IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-12240-8_7.html
   My bibliography  Save this book chapter

Robo-Advisors: A Big Data Challenge

In: Big Data in Finance

Author

Listed:
  • Federico Severino

    (Université Laval)

  • Sébastien Thierry

    (Université Laval)

Abstract

At the frontier of personal finance and FinTech, robo-advisors aim to provide customized portfolio strategies without human intervention. These new investment technologies typically propose passive strategies that match investor objectives and risk profiles at a low cost. This chapter explores how digital advisors lack precision in capturing clients’ attitudes towards risk and exposure. In this context, leveraging big data and artificial intelligence techniques can improve the principal strength of robo-advisors, i.e., their ability to provide automated, personalized investment solutions. Text data from dialogue systems, such as chatbots, can be employed to improve the client profile, while recommendation systems can use big data from financial social networks to recommend targeted investment strategies. Analysis of big data through machine learning methods can also improve the performance of the optimization algorithms employed by digital advisors. This chapter explores the vast potential for exploiting big data and artificial intelligence in automated asset management.

Suggested Citation

  • Federico Severino & Sébastien Thierry, 2022. "Robo-Advisors: A Big Data Challenge," Springer Books, in: Thomas Walker & Frederick Davis & Tyler Schwartz (ed.), Big Data in Finance, pages 115-131, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-12240-8_7
    DOI: 10.1007/978-3-031-12240-8_7
    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 search for a similarly titled item that would be available.

    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:sprchp:978-3-031-12240-8_7. 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.