Prediction of Stock Prices on The Indonesia Stock Exchange Using The Stochastic Process
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Abstract
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
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Afrizal, T., & Saputra, J. (2021). Investigating the Features of Indonesia Stock Price During Covid-19 pandemic: An Application of Merton Jump Diffusion Model.
Ardyanta, E. I., & SARI, H. (2021). A prediction of stock price movements using support vector machines in Indonesia. The Journal of Asian Finance, Economics and Business, 8(8), 399–407.
Chandra, A. S., Noviantri, V., Komsiyah, S., & Suhendar, A. (2023). Dynamic logistic models for stock price fluctuation during Covid-19 pandemic in Indonesia. AIP Conference Proceedings, 2975(1), 40001.
Di Asih, I. M., Trimono, T., & Mas’ ad, M. ad. (2022). Implementation of Stochastic Model for Risk Assessment on Indonesian Stock Exchange. Media Statistika, 15(2), 151–162.
Endri, E., Abidin, Z., Simanjuntak, T., & Nurhayati, I. (2020). Indonesian stock market volatility: GARCH model. Montenegrin Journal of Economics, 16(2), 7–17.
Fahmi, R. N., Sarno, R., Anggraini, R. N. E., & Haryono, A. T. (2024). Indonesian Stock Price Prediction Using Random Effect Model with Technical Indicators in Panel Data. 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C), 89–94.
Flavianus, M., & Maulani, A. (2025). American index exchange movement against IDX stochastic. Journal of Natural Sciences and Mathematics Research, 11(1), 92–102.
Hamdani, A. M., Iriawan, N., & Irhamah, I. (2025). Hybrid Geometric Brownian Motion-Markov Switching untuk Peramalan Harga Saham Indonesia. Euler: Jurnal Ilmiah Matematika, Sains Dan Teknologi, 13(3), 455–461.
Haryono, A. T., Sarno, R., & Sungkono, K. R. (2024). Stock price forecasting in Indonesia stock exchange using deep learning: A comparative study. International Journal of Electrical and Computer Engineering (IJECE), 14(1), 861–869.
Hersugondo, H., Ghozali, I., Handriani, E., Trimono, T., & Pamungkas, I. D. (2022). Price index modeling and risk prediction of sharia stocks in Indonesia. Economies, 10(1), 17.
Heru, M. C., Rachmawati, R. N., & Suhartono, D. (2021). Indonesian Banking Stock Price Prediction with LSTM and Random Walk Method. 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), 1, 385–390.
Irsan, M. Y. T., Antoro, S. C., & Ulya, A. H. (2024). Geometric Brownian Motion and artificial neural network in predicting stock prices of Indonesian state-owned enterprises (BUMN) banks. AIP Conference Proceedings, 2867(1), 20030.
Kahssay, M., & Miah, S. (2025). A comparative analysis of stochastic models for stock price forecasting: The influence of historical data duration and volatility regimes. Quantitative Finance and Economics (QFE), 9(3), 602–630.
Li, Z. (2025). Stock Price Prediction Based on LSTM-GBM: Evidence from Haier Smart Home.
Mardiah, R., & Sari, D. P. (2024). Determining the stock price of PT. BRI using generalization of the Wiener process and Monte Carlo simulation. AIP Conference Proceedings, 3024(1), 20014.
Paramasatya, A. E. K. A., & TJHIN, V. U. (2025). Prediction Of The Indonesia Stock Exchange Composite With Time-Series External And Technical Factors Using Artificial Neural Network. Journal of Theoretical and Applied Information Technology, 103(17).
Saepulrohman, A., Chairunnas, A., Denih, A., & Yasibang, N. D. S. (2025). Optimization of Stock Price Prediction Using Long Short-Term Memory (LSTM) Algorithm and Cross-Industry Standard Process Approach for Data Mining (CRISP-DM). International Journal of Electronics and Communications Systems, 5(1), 19–30.
Suciati, I., & Usman, M. (2023). Bayesian Structural Time Series Model for Forecasting the Composite Stock Price Index in Indonesia. Sciencestatistics: Journal of Statistics, Probability, and Its Application, 1(2), 74–83.
Suphawan, K., Kardkasem, R., & Chaisee, K. (2022). A Gaussian process regression model for forecasting stock exchange of Thailand. Trends in Sciences, 19(6), 3045.
Tampubolon, B., Tarigan, F. V. K., Daulay, N. H., & Hani, A. (2025). Apple Stock Price Prediction Using Stochastic Model: A Geometric Brownian Motion Study. Economic: Journal Economic and Business, 4(2), 117–124.