Publicação
Applying machine learning techniques to identify companies at higher risk of ESG controversy
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Han, Qiwei | |
| dc.contributor.author | Schmitz, Sophie Susanne | |
| dc.date.accessioned | 2022-10-25T14:22:14Z | |
| dc.date.available | 2022-10-25T14:22:14Z | |
| dc.date.issued | 2022-01-21 | |
| dc.date.submitted | 2021-12-16 | |
| dc.description.abstract | ESG controversies may have enormous consequences for an individual company, its customers, investors, and other stakeholders. The objective of this work is to identify companies at high risk of ESG controversy based on public ESG data. By using machine learning solutions, early indicators in ESG data can be identified that provide insight into how likely a company is to face an ESG controversy. By using Random Forest models, the proportion of companies with a controversy among the flagged companies can be increased by 93, 5.6and 4.3times for the Environmental, Social and Governance pillar, respectively. | pt_PT |
| dc.identifier.tid | 203063929 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/144990 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | Nova School of Business and Economics | |
| dc.subject | Sustainability | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Business and data analytics | pt_PT |
| dc.subject | Esg | pt_PT |
| dc.subject | Random forest | pt_PT |
| dc.subject | Esg controversy | pt_PT |
| dc.title | Applying machine learning techniques to identify companies at higher risk of ESG controversy | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UID/ECO/00124/2013 | |
| oaire.awardTitle | Nova School of Business and Economics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FECO%2F00124%2F2013/PT | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| relation.isProjectOfPublication | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| relation.isProjectOfPublication.latestForDiscovery | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsA Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics | pt_PT |