当横截面预测共享同一目标时的目标对齐、稀释与预测选择
Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target
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中文总结 AI 辅助
本文研究共享同一目标的横截面预测，将其分解为对齐与不相关分量，提出谨慎选择规则以消除稀释损失，并在语言模型和机械信号预测中验证，但组合未能胜过无信息预测。
中文摘要 AI 辅助
预测者通常根据同一标准化实现结果对每个日期的相同单位进行评分。我们证明，每个标准化预测都精确地分解为与该共同目标对齐的分量和与其不相关的分量。由此产生三个后果：预测误差相关性在很大程度上反映了预测相关性，因此不是衡量多样性的良好指标；仅当平均对齐度相对于组合的离散度足够大时，等权组合才能胜过无信息预测；增加一个预测者的收益可分解为真正的改进和单纯的稀释，而等权纳入可能会错误地奖励这种稀释。我们开发了一种谨慎的选择规则，在模拟中对其进行了研究，并将其应用于语言模型对美国股票排名的预测以及机械信号对交易所交易基金的排名。选择消除了大部分稀释损失，但没有任何组合能胜过无信息预测。
英文摘要
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.
发表机构
- University of Pisa（比萨大学）
机构由 AI 辅助整理，请以论文原文为准。
