Credit risk and companies' inter-organizational networks: Assessing impact of suppliers and buyers on CDS spreads

TL;DR

This paper investigates how companies' inter-organizational networks, including suppliers and buyers, influence credit risk as measured by CDS spreads, emphasizing the importance of network variables in credit risk assessment.

Contribution

It introduces the first empirical analysis of how supplier and customer networks impact CDS spreads, challenging assumptions that ignore inter-organizational relationships.

Findings

Diversified customer networks lower CDS spreads.

Stable partners with low default risk increase spreads.

Network variables are crucial for accurate credit risk modeling.

Abstract

Companies do not operate in a vacuum. As companies move towards an increasingly specialized production function and their reach is becoming truly global, their aptitude in managing and shaping their inter-organizational network is a determining factor in measuring their health. Current models of company financial health often lack variables explaining the inter-organizational network, and as such, assume that (1) all networks are the same and (2) the performance of partners do not impact companies. This paper aims to be a first step in the direction of removing these assumptions. Specifically, the impact is illustrated by examining the effects of customer and supplier concentrations and partners' credit risk on credit-default swap (CDS) spreads while controlling for credit risk and size. We rely upon supply-chain data from Bloomberg that provides insight into companies' relationships. The empirical results show that a well diversified customer network lowers CDS spread, while having stable partners with low default probabilities increase spreads. The latter result suggests that successful companies do not focus on building a stable eco-system around themselves, but instead focus on their own profit maximization at the cost of the financial health of their suppliers' and customers'. At a more general level, the results indicate the importance of considering the inter-organizational networks, and highlight the value of including network variables in credit risk models.