International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 1, No. 2, 29 Aug. 2011
Cover page and Table of Contents: PDF (size: 181KB)
Research on Financial Distress Early-warning of Listed Companies Based on GA-SVM
Full Text (PDF, 181KB), PP.1-7
Views: 0 Downloads: 0
Author(s)
LI Yun-fei
1,*
ZHANG Qian
2
Index Terms
Financial Distress, Early-Warning, Genetic Algorithm, Support Vector Machine
Abstract
Based on financial management and enterprises of early-warning theory, this paper constructs a financial distress early-warning model using GA-SVM. First, it uses listed companies appearing in Shanghai Stock Exchange and Shenzhen Stock Exchange in 2007-2009 as sample books. Defining ST listed companies which have abnormity of finance status as signature of the listed company's financial crisis. Then it uses the data in the financial statements known to the public as the input feature vector and combine genetic algorithm and support vector machine. Use Taking an empirical research with the financial distress early-warning model. Test results of the demonstration study shows the model has a superiority in predicting financial distress.
Cite This Paper
LI Yun-fei, ZHANG Qian,"Research on Financial Distress Early-warning of Listed Companies Based on GA-SVM", IJEME, vol.1, no.2, pp.1-7, 2011. 10.5815/ijeme.2011.02.01
Reference
[1]B. Back, T. Laitinen, K. Sere. Neural Networks and Genetic Algorithms for Bankruptcy Predictions[J]. Expert Systems with Applications. 1996, 11 (4): 407-413.
[2]G. Zhang, M. Y. Hu, B. E. Patuwo, D. C. Indro. Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis[J]. European Journal of Operational Research. 1999, 116: 16-32.
[3]Z. R. Yang, M. B. Platt, H. D. Platt. Probabilistic Neural Networks in Bankruptcy Prediction[J]. Journal of Business Research. 1999, 44: 67-74.
[4]P. C. Pendharkar. A Threshold Varying Artificial Neural Network Approach for Classification and Its Application to Bankruptcy Prediction Problem[J]. Computers & Operations Research. 2005, 32: 2561-2582.
[5]M. H. Shen, L. Xiao. Application of Support Vector Machine (SVM) in Pattern Recognition[J]. Telecommunication Engineering.2006,(4): 9-12.(in Chinese)
[6]ADANKON Mathias M, CHERIET Mohamed. Optimizing resources in model selection for support vector machine[J]. Pattern Recognition. 2007, 40(3): 953-963.
[7]Pei-Yi Hao, Jung-Hsien Chiang, Yi-Kun Tu. Hierarchically SVM classification based on support vector clustering method and its application to document categorization[J]. Expert Systems with Applications. 2007, 33(3): 627-635.
[8]Q. S. Zhang, L. L. Luo, J. M. Liu. A Study on Financial Distress Predicting of Listed Companies Based on SVM [J].Computer Applications, 2006,(6): 105-107. (in Chinese)
[9]Y. Q. Li, G. L. Tian. A Study on Early Warning for Listed Companies’ Financial Distress[J]. Journal of Northwest University(Philosophy and Social Sciences Edition).2009,39(5):79-83(in Chinese)