Netflix, Inc. share price estimation, through exponential regression analysis.

Authors

DOI:

https://doi.org/10.5377/reice.v7i14.9374

Keywords:

Exponential function, exponential linear regression, beta coefficients.

Abstract

The exponential linear regression analysis is an extremely important tool in the world of finance and economics, with this analysis you can make projections and forecasts of a dependent variable explained by an independent variable that presents a curvilinear relationship. The objective of this work was to determine an equation that allowed estimating the monthly prices of the shares of Netflix, Inc., using an exponential linear regression model. The estimated beta coefficients for the model, individually, were significant for both the base coefficient and the argument coefficient, whose values ​​were 57.8122 and 0.0333, respectively. The global test of significance of the beta coefficients was also statistically significant, presenting an F value of 543.13, thus validating the proposed model.

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References

Arya, J. C., Lardner, R. W., & Ibarra Mercado, V. H. (2009). Matemáticas aplicadas a la administración y a la economía (Quinta ed.). México: Pearson Educación.

Brenes González, H. A. (Diciembre de 2017). Aplicación del análisis de regresión lineal simple para la estimación de los precios de las acciones de Facebook, Inc. Revista Electrónica de Investigación en Ciencias Económicas, 133-155.

Chiang, A. C., & Wainwright, K. (2006). Métodos funadamentales de economía matemática (Cuarta ed.). México: McGraw-Hill Interamericana.

Haeussler, F., E. J., & Paul, R. S. (2003). Matemáticas para administración y economía (Décima ed.). México: Pearson Educación.
Yahoo! Finanzas. (s.f.). Obtenido de https://es-us.finanzas.yahoo.com/quote/NFLX/profile?p=NFLX

Published

2020-02-05

How to Cite

Brenes González, H. A. (2020). Netflix, Inc. share price estimation, through exponential regression analysis. Revista Electrónica De Investigación En Ciencias Económicas, 7(14), 54–76. https://doi.org/10.5377/reice.v7i14.9374

Issue

Section

Research Articles