Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price

Basit Öğe Kaydı
dc.authorid0000-0001-9572-8923
dc.authorid0000-0001-6530-0598
dc.authorid0000-0002-4127-357X
dc.contributor.authorKose, Nezir
dc.contributor.authorGur, Yunus Emre
dc.contributor.authorUnal, Emre
dc.date.accessioned2026-01-31T15:08:09Z
dc.date.available2026-01-31T15:08:09Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVID-19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.
dc.description.sponsorshipTUBITAK
dc.description.sponsorshipThe authors would like to thank TUBITAK for supporting this publication.
dc.identifier.doi10.1002/for.3258
dc.identifier.endpage1698
dc.identifier.issn0277-6693
dc.identifier.issn1099-131X
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85218745077
dc.identifier.scopusqualityQ1
dc.identifier.startpage1666
dc.identifier.urihttps://doi.org./10.1002/for.3258
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10598
dc.identifier.volume44
dc.identifier.wosWOS:001434810700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Forecasting
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectBitcoin price
dc.subjectdeep learning
dc.subjectgold price
dc.subjectmachine learning
dc.subjectoil price
dc.titleDeep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price
dc.typeArticle

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