DETERMINING EFFICIENCY OF INVESTMENT BANKS AFTER FINANCIAL CRISIS BY BOOTSTRAP DATA ENVELOPMENT ANALYSIS (BDEA) : A CASE OF TURKEY

Yıl 2012, Cilt: 4 Sayı: 1, 83 - 93, 01.06.2012

Funda H. Sezgin

Öz

Data Envelopment Analysis (DEA) is a mathematical programming formulation based technique that provides an efficient frontier to suggest an estimate of the relative efficiency of each decision making unit (DMU) in a problem set. DEA is developed around the concept of evaluating the efficiency of a decision alternative based on its performance of creating outputs in means of input consumption. Besides its advantages, criticisms about the potential bias of efficiency estimates of DEA has been arised. One criticism about DEA is on the sampling variation of the estimated frontier which may affect the accuracy of results. The bootstrap method is a statistical resampling method used to perform inference complex problems. The basic idea of the bootstrap method is to approximate the sampling distributions of the estimator by using the empirical distribution of resampled estimates obtained from a Monte Carlo resampling. DEA estimators introduced an approach based on “bootstrap techniques” to correct and estimate the bias of the DEA efficiency indicators. The purpose of this study is to measure the efficiency of small amount of investment banks in Turkey after the financial crisis in 2010 with the Bootstrap DEA (BDEA)

Anahtar Kelimeler

Bootstrap DEA, Investment Banks, Efficiency Analysis

Kaynakça

• Alonso, A. M., Pena, D. and Romo, J. (2006), “Introducing Model Uncertainty by
• Moving blocks Bootstrap”, Stat Paper No: 47, pp. 167-179. Bacak, A. (2007), Türkiye’de Kalkınma ve Yatırım Bankacılığı ve Finansal
• Performanslarının Değerlendirilmesi, Unpublished PhD Thesis, Muğla. Daraio, C. and Simar, L. (2007), “Conditional Nonparametric Frontier Models for
• Convex and Non Convex Technologies: a Unifying Approach”, Journal of Productivity Analysis, 28(1), pp. 13-32
• Efron, B. (1979), Bootstrap Methods: Another Look at The Jackknife. The Annals of Statistics, 7(1), pp. 1-26.
• Frazer, M. (2008), Pensions in The Context Of CrosBorder Merger and Acquisitions, Torys http://www.torys.com/Publications/Documents/Publication%20PDFs/AR2007-20T.pdf, Accessed 14.05.2011]. L.L.P.,
• Halkos, G. and Tzeremes, N. (2010), “Performance Evaluation Using Bootstrapping
• DEA Techniques: Evidence from Industry Ratio Analysis”, MPRA Paper, No: 25072, pp. 1-32.
• Hughes, J. P. and Mester, L. J. (2008), “Efficiency in Banking: Theory, Practice and Evidence”, Working Paper, No: 08-1, The Wharton School, University of Pennsylvania.
• Gardener, E. M. and Molyneux P. (1995), “Investment Banking: Theory and Practice”, Euromoney Books.
• Grifell-Tatje, E. and lovell, C. K., (1995), “A Note on the Malmquist Productivity
• Index”, Economics Letters, 47(3), pp.169–175. Radić, N., Fiordelisib, R. and Girardonec, C. (2011), “Price Competition Efficiency and Riskiness in Investment Banking”, Working Paper, Series No: 07/11, pp.1-28.
• Simar, L. and Wilson, P.W. (1998), “Sensitivity Analysis of Efficiency Scores: how to
• Bootstrap in Non- Parametric Frontier Models”, Management Science, 44(2), pp. 49-61. Simar, L. and Wilson, P.W. (2000), “A General Methodology for Bootstrapping in Non- parametric Frontier Models”, Journal of Applied Statistics , 27(6), pp.779 -802.
• Simar, L. and Wilson, P.W. (2008), “Statistical Interference in Non-parametric Frontier
• Models: Recent Developments and Perspectives, In: Fried H, Lovell CAK, Schmidt S (eds) The Measurement of Productive Efficiency and Productivity Change, Oxford University Press , New York. Şen, A. (2004), Yatırım Bankacılığı ve Sermaye Piyasaları, SPK Yeterlik Etüdü, Ankara.
• Tu, D. and Zhang, L. (1992), “On the Estimation of Skewness of a Statistic Using the Jackknife and the Bootstrap”, Stat Paper, No: 33, pp. 39-56.
• Türeoğlu, E. (1991), Yatırım Bankacılığı ve Türkiye’deki Durum, SPK Yeterlik Etüdü, Ankara.