James-Stein for eigenvectors: reducing the optimization bias in Markowitz portfolios
Lisa Goldberg, UC Berkeley
Abstract: We identify and reduce bias in the leading sample eigenvector of a high-dimensional covariance matrix of correlated variables. Our analysis illuminates how error in an estimated covariance matrix corrupts optimization. It may be applicable in finance, machine learning and genomics. Biography: Lisa Goldberg is Head of Research at Aperio and Managing Director at BlackRock. She is Professor of the Practice of Economics at University of California, Berkeley, where she co-directs the Center for Data Analysis in Risk, an industry…
