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Please use this identifier to cite or link to this item: http://lib.hpu.edu.vn/handle/123456789/23679
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dc.contributor.authorGarcia, Daviden_US
dc.contributor.authorSchweitzer, Franken_US
dc.date.accessioned2016-10-11T05:37:51Z
dc.date.available2016-10-11T05:37:51Z
dc.date.issued2015en_US
dc.identifier.otherHPU4160597en_US
dc.identifier.urihttps://lib.hpu.edu.vn/handle/123456789/23679-
dc.description.abstractThe availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders.en_US
dc.format.extent13 p.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.subjectComputer scienceen_US
dc.subjectE-scienceen_US
dc.subjectBitcoinen_US
dc.subjectComputational social scienceen_US
dc.subjectAlgorithmic tradingen_US
dc.subjectPolarizationen_US
dc.subjectSentimenten_US
dc.subjectPredictionen_US
dc.titleSocial signals and algorithmic trading of Bitcoinen_US
dc.typeArticleen_US
dc.size777KBen_US
dc.departmentEducationen_US
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