ORCID
- Keith Quille: 0000-0002-1414-5142
- Rajesh Jaiswal: 0000-0002-4530-7079
Abstract
Empirical evidence shows that short-horizon equity returns are not fully random; there is a degree of predictability, especially when traditional financial data is combined with public sentiment measures. Sentiment-based features have been used to improve prediction tasks, but there is limited explanation of their contribution. In addition, the impact of news on investor sentiment, and how that impact decays over time, has rarely been investigated. To bridge this gap, we integrate sentiment scores extracted from financial news headlines using HKUST FinBERT with daily market-based indicators to predict the next-day price direction. We evaluated two aggregation strategies: the most confident news of the day and average sentiments, and introduced a decay mechanism to attenuate the influence of older news. Predictive performance is benchmarked with an ANN, and SHAP provides model-agnostic feature attribution. Incorporating decayed sentiment from the most confident headline increases the test accuracy from 60.24% (technical indicators only) to 65.06%. SHAP highlights decayed neutral and negative sentiments, overnight sentiments and pre-market adjustments, and weekday effects as the most influential short-term predictors, consistent with prior behavioral finance evidence. These findings underscore the value of sentiment-aware, explainable AI models for short-term forecasting and highlight future improvements by using richer data and enhanced sentiment extraction.
Keywords
Explainable AI (XAI), Financial sentiment analysis, FinBERT, SHAP analysis, Stock price prediction
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
107
Last Page
113
Deposit Date
2026-05-13
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Ranade, D. J.; Bhaya, Sarthak; Bhimakari, Siddhanth; Chan, Moe Aye; Quille, Keith; and Jaiswal, Rajesh, "On Explaining the Sentiments in Prediction of Stock Movement: An XAI-Based Analysis" (2026). Research Outputs: 2025-Present. 8.
https://arrow.tudublin.ie/faccomentro/8