首页    期刊浏览 2024年11月08日 星期五
登录注册

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
  • 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
国家哲学社会科学文献中心版权所有