The Performance of Shrinkage Estimator for Stock Portfolio Selection in Case of High Dimensionality

Round 1

Reviewer 1 Report

It is well known that the sample covariance matrix is affected

by estimation error; this is particularly severe in large portfolios.

THe application of shrinkage estimator permits to stabilyze the problems and it is widly used in Markovitz framework.

The authors analyze the use of shrinkage on Vietnam stock market in the period of 2011 – 2021.

I think that, although there is no theoretical or methodological contribution, the their empirical study Vietnam stock market

could improve the state of the art.

My suggestion is to accept the paper for publication, provided the authors are

willing to address the following suggestions.

Minor comment

- I think that the authors must specify that the use of shrinkage estimator is widly used in Markovitz framework, citing also more recent references

See for example:

 1. Sun, Ruili and Ma, Tiefeng and Liu, Shuangzhe and Sathye, Milind, "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review",bvJournal of Risk and Financial Management, 2019, 12(1), 48;

 2. S. Corsaro, V. De Simone, Z. Marino, "Split Bregman iteration for multi-period mean variance portfolio optimization", Applied Mathematics and Computation, 2021, 392, 125715; 

 3. T.Bodnar, Y. Okhrin, and N.Paroly, "Optimal Shrinkage-Based Portfolio Selection in High Dimensions", JOURNAL OF BUSINESS & ECONOMIC  STATISTICS, 2021, AHEAD-OF-PRINT, 1-17

- There are typos that should be corrected (an automatic spelling checker is recommended) and grammar mistakes 

Author Response

Many thanks for the support of the reviewer. Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

see letter

Comments for author File: Comments.pdf

Author Response

Many thanks for the support of the reviewer. Please see the attachment.

Author Response File: Author Response.docx