IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1901.09143.html
   My bibliography  Save this paper

A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns

Author

Listed:
  • Leonardo Felizardo
  • Afonso Pinto

Abstract

In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the characteristics of this sample stand out and affect the neural networks. In addition, we make inferences about what kind of influence the different architectures have on the performance of neural networks. In the study, the prediction of the return of a Brazilian stock traded on the stock exchange of S\~ao Paulo to measure the error committed by the different architectures of constructed neural networks. The results are promising and indicate that some aspects of the neural network architecture have a significant impact on the assertiveness of the model.

Suggested Citation

  • Leonardo Felizardo & Afonso Pinto, 2019. "A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns," Papers 1901.09143, arXiv.org.
    • Handle: RePEc:arx:papers:1901.09143
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1901.09143
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1901.09143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.