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dc.contributor.authorHillebrand, Martin-
dc.contributor.authorSchwendner, Peter-
dc.contributor.authorWinant, Bastien-
dc.contributor.authorMravlak, Marko-
dc.date.accessioned2020-11-19T09:20:10Z-
dc.date.available2020-11-19T09:20:10Z-
dc.date.issued2019-09-05-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20832-
dc.description.abstractThe European Rescue Fund ESM has, in its role as financial backstop of the Euro area, a specific interest in a comprehensive understanding of investor behaviour in order to ensure a stable and broad market access. With numerous transaction data as well as market and macro variables, a learning machine has been trained that forecasts investor demand in syndicated transactions. Out-of-sample tests show already a decent predictive power which is intended to be further improved by intelligent methods of data enhance-ment.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titlePredicting investor behaviour in European bond markets : a machine-learning approachde_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.conference.details4th European Conference on Artificial Intelligence in Finance and Industry, Winterthur, Switzerland, 5 September 2019de_CH
zhaw.funding.euNot specifiedde_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Management and Law

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