Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review

European Journal of Operational Research

Volumn 180, Issue 1, 2007, Pages 1-28

  Ravi Kumar, P ^a   Ravi, V ^a  

^a INSTITUTE FOR DEVELOPMENT AND RESEARCH IN BANKING TECHNOLOGY   (India)

Author keywords

Bankruptcy prediction; Banks; Case based reasoning; Decision trees; Evolutionary approaches; Firms; Fuzzy logic; Intelligent techniques; Neural networks; Operations research; Rough sets; Statistics; Support vector machine and soft computing

Indexed keywords

FORECASTING; FUZZY SETS; LOGIC PROGRAMMING; NEURAL NETWORKS; OPERATIONS RESEARCH; SOCIETIES AND INSTITUTIONS; STATISTICS;

BANKRUPTCY PREDICTION; BANKS; CASE-BASED REASONING; DECISION TREES; EVOLUTIONARY APPROACHES; FIRMS; INTELLIGENT TECHNIQUES; ROUGH SETS; SOFT COMPUTING; SUPPORT VECTOR MACHINES;

FINANCIAL DATA PROCESSING;

EID: 33846314346     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2006.08.043     Document Type: Article

References (128)

1. Ahn B.A., Cho S.S., and Kim C.Y. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications 18 (2000) 65-74
2. Alam P., Booth D., Lee K., and Thordarson T. The use of fuzzy clustering algorithm and self-organizing neural network for identifying potentially failing banks: An experiment study. Expert Systems with Applications 18 (2000) 185-199
3. Altman E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (1968) 589-609
4. Altman E.I., Marco G., and Varetto F. corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance 18 (1994) 505-529
5. Altman E.I., Haldeman R.G., and Narayanan P. ZETA ANALYSIS, a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 1 (1977) 29-54
6. Andres J.D., Landajo M., and Lorca P. Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case. European Journal of Operational Research 167 (2005) 518-542
7. Atiya A.F. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks 12 4 (2001) 929-935
8. Back B., Laitinen T., and Sere K. Neural network and genetic algorithm for bankruptcy prediction. Expert Systems with Applications 11 4 (1996) 407-413
9. J. Baek and S. Cho, Bankruptcy Prediction for credit risk using an auto associative neural networks in Korean firms, in: IEEE International Conference on Computational Intelligence for Financial Engineering, Hong-Kong, 2003.
10. Banks W.J., and Prakash L.A. On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem. European Journal of Operational Research 74 23 (1994) 23-28
11. Barniv R., Anurag A., and Leach R. Predicting the out come following bankruptcy filing: A three state classification using NN. International Journal of Intelligent Systems in Accounting, Finance and Management 6 (1997) 177-194
12. Bell T.B. Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures. International Journal of Intelligent Systems in Accounting, Finance and Management 6 (1997) 249-264
13. H. Bian, L. Mazlack, Fuzzy-rough nearest neighbour classification approach, in: 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS 2003) Proceedings Chicago, 2003, pp. 500-505.
14. J.C. Bioch, V. Popova, Bankruptcy prediction with rough sets, ERIM Report Series Research in Management (ERS-2001-11-LIS), 2001.
15. Brewer E., Jackson W., and Moser J. Alligators in the Swamp: The impact of derivatives on the financial performance of depository institutions. Journal of Money, Credit and Banking 28 (1996) 478-496
16. Bryant S.M. A case-based reasoning approach to bankruptcy prediction modeling. Intelligent Systems in Accounting, Finance and Management 6 (1997) 195-214
17. Calderon T.G., and Cheh J.J. A Roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems 3 (2002) 203-236
18. Canbas S., Cabuk A., and Kilic S.B. Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case. European Journal of Operational Research 166 (2005) 528-546
19. Cielen A., Peeters L., and Vanhoof K. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154 (2004) 526-532
20. Cole R., and Gunther J. A CAMEL rating's shelf life. Federal Reserve Bank of Dallas Review (1995) 13-20
21. Cooper C.A., and Rhodes E.W.W. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (1978) 429-444
22. Cristiamini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge university press, Cambridge, England
23. M. Daubie, N. Meskens, Business failure prediction: A review and analysis of the literature. Working Paper, Department of Productions and Operations Management, Catholic University of Mons, Belgium, 2002, pp. 1-15.
24. Dietrich J.R., and Kaplan R.S. Empirical analysis of the loan classification decision. The Accounting Review 57 (1982) 18-38
25. Dimitras A.I., Zanakis S.H., and Zopounidis C. A survey of business failure with an emphasis on prediction method and industrial applications. European Journal of Operational Research 90 (1996) 487-513
26. Dimitras A.I., Slowinski R., Susmaga R., and Zopounidis C. Business failure prediction using rough sets. European Journal of Operational Research 114 (1999) 263-280
27. Elhadi M.T. Bankruptcy support system: Taking advantage of information retrieval and case-based reasoning. Export Systems with Applications 18 (2000) 215-219
28. Fahlman S.E., and Lebiere C. The cascade-correlation learning architecture. Advances in Neural Information Processing Systems (1990), Morgan Kaufmann, San Mated, CA 524-532
29. Fletcher D., and Goss E. Application forecasting with neural networks an application using bankruptcy data. Information and Management 24 (1993) 159-167
30. Fraser D. The determinants of bank profits: An analysis of extremes. Financial Review 11 (1976) 69-87
31. Frydman H., Altman E.I., and Kao D. Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance 40 1 (1985) 269-291
32. R. Gady, Anatomy of profitable medium-size banks in the fourth district, 1966-1970, Economic Review, Federal Reserve Bank of Cleveland, 1972, pp. 20-32.
33. J. Gehrke, R. Ramakrishnan, W.Y. Loh, BOAT-optimistic decision tree construction, in: Proceedings ACM SIGMOD International Conference Management of Data, Philadelphia, PA, 1999, pp. 169-180.
34. Gentry J., Newbold A., and Whitford D. Classifying bankrupt firms with funds flow components. Journal of Accounting Research 23 11(1) (1985) 146-160
35. Gersho A., and Gray R.M. Vector Quantization and Signal Compression (1992), Kluwer, Norwell, MA
36. Gorzalczany M.B., and Pista Z. Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support. Information Science 120 (1999) 45-68
37. Greco S., Matarazzo B., and Slowinski R. A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis C. (Ed). Operational Tools in the Management of Financial Risks (1998), Kluwer Academic Publishers, Dordrecht 121-136
38. Greco S., Matarazzo B., and Slowinski R. A new rough set approach to multicriteria and multiattribute classification. Rough Sets and Current Trends in Computing, Berlin (1998) 60-67
39. Greco S., Matarazzo B., and Slowinski R. Decision rule approach. In: Figueira J., Greco S., and Ehrgott M. (Eds). Multiple Criteria Decision Analysis: State of the Art Surveys (2005), Springer-Verlag, New York 507-562 (Chapter 13)
40. Greco S., Matarazzo B., and Slowinski R. Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research 138 2 (2002) 247-259
41. Greco S., Matarazzo B., and Slowinski R. Rough approximation by dominance relations. International Journal of Intelligent Systems 17 2 (2002) 153-171
42. Greco S., Matarazzo B., and Slowinski R. Multicriteria classification. In: Kloesgen W., and Zytkow J. (Eds). Handbook of Data Mining and Knowledge Discovery (2002), Oxford University Press, New York 318-328 (Chapter 16.1.9)
43. Greco S., Matarazzo B., and Slowinski R. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129 1 (2001) 1-47
44. J. Haslem, A. Scheraga, C.A. Bedingfield, P. James, An Analysis of the Foreign and Domestic Balance Sheet Strategies of the U.S. Banks and Their Association to Profitability Performance, Management International Review. First Quarter, Wiesbaden, 1992.
45. Ignizio J.P., and Soltyas J.R. Simultaneous design and training of ontogenic neural network classifier. Computers Operations Research 23 6 (1996) 535-546
46. Jeng B., Jeng Y.-M., and Liang T.-P. FILM: A fuzzy learning method for automated knowledge acquisition. Decision Support Systems 21 (1997) 61-73
47. Jo H., Han I., and lee H. Bankruptcy prediction using case-based reasoning, neural network and discriminant analysis for bankruptcy prediction. Expert Systems with Applications 13 2 (1997) 97-108
48. Jo H., and Han I. Integration of case-based forecasting, neural network and discriminant analysis for bankruptcy prediction. Expert Systems with Applications 11 4 (1996) 415-422
49. Joachimsthaler E.A., and Stam A. Four approaches to the classification problem in discriminant analysis: An experimental study. Decision Sciences 19 (1988) 322-333
50. Johnson R.A., and Wichern D.W. Applied Multivariate Statistical Analysis. second ed. (1988), Prentice-Hall, Englewood Cliffs, NJ
51. Jones S., and Hensher D.A. Predicting firm financial distress: A mixed logit model. Accounting Review 79 4 (2004) 1011-1038
52. Kao C., and Liu S.-T. Prediction bank performance with financial forecasts: A case of Taiwan commercial banks. Journal of Banking & Finance 28 (2004) 2353-2368
53. Karels G.V., and Prakash A.J. Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance and Accounting 14 4 (1987)
54. Kaski S., Sinkkonen J., and Peltonen J. Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks 12 4 (2001)
55. Kiviluoto K. Predicting bankruptcies with self organizing map. Neurocomputing 21 (1998) 191-201
56. Kohonen T. Self-Organizing Maps. second ed. (1997), Springer-Verlag, Berlin
57. Kolari J., Glennon D., Shin H., and Caputo M. Predicting large US commercial bank failures. Journal of Economics and Business 54 32 1 (2002) 361-387
58. Kolodner J.L. Case Based Reasoning (1993), Morgan Kaufman, CA
59. Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection (1992), Massachusetts Institute of Technology, Cambridge, MA
60. Kwast M., and Rose J. Pricing, Operating Efficiency and Profitability Among Large Commercial Banks. Journal of Banking and Finance 6 (1982) 233-254
61. Lachenbruch P.A. An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis. Biometrics December (1967) 639-645
62. Lacher R.C., Coats P.K., Sharma S.C., and Fantc L.F. A Neural network for classifying the financial health of a firm. European Journal of Operational Research 85 (1995) 53-65
63. Lam K.F., and Moy J.W. Combining discriminant methods in solving classification problems in two-group discriminant analysis. European Journal of Operational Research 138 (2002) 294-301
64. Lam M. Neural networks techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems 37 (2004) 567-581
65. Lee K.C., Han I., and Kwon Y. Hybrid neural network models for bankruptcy predictions. Decision Support Systems 18 (1996) 63-72
66. Lee K., Booth D., and Alam P. A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications 29 (2005) 1-16
67. Leshno M., and Spector Y. Neural network prediction analysis: The bankruptcy case. Neurocomputing 10 (1996) 125-147
68. Liang T.P. A composite approach to inducing knowledge for expert systems design. Management Science 38 1 (1992) 145-154
69. Lin F.Y., and McClean S. A datamining approach to the prediction of corporate failure. Knowledge Based Systems 14 (2001) 189-195
70. Marais M.L., Patel J., and Wolfson M. The experimental design of classification models: An application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research 22 (1984) 87-113
71. Martin D. Early warning of bank failure: A logit regression approach. Journal of Banking and Finance 1 (1977) 249-276
72. McCulloch W.S., and Pitts W.A. Logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (1943) 115-133
73. McKee T.E. Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting, Finance and Management 9 (2000) 59-173
74. McKee T.E., and Lensberg T. Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research 138 (2002) 436-451
75. McKee T.E. Rough sets bankruptcy prediction models versus auditor signaling rates. Journal of Forecasting 22 (2003) 569-589
76. Merton R. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29 (1974) 449-470
77. Messier W.F., and Hansen J. Inducing rules for expert system development: An example using default and bankruptcy data. Management Science 12 (1988) 1403-1415
78. S. Michael, D. Georgios, M. Nikolaos, Z. Constantin, A fuzzy knowledge-based decision aiding method for the assessment of financial risk: The case of corporate bankruptcy prediction, in: European Symposium on Intelligent Techniques, Crete, Greece, 1999.
79. Min J.H., and Lee Y.-C. Bankruptcy prediction using support vector machine (SVM) with optimal choice of kernel function parameters. Expert Systems with Applications 28 (2005) 603-614
80. Nanda S., and Pendharkar P. Linear models for minimizing misclassification costs in bankruptcy prediction. International Journal of Intelligent Systems in Accounting, Finance and Management 10 (2001) 155-168
81. Moody J., and Darken C.J. Fast learning in networks of locally-tuned processing units. Neural Computing 1 (1989) 281-294
82. M. Odom, R. Sharda, A neural network for bankruptcy prediction, in: IJCNN International Conference on Neural Networks, San Diego, CA, 1990.
83. Ohlson J.A. Financial rations and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 (1980) 109-131
84. O'Leary D.E. Using neural network to predict corporate failure. International Journal of Intelligent Systems in Accounting Finance and Management 7 (1998) 187-197
85. Olmeda I., and Fernandez E. Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction. Computational Economics 10 (1997) 317-335
86. Park C.-S., and Han I. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications 23 3 (2002) 255-264
87. Pawlak Z. Rough sets. International Journal of Computer and Information Science 11 (1982) 341-356
88. Pendharkar P.C., and Rodger J.A. An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification. Computers and Operations Research 31 (2004) 481-498
89. Piramuthu S., Ragavan H., and Shaw M.J. Using feature construction to improve the performance of the neural networks. Management Science 44 3 (1998)
90. Platt H.D., Platt M.B., and Pedersen J. Bankruptcy discrimination with real variables. Journal of Business Finance and Accounting 21 (1994) 491-509
91. J.R. Quinlan, Decision trees as probabilistic classifiers, in: Proceedings of 4th International Workshop Machine Learning, Irvine, CA, 1987, pp. 31-37.
92. Rahimian E., Singh S., Thammachote T., and Virmani R. Bankruptcy prediction by neural network. In: Trippi R.R., and Turban E. (Eds). Neural Networks in Finance and Investing (1996), Irwin Professional Publishing, Burr Ridge, USA
93. Rashad A.-K., and El-Sheshai K.M. Information choice and utilization in an experiment on default prediction. Journal of Accounting Research 18 2 (1980)
94. Rud O.P. Data Mining Cook Book (2000), John Wiely and Sons, New York
95. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition vol. 1 (1986), The MIT Press, Cambridge, MA 318-362
96. Russell S., and Norvig P. Artificial Intelligence. A Modern Approach. second ed. (2003), Prentice-Hall, New Jersey, USA
97. Ryu Y.U., and Yue W.T. Firm bankruptcy prediction: Experimental comparison of isotonic separation and other classification approaches. IEEE Transactions On Systems, Management and Cybernetics-Part A: Systems and Humans 35 5 (2005) 727-737
98. Salchenberger L., Mine C., and Lash N. Neural networks: A tool for predicting thrift failures. Decision Sciences 23 (1992) 899-916
99. Scott J. The probability of bankruptcy. Journal of Banking and Finance 5 (1981) 317-344
100. Serrano-Cinca C. Self organizing neural networks for financial diagnosis. Decision Support Systems 17 (1996) 227-238
101. R. Sharda, R.L. Wilson, Performance comparison issues in neural network experiments for classification problems, in: Proceedings of the 26th Hawai International Conference on System Scientists, 1993.
102. Shin K.-S., and Lee Y.-J. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications 23 3 (2002) 321-328
103. Slowinski R., Greco S., and Matarazzo B. Rough set based decision support. In: Burke E.K., and Kendall G. (Eds). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (2005), Springer-Verlag, New York 475-527 (Chapter 16)
104. Specht D.F. Probabilistic neural networks. Neural Networks 3 (1990) 110-118
105. Sung T.K., Chank N., and Lee G. Dynamics of modelling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems 16 (1999) 63-85
106. Swicegood P., and Clark J.A. Off-site monitoring for predicting bank under performance: A comparison of neural networks, discriminant analysis and professional human judgment. International Journal of Intelligent Systems in Accounting, Finance and Management 10 (2001) 169-186
107. Tam K.Y. Neural network models and the prediction of bank bankruptcy. Omega 19 5 (1991) 429-445
108. Tam K.Y., and Kiang M. Predicting bank failures: A neural network approach. Decision Sciences 23 (1992) 926-947
109. Tanaka H., and Lee H. Interval regression analysis by quadratic programming approach. IEEE Transactions on Fuzzy Systems 6 4 (1998) 473-481
110. Tay F.E.H., and Shen L. Economic and financial prediction using rough set model. European Journal of Operational Research 141 (2002) 641-659
111. In: Trippi R.R., and Turban E. (Eds). Neural Networks in Finance and Investing (1993), Probus Publishing, Chicago (Chapters 8-13)
112. Tseng F.-M., and Lin L. A quadratic interval logit model for forecasting bankruptcy. Omega 33 (2005) 85-91
113. Tsukuda J., and Baba S.-I. Predicting Japanese corporate bankruptcy in terms of finance data using neural network. Computers and industrial Engineering 27 1-4 (1994) 445-448
114. Tung W.L., Quek C., and Cheng P. GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Networks 17 (2004) 567-587
115. Vapnik V. In: Haykin S. (Ed). Statistical Learning Theory. Adaptive and Learning Systems vol. 736 (1998), John Wiley and Sons
116. Varetto F. Genetic algorithm applications in the analysis of insolvency risk. Journal of Banking and Finance 22 (1998) 1421-1439
117. Wall L. Why are some banks more profitable than others?. Journal of Bank Research 15 (1985) 240-256
118. J. Wallrafen, P. Protzel, H. Popp, Genetically optimized neural network classifiers for bankruptcy prediction, in: Proceedings of the 29 the Annual Hawaii International Conference on System Sciences, 1996.
119. West R.C. A factor analytic approach to bank condition. Journal of Banking and Finance 9 (1985) 253-266
120. Wilcox J.W. A prediction of business failure using accounting data, empirical research in accounting: Selected studies. Journal of Accounting Research Suppl. (1973) 163-179
121. Wilson R.L., and Sharda R. Bankruptcy prediction using neural networks. Decision Support Systems 11 (1994) 545-557
122. Yang Z.R., Platt M.B., and Platt H.D. Probability neural network in bankruptcy prediction. Journal of Business Research 44 (1999) 67-74
123. A.Y.N. Yip, Predicting business failure with a case-based reasoning approach, lecture notes in computer science, in: M.G. Negoita, R.J. Howlett, L.C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 3215/2004, Proceedings, Part III, 2004, pp. 20-25.
124. Zadeh L.A. Fuzzy sets. Information and Control 8 (1965) 338-353
125. Zadeh L.A. Soft computing and fuzzy logic. IEEE Software 11 6 (1994) 48-56
126. Zhang G., Hu M.Y., Patuwo B.E., and Indro D.C. Artificial neural networks and bankruptcy prediction general framework and cross-validated analysis. European Journal of Operational Research 116 (1999) 16-32
127. Zimmermann H.J. Fuzzy set theory and its applications (1996), Kluwer Academic Publishers, London
128. Zmijewski M. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 22 (1984) 59-82