Bankruptcy Prediction of Financially Distressed Companies using Independent Component Analysis and Fuzzy Support Vector Machines

The aim of research is to model the dependency of enterprises on their financial ratios for predicting bankruptcy using artificial neural networks combined with fuzzy logic. A sample of companies which are financially distressed but not yet bankrupt is considered for conducting experiments. The data extracted from financial reports of financially distressed companies for past five year forms the basis for carrying out prediction. Independent Component Analysis has been applied on the input dataset comprising of financial ratios to choose the most significant ratios to be considered as input to the neuro-fuzzy network. A data set of size 1030 consisting of five variables was used for training and of size 350 was used for testing the network’s performance. In this way the model predicts the bankruptcy status of the enterprises with minimal training errors. A linguistic diagnosis of failure or financial problems of the enterprises is done using the fuzzy rule-base for support vector machines. The proposed model can be utilized by stakeholders of a financial distressed company for determining its future status. It can also be used by managers of the enterprises to take preventive measures to deal with financial crises.