Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research

Mirazul Haque, Antony Papadimitriou, Samuel Mensah, Zhiqiang Ma, Zhijin Guo +4 more
4/22/2026
cs.AIcs.LG

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Abstract

We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research. The benchmark assesses three dimensions of report quality: qualitative rigor, quantitative forecasting and valuation accuracy, and claim credibility and verifiability. Particularly, we define corresponding qualitative and quantitative evaluation metrics and implement an automated scoring procedure to enable scalable assessment. Applying the benchmark to financial reports from frontier DR agents and comparing them with reports authored by financial professionals, we find that AI-generated reports still fall short across these dimensions. These findings underscore the need for domain-specialized DR agents tailored to finance, and we hope the work establishes a foundation for standardized benchmarking of DR agents in financial research.

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Cite this paper

@article{haque2026deep,
  title  = {Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research},
  author = {Mirazul Haque and Antony Papadimitriou and Samuel Mensah and Zhiqiang Ma and Zhijin Guo and Joy Prakash Sain and Simerjot Kaur and Charese Smiley and Xiaomo Liu},
  year   = {2026},
  eprint = {2604.21006},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2604.21006v1}
}

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