The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises. / Vinciotti, Veronica; Tosetti, Elisa; Moscone, Francesco; Lycett, Mark.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 24.08.2019, p. 1-22.

Research output: Contribution to journalArticle

E-pub ahead of print

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The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises. / Vinciotti, Veronica; Tosetti, Elisa; Moscone, Francesco; Lycett, Mark.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 24.08.2019, p. 1-22.

Research output: Contribution to journalArticle

Harvard

Vinciotti, V, Tosetti, E, Moscone, F & Lycett, M 2019, 'The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises', Journal of the Royal Statistical Society: Series A (Statistics in Society), pp. 1-22. https://doi.org/10.1111/rssa.12500

APA

Vinciotti, V., Tosetti, E., Moscone, F., & Lycett, M. (2019). The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1-22. https://doi.org/10.1111/rssa.12500

Vancouver

Vinciotti V, Tosetti E, Moscone F, Lycett M. The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2019 Aug 24;1-22. https://doi.org/10.1111/rssa.12500

Author

Vinciotti, Veronica ; Tosetti, Elisa ; Moscone, Francesco ; Lycett, Mark. / The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises. In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 2019 ; pp. 1-22.

BibTeX

@article{4c7641e8854b4ab8abdaca4e7f08a72b,
title = "The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises",
abstract = "Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm 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 a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes 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 thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.",
keywords = "SME, Credit risk modelling, Networks, Financial transactions",
author = "Veronica Vinciotti and Elisa Tosetti and Francesco Moscone and Mark Lycett",
year = "2019",
month = "8",
day = "24",
doi = "10.1111/rssa.12500",
language = "English",
pages = "1--22",
journal = "Journal of the Royal Statistical Society: Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "Wiley-Blackwell",

}

RIS

TY - JOUR

T1 - The effect of interfirm financial transactions on the credit risk of small and medium‐sized enterprises

AU - Vinciotti, Veronica

AU - Tosetti, Elisa

AU - Moscone, Francesco

AU - Lycett, Mark

PY - 2019/8/24

Y1 - 2019/8/24

N2 - Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm 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 a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes 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 thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.

AB - Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm 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 a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes 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 thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.

KW - SME

KW - Credit risk modelling

KW - Networks

KW - Financial transactions

U2 - 10.1111/rssa.12500

DO - 10.1111/rssa.12500

M3 - Article

SP - 1

EP - 22

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

SN - 0964-1998

ER -