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Forecasting of Bankruptcy with the Self-organizing Maps on the Basis of Altman's Z-score
| Content Provider | Semantic Scholar |
|---|---|
| Author | Merkevicius, Egidijus |
| Copyright Year | 2006 |
| Abstract | AbstractIn 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. |
| Starting Page | 169 |
| Ending Page | 176 |
| Page Count | 8 |
| File Format | PDF HTM / HTML |
| DOI | 10.1142/9789812772954_0018 |
| Alternate Webpage(s) | http://www.mii.lt/inys/Merkevicius.pdf |
| Alternate Webpage(s) | https://www.mii.lt/inys/Merkevicius.pdf |
| Alternate Webpage(s) | https://doi.org/10.1142/9789812772954_0018 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
