Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion
Predicting stock prices presents challenges in financial forecasting. While traditional approaches such as ARIMA and RNNs are prevalent, recent developments in Large Language Models (LLMs) offer alternative methodologies. This paper introduces an approach that integrates LLMs with daily financial news for stock price prediction. To address the challenge of processing news data and identifying relevant content, we utilize stock name embeddings within attention mechanisms. Specifically, we encode news articles using a pre-trained LLM and implement three attention-based pooling techniques -- self-attentive, cross-attentive, and position-aware self-attentive pooling -- to filter news based on...
| Authors | Pei-Jun Liao, Hung-Shin Lee, Yao-Fei Cheng et al. |
|---|---|
| Primary category | q-fin.ST |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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