IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-12240-8_1.html
   My bibliography  Save this book chapter

Big Data in Finance: An Overview

In: Big Data in Finance

Author

Listed:
  • Thomas Walker

    (Concordia University)

  • Frederick Davis

    (Concordia University)

  • Tyler Schwartz

    (Concordia University)

Abstract

In the financial sector, the importance of big data in decision-making continues to increase. This growth is facilitated by ongoing improvements and refinements in artificial intelligence (AI), where model performance in prediction tasks has continued to improve with recent innovations such as deep learning. The ability for financial models to predict more accurately, in conjunction with big data, has led to many innovations in areas such as automated lending, portfolio construction and management (robo-advising), risk management, fraud detection, quantitative and high-frequency trading, as well as customer support. However, while AI-based analyses of big data have led to many positive developments in finance, critical questions remain, including how data is collected (consumer privacy and ethical concerns), stored (environmental impacts), secured, as well as analyzed and used. This edited book critically analyzes new developments at the intersection of big data and finance and provides different perspectives on their impact on the financial sector and the way it operates. This introductory chapter summarizes the use of big data in finance and provides an overview of the eleven contributions featured in the collection.

Suggested Citation

  • Thomas Walker & Frederick Davis & Tyler Schwartz, 2022. "Big Data in Finance: An Overview," Springer Books, in: Thomas Walker & Frederick Davis & Tyler Schwartz (ed.), Big Data in Finance, pages 3-9, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-12240-8_1
    DOI: 10.1007/978-3-031-12240-8_1
    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_1. 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.