Principal Portfolios
成果类型：
Article
署名作者：
Kelly, Bryan; Malamud, Semyon; Pedersen, Lasse Heje
署名单位：
Yale University; National Bureau of Economic Research; Swiss Finance Institute (SFI); Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Copenhagen Business School
刊物名称：
JOURNAL OF FINANCE
ISSN/ISSBN：
0022-1082
DOI：
10.1111/jofi.13199
发表日期：
2023
页码：
347-387
关键词：
摘要：
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.