Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis

Tony S. Wirjanto; Danqiao Guo; Chengguo Weng

doi:10.20944/preprints202105.0031.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 2 May 2021 / Approved: 5 May 2021 / Online: 5 May 2021 (10:40:08 CEST)

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.

finance; portfolio optimization; tail eigenvalue amplification; high-dimensional setting; factor models

MATHEMATICS & COMPUTER SCIENCE, Applied Mathematics

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Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis

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