An interpretable decision-support systems for daily cryptocurrency trading

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dc.authoridDursun Delen / 0000-0001-8857-5148en_US
dc.authorscopusidDursun Delen / 55887961100en_US
dc.authorwosidDursun Delen / AGA-9892-2022
dc.contributor.authorDolatsara, Hamidreza Ahady
dc.contributor.authorKibis, Eyyub
dc.contributor.authorÇağlar, Musa
dc.contributor.authorŞimşek, Serhat
dc.contributor.authorDağ, Ali
dc.contributor.authorDolatsara, Gelareh Ahadi
dc.contributor.authorDelen, Dursun
dc.date.accessioned2022-05-20T10:01:20Z
dc.date.available2022-05-20T10:01:20Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractCryptocurrencies, especially Bitcoin (BTC), have become an important commodity for both individual and corporate investors within the last decade. The limited supply, high volatility, and random price fluctuations have increased investors' interest in BTC, especially in daily trading. Although BTC has been yielding a high rate of returns, price fluctuations and constant speculations make the investors wary of unexpected price movements. Predictive modeling suffers from the complexity of the datasets (i.e., the high number of features employed to forecast BTC movements) as well as the black-box nature of most machine learning algorithms (which is especially problematic for corporate investors since they are obligated to disclose their investment decisions to their clients). Therefore, the main goal of the current study is to assist individual and corporate investors in making transparent and interpretable daily BTC trading decisions by developing a predictive analytics framework. To address the complexities posed by the datasets, a comprehensive tri-level feature selection approach is proposed. The selected features are then, fed into the Classification & Regression Tree (C&RT) to build a highly parsimonious, transparent, and interpretable prediction model. The resultant model was not only evaluated on the test (holdout) sample but was also tested on challenging time periods, including the first half of 2020 (the start of the pandemic era) to exhibit the viability and reliability of the proposed framework. Finally, a decision support tool is developed for the practical implementation of the model. The tool can be used by short-term investors not only due to its highly simplistic, transparent, and interpretable structure, but also its higher accuracy, sensitivity, and specificity results when compared to the extant literature. © 2022 Elsevier Ltden_US
dc.identifier.citationDolatsara, H. A., Kibis, E., Caglar, M., Simsek, S., Dag, A., Dolatsara, G. A., & Delen, D. (2022). An interpretable decision-support systems for daily cryptocurrency trading. Expert Systems with Applications, 203 doi:10.1016/j.eswa.2022.117409en_US
dc.identifier.doi10.1016/j.eswa.2022.117409en_US
dc.identifier.issn0957-4174en_US
dc.identifier.scopus2-s2.0-85129770347en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.117409
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2679
dc.identifier.volume203en_US
dc.identifier.wosWOS:000803583200004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDelen, Dursun
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBitcoinen_US
dc.subjectC&RTen_US
dc.subjectCryptocurrencyen_US
dc.subjectDecision Support Systemsen_US
dc.subjectMachine Learningen_US
dc.subjectPredictive Analyticsen_US
dc.subjectPrice Predictionen_US
dc.titleAn interpretable decision-support systems for daily cryptocurrency tradingen_US
dc.typeArticleen_US

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