[Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609, http://dx.doi.org/10.1111%2Fj.1540-6261.1968.tb00843.x10.1111/j.1540-6261.1968.tb00843.x]Search in Google Scholar
[Altman, E. I. (1983). Multidimensional graphics and bankruptcy prediction: a comment. Journal of Accounting Research, 21(Spring), 297-299, http://dx.doi.org/10.2307%2F249095010.2307/2490950]Search in Google Scholar
[Back, B., Laitinen, T., Sere, K. & van Wezel, M. (1996a). Choosing Bankruptcy Predictors using Discriminant Analysis, Logit Analysis and Genetic Algorithms’, Technical Report no. 40, Turku Centre for Computer Science, Turku School of Economics and Business Administration.]Search in Google Scholar
[Back, B., Laitinen, T. & Sere, K. (1996b). Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications, 11(4), 407-413, http://dx.doi.org/10.1016%2FS0957-4174%2896%2900055-310.1016/S0957-4174(96)00055-3]Search in Google Scholar
[Becchetti, L. J. & Sierra (2003). Bankruptcy Risk and Productive Efficiency in Manufacturing Firms, Journal of Banking and Finance, 27, 2099-2120.10.1016/S0378-4266(02)00319-9]Search in Google Scholar
[Chen M.Y. & Du Y.K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2), 4075-4086, http://dx.doi.org/10.1016/j.eswa.2008.03.02010.1016/j.eswa.2008.03.020]Search in Google Scholar
[Chuang, C. C, Jeng, J. T. & Lin, P. T. (2004). Annealing robust radial basis function networks for function approximation with outliers, Neurocomputing, 56, 123-139, http://dx.doi.org/10.1016/S0925-2312(03)00436-310.1016/S0925-2312(03)00436-3]Search in Google Scholar
[Dambolena, I.G. & S.J. Khoury. (1980). Ratio Stability and Corporate Failure, The Journal of Finance, 35:1017-1026, http://dx.doi.org/10.1111%2Fj.1540-6261.1980.tb03517.x]Search in Google Scholar
[Gombola, M.J. & Ketz, J.E. (1983). A Note on Cash Flow and Classification Patterns of Financial Ratios. The Accounting Review, 58(1), 105-114, http://www.jstor.org/stable/246645]Search in Google Scholar
[Hassoun, M. H. (1995). Fundamentals of Artificial Neural Networks, MIT Press, Cambridge.]Search in Google Scholar
[Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. London: Prentice-Hall.]Search in Google Scholar
[Hong-Bao, W., Wang Fu-Sheng, W. & Xian-Fei, Y. (2012). Financial Distress Prediction Based on Cost Sensitive Learning.Information Technology Journal, 11(2), 294-300, http://dx.doi.org/10.3923/itj.2012.294.30010.3923/itj.2012.294.300]Search in Google Scholar
[Hertz, J., Krogh, A. & Palmer, R. G. (1991). Introduction to the Theory of Neural Computation, Addison-Wesley, Reading Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685, http://dx.doi.org/10.1109%2F21.256541]Search in Google Scholar
[Jang, J. S. R., Sun, C. T, & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, NJ.10.1109/TAC.1997.633847]Search in Google Scholar
[Jo, H. & Han, I. (1997). Bankruptcy Prediction Using Case- Based Reasoning, Neural Networks, and Discriminant Analysis. Expert Systems with Applications, 13, 97-108 http://dx.doi.org/10.1016/S0957-4174(97)00011-010.1016/S0957-4174(97)00011-0]Search in Google Scholar
[Karels, G.V. & Prakash A.J. (1987). Multivariate Normality and Forecasting of Corporate Bankruptcy. Journal of BusinessFinance and Accounting, 14 (4), 573-592, http://dx.doi.org/10.1111/j.1468-5957.1987.tb00113.x10.1111/j.1468-5957.1987.tb00113.x]Search in Google Scholar
[Kim, M. T., Han, H. R., & Phillips, L. (2003). Metric equivalence assessment in cross-cultural research: Using an example of the center for epidemiological studies-depression scale (CES-D).10.1891/jnum.11.1.5.52061]Search in Google Scholar
[Journal of Nursing Measurement, 11, 5-18 Laitinen, E. K. & Laitinen, T. (2000). Bankruptcy Prediction: Application of the Taylor’s Expansion in Logistic Regression, International Review of Financial Analysis, 9(4), 327-349, http://dx.doi.org/10.1016/S1057-5219(00)00039-910.1016/S1057-5219(00)00039-9]Search in Google Scholar
[Lin, F., Yeh, C.C. & Lee, M.Y. (2011). The use of hybrid manifold learning and support vector machines in the prediction of business failure, Knowledge-Based Systems, 24(1), 95-101, http://dx.doi.org/10.1016/j.knosys.2010.07.009,10.1016/j.knosys.2010.07.009]Search in Google Scholar
[Min, J.H. & Lee, Y.C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 28(4): 603-614, http://dx.doi.org/10.1016/j.eswa.2004.12.00810.1016/j.eswa.2004.12.008]Search in Google Scholar
[Nam, M. D. & Thanh, T. C. (2003). Approximation of function and its derivatives using radial basis function networks. Applied Mathematical Modeling, 27(3), 197-221. http://dx.doi.org/10.1016/j.eswa.2009.07.08110.1016/j.eswa.2009.07.081]Search in Google Scholar
[Ohlson, J. (1980).Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131, http://www.jstor.org/stable/249039510.2307/2490395]Search in Google Scholar
[Ravisankar, P. & Ravi, V. (2010). Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP. Knowledge-Based Systems, 23(8), 823-831, http://dx.doi.org/10.1016/j.knosys.2010.05.00710.1016/j.knosys.2010.05.007]Search in Google Scholar
[Scott, J. (1981). The probability of bankruptcy: A comparison of empirical prediction and theoretical model. Journal of Bankingand Finance, 5, 317-344, http://dx.doi.org/10.1016/0378-4266(81)90029-710.1016/0378-4266(81)90029-7]Search in Google Scholar
[Shin, K., Lee, T.S. & Kim, H. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systemswith Applications, 28: 127-135. http://dx.doi.org/10.1016/j.eswa.2004.08.00910.1016/j.eswa.2004.08.009]Search in Google Scholar
[Song et al. (2010). Feature selection for support vector machine in financial crisis prediction: a case study in China. ExpertSystems, 27(4), 299-310, http://dx.doi.org/10.1111/j.1468-0394.2010.00546.x10.1111/j.1468-0394.2010.00546.x]Search in Google Scholar
[Stam, A., Sun, M. & Haines, M. (1996). Artificial neural network representations for hierarchical preference structures. Computersand Operations Research, 23(12), 1191-1201, http://dx.doi.org/10.1016/S0305-0548(96)00021-410.1016/S0305-0548(96)00021-4]Search in Google Scholar
[Stiglitz, J. E., & Weiss A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(June), 393-410. http://www.jstor.org/stable/1802787]Search in Google Scholar
[Sun, J., & Li, H. (2008). Data mining method for listed companies’ financial distress prediction. Knowledge-Based Systems, 21(1), 1-5, http://dx.doi.org/10.1016/j.knosys.2006.11.003,10.1016/j.knosys.2006.11.003]Search in Google Scholar
[Surendra, R. & Krishnamurthy, A. (1997). Face recognition using transform features and neural networks, Neural Networks, 30(10), 1615-1622. http://dx.doi.org/10.1016/S0031-3203(96)00184-710.1016/S0031-3203(96)00184-7]Search in Google Scholar
[Tam, K. Y. & Kiang, M. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions, ManagementScience, 38(7), 926-947.10.1287/mnsc.38.7.926]Search in Google Scholar
[Vapnik V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York.10.1007/978-1-4757-2440-0]Search in Google Scholar
[Vythoulkas, P. C. & Koutsopoulos, H. N. (2003). Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks. TransportationResearch, 11, 51-73, http://dx.doi.org/10.1016%2FS0968-090X%2802%2900021-910.1016/S0968-090X(02)00021-9]Search in Google Scholar
[Wilson, R. L. & Sharda, R. (1994). Bankruptcy Prediction Using Neural Networks, Decision Support Systems, 11, 545-557. http://dx.doi.org/10.1016/0167-9236(94)90024-810.1016/0167-9236(94)90024-8]Search in Google Scholar
[Wong B. K., Bodnovich T.A. & Selvi Y. (1997). Neural network applications in business: a review and analysis of the literature (1988-95). Decision Support Systems, 19, 301-320, http://www.jstor.org/stable/25404710.1016/S0167-9236(96)00070-X]Search in Google Scholar
[Wong, B. & Selvi, Y. (1998). Neural network applications in finance: A review and analysis of literature (1990-1996), Information& Management, 34, 129-139. http://dx.doi.org/10.1016/S0378-7206(98)00050-010.1016/S0378-7206(98)00050-0]Search in Google Scholar
[Xu, B. (2000). The welfare implications of costly monitoring in the credit market: A note. The Economic Journal, 110(463), 576-580, http://dx.doi.org/10.1111%2F1468-0297.0053810.1111/1468-0297.00538]Search in Google Scholar
[Zmijewski, M. E. (1984). Methodological issues related to the estimated of financial distress prediction models. Journal ofAccounting Research, 22(1), 59-82, http://www.jstor.org/stable/2490859 10.2307/2490859]Search in Google Scholar