IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-16-00159.html
   My bibliography  Save this article

Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices

Author

Listed:
  • Manel Hamdi

    (International Finance Group Tunisia, El Manar University, Tunisia)

  • Chaker Aloui

    (College of Business Administration, King Saud University, Riyadh, Saudi Arabia)

  • Santosh kumar Nanda

    (Eastern Academy of Science and Technology Prachivihar, Anantapu, Odisha, 754001, India)

Abstract

In this paper a trigonometric functional link artificial neural network (FLANN) model using backpropagation rule is applied to predict the next day's spot price of US crude oil. The daily observations of these variables: US dollar index, S&P 500 stock price index, gold spot price, heating oil spot price and US crude oil spot price are employed as inputs of the proposed model. By comparing with multilayer backpropagation feedforward neural network (FNN), more accurate predictions were shown by applying the FLANN model. In fact, several performance criteria are used to assess the forecasting power of the proposed model such as the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and the hit rate. For checking the forecasting robustness of the proposed model, in addition to the other input variables, the US crude oil and biofuels production are also used to predict the next month's spot price of crude oil. Comparatively, similar conclusion was deduced and the FLANN model performs better than the standard FNN. These findings can be explained by the simplicity of FLANN structure since it consists of a single layer with only one neuron at the output thus a lower computational load on the network.

Suggested Citation

  • Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.
    • Handle: RePEc:ebl:ecbull:eb-16-00159
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2016/Volume36/EB-16-V36-I4-P235.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Siddhivinayak Kulkarni & Imad Haidar, 2009. "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices," Papers 0906.4838, arXiv.org.
    2. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    3. Dwiti Krishna Bebarta & Birendra Biswal & P.K. Dash, 2012. "Comparative study of stock market forecasting using different functional link artificial neural networks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(4), pages 398-427.
    4. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    5. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    6. Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
    7. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manel Hamdi & Walid Chkili, 2019. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers 13, Economic Research Forum, revised 21 Aug 2019.

    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. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    3. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
    4. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    5. Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
    6. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    7. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    8. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    9. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    10. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
    11. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    12. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    13. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    14. Xie Haibin & Zhou Mo & Hu Yi & Yu Mei, 2014. "Forecasting the Crude Oil Price with Extreme Values," Journal of Systems Science and Information, De Gruyter, vol. 2(3), pages 193-205, June.
    15. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    16. Heckelei, T. & Amrouk, E.M. & Grosche, S., 2018. "International interdependence between cash crop and staple food futures price indices: A wavelet-BEKK-GARCH assessment," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277376, International Association of Agricultural Economists.
    17. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    18. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    19. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    20. Mona Shazly & Alice Lou, 2016. "Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 22(4), pages 361-376, November.

    More about this item

    Keywords

    Crude oil price; Forecasting; Functional link artificial neural network (FLANN); Multilayer feedforward neural network (FNN).;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    Statistics

    Access and download statistics

    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:ebl:ecbull:eb-16-00159. 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: John P. Conley (email available below). General contact details of provider: .

    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.