DEA Modeling for Efficiency Optimization of Indian Banks with Negative Data Sets

Indian banking has experienced exponential growth after reforms of 1990s that helped to improve the profitability, performance and efficiency. However, still there are conflicting concerns of operating efficiency and risk management across the major bank categories particularly after the global financial crisis. We have used Data Envelopment Analysis (DEA) for measuring the efficiency of a set of decision making units (DMUs) which traditionally assumes that all the input and output values are non-negative. Quantitative measures of bank performance like net profits, growth rates and default portfolios frequently show negative values for output variables. We draw motivation from some studies done in other developing countries for handling the negative data sets. We cross examine the approaches for dealing with variables that are positive for some DMUs and negative for others and test the validity of Range Directional Measure Model (RDM) for examining cases when some inputs and/or outputs can take negative as well as positive values. We find some support for the RDM in handling data negative sets without the need for any transformation (conversion of the negative values with small positive values) as a measure of efficiency akin to the radial measures in traditional DEA. Our preliminary investigation indicates no significant difference between the operational efficiency and profitability of public and private banks modeled for negative data and undesirable output.