[2603.19286] Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion

Quantitative Finance > Statistical Finance arXiv:2603.19286 (q-fin) [Submitted on 8 Mar 2026] Title:Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion Authors:Pei-Jun Liao, Hung-Shin Lee, Yao-Fei Cheng, Li-Wei Chen, Hung-yi Lee, Hsin-Min Wang View a PDF of the paper titled Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion, by Pei-Jun Liao and 5 other authors View PDF HTML (experimental) Abstract: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 stock relevance. The filtered news embeddings, combined with historical stock prices, serve as inputs to the prediction model. Unlike prior studies that focus on individual stocks, our method trains a single generalized model applicable across multiple stocks. Experimental results demonstrate a 7.11% reduction in Mean Absolute Error (MAE...