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
Cite this paper
Shen, G. and Jia, W. (2014) The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine. Open Journal of Social Sciences
, 204-212. doi: 10.4236/jss.2014.29035
 Fitzpatrick, F. (1932) A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firm. Certified Public Accountant, 6, 727-731.
 Beaver, W. (1966) Financial Ratios as Predictors of Failure. Supplement to Journal of Accounting Research, 4, 71-111.
 Altman, E. (1968) Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 9, 589-609. http://dx.doi.org/10.1111/j.1540-6261.1968.tb00843.x
 Ohlson, J. (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18, 109-131. http://dx.doi.org/10.2307/2490395
 Odom, M. and Sharda, R. (1990) A Neural Network for Bankruptcy Prediction. International Joint Conference on Neural Networks, 2, 163-168.
 Min, J.H. and Lee, Y.C. (2005) Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters. Expert Systems with Applications, 28, 603-614. http://dx.doi.org/10.1016/j.eswa.2004.12.008
 Wu, S.N. and Huang, S.Z. (1986) Analysis Indexes and Prediction Model of business failures. Economic Issues in China, 6, 15-22.
 Zhou, S.H., Yang, J.H. and Wang, P. (1996) Prediction Analysis of Financial Crisis—F Fraction Mode. Accounting Research, 8, 8-11.
 Chen, J. (1999) Empirical Analysis of Listed Company Financial Deterioration Prediction. Accounting Research, 4, 31- 38.
 Wu, S.N. and Lu, X.Y. (2001) A Study of Models for Predicting Financial Distress in China’s Listed Companies. Economic Research Journal, 6, 46-56.
 Yang, S. and Huang, L. (2005) Financial Crisis Warning Model Based on BP Neural Network. System Engineering- Theory & Practice, 1, 12-19.
 Huang, X. and Zhou, A.Q. (2009) A Study of Combination Warning of Enterprise Financial Crisis Based on BP Neural Network. Research on Economics and Management, 5, P87-P91
 Vapnik, V. and Lerner, A. (1963) A Pattern Recognition Using Generalized Portrait. Automation and Remote Control, 24, 6.
 Kimeldorf, G. and Wahba, G. (1971) Some Results on Tchebycheffian Spline Functions. Journal of Mathematical Analysis and Applications, 33, 85-95.
 Vapnik, V.N. (1999) The Nature of Statistical Learning Theory. Springer-Verlag, New York.