Perbandingan Akurasi Metode Autoregressive Integrated Moving Average dan Geometric Brownian Motion untuk Peramalan Harga Saham Indonesia - Hamdani, Aldan Maulana; Widhiatmoko, Fery; Fitri, Sa'adatul (Universitas Negeri Gorontalo, 2025)
Investment is an activity of managing sources of funds with the goal of increasing profits within a certain period of time. The number of investors in the capital market, especially stock investments continue to increase. Stock movements result in returns that investors can obtain. Randomly fluctuating share prices make it difficult for investors to forecast share prices. This research helps investors in forecasting stock price movements based on PT. Gudang Garam Tbk. (GGRM) for the period 2022. This research aims to determine the level accuracy of the Geometric Brownian Motion (GBM) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting stock price movements. The accuracy level of the Mean Absolute Percentage Error (MAPE) for the GBM method is 1.68% and the ARIMA method forecasting results is 3.37%. The MAPE value of both methods is less than 10%, so it can be said that both methods are best fitting and have a high level of accuracy in forecasting stock price movements. The GBM method is better at forecasting stock prices because it is more realistic for financial asset price models because it includes volatility in the model.