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Testing macroecological theories in cryptocurrency market: neutral models cannot describe diversity patterns and their variation.
Brigatti, Edgardo; Amazonas Mendes, Estevan Augusto.
Afiliação
  • Brigatti E; Instituto de Física, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos 149, Cidade Universitária, 21941-972 Rio de Janeiro, Brazil.
  • Amazonas Mendes EA; Instituto de Física, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos 149, Cidade Universitária, 21941-972 Rio de Janeiro, Brazil.
R Soc Open Sci ; 9(4): 212005, 2022 Apr.
Article em En | MEDLINE | ID: mdl-35425637
We develop an analysis of the cryptocurrency market borrowing methods and concepts from ecology. This approach makes it possible to identify specific diversity patterns and their variation, in close analogy with ecological systems, and to characterize the cryptocurrency market in an effective way. At the same time, it shows how non-biological systems can have an important role in contrasting different ecological theories and in testing the use of neutral models. The study of the cryptocurrencies abundance distribution and the evolution of the community structure strongly indicates that these statistical patterns are not consistent with neutrality. In particular, the necessity to increase the temporal change in community composition when the number of cryptocurrencies grows, suggests that their interactions are not necessarily weak. The analysis of the intraspecific and interspecific interdependency supports this fact and demonstrates the presence of a market sector influenced by mutualistic relations. These latest findings challenge the hypothesis of weakly interacting symmetric species, the postulate at the heart of neutral models.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: R Soc Open Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: R Soc Open Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido