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

arXiv cs.LG / 4/24/2026

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Key Points

  • The paper introduces Deep FinResearch Bench, a practical evaluation framework for deep research (DR) agents focused on financial investment research.
  • It scores report quality across three key dimensions: qualitative rigor, quantitative forecasting/valuation accuracy, and claim credibility/verifiability.
  • The authors define both qualitative and quantitative metrics and build an automated scoring pipeline to support scalable benchmarking.
  • When applied to frontier DR agents, the benchmark shows AI-generated investment research reports still underperform compared with reports written by professional financial analysts.
  • The results highlight the need for domain-specialized finance DR agents and aim to establish standardized evaluation for DR systems in financial research.

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.