In this paper we provide a highly selected review and synthesis on some of the recent and notable
contribution to research in portfolio analysis. A unique perspective on this development
in the literature is offered in this paper by judiciously identifying a few sample eigenvalues
adjustment patterns in a portfolio that leads to an improvement in the out-of-sample portfolio
Sharpe ratio when the population covariance matrix admits a high-dimensional factor model.
These patterns unveil a key insight into a portfolio performance improvement and shed an
important light on the effectiveness of a few recently introduced ”robust to estimation errors”
covariance matrix estimation approaches, which were not originally designed with the goal to
improve the out-of-sample portfolio performance.