Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
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OECD FORD obor
50200 - Economics and Business
Návaznosti výsledku
Projekt
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Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Forecasting
ISSN
2571-9394
e-ISSN
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Svazek periodika
5
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
16
Strana od-do
374-389
Kód UT WoS článku
001014611200001
EID výsledku v databázi Scopus
2-s2.0-85163756347