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...

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...

This study provides operational guidance for using naïve Bayes Bayesian network (BN) models in bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors from a pool of potential variables. The method is based upon the assumption that the joint distribution of the variables is multivariate normal. Variables are selected based upon correlations...