Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target
econ.EM, cs.LG, q-fin.ST, stat.ME
Submitted: 2026-09-22
Updated: 2026-09-22
Comments: 35 pages, 5 figures, 13 tables
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Forecasters often score the same units per date against one standardized realized outcome.
Terminology
Abstract
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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