Fund Investment Decision in Support Vector Classification Based on Information Entropy

Siyang Jiang (jiangsy@snnu.edu.cn), Xiuyan Yao, Qianju Long, Jiali Chen (1174094357@qq.com) and Hui Jiang (jianghui200207@163.com)
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Siyang Jiang: School of Mathematics and Information Science, Shaanxi Normal University, CHINA
Xiuyan Yao, Qianju Long, Jiali Chen: School of Mathematics, Huizhou University, CHINA
Hui Jiang: School of Information Science and Technology, Huizhou University, CHINA

Review of Economics & Finance, 2019, vol. 15, 57-66

Abstract: 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.

Keywords: Information entropy; Support vector classification; Radius basis function neural network; K-Nearest Neighbors algorithm (search for similar items in EconPapers)
JEL-codes: C63 C81 C89 (search for similar items in EconPapers)
Date: 2019
Note: This research is funded by the national social science fund in China, No. 15BTJ024, and by the ¡®12.5¡¯ planning project of common construction subject for philosophical and social sciences in Guangdong , No. GD12XGL21.
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