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OIL PRICE PREDICTORS: MACHINE LEARNING APPROACH

Jaehyung AnCollege of Business, Hankuk University of Foreign Studies, Seoul, KoreaAlexey MikhaylovFinancial University under the Government of the Russian Federation, Moscow, RussiaNikita MoiseevDepartment of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russia

2019en

ABI

Annotatsiya

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. Keywords: oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&P500 index. JEL Classification: C51, C58, F31, G12, G15 DOI: https://doi.org/10.32479/ijeep.7597

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Identifikatorlar

DOI: 10.32479/ijeep.7597

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