Predicting Financial Distress of firms Listed in Tehran Stock Exchange Using Bayesian networks

Financial distress and bankruptcy of companies may cause the resources to be wasted and the investment opportunities to be faded. Bankruptcy prediction by providing warnings can make the companies aware of the occurrence of bankruptcy and financial distress so that they could take appropriate decisions. The aim of this study is to model financial distress prediction of listed companies in Tehran stocks exchange (TSE) using Bayesian Networks (BNs). In order to accomplish this aim, two naïve bayes models and a logistic regression model using information of listed companies in Tehran Stock Exchange are developed. The accuracy of the first naïve bayes model's performance that is based upon conditional correlation is 90% and the accuracy of the second naïve bayes model that is based upon conditional likelihood is 93% and eventually the accuracy of the logistic regression model that is a linier model is 90%.