Readable signals from the AI capital allocation benchmark

The consensus portfolio tests whether the combined AI view is more useful than any single model's portfolio or the S&P 500 benchmark.

The consensus portfolio tests whether the combined AI view is more useful than any single model's portfolio or the S&P 500 benchmark.

Benchmark difficulty matters because model scores should be interpreted against the opportunity set and the market window they faced.

Benchmark difficulty matters because model scores should be interpreted against the opportunity set and the market window they faced.

This shows whether models identified the eventual best asset before scoring, even when portfolio weights were too small to fully capture the oracle return.

This shows whether models identified the eventual best asset before scoring, even when portfolio weights were too small to fully capture the oracle return.

Confidence calibration helps readers judge whether model self-reported confidence carries useful information about realized benchmark performance.

Attribution turns a model score into an explanation of which holdings actually helped or hurt the frozen portfolio.

Attribution turns a model score into an explanation of which holdings actually helped or hurt the frozen portfolio.

Behavior profiles help readers separate model style from short-term score noise: some models seek more risk, some concentrate harder, and some change portfolios less between rounds.

Leadership that changes with the broad-market backdrop shows why a single all-history ranking can hide meaningful model strengths and weaknesses.

The score translates allocations into a common risk scale, so readers can see whether models are collectively leaning defensive, balanced, or aggressive.

Leadership that changes with the broad-market backdrop shows why a single all-history ranking can hide meaningful model strengths and weaknesses.

Agreement across horizons signals that the current model posture is not just a short-term tactical move.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.

This shows the current crowding point in model capital allocation, before the open rounds receive their final market scores.

A stronger floor identifies models whose benchmark performance is less dependent on one favorable market direction.

A stronger floor identifies models whose benchmark performance is less dependent on one favorable market direction.

Live alpha is provisional, but it shows how open model portfolios are moving before the final official score.

Similarity analysis shows whether models are independently converging on the same portfolio or expressing meaningfully different capital-allocation behavior.

A large directional split identifies models whose benchmark behavior is especially sensitive to the market backdrop.

A large directional split identifies models whose benchmark behavior is especially sensitive to the market backdrop.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.

Environment-specific returns show whether a model's all-history result is broad or depends on a particular market direction.