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Forecasting gold price changes: Rolling and recursive neural network models

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Cited by:

  1. Devendra Joshi & Premkumar Chithaluru & Divya Anand & Fahima Hajjej & Kapil Aggarwal & Vanessa Yelamos Torres & Ernesto Bautista Thompson, 2023. "RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
  2. Wanbo Lu & Tingting Qiu & Wenhui Shi & Xiaojun Sun, 2022. "International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm," Complexity, John Wiley & Sons, vol. 2022(1).
  3. 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).
  4. 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).
  5. Yang, Mo & Wang, Ruotong & Zeng, Zixun & Li, Peizhi, 2024. "Improved prediction of global gold prices: An innovative Hurst-reconfiguration-based machine learning approach," Resources Policy, Elsevier, vol. 88(C).
  6. Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, vol. 334(1), pages 679-699, March.
  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. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
  9. Martha Gutiérrez & Giovanni Franco & Carlos Campuzano, 2013. "Gold prices: Analyzing its cyclical behavior," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 79, pages 113-142.
  10. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
  11. 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.
  12. You, Wanhai & Chen, Jianyong & Xie, Haoqi & Ren, Yinghua, 2025. "Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
  13. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
  14. 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).
  15. Ruan, Qingsong & Huang, Ying & Jiang, Wei, 2016. "The exceedance and cross-correlations between the gold spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 139-151.
  16. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
  17. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.
  18. Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
  19. Mohammadhossein Rashidi & Mohammad Modarres, 2025. "Predicting the Price of Gold in the Financial Markets Using Hybrid Models," Papers 2505.01402, arXiv.org.
  20. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  21. Sroka Łukasz, 2022. "Applying Block Bootstrap Methods in Silver Prices Forecasting," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(2), pages 15-29, June.
  22. Chi-Wei Su & Xu-Yu Cai & Ran Tao, 2020. "Can Stock Investor Sentiment Be Contagious in China?," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
  23. Ntim, Collins G. & English, John & Nwachukwu, Jacinta & Wang, Yan, 2015. "On the efficiency of the global gold markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 218-236.
  24. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
  25. Xian, Lu & He, Kaijian & Lai, Kin Keung, 2016. "Gold price analysis based on ensemble empirical model decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 11-23.
  26. 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.
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