A review of artificial intelligence quality in forecasting asset prices

Barboza, Flavio; Silva, Geraldo Nunes; Fiorucci, José Augusto

Registro completo de metadatos

Campo DC	Valor	Lengua/Idioma
dc.contributor.author	Barboza, Flavio	-
dc.contributor.author	Silva, Geraldo Nunes	-
dc.contributor.author	Fiorucci, José Augusto	-
dc.date.accessioned	2023-11-20T15:11:44Z	-
dc.date.available	2023-11-20T15:11:44Z	-
dc.date.issued	2023-04-02	-
dc.identifier.citation	BARBOZA, Flavio, SILVA, Geraldo Nunes, FIORUCCI, José Augusto. A review ofartificial intelligence quality in forecasting assetprices. Journal of Forecasting, v. 42, n. 7, 1708-1728, 2023. DOI: https://doi.org/10.1002/for.2979.	pt_BR
dc.identifier.uri	http://repositorio2.unb.br/jspui/handle/10482/46872	-
dc.language.iso	eng	pt_BR
dc.publisher	John Wiley & Sons Ltd.	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	A review of artificial intelligence quality in forecasting asset prices	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Séries temporais	pt_BR
dc.subject.keyword	Aprendizado de máquina	pt_BR
dc.identifier.doi	https://doi.org/10.1002/for.2979	pt_BR
dc.relation.publisherversion	https://onlinelibrary.wiley.com/doi/10.1002/for.2979	pt_BR
dc.description.abstract1	Researchers and practitioners globally, from a range of perspectives, acknowl- edge 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.	pt_BR
dc.contributor.affiliation	Federal University of Uberlândia, School of Business and Management	pt_BR
dc.contributor.affiliation	São Paulo State University, Institute of Biosciences, Humanities and Exact Sciences, Mathematics Department	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Statistics	pt_BR
dc.description.unidade	Instituto de Ciências Exatas (IE)	pt_BR
dc.description.unidade	Departamento de Estatística (IE EST)	pt_BR
Aparece en las colecciones:	Artigos publicados em periódicos e afins