The Application Of Support Vector Machines In Funds Performance Evaluation

With the continuous establishment of new funds and the super development of funds industry in our country, properly inspecting, analyzing and evaluating funds performance is becoming more and more important. Under the background, the paper tries to discuss performance evaluation system of Chinese security investment funds. On the basis of the ways of performance evaluation in the developed countries, we hope to sump up some characteristics of Chinese funds and give some useful references to the relevant investors, asset management companies and the development of funds industry.Statistical learning theory is a theory of machine learning law dealing with small samples, and it takes into account the requirement of the generalization ability and the most excellent answer in limited conditions. Based on Statistical Learning Theory, a new machine learning method—support vector machine is put forward, and there are some virtues in dealing with the problem of pattern recognition, such as the problems of small samples, high dimensionality, non linearity.This paper gives an integrative introduction to correlative theories and models of funds performance evaluation in and abroad. On a basis of the domestic data sets of funds performance, the variables_analyzed by way of using the statistical identifying method._In this paper using the effective data mining algorithm—SVM classifier as a modeling method, and introduce Independent Component Analysis as a feature selection tool to effective select the better ratios of correlative indicators from funds data for the establishment of funds performance evaluation, thereby optimizing and improving the classification model performance based on support vector machines. By domestic funds data empirical analysis and comparison with other methods results, confirmed the validity and practicality of the funds performance evaluation model through independent component analysis and support vector machine classifier established.