title: Vast Portfolio Selection with Gross-exposure Constraints().
abstract: We introduce the large portfolio selection using gross-exposure constraints. We show that with gross-exposure constraint the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results in Jagannathan and Ma (2003). We also show that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.
authors.affiliation: Frederick L. Moore Professor, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540, USA and honorary professor, Department of Statistics, Shanghai University of Economics and Finance, Shanghai, China.
authors.forename: Jianqing
authors.initials: J
authors.lastname: Fan
authors.forename: Jingjin
authors.initials: J
authors.lastname: Zhang
authors.forename: Ke
authors.initials: K
authors.lastname: Yu