A hybrid SOM-Altman model for bankruptcy prediction

Authors:
Egidijus Merkevicius;Gintautas Garšva;Stasys Girdzijauskas
Affiliations:
Department of Informatics, Kaunas Faculty of Humanities, Vilnius University, Kaunas, Lithuania;Department of Informatics, Kaunas Faculty of Humanities, Vilnius University, Kaunas, Lithuania;Department of Informatics, Kaunas Faculty of Humanities, Vilnius University, Kaunas, Lithuania
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

Citing 2
Cited 1

Credit Risk Assessment Using Statistical and MachineLearning: Basic Methodology and Risk Modeling Applications

Computational Economics - Computational Studies at Stanford
Bankruptcy prediction for credit risk using neural networks: A survey and new results

IEEE Transactions on Neural Networks

Neuro-discriminate Model for the Forecasting of Changes of Companies Financial Standings on the Basis of Self-organizing Maps

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

This article analyzes the problems of business bankruptcy, and the methods for bankruptcy prediction. This study proposed to join two models, one is the multi-discriminate Z-Score created by Altman, and the other is the Self-organizing maps. We proposed to generate self-organizing maps based on the financial data of public companies that are included in the NASDAQ list. These maps were used for bankruptcy prediction as well as creating classification of financial risk for Lithuanian companies. Comparing the weak results of prediction we accelerated by changing of ratios weights of the Altman Z-Score model. In this way, it can fit to conditions of the Lithuanian conjuncture. Based on the original ratio weights in Altman’s Z-Score the results predicting Lithuanian bankruptcy were weak. The weights of Altman’s Z-Score model were changed to fit the Lithuanian economic circumstance.