Forecasting the Stock Price of PT. Dayamitra Telekomunikasi with Single Input Transfer Function Model - Resti Arsanti; Neva Satyahadewi; Shantika Martha (Pattimura University, 2025)
The unpredictable movement of stock prices is often a challenge for investors, so it requires a deeper understanding and consideration of various factors before making investment decisions. One of the factors that affect stock price movements is trading volume. Therefore, this study uses a single input transfer function model to forecast the daily closing stock price of PT. Dayamitra Telekomunikasi, with the closing stock price as the output variable and the stock trading volume as the input variable. The transfer function is a forecasting model that integrates ARIMA with multiple regression analysis, allowing modeling not only based on the values of the output variables, but also considering the influence of the input variables. ARIMA model estimation is performed on the input series for the prewhitening process, then the order of the transfer function is determined using cross-correlation plots, as well as model diagnostic tests to ensure its feasibility. Model accuracy is calculated to evaluate its performance in forecasting. The data used in this study are daily data from the period July 5, 2022 to October 9, 2024. The transfer function model obtained has an order of (2,0,0), with a MAPE value of 1.09%, which indicates that the model has good accuracy. Based on the forecasting results, it is estimated that there will be a decrease in the share price of PT. Dayamitra Telekomunikasi Tbk for the next five periods