Abstract
Because of the importance of companies’ financial distress prediction, this paper applies support vector machine (SVM) to the early-warning of financial distress. Taking listed companies’ three-year data before special treatment (ST) as sample data, adopting cross-validation and grid-search technique to find SVM model’s good parameters, an empirical study is carried out. By comparing the experiment result of SVM with Fisher discriminant analysis, Logistic regression and back propagation neural networks (BP-NNs), it is concluded that financial distress early-warning model based on SVM obtains a better balance among fitting ability, generalization ability and model stability than the other models.
Sponsored by National Natural Science Foundation of China (No. 70573030).
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gestel, T.V., Baesens, B., Suykens, J.A.: Bayesian Kernel Based Classification for Financial Distress Detection. European Journal of Operational Research 1, 1–2 (2005)
Beaver, W.: Financial Ratios as Predictors of Failure. Journal of Accounting Research, 71–111 (1966)
Altman, E.I.: Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23, 589–609 (1968)
Ohlson, J.A.: Financial Ratios and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18, 109–131 (1980)
Odom, M., Sharda, R.: A Neural Networks Model for Bankruptcy Prediction. In: Proceedings of the IEEE International Conference on Neural Network, pp. 163–168 (1990)
Fletcher, D., Goss, E.: Forecasting with Neural Networks: an Application Using Bankruptcy Data. Information and Management 24, 159–167 (1993)
Serrano-Cinca, C.: Self Organizing Neural Networks for Financial Diagnosis. Decision Support Systems 17, 227–238 (1996)
Parag, C.P.: A Threshold Varying Artificial Neural Network Approach for Classification and Its Application to Bankruptcy Prediction Problem. Computers & Operations Research 32, 2561–2582 (2005)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, England (2000)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Shin, K.-S., Lee, T.S., Kim, H.-J.: An Application of Support Vector Machines in Bankruptcy Prediction Model. Expert Systems with Applications 28, 127–135 (2005)
Min, J.H., Lee, Y.-C.: Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters. Expert Systems with Applications 28, 128–134 (2005)
Kim, K.J.: Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing 55, 307–319 (2003)
Tay, F.E.H., Cao, L.: Application of Support Vector Machines in Financial Time Series Forecasting. Omega 29, 309–317 (2001)
Hsu C.-W., Chang C.-C., Lin C.-J.: A Practical Guide to Support Vector Classification. Technical Report, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hui, XF., Sun, J. (2006). An Application of Support Vector Machine to Companies’ Financial Distress Prediction. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_27
Download citation
DOI: https://doi.org/10.1007/11681960_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32780-6
Online ISBN: 978-3-540-32781-3
eBook Packages: Computer ScienceComputer Science (R0)