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...

Many static neural networks have been studied extensively in financial classification problems. However, dynamic time series predictive classification using neural networks with memory, such as the Gamma Memory neural network (GMNN), may prove more accurate. In this study we compare the predictive accuracy of the GMNN to the Multilayer Perceptron neural network and the statistical approaches of...

Bankruptcy prediction has been an important decision-making process for nancial analysts. One of the most common approaches for the bankruptcy prediction problem is the Discrim-inant Analysis. Also, the k-Nearest Neighbor classiier is very successful in such domains. This paper proposes a Feature Projection based classiication algorithm, and explores its applicability to the problem of predicti...

Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machines (SVM), a powerful classification method, has been used for this task, however, the performance of SVM is sensitive to model form, parameters setting and features sele...

Business failures can cause financial damages to investors, creditors, or even society. For this reason bankruptcy prediction is one of the most challenging tasks in the field of financial decisionmaking. Business failure prediction has been an active research area since the 60s. The work of Beaver (1966) who performed univariate analysis of financial ratios and the work of Altman (1968) who em...

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...