Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is by analysis of bankruptcy prediction. This paper develops an ontological model...

The paper discusses bankruptcy prediction model in the UK during the two last decades. My study is provided to support that the Original Altman’s Z-score (1968) might not valid to predict bankruptcy since the business environment changed a lot. However, there are many firms go to bankrupt recently and there is a need to study and improve the bankruptcy predictive ability. And the result shows t...

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit ri...

Knowledge discovery in databases (KDD) is the process of discovering interesting knowledge from large amounts of data. However, real-world datasets have problems such as incompleteness, redundancy, inconsistency, noise, etc. All these problems affect the performance of data mining algorithms. Thus, preprocessing techniques are essential in allowing knowledge to be extracted from data. This work...

This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21 st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay t...

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...