Application Computational Intelligence Algorithms in Bankruptcy Prediction

This research identifies and creates a model with high accuracy of predicting and suitable performance in order to increase amount of accurate prediction in classified companies in bankrupt and nonbankrupt companies. Many academic researchers sought the best available information and techniques to explore bankruptcy prediction models and not only in developed countries but also in developing countries, researchers have tried numerously in order to make a new model and they have introduced several models. Thus, three models of Support Vector Machine and Genetic Algorithm and Algorithms of particle swarm optimization designed and implemented. Then, research hypotheses compared by ANOVA test and t-test. The results of the research indicates that of support vector machine had suitable performance compare with algorithms of particle swarm optimization in amongst listed companies in Tehran Stock Exchange for year of t and t-1. Moreover, the results show that support vector machine had better performance than algorithm genetic in year t. Create a model with high predictive ability can effectively improve the accuracy of bankruptcy forecast and on the other hand, this will lead to the discovery of better models. _________________________________________________________________________________ Mahdi Salehi Department of Accounting, Ferdowsi University of Mashhad, Iran Neda Rostami Department of Accounting, Science and Research Branch, Khorasan-e-Razavi, Neyshabur Branch, Islamic Azad University, Iran Web Journal of Chinese Management Review • Vol 18 • No 2 2 Introduction Altman (1968) believed that forced liquidation of insolvent companies is important, since it has numerous economic and social costs. Decision makers and users of financial information always have faced with making decision. Therefore, one of scientific and real index in financial decisions is correct evaluating of bankrupt probability. Bankruptcy prediction models are techniques and tools to predict the future state of the company, by using these models creditors, investors and other managers are able to predict bankruptcy many years prior its occurrence then the results of these models does the necessary measures to prevent the loss, waste of resources and stopping many researchers. Min and Jeong (2008) divided predicting bankruptcy models, first group is based on statistical techniques, and meanwhile mainstream studies have shown that the statistical models have good predictions but limited assumptions of some models have been effective like Linearity, normality, and independence of the predictive variables on the effectiveness of these methods. Thus, gradually and in accordance with the life cycle create other ways to deal with some or all of limitations and improve of performance which are based on artificial brain systems and the application of artificial intelligence in financial and accounting environment since 1990. Therefore, this study is trying to select a sample of 158 bankrupt and non-bankrupt Companies of Tehran Stock Exchange for the years 1385-1389 to examine the ability of each of the techniques in Support Vector Machine classifier, genetic algorithms and particle aggregation algorithms in predicting the bankruptcy deal and predicted before its occurrence in the consumer market, investors and others to help. Thus, in Section 2, this study examines variables and studies and procedures applied in the bankruptcy has been discussed in sections 3 and 4 of the models used and the experimental results are analyzed and the conclusions and recommendations in will be discussed in Section 5.