IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v182y2019i4p1205-1226.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12500
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12500?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1205-1226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.