Application of the Fama French 3-Factor model to the cryptocurrency and token markets
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/31260 |
Resumo: | This study investigates the ability of the Fama French 3-Factor model in predicting the returns of the cryptocurrency and token markets. The dataset is comprised of 30 cryptocurrencies and 30 tokens with a timeframe starting at 13th of November 2018 and finishing on the 15 of May 2020. In accordance to the methodology stablished in the original study, this analysis creates 6 portfolios based on the market capitalization and network to value transaction ratio in order to create the size and profitability factors, being followed by the creation of the 25 portfolios based on the same characteristics to analyse the behaviour of the model across different scenarios. The results for the cryptocurrency market show low prediction power, with all of the model’s factors not being significant in the cross-sectional regression. Moreover, the results in the token market show a high correlation across the size and profitability factors which made the analysis on this segment to follow an adapted version of what was performed for the cryptocurrency market. The size and profitability factors were analysed separately, and the results show an even lower prediction power compared to cryptocurrencies. |
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Application of the Fama French 3-Factor model to the cryptocurrency and token marketsCryptocurrencyTokenFama French 3-factor modelCriptomoedaModelo Fama French de 3 fatoresDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis study investigates the ability of the Fama French 3-Factor model in predicting the returns of the cryptocurrency and token markets. The dataset is comprised of 30 cryptocurrencies and 30 tokens with a timeframe starting at 13th of November 2018 and finishing on the 15 of May 2020. In accordance to the methodology stablished in the original study, this analysis creates 6 portfolios based on the market capitalization and network to value transaction ratio in order to create the size and profitability factors, being followed by the creation of the 25 portfolios based on the same characteristics to analyse the behaviour of the model across different scenarios. The results for the cryptocurrency market show low prediction power, with all of the model’s factors not being significant in the cross-sectional regression. Moreover, the results in the token market show a high correlation across the size and profitability factors which made the analysis on this segment to follow an adapted version of what was performed for the cryptocurrency market. The size and profitability factors were analysed separately, and the results show an even lower prediction power compared to cryptocurrencies.Este estudo investiga a capacidade do modelo Fama French de 3 fatores em prever os retornos dos mercados de criptomoedas e tokens. O conjunto de dados é composto por 30 criptomoedas e 30 tokens com prazo de 13 de novembro de 2018 a 15 de maio de 2020. De acordo com a metodologia estabelecida no estudo original, essa análise cria 6 portfólios com base na capitalização de mercado e relação de valor da transação da rede para criar fatores de tamanho e rentabilidade, seguida pela criação de 25 portfólios com base nas mesmas características para analisar o comportamento do modelo em diferentes cenários. Os resultados para o mercado de criptomoedas mostram baixo poder de previsão, com todos os fatores do modelo não sendo significativos na regressão transversal. Além disso, os resultados no mercado de tokens mostram uma alta correlação entre os fatores de tamanho e rentabilidade que fizeram a análise desse segmento seguir uma versão adaptada do que foi realizado para o mercado de criptomoedas. Os fatores de tamanho e rentabilidade foram analisados separadamente e os resultados mostram um poder de previsão ainda menor em comparação às criptomoedas.Andrade, João Freire deVeritati - Repositório Institucional da Universidade Católica PortuguesaCoelho, Diana Mara Costa2020-11-04T11:48:37Z2020-07-0720202020-07-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/31260TID:202518213enginfo: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:RCAAP2023-07-12T17:36:47Zoai:repositorio.ucp.pt:10400.14/31260Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:25:10.825434Repositório 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 |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
title |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
spellingShingle |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets Coelho, Diana Mara Costa Cryptocurrency Token Fama French 3-factor model Criptomoeda Modelo Fama French de 3 fatores Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
title_full |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
title_fullStr |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
title_full_unstemmed |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
title_sort |
Application of the Fama French 3-Factor model to the cryptocurrency and token markets |
author |
Coelho, Diana Mara Costa |
author_facet |
Coelho, Diana Mara Costa |
author_role |
author |
dc.contributor.none.fl_str_mv |
Andrade, João Freire de Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Coelho, Diana Mara Costa |
dc.subject.por.fl_str_mv |
Cryptocurrency Token Fama French 3-factor model Criptomoeda Modelo Fama French de 3 fatores Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Cryptocurrency Token Fama French 3-factor model Criptomoeda Modelo Fama French de 3 fatores Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
This study investigates the ability of the Fama French 3-Factor model in predicting the returns of the cryptocurrency and token markets. The dataset is comprised of 30 cryptocurrencies and 30 tokens with a timeframe starting at 13th of November 2018 and finishing on the 15 of May 2020. In accordance to the methodology stablished in the original study, this analysis creates 6 portfolios based on the market capitalization and network to value transaction ratio in order to create the size and profitability factors, being followed by the creation of the 25 portfolios based on the same characteristics to analyse the behaviour of the model across different scenarios. The results for the cryptocurrency market show low prediction power, with all of the model’s factors not being significant in the cross-sectional regression. Moreover, the results in the token market show a high correlation across the size and profitability factors which made the analysis on this segment to follow an adapted version of what was performed for the cryptocurrency market. The size and profitability factors were analysed separately, and the results show an even lower prediction power compared to cryptocurrencies. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-04T11:48:37Z 2020-07-07 2020 2020-07-07T00:00:00Z |
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/10400.14/31260 TID:202518213 |
url |
http://hdl.handle.net/10400.14/31260 |
identifier_str_mv |
TID:202518213 |
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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1799131965289398272 |