Compositional Classification of Financial Statement Profiles: The Weighted Case

Round 1

Reviewer 1 Report

Review of

Compositional classification of financial statement profiles.

by P. Jofre-Campuzano and G. Coenders

 

General comments:

The paper brings a nice application of the compositional theory to the field of financial accounting. The main aim of the paper, as I understood it, is to cluster a set of companies according to their economic performance and as the actual figures depend on the size of a company, the compositional approach seems to be appropriate. Moreover, in order to get more representative result, the authors compare the classical and the weighted approach. Despite the fact that I like the overall idea behind the manuscript, I ask the authors to consider my specific comments, which follow:

Specific comments:

1)      My main concern relates to the weighting. The authors introduce the weighted approach since they expect “that variables which generally take low values have the greatest variances…”. Even though I agree with this statement, I think that the word “generally” is crucial, as the high variance generally can be related to the low-valued variables, but it is not a must. The authors decided to apply a weighting algorithm introduced by Greenacre and Lewi in 2009, which uses the part means as weights, in order to down-weight the influence of low-valued compositional parts.  From my perspective the method proposed in 2017 by Hron et al. seem to be better fitting to the aim of the analysis, as this approach sets the weights according to the variance structure. Did the authors try also this approach? The authors consider the performance of the weighted approach substantially better than those obtained by the classical one and therefore they base the interpretation solely on the former one. From my perspective, the differences are rather minor (e.g. see Tables 2, 4 or 7) and I am curious if the alternative weighting algorithm would help to a more prominent improvement.

2)      Ch. 2.5. The zero replacement is done with a method designed for the imputation of values under the detection limit. Is it really an advisable approach in this kind of application? From my perspective are the zeros rather structural.

3)      Ch. 3.1. This chapter is pretty short. It discuss the clr boxplots and I think that it would be nice to see their weighted counterparts as well.

4)      Ch. 3.2. What led the authors to choose four clusters? Did they use some objective rule for it? Based solely on dendrograms, I would choose three or four clusters in the unweighted case and up to seven in the weighted one.

5)      Ch. 3.2., Table 7. I think that considering for each variable only the maximum distance between clusters can be too simplifying. To get more broader idea about the results a set of boxplots (variable ~ cluster), or visualization similar to those presented on Figs 6 and 7,  would be helpful.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors, thank you for submitting the manuscript to the editorial office. The topic of the manuscript is very interesting and the manuscript itself is very well prepared. I have just a few comments:

1. There is basically nothing visible in Figure 3 (dendrograms). This image is not even adequately described in the text.

2. Likewise, the other tables and figures in the Results section could be described better.

After incorporating these comments and those of other reviewers and carefully editing the text, the manuscript can be published as a standard article.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf