The Effectiveness of Forecasting Gold Prices Using ARIMA Models

Research Article
Open access

The Effectiveness of Forecasting Gold Prices Using ARIMA Models

Zhaozhi Wang 1*
  • 1 Faculty of Finance, City University of Macau, Macau, China    
  • *corresponding author F22090108163@cityu.edu.mo
AEMPS Vol.185
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-141-9
ISBN (Online): 978-1-80590-142-6

Abstract

The global economic situation in 2025 is extremely variable, and gold as a stable financial asset is pursued by investors in times of high market volatility, which directly leads to a significant increase in demand compared to the past, and the price of gold is on an upward trend. This study collects daily data of gold price from 2014/1/2 to 2024/12/31, builds an ARIMA model based on the criterion of minimum AIC to predict the gold price under the stable economic situation, and also carries out residual analysis and accuracy analysis of the model to judge the predictive effect of the model. The empirical results show that ARIMA (2, 1, 2) has the best prediction effect on the gold price. By analyzing the residuals, this paper find that the errors of the model are not only well normally distributed, but also consistent with the nature of white noise sequences. This study proves that the gold price meets the efficient market hypothesis in the case of economic stability. At the same time, it is possible to compare how much economic fluctuations over a period of time affect the fluctuation of gold prices.

Keywords:

ARIMA, Time series analysis, Gold price, Efficient market hypothesis

Wang,Z. (2025). The Effectiveness of Forecasting Gold Prices Using ARIMA Models. Advances in Economics, Management and Political Sciences,185,232-241.
Export citation

References

[1]. Abdullah, L. (2012). ARIMA model for gold bullion coin selling prices forecasting. International Journal of Advances in Applied Sciences, 1(4), 153-158.

[2]. Gaspareniene, L., Remeikiene, R., Sadeckas, A., & Ginevicius, R. (2018). The main gold price determinants and the forecast of gold price future trends. Economics & Sociology, 11(3), 248-264.

[3]. Bunnag, T. (2024). The Importance of Gold’s Effect on Investment and Predicting the World Gold Price Using the ARIMA and ARIMA-GARCH Model. Ekonomikalia Journal of Economics, 2(1), 38-52.

[4]. Makala, D., & Li, Z. (2021, February). Prediction of gold price with ARIMA and SVM. In Journal of Physics: Conference Series (Vol. 1767, No. 1, p. 012022). IOP Publishing.

[5]. Demirezen, S. (2021). Forecasting of Gold Prices with ARIMA and Random Forest Regression and Evaluating of Their Prediction Performance. SSRJ| Social Sciences Research Journal| Online ISSN: 2147-5237, 10(03), 747-758.

[6]. Noureen, S., Atique, S., Roy, V., & Bayne, S. (2019). A comparative forecasting analysis of arima model vs random forest algorithm for a case study of small-scale industrial load. International Research Journal of Engineering and Technology, 6(09), 1812-1821.

[7]. Box, G. (2013). Box and Jenkins: time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (pp. 161-215). London: Palgrave Macmillan UK.

[8]. Yang, X. (2019, January). The prediction of gold price using ARIMA model. In 2nd International Conference on Social Science, Public Health and Education (SSPHE 2018) (pp. 273-276). Atlantis Press.

[9]. Yaziz, S. R., Azizan, N. A., Ahmad, M. H., & Zakaria, R. (2016). Modelling gold price using ARIMA-TGARCH. Applied Mathematical Sciences, 10(28), 1391-1402.

[10]. Tyralis, H., & Papacharalampous, G. (2017). Variable selection in time series forecasting using random forests. Algorithms, 10(4), 114.

[11]. Azan, A. N. A. M., Mototo, N. F. A. M. Z., & Mah, P. J. W. (2021). The Comparison between ARIMA and ARFIMA model to forecast kijang emas (gold) prices in Malaysia using MAE, RMSE and MAPE. Journal of Computing Research and Innovation, 6(3), 22-33.

[12]. Kuo, H. H. (2018). White noise distribution theory. CRC press.

Cite this article

Wang,Z. (2025). The Effectiveness of Forecasting Gold Prices Using ARIMA Models. Advances in Economics, Management and Political Sciences,185,232-241.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN：978-1-80590-141-9(Print) / 978-1-80590-142-6(Online)
Editor：Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.185
ISSN：2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Abdullah, L. (2012). ARIMA model for gold bullion coin selling prices forecasting. International Journal of Advances in Applied Sciences, 1(4), 153-158.

[2]. Gaspareniene, L., Remeikiene, R., Sadeckas, A., & Ginevicius, R. (2018). The main gold price determinants and the forecast of gold price future trends. Economics & Sociology, 11(3), 248-264.

[3]. Bunnag, T. (2024). The Importance of Gold’s Effect on Investment and Predicting the World Gold Price Using the ARIMA and ARIMA-GARCH Model. Ekonomikalia Journal of Economics, 2(1), 38-52.

[4]. Makala, D., & Li, Z. (2021, February). Prediction of gold price with ARIMA and SVM. In Journal of Physics: Conference Series (Vol. 1767, No. 1, p. 012022). IOP Publishing.

[5]. Demirezen, S. (2021). Forecasting of Gold Prices with ARIMA and Random Forest Regression and Evaluating of Their Prediction Performance. SSRJ| Social Sciences Research Journal| Online ISSN: 2147-5237, 10(03), 747-758.

[6]. Noureen, S., Atique, S., Roy, V., & Bayne, S. (2019). A comparative forecasting analysis of arima model vs random forest algorithm for a case study of small-scale industrial load. International Research Journal of Engineering and Technology, 6(09), 1812-1821.

[7]. Box, G. (2013). Box and Jenkins: time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (pp. 161-215). London: Palgrave Macmillan UK.

[8]. Yang, X. (2019, January). The prediction of gold price using ARIMA model. In 2nd International Conference on Social Science, Public Health and Education (SSPHE 2018) (pp. 273-276). Atlantis Press.

[9]. Yaziz, S. R., Azizan, N. A., Ahmad, M. H., & Zakaria, R. (2016). Modelling gold price using ARIMA-TGARCH. Applied Mathematical Sciences, 10(28), 1391-1402.

[10]. Tyralis, H., & Papacharalampous, G. (2017). Variable selection in time series forecasting using random forests. Algorithms, 10(4), 114.

[11]. Azan, A. N. A. M., Mototo, N. F. A. M. Z., & Mah, P. J. W. (2021). The Comparison between ARIMA and ARFIMA model to forecast kijang emas (gold) prices in Malaysia using MAE, RMSE and MAPE. Journal of Computing Research and Innovation, 6(3), 22-33.

[12]. Kuo, H. H. (2018). White noise distribution theory. CRC press.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.