FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
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.
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Natapong Nitarach
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2025-06-24T08:09:20.191Z
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Natapong Nitarach (Schwyter)
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user
natnitaract
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Warit Sirichotedumrong
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Panop Pitchayarthorn
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Pittawat Taveekitworachai
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2025-06-24T08:14:47.952Z
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Pittawat Taveekitworachai
pittawat
user
pittawat
6859359b0e4ad7e21975834f
Potsawee Manakul
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Kunat Pipatanakul
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2025-07-15T19:11:48.647Z
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Kunat Pipatanakul
kunato
user
kunato
FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
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.
FinCoT, a structured chain-of-thought prompting framework, enhances financial language model performance by incorporating domain-specific expert reasoning blueprints, improving accuracy while reducing inference costs and increasing interpretability.
chain-of-thought prompting framework financial NLP CFA-style financial domains domain-specific expert reasoning structured reasoning steps FinCoT Qwen3-8B-Base Fin-R1 financial post-training inference costs interpretable reasoning