IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v61y2019icp250-260.html
   My bibliography  Save this article

Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm

Author

Listed:
  • Alameer, Zakaria
  • Elaziz, Mohamed Abd
  • Ewees, Ahmed A.
  • Ye, Haiwang
  • Jianhua, Zhang

Abstract

Developing an accurate forecasting model for long-term gold price fluctuations plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting long-term monthly gold price fluctuations. This model uses a recent meta-heuristic method called whale optimization algorithm (WOA) as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model are compared to other models, including the classic NN, particle swarm optimization for NN (PSO–NN), genetic algorithm for NN (GA–NN), and grey wolf optimization for NN (GWO–NN). Additionally, we employ ARIMA models as the benchmark for assessing the capacity of the proposed model. Empirical results indicate the superiority of the hybrid WOA–NN model over other models. Moreover, the proposed WOA–NN model demonstrates an improvement in the forecasting accuracy obtained from the classic NN, PSO–NN, GA–NN, GWO–NN, and ARIMA models by 41.25%, 24.19%, 25.40%, 25.40%, and 85.84% decrease in mean square error, respectively.

Suggested Citation

  • Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    • Handle: RePEc:eee:jrpoli:v:61:y:2019:i:c:p:250-260
    DOI: 10.1016/j.resourpol.2019.02.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420718304926
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2019.02.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Sephton, Peter & Mann, Janelle, 2018. "Gold and crude oil prices after the great moderation," Energy Economics, Elsevier, vol. 71(C), pages 273-281.
    4. Reboredo, Juan C., 2013. "Is gold a safe haven or a hedge for the US dollar? Implications for risk management," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2665-2676.
    5. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    6. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    7. Fenghua Wen & Xin Yang & Xu Gong & Kin Keung Lai, 2017. "Multi-Scale Volatility Feature Analysis and Prediction of Gold Price," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 205-223, January.
    8. Lardic, Sandrine & Mignon, Valérie, 2008. "Oil prices and economic activity: An asymmetric cointegration approach," Energy Economics, Elsevier, vol. 30(3), pages 847-855, May.
    9. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    10. Schweikert, Karsten, 2018. "Are gold and silver cointegrated? New evidence from quantile cointegrating regressions," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 44-51.
    11. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    12. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    13. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
    14. Fan, Xinghua & Wang, Li & Li, Shasha, 2016. "Predicting chaotic coal prices using a multi-layer perceptron network model," Resources Policy, Elsevier, vol. 50(C), pages 86-92.
    15. Bodart, Vincent & Candelon, Bertrand & Carpantier, Jean-Francois, 2015. "Real exchanges rates, commodity prices and structural factors in developing countries," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 264-284.
    16. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    17. Aminrostamkolaee, Behnam & Scroggs, Jeffrey S. & Borghei, Matin Sadat & Safdari-Vaighani, Ali & Mohammadi, Teymour & Hossein Pourkazemi, Mohammad, 2017. "Valuation of a hypothetical mining project under commodity price and exchange rate uncertainties by using numerical methods," Resources Policy, Elsevier, vol. 52(C), pages 296-307.
    18. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    19. Chatterjee, Snehamoy & Sethi, Manas Ranjan & Asad, Mohammad Waqar Ali, 2016. "Production phase and ultimate pit limit design under commodity price uncertainty," European Journal of Operational Research, Elsevier, vol. 248(2), pages 658-667.
    20. Bouri, Elie & Jain, Anshul & Biswal, P.C. & Roubaud, David, 2017. "Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices," Resources Policy, Elsevier, vol. 52(C), pages 201-206.
    21. 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.
    22. Klein, Tony, 2017. "Dynamic correlation of precious metals and flight-to-quality in developed markets," Finance Research Letters, Elsevier, vol. 23(C), pages 283-290.
    23. 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.
    24. Abdolreza Yazdani-Chamzini & Siamak Haji Yakhchali & Diana Volungevičienė & Edmundas Kazimieras Zavadskas, 2012. "Forecasting gold price changes by using adaptive network fuzzy inference system," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(5), pages 994-1010, April.
    25. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Awe, Olushina O., 2017. "Time series analysis of co-movements in the prices of gold and oil: Fractional cointegration approach," Resources Policy, Elsevier, vol. 53(C), pages 117-124.
    26. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    27. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    28. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    29. Raza, Syed Ali & Shah, Nida & Shahbaz, Muhammad, 2018. "Does economic policy uncertainty influence gold prices? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 57(C), pages 61-68.
    30. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    31. Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
    32. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M, 2014. "On the economic determinants of the gold–inflation relation," Resources Policy, Elsevier, vol. 41(C), pages 101-108.
    33. Bhatia, Vaneet & Das, Debojyoti & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 55(C), pages 244-252.
    34. Ciner, Cetin, 2017. "Predicting white metal prices by a commodity sensitive exchange rate," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 309-315.
    35. He, Yanan & Wang, Shouyang & Lai, Kin Keung, 2010. "Global economic activity and crude oil prices: A cointegration analysis," Energy Economics, Elsevier, vol. 32(4), pages 868-876, July.
    36. Mei-Se, Chien & Shu-Jung, Chang Lee & Chien-Chiang, Lee, 2018. "Time-varying co-movement of the prices of three metals and oil: Evidence from recursive cointegration," Resources Policy, Elsevier, vol. 57(C), pages 186-195.
    37. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
    38. Reboredo, Juan C. & Ugolini, Andrea, 2016. "The impact of downward/upward oil price movements on metal prices," Resources Policy, Elsevier, vol. 49(C), pages 129-141.
    39. Sari, Ramazan & Hammoudeh, Shawkat & Soytas, Ugur, 2010. "Dynamics of oil price, precious metal prices, and exchange rate," Energy Economics, Elsevier, vol. 32(2), pages 351-362, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    2. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
    3. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    4. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    5. Fernandez, Viviana & Pastén-Henríquez, Boris & Tapia-Griñen, Pablo & Wagner, Rodrigo, 2023. "Commodity prices under the threat of operational disruptions: Labor strikes at copper mines," Journal of Commodity Markets, Elsevier, vol. 32(C).
    6. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    7. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    8. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    9. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    10. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
    11. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    12. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    13. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    14. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    15. Yıldırım, Durmuş Çağrı & Cevik, Emrah Ismail & Esen, Ömer, 2020. "Time-varying volatility spillovers between oil prices and precious metal prices," Resources Policy, Elsevier, vol. 68(C).
    16. Umar, Zaghum & Nasreen, Samia & Solarin, Sakiru Adebola & Tiwari, Aviral Kumar, 2019. "Exploring the time and frequency domain connectedness of oil prices and metal prices," Resources Policy, Elsevier, vol. 64(C).
    17. Huang, Jianbai & Dong, Xuesong & Chen, Jinyu & Zhong, Meirui, 2022. "Do oil prices and economic policy uncertainty matter for precious metal returns? New insights from a TVP-VAR framework," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 433-445.
    18. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    19. Nekhili, Ramzi & Sultan, Jahangir & Mensi, Walid, 2021. "Co-movements among precious metals and implications for portfolio management: A multivariate wavelet-based dynamic analysis," Resources Policy, Elsevier, vol. 74(C).
    20. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:61:y:2019:i:c:p:250-260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.