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hal.structure.identifierLaboratoire d'Economie de Dauphine [LEDa]
dc.contributor.authorBenoit, Sylvain*
hal.structure.identifierSILEX INTERNATIONAL
dc.contributor.authorRaffinot, Thomas*
dc.date.accessioned2019-09-19T14:17:07Z
dc.date.available2019-09-19T14:17:07Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19851
dc.language.isoenen
dc.subjectRandom Forest
dc.subjectBoosting, Economic cycles
dc.subjectProfit maximization measures
dc.subjectModel Confidence Set
dc.subjectMachine Learning
dc.subjectTurning Points Detection
dc.subjectC53
dc.subjectE32
dc.subjectE37
dc.subjectG1
dc.subject.ddc339en
dc.subject.classificationjelC.C5.C53en
dc.subject.classificationjelE.E3.E32en
dc.subject.classificationjelE.E3.E37en
dc.subject.classificationjelG.G1.G10en
dc.titleInvesting Through Economic Cycles with Ensemble Machine Learning Algorithms
dc.typeDocument de travail / Working paper
dc.description.abstractenEnsemble machine learning algorithms (random forest and boosting) are applied to quickly and accurately detect economic turning points in the United States and in the Eurozone over the past three decades. The two key features of those algorithms are their abilities (i) to entertain a large number of predictors and (ii) to perform both variable selection and estimation simultaneously. The real-time ability to nowcast economic turning points is gauged by using investment strategies based on economic regimes induced by our models. When comparing predictive accuracy and profit measures, the model confidence set procedure is applied to avoid data snooping. We show that such investment strategies achieve impressive risk-adjusted returns: timing the market is thus possible.
dc.identifier.citationpages47
dc.relation.ispartofseriestitleSSRN Working Paper Series
dc.identifier.urlsitehttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2785583
dc.subject.ddclabelMacroéconomieen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2019-09-23T12:31:12Z
hal.identifierhal-02292317*
hal.version1*
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