Study On Credit Risk Measurement Model Based On Differential Evolutio(?)orithm

Credit risk management has always been an important part of financial risk management. With the continuous development of social informatization, the credit risk management problems present large-scale, the characteristics of complexity, uncertainty, and their management methods cannot meet the large-scale interdisciplinary credit risk measurement and assessment of the problem. On the one hand, because of the slow manual assessment methods and measurement model difficult to determine the precise level of credit risk with a large object data traffic, on the other hand in the application of complex credit risk measurement model, are inevitable on faulty assumptions and estimates. At this stage of the financial sector, the traditional financial credit risk models can’t correctly solve the problem of the risk of such complex, but the development of artificial intelligence and data mining techniques for the solution of these problems may be.The essence of management is to provide optimal decision for policymakers, credit risk management is actually an optimization problem. Differential evolution algorithm has good performance in the optimization problem, and the algorithm combines the intelligent evolution and group learning mechanism, less algorithm parameters, able to quickly find the optimal area. In order to improve the assessment of credit risk measurement model, this article will evaluate problem into optimization problem, this is conducive to the introduction of differential evolution algorithm, but in the face of the complex credit risk problem, the differential evolution algorithm itself will have this or that. Against such a situation, this article first to the traditional differential evolution algorithm is put forward their own improvement ideas, to improve the algorithm of optimal performance; Secondly, using the improved algorithm, combined with China’s listed companies of the truth, to optimize the KMV model, find the points of default conforms to national conditions; Finally using the ideas of clustering in data mining, to change, in the form of differential evolution algorithm to evaluation of listed companies in our country.From various species of parallel mechanism and random search strategy, and put forward a kind of cooperative differential evolution algorithm based on stochastic diffusion search. The algorithm is introduced into reverse the initialization of chaotic search mechanism, using stochastic diffusion search strategy population can be divided into two subgroups, success and failure and to improve the success and failure of subgroup, respectively, using different difference strategy, to overcome the defects of the single difference strategy, at the same time, regular parts of the subgroup is best and worst individuals realize one-to-one communication, so as to achieve the goal of cooperative coevolution. Through the function simulation, and compared it with other algorithms, the results show that the algorithm convergence speed and optimization can be obviously improved, has good convergence and optimization ability.In order to explore the KMV credit rating model, the default point of short term and long term ratio coefficient and investor market is the optimal combination of the subjective attitude coefficient, using the differential evolution algorithm, constructs the optimal DE-KMV default coefficient uncertainty calculation model. Through the analysis in recent years, China’s economic development environment for the listed company on the impact of default risk tendency, clarify the basic situation of the current financial market environment is good or bad, with uncertainty DE-KVM model to measure the defaults of listed companies, the optimal point of China’s listed companies default default coefficient and the coefficient of investor attitude.Establish credit risk evaluation of differential evolution automatic clustering model, and apply it to the credit risk evaluation of listed companies in China. The model does not require prior know the classification of data, on the contrary, depending on the swarm intelligence to find the optimal partition. Through data simulation and genetic algorithm, a decision tree, BP neural network model for credit risk evaluation of empirical comparative study, the results show that the model can find very accurate data corresponding partition, greatly improving the credit evaluation of accuracy, reduces the cost of risk, the credit risk management and control of high use value.