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dc.contributor.authorOhana, Jean Jacques
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorBenhamou, Éric
dc.contributor.authorSaltiel, David
dc.contributor.authorGuez, Beatrice
dc.date.accessioned2021-11-15T14:58:50Z
dc.date.available2021-11-15T14:58:50Z
dc.date.issued2021
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22202
dc.language.isoenen
dc.subjectCovid Equityen
dc.subject.ddc006.3en
dc.titleIs the Covid equity bubble rational? A machine learning answeren
dc.typeDocument de travail / Working paper
dc.description.abstractenIs the Covid Equity bubble rational? In 2020, stock prices ballooned with S&P 500 gaining 16%, and the tech-heavy Nasdaq soaring to 43%, while fundamentals deteriorated with decreasing GDP forecasts, shrinking sales and revenues estimates and higher government deficits. To answer this fundamental question, with little bias as possible, we explore a gradient boosting decision trees (GBDT) approach that enables us to crunch numerous variables and let the data speak. We define a crisis regime to identify specific downturns in stock markets and normal rising equity markets. We test our approach and report improved accuracy of GBDT over other ML methods. Thanks to Shapley values, we are able to identify most important features, making this current work innovative and a suitable answer to the justification of current equity level.en
dc.publisher.cityParisen
dc.relation.ispartofseriestitlePreprint Lamsadeen
dc.subject.ddclabelIntelligence artificielleen
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dc.description.audienceInternationalen
dc.date.updated2021-11-13T16:50:06Z
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