Sharia Bank of Indonesia Stock Price Prediction using Long Short-Term Memory
1Fahmi Poernamawatie,2I Nyoman Susipta,3Dwi Winarno
1,2,3 Gajayana University, Malang
https://doi.org/10.47191/jefms/v7-i7-94ABSTRACT:
This study explored the applicability of Long Short-Term Memory (LSTM) networks for predicting the closing prices of Sharia Bank of Indonesia stock. Utilizing historical data and a rigorous hyperparameter tuning process, the LSTM model demonstrated exceptional accuracy in forecasting closing prices, achieving a Mean Absolute Percentage Error (MAPE) of 2.46%. This significantly surpasses the benchmark for "very good" forecasting performance. The findings underscore the potential of LSTM networks for accurate stock price prediction and provide a foundation for future research to incorporate additional financial indicators and explore more complex model architectures.
KEYWORDS:
Stock Price Prediction, LSTM, Forecasting, Sharia Bank of Indonesia, Hyperparameter Tuning
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