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A Methodological Note on Eliciting Price Forecasts in Asset Market Experiments

Author

Listed:
  • Nobuyuki Hanaki

    (Université Nice Sophia Antipolis
    GREDEG-CNRS
    IUF)

  • Eizo Akiyama

    (University of Tsukuba, Japan)

  • Ryuichiro Ishikawa

    (University of Tsukuba, Japan)

Abstract

We investigate (a) whether eliciting future price forecasts influences market outcomes, and (b) whether differences in the way subjects are incentivized to submit ''accurate'' price forecasts influence the market outcomes as well as the forecasts submitted by subjects in an experimental asset market. We consider three treatments: one without forecast elicitation (NF) and two with forecast elicitations. In one of the latter treatments, subjects are paid based on both their performance of forecasting and trading (Bonus), while in the other, they are paid based only on one of the two that is chosen randomly at the end of the experiment (Unique). While we found no statistical differences in terms of mispricing, trading volumes, and trading behavior between NF and Unique treatments, there were some statistically significant differences between NF and Bonus treatments. Thus, if the aim is to avoid influencing the behavior of subjects and the market outcomes by eliciting price forecasts compared to NF treatment, then the Unique treatment seems to be better than the Bonus treatment.

Suggested Citation

  • Nobuyuki Hanaki & Eizo Akiyama & Ryuichiro Ishikawa, 2016. "A Methodological Note on Eliciting Price Forecasts in Asset Market Experiments," GREDEG Working Papers 2016-02, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    • Handle: RePEc:gre:wpaper:2016-02
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    References listed on IDEAS

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    Cited by:

    1. Michaël Assous & Muriel Dal Pont Legrand & Harald Hagemann, 2016. "Business cycles and growth," Chapters, in: Gilbert Faccarello & Heinz D. Kurz (ed.), Handbook on the History of Economic Analysis Volume III, chapter 4, pages 27-39, Edward Elgar Publishing.
    2. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2020. "Long-run expectations in a learning-to-forecast experiment: a simulation approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 75-116, January.

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    More about this item

    Keywords

    Price forecast elicitation; Experimental asset markets;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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