Sentiment-Aware Stock Market Prediction Using Financial Text and Temporal Deep Learning

Abstract

Financial market prediction has traditionally relied on quantitative price and volume data, but such representations often ignore the interpretive discourse that shapes investor expectations and trading behavior. This paper presents a system-level examination of sentiment-aware stock market prediction that integrates financial text analysis with temporal deep learning architectures. The study conceptualizes sentiment not as an exogenous indicator but as an intermediate representation derived from heterogeneous textual sources, including news, analyst commentary, regulatory filings, and social media. The system design couples contextual language modeling with recurrent and attention-based temporal learners to capture sequential dependencies in market dynamics. The paper emphasizes structural trade-offs in latency, interpretability, scalability, and data governance. It further examines infrastructure requirements for reliable ingestion, annotation, model retraining, and deployment. Robustness and fairness concerns are analyzed through the lens of distributional shift, noise propagation, and the risk of amplifying existing market asymmetries. Policy implications related to algorithmic trading, disclosure, auditability, and emerging regulatory frameworks are also considered. The discussion argues that sustainable deployment of sentiment-aware prediction requires not only predictive accuracy but also organizational oversight, data provenance, model risk management, and careful alignment with public interest. The paper contributes a systemic framework for designing, deploying, and governing integrated financial text and temporal deep learning systems in modern financial markets.