Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915876205232128 |
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| author | Liao, Pei-Jun Lee, Hung-Shin Cheng, Yao-Fei Chen, Li-Wei Lee, Hung-yi Wang, Hsin-Min |
| author_facet | Liao, Pei-Jun Lee, Hung-Shin Cheng, Yao-Fei Chen, Li-Wei Lee, Hung-yi Wang, Hsin-Min |
| contents | 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) compared to the baseline, indicating the utility of stock name embeddings for news filtering and price forecasting within a generalized framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19286 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion Liao, Pei-Jun Lee, Hung-Shin Cheng, Yao-Fei Chen, Li-Wei Lee, Hung-yi Wang, Hsin-Min Statistical Finance Artificial Intelligence Computation and Language Machine Learning 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) compared to the baseline, indicating the utility of stock name embeddings for news filtering and price forecasting within a generalized framework. |
| title | Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion |
| topic | Statistical Finance Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2603.19286 |