Understanding the structure of interactions between corporate firms is critical to identify risk concentration and the possible pathways of propagation of financial distress. In this paper we consider the in- teraction due to payments and, by investigating a large proprietary dataset of Italian firms, we characterize the topological properties of the payment network. We then focus on the relation between the net- work of payments and the risk of firms. We show the existence of an homophily of risk, i.e. the tendency of firms with similar risk pro- file to be statistically more connected among themselves. This effect is observed both when considering pairs of firms and when consider- ing communities or hierarchies identified in the network. By applying machine learning techniques, we leverage this knowledge to show that network properties of a node can be used to predict the missing rating of a firm . Our results suggest that risk assessment should take quan- titatively into account also the network of interactions among firms .
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 .
R
Understanding the
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
D
corporate
R
paper
work,
R
in- teraction due to payments and,
interactions
R
Italian firms, we characterize
payments among Italian firms. We first characterise
R
network. We then focus
networks, and then we focus our attention
R
net- work of payments
network
R
We show
Our main finding is to document
R
pro- file
profile
R
both when considering
when considering both
D
when consider- ing
R
By applying machine learning techniques, we
We
D
show that network properties of a node can be used to
R
. Our results suggest that risk assessment should take quan- titatively into account also the network of interactions among firms
using only network properties of a node by means of machine learning methods