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The application of ensemble methods in forecasting bankruptcy

Author

Listed:
  • Miklós Virág
  • Tamás Nyitrai

    (Corvinus University Budapest)

Abstract

In practice, one chosen method is generally used to solve classification tasks. Although the most modern procedures yield excellent accuracy rates, international research findings show that a concurrent (ensemble) application of methods with weaker classification performance achieves comparable rates of high accuracy. This article’s main objective is to compare the predictive power of the two ensemble methods (Adaboost and Bagging) most commonly used in bankruptcy prediction, using a sample consisting of 976 Hungarian corporations. The article’s other objective is to compare the accuracy rates of bankruptcy models built on the deviations in specific financial ratios from industry averages to those of models built on financial ratios and variables factoring in their dynamics.

Suggested Citation

  • Miklós Virág & Tamás Nyitrai, 2014. "The application of ensemble methods in forecasting bankruptcy," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 13(4), pages 178-193.
    • Handle: RePEc:mnb:finrev:v:13:y:2014:i:4:p:180-195
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    References listed on IDEAS

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    1. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
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    Cited by:

    1. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
    2. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.

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    More about this item

    Keywords

    bankruptcy prediction; ensemble methods; industry average; decision trees;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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