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

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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: da Silva Camargo, Sandro, Lopes Silva, Gabriel
Formato: Articulo
Lenguaje:Inglés
Publicado: 2023
Materias:
Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/156748
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-156748
record_format dspace
spelling I19-R120-10915-1567482023-08-23T20:04:38Z http://sedici.unlp.edu.ar/handle/10915/156748 Recommending buy/sell in brazilian stock market through long short-term memory da Silva Camargo, Sandro Lopes Silva, Gabriel 2023-05 2023-08-23T17:47:37Z en Ciencias Informáticas Variable Income Bovespa Time Series Recurrent Neural Networks 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 Articulo Articulo http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) application/pdf 37-52
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
spellingShingle Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
Finance
da Silva Camargo, Sandro
Lopes Silva, Gabriel
Recommending buy/sell in brazilian stock market through long short-term memory
topic_facet Ciencias Informáticas
Variable Income
Bovespa
Time Series
Recurrent Neural Networks
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 Articulo
Articulo
author da Silva Camargo, Sandro
Lopes Silva, Gabriel
author_facet da Silva Camargo, Sandro
Lopes Silva, Gabriel
author_sort da Silva Camargo, Sandro
title Recommending buy/sell in brazilian stock market through long short-term memory
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
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/156748
work_keys_str_mv AT dasilvacamargosandro recommendingbuysellinbrazilianstockmarketthroughlongshorttermmemory
AT lopessilvagabriel recommendingbuysellinbrazilianstockmarketthroughlongshorttermmemory
_version_ 1807221077785444352

Ejemplares similares