IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-246-0_38.html

Machine Learning Algorithm Applications in Empirical Finance: A Review of the Empirical Literature

In: Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023)

Author

Listed:
  • Xiaochuan Liao

    (University of Birmingham, School of Computer Science)

Abstract

The modern finance industry has many challenges to handle, so advanced tools are applied to assist people in trying to find a solution to those complex problems. Machine learning is a powerful tool that can help researchers tackle difficult issues, including those in the financial industry. In this paper, I reviewed literature focusing on machine learning algorithm applications in empirical finance. I divide the literature review into three sub-sectors: financial market prediction, bankruptcy prediction and credit risk analysis, and other notable aspects. It is found that researchers widely use algorithms like support vector machines and neural networks. Finally, based on my review of the literature, I provide my insights in this area.

Suggested Citation

  • Xiaochuan Liao, 2024. "Machine Learning Algorithm Applications in Empirical Finance: A Review of the Empirical Literature," Advances in Economics, Business and Management Research, in: Shehnaz Tehseen & Mohd Naseem Niaz Ahmad & Rafia Afroz (ed.), Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023), pages 311-316, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-246-0_38
    DOI: 10.2991/978-94-6463-246-0_38
    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:advbcp:978-94-6463-246-0_38. 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.