8 research outputs found
Análise da relação risco-retorno em carteiras de crédito-comparativo entre bancos tradicionais e Fintechs
Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas, Departamento de Ciências Contábeis e Atuariais, 2020.A relação risco-retorno, difundida, inicialmente, pela Teoria do Portfólio de Harry
Markowitz, vem sendo utilizada nas decisões de investimentos e na avaliação de
carteiras. Motivado pelo impacto das fintechs no mercado, com práticas de negócios
disruptivas e arrojadas, o presente trabalho tem como objetivo verificar qual dos dois
tipos de instituições financeiras, bancos tradicionais ou fintechs, detém a carteira de
crédito mais arriscada e, consequentemente, a carteira com maiores retornos, assim
como esperado pela relação risco-retorno. Para o período entre 2018 e 2020, foram
analisadas, semestralmente, as seguintes entidades como parte da amostra: Nu
Pagamentos, Original, Inter e Agibank como representantes das fintechs; e Caixa
Econômica Federal (CEF), Banco do Brasil (BB), Itaú e Bradesco como
representantes dos bancos tradicionais. Com uma abordagem quantitativa e
descritiva, foram calculados, a partir de dados coletados da base IF.Data do Banco
Central do Brasil, os percentuais das receitas de crédito e das despesas com
provisão para créditos de liquidação duvidosa em relação ao saldo da carteira de
crédito ativa das instituições, como parâmetros de nível de retorno e de risco,
respectivamente. Com taxas de risco e de retorno superiores, além de maior
dispersão nos dados, os resultados dos períodos analisados mostram que as
instituições fintechs vêm apresentando mais risco e maior retorno quando
comparados aos bancos tradicionais, corroborando a ideia de que o modus operandi
e o tipo de público alvo das fintechs, de fato, as fazem operar com maior risco do
que as instituições tradicionais, ratificando a aplicação da Teoria desenvolvida por
Markowitz.The risk-return relationship, initially disseminated by Harry Markowitz's Portfolio
Theory, is one of the main elements considered in investment decisions and portfolio
evaluation. Motivated by the impact of fintechs on the market, with disruptive and
bold business practices, the present work aims to verify which of the two types of
financial institutions, traditional banks or fintechs, holds the most risky credit portfolio
and, consequently, the portfolio with higher returns, as expected by the risk-return
ratio. For the period between 2018 and 2020, the following entities were analyzed
every six months as part of the sample: Nu Pagamentos, Original, Inter and Agibank
as representatives of the fintechs; and Caixa Econômica Federal (CEF), Banco do
Brasil (BB), Itaú and Bradesco as representatives of traditional banks. With a
quantitative and descriptive approach, the percentages of credit revenues and
expenses with allowance for loan losses in relation to the balance of the credit
portfolio were calculated from data collected from the Banco Central do Brasil’s
IF.Data database, as parameters of return and risk level, respectively. With higher
risk and return rates, in addition to greater dispersion in the data, the results for the
period analyzed show that fintechs have more risk and more return than the
traditional banks' portfolio, corroborating the idea that the modus operandi and the
type of target audience of fintechs, in fact, make them operate with greater risk than
traditional institutions, confirming the application of the theory developed by
Markowitz
RELAÇÃO RISCO-RETORNO EM CARTEIRAS DE CRÉDITO – COMPARATIVO ENTRE BANCOS TRADICIONAIS E FINTECHS
A relação risco-retorno, vem sendo utilizada nas decisões de investimentos e na avaliação de carteiras. Motivado pelo impacto das fintechs no mercado, o presente artigo tem como objetivo verificar qual dos dois tipos de instituições financeiras, bancos tradicionais ou fintechs, detém a carteira de crédito mais arriscada e, consequentemente, a carteira com maiores retornos, assim como esperado pela relação risco-retorno. Para o período entre 2018 e 2020, foram analisadas, semestralmente, as seguintes entidades: Nu Pagamentos, Original, Inter e Agibank como fintechs; e Caixa Econômica Federal (CEF), Banco do Brasil (BB), Itaú e Bradesco como bancos tradicionais. Com uma abordagem quantitativa e descritiva, foram calculados, a partir de dados coletados da base IF.Data do Banco Central do Brasil, os percentuais das receitas de crédito e das despesas com provisão para créditos de liquidação duvidosa em relação ao saldo da carteira de crédito ativa das instituições, como parâmetros de nível de retorno e de risco, respectivamente. Com taxas de risco e de retorno superiores, além de maior dispersão nos dados, os resultados dos períodos analisados mostram que as instituições fintechs vêm apresentando mais risco e maior retorno quando comparados aos bancos tradicionais, ratificando a aplicação da Teoria desenvolvida por Markowitz
Liga Acadêmica de Angiologia e Cirurgia Vascular e Endovascular - LACIVE UFRGS : relato de um projeto de extensão
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In
the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost