The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine

Guicheng Shen^*, Weiying Jia

School of Information, Beijing Wuzi University, Beijing, China.

DOI: 10.4236/jss.2014.29035                

Abstract

This paper studies financial crisis of listed companies in China Manufacture Industry, and selects 181 companies with financial crisis and 181 normal companies as its research samples, and its research is based on financial indexes three years before the financial crisis happens. Firstly the method of principle component analysis is used to abstract useful information from the training data. Secondly a prediction model of financial crisis is constructed with the method of Support Vector Machine and the accuracy of the model is 78.73% on the training data and the 79.79% on the testing data. Thirdly the advantages of this model are discussed over the other prediction models. Finally the research results show that this model uses the least number of input variables and has the highest prediction accuracy, thus this model can provide the useful information to investors, creditors, financial regulators and etc.

Keywords

Financial Crisis, Principal Component Analysis, Support Vector Machine, Kernel Function, Prediction Precision

Conflicts of Interest

The authors declare no conflicts of interest.

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