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

Korean

예측자는 종종 표준화된 실현된 결과에 대하여 날짜 당 동일한 단위를 점수합니다. 우리는 각 표준화된 예측이이 공통 목표와 일치하는 구성 요소와 관련이없는 구성 요소로 정확하게 분할된다는 것을 보여줍니다. 세 가지 결과가 따릅니다: 예측 오류 상관 관계는 주로 예측 상관 관계를 반영하고 다양성의 나쁜 측정이다; 동등한 무게가있는 조합은 단지 평균 조정이 조합의 분산에 비해 큰 경우에만 비정보 없는 예측을 이길 것이며; 예측자를 추가함으로써 얻은 이익은 진정한 개선과 단순한 희석을 분리합니다.

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)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange