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Predicting the Price of Gold in the Financial Markets Using Hybrid Models

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  • Mohammadhossein Rashidi
  • Mohammad Modarres

Abstract

Predicting the price that has the least error and can provide the best and highest accuracy has been one of the most challenging issues and one of the most critical concerns among capital market activists and researchers. Therefore, a model that can solve problems and provide results with high accuracy is one of the topics of interest among researchers. In this project, using time series prediction models such as ARIMA to estimate the price, variables, and indicators related to technical analysis show the behavior of traders involved in involving psychological factors for the model. By linking all of these variables to stepwise regression, we identify the best variables influencing the prediction of the variable. Finally, we enter the selected variables as inputs to the artificial neural network. In other words, we want to call this whole prediction process the "ARIMA_Stepwise Regression_Neural Network" model and try to predict the price of gold in international financial markets. This approach is expected to be able to be used to predict the types of stocks, commodities, currency pairs, financial market indicators, and other items used in local and international financial markets. Moreover, a comparison between the results of this method and time series methods is also expressed. Finally, based on the results, it can be seen that the resulting hybrid model has the highest accuracy compared to the time series method, regression, and stepwise regression.

Suggested Citation

  • Mohammadhossein Rashidi & Mohammad Modarres, 2025. "Predicting the Price of Gold in the Financial Markets Using Hybrid Models," Papers 2505.01402, arXiv.org.
    • Handle: RePEc:arx:papers:2505.01402
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    References listed on IDEAS

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    3. Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
    4. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
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