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PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction

Author

Listed:
  • Bohan Liang
  • Zijian Chen
  • Qi Jia
  • Kaiwei Zhang
  • Kaiyuan Ji
  • Guangtao Zhai

Abstract

Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs) have shown remarkable potential in various domains, exhibiting expert-level performance through advanced reasoning and contextual understanding. In this paper, we introduce PriceSeer, a live, dynamic, and data-uncontaminated benchmark specifically designed for LLMs performing stock prediction tasks. Specifically, PriceSeer includes 110 U.S. stocks from 11 industrial sectors, with each containing 249 historical data points. Our benchmark implements both internal and external information expansion, where LLMs receive extra financial indicators, news, and fake news to perform stock price prediction. We evaluate six cutting-edge LLMs under different prediction horizons, demonstrating their potential in generating investment strategies after obtaining accurate price predictions for different sectors. Additionally, we provide analyses of LLMs' suboptimal performance in long-term predictions, including the vulnerability to fake news and specific industries. The code and evaluation data will be open-sourced at https://github.com/BobLiang2113/PriceSeer.

Suggested Citation

  • Bohan Liang & Zijian Chen & Qi Jia & Kaiwei Zhang & Kaiyuan Ji & Guangtao Zhai, 2025. "PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction," Papers 2601.06088, arXiv.org.
    • Handle: RePEc:arx:papers:2601.06088
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    File URL: http://arxiv.org/pdf/2601.06088
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    References listed on IDEAS

    as
    1. Haofei Yu & Fenghai Li & Jiaxuan You, 2025. "LiveTradeBench: Seeking Real-World Alpha with Large Language Models," Papers 2511.03628, arXiv.org.
    2. Tianyu Fan & Yuhao Yang & Yangqin Jiang & Yifei Zhang & Yuxuan Chen & Chao Huang, 2025. "AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets," Papers 2512.10971, arXiv.org.
    3. Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2024. "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models," Papers 2402.03659, arXiv.org, revised Feb 2024.
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