Abstract:
In the present era of complex environments, banks operate in a more dynamic environment, which in turn, affects their relative efficiency. Traditional Data envelopment analysis (DEA) models are widely used to measure efficiency. However, environmental/exogenous variables can significantly influence the DEA efficiency scores. Therefore, identifying the most important environmental variables is crucial in the evaluation of bank performances. This study introduces a three-stage DEA framework that employs a random forest as a powerful ensemble method for variable selection to search for the most influential environmental variables. The direction of influence of the selected environmental variables and their predictive power for predicting bank performances are investigated in the third stage, through a regression analysis. The proposed framework is tested with a sample of 110 banks in Middle East and North Africa countries, observed over a period of 3 years (2014 till 2016). Accordingly, a relevant set of environmental variables is identified and its effects on bank efficiency are studied. The findings indicate that the country where the bank operates has a significant effect on the bank’s efficiency. Results also show that the overall average efficiency score is stable (around 87%) for all banks. The study concludes with the limitations and suggested directions for further research. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.
