Sequential Tests of the Arbitrage Pricing Theory: A Comparison of Principal Components and Maximum Likelihood Factors
比较了主成分分析和最大似然因子分析在套利定价理论横截面定价方程中的表现，发现主成分的第一向量作为风险度量效果出奇地好，甚至优于单因子或五因子模型。
We examine the cross-sectional pricing equation of the APT using the elements of eigenvectors and the maximum likelihood factor loadings of the covariance matrix of returns as measures of risk. The results indicate that, for data assumed stationary over twenty years, the first vector is a surprisingly good measure of risk when compared with either a one- or a five-factor model or a five-vector model. We conclude that in some circumstances principal components analysis may be preferred to factor analysis.