Abstract
This study attempts to use GreyART network and grey model to construct a financial distress prediction model. The inputs used to train the network are the historical data containing 17 different financial ratios of 22 healthy and 5 distressed Taiwan’s listed banks. With the help of the developed performance index, this study also proposes a growing extraction method for financial variables not only to further improve the classification ability in the training and testing phases, but also to use fewer extracted variables to build the financial distress prediction model. Simulation results show that the optimal condition is the one using four extracted variables as inputs and the vigilance threshold of 0.80. Under this condition, the proposed method generates only two clusters with corresponding classification hit rates of 96.30% and 95.24% for the training and testing results, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968)
Deakin, E.B.: A discriminant analysis of predictors of business failure. J. Account. Res. 10(1), 167–179 (1972)
Blum, M.: Fail company discriminant analysis. J. Account. Res. 12(1), 1–25 (1974)
Odom, M.D., Sharda, R.: A neural network model for bankruptcy prediction. In: Proc. Int. Joint Conf. Neural Netw., vol. 2, pp. 163–168. IEEE Press, New York (1990)
Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans. Neural Netw. 12(4), 929–935 (2001)
Kumar, P.R., Ravi, V.: Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. Eur. J. Operation Res. 180, 1–28 (2007)
Tsai, C.F., Wu, J.W.: Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst. Appl. 34, 2639–2649 (2008)
Chen, W.S., Du, Y.K.: Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst. Appl. 36, 4075–4086 (2009)
Yeh, M.F., Chiang, S.S.: GreyART network for data clustering. Neurocomputing 67, 313–320 (2005)
Deng, J.L.: Introduction to grey system theory. J. Grey Syst. 1(1), 1–24 (1989)
Carpenter, G.A., Grossberg, S.: ART 2: self-organization of stable category recognition codes for analog input patterns. Appl. Optics. 26(23), 4919–4930 (1987)
Taiwan Economic Journal Data Bank, http://www.tej.com.tw/
Hsia, K.H., Wu, J.H.: A study on the data preprocessing in grey relational analysis. J. Chinese Grey Syst. 1(1), 47–54 (1998)
Li, B.Q.: Three-data modeling of grey system theory. J. Grey Syst. 2(1), 11–20 (1990)
Hamerly, G., Elkan, C.: Learning the k in k-means. In: Proc. 17th Ann. Conf. Neural Inform. Process. Syst. MIT Press, Cambridge (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yeh, MF., Chang, CT., Leu, MS. (2010). Financial Distress Prediction Model via GreyART Network and Grey Model. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-12990-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
eBook Packages: EngineeringEngineering (R0)