Early warning model with statistical analysis procedures in Turkish insurance companies.


Isseveroglu Gulsun* and Gucenme Umit

In this study, we have developed and tested a statistical early warning model to identify companies experiencing deteriorating financial health by examining 45 insurance companies acting in non-life elementary branches of insurance during the period between 1992 and 2006. We developed the model using data regarding 45 dependent and 17 independent variables and logit model. The present study extends previous analyses by using relatively more comprehensive accounting data in logit analysis. This study compared the ability of logit, discriminant and regression analyses to predict insurance company underperformance. The same model, comprised of identical variables, was obtained as the result of the multiple regression and multiple discriminator methods. When comparing the predictive ability of all three models, the logit model showed slightly better prediction ability than the other models. The three models used information from 2003 - 2006 to predict the performance of insurance companies in 2007. The research demonstrates that logit analysis has a strong potential.

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