Corporate payments networks and credit risk rating

Elisa Letizia,Fabrizio Lillo

doi:10.1140/epjds/s13688-019-0197-5

Elisa Letizia, Fabrizio Lillo

Open Access

https://doi.org/10.1140/epjds/s13688-019-0197-5

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Abstract

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.

Highlights

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
Assessing the aggregate risk emerging in complex systems is of paramount importance in disparate fields, such as economics, finance, epidemiology, infrastructure engineering, etc
We find that the large payment networks investigated in this paper share the properties observed in other complex networks, namely they are sparse but almost entirely made of a single component, they are scale free and small world

Summary

Introduction

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. A large body of recent literature has explored, both theoretically and empirically, how risk propagates [1] and how to assess aggregate risk when the risk of each individual entity is known [2], as well as the topology of the network of interaction among them Both aspects have been shown to be important, their mutual relation is relatively less explored. Other works use more heterogeneous information to predict the rating [27,28,29,30,31]

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