Inverse and negative DEA and their application to credit risk evaluation

grantor: University of TorontoWith the increasing emphasis for financial institutions to deal with their exposures and risks, there has been an explosion of companies that develop risk management tools and methodologies. Market risk is a significant component of the enterprise wide risk management process, but credit risk still remains the most important goal for most financial institutions. The objective of this thesis is to develop a methodology utilizing the capabilities of Data Envelopment Analysis, that provides for an accurate means of evaluating the credit worthiness of corporations. Several DEA models were developed, and the best ones were identified and their optimal cut-off points found. The out of sample classification accuracies of the models were calculated and the Type I and Type II error trade-off examined. The DEA models outperformed the Z Score and Shumway models in the majority of the cases, and gave encouraging classification accuracy results. Out of sample accuracy was as high as 100% bankrupt classification accuracy and 82% nonbankrupt classification accuracy for one of the models one year prior to failure. This thesis introduces the concept of Negative DEA, an approach that identifies worst performers by placing them on or near the efficient frontier. The relationship between Normal, Inverse, and Negative DEA was investigated and it was found that the combined use of all three can increase the efficiency of the failure prediction process. Several theoretical advances were made in this area, developments which can be utilized in many other applications. Using Negative DEA, a layering technique was developed that does not rely on an optimal cut-off point but provides accurate classification results. In addition, the development of a DEA Search Engine is presented, which is used to estimate the market value of private companies.Ph.D