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Can ChatGPT assist in picking stocks?

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Listed:
  • Pelster, Matthias
  • Val, Joel

Abstract

This paper studies whether ChatGPT-4 with access to the internet is able to provide valuable investment advice and evaluate financial information in a timely manner. Using a live experiment, we find a positive correlation between ChatGPT-4 ratings and future earnings announcements and stock returns. We find evidence that ChatGPT-4 adjusts ratings in response to earnings surprises and news events information in a timely manner. An investment strategy based on “attractiveness ratings” by ChatGPT-4 yields positive returns.

Suggested Citation

  • Pelster, Matthias & Val, Joel, 2024. "Can ChatGPT assist in picking stocks?," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323011583
    DOI: 10.1016/j.frl.2023.104786
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    References listed on IDEAS

    as
    1. Christian Fieberg & Lars Hornuf & David J. Streich, 2023. "Using GPT-4 for Financial Advice," CESifo Working Paper Series 10529, CESifo.
    2. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    3. Noh, Joonki & Zhou, Dexin, 2022. "Executives’ Blaming external factors and market reactions: Evidence from earnings conference calls," Journal of Banking & Finance, Elsevier, vol. 134(C).
    4. Rennekamp, Kristina M. & Sethuraman, Mani & Steenhoven, Blake A., 2022. "Engagement in earnings conference calls," Journal of Accounting and Economics, Elsevier, vol. 74(1).
    5. Smales, Lee A., 2023. "Classification of RBA monetary policy announcements using ChatGPT," Finance Research Letters, Elsevier, vol. 58(PC).
    6. 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.
    7. Ali, Ashiq & Chen, Xuanjuan & Yao, Tong & Yu, Tong, 2020. "Can mutual funds profit from post earnings announcement drift? The role of competition," Journal of Banking & Finance, Elsevier, vol. 114(C).
    8. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    9. Jonathan A. Milian, 2015. "Unsophisticated Arbitrageurs and Market Efficiency: Overreacting to a History of Underreaction?," Journal of Accounting Research, Wiley Blackwell, vol. 53(1), pages 175-220, March.
    10. Allee, Kristian D. & Do, Chuong & Sterin, Mikhail, 2021. "Product market competition, disclosure framing, and casting in earnings conference calls," Journal of Accounting and Economics, Elsevier, vol. 72(1).
    11. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    12. Jeffrey T. Doyle & Russell J. Lundholm & Mark T. Soliman, 2006. "The Extreme Future Stock Returns Following I/B/E/S Earnings Surprises," Journal of Accounting Research, Wiley Blackwell, vol. 44(5), pages 849-887, December.
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    More about this item

    Keywords

    Information processing; Artificial intelligence (AI); ChatGPT;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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