Study on the Optimization of Default Point of China Listed Company by using Genetic Algorithm KMV Model

Objective of this paper is applying KMV Credit Risk Model to the credit assessment of China listed companies, the KMV Model needs to be modified in combination with the characteristics of listed companies, and setting an accurate default point is crucial. This paper uses several methods including Genetic Algorithm, Double Total independent sample t-test. It introduces Genetic Algorithm into KMV Model, and takes the Double Total independent sample t-test function as the fitness function of genetic algorithm to solve the optimal default point problem of China listed companies. The study shows the conclusion that the KMV model has adaptability in the credit risk assessment of China listed companies, and the optimal default point calculated by genetic algorithm make the KMV model has the strongest distinguishing ability. Introduction As the issue of the Basel III in December 2010 and the implementation of Chinese version of the Basel III, risk early warning which take credit risk, market risk and operational risk as three pillars will become the key work ofchina banking regulatory department and commercial banks in the future. Among these three major risks, the credit risk is critical and also the most important and difficult issues in the risk measurement. In 1993, America KMV company launched KMV credit risk assessment model, it is one of the key credit risk assessment model in the international community; based on Metron option pricing model, KMV credit risk assessment model evaluates the company’s future insolvency and default risk by using the financial data and stock trading data of listed companiesas references [1,2]. KMV company believes that when the company's assets are less than or equal to a default point ( DP ) which is between its short-term liabilities and total liabilities, then the risk of default is greater. Although the default point is given in the model, this point is not necessarily suit the actual situation in China, because the default point is calculated by a large number of historical date based on the American companies. Thus, based on the previous study results, this paper solves the optimization problem of default point of China listed companies by introducing genetic algorithm and Double Total independent sample t-test as the fitness function of genetic algorithm [3,4]. References Review Research on KMV Model by Domestic Scholars. The domestic scholars' studies on KMV model can be divided into three aspects: In the early stage, some domestic scholars’ studies mainly focused on the adaptability of the model in China listed companies. Wei Lu (2003) introduced the KMV model and its role in the credit risk assessment of China listed companies; Feng Xue (2003), Ling Zhang (2004), etc. affirmed the capability of KMV model in the credit risk assessment of China listed companies. After that, some domestic scholars began to focus on the industry segmentation of KMV model. Xiaohong Chen (2008) introduced the KMV model into the credit risk assessment of small and medium-sized enterprises; Yuanfan Zhou (2009) applied it to insurance company credit risk measurement to strengthen the insurance supervision; Haiyun Zhou (2015), Degong Ma (2015), Xuekai Wang (2015), etc. applied it to local government debt default analysis. 7th International Conference on Management, Education and Information (MEICI 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 156