Multidimensional Investor Sentiment and Stock Market Volatility in China: Measurement, Mechanisms and Applications

Starriver Bilingual School, 200083 Shanghai, China

^* Corresponding author: mingxuankang983@gmail.com

Abstract

This paper systematically investigates the impact of investor sentiment on stock market volatility in China. The rational hypothesis in traditional financial theory is difficult to explain many anomalies in the real market, so behavioral finance provides an important perspective. This paper firstly combed the investor sentiment of measure (direct index method, text, data analysis, multi-source data fusion method) and its correlation model, such as Vector Autoregression (VAR), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Time Varying Parameter Vector Auto-regression (TVP-VAR). On the basis of pointing out the limitations of existing research in terms of data sources, model causality and long-term rule capture, this paper proposes to deepen the mechanism analysis by constructing multi-dimensional sentiment indicators and combining long- term data and behavioral finance theory. This study not only provides a new perspective for understanding market volatility, but also provides practical enlightenment for regulators’ risk warning and investors’ strategy optimization. Future research should focus on integrating alternative data sources and developing cross-market sentiment contagion models to further enhance predictive accuracy and practical application value.