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Shiny Alternative for Finance in the Classroom

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  • Varma, Jayanth R.
  • Virmani, Vineet

Abstract

Despite the popularity of open-source languages like R and Python in modern empirical research and the data-science industry, spreadsheet programs like Microsoft Excel remain the data analysis software of choice in much of the business-school curriculum, including at IIMA. Even if instructors are comfortable with modern programming languages, they have to pitch their courses at the level of computer literacy prevalent among students. Excel then appears to be a natural choice given its popularity, but this choice constrains the depth of analysis that is possible and requires a certain amount of dumbing-down of the subject by the instructor. Recent software advances however make the ubiquitous web browser a worthy challenger to the spreadsheet. This article introduces one such browser-based tool called Shiny for bringing finance applications to the classroom and smart phones. Fueled by the availability of high-quality R packages in finance and statistics, Shiny brings together the power of HTML with the R programming language. It naturally creates an environment for the instructor to focus on the role of parameters and assumptions in analysis without the clutter of data, and allows the instructor to go beyond the toy problems that are necessitated by the nature of spreadsheets. The learning curve is short for an interested instructor with even a rudimentary exposure to programming in any language. The article ends with the discussion of a fully-worked out example of Shiny for teaching the mean variance efficient frontier in a basic investments course.

Suggested Citation

  • Varma, Jayanth R. & Virmani, Vineet, 2017. "Shiny Alternative for Finance in the Classroom," IIMA Working Papers WP 2017-03-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
  • Handle: RePEc:iim:iimawp:14565
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    References listed on IDEAS

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    1. Almiron, Marcelo G. & Lopes, Bruno & Oliveira, Alyson L. C. & Medeiros, Antonio C. & Frery, Alejandro C., 2010. "On the Numerical Accuracy of Spreadsheets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i04).
    2. Humberto Barreto, 2015. "Why Excel?," The Journal of Economic Education, Taylor & Francis Journals, vol. 46(3), pages 300-309, July.
    3. Nash, J.C., 2006. "Spreadsheets in Statistical PracticeAnother Look," The American Statistician, American Statistical Association, vol. 60, pages 287-289, August.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. McCullough, B.D. & Heiser, David A., 2008. "On the accuracy of statistical procedures in Microsoft Excel 2007," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4570-4578, June.
    6. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    7. Merton, Robert C., 1972. "An Analytic Derivation of the Efficient Portfolio Frontier," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(04), pages 1851-1872, September.
    8. Yalta, A. Talha, 2008. "The accuracy of statistical distributions in Microsoft® Excel 2007," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4579-4586, June.
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