Decision Support Using Machine Learning Indication for Financial Investment

Detalhes bibliográficos
Autor(a) principal: Oliveira, Ariel Vieira de
Data de Publicação: 2022
Outros Autores: Dazzi, Márcia Cristina Schiavi, Fernandes, Anita, Dazzi, Rudimar Luis Scaranto, Ferreira, Paulo, LEITHARDT, VALDERI
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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spelling 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
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dc.language.iso.fl_str_mv eng
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10.3390/fi14110304
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