FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation

Yuxuan Wu, Miaopei Lin, Qiufang Ying, Lin Dan, Hao Zhao, Qirui Liu, Yuteng Shen, Shaohui Wu, Weihongluo, Xiku Du, Xing Tang, Hao Chen


Abstract
Financial agents are expected to answer factual investment queries through open search, but financial multi-hop retrieval differs from general multi-hop QA in a crucial way: each intermediate answer is an exact typed constraint, such as an entity, date, event, or numerical indicator, that determines the next query. This strict answer-conditioned dependency makes small intermediate errors cascade into plausible but wrong search trajectories. We introduce FinHopper, a benchmark for complex financial agents with controlled reasoning depth from 1 to 6 hops, covering stocks, funds, and macroeconomic facts, with gold intermediate answers for process-level evaluation. To construct FinHopper at scale, we design a state-transition-based synthesis framework that composes verified atomic financial QA patterns into dependency-preserving chains, followed by targeted in-synthesis verification, model-ensemble checking, and public-search validation by financial experts. Experiments with 20 LLM-agent configurations show that public search tools are necessary but insufficient: performance drops sharply as hop depth increases, the best model remains far below human experts, and process-aware scoring reveals partial reasoning progress hidden by final-answer-only evaluation. The benchmark is available at https://anonymous.4open.science/r/FinHopper-aabb/
Anthology ID:
2026.findings-emnlp.2384
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2026
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
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Pages:
51219–51246
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URL:
https://aclanthology.org/2026.findings-emnlp.2384/
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Bibkey:
Cite (ACL):
Yuxuan Wu, Miaopei Lin, Qiufang Ying, Lin Dan, Hao Zhao, Qirui Liu, Yuteng Shen, Shaohui Wu, Weihongluo, Xiku Du, Xing Tang, and Hao Chen. 2026. FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 51219–51246, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation (Wu et al., Findings 2026)
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PDF:
https://aclanthology.org/2026.findings-emnlp.2384.pdf