The Effect of Working Capital Information in Predicting Financial Distress Based on Combination of Artificial Neural Network and Particle Swarm Optimization Algorithm

The purpose of this research is to investigate the information of circulation capital management information for predicting financial helplessness based on artificial neural networks and particle cumulative optimization algorithms. The statistical population of the research consists of 120 companies listed in Tehran Stock Exchange during the years 2008-2019. In order to achieve the goals of the research, first, by studying previous studies in the field of financial distress, 28 variables affecting financial distress and then, using the leading logistic regression model, the estimated model and 5 variables were selected. Then, in order to verify the information of the information management information in circulation, comparing the research model with attention and regardless of the circulation of circulation management based on the combination of artificial neural networks and the optimization of the cumulative particle movement. The results of the two models based on the combination of artificial neural networks and the optimization algorithm of cumulative particle movement showed that the development of the research model reduced the error of neural network training with the cumulative particle movement algorithm to 0.0641. Also, with the development of the research model, the subcutaneous level of Rock increases to 6,248 and, consequently, the research model is added to 70.5%. This result shows the effectiveness of the entry of capital management in the research model.