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Forecasting gold price changes by using adaptive network fuzzy inference system

Author

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  • Abdolreza Yazdani-Chamzini
  • Siamak Haji Yakhchali
  • Diana Volungevičienė
  • Edmundas Kazimieras Zavadskas

Abstract

Developing a precise and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination ( R -super-2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms of different performance criteria during the training and validation phases. Sensitivity analysis showed that the gold price changes are highly dependent upon the values of silver price and oil price.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jbemgt:v:13:y:2012:i:5:p:994-1010
    DOI: 10.3846/16111699.2012.683808
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Halit Yanikkaya & Mehmet Halis Saka & Hasan Karaboga, 2019. "On the Geographical Determinants of Bilateral Trade: ANFIS Approach," Working Papers 2019-01, Gebze Technical University, Department of Economics.
    3. 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.
    4. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
    5. 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.
    6. 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).
    7. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    8. Mohammad Mehdi Lotfinejad & Reza Hafezi & Majid Khanali & Seyed Sina Hosseini & Mehdi Mehrpooya & Shahaboddin Shamshirband, 2018. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies, MDPI, vol. 11(5), pages 1-15, May.
    9. 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).
    10. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

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