Forecasting Weekly Stock Price of PT. Bank Central Asia Tbk (BBCA) Using ARIMA Box-Jenkins Method

Abstract

The dynamic fluctuation of stock prices in financial markets highlights the importance of accurately forecasting future price movements, particularly for investors and financial institutions making strategic decisions, This study focuses on forecasting the weekly stock price of PT Bank Central Asia Tbk (BBCA) using the Autoregressive Integrated Moving Average (ARIMA) method based on the Box-Jenkins approach. The dataset consists of weekly closing stock prices from January 7, 2024, to December 29, 2024, totaling 51 observations obtained from Investing.com. Following data preprocessing, including stationarity testing and model selection. through ACF and PACF analysis, several ARIMA models were evaluated. The ARIMA(2,2,0) model was identified as the best-fitting model based on error metrics, achieving a Mean Absolute Percentage Error (MAPE) of 2.29%. This result demonstrates a high level of forecasting accuracy, providing valuable insights for investment planning and financial risk management. The findings confirm that ARIMA remains a reliable and practical method for short-term stock price forecasting in dynamic financial environments.