Analysis of quantitative investment strategies
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/161183 |
Resumo: | This paper tests the combination of five different sub-strategies, resembling the performance of a multi-strategy hedge fund benchmarked against the popular buy-and-hold S&P 500 investing approach. The sub-strategies are: residual momentum, value including intangibles, value and momentum, volatility forecasting, and a long short-term memory strategy, the latter two being machine-learning-based, and all investing in the U.S. universe. The combined strategy’s performance is analyzed by three weighting schemes: equal-weight, momentum, and mean-variance, resulting in a gamut of robustness and performance. The combined strategies reap diversification benefits, thereby giving investors a superior risk-reward trade-off compared to the buy-and-hold S&P 500 approach. |
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Analysis of quantitative investment strategiesSystematic trading strategyMomentumValueVolatility forecastingMachine learningNeural networksQuantitative trading strategyDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis paper tests the combination of five different sub-strategies, resembling the performance of a multi-strategy hedge fund benchmarked against the popular buy-and-hold S&P 500 investing approach. The sub-strategies are: residual momentum, value including intangibles, value and momentum, volatility forecasting, and a long short-term memory strategy, the latter two being machine-learning-based, and all investing in the U.S. universe. The combined strategy’s performance is analyzed by three weighting schemes: equal-weight, momentum, and mean-variance, resulting in a gamut of robustness and performance. The combined strategies reap diversification benefits, thereby giving investors a superior risk-reward trade-off compared to the buy-and-hold S&P 500 approach.Hirschey, NicholasRUNEusébio, Hugo2023-12-13T10:15:47Z2022-12-162022-12-162022-12-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/161183TID:203311620enginfo: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:RCAAP2024-03-11T05:44:01Zoai:run.unl.pt:10362/161183Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:24.773836Repositório 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 |
Analysis of quantitative investment strategies |
title |
Analysis of quantitative investment strategies |
spellingShingle |
Analysis of quantitative investment strategies Eusébio, Hugo Systematic trading strategy Momentum Value Volatility forecasting Machine learning Neural networks Quantitative trading strategy Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Analysis of quantitative investment strategies |
title_full |
Analysis of quantitative investment strategies |
title_fullStr |
Analysis of quantitative investment strategies |
title_full_unstemmed |
Analysis of quantitative investment strategies |
title_sort |
Analysis of quantitative investment strategies |
author |
Eusébio, Hugo |
author_facet |
Eusébio, Hugo |
author_role |
author |
dc.contributor.none.fl_str_mv |
Hirschey, Nicholas RUN |
dc.contributor.author.fl_str_mv |
Eusébio, Hugo |
dc.subject.por.fl_str_mv |
Systematic trading strategy Momentum Value Volatility forecasting Machine learning Neural networks Quantitative trading strategy Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Systematic trading strategy Momentum Value Volatility forecasting Machine learning Neural networks Quantitative trading strategy Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
This paper tests the combination of five different sub-strategies, resembling the performance of a multi-strategy hedge fund benchmarked against the popular buy-and-hold S&P 500 investing approach. The sub-strategies are: residual momentum, value including intangibles, value and momentum, volatility forecasting, and a long short-term memory strategy, the latter two being machine-learning-based, and all investing in the U.S. universe. The combined strategy’s performance is analyzed by three weighting schemes: equal-weight, momentum, and mean-variance, resulting in a gamut of robustness and performance. The combined strategies reap diversification benefits, thereby giving investors a superior risk-reward trade-off compared to the buy-and-hold S&P 500 approach. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-16 2022-12-16 2022-12-16T00:00:00Z 2023-12-13T10:15:47Z |
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/10362/161183 TID:203311620 |
url |
http://hdl.handle.net/10362/161183 |
identifier_str_mv |
TID:203311620 |
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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1799138165143896064 |