IDEAS home Printed from https://ideas.repec.org/a/aka/soceco/v35y2013i2p227-248.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Miklós Virag & Tamás Nyitrai, 2013. "Application of support vector machines on the basis of the first Hungarian bankruptcy model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 35(2), pages 227-248, August.
  • Handle: RePEc:aka:soceco:v:35:y:2013:i:2:p:227-248
    as

    Download full text from publisher

    File URL: http://www.akademiai.com/content/w459170887q183u5/fulltext.pdf
    Download Restriction: subscription

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 without new classific
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 566-585.

    More about this item

    Keywords

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

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aka:soceco:v:35:y:2013:i:2:p:227-248. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Vajda, Lőrinc). General contact details of provider: http://www.akkrt.hu .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.