Development of A Forecasting Model for Investment in Tehran Stock Exchange Based on Seasonal Coefficient

The present study aims at suggesting a model for intelligent investment, through enabling us to be Autoregressive Integrated Moving Average of ARIMA and seasonal coefficient. In this study, the researcher uses seasonal fluctuation Model. The previous trend of time series, related to the companies for a period of 11 years, from 2006 to 2017, was carried out based on seasonal data. Then the researcher predicted the final price based on moving average method. In the next stage, the proportion of real final price and predicted the final price is calculated regarding each period. Then, the seasonal coefficient average is calculated for similar seasons. In the final stage, the value of a prediction, for a given period, is calculated when moving average method is multiplied by a seasonal coefficient average. As a result, seasonal coefficient of a given stock is derived.