Can AI-Driven National ESG in Big Data Help Improve Macroeconomic Forecasting?

Hao Xiao (), Xiaofen Li (), Junyi Yang () and Xinjian Ye ()
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Hao Xiao: School of Economics & Trade, Hunan University; Institute of African Studies, Hunan University, Changsha, Hunan Province, China, 410079
Xiaofen Li: School of Economics & Trade, Hunan University, Changsha, Hunan Province, China, 410079,
Junyi Yang: School of Economics & Trade, Hunan University, Changsha, Hunan Province, China, 410079,
Xinjian Ye: School of Economics & Trade, Hunan University, Changsha, Hunan Province, China, 410079,

Journal for Economic Forecasting, 2025, issue 2, 84-103

Abstract: This study introduces a novel predictive framework for predicting South Africa's macroeconomic trends using national ESG in big data based on AI technology and deep learning. This study utilizes the GDELT database and AI-driven indicator construction methods to extract meaningful insights from 10.76 million news, generating ESG in big data at the national governance level. By combining traditional macroeconomic indicators with national ESG in big data, this study evaluates the predictive performance of econometric, machine learning, and deep learning models. The rolling out-of-sample prediction analysis shows that the LSTM model achieves the highest prediction accuracy. Subsequently, LSTM models with and without national ESG in big data were designed to evaluate the extent to which incorporating national ESG in big data improves prediction accuracy. This study demonstrates that national ESG in big data enhances the accuracy of macroeconomic forecasting, particularly improving the short-term forecasting performance of the models.

Keywords: macroeconomic forecasting; national ESG index; news data; AI; LSTM (search for similar items in EconPapers)
JEL-codes: C53 E17 O33 Q56 (search for similar items in EconPapers)
Date: 2025
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