Despite the recognized importance of interfirm financial links in determining a company's performance, only a few studies have incorporated proxies for interfirm links in credit risk models, and none of these use real financial transactions. We estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed interfirm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop several network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that includes only a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium-sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of these companies, ultimately reducing their propensity to apply excessive lending restrictions.
|Data di pubblicazione:||2019|
|Titolo:||The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises|
|Rivista:||JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, STATISTICS IN SOCIETY|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1111/rssa.12500|
|Appare nelle tipologie:||2.1 Articolo su rivista |
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