A review of artificial intelligence quality in forecasting asset prices

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dc.contributor.authorBarboza, Flavio
dc.contributor.authorNunes Silva, Geraldo [UNESP]
dc.contributor.authorAugusto Fiorucci, José
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversity of Brasilia (UnB)
dc.date.accessioned2023-07-29T16:10:32Z
dc.date.available2023-07-29T16:10:32Z
dc.date.issued2023-01-01
dc.description.abstractResearchers and practitioners globally, from a range of perspectives, acknowledge the difficulty in determining the value of a financial asset. This subject is of utmost importance due to the numerous participants involved and its impact on enhancing market structure, function, and efficiency. This paper conducts a comprehensive review of the academic literature to provide insights into the reasoning behind certain conventions adopted in financial value estimation, including the implementation of preprocessing techniques, the selection of relevant inputs, and the assessment of the performance of computational models in predicting asset prices over time. Our analysis, based on 109 studies sourced from 10 databases, reveals that daily forecasts have achieved average error rates of less than 1.5%, while monthly data only attain this level in optimal circumstances. We also discuss the utilization of tools and the integration of hybrid models. Finally, we highlight compelling gaps in the literature that provide avenues for further research.en
dc.description.affiliationSchool of Business and Management Federal University of Uberlândia (UFU), MG
dc.description.affiliationMathematics Department Institute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP), SP
dc.description.affiliationDepartment of Statistics University of Brasilia (UnB), Campus Darcy Ribeiro, DF
dc.description.affiliationUnespMathematics Department Institute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP), SP
dc.identifierhttp://dx.doi.org/10.1002/for.2979
dc.identifier.citationJournal of Forecasting.
dc.identifier.doi10.1002/for.2979
dc.identifier.issn1099-131X
dc.identifier.issn0277-6693
dc.identifier.scopus2-s2.0-85151947288
dc.identifier.urihttp://hdl.handle.net/11449/249836
dc.language.isoeng
dc.relation.ispartofJournal of Forecasting
dc.sourceScopus
dc.subjectfinancial times series
dc.subjectmachine learning
dc.subjectMAE
dc.subjectMAPE
dc.subjectRMSE
dc.titleA review of artificial intelligence quality in forecasting asset pricesen
dc.typeArtigo
unesp.author.orcid0000-0002-3449-5297[1]
unesp.author.orcid0000-0002-3574-9893[2]
unesp.author.orcid0000-0002-1201-9089[3]

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Artigos - Matemática Aplicada - IBILCE