The literature presents a clear divide between the so called market and accounting based approaches to bankruptcy prediction. With this in mind, this paper employs the discrete time hazard model framework of Shumway (2001) to test the information content of both market and accounting based models using Australian data. We …nd that market based models signi…cantly outperformed accounting based m...

The business failure has been widely researched, trying to identify the various determinants that can affect the existence of firms. However, the variety of models as well as the variety of the theoretical frameworks, illustrates the lack of consensus on how to understand the phenomenon and the difficulties in formulating a general model interpretation. One hotspot nowadays is the prediction of...

It is very important for financial institutions which are capable of accurately predicting business failure. In literature, numbers of bankruptcy prediction models have been developed based on statistical and machine learning techniques. In particular, many machine learning techniques, such as neural networks, decision trees, etc. have shown better prediction performances than statistical ones....

The aim is to find out the Altman Z-Score variables are able predict bankruptcy and determine significant effect between working capital/total assets, retained earnings/total earnings before interest taxes/total book value equity/total liabilities on predictions. Bankruptcy in Foreign Exchange BUS Indonesia for period 2013-2015. results of first test show that during 2013-2015 predicted be a he...

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purp...

In this paper models of default prediction conditional on financial statements of Austrian firms are presented. Apart from giving a discussion on the suggested 65 variables the issue of potential problems in developing rating models is raised and possible solutions are reviewed. A unique data set on credit risk analysis for the Austrian market is constructed and used to derive rating models for...

In this paper, we present a general framework for understanding the role of arti®cial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classi®cation theory. The method of cross-validation is used to examine the between-sample variation of neural net...