Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

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dc.authoridAteş, Gizem/0000-0002-2678-5999;
dc.authorwosidAteş, Gizem/ABH-5371-2020
dc.authorwosidTosunoglu, Nuray/AAI-7892-2021
dc.contributor.authorTosunoglu, Nuray
dc.contributor.authorAbaci, Hilal
dc.contributor.authorAtes, Gizem
dc.contributor.authorAkkaya, Neslihan Saygili
dc.date.accessioned2024-08-04T20:53:41Z
dc.date.available2024-08-04T20:53:41Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAnomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil's U1, and R-OOS(2) was used for out- of-sample. The Diebold-Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day- of-the-week anomaly for ETH and ADA was found.en_US
dc.identifier.doi10.1186/s40854-023-00499-x
dc.identifier.issn2199-4730
dc.identifier.issue1en_US
dc.identifier.pmid37192903en_US
dc.identifier.scopus2-s2.0-85158091024en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s40854-023-00499-x
dc.identifier.urihttps://hdl.handle.net/11616/101337
dc.identifier.volume9en_US
dc.identifier.wosWOS:000984676600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofFinancial Innovationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCryptocurrencyen_US
dc.subjectBitcoinen_US
dc.subjectEthereumen_US
dc.subjectCardanoen_US
dc.subjectDay-of-the-week anomalyen_US
dc.subjectArtificial neural networken_US
dc.titleArtificial neural network analysis of the day of the week anomaly in cryptocurrenciesen_US
dc.typeArticleen_US

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