Oil Price Predictors: Machine Learning Approach

Authors

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

Downloads

Download data is not yet available.

Author Biographies

Jaehyung An, College of Business, Hankuk University of Foreign Studies, Seoul, Korea

Assistant Professor of Operations Management

Alexey Mikhaylov, Financial University under the Government of the Russian Federation, Moscow, Russia

Department of financial markets and banks, Ph. D in Economics

Nikita Moiseev, Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, Moscow, Russia

 Associate professor, Ph.D 

Downloads

Published

2019-07-23

How to Cite

An, J., Mikhaylov, A., & Moiseev, N. (2019). Oil Price Predictors: Machine Learning Approach. International Journal of Energy Economics and Policy, 9(5), 1–6. Retrieved from https://econjournals.com/index.php/ijeep/article/view/7597

Issue

Section

Articles

Most read articles by the same author(s)