Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Tavares, Jos? Torquato Sampaio lattes
Orientador(a): Rodrigues, Carlos Alberto
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Estadual de Feira de Santana
Programa de Pós-Graduação: Mestrado em Computa??o Aplicada
Departamento: DEPARTAMENTO DE CI?NCIAS EXATAS
País: Brasil
Palavras-chave em Português:
Sistemas de negocia??o
Bolsa de valores
Redes neurais multicamadas
Finan?as quantitativas
Palavras-chave em Inglês:
Trading systems
Stock market
Neural networks MLP
Quantitative finance
Área do conhecimento CNPq:
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
Link de acesso: http://tede2.uefs.br:8080/handle/tede/747
Resumo: Predicting the behavior of the financial market has always attracted the interest of investors. Manual trading brings a number of difficulties for investors, so it is increasingly common to use automated trading systems. This work used MLP neural networks to try to predict the financial market. Two forms of training were used by the neural networks: one based on the Linear Regression Slope indicator and the other based on the index closing price the next day. A trading system with stop loss, take profit mechanisms and money management was built using the Percent Volatility strategy. The BOVA11 index was used for trading in demo accounts of a financial broker. The system was based on predictions of neural networks trained and selected periodically for trading. The trading period applied was from 07/01/2014 to 06/30/2018. Neural networks trained by closing price of the next day outperformed the one based on linear regression slope and the buy and hold strategy in the analyzed period.
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spelling Rodrigues, Carlos Alberto2824362529101927318580http://lattes.cnpq.br/0789313082327164Tavares, Jos? Torquato Sampaio2019-05-03T19:53:06Z2018-09-05TAVARES, Jos? Torquato Sampaio. Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear. 2018. 56 f. Disserta??o (Mestrado em Computa??o Aplicada) - Universidade Estadual de Feira de Santana, Feira de Santana, 2018.http://tede2.uefs.br:8080/handle/tede/747Predicting the behavior of the financial market has always attracted the interest of investors. Manual trading brings a number of difficulties for investors, so it is increasingly common to use automated trading systems. This work used MLP neural networks to try to predict the financial market. Two forms of training were used by the neural networks: one based on the Linear Regression Slope indicator and the other based on the index closing price the next day. A trading system with stop loss, take profit mechanisms and money management was built using the Percent Volatility strategy. The BOVA11 index was used for trading in demo accounts of a financial broker. The system was based on predictions of neural networks trained and selected periodically for trading. The trading period applied was from 07/01/2014 to 06/30/2018. Neural networks trained by closing price of the next day outperformed the one based on linear regression slope and the buy and hold strategy in the analyzed period.Prever o comportamento do mercado financeiro sempre atraiu o interesse dos investidores. A negocia??o manual traz uma s?rie de dificuldades para os investidores, por isso, ? cada vez mais comum o uso de sistemas autom?ticos de negocia??o. Este trabalho utilizou redes neurais MLP para tentar prever o mercado financeiro. Duas formas de treinamento foram utilizadas pelas redes neurais: uma baseada no indicador Linear Regression Slope e a outra baseada no pre?o de fechamento do ?ndice no dia seguinte. Foi constru?do um sistema de negocia??o com mecanismos de stop loss, take profit e gerenciamento de dinheiro, utilizando a estrat?gia Percentil Volatility. Foi utilizado o ?ndice BOVA11 para negocia??o em contas demo de uma corretora financeira. O sistema baseou-se nas previs?es das redes neurais treinadas e selecionadas periodicamente para negocia??o. O per?odo de negocia??o utilizado foi de 01/07/2014 a 30/06/2018. A forma de treinamento baseada no pre?o de fechamento do dia seguinte superou a baseada em regress?o linear e a estrat?gia buy and hold no per?odo analisado.Submitted by Ricardo Cedraz Duque Moliterno (ricardo.moliterno@uefs.br) on 2019-05-03T19:53:06Z No. of bitstreams: 1 tese_jose_torquato_sampaio_tavares.pdf: 1071730 bytes, checksum: a2a7a41367a63f2d4599231a2379f755 (MD5)Made available in DSpace on 2019-05-03T19:53:06Z (GMT). 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dc.title.por.fl_str_mv Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
title Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
spellingShingle Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
Tavares, Jos? Torquato Sampaio
Sistemas de negocia??o
Bolsa de valores
Redes neurais multicamadas
Finan?as quantitativas
Trading systems
Stock market
Neural networks MLP
Quantitative finance
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
title_short Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
title_full Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
title_fullStr Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
title_full_unstemmed Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
title_sort Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear
author Tavares, Jos? Torquato Sampaio
author_facet Tavares, Jos? Torquato Sampaio
author_role author
dc.contributor.advisor1.fl_str_mv Rodrigues, Carlos Alberto
dc.contributor.advisor1ID.fl_str_mv 28243625291
dc.contributor.authorID.fl_str_mv 01927318580
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/0789313082327164
dc.contributor.author.fl_str_mv Tavares, Jos? Torquato Sampaio
contributor_str_mv Rodrigues, Carlos Alberto
dc.subject.por.fl_str_mv Sistemas de negocia??o
Bolsa de valores
Redes neurais multicamadas
Finan?as quantitativas
topic Sistemas de negocia??o
Bolsa de valores
Redes neurais multicamadas
Finan?as quantitativas
Trading systems
Stock market
Neural networks MLP
Quantitative finance
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
dc.subject.eng.fl_str_mv Trading systems
Stock market
Neural networks MLP
Quantitative finance
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
description Predicting the behavior of the financial market has always attracted the interest of investors. Manual trading brings a number of difficulties for investors, so it is increasingly common to use automated trading systems. This work used MLP neural networks to try to predict the financial market. Two forms of training were used by the neural networks: one based on the Linear Regression Slope indicator and the other based on the index closing price the next day. A trading system with stop loss, take profit mechanisms and money management was built using the Percent Volatility strategy. The BOVA11 index was used for trading in demo accounts of a financial broker. The system was based on predictions of neural networks trained and selected periodically for trading. The trading period applied was from 07/01/2014 to 06/30/2018. Neural networks trained by closing price of the next day outperformed the one based on linear regression slope and the buy and hold strategy in the analyzed period.
publishDate 2018
dc.date.issued.fl_str_mv 2018-09-05
dc.date.accessioned.fl_str_mv 2019-05-03T19:53:06Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv TAVARES, Jos? Torquato Sampaio. Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear. 2018. 56 f. Disserta??o (Mestrado em Computa??o Aplicada) - Universidade Estadual de Feira de Santana, Feira de Santana, 2018.
dc.identifier.uri.fl_str_mv http://tede2.uefs.br:8080/handle/tede/747
identifier_str_mv TAVARES, Jos? Torquato Sampaio. Sistema autom?tico de negocia??o para a bolsa de valores utilizando redes neurais multilayer perceptron e regress?o linear. 2018. 56 f. Disserta??o (Mestrado em Computa??o Aplicada) - Universidade Estadual de Feira de Santana, Feira de Santana, 2018.
url http://tede2.uefs.br:8080/handle/tede/747
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dc.publisher.program.fl_str_mv Mestrado em Computa??o Aplicada
dc.publisher.initials.fl_str_mv UEFS
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv DEPARTAMENTO DE CI?NCIAS EXATAS
publisher.none.fl_str_mv Universidade Estadual de Feira de Santana
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