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.
Financial crises occurred over the past decade have shown that banking crises are usually in the core position of financial crises. Therefore, banking sectors’ stability is a key to maintain financial stability. However, the major threat to banking sectors is systemic risk, because its contagion effect makes a single bank crisis evolve into the whole banking crisis, which may cause a financial crisis. The 2007–2009 financial crisis has threatened the stability of the international monetary market, shed light on the importance of systemic risk, and revealed the necessity of predicting one in order to safeguard financial stability.
The prediction of banking systemic crisis has been done since the mid-1990s. Frankel and Rose [
In fact, banking systemic risk is a complex nonlinear phenomenon, which originates from diversity and uncertainty of risk sources, multiple contagion channels and their relationships as well as complexity and evolution of banking system structures [
In this paper, we attempt to develop an SVM-based predictive model for banking systemic risk. Our paper is organized as follows. Section
Based on statistical learning theory, Vapnik [
Consider the training dataset
In this section, we take the Chinese banking system as an example to construct an SVM-based prediction model for banking systemic risk and conduct empirical analysis based on the data of the Chinese banking system.
Microprudential indicators cannot provide a systemic perspective on banking systemic risk, while macro-prudential indicators cannot provide distress warnings from individual banks. Recent financial crises show that interbank connections play an important role in propagation mechanisms of banking systemic risk. Thus, the prediction indicators for banking systemic risk should incorporate both Microprudential and macro-prudential perspectives, as well as the characteristics of interbank connections. Based on the relative studies [
List of prediction indicators for banking systemic risk.
Indicator | Safety | Mild safety | Mild unsafety | Unsafety |
---|---|---|---|---|
Capital adequacy ratio |
|
|
|
|
Nonperforming loan ratio |
|
|
|
|
Proportion of a single maximum loan |
|
|
|
|
Return on assets |
|
|
|
|
Cost-to-income ratio |
|
|
|
|
Liquidity ratio |
|
|
|
|
Loan-to-deposit ratio |
|
|
|
|
Leverage |
|
|
|
|
Interdependence |
|
|
|
|
External linkages |
|
|
|
|
Year-on-year growth of GDP |
|
|
|
|
Year-on-year growth of CPI |
|
|
|
|
Year-on-year growth of fixed asset investment |
|
|
|
|
Year-on-year growth of national real estate index |
|
|
|
|
Volatility of the Shanghai index |
|
|
|
|
Ratio of growth of M2 to growth of GDP |
|
|
|
|
Volatility of benchmark lending rates |
|
|
|
|
In light of the lack of data on levels of banking systemic risk in China, we adopt the principal component analysis to classify levels of banking systemic risk. The basic idea is as follows: constructing three critical value samples based on data of prediction indicators processed chemotactically in Table
Classification result of levels of banking systemic risk from the first quarter of 2004 to the third quarter of 2012. In these Figures
In this section, we analyze the prediction accuracy of SVM and also compare its performance with those of back-propagation neural network (BPNN), multiple discriminant analysis (MDA), and logistic regression analysis (Logit). We take the data of prediction indicators from the first quarter of 2004 to the second quarter of 2008 as the training sample and the rest of the data as the testing sample. At the same time, we construct a safety sample based on critical values of the safety condition into the training sample, because of the lack of safety samples in the training sample. Therefore, the size of the training sample is equal to 19, and the sizes of the testing sample is equal to 17. In case of BPNN, the rest data is split into the validation sample and the testing sample, and the size of the validation sample and the testing sample are 4 and 13, respectively.
Construction of prediction models for banking systemic risk based on SVM is to choose the kernel function and the values of model parameters. In this paper, the radial basis function (RBF) is used as the basic kernel function of SVM, because it usually gets better results than other kernel functions [
According to the above method, we first analyze prediction accuracy of prediction models for banking systemic risk based on SVM. Based on the above data, we can obtain values of optimal parameters
The prediction accuracy of SVM.
Grid Search | Genetic Algorithms | Particle Swarm Optimization | |
---|---|---|---|
Training sample | 100% | 100% | 100% |
Testing sample | 94.12% | 88.24% | 88.24% |
Contour lines of Grid Search. Best
Fitness curve of Genetic Algorithms (termination of generation = 100, pop = 20). Best
Fitness curve of Particle Swarm Optimization (termination of generation = 100, pop = 20,
Back-propagation neural network (BPNN), multiple discriminant analysis (MDA), and logistic regression analysis (Logit) are widely applied methods for prediction. Therefore, in this paper, we compare the prediction accuracy of SVM with those of BPNN, MDA, and Logit. In case of BPNN, a three-layer fully connected back-propagation neural network is used as a benchmark; 5, 10, 15, 20, 25, and 30 hidden nodes in the hidden layer are analyzed; the maximum number of learning epochs is set to 1000; the learning rate is set to 0.01; the momentum term is set to 0.95; the activation function of the hidden layer and the output layer are, respectively, Tansig and Purelin; the training function is Trainlm. According to parameter adjustment, we can obtain that the best prediction accuracy of the testing sample is found when the number of hidden nodes is 20. The prediction accuracy of the testing sample, the training sample, and the validation sample is 84.62%, 100%, and 100%, respectively.
Table
The best prediction accuracy of SVM, BPNN, MDA, and Logit.
SVM | BPNN | MDA | Logit | |
---|---|---|---|---|
Training sample | 100% | 100% | 94.74% | 100% |
Testing sample | 94.12% | 84.62% | 76.47% | 76.47% |
McNemar values (
BPNN | MDA | Logit | |
---|---|---|---|
SVM | 3.37 (0.064) | 11.12 (0.001) | 10.32 (0.001) |
BPNN | 1.53 (0.175) | 1.29 (0.122) | |
MDA | 0.042 (1.000) |
In this paper, we apply SVM to predict banking systemic risk. To validate the prediction performance of this approach, we conduct a case study of an SVM-based prediction model for Chinese banking systemic risk. First, we construct the prediction indicators for banking systemic risk from both Microprudential and macro-prudential perspectives, as well as the characteristics of interbank connections. Second, we adopt the principal component analysis to classify levels of banking systemic risk. Based on the above analysis, we conduct empirical analysis of prediction accuracy of the SVM-based prediction model for banking systemic risk. The results of empirical analysis show the capability, accuracy, and high efficiency of the SVM-based prediction model. In addition, compared with the BPNN-based prediction model, multiple discriminant analysis, and logistic regression analysis, the SVM-based prediction model shows superior prediction power. With these results, we claim that SVM can serve as a promising alternative in the prediction of banking systemic risk.
This research is supported by NSFC (no. 71071034, no. 71201023), NBRR (no. 2010CB328104-02), and Humanities and Social Science Youth Foundation of the Ministry of Education of China (no. 12YJC630101).