App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Multimodal stock price trend forecasting based on large language models and attention mechanisms

Deng Liguo
Liu Shi
Sha Wendan
School of Computing, GuangDong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0471

Abstract

To address the challenges of high noise in financial news and the difficulty of capturing dynamic correlations in heterogeneous data, a multimodal stock price trend prediction model, PLCAS, integrating Large Language Models (LLM) and attention mechanisms, was proposed. The model aimed to overcome the limitations of insufficient semantic understanding of unstructured text and unbalanced multimodal fusion. For stock price data, an enhanced feature matrix comprising indicators such as Moving Average (MA) , Smoothed Moving Average (SMA) , and Bollinger Bands (BOLL) was constructed, and a patched Transformer encoder was utilized to strengthen the extraction of long sequence features. Regarding stock news data, LLM was employed to deeply parse news semantics, leveraging contextual understanding for information denoising and summarization. A multimodal fusion module was designed to facilitate sufficient interaction between news semantics and trading features. Experimental results showed that on the CMIN-US dataset, the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) improved by 0.18% and 0.011, respectively, compared to baseline models. On the CMIN-CN dataset, the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) increased by 1.78% and 0.01, respectively. Research demonstrates that the deep integration of news semantics and technical indicators effectively improves trend prediction accuracy, providing a scientific reference for investment decisions in complex market environments.

Foundation Support

国家自然科学基金项目(66207212)
广东省自然科学基金项目(2025A515010164,2025A1515011397)
青年科学基金项目(C类)(662506082)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0471
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-25] Accepted Paper

Cite This Article

邓立国, 刘石, 沙文丹. 基于大语言模型与注意力机制的多模态股票价格趋势预测 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0471. (Deng Liguo, Liu Shi, Sha Wendan. Multimodal stock price trend forecasting based on large language models and attention mechanisms [J]. Application Research of Computers, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0471. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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