the artificial neural networks for investigation of correlation between economic variables and stock market indices

‎in this research‎, ‎we investigated the interactive effects between the macroeconomic variables of currency‎, ‎gold‎, ‎and oil on two indicators of total and equal weighted indices considering the importance of correlation between economic variables and stock market indices‎. ‎in this regard‎, ‎the analysis of pearson correlation and regression coefficients have been used to investigate the existence of an interactive effect among them‎, ‎and a multi-layer perceptron neural network (mlp nn) model has been used to simulate this effect‎. ‎the models have been fitted as a time series based on the daily data related to the economic variables and the mentioned indicators during march 2016 to that of 2021‎. ‎investigating the interactive effects between variables has been done using spss statistical software‎, ‎and artificial neural network (ann) simulation developed in matlab programming environment‎. ‎the extracted results indicate the existence of an interactive effect among these economic variables‎. ‎the simulation results show the high ability of ann in modeling and predicting the total price and equal-weighted indices‎, ‎and this model has been able to make more accurate predictions by considering these interactive effects as well‎.