Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data

Discrete Dynamics in Nature and Society, Mar 2018

Based on the loan data of Chinese listed companies from 2008 to 2016, this paper constructs a co-loan network of the Chinese banking system and analyzes the topological structures and corresponding evolvement characteristics from the perspective of complex network. Through the empirical studies, we find that the co-loan network always displays a core-periphery structure; for example, ten banks including four state banks and six large commercial banks are always in the core region of the Chinese banking system for nine consecutive years. Furthermore, the co-loan network is a small-world network lasting for nine years.

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Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data

Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data Liang Li,1 Qianting Ma,1 Jianmin He,1 and Xin Sui2 1School of Economics and Management, Southeast University, Nanjing 211189, China 2School of Finance, Nanjing University of Finance and Economics, Nanjing 210023, China Correspondence should be addressed to Liang Li; moc.621@62uesgnailil Received 6 December 2017; Accepted 11 February 2018; Published 11 March 2018 Academic Editor: Ricardo López-Ruiz Copyright © 2018 Liang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Based on the loan data of Chinese listed companies from 2008 to 2016, this paper constructs a co-loan network of the Chinese banking system and analyzes the topological structures and corresponding evolvement characteristics from the perspective of complex network. Through the empirical studies, we find that the co-loan network always displays a core-periphery structure; for example, ten banks including four state banks and six large commercial banks are always in the core region of the Chinese banking system for nine consecutive years. Furthermore, the co-loan network is a small-world network lasting for nine years. 1. Introduction Banking is not only a significant part of the modern financial system, but also an indispensable financial intermediary for the healthy operation of the entire economic system. Banking is highly interdependent due to all sorts of connections and forms a huge banking network. Interconnection between banks not only brings many economic benefits, but also provides channels for risk transmission. The collapse of small and medium banks in the United States triggered by the subprime crisis in 2007 is a typical example. Therefore, it is important to study the structural characteristics of banking networks to maintain the stability of banking systems. In reality, there are two main ways to construct the banking network. One way constructs the network directly by interbank balance sheets or payments. There is growing literature on network structures of real banking systems in different countries. For instance, Boss et al. [1] discover that the Austrian banking network of debt relationships shows a small-world property and a community structure, and the degree distribution displays two different power-law exponents. Inaoka et al. [2] prove that the Japanese interbank network of monetary transactions is a scale-free network described by a power-law degree distribution. De Masi et al. [3] find that the Italian interbank network composed of different banks exchanging on a daily basis loans and debts of liquidity shows a community structure. Soramäki et al. [4] suggest that the American interbank payment network is compact despite low connectivity, and the degree distribution is scale-free over a substantial range. Blasques et al. [5] argue that the interbank lending network in Netherlands can be characterized as a structure with multiple monetary centers, which also exists in Germany (2004), Hungary (2006), Belgium (2007), and Finland (2009). Furthermore, the interbank network also presents the characteristic of dynamic evolution in Brazil (2008) and Mexico (2014). The other way constructs the network indirectly by cross-holding or co-holding relationships of assets or liabilities. For example, Bubna et al. [6] discover that the co-lending network of the American banking system shows a community structure and a centrality structure. Gong et al. [7] find that the co-lending network of the Chinese banking system shows a small-world property and a core-periphery structure. Elliott et al. [8] analyze the European debt cross-holding network, which exhibits realistic structural features such as core-periphery and segregation structure. In addition, there are other significant studies in this area; see Hernandez et al. [9], Gathergood and Weber [10], Burdick et al. [11], Lux [12], and so forth. In addition to the empirical researches, scholars also carry out simulation studies to explain the formation mechanism of banking networks. For instance, Li et al. [13] put forward an interbank network based on interbank credit lending relationships and it generates some network features including a low clustering coefficient, a relatively short average path length, a community structure, and a two-power-law distribution of degree. Fricke et al. [14] construct a scare-free network of the banking system based on the balance sheets and Monte-Carlo simulations. Lux [15] presents a dynamic model of interbank credit relationships, which forms a core-periphery structure. Furthermore, there are other simulation studies on the banking network; see Georg [16], Craig and Von Peter [17], Anand et al. [18], González-Avella et al. [19], and so forth. As for the Chinese banking system, i (...truncated)


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Liang Li, Qianting Ma, Jianmin He, Xin Sui. Co-Loan Network of Chinese Banking System Based on Listed Companies’ Loan Data, Discrete Dynamics in Nature and Society, 2018, 2018, DOI: 10.1155/2018/9565896