IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v102y2016icp365-374.html
   My bibliography  Save this article

Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

Author

Listed:
  • Wang, Jie
  • Wang, Jun

Abstract

In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models.

Suggested Citation

  • Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:365-374
    DOI: 10.1016/j.energy.2016.02.098
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216301608
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.02.098?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. Saif Ghouri, Salman, 2006. "Assessment of the relationship between oil prices and US oil stocks," Energy Policy, Elsevier, vol. 34(17), pages 3327-3333, November.
    2. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    3. Yao Yu & Jun Wang, 2012. "Lattice-oriented percolation system applied to volatility behavior of stock market," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 785-797, August.
    4. Xiao, Di & Wang, Jun, 2012. "Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4827-4838.
    5. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816.
    6. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    7. Wang, Tiansong & Wang, Jun & Zhang, Junhuan & Fang, Wen, 2011. "Voter interacting systems applied to Chinese stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2492-2506.
    8. Yoshio Kajitani & A. Ian Mcleod & Keith W. Hipel, 2005. "Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 105-117.
    9. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
    10. Oberndorfer, Ulrich, 2009. "Energy prices, volatility, and the stock market: Evidence from the Eurozone," Energy Policy, Elsevier, vol. 37(12), pages 5787-5795, December.
    11. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    12. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    13. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    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. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    2. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    3. Xiao, Di & Wang, Jun, 2021. "Attitude interaction for financial price behaviours by contact system with small-world network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    4. Di Xiao & Jun Wang & Hongli Niu, 2016. "Volatility Analysis of Financial Agent-Based Market Dynamics from Stochastic Contact System," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 607-625, December.
    5. Zhang, Wei & Wang, Jun, 2017. "Nonlinear stochastic exclusion financial dynamics modeling and time-dependent intrinsic detrended cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 29-41.
    6. Niu, Hongli & Wang, Jun & Lu, Yunfan, 2016. "Fluctuation behaviors of financial return volatility duration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 30-40.
    7. Zhang, Bo & Wang, Jun & Fang, Wen, 2015. "Volatility behavior of visibility graph EMD financial time series from Ising interacting system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 301-314.
    8. Niu, Hongli & Wang, Jun, 2013. "Complex dynamic behaviors of oriented percolation-based financial time series and Hang Seng index," Chaos, Solitons & Fractals, Elsevier, vol. 52(C), pages 36-44.
    9. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    10. Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.
    11. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    12. Zhang, Yali & Wang, Jun, 2017. "Nonlinear complexity of random visibility graph and Lempel-Ziv on multitype range-intensity interacting financial dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 741-756.
    13. Jia, Linlu & Ke, Jinchuan & Wang, Jun, 2019. "Volatility aggregation intensity energy futures series on stochastic finite-range exclusion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 370-383.
    14. Zhang, Bo & Wang, Guochao & Wang, Yiduan & Zhang, Wei & Wang, Jun, 2019. "Multiscale statistical behaviors for Ising financial dynamics with continuum percolation jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1012-1025.
    15. Wen, Xiaoqian & Guo, Yanfeng & Wei, Yu & Huang, Dengshi, 2014. "How do the stock prices of new energy and fossil fuel companies correlate? Evidence from China," Energy Economics, Elsevier, vol. 41(C), pages 63-75.
    16. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2020. "Fluctuation and volatility dynamics of stochastic interacting energy futures price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    17. Li, Su-Fang & Zhu, Hui-Ming & Yu, Keming, 2012. "Oil prices and stock market in China: A sector analysis using panel cointegration with multiple breaks," Energy Economics, Elsevier, vol. 34(6), pages 1951-1958.
    18. Zeng, Yayun & Wang, Jun & Xu, Kaixuan, 2017. "Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 364-376.
    19. Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
    20. Zolfaghari, Mehdi & Ghoddusi, Hamed & Faghihian, Fatemeh, 2020. "Volatility spillovers for energy prices: A diagonal BEKK approach," Energy Economics, Elsevier, vol. 92(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:energy:v:102:y:2016:i:c:p:365-374. 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.journals.elsevier.com/energy .

    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.