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Good Foundation Inc.
http://goodfoundation.ca/

has been cited by the following article:

  • TITLE: The Models of Investing Schools

    AUTHORS: Jun’e Liu, Lei Chai, Zina Xu

    KEYWORDS: Investment, Factors Model, LP Model, ROI Model

    JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.4 No.6, June 16, 2016

    ABSTRACT: In this paper, we build the Linear Programming (LP) model, factor analysis model and return on investment model to measure the investment amount and which year to invest of each selected schools. We firstly analyze the indicators from attached files, and select effective indexes to choose schools donated. Then we select 17 indexes out after preprocessing all the indices. Secondly, we extract 1064 schools by MATLAB which is the Potential Candidate Schools from the table of attached files; we extract 10 common factors of these schools by factor analysis. After calculation, we rank the universities and select the top 100. We calculate the Return on Investment (ROI) based on these 17 indexes. Thirdly, we figure out the investment amount by conducting LP model through MATLAB. According to the property of schools, we calculate the annual limit investment and the mount of investment of each school. Fourthly, we determine which year to invest by ROI model which is operated by LINGO. In order to achieve optimal investment strategy and not duplication of investment, for five years, starting July 2016, we assume that the time duration that the organization’s money should be provided is one year, and the school return to the Good grant Foundation only one year. Then we can get the investment amount per school, the return on that investment, and which years to invest. Fifthly, by changing parameter, the sensitivity analysis is conducted for our models. The result indicates that our models are feasible and robust. Finally, we evaluate our models, and point out the strengths and weakness. Through previous analysis, we can find that our models can be applied to many fields, which have a relatively high generalization.