Oil Price Predictors: Machine Learning Approach

Jaehyung An, Alexey Mikhaylov and Nikita Moiseev
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Jaehyung An: College of Business, Hankuk University of Foreign Studies, Seoul, Korea
Nikita Moiseev: Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russia

International Journal of Energy Economics and Policy, 2019, vol. 9, issue 5, 1-6

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.

Keywords: oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&P500 index. (search for similar items in EconPapers)
JEL-codes: C51 C58 F31 G12 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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