Classifying earnings conference calls
Autor(a) principal: | |
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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/10362/143253 |
Resumo: | This study examines whether it is possible to classify the sentiment of earnings conference calls of U.S. publicly traded companies not by using standard metrics such as standardized unexpected earnings, but by but using the general sentiments, opinions and affective states present in the earnings calls. This classification task is attempted using the naïve Bayes classifier. Results show that due to the high signal to noise ratio present in the earnings calls, the classifier of choice was unable to adequately distinguish positive earnings calls from negative ones and vice-versa. Nonetheless, the classifier did shed light on the extent to which company CEOs tend to be overly optimistic when partaking in the conference calls. |
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Classifying earnings conference callsMachine learningEvent studyNatural language processingDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis study examines whether it is possible to classify the sentiment of earnings conference calls of U.S. publicly traded companies not by using standard metrics such as standardized unexpected earnings, but by but using the general sentiments, opinions and affective states present in the earnings calls. This classification task is attempted using the naïve Bayes classifier. Results show that due to the high signal to noise ratio present in the earnings calls, the classifier of choice was unable to adequately distinguish positive earnings calls from negative ones and vice-versa. Nonetheless, the classifier did shed light on the extent to which company CEOs tend to be overly optimistic when partaking in the conference calls.Hirschey, Nicholas H.RUNSalomão, Antônio Elias Xavier2022-08-24T14:42:03Z2022-01-102021-12-172022-01-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/143253TID:203050100enginfo: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:RCAAP2024-03-11T05:21:30Zoai:run.unl.pt:10362/143253Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:47.562196Repositório 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 |
Classifying earnings conference calls |
title |
Classifying earnings conference calls |
spellingShingle |
Classifying earnings conference calls Salomão, Antônio Elias Xavier Machine learning Event study Natural language processing Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Classifying earnings conference calls |
title_full |
Classifying earnings conference calls |
title_fullStr |
Classifying earnings conference calls |
title_full_unstemmed |
Classifying earnings conference calls |
title_sort |
Classifying earnings conference calls |
author |
Salomão, Antônio Elias Xavier |
author_facet |
Salomão, Antônio Elias Xavier |
author_role |
author |
dc.contributor.none.fl_str_mv |
Hirschey, Nicholas H. RUN |
dc.contributor.author.fl_str_mv |
Salomão, Antônio Elias Xavier |
dc.subject.por.fl_str_mv |
Machine learning Event study Natural language processing Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Machine learning Event study Natural language processing Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
This study examines whether it is possible to classify the sentiment of earnings conference calls of U.S. publicly traded companies not by using standard metrics such as standardized unexpected earnings, but by but using the general sentiments, opinions and affective states present in the earnings calls. This classification task is attempted using the naïve Bayes classifier. Results show that due to the high signal to noise ratio present in the earnings calls, the classifier of choice was unable to adequately distinguish positive earnings calls from negative ones and vice-versa. Nonetheless, the classifier did shed light on the extent to which company CEOs tend to be overly optimistic when partaking in the conference calls. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-17 2022-08-24T14:42:03Z 2022-01-10 2022-01-10T00:00:00Z |
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/10362/143253 TID:203050100 |
url |
http://hdl.handle.net/10362/143253 |
identifier_str_mv |
TID:203050100 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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openAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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