Empirical Study On Credit Risk Measurement Based On GA-KMV Model

At present,emerging industries are the key areas of China’s economic restructuring and upgrading,which have been promoted to the national strategic level.By the first half of 2017,listed companies in emerging industries accounted for 38.3% of the total number of listed companies in a-share market.High-speed rail system and remote sensing satellite system show that its quality has also been greatly developed.However,in the fields of Internet finance,photovoltaic industry and high-end equipment manufacturing,frequent corporate defaults have affected the market’s investment confidence and thus restricted the healthy and sustainable development of China’s emerging industries.In addition,domestic scholars have done a lot of research on enterprise credit risk measurement,but its credit risk measurement method is complex and poor operability.Therefore,how to effectively and accurately measure the credit risk of emerging enterprises? How to guide investors to invest in emerging enterprises rationally? It is an urgent need to solve the problems.This paper summarizes the research context of enterprise credit risk measurement: the research of enterprise credit risk has gone through a process from rough qualitative analysis to precise quantitative analysis,and it has made a breakthrough with the introduction of modern financial theory and computer technology.By comparing the advantages and disadvantages of the model of enterprise credit risk measurement,the KMV model is improved from three aspects.Firstly,the calculation method of model input parameters is improved.GARCH model is used to calculate the volatility of listed companies’ assets.Secondly,genetic algorithm is used to improve the parameters of the default point.Thirdly,the default distance is directly used to measure the credit risk of listed companies.Based on the above analysis,GA-KMV model was constructed to measure the credit risk of listed companies in emerging industries,which is from the perspective of default point parameter optimization.The empirical test selected 148 listed companies in emerging industries and used default distance to conduct quantitative analysis on the credit risk of emerging listed companies.The results show that :(1)According to GA-KMV model,the default point parameters α and β of listed companies in emerging industries are 3.922 and 1.626 respectively,both of which are bigger than the default point parameter of KMV model.It indicates that China’s market has a higher tolerance for corporate default than that of the United States.(2)From the perspective of default distance,the recognition accuracy of GAKMV model is about 75%,which is greatly improved compared with the 40-50% recognition accuracy of KMV model.Therefore,based on GA-KMV model,this paper proposes to build a sustainable and integrated data platform led by the regulatory authorities,which includes the quantification,information tracking and supervision of corporate credit default risk.(3)We should pay attention to the default distance between the three emerging industries of network economy,green low-carbon and high-end equipment manufacturing,and different investment and financing strategies should be proposed for high-risk and low-risk enterprises to guide the rational flow of capital.