Show simple item record

dc.contributor.authorHaddad, Michel Ferreira Cardia
dc.date.accessioned2021-04-22T14:15:53Z
dc.date.available2021-04-22T14:15:53Z
dc.date.issued2021-04-22
dc.date.submitted2019-07-08
dc.identifier.issn2577-8196
dc.identifier.othereng212329
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/321439
dc.descriptionFunder: Cambridge Commonwealth, European and International Trust; Id: http://dx.doi.org/10.13039/501100003343
dc.descriptionFunder: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES): BEX 2220/15‐6; Id: http://dx.doi.org/10.13039/501100002322
dc.description.abstractAbstract: In the present paper a data‐driven hard cluster analysis derived from a novel similarity measure is proposed to support financial investors in their portfolio management decision‐making process. The main objective of the proposed method is to provide a less arbitrary learning procedure to quantify similarity levels between investment alternatives (pairwise) as well as revealing clustering patterns (whole sample). This is especially useful during periods of high volatility, when investment alternatives tend to become more similar and, therefore, harder to distinguish between themselves. The method dynamics may be readily interpreted through a clear data visualisation. The advantages and caveats of the proposed method is compared to the most popular class of cluster analysis, applied to the well‐known Fisher's Iris dataset. Such results show a slightly superior performance of the proposed method but, most importantly, through remarkably different clustering allocation approaches. Moreover, further empirical results applied to daily data reflecting a period of 15 years of time series of economies/stock markets of the Group of Seven (G7) illustrate the potential practical usefulness of the proposed unsupervised learning method, particularly, for portfolio strategy, asset allocation, and investment diversification.
dc.languageen
dc.publisherJohn Wiley & Sons, Inc.
dc.subjectRESEARCH ARTICLE
dc.subjectRESEARCH ARTICLES
dc.subjectcluster analysis
dc.subjectpattern recognition
dc.subjectportfolio management
dc.subjectsimilarity measure
dc.subjectunsupervised learning
dc.titleHarnessing the power of intersection for pattern recognition: a novel unsupervised learning method and its application to financial engineering
dc.typeArticle
dc.date.updated2021-04-22T14:15:51Z
prism.issueIdentifier4
prism.publicationNameEngineering Reports
prism.volume3
dc.identifier.doi10.17863/CAM.68562
dcterms.dateAccepted2020-10-23
rioxxterms.versionofrecord10.1002/eng2.12329
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.contributor.orcidHaddad, Michel Ferreira Cardia [0000-0002-0978-9525]


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record