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The Model Design for Education Fund Investment Issues
Science Journal of Applied Mathematics and Statistics
Volume 4, Issue 4, August 2016, Pages: 168-174
Received: Jun. 21, 2016; Accepted: Jun. 30, 2016; Published: Jul. 28, 2016
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Authors
Yawei Li, Graduate School, Beijing Wuzi University, Beijing, China
Hongqing Sang, Graduate School, Beijing Wuzi University, Beijing, China
Zilong Song, Graduate School, Beijing Wuzi University, Beijing, China
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Abstract
In order to help the Goodgrant Foundation Education Fund to invest on higher education of American, this report makes the following scheme. Based on minimal risk principle, we employ statistical analysis and Python programming, to screen the 93 from about 3,000 schools, which are necessary to be invested. Structure Entropy and Factor Analysis are used to select the key indicators closely related investment returns. Then we design four-investment strategies, based upon the ratio of faculty and student. We consider the risk factors and total revenue, and then establish the investment return and risk model. According to investment benefit of first year, we make the investment strategy of the next few years. The next few years are with rule that returns on investments over the last year. This report will effectively help solve the Goodgrant Foundation Education Fund investment issues.
Keywords
Structure Entropy, Factor Analysis Optimization Model, FAHP Model, Investment Return and Risk Model, Investment Strategy
To cite this article
Yawei Li, Hongqing Sang, Zilong Song, The Model Design for Education Fund Investment Issues, Science Journal of Applied Mathematics and Statistics. Vol. 4, No. 4, 2016, pp. 168-174. doi: 10.11648/j.sjams.20160404.17
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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