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Predicting corporate credit risk: Network contagion via trade credit

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagati...

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Autores principales: Berloco, Claudia, De Francisci Morales, Gianmarco, Frassineti, Daniele, Greco, Greta, Kumarasinghe, Hashani, Lamieri, Marco, Massaro, Emanuele, Miola, Arianna, Yang, Shuyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8084139/
https://www.ncbi.nlm.nih.gov/pubmed/33914764
http://dx.doi.org/10.1371/journal.pone.0250115
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author Berloco, Claudia
De Francisci Morales, Gianmarco
Frassineti, Daniele
Greco, Greta
Kumarasinghe, Hashani
Lamieri, Marco
Massaro, Emanuele
Miola, Arianna
Yang, Shuyi
author_facet Berloco, Claudia
De Francisci Morales, Gianmarco
Frassineti, Daniele
Greco, Greta
Kumarasinghe, Hashani
Lamieri, Marco
Massaro, Emanuele
Miola, Arianna
Yang, Shuyi
author_sort Berloco, Claudia
collection PubMed
description Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.
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spelling pubmed-80841392021-05-06 Predicting corporate credit risk: Network contagion via trade credit Berloco, Claudia De Francisci Morales, Gianmarco Frassineti, Daniele Greco, Greta Kumarasinghe, Hashani Lamieri, Marco Massaro, Emanuele Miola, Arianna Yang, Shuyi PLoS One Research Article Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline. Public Library of Science 2021-04-29 /pmc/articles/PMC8084139/ /pubmed/33914764 http://dx.doi.org/10.1371/journal.pone.0250115 Text en © 2021 Berloco et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Berloco, Claudia
De Francisci Morales, Gianmarco
Frassineti, Daniele
Greco, Greta
Kumarasinghe, Hashani
Lamieri, Marco
Massaro, Emanuele
Miola, Arianna
Yang, Shuyi
Predicting corporate credit risk: Network contagion via trade credit
title Predicting corporate credit risk: Network contagion via trade credit
title_full Predicting corporate credit risk: Network contagion via trade credit
title_fullStr Predicting corporate credit risk: Network contagion via trade credit
title_full_unstemmed Predicting corporate credit risk: Network contagion via trade credit
title_short Predicting corporate credit risk: Network contagion via trade credit
title_sort predicting corporate credit risk: network contagion via trade credit
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8084139/
https://www.ncbi.nlm.nih.gov/pubmed/33914764
http://dx.doi.org/10.1371/journal.pone.0250115
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