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A GA-based Binary Classification Method for Bankruptcy Prediction  

Min, Jae-H. (서강대학교 경영학과)
Jeong, Chul-Woo (서강대학교 경영학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.33, no.2, 2008 , pp. 1-16 More about this Journal
Abstract
The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.
Keywords
Bankruptcy Prediction; Binary Classification; Genetic Algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 민재형, 정철우, "유전 알고리듬 기반 집단분류 기법의 개발과 성과평가：채권등급 평가를 중심으로", 한국경영과학회지, 제32권, 제1호(2007), pp.61-76   과학기술학회마을
2 Atiya, A.F., "Bankruptcy prediction for credit risk using neural networks：A survey and new results," IEEE Transactions on Neural Networks, Vol.12, No.4(2001), pp.929-935   DOI   ScienceOn
3 Kaski, S., J. Sinkkonen, and J. Peltonen, "Bankruptcy analysis with self-organizing maps in learning metrics," IEEE Transaction on Neural Networks, Vol.12, No.4(2001), pp.936-947   DOI   ScienceOn
4 Lam, M., "Neural networks techniques for financial performance prediction：integrating fundamental and technical analysis," Decision Support Systems, Vol.34, No.4(2004), pp.567-581
5 Leshno, M. and Y. Spector, "Neural network prediction analysis：The bankruptcy case," Neurocomputing, Vol.10, No.2(1996), pp.125-147   DOI   ScienceOn
6 Lin, H.J., F.W. Yang and Y.T. Kao, "An Efficient GA-based Clustering Technique," Tamkang Journal of Science and Engineering, Vol.8, No.2(2005), pp.113-122
7 Marais, M.L., J. Patel, and M. Wolfson, "The experimental design of classification models：An application of recursive partitioning and bootstrapping to commercial bank loan classifications," Journal of Accounting Research, Vol.22, Supplement(1984), pp.87-114   DOI   ScienceOn
8 Ohlson, J.A., "Financial Ratios and the Probabilistic Prediction of Bankruptcy," Journal of Accounting Research, Vol.18, No.1(1980), pp.109-131   DOI   ScienceOn
9 Varetto, F., "Genetic algorithm applications in the analysis of insolvency risk," Journal of Banking and Finance, Vol.22, No.10-11(1998), pp.1421-1439   DOI   ScienceOn
10 Wilson, R.L. and R. Sharda, "Bankruptcy prediction using neural networks," Decision Suppot Systems, Vol.11, No.5(1994), pp.545-557   DOI   ScienceOn
11 Yang, Z.R., M.B. Platt, and H.D Platt, "Probability neural network in bankruptcy prediction," Journal of Business Research, Vol.44, No.2(1999), pp.67-74   DOI   ScienceOn
12 Frydman, H., E.I. Altman, and D. Kao, "Introducing recursive partitioning for financial classification：The case of financial distress," Journal of Finance, Vol.40, No.1(1985), pp.269-291   DOI
13 Zimmermann, H.J., Fuzzy set theory and its applications, Kluwer Academic Publishers, London, 1996
14 Zmijewski, M.E., "Methodological Issues Related to the Estimation of Financial Distress Prediction Models," Journal of Accounting Research, Vol.22, Supplement(1984), pp.59-82   DOI   ScienceOn
15 Lacher, R.C., P.K. Coats, S.C. Sharma, and L.F. Fantc, "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Vol.85, No.1(1995), pp.53-65   DOI   ScienceOn
16 McKee, T.E., "Rough sets bankruptcy prediction models versus auditor signaling rates," Journal of Forecasting, Vol.22, No.8(2003), pp.569-589   DOI   ScienceOn
17 Shin, K.S. and Y.J. Lee, "A genetic algorithm application in bankruptcy prediction modeling," Expert Systems with Applications, Vol.23, No.3(2002), pp.321-328   DOI   ScienceOn
18 Tam, K.Y., "Neural network models and the prediction of bank bankruptcy," Omega, Vol.19, No.5(1991), pp.429-445   DOI   ScienceOn
19 Park, C.S. and I. Han, "A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction," Expert Systems with Applications, Vol.23, No.3(2002), pp.255-264   DOI   ScienceOn
20 Bryant, S.M., "A case-based reasoning approach to bankruptcy prediction modeling," Intelligent Systems in Accounting, Finance and Management, Vol.6, No.3(1997), pp.195-214   DOI   ScienceOn
21 Lee, K.C., I. Han, and Y. Kwon, "Hybrid neural network models for bankruptcy predictions," Decision Support Systems, Vol.18, No.1(1996), pp.63-72   DOI   ScienceOn
22 Altman, E.I., "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy," Journal of Finance, Vol.23, No.4(1968), pp.589-609   DOI
23 Jo, H., I. Han, and H. Lee, "Bankruptcy prediction using case-based reasoning, neural network and discriminant analysis for bankruptcy prediction," Expert Systems with Applications, Vol.13, No.2(1997), pp.97-108   DOI   ScienceOn
24 Holland, J.H., Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, 1975
25 Salchenberger, L., C. Mine, and N. Lash, "Neural networks：A tool for predicting thrift failures," Decision Sciences, Vol.23, No.4(1992), pp.899-916   DOI
26 Swicegood, P. and J.A. Clark, "Off-site monitoring systems for predicting bank underperformance：A comparison of neural networks, discriminant analysis and professional human judgment," International Journal of Intelligent Systems in Accounting, Finance and Management, Vol.10, No.3(2001), pp.169-186   DOI   ScienceOn
27 문병로, 유전 알고리즘, 1판, 다성출판사, 2001
28 Bandyopadhyay, S. and U. Maulik, "Genetic clustering for automatic evolution of clusters and applicaton to image classification," Pattern Recognition, Vol.35, No.6(2002), pp.1197-1208   DOI   ScienceOn
29 McKee, T.E., "Developing a bankruptcy prediction model via rough sets theory," International Journal of Intelligent Systems in Accounting, Finance and Management, Vol.9, No.3(2000), pp.59-173
30 Lee, K, D. Booth, and P. Alam, "A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms," Expert Systems with Applications, Vol.29, No.1(2005), pp.1-16   DOI   ScienceOn
31 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, Vol.6, No.3(1997), pp.249-264   DOI   ScienceOn
32 Beaver, W.H., "Financial Ratios and Predictions of Failure," Journal of Accounting Research, Vol.4, Supplement(1966), pp.71-111   DOI   ScienceOn
33 Dimitras, A.I., R. Slowinski, R. Susmaga, and C. Zopounidis, "Business failure prediction using rough sets," European Journal of Operational Research, Vol.114, No.2(1999), pp.263-280   DOI   ScienceOn
34 Kumar, P.R. and V. Ravi, "Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review," European Journal of Operational Research, Vol.180, No.1(2007), pp.1-28   DOI   ScienceOn