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Prediction Of Insolvency In Non-Life Insurance Companies Using Support Vector Machines, Genetic Algorithms And Simulated Annealing

Author

Listed:
  • Segovia-Vargas, María Jesús

    (Universidad Complutense de Madrid)

  • Salcedo-Sanz, Sancho

    (Universidad Carlos III de Madrid and The University of Birmingham)

  • Bousoño-Calzón, Carlos

    (Universidad Carlos III de Madrid)

Abstract

In this paper we propose an approach to predict insolvency of non-life insurance companies based on the application of Support Vector Machines (SVMs), hybridized with two global search heuristics: a Genetic Algorithm (GA) and a Simulated Annealing (SA). A SVM is used to classify firms as failed or non-failed, whereas a GA and a SA are used to perform on-line feature selection in the ratios space of the SVM, in order to improve its perfor-mance. We use general financial ratios and also other specific ratios which have been proposed for evaluating insolvency of insurance sector. In the simulations section, we compare the performance of the GA and SA as part of the proposed algorithm. The results obtained with both techniques show that the proposed algorithm can be a useful tool for parties interested in evaluating insolvency of non-life insurance firms.

Suggested Citation

  • Segovia-Vargas, María Jesús & Salcedo-Sanz, Sancho & Bousoño-Calzón, Carlos, 2004. "Prediction Of Insolvency In Non-Life Insurance Companies Using Support Vector Machines, Genetic Algorithms And Simulated Annealing," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 79-94, May.
    • Handle: RePEc:fzy:fuzeco:v:ix:y:2004:i:1:p:79-94
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    More about this item

    Keywords

    insolvency; non-life insurance companies; support vector machines; genetic algorithms; simulated annealing.;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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