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Original Articles

El Análisis Discriminante en la Previsión de la Insolvencia en las Empresas de Seguros de no Vida

The Discriminant Analysis in the prediction of failure for non-life insurence companies

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Pages 183-233 | Received 05 Sep 2000, Accepted 20 Dec 2001, Published online: 15 Jan 2014
 

RESUMEN

En este trabajo se aplica el análisis discriminante a la previsión de la insolvencia de las empresas de seguros no vida utilizando ratios financieros como variables explicativas. En concreto, se construyen reglas de clasificación que permiten asignar una empresa al grupo de las potencialmente insolventes o al grupo de las potencialmente solventes en t + 1, utilizando datos de las cuentas anuales de períodos anteriores. El estudio se limita a cinco desfases, es decir, podremos realizar el diagnóstico de la situación en t + 1 utilizando datos desde t hasta t−4. Se utilizan conjuntos de ratios distintos para construir las reglas de clasificación en función del desfase de los datos. Estas reglas están basadas, por una parte, en la estimación de las funciones de distribución multivariantes de los conjuntos de ratios en cada grupo, incorporando también tanto las probabilidades a priori como los costes de clasificación errónea.

SUMMARY

In this study a discriminating analysis is applied to insolvency forecasts for non-lite insurance companies using financial ratios as explicative variables. Specifically, classification rules are constructed which allow a company to be assigned to the group of potentially insolvent companies in t + 1, using data from annual accounts of former periods. The study is confinad to five time periods, that is, we will be able to carry out the diagnosis of the situation in t + 1 using data from t to t−4. Combinations of diferents ratios are used to construct the classification rules according to the period covered by the data. These rules are basad on the one hand on the estimate of multivariable distribution functions of the combined ratios for each group, also incorporating both the a priori probabilities and the costs of mistaken classification.

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