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Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment

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  • Tiffany Tsz Kwan Tse
  • Nobuyuki Hanaki
  • Bolin Mao

Abstract

We experimentally investigated the relationship between participants’ reliance on algorithms, their familiarity with the task, and the performance level of the algorithm. We found that when participants were given the freedom to submit any number as their final forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average degree of reliance on high and low performing algorithms did not significantly differ when there was no practice stage. Participants relied less on the algorithm when there was practice stage, regardless of its performance level. The reliance on the low performing algorithm was positive even when participants could infer that they outperformed the algorithm. Indeed, participants would have done better without relying on the low performing algorithm at all. Our results suggest that, at least in some domains, excessive reliance on algorithms, rather than algorithm aversion, should be a concern.
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Suggested Citation

  • Tiffany Tsz Kwan Tse & Nobuyuki Hanaki & Bolin Mao, 2022. "Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment," ISER Discussion Paper 1194, Institute of Social and Economic Research, The University of Osaka.
    • Handle: RePEc:dpr:wpaper:1194
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    File URL: https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/2022/DP1194.pdf
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    References listed on IDEAS

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    1. Nobuyuki Hanaki & Keigo Inukai & Takehito Masuda & Yuta Shimodaira, 2021. "Participants’ Characteristics at ISER-Lab in 2020," ISER Discussion Paper 1141, Institute of Social and Economic Research, The University of Osaka.
    2. Charles N. Noussair & Stefan T. Trautmann & Gijs van de Kuilen, 2014. "Higher Order Risk Attitudes, Demographics, and Financial Decisions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(1), pages 325-355.
    3. Takehito Masuda & Eungik Lee, 2019. "Higher order risk attitudes and prevention under different timings of loss," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 197-215, March.
    4. Takehito Masuda & Eungik Lee, 2018. "Higher order risk attitudes and prevention under different timings of loss," ISER Discussion Paper 1034, Institute of Social and Economic Research, The University of Osaka.
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    Cited by:

    1. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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    More about this item

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • G1 - Financial Economics - - General Financial Markets
    • G4 - Financial Economics - - Behavioral Finance
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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