Forecasting Weekly Stock Price of Apple Inc by using ARIMA model

Auriellia Theresia Kusmanto

Abstract


Stock price forecasting remains a critical field in financial research, offering practical benefits for portfolio optimization and risk management. This study focuses on forecasting the weekly closing prices of Apple Inc. (AAPL) from March to April 2025 using the Autoregressive Integrated Moving Average (ARIMA) model. The objective is twofold: to assess the forecasting accuracy and to validate the statistical adequacy of the model. Employing the Box-Jenkins methodology, the process includes stationarity testing via the Augmented Dickey-Fuller test, model identification through ACF and PACF plots, parameter estimation, and residual diagnostics using the Shapiro-Wilk and Ljung-Box tests. ARIMA(6,1,0) was selected based on the lowest AIC and BIC values and diagnostic compliance. The model achieved a low Mean Absolute Percentage Error (MAPE) of 2.6%, indicating strong predictive accuracy. Forecasts were accompanied by 95% confidence intervals, enhancing interpretability. The results confirm that the ARIMA model is suitable for short-term financial forecasting where model transparency and statistical validity are essential.


Keywords


Forecasting; Apple; ARIMA; Box-Jenkins method

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References


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