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

TL;DR

The paper tackles gold price forecasting in financial markets by proposing a hybrid pipeline that integrates ARIMA time-series models, stepwise regression-based variable selection, and a neural network. It demonstrates that separate ARIMA models for gold, oil, and EUR/USD can capture distinct dynamics, while stepwise regression identifies a compact, informative feature set that enhances predictive power. Feeding these selected features into a neural network yields a final forecast with state-of-the-art accuracy, achieving up to $99.29\%$ on the test set, surpassing traditional ARIMA and regression approaches. The work highlights the value of combining statistical, regression-based, and nonlinear learning methods for financial time series and suggests directions for extending the approach to other assets and incorporating alternative variable-selection techniques.

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.

Figures (14)

• Figure 1: The chart of daily closing prices in $ for gold in a specific time period.
• Figure 2: ACF for trained data of Gold daily prices.
• Figure 3: PACF for trained data of Gold daily prices.
• Figure 4: One difference in the closing price of gold for training data.
• Figure 5: ACF for one difference of Gold daily prices.