Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks

Bankruptcy prediction is one of the major business classification problems. In this paper, we use four different techniques (1) logit model, (2) quadratic interval logit model, (3) backpropagation multi-layer perceptron (i.e., MLP), and (4) radial basis function network (i.e., RBFN) to predict bankrupt and non-bankrupt firms in England. The average hit ratio of four methods range from 91.15% to 77.05%. The original classification accuracy and the validation test results indicate that RBFN outperforms the other models.

[1]  J. Watada,et al.  Possibilistic linear systems and their application to the linear regression model , 1988 .

[2]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[3]  J. Stiglitz,et al.  Credit Rationing in Markets with Imperfect Information , 1981 .

[4]  Minghe Sun,et al.  Artificial neural network representations for hierarchical preference structures , 1996, Comput. Oper. Res..

[5]  Gordon V. Karels,et al.  Multivariate Normality and Forecasting of Business Bankruptcy , 1987 .

[6]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[7]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[8]  Lin Lin,et al.  Identification of corporate distress in UK industrials: a conditional probability analysis approach , 2004 .

[9]  Fang-Mei Tseng,et al.  A quadratic interval logit model for forecasting bankruptcy , 2005 .

[10]  Marc Blum.  FAILING COMPANY DISCRIMINANT-ANALYSIS , 1974 .

[11]  Haris N. Koutsopoulos,et al.  Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks , 2003 .

[12]  Kaisa Sere,et al.  Neural networks and genetic algorithms for bankruptcy predictions , 1996 .

[13]  Kar Yan Tam,et al.  A Comparative Analysis of Inductive-Learning Algorithms , 1993 .

[14]  Nam Mai-Duy,et al.  Approximation of function and its derivatives using radial basis function networks , 2003 .

[15]  Joseph E. Stiglitz,et al.  Some Aspects of the Pure Theory of Corporate Finance: Bankruptcies and Take-Overs: Reply , 1972 .

[16]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  I. G. Dambolena,et al.  Ratio Stability and Corporate Failure , 1980 .

[18]  Surendra Ranganath,et al.  Face recognition using transform features and neural networks , 1997, Pattern Recognit..

[19]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[20]  Kyung-shik Shin,et al.  A genetic algorithm application in bankruptcy prediction modeling , 2002, Expert Syst. Appl..

[21]  Krishna G. Palepu,et al.  Predicting takeover targets: A methodological and empirical analysis , 1986 .

[22]  Jin-Tsong Jeng,et al.  Annealing robust radial basis function networks for function approximation with outliers , 2004, Neurocomputing.

[23]  Ingoo Han,et al.  The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms , 2003, Expert Syst. Appl..

[24]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[25]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[26]  E. Altman.  Corporate financial distress : a complete guide to predicting, avoiding, and dealing with bankruptcy , 1983 .

[27]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[28]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[29]  Frederick M. Richardson,et al.  AN EXPLORATION INTO BANKRUPTCY DISCRIMINANT MODEL SENSITIVITY , 1983 .

[30]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[31]  E. Altman.  A Further Empirical Investigation of the Bankruptcy Cost Question , 1984 .

[32]  Ingoo Han,et al.  Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis , 1997 .

[33]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[34]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[35]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[36]  Ali Zilouchian,et al.  Adaptive receptive fields for radial basis functions , 2001 .

[37]  Ingoo Han,et al.  Integration of Case-based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction , 1996 .

[38]  C. Zavgren.  ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS , 1985 .

[39]  L. Becchetti,et al.  Bankruptcy risk and productive efficiency in manufacturing firms , 2003 .

[40]  Hideo Tanaka,et al.  Interval regression analysis by quadratic programming approach , 1998, IEEE Trans. Fuzzy Syst..

[41]  Witold Pedrycz,et al.  Evaluation of fuzzy linear regression models , 1991 .

[42]  Joseph E. Stiglitz,et al.  Credit Rationing: Reply , 1987 .

[43]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[44]  Bin Xu.  The Welfare Implications of Costly Monitoring in the Credit Market: A Note , 2000 .

[45]  Thomas G. Dietterich,et al.  Improving the Performance of Radial Basis Function Networks by Learning Center Locations , 1991, NIPS.

[46]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[47]  Jatinder N. D. Gupta,et al.  Neural networks in business: techniques and applications for the operations researcher , 2000, Comput. Oper. Res..

[48]  L. Becchetti,et al.  Bankruptcy Risk and Productive Efficiency in Manufacturing Firms , 2003 .

[49]  James H. Scott.  The probability of bankruptcy: A comparison of empirical predictions and theoretical models , 1981 .

[50]  Gwo-Hshiung Tzeng,et al.  A fuzzy seasonal ARIMA model for forecasting , 2002, Fuzzy Sets Syst..

[51]  Jianguo Sun,et al.  Two-grade fuzzy synthetic decision-making system with use of an analytic hierarchy process for performance evaluation of grinding fluids , 2001 .

[52]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[53]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..