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Valuing Algorithms Over Experts: Evidence from a Stock Price Forecasting Experiment

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Listed:
  • Nobuyuki Hanaki
  • Bolin Mao
  • Tiffany Tsz Kwan Tse
  • Wenxin Zhou

Abstract

This study examined participants’ willingness to pay for stock price forecasts provided by an algorithm, financial experts, and peers. Participants valued algorithmic advice more highly and relied on it as much as expert advice. This preference for algorithms – despite their similar or even lower performance – suggests a shift in perception, particularly among students, toward viewing AI as a reliable and valuable source. However, this “algorithm appreciation” reduced participants’ payoffs, as they overpaid for advice that did not sufficiently enhance performance. These findings underscore the need to develop tools and policies that enable individuals to better assess algorithm performance.

Suggested Citation

  • Nobuyuki Hanaki & Bolin Mao & Tiffany Tsz Kwan Tse & Wenxin Zhou, 2024. "Valuing Algorithms Over Experts: Evidence from a Stock Price Forecasting Experiment," ISER Discussion Paper 1268, Institute of Social and Economic Research, Osaka University.
    • Handle: RePEc:dpr:wpaper:1268
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    File URL: https://www.iser.osaka-u.ac.jp/library/dp/2024/DP1268.pdf
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    References listed on IDEAS

    as
    1. Bao, Te & Corgnet, Brice & Hanaki, Nobuyuki & Riyanto, Yohanes E. & Zhu, Jiahua, 2023. "Predicting the unpredictable: New experimental evidence on forecasting random walks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    2. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    3. Felix Holzmeister & Martin Holmén & Michael Kirchler & Matthias Stefan & Erik Wengström, 2023. "Delegation Decisions in Finance," Management Science, INFORMS, vol. 69(8), pages 4828-4844, August.
    4. Mark Egan & Gregor Matvos & Amit Seru, 2019. "The Market for Financial Adviser Misconduct," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 233-295.
    5. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    6. repec:bla:jfinan:v:53:y:1998:i:5:p:1589-1622 is not listed on IDEAS
    7. Stephen Foerster & Juhani T. Linnainmaa & Brian T. Melzer & Alessandro Previtero, 2017. "Retail Financial Advice: Does One Size Fit All?," Journal of Finance, American Finance Association, vol. 72(4), pages 1441-1482, August.
    8. Daniel Hoechle & Stefan Ruenzi & Nic Schaub & Markus Schmid, 2018. "Financial Advice and Bank Profits," The Review of Financial Studies, Society for Financial Studies, vol. 31(11), pages 4447-4492.
    9. Juhani T. Linnainmaa & Brian T. Melzer & Alessandro Previtero, 2021. "The Misguided Beliefs of Financial Advisors," Journal of Finance, American Finance Association, vol. 76(2), pages 587-621, April.
    10. Stephen G. Dimmock & William C. Gerken & Nathaniel P. Graham, 2018. "Is Fraud Contagious? Coworker Influence on Misconduct by Financial Advisors," Journal of Finance, American Finance Association, vol. 73(3), pages 1417-1450, June.
    11. Timothy N. Cason & Charles R. Plott, 2014. "Misconceptions and Game Form Recognition: Challenges to Theories of Revealed Preference and Framing," Journal of Political Economy, University of Chicago Press, vol. 122(6), pages 1235-1270.
    12. Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
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