PONE-D-20-32164

Predicting Corporate Credit Risk: Network Contagion via Trade Credit

PLOS ONE

Dear Dr. De Francisci Morales,

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper develops a model to predict firm default based on a supplier-customer transaction network, used as a proxy for the trade credit (i.e. the payment extension granted by the supplier firm to the customers) network. With that purpose, two models are shown: the first one includes two different but strictly related "sub-models", i.e. the standard econometric method of computing the probability of default, P(d), with the conditioning financial information owned by the bank and a more complex model which leverages the novelty of the network characteristics and integrates them in the predictive model, taking into account not just their own risk but also the risk deriving from the neighborhood of customers and suppliers. The second model presented combines the aforementioned approaches by creating a wide set of features potentially able to predict the firm default. At a later stage, these potentially predictive features are selected and corrected for collinearity. Several prediction models are compared and XGBoost appears to be the most efficient.

The paper shows a sound approach to future firm default prediction, it is well structured and highlights clearly its main contributions with respect to the current state of art. The references list is comprehensive. Yet, some issues need to be addressed or clarified:

Some notation, even if already correct, can be improved for the sake of clarity. Although later specified, AR and FGR are used before a proper definition. Moreover, "Larger customers, in terms of P_i" would be definitely clearer if 'P_i' was replaced by 'purchases', independently from its definition.

Plots could be made clearer. For instance, in figure 2b the colours chosen does not allow to distinguish between the two degree distributions of the transaction network. The following score plots present blurred labels. In figure 11 one of the time-tick labels is misplaced. Also, some of the plots in the 'supporting information' sheets some plots are blurred and then not clear.

I personally find slightly misleading the following quote from the abstract: "which can represent a vulnerability to customer decisions for suppliers". To avoid ambiguity, given the importance of the abstract, I suggest to rephrase.

From the abstract it is mentioned that two models are presented, but from my understanding the first one (especially the second step) is used to test network features (or in general, features) can be efficiently integrated in the following hybrid model. My concerns (to be clarified and not necessarily to cause any changes in the paper) regard the method itself which could be seen as constituted by two blocks: first, the feature creation and valuation and second, the hybrid model for the forecasts.

As for the human validation step for the hybrid model, I can understand what could have guided you in the substituting process (common sense, in all likelihood), but it is not clear what could have guided you in the removing process. I would be grateful if you could further clarify.

Both the R@K and P@K shows negative peaks for the 2017Q4 for the Random Forest model (two steps model, step 1) and for the hybrid model. Could it be affected by any systematic feature in the dataset? Have you investigated further?

Reviewer #2: This paper is written in good english and it is well structured. The study is interesting and the authors use a unique data-set. However, this paper has several shortcomings. 1) I feel that the text is often vague, especially in the section "Data Description". For example, it is not clear which information from yearly financial statements is used and what the official risk measures computed by the bank are. 2) In many parts of the papers relevant citations are missing. For example, on page 4 the authors write "aligned with typical early warning models", but they do not explain what typical early warning models are nor do they cite relevant literature. 3) The authors do not compare their results with state of the art credit risk models. 4) The figures are often pixelated and not displayed in a consistent form throughout the paper. 5) Often, it is not clear why the authors perform certain analyses and not others.

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Reviewer #1: No

Reviewer #2: No

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Predicting Corporate Credit Risk: Network Contagion via Trade Credit

PONE-D-20-32164R1

Dear Dr. De Francisci Morales,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Stefan Cristian Gherghina, PhD. Habil.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: All the comments done by me have been addressed. The sentences have been clarified accordingly and the figures are now definitely clearer. The authors have also been addressed and commented properly my remark on the Random Forest model.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No