Prediction of Stock Prices on The Indonesia Stock Exchange Using The Stochastic Process

Santoso, Eko Budi

Immortalis Journal of Interdisciplinary Studies
Vol. 2 No. 1 (2026): January - March

This study aims to predict stock prices on the Indonesia Stock Exchange (IDX) using the Geometric Brownian Motion (GBM) model, a widely used stochastic process in financial markets. While previous research has applied GBM in global markets, its application in emerging markets, particularly Indonesia, remains limited. This research seeks to fill this gap by analyzing the potential of GBM in simulating stock price movements on the IDX. The method involves applying the GBM model to historical stock price data from IDX, using Monte Carlo simulations to generate multiple future price paths. The results show that the GBM model provides useful insights into potential future stock price trends and variability, but it is limited by assumptions of constant volatility and log-normal distribution. The novelty of this study lies in its application of GBM to the IDX, a market characterized by higher volatility and influenced by political and economic factors, offering new perspectives on financial forecasting in emerging markets. This research contributes to the body of knowledge by highlighting the strengths and limitations of using stochastic models in volatile market conditions