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....

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...

Credit-risk models that are designed for general application across sectors may not be suitable the construction industry, which has unique characteristics and financial risks require specialised modelling approaches. Moreover, advanced bankruptcy-prediction often used to achieve highest accuracy in large modern datasets. Therefore, aim of this research is creation enterprise-bankruptcy predict...

In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small Medium Enterprises (SME’s) during the period 2002–2012. Using above-mentioned techniques, bankruptcies with global accuracy 82–83% only three ratios: ...

Bankruptcy prediction has been a subject of interests for almost a century and it still ranks high among hottest topics in economics. The aim of predicting financial distress is to develop a predictive model that combines various econometric measures and allows to foresee a financial condition of a firm. In this domain various methods were proposed that were based on statistical hypothesis test...