A machine learning algorithm applied to macroeconomic factor investing
Autor(a) principal: | |
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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/10362/144695 |
Resumo: | This paper examines the extent to which macroeconomic indicators can be used to determine the optimal allocation of an extended Fama French 5-Factor model which includes the risk-free rate. The study is based on Modern Portfolio Theory (MPT) as developed by Markowitz(1952) and Smart Beta Investing. The algorithm combines MPT with two Machine Learning (ML) Algorithms (K-means Clustering and Random Forest) to predict the macroeconomic state and arrive at the according optimal ‘tactical’ portfolio allocation of each security over the investment period. The research contributes to the existing literature of ML Algorithm performance applied to Smart Beta macroeconomic strategies. |
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A machine learning algorithm applied to macroeconomic factor investingCorporate financePrivate equityDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis paper examines the extent to which macroeconomic indicators can be used to determine the optimal allocation of an extended Fama French 5-Factor model which includes the risk-free rate. The study is based on Modern Portfolio Theory (MPT) as developed by Markowitz(1952) and Smart Beta Investing. The algorithm combines MPT with two Machine Learning (ML) Algorithms (K-means Clustering and Random Forest) to predict the macroeconomic state and arrive at the according optimal ‘tactical’ portfolio allocation of each security over the investment period. The research contributes to the existing literature of ML Algorithm performance applied to Smart Beta macroeconomic strategies.Ribeiro, Gonçalo SommerAnjos, FernandoRUNSimões, Ana Bárbara Moura Da Luz Delgado2022-10-14T09:53:43Z2022-04-052021-12-172022-04-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/144695TID:203064828enginfo: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:RCAAP2024-03-11T05:24:31Zoai:run.unl.pt:10362/144695Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:42.053097Repositório 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 algorithm applied to macroeconomic factor investing |
title |
A machine learning algorithm applied to macroeconomic factor investing |
spellingShingle |
A machine learning algorithm applied to macroeconomic factor investing Simões, Ana Bárbara Moura Da Luz Delgado Corporate finance Private equity Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
A machine learning algorithm applied to macroeconomic factor investing |
title_full |
A machine learning algorithm applied to macroeconomic factor investing |
title_fullStr |
A machine learning algorithm applied to macroeconomic factor investing |
title_full_unstemmed |
A machine learning algorithm applied to macroeconomic factor investing |
title_sort |
A machine learning algorithm applied to macroeconomic factor investing |
author |
Simões, Ana Bárbara Moura Da Luz Delgado |
author_facet |
Simões, Ana Bárbara Moura Da Luz Delgado |
author_role |
author |
dc.contributor.none.fl_str_mv |
Ribeiro, Gonçalo Sommer Anjos, Fernando RUN |
dc.contributor.author.fl_str_mv |
Simões, Ana Bárbara Moura Da Luz Delgado |
dc.subject.por.fl_str_mv |
Corporate finance Private equity Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Corporate finance Private equity Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
This paper examines the extent to which macroeconomic indicators can be used to determine the optimal allocation of an extended Fama French 5-Factor model which includes the risk-free rate. The study is based on Modern Portfolio Theory (MPT) as developed by Markowitz(1952) and Smart Beta Investing. The algorithm combines MPT with two Machine Learning (ML) Algorithms (K-means Clustering and Random Forest) to predict the macroeconomic state and arrive at the according optimal ‘tactical’ portfolio allocation of each security over the investment period. The research contributes to the existing literature of ML Algorithm performance applied to Smart Beta macroeconomic strategies. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-17 2022-10-14T09:53:43Z 2022-04-05 2022-04-05T00:00:00Z |
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/10362/144695 TID:203064828 |
url |
http://hdl.handle.net/10362/144695 |
identifier_str_mv |
TID:203064828 |
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 |
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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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1799138109865066496 |