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Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction

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  • Olmeda, Ignacio
  • Fernandez, Eugenio

Abstract

This paper compares the accuracy of parametric and nonparametric classifiers on the problem of predicting Bankruptcy. Among the single classifiers an artificial neural network is found to provide the best results. Two ways of combining classifiers are considered and an additive aggregation method is proposed. We show that both ways of combining produce classifiers whose forecasts are more accurate than the ones obtained with any single model. We suggest that an optimal system for risk rating should combine two or more different techniques. Citation Copyright 1997 by Kluwer Academic Publishers.

Suggested Citation

  • Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 10(4), pages 317-335, November.
  • Handle: RePEc:kap:compec:v:10:y:1997:i:4:p:317-35
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    Cited by:

    1. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    2. Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 95-121, June.
    3. Федорова Е.А. & Гиленко Е.В., 2013. "Применение Моделей Бинарного Выбора Для Прогнозирования Банкротства Банков," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 49(1), pages 106-118, январь.
    4. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2014. "Forecasting Financial Failure of Firms via Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 133-157, February.
    5. Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 45-62, June.
    6. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével
      [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    7. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    8. Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
    9. Ivo Casagranda Ivo & Giorgio Costantino & Greta Falavigna & Raffaello Furlan & Roberto Ippoliti, 2014. "Artificial Neural Networks and risk stratification in Emergency department," CERIS Working Paper 201412, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    10. Maiya Anokhina & Henry Penikas & Victor Petrov, 2014. "Identifying SIFI Determinants for Global Banks and Insurance Companies: Implications for D-SIFIs in Russia," DEM Working Papers Series 085, University of Pavia, Department of Economics and Management.
    11. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    12. Casagranda, Ivo & Costantino, Giorgio & Falavigna, Greta & Furlan, Raffaello & Ippoliti, Roberto, 2016. "Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective," Health Policy, Elsevier, vol. 120(1), pages 111-119.
    13. repec:kap:compec:v:49:y:2017:i:4:d:10.1007_s10614-016-9623-y is not listed on IDEAS
    14. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    15. Greta Falavigna, 2011. "An artificial neural network approach for assigning rating judgements to Italian Small Firms," CERIS Working Paper 201104, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    16. John A. Tatom & Reza Houston, 2011. "Predicting Failure in the Commercial Banking Industry," NFI Working Papers 2011-WP-27, Indiana State University, Scott College of Business, Networks Financial Institute.
    17. Enrique García Pérez & Benjamín Manchado, 1998. "Un modelo econométrico del fraude académico en una universidad española," Documentos de trabajo de la Facultad de Ciencias Económicas y Empresariales 98-20, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.

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