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DEA additive model to evaluate banking efficiency….29
Chapter IV Data description…………………………………………………....35
4.1 Additive model of DEA analysis in evaluating bank efficiency……………..35
Chapter V Results and Conclusion……………………………………………..41
5.1 Results………………………………………………………………………...41
5.2 conclusion……………………………………………………………………..51
References………………………………………………………………………..59

List of Tables and Figures

Table 1 Input-Output correlation…………………………………………………37
Table 2 Mean value, standard deviation 6, minimal value and maximal value of outputs and inputs for 600 local branch offices of the selected banks…………..38
Table 3 Productivity Change Indices……………………………………………....41
Table 4 Mean Absolute Percent Error…………………………………………….42

Figure 1 The difference between DEA additive model and CCR model……….61
Figure 2 Slope of scale efficiencies………………………………………………..62
Figure 3 Chart of efficiency subset and productivity……………………………62

Chapter I Introduction

1.1 Brief introduction to DEA analysis
Previous researches conducted by prestigious scholars and researchers have shown that the only correct and efficient way to evaluate the performance of large banks is through the applications of DEA (Data Envelopment Analysis) and with an efficiency oriented additive model. Recent studies have also shown that such applications have been often used to examine the efficiency of regular firms. According to the Silk (2007), who extensively reported the widely usage of Data Envelopment Analysis in the Europe academic circle, Patrick and Smith (2005), Birgin and Chen (2008) who advocate efficiency oriented additive model successfully demonstrated the applications of efficiency analysis on some of the largest-scale commercial banks in the world.

This dissertation mainly focuses on the usage of DEA to analyze European commercial banks with a larger operational structure. Meanwhile, in order to avoid miscalculations and make the research process more efficient, computational research method is used in this study. This method on the other hand can enable us to assess the data of different units from a more personal point of view. According to Birgin and Chen (2008), this method emphasizes the existence of violation of the assumption of the positive inputs or outputs.

References
Aubestine, G. 2007. Banking Efficiency and Productivity Analysis, Silver Lake Publishing, 122-130
Birgin, H. & Chen, G. 2008. Data Envelopment Analysis and commercial bank performance, Bloomberg Press, 90-95
Berger, M. & Young, R. 1997. Measuring the efficiency of European bank’s technical efficiency, South-Western College Publishing, 4/8, 11-15
Bottenburg, P. 2006. Cost and Productivity Efficiency in European Banking industry, Journal of International Financial Markets, 9/12, 44-48
Demin, H. & Sarin, B. 2002. The principles of frontier cost functions and banking efficiency. European Economic Journal, 16/4, 33-40
Foreman, P.O. 2007. A quick guide to the Data Envelopment Analysis and application, European Journal of Economic Research, 7/8, 33-36
Green, B.C. 2008. The technology-inspired efficiency revolution in European Banking, Harvard Press, 90-101
Johnny, K. & Hinlen, P. 1996. Productivity, Risks and Efficiency Evaluation of European Banking Systems, Journal of European Productivity Study, 8/11, 67-71
Keung, Y. 2004. Cross-sector Performance in European, Banking industry, 本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。