Identifying key features for ESG score improvement in the Eu market - a machine learning approach

Detalhes bibliográficos
Autor(a) principal: Bellon, Diego
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/10362/144988
Resumo: The growing importance of sustainability in business practices has led to increases in non-financial reporting policies. With the upcoming Corporate Sustainability Reporting Directive, more companies within the European Union must prepare to identify and capture ESG data to stay competitive. This paper determines multiple key metrics for improving ESG performance by applying the Random Forest and Rule Fit machine learning methods. The findings indicate that while company size is a dominant predictor, other features can significantly set its ESG performance apart and increase its score.
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spelling Identifying key features for ESG score improvement in the Eu market - a machine learning approachMachine learningBusiness and data analyticsEsgEsg ratingsRefinitivDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe growing importance of sustainability in business practices has led to increases in non-financial reporting policies. With the upcoming Corporate Sustainability Reporting Directive, more companies within the European Union must prepare to identify and capture ESG data to stay competitive. This paper determines multiple key metrics for improving ESG performance by applying the Random Forest and Rule Fit machine learning methods. The findings indicate that while company size is a dominant predictor, other features can significantly set its ESG performance apart and increase its score.Han, QiweiRUNBellon, Diego2022-01-212021-12-172025-12-17T00:00:00Z2022-01-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/144988TID:203063937enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:25:02Zoai:run.unl.pt:10362/144988Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:51.339314Repositório 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 Identifying key features for ESG score improvement in the Eu market - a machine learning approach
title Identifying key features for ESG score improvement in the Eu market - a machine learning approach
spellingShingle Identifying key features for ESG score improvement in the Eu market - a machine learning approach
Bellon, Diego
Machine learning
Business and data analytics
Esg
Esg ratings
Refinitiv
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Identifying key features for ESG score improvement in the Eu market - a machine learning approach
title_full Identifying key features for ESG score improvement in the Eu market - a machine learning approach
title_fullStr Identifying key features for ESG score improvement in the Eu market - a machine learning approach
title_full_unstemmed Identifying key features for ESG score improvement in the Eu market - a machine learning approach
title_sort Identifying key features for ESG score improvement in the Eu market - a machine learning approach
author Bellon, Diego
author_facet Bellon, Diego
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Bellon, Diego
dc.subject.por.fl_str_mv Machine learning
Business and data analytics
Esg
Esg ratings
Refinitiv
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Business and data analytics
Esg
Esg ratings
Refinitiv
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description The growing importance of sustainability in business practices has led to increases in non-financial reporting policies. With the upcoming Corporate Sustainability Reporting Directive, more companies within the European Union must prepare to identify and capture ESG data to stay competitive. This paper determines multiple key metrics for improving ESG performance by applying the Random Forest and Rule Fit machine learning methods. The findings indicate that while company size is a dominant predictor, other features can significantly set its ESG performance apart and increase its score.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-01-21
2022-01-21T00:00:00Z
2025-12-17T00:00:00Z
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/10362/144988
TID:203063937
url http://hdl.handle.net/10362/144988
identifier_str_mv TID:203063937
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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
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