Can FinBERT2-Based Investor Sentiment Predict Gold Futures Volatility? A Fine-Grained Sentiment Category Analysis

This paper focuses on China’s gold futures market. Using East Money investor posts and the FinBERT2 model, we construct a multidimensional sentiment indicator comprising overall sentiment, positive/negative intensity, and six fine-grained sentiment categories (happiness, sadness, fear, anger, disgust, and neutral). With high-frequency 5 min data, we compute realized volatility and, within the HAR-RV framework, systematically examine the in-sample and out-of-sample predictive power, asymmetry, and inter-period heterogeneity of sentiment dimensions. The results show that investor sentiment significantly and robustly predicts volatility, with gains increasing over horizons, relying on multi-scale cumulative effects. Predictions are asymmetric: negative sentiment drives volatility while positive sentiment does not. Among fine-grained sentiments, happiness and anger are strongest; fear and sadness are ineffective; and disgust has an effect only in long-term routine forecasts but fails under extreme volatility. During the Russia–Ukraine conflict, sadness replaces happiness and anger as the dominant predictor (long-term MSE: 0.01579 vs. benchmark 0.04384). In trending bull markets, the predictive power of happiness and positive/negative intensity is amplified (full-sample R2 gains: 2.70% and 2.89%, vs. 17.08% and 9.69% in bull periods). This study reveals the multidimensional, asymmetric effects and intertemporal heterogeneity of sentiment on forecasts of gold futures volatility, providing a theoretical and empirical foundation for regime-adaptive early-warning systems and risk management.