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Prediction of Banking Systemic Risk Based on Support Vector Machine

Author

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  • Shouwei Li
  • Mingliang Wang
  • Jianmin He

Abstract

Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM) to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

Suggested Citation

  • Shouwei Li & Mingliang Wang & Jianmin He, 2013. "Prediction of Banking Systemic Risk Based on Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-5, May.
    • Handle: RePEc:hin:jnlmpe:136030
    DOI: 10.1155/2013/136030
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    Cited by:

    1. Le, Richard & Ku, Hyejin, 2022. "Reducing systemic risk in a multi-layer network using reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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