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

Lei Zhou · Yuqi Zhang · Jian Yu · Guiling Grace Wang · Zhizhong Liu · Sira Yongchareon

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

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.

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References

2011Twitter mood predicts the stock market 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2021Applications of deep learning in stock market prediction: Recent progress 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data 2008Short term forecasting with support vector machines and application to stock price prediction 2021Stock Market Prediction Using Linear Regression and SVM 2021Stock values predictions using deep learning based hybrid models

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