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dc.contributor.authorMarček, Dušan
dc.date.accessioned2018-05-28T12:02:30Z
dc.date.available2018-05-28T12:02:30Z
dc.date.issued2018
dc.identifier.citationComplex & Intelligent Systems. 2018, vol. 4, issue 2, p. 95-104.cs
dc.identifier.issn2199-4536
dc.identifier.issn2198-6053
dc.identifier.urihttp://hdl.handle.net/10084/127195
dc.description.abstractFirst, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.cs
dc.format.extent836560 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofseriesComplex & Intelligent Systemscs
dc.relation.urihttps://doi.org/10.1007/s40747-017-0056-6cs
dc.rights© The Author(s) 2017cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectRBF neural networkscs
dc.subjectsupport vector machinescs
dc.subjectARMA/GARCH modelscs
dc.subjectvolatility modellingcs
dc.subjecterror-correction mechanismcs
dc.titleForecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statisticscs
dc.typearticlecs
dc.identifier.doi10.1007/s40747-017-0056-6
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume4cs
dc.description.issue2cs
dc.description.lastpage104cs
dc.description.firstpage95cs
dc.identifier.wos000432236000002


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© The Author(s) 2017
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