<![CDATA[Stock price volatility is one of the most critical investment risk indicators, particularly in Islamic banking stocks that face the dual challenges of conventional capital market dynamics and compliance with Islamic principles. This study compares the predictive performance of two modeling approaches: Long Short-Term Memory (LSTM), a deep learning architecture based on Recurrent Neural Networks, and the classical econometric ARIMA-GARCH model, in predicting stock price volatility of four Islamic banking issuers in Indonesia, namely BRIS (Bank Syariah Indonesia), BSIM (Bank Sinarmas Syariah), PNBS (Bank Panin Dubai Syariah), and BTPNS (Bank BTPN Syariah), for the period 2019–2024. Daily closing price data was obtained from the Indonesia Stock Exchange (IDX). The ARIMA-GARCH model was built through the stages of identification, estimation, and Box-Jenkins diagnostic testing, while the LSTM model was optimized through hyperparameter tuning with a 60-day rolling window. Predictive performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the LSTM model consistently produces lower MAPE values than ARIMA-GARCH for all issuers studied, especially during periods of high volatility such as the COVID-19 pandemic (2020) and global interest rate instability (2022–2023). However, the ARIMA-GARCH model provides better interpretability and is more stable under calm market conditions. This research contributes to the literature on Sharia-based quantitative finance in Indonesia and provides practical implications for investors and risk managers.]]>
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