Forecasting classification of operating performance of enterprises by ZSCORE combining ANFIS and genetic algorithm

Original Article


Classification of operating performance of the enterprises is not only a hot issue emphasized by the management, but it is an important reference for investors too in their decision-making. Generally speaking, when predicting or analyzing business performance classification, most researchers adopt corporate financial early warning or credit-rating models, which pretty much use previous data and facts. Therefore, this paper brings about an alternative method to discriminate between excellent and poor business management, so as to take preventive measures prior to business crisis or bankruptcy. We collected the financial reports and financial ratios from the listed firms in mainland China and Taiwan as our samples to build up four kinds of forecasting models for business performance. The empirical results show that the hybrid model provides better classification forecasting capability than the other models, while the ANFIS model adjusted by genetic algorithm could effectively enhance the classification forecasting capability.


ANFIS Genetic algorithm Grey relational analysis ZSCORE Hybrid model 


  1. 1.
    Beaver WH (1996) Financial ratios as predictors of failure. J Account Res 4:72–102Google Scholar
  2. 2.
    Tam KY, Kiang MY (1992) Managerial application of neural networks: the case of bank failure predictions. Manage Sci 38(7):926–947. doi: 10.1287/mnsc.38.7.926 MATHCrossRefGoogle Scholar
  3. 3.
    Odom MD, Sharda R (1990) A neural network model for bankruptcy prediction. IEEE INNS IJCNN 2:163–168Google Scholar
  4. 4.
    Chang TC (2003) Probability of default and credit rating model, quarterly publication of Taiwan Academy Banking and Finance, vol 4, no 1Google Scholar
  5. 5.
    Yen CH, Tsai MC (2006) The establishment of credit rating model of domestic banks by applying neural network—analyzed by CAMELS, conference on innovation of industry management, fourth termGoogle Scholar
  6. 6.
    Altman EI (1968) Financial ratios discriminate analysis and the prediction of corporate bankruptcy. J Finance 123(4):589–609CrossRefGoogle Scholar
  7. 7.
    Altman EI (2002) Revisiting credit scoring models in a Basel 2 environment. In: Ong M (ed) Credit ratings, methodologies, rationale and default risk. Risk Books, London. ISBN-10: 1-899332-69-3 and ISBN-13: 978-1-899332-69-4Google Scholar
  8. 8.
    Pai PF, Lin CS (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33:497–505. doi: 10.1016/ CrossRefGoogle Scholar
  9. 9.
    Tong LL, Shih BC (2001) Predict the financial crisis by using grey relation analysis, neural network, and case-based reasoning, Chinese. Manage Rev 4(2):25–37Google Scholar
  10. 10.
    Deng J (1982) The control problems of grey system. Syst Contr Lett 5:288–294Google Scholar
  11. 11.
    Wen KL, Chang Chien SK, Yeah CK, Wang CW, Lin HS (2006) Apply matlab in grey system theory. Chuan Hwa Book Co. Ltd, Taiwan. ISBN: 975-21-5278-5Google Scholar
  12. 12.
    Lin SY (2004) Evaluation of business reputation in information service industry—an application of grey relational analysis. J Inform Technol Soc 2:79Google Scholar
  13. 13.
    Holland J (1992) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  14. 14.
    Wu Li S, Lin CH (2002) Matlab Auxiliary Fuzzy System. Xidian University Publication, ChinaGoogle Scholar
  15. 15.
    Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159. doi: 10.1016/S0031-3203(96)00142-2 CrossRefGoogle Scholar
  16. 16.
    Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve to multiple class classification problems. Mach Learn 45(2):171–186. doi: 10.1023/A:1010920819831 MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.TaipeiTaiwan, ROC
  2. 2.Department of FinanceJinwen University of Science and TechnologyTaipeiTaiwan, ROC

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