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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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
record_format dspace
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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