文章基本信息

标题：Oil Price Predictors: Machine Learning Approach
本地全文：下载
作者：Jaehyung An ; Alexey Mikhaylov ; Nikita Moiseev 等
期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2019
卷号：9
期号：5
页码：1-6
DOI：10.32479/ijeep.7597
出版社：EconJournals
摘要：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 and P500 index, Volatility 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.
关键词：Energy Innovative Start-ups; Knowledge-based Strategy; Human Capital Efficiency; Value Creation
其他关键词：oil price shocks; economic growth; oil impact; factors; dollar index; inflation; key rate; volatility index; S&P500 index