Ranking with a Euclidean Common Set of Weights in Data Envelopment Analysis: With Application to the Eurozone Banking Sector

Purpose - This paper provides a new method to define an Euclidean common set of weights (ECSW) in data developme+E58nt analysis (DEA) that (i) allows ranking both efficient and inefficient firms, (ii) is more realistic in terms of weight determination, and (iii) generates rankings consistent with the credit ratings system. Design/methodology/approach - We first use DEA to determine the efficient frontier and then estimate a common set of weights that can minimize the Euclidean distance between the firms and that frontier. This process is illustrated by a simple numerical example and is also extended to a real life situation using the Eurozone banking sector. Findings - Our ECSW approach outperforms other common set of weights approaches in both numerical and real life examples. Additionally, the ECSW also outperforms the others in terms of providing consistent rankings to the credit ratings system. Research limitations/implications ? We did not examine the variable returns to scale assumption in DEA nor analyze productivity changes over time (e.g., the Malmquist index approach). It would be also interesting to extend the real life sample (e.g., U.S. banks or Asian banks) to test the robustness and stability of our ECSW approach. Practical implications - The paper suggests a new approach to banks' ranking alternates to the credit ratings system. Originality/value - The paper provides a more realistic approach to estimate the common set of weight in DEA that also can be an alternative for the credit ratings system.