Finding Default Barrier and Optimal Cutoff Rate in KMV Structural Model based on the best Ranking of Companies

According to the adverse consequences that are brought by financial distress for companies, economy and financial –monetary institutions, the use of methods that can predict the occurrence of financial failure and prevent the loss of wealth is of great importance. The major models of credit risk assessment are based on retrospective information and using the methods which use the updated market data for prediction of the probability of default can lead to the increase of the reliability of results. The purpose of this study is to obtain optimal default barrier in KMV model by using an approach based on genetic algorithm and compare the performance of the proposed model to KMV model. Research data included all data of listed companies in the Tehran stock exchange that were bankrupted from 2009 to 2014 according to the article 141 of the commercial code. In total, 25 companies were considered as distressed companies and 50 non-bankrupted companies were also selected as the control group and then results of the two models were compared. The study results showed that the performance of the presented model in prediction of bankruptcy and separating distressed from non-distressed companies is better than KMV model. At the end, the optimal cut off rate was calculated to determine whether a specific company will be bankrupt or healthy according to its probability of default. The results showed that the calculated optimal value led to 80% correct prediction in 2015.