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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
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    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.
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    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.

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    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

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