A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search

Detalhes bibliográficos
Autor(a) principal: Lousão, Francisca Inês Augusto
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/10362/145565
Resumo: Dissertation 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 A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local SearchIndex fundVeganGenetic AlgorithmHeuristic Local SearchESGDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThe curiosity of investors regarding Environmental, Social and Governance (ESG) factors has seen a growth in the last few years (Alcoforado, 2016), as the world faces some of its biggest problems to date, such as Climate Change and Ecological Collapse. As these issues are not to be taken lightly, individuals have started to act, in the hopes of creating a ‘greener’ world. As individuals hope to align with principles such as Sustainability and Veganism, the proposed project hopes to build a Vegan and Sustainable Index Fund, as “An investment is not an investment if it is destroying our planet.” (Shiva, 2017). The aim of the proposed work is, consequently, to build and optimize an Industry and Geographical diversified Index Fund, using a Genetic Algorithm (GA), demonstrating this through the incorporation of Vegan and Sustainable companies, in addition to the global top-50 ESG ranked firms. Index Funds, which are mutual or Exchange-Traded Funds (ETF), are known to be passively managed portfolios, which have been broadly used in hedge trading (Orito, Inoguchi, & Yamamoto, 2008). This study uses historical data from Vegan, Sustainable and ESG-ranked companies as sample data, replacing traditional optimization methods using a Genetic Algorithm. The GA method was applied to a sample of 61 assets, regarding vegan and sustainable companies, further obtaining a well-diversified and non-centred asset allocation. The obtained results confirm the possible efficiency of genetic algorithms, given their high-speed convergence towards a better solution. A few functions were presented in the algorithm, for example the penalty function method, to perform portfolio optimization which expects to maximize profits and minimize risks. Some flaws have been identified in regard to the method applied.Castelli, MauroRUNLousão, Francisca Inês Augusto2022-11-16T15:31:08Z2022-10-262022-10-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145565TID:203126068enginfo: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:RCAAP2024-03-11T05:26:04Zoai:run.unl.pt:10362/145565Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:09.297494Repositório 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 Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
title A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
spellingShingle A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
Lousão, Francisca Inês Augusto
Index fund
Vegan
Genetic Algorithm
Heuristic Local Search
ESG
title_short A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
title_full A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
title_fullStr A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
title_full_unstemmed A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
title_sort A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
author Lousão, Francisca Inês Augusto
author_facet Lousão, Francisca Inês Augusto
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Lousão, Francisca Inês Augusto
dc.subject.por.fl_str_mv Index fund
Vegan
Genetic Algorithm
Heuristic Local Search
ESG
topic Index fund
Vegan
Genetic Algorithm
Heuristic Local Search
ESG
description Dissertation 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 2022-11-16T15:31:08Z
2022-10-26
2022-10-26T00:00:00Z
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/10362/145565
TID:203126068
url http://hdl.handle.net/10362/145565
identifier_str_mv TID:203126068
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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