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

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  • Veronica Vinciotti
  • Elisa Tosetti
  • Francesco Moscone
  • Mark Lycett

Abstract

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.

Suggested Citation

  • Veronica Vinciotti & Elisa Tosetti & Francesco Moscone & Mark Lycett, 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, Royal Statistical Society, vol. 182(4), pages 1205-1226, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1205-1226
    DOI: 10.1111/rssa.12500
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    Cited by:

    1. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    2. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    3. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org, revised Jun 2024.

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