Oil Price Predictors: Machine Learning Approach

Oil Price Predictors: Machine Learning Approach

<p>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&amp;P500 index, VIX index, US consumer price index. After analyzing the results and comparing the accuracy of the model fir...

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Main Authors: Jaehyung An, Alexey Mikhaylov, Nikita Moiseev
Format: Article
Language:English
Published: EconJournals 2019-07-01
Series:International Journal of Energy Economics and Policy
Online Access:https://www.econjournals.com/index.php/ijeep/article/view/7597
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spelling doaj-8bfea3d7222f48b58a9e000fca11f6192020-11-25T03:00:09ZengEconJournalsInternational Journal of Energy Economics and Policy2146-45532019-07-0195163963Oil Price Predictors: Machine Learning ApproachJaehyung An0Alexey Mikhaylov1Nikita Moiseev2College of Business, Hankuk University of Foreign Studies, Seoul, KoreaFinancial University under the Government of the Russian Federation, Moscow, RussiaDepartment of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russia<p>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&amp;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.</p><p><strong>Keywords: </strong>oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&amp;P500 index.</p><p><strong>JEL Classification:</strong> C51, C58, F31, G12, G15</p><p>DOI: <a href="https://doi.org/10.32479/ijeep.7597">https://doi.org/10.32479/ijeep.7597</a></p>https://www.econjournals.com/index.php/ijeep/article/view/7597
collection DOAJ
language English
format Article
sources DOAJ
author Jaehyung An
Alexey Mikhaylov
Nikita Moiseev
spellingShingle Jaehyung An
Alexey Mikhaylov
Nikita Moiseev
Oil Price Predictors: Machine Learning Approach
International Journal of Energy Economics and Policy
author_facet Jaehyung An
Alexey Mikhaylov
Nikita Moiseev
author_sort Jaehyung An
title Oil Price Predictors: Machine Learning Approach
title_short Oil Price Predictors: Machine Learning Approach
title_full Oil Price Predictors: Machine Learning Approach
title_fullStr Oil Price Predictors: Machine Learning Approach
title_full_unstemmed Oil Price Predictors: Machine Learning Approach
title_sort oil price predictors: machine learning approach
publisher EconJournals
series International Journal of Energy Economics and Policy
issn 2146-4553
publishDate 2019-07-01
description <p>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&amp;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.</p><p><strong>Keywords: </strong>oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&amp;P500 index.</p><p><strong>JEL Classification:</strong> C51, C58, F31, G12, G15</p><p>DOI: <a href="https://doi.org/10.32479/ijeep.7597">https://doi.org/10.32479/ijeep.7597</a></p>
url https://www.econjournals.com/index.php/ijeep/article/view/7597
work_keys_str_mv AT jaehyungan oilpricepredictorsmachinelearningapproach
AT alexeymikhaylov oilpricepredictorsmachinelearningapproach
AT nikitamoiseev oilpricepredictorsmachinelearningapproach
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