IHSG Closing Price Prediction on the Indonesian Stock Exchange using the Geometric Brownian Motion Model - Sukra Hamna; Devni Prima Sari (UNP Journal of Statistics and Data Science, 2026)
Being among the leading primary benchmarks reflecting the health of the equity market in Indonesia, the Jakarta Composite Index (IHSG) experiences ongoing price movements shaped by a wide spectrum of domestic and international forces. The inherent unpredictability of these movements underscores the critical need for reliable forecasting methods to guide investors in their decision-making process. In response to this, the present study applies the Geometric Brownian Motion model as a tool for projecting the daily closing values of the IHSG, owing to its well-recognized ability to represent the random characteristics inherent in financial time series. The dataset utilized comprises daily closing price records of the IHSG throughout 2025. The analysis includes the calculation of log returns, normality testing using the Kolmogorov-Smirnov test, and estimation of drift and volatility parameters. Forecasting is performed using simulation with 50 and 1000 iterations, where the initial value is based on the last observed closing price. The findings reveal that the Geometric Brownian Motion model demonstrates a solid capacity to reflect the volatile behavior of IHSG movements, yielding MAPE figures of 4.50% and 2.81%, which correspond to a very high level of predictive precision. A greater number of iterations was found to produce more consistent and dependable projections, while the estimated values broadly align with the overall trajectory of historical data, notwithstanding the element of randomness embedded in the model. Therefore, the GBM model can be considered an effective method for forecasting stock price movements, particularly for highly volatile market indices such as the IHSG.