Application of the ARIMAX Model in Assessing the Impact of Global Stock Price Index on Forecasting Indonesia's IHSG - Marzuki, Marzuki; Riswanda, Muhammad; Nurhasanah, Nurhasanah; Rusyana, Asep (Transcendent Journal of Mathematics and Applications, 2024)
Time series analysis can be classified into two parts when viewed based on the analysis data variables, namely univariate and multivariate time series analysis. The ARIMAX model is the development of the ARIMA model. The ARIMAX model is a multivariate time series analysis method consisting of exogenous and endogenous variables. This study aims to forecast the Composite Stock Price Index (IHSG) using the ARIMAX model, by looking at the influence of global stock price indices, namely the American stock price index (DJIA), Japanese stock price index (N225), and Chinese stock price index (SSEC). The results of the study show that the model used to forecast the 2019 IHSG is the ARIMAX model (4,1,4). The results of the 2019 IHSG forecast produce fluctuating data. The highest IHSG share price of 6,958.419 occurred in November, while the lowest share price of 6.591.566 occurred in January. Forecast accuracy measured using RMSE and MAPE obtained results of 144.5387 and 2.5121 respectively.