IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-58405-3_20.html
   My bibliography  Save this book chapter

Artificial Intelligence and Daily Return Forecasts

In: Operations Research Proceedings 2023

Author

Listed:
  • Theo Berger

    (University of Applied Sciences Harz
    University of Bremen)

Abstract

This study provides an assessment of machine learning applied to the field of univariate financial time series analysis. Drawing on widely accepted ARIMA models, competing machine learning approaches are compared and various training scenarios and network architectures are discussed to identify the optimal network approach for daily return series forecasts. As a result of this exercise, plain vanilla recurrent neural networks with one layer describe a sensible choice.

Suggested Citation

  • Theo Berger, 2025. "Artificial Intelligence and Daily Return Forecasts," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 155-160, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_20
    DOI: 10.1007/978-3-031-58405-3_20
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:lnopch:978-3-031-58405-3_20. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.