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The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

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  • Wu, Binghui
  • Duan, Tingting

Abstract

The price of gold future is affected by many factors, which include the fluctuation of gold price and the change of trading environment. Fractal analysis can help investors gain better understandings of the price fluctuation and make reasonable investment decisions in the gold future market. After analyzing gold future price from January 2th, 2014 to April 12th, 2016 at the Shanghai Futures Exchange (SFE) in China, the conclusion is drawn that the gold future market has sustainability in each trading day, with all Hurst indexes greater than 0.5. The changing features of Hurst index indicate the sustainability of gold future market is strengthened first and weakened then. As a complicatedly nonlinear system, the gold future market can be well reflected by Elman neural network, which is capable of memorizing previous prices and particularly suited for forecasting time series in comparison with other types of neural networks. After analyzing the price trend in the gold future market, the results show that the relative error between the actual value of gold future and the predictive value of Elman neural network is smaller. This model that has a better performance in data fitting and predication, can help investors analyze and foresee the price tendency in the gold future market.

Suggested Citation

  • Wu, Binghui & Duan, Tingting, 2017. "The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 99-106.
    • Handle: RePEc:eee:phsmap:v:474:y:2017:i:c:p:99-106
    DOI: 10.1016/j.physa.2016.12.048
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    References listed on IDEAS

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    1. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    2. Kedong YIN & Hengda ZHANG & Wenbo ZHANG & Qian WEI, 2013. "Fractal Analysis of the Gold Market in China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 144-163, October.
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    Cited by:

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    2. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
    3. 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.
    4. Un, Kuok Sin & Ausloos, Marcel, 2022. "Equity premium prediction: Taking into account the role of long, even asymmetric, swings in stock market behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    5. Xiaowen Wang & Ying Ma & Wen Li, 2021. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    6. Thi Hong Van Hoang & Zhenzhen Zhu & Bing Xiao & Wing‐Keung Wong, 2020. "The seasonality of gold prices in China does the risk‐aversion level matter?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(3), pages 2617-2664, September.
    7. Emrah BALKAN & Umut UYAR, 2022. "The Fractal Structure of CDS Spreads: Evidence from the OECD Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 106-121, April.
    8. Depren, Özer & Kartal, Mustafa Tevfik & Kılıç Depren, Serpil, 2021. "Changes of gold prices in COVID-19 pandemic: Daily evidence from Turkey's monetary policy measures with selected determinants," Technological Forecasting and Social Change, Elsevier, vol. 170(C).

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