Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002600" target="_blank" >RIV/75081431:_____/23:00002600 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2571-9394/5/2/20" target="_blank" >https://www.mdpi.com/2571-9394/5/2/20</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns

Original language description

The price of oil is nowadays a hot topic as it affects many areas of the world economy. The price of oil also plays an essential role in how the economic situation is currently developing (such as the COVID-19 pandemic, inflation and others) or the political situation in surrounding countries. The paper aims to predict the oil price movement in stock markets and to what extent the COVID-19 pandemic has affected stock markets. The experiment measures the price of oil from 2000 to 2022. Time-series-smoothing techniques for calculating the results involve multilayer perceptron (MLP) networks and radial basis function (RBF) neural networks. Statistica 13 software, version 13.0 forecasts the oil price movement. MLP networks deliver better performance than RBF networks and are applicable in practice. The results showed that the correlation coefficient values of all neural structures and data sets were higher than 0.973 in all cases, indicating only minimal differences between neural networks. Therefore, we must validate the prediction for the next 20 trading days. After the validation, the first neural network (10 MLP 1-18-1) closest to zero came out as the best. This network should be further trained on more data in the future, to refine the results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50200 - Economics and Business

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Forecasting

ISSN

2571-9394

e-ISSN

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Volume of the periodical

5

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

374-389

UT code for WoS article

001014611200001

EID of the result in the Scopus database

2-s2.0-85163756347