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Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support

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  • Tianyi Zhang
  • Mu Chen

Abstract

Financial advisors and investors struggle with information overload from financial news, where irrelevant content and noise obscure key market signals and hinder timely investment decisions. To address this, we propose a novel Chain-of-Thought (CoT) summarization framework that condenses financial news into concise, event-driven summaries. The framework integrates user-specified keywords to generate personalized outputs, ensuring that only the most relevant contexts are highlighted. These personalized summaries provide an intermediate layer that supports language models in producing investor-focused narratives, bridging the gap between raw news and actionable insights.

Suggested Citation

  • Tianyi Zhang & Mu Chen, 2025. "Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support," Papers 2511.05508, arXiv.org, revised Nov 2025.
    • Handle: RePEc:arx:papers:2511.05508
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    References listed on IDEAS

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    1. Shangyang Mou & Qiang Xue & Xunquan Chen & Jinhui Chen & Ryoichi Takashima & Tetsuya Takiguchi & Yasuo Ariki, 2025. "Prefix tuning with prompt augmentation for efficient financial news summarization," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-16, February.
    2. Haoqiang Kang & Xiao-Yang Liu, 2023. "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination," Papers 2311.15548, arXiv.org.
    3. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
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