Bank-Firm Credit Network in Japan. An Analysis of a Bipartite Network

Abstract

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the overexpression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-yearlong analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

Long-term ecology of investors in a financial market

“…To relate investors of a cluster in a given year to investors of clusters in the successive year, we use a statistical test of the over-representation of the number of investors that are present in both clusters against a null hypothesis that takes into account the heterogeneity of the size of the clusters. We perform the test as indicated in (Marotta et al 2015). The test is performed on all clusters with more than five investors.…”

“…This methodology (Marotta et al 2015) allows us to select clusters in year k+1 that present an over-expressed number of investors that were also present in clusters at year k. When the pvalue is below a statistical threshold we connect with a directed link the clusters selected by the statistical test. Also in this case we are performing a multiple comparison procedure and therefore a multiple hypothesis test correction is necessary.…”

Sparse Network Asymptotics for Logistic Regression under Possible Misspecification

“…, M locations; see KPMG (2016)). Other many-to-many matching problems that have drawn economists' interest include (i) bank-firm lending relationships (e.g., Marotta et al, 2015), (ii) the matching of venture capital with start-ups (e.g., Bengtsson and Hsu, 2015), and (iii) supply chain settings with strong bi-partite structure (e.g., automakers and parts supplies as in Fox (2018)). When i = 1, .…”

Sparse network asymptotics for logistic regression

“…, M locations; see KPMG (2016)). Other many-to-many matching problems that have drawn economists' interest include (i) bank-firm lending relationships (e.g., Marotta et al, 2015), (ii) the matching of venture capital with start-ups (e.g., Bengtsson and Hsu, 2015), and (iii) supply chain settings with strong bi-partite structure (e.g., automakers and parts supplies as in Fox (2018)). When i = 1, .…”