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The aim of the paper is to present the results of application artificial neural networks to firm classification and to verification the credit granting decision made by the bank experts. The experiments are provided on the basis of data regarding 115 small enterprises that applied for a credit in two regional banks in Poland. The accuracy of classification is evaluated in terms of classification errors. To evaluate the efficiency of artificial neural networks we compare the ANN results to the ones that were obtained applying linear discriminant analysis and k-means method.
Czasopismo
Rocznik
Tom
Strony
17--34
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
- Warsaw Agricultural University, Department of Econometrics and Informatics, ul. Nowoursynowska 166, 02-787 Warszawa, Poland
autor
- Technical University of Lodz, Faculty of Process and Environmental Engineering, Wólczańska 215, 90-924 Lodz, Poland
Bibliografia
- [1] Altaian E.: Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance Vol. XXIII, No. 4, 1968, s. 589-603.
- [2] Altaian E.: Corporate Bankruptcy in America, Heath Lexington Books, Lexington, 1971.
- [3] Altman E.: The Prediction of Corporate Bankruptcy, Grandland Publishing, New York, 1988.
- [4] Altman E.: Corporate Financial Distress and Bankruptcy, J. Wiley & Sons. Chichester, New York, 1993.
- [5] Azoff E.M.: Neural Network Time Series Forecasting of Financial Markets, John Wiley & Sons Ltd., Chichester, 1994.
- [6] Baetge J., Krause C: The Classification of Companies by Means of Neural Networks, Journal of Information Science and Technology, 3, 1, October 1993, pp.96- 112.
- [7] Bishop CM.: Neural Networks in Pattern Recognition, Clarenton Press, Oxford, 1995.
- [8] Kamiński W., Strumiłło P.: Kernel Oitogonalization in Radial basis Function Neural Networks, IEEE Transactions on Neural Networks, Vol. 8, No. 5, 1997, p. 1177-1183.
- [9] Odom M.D., Sharda R.: A Neural Network Model for Bankruptcy Prediction, in Trippi R.R., Turban E., ed., Neural Networks in Finance and Investing, Probus Publishing Company, Chicago- London, 1993, pp. 177 - 185.
- [10] Raghupathi W., Schkade L.L., Raju B.S.: A Neural Network Approach to Bankruptcy Prediction, in: Trippi R.R., Turban E., ed., Neural Networks in Finance and Investing, Probus Publishing Company, Chicago- London, 1993, pp. 141 - 158
- [11] Rahimian E., Singh S., Thammachote T., Virmani R.: Bankruptcy Prediction by Neural Network in Trippi R.R., Turban E., ed., Neural Networks in Finance and Investing, Probus Publishing Company, Chicago- London, 1993, pp. 159 - 176.
- [12] Rehkugler H., Schmidt-von Rhein A.: Kreditwurdigkeitsanalyse und Prognose fur Privatkundenkredite Mittels Statischer Methoden und Kunstlicher Neuronaler Netze. Eine empirisch - vergleichende Studie", Bamberger Betriebswirtschaftliche Beitrage, Otto Friedrich Universität, Bamberg, discussion paper 93/1993.
- [13] Schurmann J.: Pattern Classification. A Unified View of Statistical and Neural Approaches, John Wiley & Sons, INC., New York, Chichester, Brisbane, Toronto, Singapore, 1996.
- [14] Wilson R.L., Sharda R.: Bankruptcy Prediction Using Neural Networks, Decision Support Systems, 11, 1994, pp. 545 - 557.
- [15] Witkowska D.: Application of Artificial Neural Networks to Bank - Decision Simulations, International Advances in Economic Research, No 5, 3, 1999, pp. 350 - 368.
- [16] Witkowska D.: Credit Decision Verification by Means of Artificial Neural Networks, Studia Prawno-Ekonomiczne, No 58, 1998, pp. 241 - 256 (in Polish).
- [17] Witkowska D., Kamiński W., Stanieć I.: The Loan Granting Procedure: Artificial Networks, Discriminant Analysis, K-Means Method in: R. Neck (ed.), Modeling and Control of Economic Systems 2001 (SME 2001), International Federation of Automatic Control, Pergamon, Elsevier Science Ltd., Oxford, 2003, s. 383 - 387.
- [18] Witkowska D., Kamiński W., Kompa K, Stanieć I.: Neural networks as a Supporting Tool in Credit Granting Procedure, Information Technology for Economics and Management, (ITEM) e-journal, Vol. 2, No. 1, Paper 1, 2004, http://www.item.woiz.polsl.p1/issue2.l/iournal2.l.htm
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD2-0001-0015