Risk management and value creation in banking institutions : analysis to the risk adjusted performance measures

Mestrado em FinançasAs métricas tradicionais, com base nas demonstrações financeiras, foram até à década de 80, a metodologia privilegiada para avaliar a performance bancária, mas estas demonstraram um afastamento significativo entre a realidade contabilística e económica, e como tal, insuficientes para análise à percepção se as intituições estariam ou não a criar valor para os seus accionistas e principalmente denotou-se que não estavam a incluir uma correcta gestão dos diferentes riscos a que as instituições financeiras estão expostas. Emergiram assim, novas métricas de avaliação e gestão da performance baseadas no valor ajustada ao risco, sendo a mais utilizada o Risk Adjusted Return on Capital (RAROC), em contraposição com estes indicadores mais tradicionais. Este trabalho é desenvolvido tendo por base este contexto. São descritas algumas das métricas tradicionais utilizadas, inferindo sobre as suas vantagens e desvantagens. E por fim, é efectuada uma introdução abrangente da métrica RAROC, adicionalmente acrescido de um estudo empiríco prático de implementação do modelo, como qual se pretende-se contribuir com uma possível abordagem de implementação e uma maior compreensão e adopção da medida RAROC. Conclui-se, que com o uso de modelos de avaliação e quantificação das rentabilidades ajustadas ao risco subjacente às operações bancárias, é possível a obtenção de decisões de crédito e alocação de capital mais consistente, eficientes e concretas, porque se evidenciam e corrigem as inconsistências verificadas entre os critérios tradicionais e os critérios que utilizam a componente de risco.Until the 1980s, traditional metrics based on financial statements have been the primary methodology used to assess banking performance. However, such metrics have shown significant divergence between accounting and economic realities, therefore becoming inadequate to analyze the perception of institutions in terms of value creation for its shareholders and, most importantly, it has become clear that they weren't including a correct management of the several risks to which financial institutions are exposed. New value-based corporate performance assessment metrics have emerged, and risk-adjusted value-based management systems started to be implemented, as opposed to the more traditional indicators. Thus, the so-called RAPM - Risk-Adjusted Performance Measures arose. The dichotomy between accounting indicators and value-based indicators is the focus of this work, whose main objective is the study of the RAROC metric - Risk-Adjusted Return on Capital, to infer about its advantages and disadvantages. We intend to contribute with a possible implementation approach, to have a better understanding of and to adopt the RAROC methodology through a practical experiment which implements this framework. In conclusion, the use of risk-adjusted profitability assessment and measurement frameworks, with such risk being inherent to banking operations, proves to be extremely important, so that we can avert the inconsistencies shown by traditional and risk-based criteria

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