Masoud Soleimani · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2609.26303 · cited by 0 works (OpenAlex) · open-access PDF
Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error correlation largely mirrors forecast correlation and is therefore a poor measure of diversity; an equally weighted combination beats a no-information forecast only when average alignment is large relative to the combination's dispersion; and the gain from adding a forecaster separates into genuine improvement and mere dilution, which equal-weight admission can mistakenly reward. We develop a cautious selection rule, study it in simulations, and apply it to language-model forecasts of US equity rankings and mechanical signals ranking exchange-traded funds. Selection removes most dilution losses, but no combination beats the no-information forecast.
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Topics: Selection (genetic algorithm), Uncorrelated, Econometrics, Statistics, Ranking (information retrieval), Measure (data warehouse)
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