NDLI: Corporate Payments Networks and Credit Risk Rating

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Corporate Payments Networks and Credit Risk Rating

Content Provider	Scilit
Author	Letizia, Elisa Lillo, Fabrizio
Copyright Year	2018
Description	Journal: SSRN Electronic Journal 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.
Related Links	https://cris.unibo.it/bitstream/11585/720762/2/EPJdata_science8-1-2019.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3075019
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.3075019
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2018-01-23
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Information Systems Applied Ethics Complex Networks Corporate Networks Credit Risk Rating Data Science
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health

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