Abstract

ABSTRACT This paper introduces a robust data envelopment analysis (DEA) model to address the limitations of traditional models in handling nonpositive data and applies it to measure the profitability efficiency of banks, including those facing financial losses. The proposed model offers several advantages over current approaches. The model is designed to resist the influence of extreme values and effectively handle variables with positive values for some banks and nonpositive values for others, can be applied under various returns-to-scale conditions and does not require any prior knowledge, overcomes the lack of translation invariance, considers nonradial input or output slack, and uses benchmark targets with economic interpretation. Numerical examples are provided to illustrate its effectiveness, and the results are compared with those obtained from commonly used methods, highlighting its superior performance in handling nonpositive data. Finally, the proposed method is applied to measure the profitability efficiency of commercial banks, demonstrating its practicality in real-world scenarios.

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