Decision Support Using Machine Learning Indication for Financial Investment
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.26/43553 |
Resumo: | To support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniqueswere implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%. |
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Decision Support Using Machine Learning Indication for Financial InvestmentTo support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniqueswere implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.Repositório ComumOliveira, Ariel Vieira deDazzi, Márcia Cristina SchiaviFernandes, AnitaDazzi, Rudimar Luis ScarantoFerreira, PauloLEITHARDT, VALDERI2023-02-01T18:12:32Z2022-10-252022-10-26T16:28:49Z2022-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/43553eng1999-5903cv-prod-306560310.3390/fi14110304info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-04T10:30:24Zoai:comum.rcaap.pt:10400.26/43553Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:46:42.567282Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Decision Support Using Machine Learning Indication for Financial Investment |
title |
Decision Support Using Machine Learning Indication for Financial Investment |
spellingShingle |
Decision Support Using Machine Learning Indication for Financial Investment Oliveira, Ariel Vieira de |
title_short |
Decision Support Using Machine Learning Indication for Financial Investment |
title_full |
Decision Support Using Machine Learning Indication for Financial Investment |
title_fullStr |
Decision Support Using Machine Learning Indication for Financial Investment |
title_full_unstemmed |
Decision Support Using Machine Learning Indication for Financial Investment |
title_sort |
Decision Support Using Machine Learning Indication for Financial Investment |
author |
Oliveira, Ariel Vieira de |
author_facet |
Oliveira, Ariel Vieira de Dazzi, Márcia Cristina Schiavi Fernandes, Anita Dazzi, Rudimar Luis Scaranto Ferreira, Paulo LEITHARDT, VALDERI |
author_role |
author |
author2 |
Dazzi, Márcia Cristina Schiavi Fernandes, Anita Dazzi, Rudimar Luis Scaranto Ferreira, Paulo LEITHARDT, VALDERI |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Oliveira, Ariel Vieira de Dazzi, Márcia Cristina Schiavi Fernandes, Anita Dazzi, Rudimar Luis Scaranto Ferreira, Paulo LEITHARDT, VALDERI |
description |
To support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniqueswere implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-25 2022-10-26T16:28:49Z 2022-10-25T00:00:00Z 2023-02-01T18:12:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.26/43553 |
url |
http://hdl.handle.net/10400.26/43553 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1999-5903 cv-prod-3065603 10.3390/fi14110304 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130938400047104 |