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Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress

Author

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  • Guvenir, H. Altay
  • Cakir, Murat

Abstract

Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms.

Suggested Citation

  • Guvenir, H. Altay & Cakir, Murat, 2009. "Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress," MPRA Paper 21595, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:21595
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    References listed on IDEAS

    as
    1. Fukuda, Shin-ichi & Kasuya, Munehisa & Akashi, Kentaro, 2009. "Impaired bank health and default risk," Pacific-Basin Finance Journal, Elsevier, vol. 17(2), pages 145-162, April.
    2. G. Lanine & R. Vander Vennet, 2005. "Failure prediction in the Russian bank sector with logit and trait recognition models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/329, Ghent University, Faculty of Economics and Business Administration.
    3. Shin-ichi Fukuda & Munehisa Kasuya & Kentaro Akashi, 2008. "Impaired Bank Health and Default Risk ( Forthcoming in "Pacific-Basin Finance Journal". )," CARF F-Series CARF-F-122, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
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    Cited by:

    1. Murat Cakir, 2017. "A conceptual design of "what and how should a proper macro-prudential policy framework be?" A globalistic approach to systemic risk and procuring the data needed," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Uses of central balance sheet data offices' information, volume 45, Bank for International Settlements.

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    More about this item

    Keywords

    Classification; Voting; Feature construction; Financial distress; Feature projections;
    All these keywords.

    JEL classification:

    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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