Optimizing Banking Stock Prediction with Single Layer Long Short-Term Memory

Abstract

The inherent volatility of the stock market and increasing investor engagement, especially in the banking sector, highlight the critical need for accurate stock price prediction. This research develops and evaluates a single-layer Long Short-Term Memory (LSTM) model to forecast the closing prices of six major Indonesian banking stocks: PT Bank Central Asia Tbk (BBCA.JK), PT Bank Rakyat Indonesia Tbk (BBRI.JK), PT Bank Mandiri Tbk (BMRI.JK), PT Bank Negara Indonesia Tbk (BBNI.JK), PT Bank MEGA Tbk (MEGA.JK), and PT Bank Tabungan Negara Tbk (BBTN.JK). A single-layer LSTM architecture was systematically tested using 4 distinct hyperparameter including number of neurons, batch size, optimizers, and learning rates. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that a single-layer LSTM model with 100 neurons achieved superior accuracy with lowest MAPE of 1.95% and RMSE of 106.54 on BBNI stock compared to configurations with 50 or 200 neurons. Furthermore, with 100 neuron and 0.001 learning rate setups, the Adam and Adamax optimizers consistently outperformed RMSProp across all stocks prediction. These findings provide practical guidance on hyperparameter tuning for LSTM-based stock prediction in the Indonesian banking industry.