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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan STATISTIKA
Lee, Muhammad Hisyam
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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STRENGTHENING SYARIAH FINANCIAL MARKETS WITH GARCH-BASED STOCK PRICE FORECASTING AND VAR-RISK ASSESSMENT Darmanto, Darmanto; Darti, Isnani; Astutik, Suci; Nurjannah, Nurjannah; Lee, Muhammad Hisyam; Damayanti, Rismania Hartanti Putri Yulianing; Irsandy, Diego
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1217-1236

Abstract

Indonesia, as the largest Muslim-majority country, has significant potential to enhance its Shariah financial sector, which has been growing rapidly, around 7.43% from 2023 to 2024, and contributing to the national economy. However, political and natural disasters have influenced the economy and Shariah-compliant stocks. This study focuses on forecasting Shariah-compliant stock prices using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and estimating investment risks via Value at Risk (VaR) for four Islamic banks listed in IDX: BRIS, BTPS, BANK, and PNBS. The findings indicate that GARCH models effectively capture stock price dynamics and provide accurate 10-day forecasts. Additionally, the models reliably predict VaR, validated through backtesting at various confidence levels. These insights are valuable for financial regulators and risk managers, aiding in policy design to ensure market stability by enabling the implementation of measures such as stricter capital reserve requirements for institutions with high-risk exposure and mandatory adoption of advanced risk management techniques like dynamic stress testing. Such policies not only mitigate systemic risks during periods of financial volatility but also enhance the overall resilience and robustness of the financial system. For investors, accurate risk predictions support informed decision-making, enhance portfolio protection, and optimize risk management.
Optimizing Train-Test Splits for LSTM and MLP Models in Bitcoin Price Forecasting Accuracy Kamisan, Nur Arina Bazilah; Lee, Muhammad Hisyam; Sulandari, Winita
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.6989

Abstract

Abstract. This study investigates the application and efficiency of two machine learning models, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP), for cryptocurrency price forecasting, using Bitcoin as a case study. MLP is a feedforward neural network that learns patterns from independent data, while LSTM is a recurrent network that remembers past information to handle sequential or time-series data. The rapid growth and volatility of cryptocurrencies underscore the need for accurate price predictions to support investor’s and trader’s decision-making. The study aims to identify the optimal train-test splitting ratio for each machine learning model and to forecast Bitcoin prices over a 120 days. The daily Bitcoin price data is obtained from the Bitcoin website recorded from January 2018 until March 2021. Model performance was evaluated using Akaike Information Criterion (AIC), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that both models exhibit strong predictive capabilities; the LSTM model consistently outperforms MLP in accuracy and reliability, achieving lower MAE, MAPE, and AIC values. These findings highlight LSTM’s effectiveness for forecasting volatile financial data and provide insights into selecting appropriate data-splitting ratios to improved model performance.
Co-Authors Damayanti, Rismania Hartanti Putri Yulianing Darmanto Darmanto Irsandy, Diego Isnani Darti Kamisan, Nur Arina Bazilah Nurjannah Nurjannah Suci Astutik Winita Sulandari