Factors That Fit the Time Series and Cross-Section of Stock Returns
提出一种新的估计方法，通过惩罚预期收益的定价误差来扩展主成分分析，能够发现高夏普比率的弱因子，并找到五个有经济含义的因子，其样本外最大夏普比率是PCA的两倍。
Abstract We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.