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

TL;DR

Deep FinResearch Bench provides a comprehensive framework for evaluating AI-driven financial research reports across multiple quality dimensions, highlighting current AI limitations.

Contribution

It introduces a scalable, automated benchmark for assessing AI financial research agents, emphasizing the need for domain-specific improvements.

Findings

AI reports lag behind professional financial reports in quality and accuracy

The benchmark enables scalable, automated assessment of AI research reports

Results highlight the need for domain-specialized AI financial research agents

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.