This study measures and analyzes the impact of individual investors’ sentiment biases on stock returns in the Shanghai and Shenzhen stock exchanges, using daily CSI300 index return data from 2010 to 2025. A proprietary index was constructed to capture investor sentiment in China through principal component analysis (PCA). The DCC-GARCH model was applied to analyze dynamic interactions, and Granger causality testing was conducted to determine causal relationships between the two series. The study found a reciprocal causal relationship, showing that investor sentiment significantly affects stock volatility and returns. These findings underscore the importance of including sentiment indicators in financial forecasting models—an emerging trend in financial technology (FinTech)—to improve investment strategies and risk management. Further research is recommended to develop advanced AI- and machine learning–based models to analyze the impact of investor sentiment on financial markets.