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

2025-05-02

AI Summary

This paper addresses the critical financial time-series forecasting problem of gold price prediction by proposing a novel three-stage hybrid model—ARIMA–stepwise regression–artificial neural network (ANN). The method first applies ARIMA to capture linear temporal dynamics, then models its residuals via stepwise regression incorporating technical indicators (RSI, MACD) and behavioral finance variables for interpretable feature selection, and finally employs an ANN to capture residual nonlinearities. This design achieves deep synergy among time-series dynamics, market technical signals, and heterogeneous investor sentiment features. Experimental results demonstrate substantial improvements over baseline models (single ARIMA, linear regression, and stepwise regression): the proposed model reduces mean absolute error (MAE) by 37.2% and achieves an R² of 0.941. These results validate the effectiveness and advancement of multi-source feature coupling in precious metal price forecasting.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Predict gold prices accurately using hybrid models
• Combine ARIMA, stepwise regression, and neural networks
• Compare hybrid model accuracy with traditional methods

Innovation

• Hybrid ARIMA and neural network model
• Stepwise regression for variable selection
• Combines technical and psychological factors