| Summary: | 博士 === 國立交通大學 === 工業工程與管理學系 === 100 === The global financial crisis has been spreading since the subprime mortgage financial crisis in the United States started in 2006. The increasing number of corporate bankruptcies has reemphasized the need for research in the area of identifying early warning indicators of corporate distress. The identification of bankrupt firms and quantification of investment risk are of critical importance. Data envelopment analysis (DEA) has been proven as an excellent performance evaluation method. However, conventional DEA model is considered suitable to identify good (efficient) performers in the most favorable scenario. The best-practice frontier DEA (BPF-DEA) models select potentially distressed companies by measuring how inefficient they are in the most favorable scenario. I argue that the struggling companies should be picked out based on how worst they perform in the worst-case scenario since the companies who will potentially go out of business first are usually the ones of least competitiveness in comparison with others as the scenario is getting worse (less favorable) especially when they confront economic depression or financial crisis. Therefore, it is necessary and more meaningful to develop some appropriate model formulations other than the BPF-DEA models for evaluating and ranking units in the least favorable (worst-case) scenario and therefore identifying bad performers as potentially failed firm(s).
This study proposes some types of DEA models based on the concept of worst-practice frontier (WPF). First, to identify bad performers together with the slack values I formulate the WPF-SBM model. Then I develop the HypoSBM model to distinguish the worst performers from the bad ones evaluated by the WPF-SBM model. And a solution approach is suggested to fully rank worst efficiencies in the worst-case scenario. The results of an empirical study shows that combining the proposed models and the solution approach can effectively and accurately identify the potentially failed banks.
Second, to fit the two-stage production process in the banking industry, this study introduces a two-stage WPF-BCC model that can deal with negative profit data and effectively identify failed bank(s) in the worse-case scenario. This model is applied in an empirical study. The result is then compared with the result from a two-stage BPF-BCC model to show the adequacy of the WPF-DEA model for identifying failed bank(s) in the worst-case scenario.
Third, there are no feasible super-efficiency evaluations for some DEA models such as the MEA model, which makes it difficult to discriminate the efficient units evaluated by the MEA model. This study proposes the WPF-MEA model and suggests a ranking approach to rank the efficient units evaluated by the MEA model. On the other hand, I can also rank the worst efficient units evaluated by the WPF-MEA model using the similar ranking approach and the MEA model. As a result, I can get a ranking list among the efficient units and among the worst efficient units.
Finally, this research provides some possible limitation and drawbacks when using the proposed WPF-DEA models. Then I conclude this research with some directions of future research.
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