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Bank-firm credit network in Japan. An analysis of a bipartite network

Author

Listed:
  • Luca Marotta
  • Salvatore Miccich`e
  • Yoshi Fujiwara
  • Hiroshi Iyetomi
  • Hideaki Aoyama
  • Mauro Gallegati
  • Rosario N. Mantegna

Abstract

We present an analysis of the credit market of Japan. The analysis is performed by investigating the bipartite network of banks and firms which is obtained by setting a link between a bank and a firm when a credit relationship is present in a given time window. In our investigation we focus on a community detection algorithm which is identifying communities composed by both banks and firms. We show that the clusters 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. Specifically, we obtain communities of banks and networks for each of the 32 investigated years, and we introduce a method to track the time evolution of these communities on a statistical basis. We then characterize communities by detecting the simultaneous over-expression 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 year long analysis we detect a persistence of the over-expression of attributes of clusters of banks and firms together with a slow dynamics 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.

Suggested Citation

  • Luca Marotta & Salvatore Miccich`e & Yoshi Fujiwara & Hiroshi Iyetomi & Hideaki Aoyama & Mauro Gallegati & Rosario N. Mantegna, 2014. "Bank-firm credit network in Japan. An analysis of a bipartite network," Papers 1407.5429, arXiv.org.
  • Handle: RePEc:arx:papers:1407.5429
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    Cited by:

    1. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    2. Bikramjit Das & Vicky Fasen-Hartmann, 2025. "Measuring risk contagion in financial networks with CoVaR," Finance and Stochastics, Springer, vol. 29(3), pages 707-755, July.
    3. Rocco, Claudio M. & Moronta, José & Ramirez-Marquez, José E. & Barker, Kash, 2017. "Effects of multi-state links in network community detection," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 46-56.
    4. Yanquen, Eduardo & Livan, Giacomo & Montañez-Enriquez, Ricardo & Martinez-Jaramillo, Serafin, 2022. "Measuring systemic risk for bank credit networks: A multilayer approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    5. Luca Marotta & Salvatore Miccich`e & Yoshi Fujiwara & Hiroshi Iyetomi & Hideaki Aoyama & Mauro Gallegati & Rosario N. Mantegna, 2015. "Backbone of credit relationships in the Japanese credit market," Papers 1511.06870, arXiv.org.
    6. Wu, Yujia & Lan, Wei & Fan, Xinyan & Fang, Kuangnan, 2024. "Bipartite network influence analysis of a two-mode network," Journal of Econometrics, Elsevier, vol. 239(2).
    7. Landaberry, Victoria & Caccioli, Fabio & Rodriguez-Martinez, Anahi & Baron, Andrea & Martinez-Jaramillo, Serafin & Lluberas, Rodrigo, 2021. "The contribution of the intra-firm exposures network to systemic risk," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
    8. Diaz de la Fuente Manuel, 2023. "Análisis de la Topología de las relaciones entre Bancos y Firmas mediante Redes Complejas: comparación del caso de Argentina e Italia," Asociación Argentina de Economía Política: Working Papers 4647, Asociación Argentina de Economía Política.
    9. Bikramjit Das & Vicky Fasen-Hartmann, 2023. "Measuring risk contagion in financial networks with CoVaR," Papers 2309.15511, arXiv.org, revised May 2025.
    10. Barón, Andrea & Landaberry, María Victoria & Lluberas, Rodrigo & Ponce, Jorge, 2021. "Commercial and banking credit network in Uruguay," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(3).
    11. Margarita Baltakienė & Kęstutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around initial public offering," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 5(1), pages 1-14, December.
    12. Chakraborty, Abhijit & Krichene, Hazem & Inoue, Hiroyasu & Fujiwara, Yoshi, 2019. "Characterization of the community structure in a large-scale production network in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 210-221.
    13. Bryan S. Graham, 2020. "Sparse network asymptotics for logistic regression," Papers 2010.04703, arXiv.org.
    14. Opeoluwa Banwo & Fabio Caccioli & Paul Harrald & Francesca Medda, 2016. "The Effect Of Heterogeneity On Financial Contagion Due To Overlapping Portfolios," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-20, December.
    15. Ramirez-Marquez, J.E. & Rocco, C.M. & Moronta, J. & Gama Dessavre, D., 2016. "Robustness in network community detection under links weights uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 88-95.
    16. Sebastian Poledna & Abraham Hinteregger & Stefan Thurner, 2018. "Identifying systemically important companies in the entire liability network of a small open economy," Papers 1801.10487, arXiv.org.
    17. Ermanno Catullo & Antonio Palestrini & Ruggero Grilli & Mauro Gallegati, 2018. "Early warning indicators and macro-prudential policies: a credit network agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 81-115, April.

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