Providing a neural network model to predict the profits of companies listed on the Tehran Stock Exchange and comparing its accuracy with HDZ and ARIMA models‏‏

Profit forecasting is an important criterion for companies and companies listed on the Tehran Stock Exchange must be very careful in forecasting their profits. This study aims to provide a neural network model to predict the profits of companies listed on the Tehran Stock Exchange and compare its accuracy with ARIMA and HDZ models. The research method is an applied research in terms of purpose, an inductive research in terms of logic and a quantitative research in terms of data nature. In order to collect data, the basic financial statements of companies in the period 1398-1393 were used. In this study, neural network method was used to predict corporate profits and two models, ARIMA and HDZ, were evaluated. The results show that the rate of data convergence and regression in the first phase and in the HDZ method equal to 0.79087, in the second phase, in the ARIMA method, it is equal to 0.79184, and in the artificial neural network method, it is equal to 0.79464, which has a higher degree of convergence and regression coefficient. Based on the results, it can be seen that the designed neural network has the ability to predict stock price trends using general and industry indicators, and this, in addition to confirming the neural network's ability to predict financial areas and profitability it also confirms strategy of the price forecast on the Tehran Stock Exchange.‏ ‏‏