IDEAS home Printed from https://ideas.repec.org/p/jbs/wpaper/20202001.html

Artificial intelligence in asset management

Author

Listed:
  • Söhnke M. Bartram
  • Jürgen Branke
  • Mehrshad Motahari

    (Cambridge Judge Business School, University of Cambridge)

Abstract

Artificial intelligence (AI) has a growing presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and returns forecasts and under more complex constraints. Trading algorithms utilize AI to devise novel trading signals and execute trades with lower transaction costs, and AI improves risk modelling and forecasting by generating insights from new sources of data. Finally, robo-advisors owe a large part of their success to AI techniques. At the same time, the use of AI can create new risks and challenges, for instance as a result of model opacity, complexity, and reliance on data integrity.

Suggested Citation

  • Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:jbs:wpaper:20202001
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3560333
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francois Mercier & Makesh Narsimhan, 2022. "Discovering material information using hierarchical Reformer model on financial regulatory filings," Papers 2204.05979, arXiv.org.
    2. Adzman Shah Mohd Ariffin & Fitriyah Razali & Muhammad Najib Razali & Muhamad Amir Afiq Lokman, 2025. "Adoption of Artificial Intelligence and Technology in Customer Relationship Management for Property Management: A Systematic Literature Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 3312-3320, January.
    3. Scherer, Bernd & Lehner, Sebastian, 2025. "What drives robo-advice?," Journal of Empirical Finance, Elsevier, vol. 80(C).

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:jbs:wpaper:20202001. 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: Ruth Newman (email available below). General contact details of provider: https://edirc.repec.org/data/jicamuk.html .

    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.