A former hedge fund analyst built custom evaluations to measure AI agent quality on equity research, finding that standard finance benchmarks fail to capture nuance that matters for investment decisions.

What happened: The author, who spent three years testing AI agents on stock research after leaving a hedge fund desk, developed internal evaluation methods because public finance benchmarks rely too heavily on factual retrieval or mechanical modeling tasks—neither of which measures actual investment judgment. When testing an agent on an adjusted cash flow analysis of Copart (CPRT), the agent outperformed the baseline fixed-pipeline approach by handling operating leases more rigorously and explaining uncertainty more clearly, even though a standard rubric scoring system had rated both outputs identically.

Why it matters: Equity research requires judgment calls and reasoning that have no single correct answer—one analyst may interpret margin pressure as temporary overinvestment while another sees structural competition, and both can be financially sound. Because absolute scoring systems max out once an agent reaches basic competence, they cannot distinguish between good and great research, which is where real investment value lives. Internal benchmarks using relative comparison (where an AI judge scores agents against each other rather than in isolation) proved better at capturing these distinctions.

What to watch: The author notes that the next step is live earnings coverage, described as the beginning of truly autonomous research—suggesting the evaluation framework is being readied to assess agent performance on real-time financial events.