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Analysis and projection of Pfizer's stock returns, in the period 2018-2020, through differential neural networks

Author

Listed:
  • Alfonso Aja Kindelan

    (Socio fundador de El Pescau)

  • Leovardo Mata Mata

    (Universidad Anáhuac México)

  • Jaime Humberto Beltrán Godoy

    (Universidad Anáhuac México)

Abstract

In this paper, a differential neural network (DNN) is used to project Pfizer’s stock returns in the 2018-2020 period. The model uses quarterly data, at the end of the period, the price of the company’s stock (P), net sales (NS), total assets (TA) and accounts receivable (AR). The results are compared with the classic regression models and there is evidence of the superior goodness of fit of the DNN, compared to conventional methods, since the error in out sample forecast is less than 5.

Suggested Citation

  • Alfonso Aja Kindelan & Leovardo Mata Mata & Jaime Humberto Beltrán Godoy, 2019. "Analysis and projection of Pfizer's stock returns, in the period 2018-2020, through differential neural networks," The Anahuac Journal, Business and Economics School. Anahuac University (Mexico)., vol. 19(1), pages 13-34, June.
    • Handle: RePEc:amj:journl:v:19:y:2019:i:1:p:13-34
    DOI: 10.36105/theanahuacjour.2019v19n1.01
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    More about this item

    Keywords

    neural networks; forecast; stock returns;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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