Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 22 Apr 2026.
Builds on
8 of 24 references.
References · 8
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
- 2025DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agentscited by 248
- 2024TradingAgents: Multi-Agents LLM Financial Trading Frameworkcited by 188
- 2025DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesiscited by 34
- 2026DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reportscited by 34
- 2025Working with AI: Measuring the Applicability of Generative AI to Occupationscited by 19
- 2025FinResearchBench: A Logic Tree based Agent-as-a-Judge Evaluation Framework for Financial Research Agentscited by 12
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Citing papers per year: 2026: 2.
2 papers.