Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.21006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911616442826752 |
|---|---|
| author | Haque, Mirazul Papadimitriou, Antony Mensah, Samuel Ma, Zhiqiang Guo, Zhijin Sain, Joy Prakash Kaur, Simerjot Smiley, Charese Liu, Xiaomo |
| author_facet | Haque, Mirazul Papadimitriou, Antony Mensah, Samuel Ma, Zhiqiang Guo, Zhijin Sain, Joy Prakash Kaur, Simerjot Smiley, Charese Liu, Xiaomo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research Haque, Mirazul Papadimitriou, Antony Mensah, Samuel Ma, Zhiqiang Guo, Zhijin Sain, Joy Prakash Kaur, Simerjot Smiley, Charese Liu, Xiaomo Artificial Intelligence Machine Learning 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. |
| title | Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.21006 |