, China

Abstract

This study explores the integration of Environmental, Social, and Governance (ESG) data with deep learning models for stock price prediction. We propose a hybrid framework combining FinBERT, a pre-trained financial language model, for sentiment analysis of ESG-related texts, and Long Short-Term Memory (LSTM) networks for time-series forecasting. Experimental results on Ping An Bank (stock code: 000001.SZ) demonstrate that the model achieves a Mean Absolute Error (MAE) of 0.107 and Root Mean Squared Error (RMSE) of 0.163, indicating robust convergence. The analysis highlights the potential of ESG sentiment as a predictive factor and underscores challenges such as data scarcity and inconsistent ESG ratings. Future research directions include multi-enterprise validation and model comparison.

Keywords

ESG Investment; FinBERT; LSTM; Stock Prediction; Natural Language Processing

References

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