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Reducing information asymmetry in used-car markets by using machine learning models

2020-01-14Dissertação de mestrado Acesso aberto
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datacite.subject.fosCiências Sociais::Economia e Gestãopt_PT
dc.contributor.advisorHan, Qiwei
dc.contributor.advisorLello, Enrico Di
dc.contributor.authorKocks, Leonard Werner
dc.date.accessioned2020-10-20T13:00:18Z
dc.date.available2020-10-20T13:00:18Z
dc.date.issued2020-01-14
dc.date.submitted2020-01-03
dc.description.abstractInformation asymmetry in used-car markets results from knowledge differences between buyers and sellers about used cars. Naturally, someone who owns a used car for a certain period, develops a deeper understanding of the real value opposed to someone who did not. The goal of this work is to attempt to reduce information asymmetry in used-car markets by using state-of-the-art machine learning models. With data provided by a Polish used-car online marketplace, a price range estimation as well as a point estimation will be made for every car. A Median Absolute Percentage Error of 7.86%and Target Zone of 58.38% are achieved.pt_PT
dc.identifier.tid202494446pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/105919
dc.language.isoengpt_PT
dc.subjectInformation asymmetrypt_PT
dc.subjectMachine learningpt_PT
dc.subjectPrice range estimationpt_PT
dc.subjectUsed-car marketspt_PT
dc.titleReducing information asymmetry in used-car markets by using machine learning modelspt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameA Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economicspt_PT

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