Determining Efficiency of Investment Banks after Financial Crisis by Bootstrap Data Envelopment Analysis (bdea) : a Case of Turkey

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).