Fund Investment Decision in Support Vector Classification Based on Information Entropy
As to the complex investment decision with big data, how to portray the essential characteristics to answer its evolving complexity of the mechanism, and how to make risk identification, assessment and measurement have become problems which urgently need to be addressed by investors. In this paper, the support vector classification based on information entropy (IE-SVC) is put forward to improve the accuracy in the field of capital investment decisions. Two classic methods, the K-Nearest Neighbors algorithm (K-NN) and the Radius Basis Function Neural Network (RBFNN), are applied to compare the performance. In the experiment of Gates foundation investment decision, its results show that the IE-SVC can be faster and higher accuracy than those of other methods.
Information entropy; Support vector classification; Radius basis function neural network; K-Nearest Neighbors algorithm
C63, C81, C89
Jiang, S. , X. Yao, Q. Long, J. Chen, and H. Jiang(2019). "Fund Investment Decision in Support Vector Classification Based on Information Entropy", Review of Economics & Finance, vol.15, no.1, pp. 57-66.