Bankruptcy prediction for Russian companies: Application of combined classifiers

The problem of bankruptcy forecasting is one of the most actively studied nowadays, posing the task of building effective classifiers as well as the task of dealing with dataset imbalance. In this paper, we apply different combinations of modern learning algorithms (MDA, LR, CRT, and ANNs) in order to try to identify the most effective approach to bankruptcy prediction for Russian manufacturing companies. Simultaneously, we try to find out whether the financial indicators stipulated by Russian legislation provide an effective set of indicators for bankruptcy prediction.