Abstract: Estimation error has plagued quantitative finance since Harry Markowitz launched modern

portfolio theory in 1952. Using random matrix theory, we characterize a source of bias in the sample

eigenvectors of financial covariance matrices. Unchecked, the bias distorts weights of minimum

variance portfolios and leads to risk forecasts that are severely biased downward. To address these

issues, we develop an eigenvector bias correction. Our approach is distinct from the regularization

and eigenvalue shrinkage methods found in the literature. We provide theoretical guarantees on the

improvement our correction provides as well as estimation methods for computing the optimal

correction from data.  We will illustrate the effectiveness of our method with numerical examples.

Working paper:  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3071328