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Abstract
In the current era, many people have made investments, namely capital investment activities within a certain period to seek and get profits. One of the most popular investment instruments in the capital market is stocks, which consist of conventional stocks and Islamic stocks. Conventional stocks are shares traded on the stock market without adhering to Sharia principles. In contrast, Sharia-compliant stocks meet Islamic principles and are traded in the sharia capital market. One form of development of the Islamic capital market in Indonesia is the existence of the Indonesian Sharia Stock Index (ISSI), which projects the movement of all Islamic stocks on the Indonesia Stock Exchange (IDX). Stock prices change every day so modeling is needed that can be used by investors to determine decisions. The Autoregressive Integrated Moving Average (ARIMA) model is one of the forecasting models that is applicable. Stock prices have volatility that tends to be high, this results in variance that is not constant or there is a heteroscedasticity problem, at the same time the ARIMA model must fulfill the assumption of homoscedasticity. Therefore, it is necessary to combine the ARIMA model with a model that can overcome the problem of heteroscedasticity, namely the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This research aims to get the best hybrid ARIMA-GARCH model that will be used to forecast the stock price of the ISSI. The daily closing data of the ISSI stock price from May 4, 2020, to January 13, 2023, is the data that was used. The study’s findings suggest that ARIMA (0,1,3)-GARCH (2,0) is the best model among all possible models for ISSI stock price forecasting. By evaluating the predictive accuracy of the model using Mean Absolute Percentage Error (MAPE), the forecasting result for ISSI stock prices using the best model, ARIMA(0,1,3)-GARCH(2,0) at 0,6092%, shows a forecasting that is close to the actual data, which means that the model used is highly effective at forecasting stock priced
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