javad masodi

Today, profit rates and the possibility of managing and manipulating the profits are clear to all, and researchers have always sought solutions to remove the uncertainties facing investors and stakeholders when making their financial decisions. To clarify users' decision path of financial data users, Beneish (1999) has developed a profit-management predicting model that has yielded different results in different societies. Thus, this article aims to optimize and localize Beneish’s model by adding the Tunneling variable to Beneish’s variable and using a modern neural network and particle swarm algorithms. The statistical research population consisted of 196 companies listed at the Tehran Stocks Exchange from 2014 to 2019. The research method was a descriptive-library method in which the variables are interrelated through the causal-correlational method. From an objective point of view, it is an Ex-Post Facto research design. To analyze the data, the regression method and artificial neural and the PSO algorithms were used. The model analysis results suggested that all financial ratios had significant effects on Beneish’s profit management, as the Tunneling phenomenon and the financial leverage had the highest and lowest effects on predicting Beneish’s profit management, respectively.

The purpose of this study is to optimize the Bayesian profit management model with tunneling phenomenon and cumulative particle motion optimization algorithm. The statistical population of the study included companies listed in the Tehran Stock Exchange and the number of companies under study, including 196 companies listed during the years 2015 to 2020. The research method is descriptive-correlational and in terms of causal-correlational variables and in terms of purpose and event, it is post-event. In order to analyze the data, regression and artificial neural network and cumulative particle motion optimization algorithm were used. The results of the model analysis showed that all financial ratios had a significant effect on the earnings management prediction of insight and the greatest impact on the prediction of earnings management was on the INE tunneling phenomenon and the least on financial leverage. The results of the estimation of the designed neural networks show that the use of cumulative particle optimization algorithm to predict the Profit management for companies listed in Tehran Stock Exchange is acceptable.