Apilado V.P., Warner D.C., Dauten J.J., (1974) “Evaluation techniques in consumer
finance”, Journal of Financial and Quantitative Analysis, March, 275-283
Altman E.I., Marco G., Varetto F., (1994) “Corporate distress diagnosis: comparison
using linear disriminant analysis and neural networks (the Italian experience)”, Journal
of Banking and Finance, 18, 505-529.
Durand D., (1941) “Risk elements in consumer installment financing”, National Bureau
of Economic Research, New York.
Eisenbeis R.A., (1977) “Pitfalls in the applicationof discriminant analysis in business,
finance and economics”, Journal of Finance, 32, 875-900.
Fisher R. A., (1936) “The use of multiple measurements in taxonomic problems”,
Annals of Eugenics, 7, 179-188
Hardy W.E., Adrian J. L., (1985) “A linear programming alternative to discriminant
analysis in credit scoring”, Abribus, 1, 285-292.
Haykin S. (1994) Neural Networks: A Comprehensive Foundation, New Jersey,
Prentice Hall.
Hsieh N.C. (2005) “Hybrid mining approach in the design of credit scoring models”,
Expert System with Applications, 28, 655-665
Murat Çinko
152
Jensen H. L., (1992) “Using neural networks for credit scoring”, Managerial Finance,
18, 15-26
Jo H., Han I., Lee H. (1997) “Bankruptcy predictionusing case-based reasoning, Neural
Networks, and Discriminant Analysis”, Expert SystemWith Application Cilt:13, Sayı:2,
97-108
Kolesar P., Showers J.L., (1985) “A robust credit screening model using categorical
data”, Management Science 31(2), 123-133
Lane S., (1972) “Submarginal credit risk classification”, Journal of Financial and
Quantitative Analysis, January, 1379-1385
Lee T.S., Chiu C.C., Lu C.J., Chen I.F., (2002) “Credit scoring using the hybrid neural
discriminant technique”, Expert System With Application, 23, 245-254
Lee T.S., Chen I.F., (2005) “A two stage hybrid credit scoring model using artificial
neural networks and multivariate adaptive regression splines”, Expert System With
Application, 28, 743-752
Malhotra R., Malhotra D.K., (2003) “Evaluating consumer loans using neural
networks”, Omega 31, 83-96
Ong C.S., Huang J.J., Tzeng G.H., (2005) “Building credit scoring models using genetic
programming”, Expert System With Application, 29, 41-47
Orgler Y.E., (1970) “A credit scoring models for commercial loans”, Journal of Money
Credit Banking, November, 31-37
Piramuthu S. (1999) “Financial credit-risk evaluation with neural and neurofuzzy
systems”, European Journal of Operational Research,112, 310-321
Reichert A. K., Cho C.C., Wagner G.M., (1983) “An examination of the conceptual
issues involved in developing credit scoring models”, Journal of Business and
Economic Statistics, 1, 101-114
Rosenberg E., Gleit A., (1994) “Quantitative methods in credit management: a survey”,
Operational Research, 42, 589-613
Srinivasan V. Kim Y.H., (1987) “Credit granting: a comparative analysis of
classification procedures”, Journal of Finance, 42,665-683
Tam K.Y., Kiang M.Y., (1992) “Managerial applications of neural networks: the case of
bank failure prediction”, Management Science, 38, 926-947.
Thomas L. C., (2000) “A survey of credit and behavioral scoring: forecasting financial
risk of lending to consumer”, International Journalof Forecasting, 16, 149-172
Bahar 2006 / 1
153
Vellido A., Lisboa P.J.G., Vaughan J., (1999) “Neural networks in business: a survey of
application (1992-1998)”, Expert System With Application, 17, 51-70
West D., Dellana S., Qian J., (2005) “Neural network ensemble strategies for financial
decision applications”, Computer and Operation Research, 32, 2543-2559
Westgaard S., Wijst N. (2001) “Default probabilities in a corporate bank portfolio: a
logistic model approach”, European Journal of Operational Research, 135, 338-349
Wiginton J.C., (1980) “A note on the comparison of logit and discriminant models of
consumer credit behavior”, Journal of Financial andQuantitative Analysis XV,(3), 757-770
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http://www.hazine.gov.tr/stat/egosterge/I-Uretim/I_1.xls, (24.06.2006, WEB)
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