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Mathematical Problems in Engineering
Volume 2013, Article ID 136030, 5 pages
Research Article

Prediction of Banking Systemic Risk Based on Support Vector Machine

School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China

Received 1 February 2013; Accepted 22 April 2013

Academic Editor: Wei-Chiang Hong

Copyright © 2013 Shouwei 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.


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.