Research on Stock Price Forecasting Based on the ARIMA-GARCH Model: A Case Study of Apeloa Pharmaceutical

This study primarily investigates the application of the ARIMA-GARCH model in forecasting pharmaceutical industry stocks, with Apeloa Pharmaceutical chosen as the sample stock and the sample period spanning from 2020 to 2024. Initially, this research determines the order of the ARIMA model using information criteria, and subsequently conducts joint volatility modeling on the residual series of the ARIMA model. The modeling results demonstrate that all parameters exhibit favorable statistical significance. Based on the established model, backward forecasting is performed, yielding the MAPE of 3.81% between the forecasted values and actual values for the subsequent five periods. This indicates that the ARIMA-GARCH model can effectively contribute to the process of stock price forecasting. The empirical conclusions of this study provide robust theoretical underpinnings by validating the ARIMA-GARCH model's efficacy for pharmaceutical stocks. Practically, these findings enable more accurate equity price forecasts, refine portfolio risk management strategies through volatility insights, and guide dynamic asset allocation optimization.