Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

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dc.citation.journalTitleComputational Economicseng
dc.contributor.affiliationUniversidad EAFIT
dc.contributor.authorPantoja Robayo, Javier Orlando
dc.contributor.authorAlemán Muñoz, Julián Alberto
dc.contributor.authorTellez-Falla, Diego F.
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.creator.emaildftellezf@eafit.edu.co
dc.date.accessioned2025-01-08T20:12:21Z
dc.date.available2025-01-08T20:12:21Z
dc.date.issued2024-09-11
dc.description.abstractWe suggest using deep learning networks to create expert opinions as part of an iterative active portfolio management process. These opinions would be based on posts from the X platform and the fundamentals of stocks listed in the S&P 500 index. Expert views are integral to active portfolio management, as proposed by Black–Litterman. The method we propose addresses the original subjectivity of the opinions by incorporating innovation and accuracy to generate views using analytical techniques. We utilize daily data from 2010 to 2022 for stocks from the S&P 500 and daily posts from Twitter API v2, collected under a research account license spanning the same period. We found that incorporating sentiment factors with machine learning techniques into the view generation process of the Black–Litterman model improves optimal portfolio allocation. Empirically, our results notably outperform the S&P 500 market when considering the annualized alpha.eng
dc.identifier.doi10.1007/s10614-024-10702-5
dc.identifier.issn1572-9974
dc.identifier.jelG12
dc.identifier.jelG11
dc.identifier.urihttps://hdl.handle.net/10784/34858
dc.language.isoeng
dc.publisherSpringereng
dc.publisher.departmentUniversidad EAFIT. Escuela de Finanzas, Economía y Gobierno. Área Mercados y Estrategia Financieraspa
dc.publisher.placeMedellínspa
dc.publisher.programGrupo de Investigación en Finanzas y Bancaspa
dc.relation.ispartofComput Econ (2024)
dc.relation.isversionofhttps://link.springer.com/article/10.1007/s10614-024-10702-5
dc.relation.urihttps://link.springer.com/article/10.1007/s10614-024-10702-5
dc.rightsCopyright © 2024 Springer. All rights reserved.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subject.keywordBlack–littermaneng
dc.subject.keywordActive portfolioeng
dc.subject.keywordTweetseng
dc.subject.keywordSentimentseng
dc.subject.keywordLSTMeng
dc.titleIterative Deep Learning Approach to Active Portfolio Management with Sentiment Factorseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.type.hasVersionpublishedVersioneng
dc.type.localArtículospa

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