Test Assets and Weak Factors
研究发现实证资产定价中的弱因子与测试资产选择密切相关，通过适当选择测试资产可增强因子强度，并提出了监督主成分分析方法来估计风险溢价和诊断因子模型。
ABSTRACT We show that two important issues in empirical asset pricing—the presence of weak factors and the selection of test assets—are deeply connected. Since weak factors are those to which test assets have limited exposure, an appropriate selection of test assets can improve the strength of factors. Building on this insight, we introduce supervised principal component analysis (SPCA), a methodology that iterates supervised selection, principal‐component estimation, and factor projection. It enables risk premia estimation and factor model diagnosis even when weak factors are present and not all factors are observed. We establish SPCA's asymptotic properties and showcase its empirical applications.