Dynamic Asset Pricing: Integrating FinBERT-Based Sentiment Quantification with the Fama--French Five-Factor Model

Abstract

This paper presents a comprehensive study on the integration of text-derived, time-varying sentiment factors into traditional multi-factor asset pricing models. Leveraging FinBERT, a domain-specific deep learning language model, we construct a dynamic sentiment index and its volatility from large-scale financial news and social media data covering 2020 to 2022. By embedding these sentiment measures into the Fama French five-factor regression, we rigorously examine whether sentiment significantly explains variations in daily stock returns and how its impact evolves across different market volatility regimes. Empirical results demonstrate that sentiment has a consistently positive impact on returns during normal periods, while its effect is amplified or even reversed under extreme market conditions. Rolling regressions reveal the time-varying nature of sentiment sensitivity, and an event study around the June 15, 2022 Federal Reserve 75 basis point rate hike shows that a sentiment-augmented five-factor model better explains abnormal returns relative to the baseline model. Our findings support the incorporation of high-frequency, NLP-derived sentiment into classical asset pricing frameworks and suggest implications for investors and regulators.