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Real-time and out-of-sample.
The benchmark is free from look-ahead bias and fully reflects the reflexivity of financial markets.
About the research
The research behind
the benchmark.
Explaining Markets is an open, out-of-sample benchmark for AI systems that explain stock returns around earnings announcements — using any information available prior to the announcement and the earnings call summary. It is the benchmark introduced in Koijen and Levy, and it ships with an SDK so researchers can evaluate optimized agentic AI systems for asset pricing 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 on SSRN ->Core principles behind the benchmark
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Relevance of the benchmark.
Saturating this benchmark would represent fundamental progress in understanding how capital markets process firm-level information.
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Competition.
Compete against peers to rise to the top of the leaderboard each quarter.
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