A review of artificial intelligence quality in forecasting asset prices
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1002/for.2979 http://hdl.handle.net/11449/249836 |
Resumo: | Researchers 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. |
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A review of artificial intelligence quality in forecasting asset pricesfinancial times seriesmachine learningMAEMAPERMSEResearchers 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.School of Business and Management Federal University of Uberlândia (UFU), MGMathematics Department Institute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP), SPDepartment of Statistics University of Brasilia (UnB), Campus Darcy Ribeiro, DFMathematics Department Institute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP), SPUniversidade Federal de Uberlândia (UFU)Universidade Estadual Paulista (UNESP)University of Brasilia (UnB)Barboza, FlavioNunes Silva, Geraldo [UNESP]Augusto Fiorucci, José2023-07-29T16:10:32Z2023-07-29T16:10:32Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/for.2979Journal of Forecasting.1099-131X0277-6693http://hdl.handle.net/11449/24983610.1002/for.29792-s2.0-85151947288Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Forecastinginfo:eu-repo/semantics/openAccess2023-07-29T16:10:32Zoai:repositorio.unesp.br:11449/249836Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T16:10:32Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A review of artificial intelligence quality in forecasting asset prices |
title |
A review of artificial intelligence quality in forecasting asset prices |
spellingShingle |
A review of artificial intelligence quality in forecasting asset prices Barboza, Flavio financial times series machine learning MAE MAPE RMSE |
title_short |
A review of artificial intelligence quality in forecasting asset prices |
title_full |
A review of artificial intelligence quality in forecasting asset prices |
title_fullStr |
A review of artificial intelligence quality in forecasting asset prices |
title_full_unstemmed |
A review of artificial intelligence quality in forecasting asset prices |
title_sort |
A review of artificial intelligence quality in forecasting asset prices |
author |
Barboza, Flavio |
author_facet |
Barboza, Flavio Nunes Silva, Geraldo [UNESP] Augusto Fiorucci, José |
author_role |
author |
author2 |
Nunes Silva, Geraldo [UNESP] Augusto Fiorucci, José |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Uberlândia (UFU) Universidade Estadual Paulista (UNESP) University of Brasilia (UnB) |
dc.contributor.author.fl_str_mv |
Barboza, Flavio Nunes Silva, Geraldo [UNESP] Augusto Fiorucci, José |
dc.subject.por.fl_str_mv |
financial times series machine learning MAE MAPE RMSE |
topic |
financial times series machine learning MAE MAPE RMSE |
description |
Researchers 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T16:10:32Z 2023-07-29T16:10:32Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1002/for.2979 Journal of Forecasting. 1099-131X 0277-6693 http://hdl.handle.net/11449/249836 10.1002/for.2979 2-s2.0-85151947288 |
url |
http://dx.doi.org/10.1002/for.2979 http://hdl.handle.net/11449/249836 |
identifier_str_mv |
Journal of Forecasting. 1099-131X 0277-6693 10.1002/for.2979 2-s2.0-85151947288 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Forecasting |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803649998758346752 |