FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging

• URL: http://arxiv.org/abs/2506.05828v2
• Date: Wed, 06 Aug 2025 17:19:50 GMT
• Title: FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging
• Authors: Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng,
• Abstract summary: FinanceReasoning is a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems.<n>We update 15.6% of the questions from four public datasets, annotating 908 new questions with detailed Python solutions.<n>We construct 3,133 Python-formatted functions, which enhances LRMs' financial reasoning capabilities.
• Score: 10.175739273593985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compared to existing benchmarks, our work provides three key advancements. (1) Credibility: We update 15.6% of the questions from four public datasets, annotating 908 new questions with detailed Python solutions and rigorously refining evaluation standards. This enables an accurate assessment of the reasoning improvements of LRMs. (2) Comprehensiveness: FinanceReasoning covers 67.8% of financial concepts and formulas, significantly surpassing existing datasets. Additionally, we construct 3,133 Python-formatted functions, which enhances LRMs' financial reasoning capabilities through refined knowledge (e.g., 83.2% $\rightarrow$ 91.6% for GPT-4o). (3) Challenge: Models are required to apply multiple financial formulas for precise numerical reasoning on 238 Hard problems. The best-performing model (i.e., OpenAI o1 with PoT) achieves 89.1% accuracy, yet LRMs still face challenges in numerical precision. We demonstrate that combining Reasoner and Programmer models can effectively enhance LRMs' performance (e.g., 83.2% $\rightarrow$ 87.8% for DeepSeek-R1). Our work paves the way for future research on evaluating and improving LRMs in domain-specific complex reasoning tasks.

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