TITLE:
The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine
AUTHORS:
Guicheng Shen, Weiying Jia
KEYWORDS:
Financial Crisis, Principal Component Analysis, Support Vector Machine, Kernel Function, Prediction Precision
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.2 No.9,
August
27,
2014
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.