2025/04/22 by Chi Zhang, Zhang, Chi · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Computational Engineering #FOS: Computer and information sciences #Finance #Financial Markets and Investment Strategies #Stock Market Forecasting Methods #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2505.01432
openalex publication_date 2025/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.