Recommending buy/sell in brazilian stock market through long short-term memory

Autores
da Silva Camargo, Sandro; Lopes Silva, Gabriel
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work aims to evaluate the accuracy of Long Short-Term Memory Neural Networks to recommend Buy/Sell signals of some Brazilian Stock Market Blue Chips. The population of this study was composed by top 5 volume stocks, which represented nearly 40% of the total volume of Brazilian Stock Market in 2019. It was analyzed the following features: volume traded, closing and opening price, maximum and minimum price, and last five-day closing prices. Models created can forecast the next day’s opening or closing price. Obtained results show that forecasting and real values have a coefficient of determination (R2) from 0.91 to 0.99, depending on the stock.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/156748
Acceder
id SEDICI_ebc031248938eb0980e29b6324d5443d
oai_identifier_str oai:sedici.unlp.edu.ar:10915/156748
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Recommending buy/sell in brazilian stock market through long short-term memoryda Silva Camargo, SandroLopes Silva, GabrielCiencias InformáticasVariable IncomeBovespaTime SeriesRecurrent Neural NetworksFinanceThis work aims to evaluate the accuracy of Long Short-Term Memory Neural Networks to recommend Buy/Sell signals of some Brazilian Stock Market Blue Chips. The population of this study was composed by top 5 volume stocks, which represented nearly 40% of the total volume of Brazilian Stock Market in 2019. It was analyzed the following features: volume traded, closing and opening price, maximum and minimum price, and last five-day closing prices. Models created can forecast the next day’s opening or closing price. Obtained results show that forecasting and real values have a coefficient of determination (R2) from 0.91 to 0.99, depending on the stock.Sociedad Argentina de Informática e Investigación Operativa2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf37-52http://sedici.unlp.edu.ar/handle/10915/156748enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/466info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:40:46Zoai:sedici.unlp.edu.ar:10915/156748Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:40:47.04SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Recommending buy/sell in brazilian stock market through long short-term memory
title Recommending buy/sell in brazilian stock market through long short-term memory
spellingShingle Recommending buy/sell in brazilian stock market through long short-term memory
da Silva Camargo, Sandro
Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
title_short Recommending buy/sell in brazilian stock market through long short-term memory
title_full Recommending buy/sell in brazilian stock market through long short-term memory
title_fullStr Recommending buy/sell in brazilian stock market through long short-term memory
title_full_unstemmed Recommending buy/sell in brazilian stock market through long short-term memory
title_sort Recommending buy/sell in brazilian stock market through long short-term memory
dc.creator.none.fl_str_mv da Silva Camargo, Sandro
Lopes Silva, Gabriel
author da Silva Camargo, Sandro
author_facet da Silva Camargo, Sandro
Lopes Silva, Gabriel
author_role author
author2 Lopes Silva, Gabriel
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
topic Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
dc.description.none.fl_txt_mv This work aims to evaluate the accuracy of Long Short-Term Memory Neural Networks to recommend Buy/Sell signals of some Brazilian Stock Market Blue Chips. The population of this study was composed by top 5 volume stocks, which represented nearly 40% of the total volume of Brazilian Stock Market in 2019. It was analyzed the following features: volume traded, closing and opening price, maximum and minimum price, and last five-day closing prices. Models created can forecast the next day’s opening or closing price. Obtained results show that forecasting and real values have a coefficient of determination (R2) from 0.91 to 0.99, depending on the stock.
Sociedad Argentina de Informática e Investigación Operativa
description This work aims to evaluate the accuracy of Long Short-Term Memory Neural Networks to recommend Buy/Sell signals of some Brazilian Stock Market Blue Chips. The population of this study was composed by top 5 volume stocks, which represented nearly 40% of the total volume of Brazilian Stock Market in 2019. It was analyzed the following features: volume traded, closing and opening price, maximum and minimum price, and last five-day closing prices. Models created can forecast the next day’s opening or closing price. Obtained results show that forecasting and real values have a coefficient of determination (R2) from 0.91 to 0.99, depending on the stock.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/156748
url http://sedici.unlp.edu.ar/handle/10915/156748
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/466
info:eu-repo/semantics/altIdentifier/issn/1514-6774
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.format.none.fl_str_mv application/pdf
37-52
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616280218271744
score 13.070432

Publicaciones similares