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  • 1 Corvinus University of Budapest Department of Enterprise Finances, School of Business Administration Budapest Hungary
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In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

  • Boyacioglu, M. A. — Kara, Y. — Baykan, Ö. K. (2009): Predicting Bank Financial Failures Using Neural Networks, Support Vector Machines, and Multivariate Statistical Methods: A Comparative Analysis in the Sample of Savings Deposit Insurance Fund (SDIF) Transferred Banks in Turkey. Expert Systems with Applications 36: 3355–3366.

    Baykan Ö. K. K. , 'Predicting Bank Financial Failures Using Neural Networks, Support Vector Machines, and Multivariate Statistical Methods: A Comparative Analysis in the Sample of Savings Deposit Insurance Fund (SDIF) Transferred Banks in Turkey ' (2009 ) 36 Expert Systems with Applications : 3355 -3366.

    • Search Google Scholar
  • Burges, C. J. C. (1998): A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2): 955–974.

    Burges C. J. C. , 'A Tutorial on Support Vector Machines for Pattern Recognition ' (1998 ) 2 Data Mining and Knowledge Discovery : 955 -974.

    • Search Google Scholar
  • Ding, Y. — Song, X. — Zen, Y. (2008): Forecasting Financial Condition of Chinese Listed Companies Based on Support Vector Machines. Expert Systems with Applications 34: 3081–3089.

    Zen Y. , 'Forecasting Financial Condition of Chinese Listed Companies Based on Support Vector Machines ' (2008 ) 34 Expert Systems with Applications : 3081 -3089.

    • Search Google Scholar
  • Erdal, H. I. — Ekinci, A. (2012): A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures. Computational Economics Online First Articles, http://link.springer.com/article/10.1007/s10614-012-9332-0 (accessed 4 November 2012)

  • Fan, A. — Palaniswami, N. (2000): Selecting Bankruptcy Predictors Using a Support Vector Machine Approach. Proceedings of the International Joint Conference on Neural Networks.

  • Hearst, M. A. (1998): Support Vector Machines. IEEE Intelligent Systems 13(4): 18–28.

    Hearst M. A. , 'Support Vector Machines ' (1998 ) 13 IEEE Intelligent Systems : 18 -28.

  • Huang, Z. — Chen, H. — Hsu, C. H. — Chen, W. H. — Wu, S. (2004): Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems 37: 543–558.

    Wu S. , 'Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study ' (2004 ) 37 Decision Support Systems : 543 -558.

    • Search Google Scholar
  • Kim, H. K. — Sohn, S. Y. (2010): Support Vector Machines for Default Prediction of SMEs Based on Technology Credit. European Journal of Operational Research 201: 838–846.

    Sohn S. Y. , 'Support Vector Machines for Default Prediction of SMEs Based on Technology Credit ' (2010 ) 201 European Journal of Operational Research : 838 -846.

    • Search Google Scholar
  • Kristóf, T. — Virág, M. (2012): Data Reduction and Univariate Splitting — Do they Together Provide Better Corporate Bankruptcy Prediction? Acta Oeconomica 62(2): 205–227.

    Virág M. , 'Data Reduction and Univariate Splitting — Do they Together Provide Better Corporate Bankruptcy Prediction? ' (2012 ) 62 Acta Oeconomica : 205 -227.

    • Search Google Scholar
  • Lee, M. C. — To, C. (2010): Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress. International Journal of Artificial Intelligence & Applications 1(3): 31–43.

    To C. , 'Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress ' (2010 ) 1 International Journal of Artificial Intelligence & Applications : 31 -43.

    • Search Google Scholar
  • Lensberg, T. L. — Eilifsen, A. — McKee, T. E. (2006): Bankruptcy Theory Development and Classification via Genetic Programming. European Journal of Operational Research 169: 677–697.

    McKee T. E. , 'Bankruptcy Theory Development and Classification via Genetic Programming ' (2006 ) 169 European Journal of Operational Research : 677 -697.

    • Search Google Scholar
  • Min, J. H. — Lee, Y. C. (2005): Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters. Expert Systems with Applications 28: 603–614.

    Lee Y. C. , 'Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters ' (2005 ) 28 Expert Systems with Applications : 603 -614.

    • Search Google Scholar
  • Moradi, M. — Sardasht, M. S. — Ebrahimpour, M. (2012): An Application of Support Vector Machines in Bankruptcy Prediction; Evidence from Iran. World Applied Sciences Journal 17(6): 710–717.

    Ebrahimpour M. , 'An Application of Support Vector Machines in Bankruptcy Prediction; Evidence from Iran ' (2012 ) 17 World Applied Sciences Journal : 710 -717.

    • Search Google Scholar
  • Moro, R. — Hardle, W. — Aliakbari, S. — Hoffmann, L. (2011): Forecasting Corporate Distress in the Asian and Pacific Region, Economics and Finance Working Paper No. 11-08. Department of Economics and Finance, Brunel University.

  • Rüping, S. (2000): MySVM — Manual. http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/mysvm-manual.pdf (accessed 20 November 2012)

  • Shin, K. S. — Lee, T. S. — Kim, H. J. (2005): An Application of Support Vector Machines in Bankruptcy Prediction Model. Expert Systems with Applications 28: 127–135.

    Kim H. J. , 'An Application of Support Vector Machines in Bankruptcy Prediction Model ' (2005 ) 28 Expert Systems with Applications : 127 -135.

    • Search Google Scholar
  • Sun, L. — Shenoy, P. P. (2007): Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues. European Journal of Operational Research 180: 738–753.

    Shenoy P. P. , 'Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues ' (2007 ) 180 European Journal of Operational Research : 738 -753.

    • Search Google Scholar
  • Szûcs, I. (2010): Support vector gépek alkalmazása hitelpontozó kártyák fejlesztésében [The Application of Support Vector Machines in Developing Scorecards]. Acta Agraria Kaposváriensis 14(3): 173–182.

    Szûcs I. , 'Support vector gépek alkalmazása hitelpontozó kártyák fejlesztésében ' (2010 ) 14 Acta Agraria Kaposváriensis : 173 -182.

    • Search Google Scholar
  • Vapnik, V. M. (1995): The Nature of Statistical Learning Theory. New York: Springer.

    Vapnik V. M. , '', in The Nature of Statistical Learning Theory , (1995 ) -.

  • Vapnik, V. M. (1998): Statistical Learning Theory. New York: Springer.

    Vapnik V. M. , '', in Statistical Learning Theory , (1998 ) -.

  • Virág, M. — Hajdu, O. (1996): Pénzügyi mutatószámokon alapuló csõdmodell-számítások [Financial Ratio Based Bankruptcy Model Calculations]. Bankszemle 15(5): 42–53.

    Hajdu O. , 'Pénzügyi mutatószámokon alapuló csõdmodell-számítások ' (1996 ) 15 Bankszemle : 42 -53.

    • Search Google Scholar
  • Virág, M. — Kristóf, T. (2005): Neural Networks in Bankruptcy Prediction — a Comparative Study on the Basis of the First Hungarian Bankruptcy Model. Acta Oeconomica 55(4): 403–425.

    Kristóf T. , 'Neural Networks in Bankruptcy Prediction — a Comparative Study on the Basis of the First Hungarian Bankruptcy Model ' (2005 ) 55 Acta Oeconomica : 403 -425.

    • Search Google Scholar
  • Virág, M. — Kristóf, T. (2008): Vizuális klaszterezõ csõdmodellezés többdimenziós skálázás segítségével [Bankruptcy Modeling with Visual Clustering Using Multidimensional Scaling]. In: Bartók, István — Simon, Judit (eds): 60 éves a Közgáz: A jubileumi tudományos konferencia alkalmából készült tanulmányok. Budapest: Aula Kiadó, pp. 11–24.

    Kristóf T. , '', in 60 éves a Közgáz: A jubileumi tudományos konferencia alkalmából készült tanulmányok , (2008 ) -.

  • Yang, Y. (2007): Adaptive Credit Scoring with Kernel Learning Methods. European Journal of Operational Research 183: 1521–1536.

    Yang Y. , 'Adaptive Credit Scoring with Kernel Learning Methods ' (2007 ) 183 European Journal of Operational Research : 1521 -1536.

    • Search Google Scholar
  • Yoon, J. — Kwon, J. S. — Lee, C. H. (2008): Bankruptcy Prediction for Small Businesses Using Credit Card Sales Information: Comparison of Classification Performance. Proceedings of the 9th Asia Pacific Industrial Engineering & Management Systems Conference, pp. 2920–2935.