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Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm

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  • Jiannan Yu
  • Jinlou Zhao

Abstract

It is well known that the interbank market is able to effectively provide financial liquidity for the entire banking system and maintain the stability of the financial market. In this paper, we develop an innovative complex network approach to simulate an interbank network with systemic risk contagion that takes into account the balance sheet of each bank, from which we can identify if the financial institutions have sufficient capital reserves to prevent risk contagion. Cascading defaults are also generated in the simulation according to different crisis-triggering (targeted defaults) methods. We also use machine learning techniques to identify the synthetic features of the network. Our analysis shows that the topological factors and market factors in the interbank network have significant impacts on the risk spreading. Overall, this paper provides a scientific method for policy-makers to select the optimal management policy for handling systemic risk.

Suggested Citation

  • Jiannan Yu & Jinlou Zhao, 2020. "Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm," Complexity, Hindawi, vol. 2020, pages 1-13, August.
    • Handle: RePEc:hin:complx:6035372
    DOI: 10.1155/2020/6035372
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    Cited by:

    1. Jorge Omar Razo-De-Anda & Luis Lorenzo Romero-Castro & Francisco Venegas-Martínez, 2023. "Contagion Patterns Classification in Stock Indices: A Functional Clustering Analysis Using Decision Trees," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    2. Huaming Du & Xingyan Chen & Yu Zhao & Qing Li & Fuzhen Zhuang & Fuji Ren & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised Mar 2025.
    3. Xi, Xian & Gao, Xiangyun & Zhou, Jinsheng & Zheng, Huiling & Ding, Jiazheng & Si, Jingjian, 2021. "Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels," International Review of Financial Analysis, Elsevier, vol. 76(C).
    4. Mónica Andrea Arauco Ballesteros & Elio Agustín Martínez Miranda, 2025. "Stock Market Forecasting Using a Neural Network Through Fundamental Indicators, Technical Indicators and Market Sentiment Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1715-1745, August.
    5. R. Giacometti & G. Torri & G. Farina & M. E. Giuli, 2020. "Risk attribution and interconnectedness in the EU via CDS data," Computational Management Science, Springer, vol. 17(4), pages 549-567, December.
    6. Chen, Ren-Raw & Zhang, Xiaohu, 2024. "From liquidity risk to systemic risk: A use of knowledge graph," Journal of Financial Stability, Elsevier, vol. 70(C).

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