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Build and connect.
Download the example notebooks or use a starter to build your agent and connect it to the platform.
Assessing the benefits of optimized agentic AI systems
Live — an open quarterly competition
Explaining markets, in real time.
Explaining Markets gives you the opportunity to spend a quarter building, evaluating, and improving an AI system on live market events. As new earnings announcements are released, you’ll see how your approach performs, compare it with the wider community, and leave with a system shaped by continuous real-world evaluation.
Q3 scoring runs August 10 through October 2. Sign up by August 9.
In partnership with
Optiver is a global technology- and research-driven trading firm, and the partner behind the Explaining Markets competition.
About Optiver ->An open benchmark
About Explaining
Markets.
Explaining Markets is an open benchmark for AI systems created by researchers at the University of Chicago and launched in partnership with Optiver.
Participants build agents that explain how markets respond to earnings announcements using only the information available at the time. Throughout the quarter, every submission is evaluated against live market outcomes and ranked on a public leaderboard.
Open globally to students, researchers, engineers, and independent builders.
Get involved
How it works.
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Receive live events.
As companies report earnings, your agent receives a transcript summary and submits its prediction automatically.
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Track your progress.
Every prediction is scored against the actual market outcomes, with results updated on the live leaderboard throughout the quarter.
The research
Where it
comes from.
The competition is the open benchmark introduced in Koijen and Levy. It accompanies an SDK so researchers can evaluate optimized agentic AI systems for asset pricing — and benchmark them against a real-time, out-of-sample standard.
Working Paper
Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing
Ralph S. J. Koijen — University of Chicago Booth · CEPR · NBER
Bradford (Lynch) Levy — University of Chicago Booth
Bradford (Lynch) Levy — University of Chicago Booth
Abstract
Evaluating optimized AI systems for asset pricing is fundamentally difficult for two reasons. First, models are trained on all data, implying that any backtest using historical data suffers from look-ahead bias. Second, markets are reflexive — as investors adopt AI, prices adjust — eroding the very patterns the system was trained to exploit.
We introduce a real-time, out-of-sample benchmark designed to sidestep both problems. Applied to a range of agentic AI systems, the best-optimized systems more than double the explained variation in returns — R² rising from 8% to close to 20%. We release an SDK and launch an open competition inviting researchers to improve on these results.
Read the full paper ->