IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.06260.html
   My bibliography  Save this paper

ChatGPT-based Investment Portfolio Selection

Author

Listed:
  • Oleksandr Romanko
  • Akhilesh Narayan
  • Roy H. Kwon

Abstract

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.

Suggested Citation

  • Oleksandr Romanko & Akhilesh Narayan & Roy H. Kwon, 2023. "ChatGPT-based Investment Portfolio Selection," Papers 2308.06260, arXiv.org.
    • Handle: RePEc:arx:papers:2308.06260
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2308.06260
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    2. Kim, Jang Ho, 2023. "What if ChatGPT were a quant asset manager," Finance Research Letters, Elsevier, vol. 58(PD).
    3. Ullah, Rafid & Ismail, Hishamuddin Bin & Islam Khan, Mohammad Tariqul & Zeb, Ali, 2024. "Nexus between Chat GPT usage dimensions and investment decisions making in Pakistan: Moderating role of financial literacy," Technology in Society, Elsevier, vol. 76(C).
    4. Jang Ho Kim & Seyoung Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2025. "Enhancing mean–variance portfolio optimization through GANs-based anomaly detection," Annals of Operations Research, Springer, vol. 346(1), pages 217-244, March.
    5. Ko, Hyungjin & Lee, Jaewook, 2024. "Can ChatGPT improve investment decisions? From a portfolio management perspective," Finance Research Letters, Elsevier, vol. 64(C).

    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.
    1. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    2. Ahmet Faruk Aysan & Ibrahim Musa Unal, 2024. "The Institutionalization of Islamic Finance: Historical Context, Current Developments, and Future Directions," Post-Print hal-04669980, HAL.
    3. Rangapriya Saivasan & Madhavi Lokhande, 2023. "Exploring Use Cases of Generative AI and Metaverse in Financial Analytics: Unveiling the Synergies of Advanced Technologies," International Journal of Global Business and Competitiveness, Springer, vol. 18(1), pages 77-86, December.
    4. Oleksandr Romanko & Akhilesh Narayan & Roy H. Kwon, 2023. "ChatGPT-Based Investment Portfolio Selection," SN Operations Research Forum, Springer, vol. 4(4), pages 1-27, December.

    More about this item

    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:arx:papers:2308.06260. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.