Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/93816
DC FieldValueLanguage
dc.contributor.authorSebastião, Helder Miguel Correia Virtuoso-
dc.contributor.authorGodinho, Pedro Manuel Cortesão-
dc.date.accessioned2021-03-19T21:29:04Z-
dc.date.available2021-03-19T21:29:04Z-
dc.date.issued2021-
dc.identifier.issn2199-4730pt
dc.identifier.urihttps://hdl.handle.net/10316/93816-
dc.description.abstractThis study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.pt
dc.language.isoengpt
dc.publisherSpringerpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectBitcoinpt
dc.subjectEthereumpt
dc.subjectLitecoinpt
dc.subjectMachine learningpt
dc.subjectForecastingpt
dc.subjectTradingpt
dc.titleForecasting and trading cryptocurrencies with machine learning under changing market conditionspt
dc.typearticle-
degois.publication.firstPage1pt
degois.publication.lastPage30pt
degois.publication.issue1pt
degois.publication.titleFinancial Innovationpt
dc.peerreviewedyespt
dc.identifier.doi10.1186/s40854-020-00217-xpt
degois.publication.volume7pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.openairetypearticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
crisitem.author.researchunitGroup for Monetary and Financial Studies-
crisitem.author.researchunitCeBER – Centre for Business and Economics Research-
crisitem.author.researchunitCeBER – Centre for Business and Economics Research-
crisitem.author.orcid0000-0002-1743-6869-
crisitem.author.orcid0000-0003-2247-7101-
Appears in Collections:FEUC- Artigos em Revistas Internacionais
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