Information Theory Quantifiers in Cryptocurrency Time Series Analysis

Autores
Suriano, Micaela Paula; Caram, Leonidas Facundo; Caiafa, César Federico; Merlino, Hernán Daniel; Rosso, Osvaldo Anibal
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.
Fil: Suriano, Micaela Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Hidráulica; Argentina
Fil: Caram, Leonidas Facundo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electrónica; Argentina
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Merlino, Hernán Daniel. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Rosso, Osvaldo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
Materia
permutation entropy
statistical complexity
cryptocurrency
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/276599
Acceder
id CONICETDig_f5f45cd0f3ee2896597e85d867a16974
oai_identifier_str oai:ri.conicet.gov.ar:11336/276599
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Information Theory Quantifiers in Cryptocurrency Time Series AnalysisSuriano, Micaela PaulaCaram, Leonidas FacundoCaiafa, César FedericoMerlino, Hernán DanielRosso, Osvaldo Anibalpermutation entropystatistical complexitycryptocurrencyhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.Fil: Suriano, Micaela Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Hidráulica; ArgentinaFil: Caram, Leonidas Facundo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electrónica; ArgentinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Merlino, Hernán Daniel. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Rosso, Osvaldo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaMolecular Diversity Preservation International2025-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/276599Suriano, Micaela Paula; Caram, Leonidas Facundo; Caiafa, César Federico; Merlino, Hernán Daniel; Rosso, Osvaldo Anibal; Information Theory Quantifiers in Cryptocurrency Time Series Analysis; Molecular Diversity Preservation International; Entropy; 27; 4; 4-2025; 1-161099-4300CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/27/4/450info:eu-repo/semantics/altIdentifier/doi/10.3390/e27040450info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-23T13:17:07Zoai:ri.conicet.gov.ar:11336/276599instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-12-23 13:17:07.965CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Information Theory Quantifiers in Cryptocurrency Time Series Analysis
title Information Theory Quantifiers in Cryptocurrency Time Series Analysis
spellingShingle Information Theory Quantifiers in Cryptocurrency Time Series Analysis
Suriano, Micaela Paula
permutation entropy
statistical complexity
cryptocurrency
title_short Information Theory Quantifiers in Cryptocurrency Time Series Analysis
title_full Information Theory Quantifiers in Cryptocurrency Time Series Analysis
title_fullStr Information Theory Quantifiers in Cryptocurrency Time Series Analysis
title_full_unstemmed Information Theory Quantifiers in Cryptocurrency Time Series Analysis
title_sort Information Theory Quantifiers in Cryptocurrency Time Series Analysis
dc.creator.none.fl_str_mv Suriano, Micaela Paula
Caram, Leonidas Facundo
Caiafa, César Federico
Merlino, Hernán Daniel
Rosso, Osvaldo Anibal
author Suriano, Micaela Paula
author_facet Suriano, Micaela Paula
Caram, Leonidas Facundo
Caiafa, César Federico
Merlino, Hernán Daniel
Rosso, Osvaldo Anibal
author_role author
author2 Caram, Leonidas Facundo
Caiafa, César Federico
Merlino, Hernán Daniel
Rosso, Osvaldo Anibal
author2_role author
author
author
author
dc.subject.none.fl_str_mv permutation entropy
statistical complexity
cryptocurrency
topic permutation entropy
statistical complexity
cryptocurrency
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.
Fil: Suriano, Micaela Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Hidráulica; Argentina
Fil: Caram, Leonidas Facundo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electrónica; Argentina
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Merlino, Hernán Daniel. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Rosso, Osvaldo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
description This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.
publishDate 2025
dc.date.none.fl_str_mv 2025-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/276599
Suriano, Micaela Paula; Caram, Leonidas Facundo; Caiafa, César Federico; Merlino, Hernán Daniel; Rosso, Osvaldo Anibal; Information Theory Quantifiers in Cryptocurrency Time Series Analysis; Molecular Diversity Preservation International; Entropy; 27; 4; 4-2025; 1-16
1099-4300
CONICET Digital
CONICET
url http://hdl.handle.net/11336/276599
identifier_str_mv Suriano, Micaela Paula; Caram, Leonidas Facundo; Caiafa, César Federico; Merlino, Hernán Daniel; Rosso, Osvaldo Anibal; Information Theory Quantifiers in Cryptocurrency Time Series Analysis; Molecular Diversity Preservation International; Entropy; 27; 4; 4-2025; 1-16
1099-4300
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/27/4/450
info:eu-repo/semantics/altIdentifier/doi/10.3390/e27040450
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1852335019722276864
score 12.952241

Publicaciones similares