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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
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    Citations

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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 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.
    2. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    3. Botond Benedek & Balint Zsolt Nagy, 2023. "Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 22(2), pages 77-98.
    4. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.

    More about this item

    Keywords

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

    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

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