Stock Price Prediction Using Univariate and Multivariate Historical Data with Post-Interpretation via Large Language Models
Issued Date
2026-01-01
Resource Type
ISSN
18650929
eISSN
18650937
Scopus ID
2-s2.0-105011987910
Journal Title
Communications in Computer and Information Science
Volume
2380 CCIS
Start Page
30
End Page
47
Rights Holder(s)
SCOPUS
Bibliographic Citation
Communications in Computer and Information Science Vol.2380 CCIS (2026) , 30-47
Suggested Citation
Phalangpatanakij H., Deemee C., Chen S.C., Thaipisutikul T. Stock Price Prediction Using Univariate and Multivariate Historical Data with Post-Interpretation via Large Language Models. Communications in Computer and Information Science Vol.2380 CCIS (2026) , 30-47. 47. doi:10.1007/978-981-96-6291-3_3 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114728
Title
Stock Price Prediction Using Univariate and Multivariate Historical Data with Post-Interpretation via Large Language Models
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
In this study, we propose a hybrid approach that utilizes both univariate and multivariate historical data from key variables and related factors across four distinct groups. We developed a hybrid approach combining Volume Features, Valuation Metrics, Technical Indicators, and Market Sentiment for stock price prediction and investigate several state-of-the-art models, including Artificial Neural Networks (ANN), Gated Recurrent Units (GRU), Bidirectional GRU (BI-GRU), and Transformer-based Time Series (TST) models, while experimenting with different lags of inputs to capture intricate temporal patterns in stock price movements. Our experiments, conducted on seven stocks from various sectors, allow us to evaluate the robustness and generalizability of the models across different industries. To enhance interpretability, we employ large language models (LLMs) in the post-prediction phase, which transform the predictive outputs into human-readable narratives explaining the factors driving stock price predictions. Empirical results demonstrate that our approach, incorporating advanced deep learning models like ANN, GRU, BI-GRU, and TST with varying input lags, significantly improves prediction accuracy over traditional methods while providing actionable insights for financial decision-making.