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Volumn 28, Issue 1, 2005, Pages 127-135

An application of support vector machines in bankruptcy prediction model

Author keywords

Bankruptcy prediction; Support vector machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; OPTIMAL CONTROL SYSTEMS; PARAMETER ESTIMATION; PERSONNEL TRAINING; SIGNAL FILTERING AND PREDICTION; STATISTICAL METHODS;

EID: 9244259665     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2004.08.009     Document Type: Article
Times cited : (643)

References (48)
  • 1
    • 84980104458 scopus 로고
    • Financial ratios, discriminant analysis and the prediction of corporate bankruptcy
    • Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589-609.
    • (1968) The Journal of Finance , vol.23 , pp. 589-609
    • Altman, E.1
  • 3
    • 0035391093 scopus 로고    scopus 로고
    • Bankruptcy prediction for credit risk using neural networks: A survey and new results
    • Atiya, A. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks, 12(4).
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.4
    • Atiya, A.1
  • 4
    • 0001659342 scopus 로고    scopus 로고
    • Predicting the outcome following bankruptcy filing: A three-state classification using neural networks
    • Barniv, R., Agarwal, A., & Leach, R. (1997). Predicting the outcome following bankruptcy filing: a three-state classification using neural networks. Intelligent Systems in Accounting, Finance and Management, 6, 177-194.
    • (1997) Intelligent Systems in Accounting, Finance and Management , vol.6 , pp. 177-194
    • Barniv, R.1    Agarwal, A.2    Leach, R.3
  • 5
    • 0002554419 scopus 로고
    • Financial ratios as prediction of failure. Empirical research in accounting: Selected studies
    • Beaver, W. (1966). Financial ratios as prediction of failure. Empirical research in accounting: selected studies. Journal of Accounting Research, 4, 71-111.
    • (1966) Journal of Accounting Research , vol.4 , pp. 71-111
    • Beaver, W.1
  • 6
    • 0000279964 scopus 로고    scopus 로고
    • Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures
    • Bell, T. (1997). Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures. Intelligent Systems in Accounting, Finance and Management, 6, 249-264.
    • (1997) Intelligent Systems in Accounting, Finance and Management , vol.6 , pp. 249-264
    • Bell, T.1
  • 7
    • 0031208638 scopus 로고    scopus 로고
    • Learning distributions by their density levels: A paradigm for learning without a teacher
    • Ben-David, S., & Lindenbaum, M. (1997). Learning distributions by their density levels: a paradigm for learning without a teacher. Journal of Computer and System Sciences, 53, 171-182.
    • (1997) Journal of Computer and System Sciences , vol.53 , pp. 171-182
    • Ben-David, S.1    Lindenbaum, M.2
  • 8
    • 0029492199 scopus 로고
    • Effectiveness of neural networks types for prediction of business failure
    • Boritz, J., & Kennedy, D. (1995). Effectiveness of neural networks types for prediction of business failure. Expert Systems with Applications, 9, 503-512.
    • (1995) Expert Systems with Applications , vol.9 , pp. 503-512
    • Boritz, J.1    Kennedy, D.2
  • 9
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 955-974.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 955-974
    • Burges, C.J.C.1
  • 10
    • 84898957872 scopus 로고    scopus 로고
    • Improving the accuracy and speed of support vector machines
    • M. Mozer, M. Jordan, & T. Petsche (Eds.), Cambridge, MA: MIT Press
    • Burges, C. J. C., & Scholkopf, B. (1997). Improving the accuracy and speed of support vector machines. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems (pp. 475-481). Cambridge, MA: MIT Press.
    • (1997) Advances in Neural Information Processing Systems , pp. 475-481
    • Burges, C.J.C.1    Scholkopf, B.2
  • 11
    • 0003417806 scopus 로고    scopus 로고
    • University of East Anglia, School of Information Systems available on
    • Cawley, G. C. (2000). Support vector machine toolbox University of East Anglia, School of Information Systems available on.
    • (2000) Support Vector Machine Toolbox
    • Cawley, G.C.1
  • 12
    • 0034562990 scopus 로고    scopus 로고
    • Comparative analysis of artificial neural network models: Application in bankruptcy prediction
    • Charalambous, C., Charitous, A., & Kaourou, F. (2000). Comparative analysis of artificial neural network models: application in bankruptcy prediction. Annals of Operations Research, 99, 403-425.
    • (2000) Annals of Operations Research , vol.99 , pp. 403-425
    • Charalambous, C.1    Charitous, A.2    Kaourou, F.3
  • 14
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes, C., & Vapnik, V. N. (1995). Support vector networks. Machine Learning, 20, 273-297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.N.2
  • 15
    • 0000275837 scopus 로고
    • A discriminant analysis of predictors of business failure
    • Deakin, B. E. (1976). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 167-179.
    • (1976) Journal of Accounting Research , pp. 167-179
    • Deakin, B.E.1
  • 16
    • 0000388853 scopus 로고    scopus 로고
    • A comparison of the relative costs of financial distress models: Artificial neural networks, logit and multivariate discriminant analysis
    • Etheridge, H., & Sriram, R. (1997). A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis. Intelligent Systems in Accounting, Finance and Management, 6, 235-248.
    • (1997) Intelligent Systems in Accounting, Finance and Management , vol.6 , pp. 235-248
    • Etheridge, H.1    Sriram, R.2
  • 18
    • 0000152448 scopus 로고
    • Forecasting with neural networks: An application using bankruptcy data
    • Fletcher, D., & GOSS, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information and Management, 24(3), 159-167.
    • (1993) Information and Management , vol.24 , Issue.3 , pp. 159-167
    • Fletcher, D.1    Goss, E.2
  • 20
    • 9244260899 scopus 로고    scopus 로고
    • The limitations of bankruptcy prediction models: Some cautions for the researcher
    • Grice, S. J., & Dugan, T. M. (2001). The limitations of bankruptcy prediction models: some cautions for the researcher. Review of Quantitative Finance and Accounting, 17, 151-166.
    • (2001) Review of Quantitative Finance and Accounting , vol.17 , pp. 151-166
    • Grice, S.J.1    Dugan, T.M.2
  • 23
    • 0031199479 scopus 로고    scopus 로고
    • Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis
    • Jo, H., Han, I., & Lee, H. (1997). Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems With Applications, 13(2), 97-108.
    • (1997) Expert Systems with Applications , vol.13 , Issue.2 , pp. 97-108
    • Jo, H.1    Han, I.2    Lee, H.3
  • 25
    • 0242351905 scopus 로고    scopus 로고
    • Financial time series forecasting using support vector machines
    • Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1/2), 307-319.
    • (2003) Neurocomputing , vol.55 , Issue.1-2 , pp. 307-319
    • Kim, K.J.1
  • 26
    • 0030241497 scopus 로고    scopus 로고
    • Hybrid neural network models for bankruptcy predictions
    • Lee, K. C., Han, I. G., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18, 63-72.
    • (1996) Decision Support Systems , vol.18 , pp. 63-72
    • Lee, K.C.1    Han, I.G.2    Kwon, Y.3
  • 27
    • 0030110988 scopus 로고    scopus 로고
    • Neural network prediction analysis: The bankruptcy case
    • Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: the bankruptcy case. Neurocomputing, 10, 125-247.
    • (1996) Neurocomputing , vol.10 , pp. 125-247
    • Leshno, M.1    Spector, Y.2
  • 28
    • 0002936377 scopus 로고
    • Integrating statistical and inductive learning methods for knowledge acquisition
    • Liang, T. P., Chandler, J. S., & Han, I. (1990). Integrating statistical and inductive learning methods for knowledge acquisition. Expert Systems with Applications, 1, 391-401.
    • (1990) Expert Systems with Applications , vol.1 , pp. 391-401
    • Liang, T.P.1    Chandler, J.S.2    Han, I.3
  • 29
    • 0002944481 scopus 로고    scopus 로고
    • Inducing rules for expert system development: An example using default and bankruptcy data
    • Messier, W., & Hansen, J. (1998). Inducing rules for expert system development: an example using default and bankruptcy data. Management Science, 34(12), 1403-1415.
    • (1998) Management Science , vol.34 , Issue.12 , pp. 1403-1415
    • Messier, W.1    Hansen, J.2
  • 32
    • 0000666375 scopus 로고
    • Financial ratios and the probabilistic prediction of bankruptcy
    • Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
    • (1980) Journal of Accounting Research , vol.18 , Issue.1 , pp. 109-131
    • Ohlson, J.1
  • 34
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 35
    • 84988828223 scopus 로고
    • Neural networks: A new tool for predicting thrift failures
    • Salchenberger, L., Cinar, E., & Lash, N. (1992). Neural networks: a new tool for predicting thrift failures. Decision Sciences, 23, 899-916.
    • (1992) Decision Sciences , vol.23 , pp. 899-916
    • Salchenberger, L.1    Cinar, E.2    Lash, N.3
  • 36
    • 0025387848 scopus 로고
    • Inductive learning for risk classification
    • Shaw, M., & Gentry, J. (1990). Inductive learning for risk classification. IEEE Expert, 47-53.
    • (1990) IEEE Expert , pp. 47-53
    • Shaw, M.1    Gentry, J.2
  • 38
    • 0032664993 scopus 로고    scopus 로고
    • Improving the manufacturability of electronic designs
    • Stoneking, D. (1999). Improving the manufacturability of electronic designs. IEEE Spectrum, 36(6), 70-76.
    • (1999) IEEE Spectrum , vol.36 , Issue.6 , pp. 70-76
    • Stoneking, D.1
  • 39
    • 84997479670 scopus 로고
    • Managerial applications of neural networks: The case of bank failure predictions
    • Tam, K., & Kiang, M. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7), 926-947.
    • (1992) Management Science , vol.38 , Issue.7 , pp. 926-947
    • Tam, K.1    Kiang, M.2
  • 41
    • 0001023715 scopus 로고    scopus 로고
    • Application of support vector machines in financial time series forecasting
    • Tay, F. E. H., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29, 309-317.
    • (2001) Omega , vol.29 , pp. 309-317
    • Tay, F.E.H.1    Cao, L.2
  • 45
    • 0028444978 scopus 로고
    • Bankruptcy prediction using neural networks
    • Wilson, R., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557.
    • (1994) Decision Support Systems , vol.11 , Issue.5 , pp. 545-557
    • Wilson, R.1    Sharda, R.2
  • 47
    • 0032640524 scopus 로고    scopus 로고
    • Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
    • Zhang, G., Hu, Y. M., Patuwo, E. B., & Indro, C. D. (1999). Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research, 116, 16-32.
    • (1999) European Journal of Operational Research , vol.116 , pp. 16-32
    • Zhang, G.1    Hu, Y.M.2    Patuwo, E.B.3    Indro, C.D.4
  • 48
    • 0001953906 scopus 로고
    • Methodological issues related to the estimated of financial distress prediction models
    • Zmijewski, M. E. (1984). Methodological issues related to the estimated of financial distress prediction models. Journal of Accounting Research, 22(1), 59-82.
    • (1984) Journal of Accounting Research , vol.22 , Issue.1 , pp. 59-82
    • Zmijewski, M.E.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.