ST Risk Warning Of A-share Small Listed Companies

With the rapid development of economy, the relationship between various enterprises is becoming more and more closely. The credit risk has become one of the most basic and important risks for banks and even all financial institutions. This year, the amount of non-performing loans of commercial banks in China is increasing, which affects the operation and profit level of banks. The default of the companies will bring huge losses to the banks. Therefore, as a primary risk, the credit risk affects all aspects of the economic. If we can measure and control the credit risk, we can optimize the allocation the capital and improve the economic benefit of the bank. The method to measure credit risk in our country is relatively backward. The credit rating is not perfect. As an active part of the national economy, China’s small and medium enterprises have larger default risk due to the limit of the capital. So how to identify and prevent the credit risk better? What model can better measure its default risk? This paper is to discuss and do empirical research on these problems.In the paper, we first introducedomestic and overseas research status as well as related theories. We give the definition and characteristics of credit risk and summarize the traditional and modern credit risk models. Then we sum up the advantages of KMV after comparing the four modern credit risk measuring models. Next we introduce the development and calculation process of the KMV model. We select 15 ST shares and 15 non-ST shares of small and medium-sized listed companies as a sample, calculating their distance to default respectively and contrast them. This is to verify the KMV model applies to the listed companies in our country. Finally, we reach a conclusion and put forward the corresponding suggestions. This paper draws the conclusions:the average default distance between ST and non-ST has a significant difference. KMV model can predict the default risk of the listed companies in China; the result is improved by the variable parameters of KMV model.The main innovation points of this paper are:the development of the credit models is introduced and compared explicitly and clearly. And the KMV model is selected as the research model of this paper. Considering the actual situation of our country, the parameters of KMV model are improved. We change the coefficient of default point when calculating it. The result shows it is more suitable for measurement in our country. Besides, this paper selects data from 2012 to 2014 of the listed companies. The data in this paper is complete and new considering the time period.