Abstract
Financial news sentiment analysis is crucial for anticipating market movements. With the rise of AI techniques such as Large Language Models (LLMs), which demonstrate strong text understanding capabilities, there has been renewed interest in enhancing these systems. Existing methods, however, often struggle to capture the complex economic context of news and lack transparent reasoning, which undermines their reliability. We propose Analogy-Driven Financial Chain-of-Thought (AD-FCoT), a prompting framework that integrates analogical reasoning with chain-of-thought (CoT) prompting for sentiment prediction on historical financial news. AD-FCoT guides LLMs to draw parallels between new events and relevant historical scenarios with known outcomes, embedding these analogies into a structured, step-by-step reasoning chain. To our knowledge, this is among the first approaches to explicitly combine analogical examples with CoT reasoning in finance. Operating purely through prompting, AD-FCoT requires no additional training data or fine-tuning and leverages the model's internal financial knowledge to generate rationales that mirror human analytical reasoning. Experiments on thousands of news articles show that AD-FCoT outperforms strong baselines in sentiment classification accuracy and achieves substantially higher correlation with market returns. Its generated explanations also align with domain expertise, providing interpretable insights suitable for real-world financial analysis.
Abstract (translated)
财务新闻情绪分析对于预测市场走势至关重要。随着人工智能技术，如大型语言模型（LLMs）的兴起，这些技术展现出强大的文本理解能力，增强了改进此类系统的兴趣。然而，现有的方法往往难以捕捉复杂的经济背景，并且缺乏透明的推理过程，这削弱了它们的可靠性。我们提出了一个基于类比驱动的金融链式思维（AD-FCoT）框架，该框架将类比推理与链式思考（CoT）提示结合在一起，用于对历史财务新闻的情绪预测。通过引导大型语言模型在新事件和相关的历史场景之间建立联系，并利用这些历史场景已知的结果构建结构化的、逐步的推理链条，AD-FCoT能够提升情绪分析的准确性。 据我们所知，这种方法是首个明确将类比示例与CoT推理相结合应用于金融领域的尝试。AD-FCoT完全依靠提示操作，并不需要额外的数据训练或微调，而是利用模型内部的财务知识生成类似于人类分析师的解释性理由。实验结果表明，在对数千篇新闻文章进行分析后，AD-FCoT在情绪分类准确性方面超越了强大的基准方法，并且与市场收益的相关度显著提高。此外，其生成的解释也符合领域专家的知识，提供了适合实际金融分析的可解释见解。 该框架不仅提高了财务新闻情绪预测的准确性和透明性，而且还增强了模型输出结果的应用价值，为金融市场投资者和分析师提供更加可靠的工具和支持。
URL
https://arxiv.org/abs/2509.12611
