BigFinanceBench: A Workflow-Grounded Benchmark for Financial-Research Agents
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
Financial-research answers are decision-relevant only when another analyst can audit how they were produced: which source was chosen, which period and accounting definition were used, which assumptions were made, and how the calculation was performed. Existing finance benchmarks largely evaluate isolated subskills or final answers, leaving the auditable derivation itself under-measured. We introduce BigFinanceBench, a 928-item expert-authored benchmark of open-ended financial-research tasks in which each item pairs a ground-truth reference answer with a point-weighted rubric that decomposes the derivation into independently checkable steps. BigFinanceBench is workflow-grounded in that it evaluates the full derivation rather than only the final output. Across 36,241 rubric points, the benchmark supports partial-credit evaluation and localization of failures across the analyst workflow. Evaluating ten current frontier and open-weight agents, we find substantial headroom: the best system reaches only 58.8% rubric score, final-answer accuracy is a useful but lossy proxy for derivation quality, and model capability varies non-uniformly across financial workflows.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 29 of its 39 references that are in the map. First posted on arXiv 2 Jun 2026.
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29 of 39 references.
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Other references · 28
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Citing papers per year: 2026: 7.
7 papers.
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- 2026One Analyst Is Not Ground Truth: Grading Agent-Built Financial Models Against Observed Professional Practice27 Jul 2026
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