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Risk contagion of bank-firm loan network: evidence from China

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Abstract

Starting from Chinese A-listed firms’ loan announcements, this research creatively constructs a dynamic, variant-linkage, and more comprehensive banking network in China during 2007 and 2016. Exploiting techniques from the literature on complex networks, we find that China’s banking network exhibits more clustering, more coherence, higher centrality, and even more heterogeneity. Empirical results show that the above network features, especially the heterogeneity of the network, have a great impact on financial systemic risk. A network with higher clustering coefficient, higher coherence, lower centrality, and greater heterogeneity is associated with a lower financial systemic risk. A range of policy measures can be drawn from our results, including macro-prudential policy, increasing network stability, and applying surcharges for systemically important financial institutions so as to minimize financial systemic risk.

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Data availability

Data are available from the authors upon request.

Notes

  1. In January 2007, the Ministry of Finance officially implemented “The Accounting Standards for Business Enterprises”, which further standardized the financial information disclosure of enterprises.

  2. In 2017, the People’s Bank of China, China Banking Regulatory Commission, China Securities Regulatory Commission, China Insurance Regulatory Commission, and China Foreign Exchange Administration jointly published “Guidance on standardizing the asset management business of financial institutions” to solicit opinions from the public. The guidance aims to regulate the development of the shadow banking system in China and has critical impact on China’s banking system.

  3. The variance inflation factor (VIF) tests statistics for CC, CO, CN and HN are 5.60, 5.27, 2.25 and 5.15, respectively. According to the rule of thumb for interpreting the VIF, though there is no multicollinearity (i.e., the statistics of VIF is larger than 10) in regression analysis in column (6), variables are moderately or highly correlated with each other.

  4. We retrieve the EPU data from http://www.policyuncertainty.com/scmp_monthly.html.

  5. In the heterogeneity tests, we don’t add all four network structural features into the regression simultaneously for at least two reasons. For one, the focus of this section is the heterogeneity effect of EPU on each network feature-FSI relationship. For another, the correlations between four features may contaminate the heterogeneity effects of EPU.

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Acknowledgements

The authors thank Professor Feifei Zhu (Central University of Finance and Economics) for helpful comments and suggestions.

Funding

XX. Hao is grateful to the National Social Science Foundation of China for financial support through Grant No: 20BGL036.

Author information

Authors and Affiliations

  1. College of Management and Economics, Tianjin University, Tianjin, China

    Qingmin Hao

  2. Department of Applied Economics, School of Management, Fudan University, Fudan, China

    Jim Huangnan Shen

  3. The Growth Lab, Center for International Development, Harvard Kennedy School, Harvard University, Harvard, US

    Jim Huangnan Shen

  4. School of Economics and Management, Nanchang University, Nanchang, Jiangxi, China

    Chien-Chiang Lee

  5. Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang, China

    Chien-Chiang Lee

  6. Core China Research Center, School of Economics and Business, University Of Navarra , Pamplona , Spain

    Jim Huangnan Shen

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  1. Qingmin Hao
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These authors contributed equally to this study and share first authorship.

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Correspondence to Chien-Chiang Lee.

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Hao, Q., Shen, J.H. & Lee, CC. Risk contagion of bank-firm loan network: evidence from China. Eurasian Bus Rev 13, 341–361 (2023). https://doi.org/10.1007/s40821-022-00237-w

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