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See5 Algorithm versus Discriminant Analysis. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies

  • Zuleyca Díaz Martínez

    ()

    (Universidad Complutense de Madrid. Facultad de Económicas y Empresariales.Departamento de Economía Financiera y Contabilidad I.)

  • José Fernández Menéndez

    (Universidad Complutense de Madrid. Facultad de Económicas y Empresariales.Departamento de Organización de Empresas.)

  • Paloma Martínez Almodovar

    (Universidad Complutense de Madrid. Facultad de Económicas y Empresariales.Departamento de Organización de Empresas.)

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    Prediction of insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem. Most methods applied in the past to tackle this question are traditional statistical techniques which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods.In this paper, a comparative study of the performance of a well-known parametric statistical technique (Linear Discriminant Analysis) and a non-parametric machine learning technique (See5) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies upon the basis of a set of financial ratios. Results indicate a higher performance of the machine learning technique, what shows that this method can be a useful tool to evaluate insolvency of insurance firms.

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    File URL: http://eprints.ucm.es/6836/1/0412.pdf
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    Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales in its series Documentos de trabajo de la Facultad de Ciencias Económicas y Empresariales with number 04-12.

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    Date of creation: 2004
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    Handle: RePEc:ucm:doctra:04-12
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    1. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    2. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    3. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
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