See5 Algorithm versus Discriminant Analysis. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies
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.
|Date of creation:||2004|
|Contact details of provider:|| Phone: +34 913942604|
Web page: http://economicasyempresariales.ucm.es/
More information through EDIRC
|Order Information:|| Postal: Facultad de Ciencias Económicas y Empresariales. Campus de Somosaguas, 28223 - POZUELO DE ALARCÓN (MADRID)|
Web: https://economicasyempresariales.ucm.es/working-papers-ccee Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
- 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.
When requesting a correction, please mention this item's handle: RePEc:ucm:doctra:04-12. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Águeda González Abad)
If references are entirely missing, you can add them using this form.