FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
Authors: name: Natapong Nitarach; name: Warit Sirichotedumrong; name: Panop Pitchayarthorn; name: Pittawat Taveekitworachai; name: Potsawee Manakul; name: Kunat Pipatanakul
Published: 2025-06-19 08:18:55

Abstract: This paper presents FinCoT, a structured chain-of-thought (CoT) prompting
approach that incorporates insights from domain-specific expert financial
reasoning to guide the reasoning traces of large language models. We
investigate that there are three main prompting styles in FinNLP: (1) standard
prompting--zero-shot prompting; (2) unstructured CoT--CoT prompting without an
explicit reasoning structure, such as the use of tags; and (3) structured CoT
prompting--CoT prompting with explicit instructions or examples that define
structured reasoning steps. Previously, FinNLP has primarily focused on prompt
engineering with either standard or unstructured CoT prompting. However,
structured CoT prompting has received limited attention in prior work.
Furthermore, the design of reasoning structures in structured CoT prompting is
often based on heuristics from non-domain experts. In this study, we
investigate each prompting approach in FinNLP. We evaluate the three main
prompting styles and FinCoT on CFA-style questions spanning ten financial
domains. We observe that FinCoT improves performance from 63.2% to 80.5% and
Qwen-2.5-7B-Instruct from 69.7% to 74.2%, while reducing generated tokens
eight-fold compared to structured CoT prompting. Our findings show that
domain-aligned structured prompts not only improve performance and reduce
inference costs but also yield more interpretable and expert-aligned reasoning
traces.