A machine learning approach to predicting stock returns

Detalhes bibliográficos
Autor(a) principal: Silva, Francisco Trindade De Oliveira
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/138154
Resumo: Machine learning approaches to stock market forecasting have become increasingly popular throughout the years due to their predictive power and ability to identify hidden patterns in the data. However, considering the inherent volatility and complexity of stock markets, this is a challenging problem to model. This paper presents a comparative analysis of the performance of various machine learning regression algorithms in predicting stock returns. Several leading and technical indicators are considered as features to predict the monthly return of the S&P 500 Index, a market-capitalization-weighted index of the 500 largest publicly traded companies in the United States.
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spelling A machine learning approach to predicting stock returnsPredictionMachine learning algorithmsPythonStock marketForecasting stock returnsDomínio/Área Científica::Ciências Sociais::Economia e GestãoMachine learning approaches to stock market forecasting have become increasingly popular throughout the years due to their predictive power and ability to identify hidden patterns in the data. However, considering the inherent volatility and complexity of stock markets, this is a challenging problem to model. This paper presents a comparative analysis of the performance of various machine learning regression algorithms in predicting stock returns. Several leading and technical indicators are considered as features to predict the monthly return of the S&P 500 Index, a market-capitalization-weighted index of the 500 largest publicly traded companies in the United States.Rodrigues, Paulo Manuel MarquesRUNSilva, Francisco Trindade De Oliveira2022-05-18T15:27:04Z2021-06-292021-05-212021-06-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/138154TID:202894975enginfo: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:RCAAP2024-03-11T05:15:30Zoai:run.unl.pt:10362/138154Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:59.082506Repositório 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 A machine learning approach to predicting stock returns
title A machine learning approach to predicting stock returns
spellingShingle A machine learning approach to predicting stock returns
Silva, Francisco Trindade De Oliveira
Prediction
Machine learning algorithms
Python
Stock market
Forecasting stock returns
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short A machine learning approach to predicting stock returns
title_full A machine learning approach to predicting stock returns
title_fullStr A machine learning approach to predicting stock returns
title_full_unstemmed A machine learning approach to predicting stock returns
title_sort A machine learning approach to predicting stock returns
author Silva, Francisco Trindade De Oliveira
author_facet Silva, Francisco Trindade De Oliveira
author_role author
dc.contributor.none.fl_str_mv Rodrigues, Paulo Manuel Marques
RUN
dc.contributor.author.fl_str_mv Silva, Francisco Trindade De Oliveira
dc.subject.por.fl_str_mv Prediction
Machine learning algorithms
Python
Stock market
Forecasting stock returns
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Prediction
Machine learning algorithms
Python
Stock market
Forecasting stock returns
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Machine learning approaches to stock market forecasting have become increasingly popular throughout the years due to their predictive power and ability to identify hidden patterns in the data. However, considering the inherent volatility and complexity of stock markets, this is a challenging problem to model. This paper presents a comparative analysis of the performance of various machine learning regression algorithms in predicting stock returns. Several leading and technical indicators are considered as features to predict the monthly return of the S&P 500 Index, a market-capitalization-weighted index of the 500 largest publicly traded companies in the United States.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-29
2021-05-21
2021-06-29T00:00:00Z
2022-05-18T15:27:04Z
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.uri.fl_str_mv http://hdl.handle.net/10362/138154
TID:202894975
url http://hdl.handle.net/10362/138154
identifier_str_mv TID:202894975
dc.language.iso.fl_str_mv eng
language eng
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)
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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
repository.mail.fl_str_mv
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