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Fostering the adoption of robo-advisors: A 3-weeks online stock-trading experiment

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  • Alexia Gaudeul
  • Caterina Giannetti

Abstract

We consider how to increase the take-up of robo-advisors to help investors cope with the disposition effect. In a 3-weeks online stock-trading experiment, participants traded freely in week 1, were assisted by trading algorithms in week 2, and chose whether to be assisted in week 3. Passive algorithms prevented trading, active ones traded according to Bayesian rules. Participants could override algorithm choices in some treatments. Only a minority adopted robo-advisors in week 3, with the worst performers being the least likely to do so. Algorithm aversion was reduced if the algorithm traded actively and could be overridden.

Suggested Citation

  • Alexia Gaudeul & Caterina Giannetti, 2021. "Fostering the adoption of robo-advisors: A 3-weeks online stock-trading experiment," Discussion Papers 2021/275, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2021/275
    Note: ISSN 2039-1854
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    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2021-275.pdf
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    References listed on IDEAS

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    Cited by:

    1. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G40 - Financial Economics - - Behavioral Finance - - - General

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