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 ...

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great att...

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...

Numerous studies on bankruptcy prediction have widely applied data mining techniques to finding out the useful knowledge automatically from financial databases, while few studies have proposed qualitative data mining approaches capable of eliciting and representing experts’ problem-solving knowledge from experts’ qualitative decisions. In an actual risk assessment process, the discovery of bank...

This paper presents a methodology which aims at detecting mislabeled samples, with a practical example in the field of bankruptcy prediction. Mislabeled samples are found in many classification problems and can bias the training of the desired classifier. This paper proposes a new method based on Extreme Learning Machine (ELM) which allows for identification of the most probable mislabeled samp...

Aims: This study aimed at predicting bankruptcy based on two data mining techniques, i.e. logistic regression and classification and regression tree (CART). Study design: This was an applied, descriptiveanalytical, cross-sectional study. Place and Duration of Study: This research was carried out in Iran. Annual financial statements of companies in Tehran stock market (Iran) during 1999-2010 wer...