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Application of support vector machines on the basis of the first Hungarian bankruptcy model


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  • Miklós Virag

    (Corvinus University of Budapest, Department of Enterprise Finances, School of Business Administration, Budapest, Hungary)

  • Tamás Nyitrai

    (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.

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    Bibliographic Info

    Article provided by Akadémiai Kiadó, Hungary in its journal Society and Economy.

    Volume (Year): 35 (2013)
    Issue (Month): 2 (August)
    Pages: 227-248

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    Handle: RePEc:aka:soceco:v:35:y:2013:i:2:p:227-248

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    Postal: Akadémiai Kiadó Zrt., Prielle K. u. 21-35. Budapest, 1117, Hungary

    Related research

    Keywords: bankruptcy prediction; classification; data preparation; outliers; support vector machines (SVM); ROC curve analysis;

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    Cited by:
    1. Nyitrai, Tamás, 2014. "Növelhető-e a csőd-előrejelző modellek előre jelző képessége az új klasszifikációs módszerek nélkül?
      [Can the predictive capacity of bankruptcy forecasting models be increased withou
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 566-585.


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