Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market

Detalhes bibliográficos
Autor(a) principal: Quintella, Vitor M.
Data de Publicação: 2019
Outros Autores: Quintella, Cristina M., Silva Junior, Antônio Francisco A., Fontes, Cristiano de Oliveira Hora
Tipo de documento: Artigo
Idioma: por
Título da fonte: Cadernos de Prospecção (Online)
Texto Completo: https://periodicos.ufba.br/index.php/nit/article/view/27260
Resumo: This is a prospective study of the uses of machine learning technology applied to financial market trading and investment. Quick expansion on TI technology generated effective changes on daily financial routine. As futher changes may be expected, this prospection analyzed 257 patents from Espacenet database processed via Questel Orbit® software. It was found in 2016 a peak in 1st patent deposits that remains until the present. Most of applications was on outliers, feature selection and clustering. No holder was identified with financial core activity, pointing out the possibility of trade secret strategies. China and USA were the biggest depositors and the biggest market of these applications. The outstanding growth of the market and the identification of a horizon of technological maturation processes is a consequence of the recent development in process power, data readiness and cloud-based technology.
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spelling Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial MarketEstudo Prospectivo Exploratório das Patentes de Métodos de Aprendizagem de Máquina Aplicados ao Mercado FinanceiroAprendizagem de máquinaMercado financeiroInvestimentoNegociação.Machine LearningFinancial MarketInvestmentTradingProspection.This is a prospective study of the uses of machine learning technology applied to financial market trading and investment. Quick expansion on TI technology generated effective changes on daily financial routine. As futher changes may be expected, this prospection analyzed 257 patents from Espacenet database processed via Questel Orbit® software. It was found in 2016 a peak in 1st patent deposits that remains until the present. Most of applications was on outliers, feature selection and clustering. No holder was identified with financial core activity, pointing out the possibility of trade secret strategies. China and USA were the biggest depositors and the biggest market of these applications. The outstanding growth of the market and the identification of a horizon of technological maturation processes is a consequence of the recent development in process power, data readiness and cloud-based technology. Neste trabalho é realizada uma prospecção tecnológica do uso da tecnologia de aprendizagem de máquina aplicada à negociação e ao investimento financeiro. A rápida expansão das aplicações de Tecnologia da Informação (TI) no campo das finanças tem gerado uma série de mudanças nas suas práticas impactando a vida de investidores e de todos os cidadãos. Foi utilizada base de dados de documentos de patentes da Espacenet, o escopo de pesquisa permitiu a análise de 257 patentes. As patentes analisadas no escopo são datadas de 1991 a 2018. O software Questel Orbit® foi utilizado para análise dos dados. As principais aplicações são orientadas para a identificação de outliers e a realização de feature selection e clustering. Não foi identificada empresa que tenha como atividade-fim atuar no mercado financeiro, indicando a existência de estratégias de manutenção de segredo de negócio. Foi verificado o rápido crescimento do mercado e a possibilidade de amadurecimento da tecnologia diante do cenário contemporâneo.Universidade Federal da Bahia2019-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionProspecçãoapplication/pdfhttps://periodicos.ufba.br/index.php/nit/article/view/2726010.9771/cp.v12i1.27260Cadernos de Prospecção; Vol. 12 No. 1 (2019); 113Cadernos de Prospecção; v. 12 n. 1 (2019); 1132317-00261983-1358reponame:Cadernos de Prospecção (Online)instname:Universidade Federal da Bahia (UFBA)instacron:UFBAporhttps://periodicos.ufba.br/index.php/nit/article/view/27260/16966Copyright (c) 2019 Cadernos de Prospecçãohttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessQuintella, Vitor M.Quintella, Cristina M.Silva Junior, Antônio Francisco A.Fontes, Cristiano de Oliveira Hora2022-07-08T20:55:18Zoai:ojs.periodicos.ufba.br:article/27260Revistahttps://periodicos.ufba.br/index.php/nitPUBhttps://periodicos.ufba.br/index.php/nit/oaicadernosdeprospeccao@gmail.com || maliceribeiro@yahoo.com.br || cadernosdeprospeccao@gmail.com || saionaraluna@gmail.com2317-00261983-1358opendoar:2022-07-08T20:55:18Cadernos de Prospecção (Online) - Universidade Federal da Bahia (UFBA)false
dc.title.none.fl_str_mv Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
Estudo Prospectivo Exploratório das Patentes de Métodos de Aprendizagem de Máquina Aplicados ao Mercado Financeiro
title Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
spellingShingle Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
Quintella, Vitor M.
Aprendizagem de máquina
Mercado financeiro
Investimento
Negociação.
Machine Learning
Financial Market
Investment
Trading
Prospection.
title_short Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
title_full Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
title_fullStr Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
title_full_unstemmed Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
title_sort Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market
author Quintella, Vitor M.
author_facet Quintella, Vitor M.
Quintella, Cristina M.
Silva Junior, Antônio Francisco A.
Fontes, Cristiano de Oliveira Hora
author_role author
author2 Quintella, Cristina M.
Silva Junior, Antônio Francisco A.
Fontes, Cristiano de Oliveira Hora
author2_role author
author
author
dc.contributor.author.fl_str_mv Quintella, Vitor M.
Quintella, Cristina M.
Silva Junior, Antônio Francisco A.
Fontes, Cristiano de Oliveira Hora
dc.subject.por.fl_str_mv Aprendizagem de máquina
Mercado financeiro
Investimento
Negociação.
Machine Learning
Financial Market
Investment
Trading
Prospection.
topic Aprendizagem de máquina
Mercado financeiro
Investimento
Negociação.
Machine Learning
Financial Market
Investment
Trading
Prospection.
description This is a prospective study of the uses of machine learning technology applied to financial market trading and investment. Quick expansion on TI technology generated effective changes on daily financial routine. As futher changes may be expected, this prospection analyzed 257 patents from Espacenet database processed via Questel Orbit® software. It was found in 2016 a peak in 1st patent deposits that remains until the present. Most of applications was on outliers, feature selection and clustering. No holder was identified with financial core activity, pointing out the possibility of trade secret strategies. China and USA were the biggest depositors and the biggest market of these applications. The outstanding growth of the market and the identification of a horizon of technological maturation processes is a consequence of the recent development in process power, data readiness and cloud-based technology.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Prospecção
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufba.br/index.php/nit/article/view/27260
10.9771/cp.v12i1.27260
url https://periodicos.ufba.br/index.php/nit/article/view/27260
identifier_str_mv 10.9771/cp.v12i1.27260
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufba.br/index.php/nit/article/view/27260/16966
dc.rights.driver.fl_str_mv Copyright (c) 2019 Cadernos de Prospecção
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2019 Cadernos de Prospecção
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal da Bahia
publisher.none.fl_str_mv Universidade Federal da Bahia
dc.source.none.fl_str_mv Cadernos de Prospecção; Vol. 12 No. 1 (2019); 113
Cadernos de Prospecção; v. 12 n. 1 (2019); 113
2317-0026
1983-1358
reponame:Cadernos de Prospecção (Online)
instname:Universidade Federal da Bahia (UFBA)
instacron:UFBA
instname_str Universidade Federal da Bahia (UFBA)
instacron_str UFBA
institution UFBA
reponame_str Cadernos de Prospecção (Online)
collection Cadernos de Prospecção (Online)
repository.name.fl_str_mv Cadernos de Prospecção (Online) - Universidade Federal da Bahia (UFBA)
repository.mail.fl_str_mv cadernosdeprospeccao@gmail.com || maliceribeiro@yahoo.com.br || cadernosdeprospeccao@gmail.com || saionaraluna@gmail.com
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