A COMPARATIVE ANALYSIS OF FINANCIAL PERFORMANCE FORECASTING MODELS: ARIMA, ARIMA-GARCH & LSTM IN INDONESIAN BANKING STOCKS
DOI:
https://doi.org/10.35794/jmbi.v12i1.61515Abstract
The banking sector is a crucial generator of economic activity and financial stability in Indonesia's stock market, so precise forecasting of bank stock prices is critical for making informed investment decisions. This study evaluates the forecasting performance of ARIMA, ARIMA-GARCH, and Long Short-Term Memory (LSTM) models for predicting daily closing prices of five key Indonesian banking stocks: BBCA.JK, BBNI.JK, BBRI.JK, BMRI.JK, and BNGA.JK. Using historical data from January 2022 to December 2024, the models are evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the ARIMA model consistently outperforms ARIMA-GARCH and LSTM across all equities and measures. ARIMA has the lowest average MAE, RMSE, and MAPE values, at 74.46, 93.01, and 1.297%, respectively, demonstrating its reliability for static short-term price projections, surpassing both ARIMA-GARCH and LSTM in static forecasting. While ARIMA- GARCH incorporates volatility modelling, it provides only marginal improvements, and LSTM exhibits the weakest performance with higher error rates. These findings indicate that traditional econometric models, particularly ARIMA, are still excellent tools for projecting stock values in Indonesia's banking sector, allowing investors and analysts to make more informed decisions.
Keywords: Stock price prediction, Banking, ARIMA, ARIMA-GARCH, LSTM
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