Incorporating News Sentiment into FIGARCH Models for Asset Returns and Volatility

This paper introduces a novel sentiment-driven framework for modeling asset return volatility and for comparing FIGARCH-based specifications by integrating machine learning and time-series econometric techniques. Leveraging a dataset of over 347,000 asset-specific news headlines and financial data spanning from March 2022 to January 2025, we first develop a Random Forest classifier to assess the likelihood that daily news headlines will significantly affect asset returns, either positively or negatively, across four key time frames: Open-to-Close, Open-to-Open, Close-to-Close, and Close-to-Open. The Random Forest model generates impact probability scores for each of the four time frames, which are then incorporated as exogenous variables into a GARCH model to enhance its predictive power over return volatility. In parallel, we utilize the FinBERT model to extract average sentiment scores from headlines and construct sentiment-based exogenous variables. These features are incorporated into an extended FIGARCH(1, 1) model to examine their predictive contributions to return volatility. Empirical results across 37 assets from stock, cryptocurrency, and commodity markets demonstrate that the inclusion of sentiment and impact probability features improves model performance (measured by AIC, BIC, and log-likelihood), particularly in time frames sensitive to market openings and overnight news. Our findings underscore the value of combining textual sentiment indicators produced by the FinBERT model, which is specifically trained on financial data, alongside directional time frame-based impact probabilities computed by the Random Forest model with advanced volatility modeling to enhance return and volatility models and support informed model selection for asset returns.