Tests of Predictability in Cryptocurrency Markets

Detalhes bibliográficos
Autor(a) principal: Rubio, Isabella Regina da Silva
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/161114
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Tests of Predictability in Cryptocurrency MarketsCryptocurrencyPrice VolatilityLSTMFinancial Market PredictionDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThe cryptocurrency market has grabbed the curiosity of both seasoned and novice investors as a developing and increasingly popular financial arena. This rise in attention warrants a closer look at Bitcoin pricing trends and the market's potential predictability. To solve the core research topic, a deductive technique was used in response to these study aims. To help this analysis, the researcher used Long Short-Term Memory (LSTM) networks, a type of recurrent neural network known for its ability to capture order dependencies within sequential data. The study's findings highlight the capacity of LSTM networks to deliver cryptocurrency price forecasts, putting light on the promising potential of LSTM in cryptocurrency market analysis. This study goes beyond standard ways to investigate cryptocurrency market prediction, using data from 2015 to 2023. The data scope, together with the use of LSTM and GRU models, adds to a more comprehensive and accurate analysis, meeting the need for a more in-depth understanding of Bitcoin market dynamics.Damásio, Bruno Miguel PintoRUNRubio, Isabella Regina da Silva2023-12-12T14:10:00Z2023-10-262023-10-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/161114TID:203418719enginfo: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:RCAAP2024-03-11T05:43:54Zoai:run.unl.pt:10362/161114Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:20.547467Repositório 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 Tests of Predictability in Cryptocurrency Markets
title Tests of Predictability in Cryptocurrency Markets
spellingShingle Tests of Predictability in Cryptocurrency Markets
Rubio, Isabella Regina da Silva
Cryptocurrency
Price Volatility
LSTM
Financial Market Prediction
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Tests of Predictability in Cryptocurrency Markets
title_full Tests of Predictability in Cryptocurrency Markets
title_fullStr Tests of Predictability in Cryptocurrency Markets
title_full_unstemmed Tests of Predictability in Cryptocurrency Markets
title_sort Tests of Predictability in Cryptocurrency Markets
author Rubio, Isabella Regina da Silva
author_facet Rubio, Isabella Regina da Silva
author_role author
dc.contributor.none.fl_str_mv Damásio, Bruno Miguel Pinto
RUN
dc.contributor.author.fl_str_mv Rubio, Isabella Regina da Silva
dc.subject.por.fl_str_mv Cryptocurrency
Price Volatility
LSTM
Financial Market Prediction
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Cryptocurrency
Price Volatility
LSTM
Financial Market Prediction
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2023
dc.date.none.fl_str_mv 2023-12-12T14:10:00Z
2023-10-26
2023-10-26T00:00:00Z
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/10362/161114
TID:203418719
url http://hdl.handle.net/10362/161114
identifier_str_mv TID:203418719
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
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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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