Implementing machine learning in the stock picking process of Nova students portfolio

Detalhes bibliográficos
Autor(a) principal: Afonso, Miguel Pardal
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/102627
Resumo: In a time when algorithmic trading accounts for over 50% of US equities’ traded volume, this work project proposes a holistic approach to the implementation of Machine Learning in the Stock Picking process of the Nova Students Portfolio. The presented algorithms can help investors in the identification of the features that drive stock returns and results show that our predictive algorithm provides an edge in the selection of outperforming stocks. An investor using our method from 2006 to 2019 would have achieved an annualized return of 4.8% in excess of the S&P 500 and an Info Sharpe gain of 0.2.
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spelling Implementing machine learning in the stock picking process of Nova students portfolioMachine learningStock pickingPortfolio managementDomínio/Área Científica::Ciências Sociais::Economia e GestãoIn a time when algorithmic trading accounts for over 50% of US equities’ traded volume, this work project proposes a holistic approach to the implementation of Machine Learning in the Stock Picking process of the Nova Students Portfolio. The presented algorithms can help investors in the identification of the features that drive stock returns and results show that our predictive algorithm provides an edge in the selection of outperforming stocks. An investor using our method from 2006 to 2019 would have achieved an annualized return of 4.8% in excess of the S&P 500 and an Info Sharpe gain of 0.2.Ribeiro, Gonçalo SommerRUNAfonso, Miguel Pardal2020-10-13T00:30:46Z2020-01-132020-08-202020-01-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/102627TID:202495396enginfo: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:RCAAP2024-03-11T04:48:22Zoai:run.unl.pt:10362/102627Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:45.115759Repositório 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 Implementing machine learning in the stock picking process of Nova students portfolio
title Implementing machine learning in the stock picking process of Nova students portfolio
spellingShingle Implementing machine learning in the stock picking process of Nova students portfolio
Afonso, Miguel Pardal
Machine learning
Stock picking
Portfolio management
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Implementing machine learning in the stock picking process of Nova students portfolio
title_full Implementing machine learning in the stock picking process of Nova students portfolio
title_fullStr Implementing machine learning in the stock picking process of Nova students portfolio
title_full_unstemmed Implementing machine learning in the stock picking process of Nova students portfolio
title_sort Implementing machine learning in the stock picking process of Nova students portfolio
author Afonso, Miguel Pardal
author_facet Afonso, Miguel Pardal
author_role author
dc.contributor.none.fl_str_mv Ribeiro, Gonçalo Sommer
RUN
dc.contributor.author.fl_str_mv Afonso, Miguel Pardal
dc.subject.por.fl_str_mv Machine learning
Stock picking
Portfolio management
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Stock picking
Portfolio management
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description In a time when algorithmic trading accounts for over 50% of US equities’ traded volume, this work project proposes a holistic approach to the implementation of Machine Learning in the Stock Picking process of the Nova Students Portfolio. The presented algorithms can help investors in the identification of the features that drive stock returns and results show that our predictive algorithm provides an edge in the selection of outperforming stocks. An investor using our method from 2006 to 2019 would have achieved an annualized return of 4.8% in excess of the S&P 500 and an Info Sharpe gain of 0.2.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-13T00:30:46Z
2020-01-13
2020-08-20
2020-01-13T00:00:00Z
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/10362/102627
TID:202495396
url http://hdl.handle.net/10362/102627
identifier_str_mv TID:202495396
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)
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
repository.mail.fl_str_mv
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