Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис: http://hdl.handle.net/20.500.12188/27395
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dc.contributor.authorTodorovska, Anaen_US
dc.contributor.authorMilev, Stefanen_US
dc.contributor.authorVodenska, Irenaen_US
dc.contributor.authorT. Chitkushev, Lubomiren_US
dc.contributor.authorTrajanov, Dimitaren_US
dc.date.accessioned2023-08-15T06:30:18Z-
dc.date.available2023-08-15T06:30:18Z-
dc.date.issued2023-07-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/27395-
dc.description.abstractIn a global world, no country, market, or economy is isolated and can function independently. Interconnectivity is a fundamental feature of economic systems, including both traditional financial markets and novel markets. This study aims to explore the relationships between traditional markets and commodities trade. We develop a methodology for analyzing the relationships between five stock market indexes (S&P500, Dow Jones, BSE, Hang Seng, FTSE) and three commodities (crude oil, natural gas, gold), based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of direc tional associations, we develop an Explainable ML model that first learns the dependencies between different assets and then explains them in a form understandable by humans. We apply our methodology to analyze connectivity networks between the assets and discuss our conclusions.en_US
dc.publisherSs Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedoniaen_US
dc.relation.ispartofseriesCIIT 2023 papers;20;-
dc.subjectstock market indexes, gold, crude oil, natural gas, networks, NLP, sentimenten_US
dc.titleAnalysis of the relationship between traditional markets and commodities tradeen_US
dc.typeProceeding articleen_US
dc.relation.conference20th International Conference on Informatics and Information Technologies - CIIT 2023en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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