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Oil Price Predictors: Machine Learning Approach

Author

Listed:
  • Jaehyung An

    (College of Business, Hankuk University of Foreign Studies, Seoul, Korea)

  • Alexey Mikhaylov

    (Department of Financial Markets and Banks, Financial University Under the Government of the Russian Federation, Moscow, Russia,)

  • Nikita Moiseev

    (Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russia)

Abstract

The paper proposes a machine-learning approach to predict oil price. Market participants can forecast prices using such factors as: US key rate, US dollar index, S&P500 index, VIX index, US consumer price index. After analyzing the results and comparing the accuracy of the model first, we can conclude that oil prices in 2019-2022 will have a slight upward trend and will generally be stable. At the time of the fall in June 2012 the price of Brent fell to a minimum of 17 months. The reason for this was the weak demand for oil futures, which was caused by poor data on the state of the US labor market.

Suggested Citation

  • Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    • Handle: RePEc:eco:journ2:2019-05-1
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    oil price shocks; economic growth; oil impact; factors; dollar index; inflation; key rate; volatility index; S&P500 index.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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