AI Stock Picks Beat Benchmark in Live Market Test: Study
AI autonomously searched the web, scored all Russell 1000 stocks, and constructed a daily portfolio.
RESEARCH
Many AI + investing research papers suffer from the same problem: the models were trained on historical internet data that often contains the outcomes they are asked to predict. Ask a model today what happened to a stock in 2022, and it may already know.
This look-ahead bias makes me skeptical of many papers with “big” conclusions. But since the models have become ubiquitous, researchers can test their theories in real time.
Two Peking University researchers, Zefeng Chen and Darcy Pu, did just that. They ran a live, nine-month experiment asking a frontier AI model to pick stocks every night across the Russell 1000.
Here’s what they did:
Every night from April 2025 through January 2026, they queried a leading U.S. frontier AI model via its web interface with live search enabled, with no pre-selected news or filings fed to the model. The model autonomously searched the web, synthesized what it found, and returned a score (−5 to +5) for each Russell 1000 stock.
Signals were generated after the 4pm close and before the next open. Portfolios were entered at the opening auction and exited the following open.
They ranked about 1,000 stocks by the model’s daily score, built a portfolio of the top 20 weighted by market value, and tested its performance using standard factor models.
Here’s what they found:
