Bankruptcy assessment provides valuable information for the governments and investors to base their decisions in order to prevent possible financial losses. Data envelopment analysis (DEA) has generally been used to assess relative efficiency of decision making units. Recently, several approaches have appeared that reformulate DEA as a bankruptcy prediction tool. However, only several studies h...

Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important...

This study reintroduces the famous discriminant functions from Edward Altman and Begley, Ming and Watts (BMW) that were used to predict bankrupts. We will formulate three new discriminant functions which differ from Altman’s and BMW’s re-estimated Altman model. Altman’s models as well as Begley, Ming and Watts’s re-estimated Altman model apply to publicly traded industries, whereas the new mode...

Bankruptcy prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learn...

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble techniques are known to be very useful in improving the generalization ability of a classifier. The random subspace ensemble technique is a simple but effective method of constructing ensemble classifiers, in which some features are randomly d...

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy predict...

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversamp...

Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and ...