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dc.contributor.advisorSantos, Francisco
dc.contributor.authorBjørneklett, Martin Rognan
dc.contributor.authorMangal, Asad
dc.date.accessioned2022-09-09T11:02:31Z
dc.date.available2022-09-09T11:02:31Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/11250/3016857
dc.description.abstractSustainable investing has seen exponential growth among investors in recent years. To this end, ESG ratings are the tool used by investors to gauge environmental sustainability in firms. Recently listed firms lack ESG ratings, creating a market imperfection. Additionally, researchers found no correlation between carbon emissions and ESG ratings. This thesis proposes a textual analysis methodology using cosine similarity for computing environmental sustainability scores (E-scores) based on firm activities. Primarily, we provide E-scores for 366 recently listed US firms. We use selection criteria established in IPO literature. Additionally, we compute E-scores for all publicly listed US firms in 2020, approximately 10000 firms. We indirectly verify the proposed method through an event study of the 2020 US presidential election. Firms with high E-scores had cumulative abnormal returns of 2 percent, while firms with low E-scores achieved only -0.28 percent. Through significance testing, we conclude that this difference in cumulative abnormal returns is statistically significant. Due to the lack of ESG ratings of recently listed firms, little research has been conducted on the relation between environmental sustainability and underpricing. In this thesis, we use the proposed E-scores to establish this link. During the sample period between 2019 and 2021, we found that firms with high E-scores were underpriced by 1.44 percent, while firms with low E-scores had underpricing equal to -0.73 percent.en_US
dc.language.isoengen_US
dc.subjectfinancial economicsen_US
dc.titleGreen Stocks and How to Find Them: Identifying environmentally sustainable IPO firms using textual analysis and assessing their profitabilityen_US
dc.typeMaster thesisen_US
dc.description.localcodenhhmasen_US


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