The Effect of Interfirm Financial Transactions on the Credit Risk of Small and Medium-Sized Enterprises

Abstract

Summary 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.

Credit Risk Evaluation for SMEs Using Reputation Information

“…al (2010) built a default prediction model by using financial ratios, available non-financial, regulatory compliance, and event data. Vinciotti et. al (2019) proposed an improved random forest model by using financial ratios and transaction network data to predict the default risk of SMEs.…”

Network log-ARCH models for forecasting stock market volatility

“…As shown by many authors (e.g., see Diebold and Yılmaz, 2014;Demiris et al, 2014;Barigozzi and Hallin, 2017;Vinciotti et al, 2019;Betancourt et al, 2020;Liu et al, 2021;Zhou et al, 2023), the financial market is well represented by networks where stocks are the nodes, and the edges reflect the degree of similarity across them. We introduce the spillovers from adjacent nodes in an ARCH-like manner.…”