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

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 2...

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Detalles Bibliográficos
Autores principales:	Lopes Silva, Gabriel, Silva Camargo, Sandro da
Formato:	Objeto de conferencia
Lenguaje:	Español
Publicado:	2022
Materias:	Ciencias Informáticas Variable Income Bovespa Time Series LSTM Finance
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/151695 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/266/217
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-151695
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spelling	I19-R120-10915-1516952023-05-03T20:02:12Z http://sedici.unlp.edu.ar/handle/10915/151695 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/266/217 issn:2451-7496 Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks Lopes Silva, Gabriel Silva Camargo, Sandro da 2022-10 2022 2023-04-18T18:26:57Z es Ciencias Informáticas Variable Income Bovespa Time Series LSTM Finance 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 Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 75-87
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Español
topic	Ciencias Informáticas Variable Income Bovespa Time Series LSTM Finance
spellingShingle	Ciencias Informáticas Variable Income Bovespa Time Series LSTM Finance Lopes Silva, Gabriel Silva Camargo, Sandro da Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
topic_facet	Ciencias Informáticas Variable Income Bovespa Time Series LSTM Finance
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.
format	Objeto de conferencia Objeto de conferencia
author	Lopes Silva, Gabriel Silva Camargo, Sandro da
author_facet	Lopes Silva, Gabriel Silva Camargo, Sandro da
author_sort	Lopes Silva, Gabriel
title	Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks
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
publishDate	2022
url	http://sedici.unlp.edu.ar/handle/10915/151695 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/266/217
work_keys_str_mv	AT lopessilvagabriel recommendingbuysellinbrazilianstockmarketthroughrecurrentneuralnetworks AT silvacamargosandroda recommendingbuysellinbrazilianstockmarketthroughrecurrentneuralnetworks
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Recommending Buy/Sell in Brazilian Stock Market through Recurrent Neural Networks

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