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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

Chinese

我们表明,每个标准化预测都会分成一个与这个共同目标一致的组成部分,一个与之无关的组成部分。三个后果:预测错误的相关性大多反映了预测的相关性,因此是差异的衡量;一个均衡的组合只有当平均对比与组合的分散相对较大时才会打败一个没有信息的预测;加上预测错误的相关性将真正的改进和简单的稀释分开,而平衡的承认可能会错误地奖励;我们开发一个谨慎的选择规则,在模拟中研究它,并应用到美国股票排名和机械信号排名的语言模型预测。

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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