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Oil Market Efficiency under a Machine Learning Perspective

Author

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  • Athanasia Dimitriadou

    (Department of Economics, Democritus University of Thrace, Komotini 69100, Greece)

  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, Komotini 69100, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, Komotini 69100, Greece)

  • Vasilios Plakandaras

    (Department of Economics, Democritus University of Thrace, Komotini 69100, Greece)

Abstract

Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Suggested Citation

  • Athanasia Dimitriadou & Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras, 2018. "Oil Market Efficiency under a Machine Learning Perspective," Forecasting, MDPI, vol. 1(1), pages 1-12, October.
    • Handle: RePEc:gam:jforec:v:1:y:2018:i:1:p:11-168:d:175388
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    References listed on IDEAS

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    2. Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.

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