On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II): Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment

Round 1

Reviewer 1 Report

The paper being evaluated is written in a clear and transparent manner which does not raise any objections. The tools and methods used are fully correct and the quality of graphics and references are appropriate.

However taking into account the above, in my view, two additional elements are required:
1. The paperer does not contain a list of acronyms/abbreviation - the multiplicity and diversity of which significantly hinder the study of the paper, and for the readers who want to get acquainted with a part of the paper - they effectively prevent from understanding it.
2. It would be worth to indicate/develop why applying MLT for Unbiased Enterprise Value Assessment it is worth to use.

Author Response

Thank you for the comments. In the new version of the manuscript, changes with respect to the old version are indicated in red. Some minor changes and corrected typos are not indicated. We think that the paper has improved after the response to the suggestions and indications, and we hope it can reach the quality required for the MDPI-Entropy audience.

Author Response File: Author Response.docx

Reviewer 2 Report

This article presents an extensive application of ML techniques for enterprise value assessment. The article is well-structured and its objectives are clear. Taking into account the complementary article, the authors provide a solid and extensive analysis of many well-known classification ad prediction algorithms. However, these ML approaches have already implemented for many classification for numerous similar problems. Thus, the novelty of the article is not strong. The authors have correctly concluded that the utilization of ML techniques is case-specific and requires a lot of parameter tuning. In my opinion, the authors should address the following comments to further improve the quality of their work.

1. Page 9, 2nd paragraph, "The analysis of over 30 .... in the prediction variance". This remark is not supported from data of Table 3. In this table, the usage of exogeneous variables do not significantly improve in general but only in some cases. In this point, the authors must better explain the results of their analysis.
2.  Figure 1. Add legend in every graph. Also in gray scale, red and blue are not clearly separated. Please use different line type (dotted or dashed).
3. Table 1. In Utilities results GPR score (1.21) is not highlighted.
4. Move Table 3 in page 9.
5. Make a careful proofread. There are few typos, such as 
   • Page 5 (three lines  at the bottom) didn't --> did not
   • Section 3.3, 1st paragraph could limit --> could limit
   • Section 3.4, 1st paragraph of a the --> of the

Author Response

Thank you for the comments.  In the new version of the manuscript, changes with respect to the old version are indicated in red. Some minor changes and corrected typos are not indicated.  We think that the paper has improved after the response to the suggestions and indications, and we hope it can reach the quality required for the MDPI-Entropy audience.

Author Response File: Author Response.docx