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Fund Investment Decision in Support Vector Classification Based on Information Entropy

Author

Listed:
  • 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)

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.

Suggested Citation

  • Siyang Jiang & Xiuyan Yao, Qianju Long, Jiali Chen & Hui Jiang, 2019. "Fund Investment Decision in Support Vector Classification Based on Information Entropy," Review of Economics & Finance, Better Advances Press, Canada, vol. 15, pages 57-66, February.
    • Handle: RePEc:bap:journl:190106
    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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    More about this item

    Keywords

    Information entropy; Support vector classification; Radius basis function neural network; K-Nearest Neighbors algorithm;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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