LLM-Augmented Linear Transformer–CNN for Enhanced Stock Price Prediction

Lei Zhou (), Yuqi Zhang, Jian Yu, Guiling Wang, Zhizhong Liu, Sira Yongchareon and Nancy Wang
Additional contact information
Lei Zhou: Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand
Yuqi Zhang: Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand
Jian Yu: Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand
Guiling Wang: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Zhizhong Liu: The School of Computer and Control Engineering, Yantai University, Yantai 254005, China
Sira Yongchareon: Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand
Nancy Wang: Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand

Mathematics, 2025, vol. 13, issue 3, 1-18

Abstract: Accurately predicting stock prices remains a challenging task due to the volatile and complex nature of financial markets. In this study, we propose a novel hybrid deep learning framework that integrates a large language model (LLM), a Linear Transformer (LT), and a Convolutional Neural Network (CNN) to enhance stock price prediction using solely historical market data. The framework leverages the LLM as a professional financial analyst to perform daily technical analysis. The technical indicators, including moving averages (MAs), relative strength index (RSI), and Bollinger Bands (BBs), are calculated directly from historical stock data. These indicators are then analyzed by the LLM, generating descriptive textual summaries. The textual summaries are further transformed into vector representations using FinBERT, a pre-trained financial language model, to enhance the dataset with contextual insights. The FinBERT embeddings are integrated with features from two additional branches: the Linear Transformer branch, which captures long-term dependencies in time-series stock data through a linearized self-attention mechanism, and the CNN branch, which extracts spatial features from visual representations of stock chart data. The combined features from these three modalities are then processed by a Feedforward Neural Network (FNN) for final stock price prediction. Experimental results on the S&P 500 dataset demonstrate that the proposed framework significantly improves stock prediction accuracy by effectively capturing temporal, spatial, and contextual dependencies in the data. This multimodal approach highlights the importance of integrating advanced technical analysis with deep learning architectures for enhanced financial forecasting.

Keywords: stock price prediction; Linear Transformer; CNN; LLM; deep learning; financial forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/3/487/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/3/487/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:3:p:487-:d:1581357

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().