Least-distance Data Envelopment Analysis Model for Bankruptcy-based Performance Assessment

Abstract

In this paper, the use of the Data envelopment analysis(DEA) as a quick-and-easy approach for bankruptcy-based performance assessment is presented. The attractive advantage of DEA is that it can provide an efficient target(improvement goal) for inefficient decision-making units(DMUs). The DMUs under evaluation are divided into two groups: efficient and inefficient, regarding cases of bankruptcy analysis, they are divided into non-default firms and default firms. Moreover, the least-distance(LD)DEA model has been actively researched and applied, because it can provide the closest efficient target that is achievable with the least effort. Thus, using the LD-DEA model for bankruptcy-based performance assessment can give an early warning of a firm’s financial performance and provide an improvement goal that can be easily achieved for default firms. As a case study, we demonstrate this approach using financial data of 61 Japanese banks. From the results, we find that our approach provides an improvement goal that can be achieved with fewer total modifications of inputs and outputs compared with that provided by slacks-based measure(SBM) model.