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All Journal Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Hamdani, Aldan Maulana
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Computer Science & IT Mathematics

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Perbandingan Akurasi Metode Autoregressive Integrated Moving Average dan Geometric Brownian Motion untuk Peramalan Harga Saham Indonesia Hamdani, Aldan Maulana; Widhiatmoko, Fery; Fitri, Sa'adatul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.30760

Abstract

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.
Hybrid Geometric Brownian Motion-Markov Switching untuk Peramalan Harga Saham Indonesia Hamdani, Aldan Maulana; Iriawan, Nur; Irhamah, Irhamah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.35300

Abstract

This study aims to analyse the performance of a stock price forecasting model based on Geometric Brownian Motion (GBM) modified with a Markov Switching (MS) approach. The research gap addressed is the limitation of the classical GBM model, which assumes constant volatility and is therefore unable to capture sudden changes in market regimes. To address these limitations, this study proposes a hybrid GBM-MS model as its main scientific contribution, in whichthe drift and volatility parameters are dynamically estimated following changes in market conditions through a switching mechanism between regimes. Parameter estimation is performed using the Hidden Markov Model. The model's performance is compared with the classical GBM as a benchmark. The research uses daily closing price data of PT Bank Central Asia Tbk. (BBCA) shares for the period 1 July – 30 December 2024. The results show that the hybrid GBM-MS model provides better forcasting accuracy with a MAPE value of 2.25% on training data and 1.38% on testing data, lower than the classical GBM model. These findings confirm that the integration of Markov Switching enhances the model's adaptability in capturing structural changes and market volatility. Practically, the hybrid GBM-MS model can be used as a more reliable forecasting and risk management tool to support investment decision-making, especially in dynamic and unstable market environments.
Co-Authors Fery Widhiatmoko Fitri, Sa'adatul Irhamah - Nur Iriawan