Abstract
Composite Stock Price Index (CSPI) can be used as a reflection of the national economic condition of a country because it is an indicator to know the development the capital market in a country. Therefore, the movement in the future needs to be forecast. This study aims to build a model for the time series forecasting of Indonesia Composite Index (ICI) using the ARIMA model. The data used is the monthly data of ICI in Indonesia Stock Exchange (IDX) from January 2000 until December 2017 as many as 216 data. The method used in this research is the Box-Jenkins method. The autocorrelation (ACF) and partial autocorrelation function (PACF) are used for stationary test and model identification. The maximum estimated likelihood is used to estimate the parameter model. In addition, to select a model then used Akaike’s Information Criterion (AIC). Ljung-Box Q statistics are used for diagnostic tests. In addition, to show the accuracy of the model, we use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and the most appropriate model is ARIMA (0, 1, 1).
Highlights
Investment managers need to accurately predict the CPSI in order to minimize the risk of the decision
This paper focuses on constructing a time series forecasting model with the Autoregressive Integrated Moving Average (ARIMA) model to be applied to Composite Stock Price Index (CSPI) data forecasting
The ARIMA model was first popularized by Box and Jenkins (1976), known as the Box-Jenkins method or the Box-Jenkins model
Summary
Investment managers need to accurately predict the CPSI in order to minimize the risk of the decision. Most of the central banks in the world generally use CPSI data as one of the considerations to determine monetary policy. Monetary policy was decided by considering the upcoming CPSI value. One tool to predict the CPSI value is to use a time series model. Capital market is the part of the financial system concerned with raising capital by dealing in shares, bonds, and other long-term investments. Where, it can support the development of the national www.scholink.org/ojs/index.php/ijafs
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