Clustering of Assets and its relationship with macroeconomic variables and the financial indexes
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/12421 |
Resumo: | The advent of the financial market is one of the most fascinating events of our time. Over the years, researchers and investors have been interested in developing tools to assist in making decisions regarding capital allocation. This work proposes clustering as a metric to classify a set of assets, through a grouping method that maximizes similarity between elements of the same group and minimizes similarity between different groups in order to mitigate portfolio risk. Additionally, we use multiple linear regressions (MLR) to show whether the assets belonging to the clusters respond in a similar way to some macroeconomic variables and financial indexes. For the analyzed period -January 2019 to January 2020 - we obtained 8 different clusters of assets with a minimum of 1 asset (1.32 % of total assets) and a maximum of 30 assets (42.86% of total assets). With respect to the relationships with the selected variables, the ANBIMA market index (IMAB) and the smll index (smalll caps) are the most closely variables related to the clusters whereas that the IPCA and Ibovespa indexes are the least significant variables in the econometric application proposed in this paper. |
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Clustering of Assets and its relationship with macroeconomic variables and the financial indexesClustering de Activos y su relación con variables macroeconómicas e índices financierosClusterização de Ativos e suas relações com variáveis macroeconômicas e índices financeirosFinancial assetsClusterFinancial decisions.Activos financierosRacimoDecisiones financieras.Ativos financeirosClusterDecisões financeiras.The advent of the financial market is one of the most fascinating events of our time. Over the years, researchers and investors have been interested in developing tools to assist in making decisions regarding capital allocation. This work proposes clustering as a metric to classify a set of assets, through a grouping method that maximizes similarity between elements of the same group and minimizes similarity between different groups in order to mitigate portfolio risk. Additionally, we use multiple linear regressions (MLR) to show whether the assets belonging to the clusters respond in a similar way to some macroeconomic variables and financial indexes. For the analyzed period -January 2019 to January 2020 - we obtained 8 different clusters of assets with a minimum of 1 asset (1.32 % of total assets) and a maximum of 30 assets (42.86% of total assets). With respect to the relationships with the selected variables, the ANBIMA market index (IMAB) and the smll index (smalll caps) are the most closely variables related to the clusters whereas that the IPCA and Ibovespa indexes are the least significant variables in the econometric application proposed in this paper.La llegada del mercado financiero es uno de los acontecimientos más fascinantes de nuestro tiempo. A lo largo de los años, los investigadores e inversores se han interesado en desarrollar herramientas para ayudar a tomar decisiones sobre la asignación de capital. Este artículo propone el agrupamiento como métrica para separar un conjunto de activos, utilizando un método de agrupamiento que maximiza la similitud entre grupos y minimiza la similitud entre diferentes grupos para mitigar el riesgo de cartera. Además, utilizamos regresiones lineales múltiples para mostrar si los activos que pertenecen a los clústeres responden de manera similar a algunas variables macroeconómicas e índices financieros. para el período analizado - enero de 2019 a enero de 2020 - obtuvimos 8 grupos de activos diferentes con un mínimo de 1 activo (1,32% del activo total) y un máximo de 30 activos (42,86% del activo total). En cuanto a las relaciones con las variables seleccionadas, el índice de mercado ANBIMA (IMAB) y el índice SMLL (smalll caps) son las variables que más se relacionan con los clusters y las variables IPCA e Ibovespa son las menos significativas en la aplicación econométrica propuesta en este artículo.O advento do mercado financeiro é um dos acontecimentos mais fascinantes do nosso tempo. Ao longo dos anos, pesquisadores e investidores se interessaram em desenvolver ferramentas para auxiliar na tomada de decisões referentes a alocação de capital. o presente artigo propõe a clusterização como uma métrica para separar um conjunto de ativos, através de um método de agrupamento que maximiza a semelhança entre grupos e minimiza a semelhança entre diferentes grupos com a finalidade de atenuar o risco do portfólio. Adicionalmente, utilizamos regressões lineares múltiplas para evidenciar se os ativos pertencentes aos clusters respondem de forma similar a algumas variáveis macroeconômicas e índices financeiros. para o período analisado - janeiro de 2019 a janeiro de 2020 - obtivemos 8 diferentes clusteres de ativos com um mínimo de 1 ativo (1,32% do total de ativos) e máximo de 30 ativos (42,86% do total de ativos). No que tange as relações com as variáveis selecionadas, o índice de mercado ANBIMA (IMAB) e o índice SMLL (smalll caps) são as variáveis que mais se relacionam com os clusteres e as variáveis IPCA e Ibovespa são as que menos apresentaram significância na aplicação econométrica proposta neste artigo.Research, Society and Development2021-02-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1242110.33448/rsd-v10i2.12421Research, Society and Development; Vol. 10 No. 2; e22910212421Research, Society and Development; Vol. 10 Núm. 2; e22910212421Research, Society and Development; v. 10 n. 2; e229102124212525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/12421/11201Copyright (c) 2021 Daiane Rodrigues dos Santos; Tuany Esthefany Barcellos de Carvalho Silva; Campo Elias Suárez Villagrán; Tiago Costa Ribeirohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantos, Daiane Rodrigues dos Silva, Tuany Esthefany Barcellos de Carvalho Villagrán, Campo Elias Suárez Ribeiro, Tiago Costa 2021-03-02T09:32:39Zoai:ojs.pkp.sfu.ca:article/12421Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:34:00.041227Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes Clustering de Activos y su relación con variables macroeconómicas e índices financieros Clusterização de Ativos e suas relações com variáveis macroeconômicas e índices financeiros |
title |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
spellingShingle |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes Santos, Daiane Rodrigues dos Financial assets Cluster Financial decisions. Activos financieros Racimo Decisiones financieras. Ativos financeiros Cluster Decisões financeiras. |
title_short |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
title_full |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
title_fullStr |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
title_full_unstemmed |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
title_sort |
Clustering of Assets and its relationship with macroeconomic variables and the financial indexes |
author |
Santos, Daiane Rodrigues dos |
author_facet |
Santos, Daiane Rodrigues dos Silva, Tuany Esthefany Barcellos de Carvalho Villagrán, Campo Elias Suárez Ribeiro, Tiago Costa |
author_role |
author |
author2 |
Silva, Tuany Esthefany Barcellos de Carvalho Villagrán, Campo Elias Suárez Ribeiro, Tiago Costa |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, Daiane Rodrigues dos Silva, Tuany Esthefany Barcellos de Carvalho Villagrán, Campo Elias Suárez Ribeiro, Tiago Costa |
dc.subject.por.fl_str_mv |
Financial assets Cluster Financial decisions. Activos financieros Racimo Decisiones financieras. Ativos financeiros Cluster Decisões financeiras. |
topic |
Financial assets Cluster Financial decisions. Activos financieros Racimo Decisiones financieras. Ativos financeiros Cluster Decisões financeiras. |
description |
The advent of the financial market is one of the most fascinating events of our time. Over the years, researchers and investors have been interested in developing tools to assist in making decisions regarding capital allocation. This work proposes clustering as a metric to classify a set of assets, through a grouping method that maximizes similarity between elements of the same group and minimizes similarity between different groups in order to mitigate portfolio risk. Additionally, we use multiple linear regressions (MLR) to show whether the assets belonging to the clusters respond in a similar way to some macroeconomic variables and financial indexes. For the analyzed period -January 2019 to January 2020 - we obtained 8 different clusters of assets with a minimum of 1 asset (1.32 % of total assets) and a maximum of 30 assets (42.86% of total assets). With respect to the relationships with the selected variables, the ANBIMA market index (IMAB) and the smll index (smalll caps) are the most closely variables related to the clusters whereas that the IPCA and Ibovespa indexes are the least significant variables in the econometric application proposed in this paper. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/12421 10.33448/rsd-v10i2.12421 |
url |
https://rsdjournal.org/index.php/rsd/article/view/12421 |
identifier_str_mv |
10.33448/rsd-v10i2.12421 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/12421/11201 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 2; e22910212421 Research, Society and Development; Vol. 10 Núm. 2; e22910212421 Research, Society and Development; v. 10 n. 2; e22910212421 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052669719740416 |