Abstract
This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.
Highlights
Kijang Emas is a gold bullion investment coin issued by the Bank Negara Malaysia
This study investigates the potential of Deep Learning techniques, Long Short-Term Memory (LSTM) networks, in forecasting Kijang Emas future value over a long period
The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average root mean square error (RMSE) was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models
Summary
Kijang Emas is a gold bullion investment coin issued by the Bank Negara Malaysia. Functioning primarily as an investment instrument, the Kijang Emas actual price is determined by the international gold market, despite having a RM200 face value. While the forecasting of gold bullion price is a well-researched area (Alameer et al, 2019; Hadavandi et al, 2010; He et al, 2019; Livieris et al, 2020; Pandey et al, 2019; Ping et al, 2013; Vidal & Kristjanpoller, 2020; Zhang & Liao, 2014), studies that predicts the Kijang Emas gold coin price remains limited. Yussof et al (2016) combined the GARCH model with a three-layer feed-forward Artificial Neural Network (ANN) that improved forecasting accuracy
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