An Application of Support Vector Machines in Bankruptcy Prediction; Evidence from Iran
نویسندگان
چکیده
Ability to predict corporate bankruptcy as one of the areas of risk management has various social and individual aspects. Timely warning of bankruptcy risk makes managers and investors able to do preventative measures. These measures consist of changing operational policy, financial restructuring and even optional treatment which by reducing potential losses, improve social and individual resource allocation finally. The purpose of this study is to improve predicting bankruptcy relying on two essential parts. In the first part of this paper, we focused on the predictor variables and in the second part, two major models of corporate bankruptcy prediction underlined. Therefore, financial ratios and efficiency of corporate entering the two support vector machines and multiple discriminant analysis predictive models. Final Results in the presence of efficiency and without it, suggest no change on the overall accuracy of these models. In spite of more accurate in performance of support vector machines compare with multiple discriminant analysis, because of the lack of significant difference, the findings not prove finality.
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