Modeling of autoregressive moving average and vector autoregressive for forecasting stock price index in ASEAN countries

A country's stock price index is an important part to see, because it shows the country's indicators of high or low economic growth or development. A country is said to have a high economic growth rate if the country's stock price index increases every day. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the future period. In this research, modeling of share price of Indonesia with ASEAN countries (Association of South East Asia Nations) including developed and developing countries such as Malaysia, Singapore, Thailand, Philippines. These countries are the founders of ASEAN and have a good stock price index. The Indonesian stock price index (IDX, Indonesia Stock Exchange), Malaysia (KLCI, Kuala Lumpur Composite Index), Singapore (SGX, Singapore Exchange), Thailand (SETI), Thai Stock Exchange) and the Philippines (PSE, Philippine Stock Exchange) will affect each other one another. For this we need a model that is suitable for the case above, namely the pattern of relations between the country's stock price index. By using the ARIMA method (Autoregressive Moving Average) and VAR (Vector Autoregressive) the best model is obtained for the ASEAN stock price index. By using MAPE (Mean Absolute Percentage Error), the results for the best model data in ARIMA are obtained, except for Thailand, the best model is VAR. As for the sample data, the best model ARIMA was obtained for Thailand, Singapore, the Philippines and VAR for Indonesia and Malaysia.