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

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

Resumos concluídos - Saúde Coletiva

Resumos concluídos - Saúde Coletiv