https://doi.org/10.1140/epjds/s13688-019-0197-5
Regular article
Corporate payments networks and credit risk rating
1
Scuola Normale Superiore, Pisa, Italy
2
Department of Mathematics, University of Bologna, Bologna, Italy
3
Center for Analysis, Decisions, and Society, Human Technopole, Milano, Italy
* e-mail: elisa.letizia@sns.it
Received:
29
September
2018
Accepted:
20
May
2019
Published online:
1
June
2019
Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risk of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with its topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show 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 show the predictability of the missing rating of a firm using only the network properties of the associated node.
Key words: Financial networks / Corporate networks / Credit risk / Credit rating / Machine learning
© The Author(s), 2019
