Newest papers

Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target

Masoud Soleimani · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.26303 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

ونشير إلى أن كل استطلاع يتم تقسيمه بشكل صحيح إلى جزء يتوافق مع هذا الهدف المشترك ومكون لا يتوافق معه. تتبع ثلاثة عواقب: تتبع التوقيت-خطأ التوافق بشكل كبير تتبع التوقيت التوافق، وبالتالي هو قياس ضئيل للتنوع، وتقسيم متساوي يتجاوز التوقيت غير المعلومات فقط عندما يكون التوافق المتوسط كبير مقارنة بتنوع التوافق؛ والربح من إضافة التوقيت يختلف في تحسين حقيقي وتقسيم بسيط، والتي يمكن أن تمنح التوافق المساواة في الخطأ.

Machine translation (Meta M2M100); the English original follows.

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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Topics: Selection (genetic algorithm), Uncorrelated, Econometrics, Statistics, Ranking (information retrieval), Measure (data warehouse)

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