ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF)) - MUHAMMAD RIZAL; ALBERTUS EKA PUTRA HARYANTO; NI KADEK JULIARINI (Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang, 2024)
Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.