Forecasting Weekly Stock Price of Gold Futures (GCM5) Using ARIMA Box-Jenkins Method
Keywords:
ARIMA, Forecasting, Stock PriceAbstract
The pattern of gold futures prices demands accurate forecasting to support rational decision-making on the part of investors and market experts. The study applies the ARIMA (Autoregressive Integrated Moving Average) model using the Box-Jenkins method for forecasting the weekly Gold Futures stock price (GCM5). The model was fit with 52 weekly closing prices from May 5, 2024, to April 27, 2025. For stationarity. Augmented Dickey-Fuller (ADF) test was conducted and then differencing. ACF, PACF, and "auto.arima(data)" were used to identify the model, and ARIMA(0,1,1) was found to be the best-fitting model. Diagnostic tests like Shapiro-Wilk and Ljung-Box tests confirmed the model fitness with residuals normally distributed and not having any serious autocorrelation. The forecast accuracy was checked using the Mean Absolute Percentage Error (MAPE), which yielded a value of 0.72%, indicating a very precise prediction. These results confirm that the ARIMA(0,1,1) model is a good representation of the trend and volatility of gold futures prices. This study provides real-world information to market participants and demonstrates the continued relevance of traditional time series models to financial forecasting.References
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