Version 1
: Received: 2 May 2021 / Approved: 5 May 2021 / Online: 5 May 2021 (10:40:08 CEST)
How to cite:
Wirjanto, T.S.; Guo, D.; Weng, C. Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis. Preprints2021, 2021050031. https://doi.org/10.20944/preprints202105.0031.v1
Wirjanto, T.S.; Guo, D.; Weng, C. Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis. Preprints 2021, 2021050031. https://doi.org/10.20944/preprints202105.0031.v1
Wirjanto, T.S.; Guo, D.; Weng, C. Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis. Preprints2021, 2021050031. https://doi.org/10.20944/preprints202105.0031.v1
APA Style
Wirjanto, T.S., Guo, D., & Weng, C. (2021). Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis. Preprints. https://doi.org/10.20944/preprints202105.0031.v1
Chicago/Turabian Style
Wirjanto, T.S., Danqiao Guo and Chengguo Weng. 2021 "Taking Stock of Some Recent and Notable Contribution to Research in Portfolio Analysis" Preprints. https://doi.org/10.20944/preprints202105.0031.v1
Abstract
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.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
