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2020-03-27Zeitschriftenartikel DOI: 10.18452/23703
Forex exchange rate forecasting using deep recurrent neural networks
dc.contributor.authorDautel, Alexander Jakob
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorLessmann, Stefan
dc.contributor.authorSeow, Hsin-Vonn
dc.date.accessioned2021-11-24T15:31:28Z
dc.date.available2021-11-24T15:31:28Z
dc.date.issued2020-03-27none
dc.identifier.other10.1007/s42521-020-00019-x
dc.identifier.urihttp://edoc.hu-berlin.de/18452/24365
dc.description.abstractDeep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learningeng
dc.subjectFinancial time series forecastingeng
dc.subjectRecurrent neural networkseng
dc.subjectForeign exchange rateseng
dc.subject.ddc330 Wirtschaftnone
dc.titleForex exchange rate forecasting using deep recurrent neural networksnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/24365-3
dc.identifier.doihttp://dx.doi.org/10.18452/23703
dc.type.versionpublishedVersionnone
local.edoc.container-titleDigital financenone
local.edoc.pages28none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionWirtschaftswissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameSpringer Nature Switzerland AGnone
local.edoc.container-publisher-placeChamnone
local.edoc.container-volume2none
local.edoc.container-issue1-2none
local.edoc.container-firstpage69none
local.edoc.container-lastpage96none
dc.description.versionPeer Reviewednone
dc.identifier.eissn2524-6186

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