Data Envelopment Analysis In The Financial Services Industry

This book presents the methodology and applications of Data Envelopment Analysis (DEA) in measuring productivity, efficiency and effectiveness in Financial Services firms such as banks, bank branches, stock markets, pension funds, mutual funds, insurance firms, credit unions, risk tolerance, and corporate failure prediction. Financial service DEA research includes banking; insurance businesses; hedge, pension and mutual funds; and credit unions. Significant business transactions among financial service organizations such as bank mergers and acquisitions and valuation of IPOs have also been the focus of DEA research. The book looks at the range of DEA uses for financial services by presenting prior studies, examining the current capabilities reflected in the most recent research, and projecting future new uses of DEA in finance related applications.

An authoritative introduction to efficiency and productivity analysis with applications in both the banking and finance industry In light of the recent global financial crisis, several studies have examined the efficiency of financial institutions. A number of open questions remain and this book reviews recent issues and state-of-the-art techniques in the assessment of the efficiency and productivity of financial institutions. Written by an international team of experts, the first part of the book links efficiency with a variety of topics like Latin American banking, market discipline and governance, economics of scale, off-balance-sheet activities, productivity of foreign banks, mergers and acquisitions, and mutual fund ratings. The second part of the book compares existing techniques and state-of-the-art techniques in the bank efficiency literature, including among others, network data envelopment analysis and quantile regression. The book is suitable for academics and professionals as well as postgraduate research students working in banking and finance. Efficiency and Productivity Growth: Provides an authoritative introduction to efficiency and productivity analysis with applications in both the banking and mutual funds industry such as efficiency of Asian banks, cooperatives and not-for-profit credit associations. Explores contemporary research issues in the area of efficiency and productivity measurement in the financial sector. Evaluates the most suitable approaches to selecting inputs and outputs as well as selecting the most efficient techniques, such as parametric and non-parametric, to estimate the models.

In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.

The efficient operation of financial intermediaries--banks, insurance and pension fund firms, government agencies and so on--is instrumental for the efficient functioning of the financial system and the fueling of the economies of the twenty-first century. But what drives the performance of these institutions in today's global environment? In this volume, world-renowned scholars bring their expertise to bear on the issues. Primary among them are the definition and measurement of efficiency of a financial institution, benchmarks of efficiency, identification of the drivers of performance and measurement of their effects on efficiency, the impact of financial innovation and information technologies on performance, the effects of process design, human resource management policies, as well as others.

This book applies econometric techniques to test the relationship between efficiency and risk within the banking industry in China. Chapters examine how efficiency has been affected by different types of risk-taking behaviour and how risk has been an important determinant of bank efficiency in the context of the series of reforms impacting banks in China since 1978. The author begins by unpacking these reforms and proceeds to explain relevant theories of efficiency and bank risk before reviewing empirical literature in evaluating risk and efficiency in the banking industry. He then investigates the issues of efficiency and risk in the Chinese banking industry using a number of modern econometric techniques. The final chapters present the results of original empirical research conducted by the author, and provide valuable implications to Chinese government as well as banking regulatory authorities to make relevant policies.

Inhaltsangabe:Abstract: Part one of the thesis states the changes and developments that have markedly altered the banking world. This analysis is followed by a discussion about the consequences these transformations have on credit institutions in general. Special focus is devoted to the universal bank, the advantages and disadvantages of which are linked to the changes mentioned above. Part two begins with the introduction of a prominent member of the universal banking type: ING Group and its banking operations in the Netherlands, ING Bank, respectively. Both are shortly introduced and their performance measured with the help of "traditional" tools. To go deeper than the mere standard comparison of data given in annual reports, however, a new method will be introduced that approximates the relative efficiency and productivity of companies - in this case banks. This method is called "Data Envelopment Analysis" (DEA) and offers some advantages that complement "traditional" ways of assessing company performance. DEA is then run with the help of a special software programme, processing data about 65 credit institutions that are operating in the Dutch market. The focal point of interest in this analysis is on the performance of ING Bank as measured by DEA. This kind of "benchmarking" will be performed in two ways: first statically (comparison within a certain year), then dynamically (comparison over the years 1992 to 1995). Subsequently, the credit institutions will be summed up into categories like "best-practice" or "worst-practice" to conduct further analyses. The conclusions from this analysis is then taken as a basis to evaluate, whether ING Bank has been able to adopt to the growing demands of an ever faster changing banking environment. The concluding chapter relates mainly to an additional usage of DEA that is not related to benchmarking directly. DEA and the results it yields can be used to find out, which firms are especially successful in incorporating the all-important "soft factors" like firm culture, tradition, values, employee motivation, and the like. This application uses DEA as a kind of "navigator", that helps annalists to save time by pointing directly to potential owners of "soft factors". The appendix gives the sources of data used and the quality and reliability thereof. Furthermore, the credit institutions used in the analysis will be stated in name. A list of mathematical formulae that make up the DEA analysis and the logic [...]

This study examines the effect of financial-sector reform on bank performance in selected Middle Eastern and North African (MENA) countries in the period 1994 -2008. We evaluate bank efficiency in Egypt, Jordan, Morocco, Lebanon and Tunisia by means of Data Envelopment Analysis (DEA) and we employ a meta-frontier approach to calculate efficiency scores in a cross-country setting. We then employ a second-stage regression to investigate the impact of institutional, financial, and bank specific variables on bank efficiency. Overall, the analysis shows that, despite similarities in the process of financial reforms undertaken in the five MENA countries, the observed efficiency levels of banks vary substantially across markets, with Morocco consistently outperforming the rest of the region.Differences in technology seem to be crucial in explaining efficiency differences. To foster banking sector performance, policies should be aimed at giving banks incentives to improve their risk management and portfolio management techniques. Improvements in the legal system and in the regulatory and supervisory bodies would also help to reduce inefficiency.

Data Envelopment Analysis (DEA) represents a milestone in the progression of a continuously advancing methodology for data analysis, which finds extensive use in industry, society and even in education. This book is a handy encyclopedia for researchers, students and practitioners looking for the latest and most comprehensive references in DEA. J.K. Mantri has specifically selected 22 research papers where DEA is applied in different fields so that the techniques discussed in this book can be used for various applications. In A Bibliography of Data Envelopment Analysis (1978-2001), Gabriel Tavares states that DEA is a mathematical programme for measuring performance efficiency of organizations popularly named as decision-making units (DMU). The DMU can be of any kind such as manufacturing units, a number of schools, banks, hospitals, police stations, firms, etc. DEA measures the performance efficiency of these kinds of DMUs, which share a common characteristic: they have a non-profit organization where measurement is difficult. DEA assumes the performance of the DMU using the concepts of efficiency and productivity, which are measured as the ratio of total outputs to total inputs. The efficiencies estimated are relative to the best performing DMU, which is given a score of 100%. The performance of other DMUs varies between 0% and 100%.