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Forecasting Of Bankruptcy With The Self-Organizing Maps On The Basis Of Altman'S Z-Score

In: Computer Aided Methods In Optimal Design And Operations

Author

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  • E. MERKEVIČIUS

    (Informatics Department, Kaunas faculty of Humanities, Vilnius University, Muitines str.8, Kaunas, Lithuania)

Abstract

In financial institutions statistical and artificial intelligence methods have been used for determination of the credit risk classes. Recently, algorithms of the artificial neural networks were often applied, one of them is a self-organizing map (SOM): this is the two-dimensional map of the credit units in the process that is generated by similar characteristics (attributes), however, this process is not specified by network outputs. If in the cluster dominate credit units of one class, and then it is reasonable to use SOMs in forecast of bankruptcy of the company. Here was used the core statistical methodology on prediction for bankruptcy of corporate companies created by Altman so called Z-score model. The factors of bankruptcy were used as input data and Z-score values were used to define clusters of generated SOM. The results of our investigations were presented and they show that SOM is reliable method for bankruptcy prediction.

Suggested Citation

  • E. Merkevičius, 2006. "Forecasting Of Bankruptcy With The Self-Organizing Maps On The Basis Of Altman'S Z-Score," World Scientific Book Chapters, in: I D L Bogle & J Žilinskas (ed.), Computer Aided Methods In Optimal Design And Operations, chapter 18, pages 169-176, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812772954_0018
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