类比驱动的金融思维链（AD-FCoT）：一种用于金融情感分析的提示方法
Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis
AI总结：
针对金融新闻情感分析中复杂经济背景捕捉难和推理透明度低的问题，提出AD-FCoT提示框架，结合类比推理与思维链引导LLM进行逐步推理，无需微调即在分类准确率、市场回报相关性及可解释性上超越基线。
摘要：
金融新闻情感分析对于预测市场走势至关重要。随着大型语言模型（LLMs）等AI技术的兴起，这些系统展现出强大的文本理解能力，人们对其增强产生了新的兴趣。然而，现有方法通常难以捕捉新闻的复杂经济背景，且缺乏透明的推理过程，这削弱了其可靠性。我们提出了类比驱动的金融思维链（AD-FCoT），这是一种将类比推理与思维链（CoT）提示相结合的提示框架，用于对历史金融新闻进行情感预测。AD-FCoT引导LLMs将新事件与已知结果的相关历史场景进行类比，并将这些类比嵌入到结构化的、逐步的推理链中。据我们所知，这是首批在金融领域明确将类比示例与CoT推理相结合的方法之一。AD-FCoT完全通过提示操作，无需额外的训练数据或微调，并利用模型的内部金融知识生成反映人类分析推理的依据。对数千篇新闻文章的实验表明，AD-FCoT在情感分类准确性上优于强基线，并与市场回报实现了显著更高的相关性。其生成的解释也符合领域专业知识，提供了适用于现实世界金融分析的可解释见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
