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

TL;DR

This study integrates FinBERT-derived sentiment indices into the Fama-French five-factor model, demonstrating that sentiment significantly influences stock returns, especially during market extremes, and enhances asset pricing accuracy.

Contribution

It introduces a novel method of incorporating dynamic, NLP-based sentiment measures into traditional asset pricing models, revealing their time-varying impact on returns.

Findings

Sentiment positively affects returns during normal periods.

Impact of sentiment is amplified or reversed in extreme market conditions.

Sentiment improves explanation of abnormal returns around major events.

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.