This paper presents FinCoT, a structured chain-of-thought (CoT) prompting framework that embeds domain-specific expert financial reasoning blueprints to guide large language models' behaviors. We identify three main prompting styles in financial NLP (FinNLP): (1) standard prompting (zero-shot), (2) unstructured CoT (free-form reasoning), and (3) structured CoT (with explicitly structured reasoning steps). Prior work has mainly focused on the first two, while structured CoT remains underexplored and lacks domain expertise incorporation. Therefore, we evaluate all three prompting approaches across ten CFA-style financial domains and introduce FinCoT as the first structured finance-specific prompting approach incorporating blueprints from domain experts. FinCoT improves the accuracy of a general-purpose model, Qwen3-8B-Base, from 63.2% to 80.5%, and boosts Fin-R1 (7B), a finance-specific model, from 65.7% to 75.7%, while reducing output length by up to 8.9x and 1.16x compared to structured CoT methods, respectively. We find that FinCoT proves most effective for models lacking financial post-training. Our findings show that FinCoT does not only improve performance and reduce inference costs but also yields more interpretable and expert-aligned reasoning traces.

翻译：本文提出FinCoT，一种结构化的思维链提示框架，通过嵌入领域特定的专家金融推理蓝图来指导大语言模型的行为。我们识别了金融自然语言处理中的三种主要提示风格：(1) 标准提示（零样本），(2) 非结构化思维链（自由形式推理），(3) 结构化思维链（具有显式结构化推理步骤）。先前研究主要集中在前两种方法，而结构化思维链仍未被充分探索且缺乏领域专业知识整合。因此，我们在十个CFA风格的金融领域中评估了所有三种提示方法，并引入FinCoT作为首个融合领域专家蓝图的结构化金融专用提示方法。FinCoT将通用模型Qwen3-8B-Base的准确率从63.2%提升至80.5%，并将金融专用模型Fin-R1（7B）的准确率从65.7%提升至75.7%，同时相较于结构化思维链方法，分别将输出长度减少至8.9倍和1.16倍。我们发现FinCoT对于缺乏金融领域后训练的模型最为有效。研究结果表明，FinCoT不仅能提升性能并降低推理成本，还能产生更具可解释性且与专家思维对齐的推理轨迹。