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.

The prediction of stock market price trends has always been a challenging issue, attracting widespread attention from both economists and computer scientists. Recently, integrating stock prices with news data has been shown to be an effective strategy for enhancing the accuracy of the prediction task. Yet, many current methods fail to fully leverage the intricate inter-stock relationships inherent in stock news.Applying deep learning, especially Graph Convolutional Networks (GCNs), to predict stock trends has demonstrated advanced performance. This method employs a message-passing architecture, enabling nodes to progressively aggregate information from neighboring nodes across multiple layers. In this paper, we propose a novel approach: the Motif-based Graph Convolutional Network for Stock Prediction (MGCN-SP). This model mitigates the over-smoothing problem by incorporating network motifs into the layer propagation process. Specifically, we first generate a motif graph by correlating stocks with stock news. Then, we encode the stock price information and stock news into features using the scaled dot product attention adapted from the transformer architecture. After that, we apply the motif-based graph convolutional network. This framework is designed to jointly refine the embeddings of both stock news and stock time series data using a transformer encoder and to estimate the likelihood of target movements. Finally, we conducted extensive implementation in the U.S. stock market, and the experimental results demonstrate that our method outperforms some state-of-the-art approaches.