Probabilistic Forcasting of Stock Prices Using a Hybrid ARIMA-Monte Carlo Simulation Approach

Febi Febrianti, Syaharuddin Syaharuddin, Vera Mandailina

Abstract


Abstract: This study aims to conduct probabilistic forecasting of the Farmer Exchange Rate (NTP) in Indonesia during the period 2015-2024 using an experimental quantitative approach through three methods: ARIMA, Monte Carlo Simulation (MCS), and the ARIMA-Monte Carlo hybrid model. The ARIMA model is used to capture linear patterns in historical data, while the Monte Carlo method is used to simulate stochastic uncertainty based on probability distributions. Furthermore, a hybrid approach was developed to integrate the advantages of both methods to improve accuracy and probabilistic representation in forecasting. Model performance was evaluated using MSE and MAPE. The results show that the Monte Carlo method has the best performance with an MSE value of 0.9051 and a MAPE of 0.72%. Meanwhile, the ARIMA method produces an MSE of 5.6991 and a MAPE of 1.46%. The ARIMA-Monte Carlo hybrid model shows an MSE value of 6.3288 and a MAPE of 1.87%. These findings indicate that in the context of NTP data, the Monte Carlo stochastic approach is superior to the ARIMA method and hybrid model in terms of prediction accuracy. This study contributes to the development of probabilistic forecasting methods that are more suitable for supporting decision-making in the agricultural sector.

Keywords


Farmers' Exchange Rate (NTP), ARIMA, Monte Carlo Simulation, ARIMA-Monte Carlo Hybrid Model.

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References


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  • Dr. Syaharuddin : +62 878-6400-3847
  • Dr. Intan Dwi Hastuti : +62 812-1611-9880