Portfolio optimization: from markowitz to machine learning

Detalhes bibliográficos
Autor(a) principal: Bernarda, Mariana Serrano Lopes da
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/10362/134510
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Portfolio optimization: from markowitz to machine learningPortfolio OptimizationMachine LearningRandom ForestMarkowitzSharpe RatioSDG 9 - Industry, innovation and infrastructureProject Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the past few decades, substantial progress has been made in portfolio optimization, especially with the emergence of machine learning. Therefore, it is essential to find the models that not only achieve the best results but also simplify the process. This project aims to demonstrate that to achieve optimal portfolios cannot be based only on traditional statistical methods. Therefore the Random Forest regression model, a machine learning model, was chosen to predict stock prices to complement the Markowitz model, a classical portfolio selection model. To evaluate the efficacy of the modified model compared to the classical model the following methodology was adopted: data was collected (from 2012 to 2019 from 10 companies and it was divided in 15 periods) and treated; some common technical indicators were extracted; one stock price was predicted per period; expected returns and partially estimated volatility were derived from the predictions and introduced in the classical model; 15 portfolios were constructed by each model; and finally, a performance analysis was conducted. The results obtained show that the 1-day predictions were quite accurate, almost 90%, and the modified model’s portfolios’ outperformed the classical model’s portfolios for most periods analyzed.Gonçalves, Rui Alexandre HenriquesRUNBernarda, Mariana Serrano Lopes da2022-03-15T15:57:48Z2022-02-242022-02-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/134510TID:202964256enginfo: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:RCAAP2024-03-11T05:12:58Zoai:run.unl.pt:10362/134510Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:08.941287Repositório 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 Portfolio optimization: from markowitz to machine learning
title Portfolio optimization: from markowitz to machine learning
spellingShingle Portfolio optimization: from markowitz to machine learning
Bernarda, Mariana Serrano Lopes da
Portfolio Optimization
Machine Learning
Random Forest
Markowitz
Sharpe Ratio
SDG 9 - Industry, innovation and infrastructure
title_short Portfolio optimization: from markowitz to machine learning
title_full Portfolio optimization: from markowitz to machine learning
title_fullStr Portfolio optimization: from markowitz to machine learning
title_full_unstemmed Portfolio optimization: from markowitz to machine learning
title_sort Portfolio optimization: from markowitz to machine learning
author Bernarda, Mariana Serrano Lopes da
author_facet Bernarda, Mariana Serrano Lopes da
author_role author
dc.contributor.none.fl_str_mv Gonçalves, Rui Alexandre Henriques
RUN
dc.contributor.author.fl_str_mv Bernarda, Mariana Serrano Lopes da
dc.subject.por.fl_str_mv Portfolio Optimization
Machine Learning
Random Forest
Markowitz
Sharpe Ratio
SDG 9 - Industry, innovation and infrastructure
topic Portfolio Optimization
Machine Learning
Random Forest
Markowitz
Sharpe Ratio
SDG 9 - Industry, innovation and infrastructure
description Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2022
dc.date.none.fl_str_mv 2022-03-15T15:57:48Z
2022-02-24
2022-02-24T00:00:00Z
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/10362/134510
TID:202964256
url http://hdl.handle.net/10362/134510
identifier_str_mv TID:202964256
dc.language.iso.fl_str_mv eng
language eng
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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
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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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