Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks

Autores
Lopes Silva, Gabriel; Silva Camargo, Sandro da
Año de publicación
2022
Idioma
español castellano
Tipo de recurso
documento de conferencia
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
LSTM
Finance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/151695
Acceder
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network_name_str SEDICI (UNLP)
spelling Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural NetworksLopes Silva, GabrielSilva Camargo, Sandro daCiencias InformáticasVariable IncomeBovespaTime SeriesLSTMFinanceThis 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 Operativa2022-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf75-87http://sedici.unlp.edu.ar/handle/10915/151695spainfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/266/217info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:19:58Zoai:sedici.unlp.edu.ar:10915/151695Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:19:58.413SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
title Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
spellingShingle Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
Lopes Silva, Gabriel
Ciencias Informáticas
Variable Income
Bovespa
Time Series
LSTM
Finance
title_short Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
title_full Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
title_fullStr Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
title_full_unstemmed Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
title_sort Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
dc.creator.none.fl_str_mv Lopes Silva, Gabriel
Silva Camargo, Sandro da
author Lopes Silva, Gabriel
author_facet Lopes Silva, Gabriel
Silva Camargo, Sandro da
author_role author
author2 Silva Camargo, Sandro da
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Variable Income
Bovespa
Time Series
LSTM
Finance
topic Ciencias Informáticas
Variable Income
Bovespa
Time Series
LSTM
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 2022
dc.date.none.fl_str_mv 2022-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/151695
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info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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