Quantitative Statistical Study of Financial Market Sentiment on Economic Cycles: An Analysis Based on the FinBERT Model and TVP-VAR

Abstract

Amid global financial market turmoil, the relationship between market sentiment and macroeconomic cycles has garnered significant attention. This study leverages big data from financial markets to quantitatively analyze market sentiment using the FinBERT model and investigates its impact on macroeconomic cycles with the TVP-VAR method. Based on textual data from the Shanghai Stock Exchange Index forums and Baidu Index online engagement metrics, the study employs GIS technology to analyze regional emotional responses to financial market fluctuations and economic activity trends.The research reveals significant regional differences in China's financial sentiment index during 2022-2023, with hotspots in the eastern coastal regions and cold spots in the west. Economically developed areas exhibit higher sensitivity to market fluctuations. TVP-VAR analysis indicates that changes in market sentiment have a minor impact on macroeconomic cycle volatility, typically exerting a mild negative effect at year's end, though the effects are not significant. This study unveils the dynamic relationship between financial market sentiment and macroeconomics, demonstrating the potential of using social media and online data for macroeconomic analysis. It offers practical recommendations for policymakers on leveraging market sentiment data for forecasting and regulating the macroeconomy, fostering interdisciplinary development in economics and financial engineering.