Short-term prediction of Indonesian gold prices using a weighted Markov chain approach
DOI:
https://doi.org/10.55749/rmm.v1i1.179Keywords:
Gold Investment, Gold price prediction, Indonesian gold price, MAPE, Weighted Markov chainAbstract
Gold is one of the most widely used investment assets because of its high economic value and relatively stable role during economic uncertainty. However, fluctuations in gold prices may affect investors’ potential gains and losses, making price prediction important as a consideration in investment decision-making. This study applies the weighted Markov chain method to predict Indonesian gold prices using weekly gold price data from January 2019 to December 2020. The data were grouped into two state classifications, namely six states and ten states, to evaluate the effect of state formation on prediction accuracy. Transition probability matrices were constructed for each classification, while the weights were determined based on autocorrelation coefficients. Prediction accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the predicted gold prices tend to remain in the same state over the prediction period. In the six-state classification, the predicted price was in state 5 with an estimated value of Rp833,701 and a MAPE of 0.66%. In the ten-state classification, the predicted price was in state 9 with an estimated value of Rp847,580 and a MAPE of 1.67%. These results indicate that the weighted Markov chain method provides very good short-term prediction accuracy. However, for longer prediction periods, the predicted values tend to become constant and are less able to capture volatile movements in actual gold prices. Therefore, the weighted Markov chain method is more suitable for short-term gold price prediction than for long-term forecasting.
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