AI Equity Research Benchmark Methodology

Direct Answer

A credible AI equity research benchmark uses pre-registered tasks, primary-source answer keys, consistent test conditions, transparent scoring, independent review, and published limitations. It should report performance by task and dimension rather than forcing every product into a single winner ranking.

Key Takeaways

• AI is a research aid, not a guarantee of investment performance.
• Verify material financial claims against current primary sources.
• Record source dates, data periods, assumptions, and limitations.

Define eligible tasks

Use factual retrieval, filing evidence, calculations, historical events, earnings calls, citations, temporal accuracy, cross-document reasoning, thesis structure, and uncertainty handling.

Create answer keys

Each question should have expected facts, acceptable sources, calculation rules, and a clear treatment for unavailable data. Publish the task set where licensing permits.

Score transparently

Separate factual accuracy, citation correctness, evidence retrieval, calculation accuracy, completeness, reasoning quality, and uncertainty handling. Include reviewer agreement where subjective scoring is used.

Report limitations

Disclose model versions, paid tiers, browsing state, data access, run dates, sample size, prompt wording, and run-to-run variability. Do not design the test to make a sponsor win.

Sources and methodology