| Abstract [eng] |
During bankruptcies in Lithuania creditors in average receive 11,4 % of their claims. Bankruptcy prediction could reduce looses that shareholders and creditors encounter if it is done in time. This work describes definitions of the insolvency of commercial entities in Lithuania. It should be noticed that these definitions do not use companies financial data rather depending on different assessment methodologies. Classical bankruptcy prediction models are reviewed and used to build new prediction models based on neural networks. The new bankruptcy prediction model that was developed outperforms classical Z-Score models. This shows that neural networks can be used in upgrading old bankruptcy prediction models. One of the conclusions this research provides is that using detailed business activity category of commercial entity (EVRK code) combined with various financial ratios in prediction process improves its results. Historical financial accounting data of Lithuanian commercial entities has been used to train neural networks. The bankruptcy predictions model that was developed is optimized for Lithuanian commercial entities. |
