Designing a model for predicting the financial bankruptcy of companies listed in the Tehran Stock Exchange using artificial neural networks and comparing it with the logit regression model

Considering the concerns that investors have about the return of principal and capital gains and the consequences and costs that bankruptcy can cause for companies and the country's economy and other individuals and institutions, the design of a reliable model In order to predict the probability of bankruptcy of companies, it seems necessary to guide decision makers such as investment companies, banks and the government.In this research, the artificial neural network method and the logit regression method were used to predict the bankruptcy of a number of companies admitted to the Tehran Stock Exchange during the years 2015 to 2019, and the results were compared with the logit regression method. The overall prediction accuracy of the artificial neural network method for each of the years t, t-1, t-2 and t-3 is equal to 96.55%, 96.55%, 92.24% and 24/2 respectively. 92% and for the logit regression method for the same years, it is 94%, 94.82%, 90.51% and 87.06% respectively, which showed that the artificial neural network method has a higher accuracy than the logit regression method. is. Therefore, it can be concluded that the artificial neural network method provides a more appropriate tool for predicting the bankruptcy of companies.