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• W2963738379 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. JEL Classification: C51, C58, F31, G12, G15 DOI: https://doi.org/10.32479/ijeep.7597" @default.
• W2963738379 created "2019-07-30" @default.
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• W2963738379 date "2019-09-01" @default.
• W2963738379 modified "2023-10-18" @default.
• W2963738379 title "OIL PRICE PREDICTORS: MACHINE LEARNING APPROACH" @default.
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• W2963738379 doi "https://doi.org/10.32479/ijeep.7597" @default.
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