Bankruptcy Prediction by Support Vector Machines and Multiple Discriminate Analysis Models

Ability to predict financial distress as one of the areas of risk management has various social and individual aspects. The aim of this study is to improve predicting financial distress process relying on two important processes. The first part of this article focuses on the financial distress predictor variables and then the two major models of financial distress prediction have been emphasized. For the goal, 20 financial ratios and efficiency of corporate as non-financial predictive variable, entering the two predictive models also differ in nature mean support vector machines and multiple discriminate analyses. The efficiency was computed by DEA. In order to have a real and effective estimate of predictive variables, by using feature selection method among variables, 10 financial ratios beside efficiency have chosen. Also to avoid of over fitting, cross-validation method with 12 v-folds has been conducted. As well as to compute the influence of efficiency on prediction accuracy, the prediction models were conducted by with and without efficiency respectively. Because of lack of the significant difference in the results of mentioned models, all hypotheses were rejected.