Credit Risk Analysis Of My Country's Commercial Banks Based On The Genetic Algorithm KMV Model

The deposit and loan margin,which is the main profit source of China's commercial banks,has gradually narrowed due to the completion of the interest rate marketization.At the same time,the industies have transformed and accelerated,and the capital chain of the enterprises in some industries was tight,especially in the 2015 overcapacity and low end manufacturing solvency and the rapid increase of default risk,which made the quality of the bank's assets down..Because of its particularity,banks decide that credit risk is the most important risk for all risks faced by banks.It is generally believed that the credit risk of commercial banks is mainly focused on the loan business,but with the development of intermediary business,credit risk is also contained in the business of commercial banks' funds,receivables,out of order credit and so on.By the end of 2016,according to the data published by the China Banking Regulatory Commission's website,the rate of non-performing loans in China's commercial banks was up to 1.74%,and the index continued to rise in the past few years.The financial crisis of 2008 shows that the credit risk of commercial banks will have a great impact on all aspects of society,and it also affects macroeconomic decision-making and future development of a country.With the further adjustment and optimization of China's domestic industrial structure,the commercial banks will face greater credit risk,so it is very important to judge and control the bank's credit risk in time for the financial industry and even the economic development of our country.This paper first analyzes the current situation of credit risk in China's commercial banks,and then compares the traditional and modern credit risk measurement models.On this basis,the KMV model is selected and the artificial intelligence model genetic algorithm(GA)is introduced to improve the accuracy of KMV model.Secondly,we select 16 listed commercial banks for 2012-2017 years' annual financial data and stock trading data for empirical research.The analysis of the total default distance between the 16 banks and the actual economic development in China proves that the results of the GA-KMV model are in good agreement with the economic development trend,indicating that the model has good applicability.The fitting analysis of the breach distance and the expected default rate shows that the model fitting is better.The sensitivity of the breach distance is tested.The analysis shows that the three variables of equity value,default point and the volatility of equity value affect the distance of default of the bank,and the volatility of equity value has the greatest impact on the distance of default.Finally,some suggestions are put forward according to the empirical results.