IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v53y2023ics1544612323000363.html
   My bibliography  Save this article

ChatGPT for (Finance) research: The Bananarama Conjecture

Author

Listed:
  • Dowling, Michael
  • Lucey, Brian

Abstract

We show, based on ratings by finance journal reviewers of generated output, that the recently released AI chatbot ChatGPT can significantly assist with finance research. In principle, these results should be generalisable across research domains. There are clear advantages for idea generation and data identification. The technology, however, is weaker on literature synthesis and developing appropriate testing frameworks. Importantly, we further demonstrate that the extent of private data and researcher domain expertise input, are key factors in determining the quality of output. We conclude by considering the implications, particularly the ethical implications, which arise from this new technology.

Suggested Citation

  • Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:finlet:v:53:y:2023:i:c:s1544612323000363
    DOI: 10.1016/j.frl.2023.103662
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323000363
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.103662?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Matthew Hutson, 2022. "Could AI help you to write your next paper?," Nature, Nature, vol. 611(7934), pages 192-193, November.
    2. Wenzlaff, Karsten & Spaeth, Sebastian, 2022. "Smarter than humans? Validating how OpenAI's ChatGPT model explains crowdfunding, alternative finance and community finance," WiSo-HH Working Paper Series 75, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Saggu, Aman & Ante, Lennart, 2023. "The influence of ChatGPT on artificial intelligence related crypto assets: Evidence from a synthetic control analysis," Finance Research Letters, Elsevier, vol. 55(PB).
    2. Amina Badreddine & Hadjira Larbi Cherif, 2023. "ChatGPT in Academic Research: Demonstrating Limitations through Real Practical Examples," Post-Print hal-04379581, HAL.
    3. Pawe{l} Niszczota & Sami Abbas, 2023. "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice," Papers 2309.00649, arXiv.org.
    4. Christian Fieberg & Lars Hornuf & David J. Streich, 2023. "Using GPT-4 for Financial Advice," CESifo Working Paper Series 10529, CESifo.
    5. Hassnian Ali & Ahmet Faruk Aysan, 2023. "What will ChatGPT revolutionize in the financial industry?," Modern Finance, Modern Finance Institute, vol. 1(1), pages 116-129.
    6. Konstantinos I. Roumeliotis & Nikolaos D. Tselikas, 2023. "ChatGPT and Open-AI Models: A Preliminary Review," Future Internet, MDPI, vol. 15(6), pages 1-24, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      ChatGPT; Artificial intelligence; Finance research; Ethics;
      All these keywords.

      JEL classification:

      • G00 - Financial Economics - - General - - - General
      • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

      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:eee:finlet:v:53:y:2023:i:c:s1544612323000363. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

      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.