Corporate Payments Networks and Credit Risk Rating

39 Pages Posted: 30 Jan 2018

See all articles by Elisa Letizia

Elisa Letizia

Scuola Normale Superiore

Fabrizio Lillo

Università di Bologna

Date Written: January 23, 2018

Abstract

This paper provides empirical evidences that corporate firms risk assessment could benefit from taking quantitatively into account the network of interactions among firms. Indeed, the structure of interactions between firms is critical to identify risk concentration and the possible pathways of propagation of financial distress. In this work, we consider the interactions by investigating a large proprietary dataset of payments among Italian firms. We first characterise the topological properties of the payment networks, and then we focus our attention on the relation between the network and the risk of firms. Our main finding is to document the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to predict the missing rating of a firm using only network properties of a node by means of machine learning methods.

Keywords: Complex Networks, Corporate Networks, Credit Risk Rating, Data Science

JEL Classification: C45, C88, G21, G33, L14

Suggested Citation

Letizia, Elisa and Lillo, Fabrizio, Corporate Payments Networks and Credit Risk Rating (January 23, 2018). Available at SSRN: https://ssrn.com/abstract=3075019 or http://dx.doi.org/10.2139/ssrn.3075019

Elisa Letizia (Contact Author)

Scuola Normale Superiore ( email )

Piazza dei Cavalieri, 7
Pisa, 56126
Italy

Fabrizio Lillo

Università di Bologna ( email )

Via Zamboni, 33
Bologna, 40126
Italy

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