Principal Portfolios
提出一个允许所有证券信号预测彼此收益的资产定价框架，推导出最优策略（主成分投资组合），并分解为alpha和beta，实证显示该策略在多个数据集中产生显著的样本外alpha。
ABSTRACT We propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own‐signal predictability, assuming equal strength across securities, our framework includes cross‐predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out‐of‐sample alphas to standard factors in several data sets.