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
Highlights
For many years, oil has remained one of the most important sources of energy
The economy of many countries is based on oil production and trade in oil and oil products, forecasting oil prices is an important task
The purpose of this work is to identify factors affecting the price of oil and the creation of algorithm and machine learning based on a modified linear regression
Summary
Oil has remained one of the most important sources of energy. All countries are consumers of oil and oil products. The prices of oil and its derivatives are of interest to both producers and consumers. The dynamics of oil prices affect the level of costs in all sectors of production. The economy of many countries is based on oil production and trade in oil and oil products, forecasting oil prices is an important task. It is worth noting that some sectors of the economy are directly dependent on oil prices
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