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No-Trade in the Laboratory

Author

Listed:
  • Angrisani Marco

    (RAND Corporation, mangrisa@rand.org)

  • Guarino Antonio

    (University College London, a.guarino@ucl.ac.uk)

  • Huck Steffen

    (University College London, s.huck@ucl.ac.uk)

  • Larson Nathan C

    (University of Virginia, nl2a@virginia.edu)

Abstract

We construct laboratory financial markets in which subjects can trade an asset whose value is unknown. Subjects receive private clues about the asset value and then set bid and ask prices at which they are willing to buy or to sell from the other participants. In some of our markets (experimental treatments), there are gains from trade, while in others there are no gains: trade is zero sum. Celebrated no-trade theorems state that differences in private information alone cannot explain trade in the zero sum case. We study whether purely informational trade is eliminated in our experimental markets with no gains. The comparison of our results for gains and no-gains treatments shows that subjects fail to reach the no-trade outcome by pure introspection, but they approach it over time through market feedback and learning. Furthermore, the less noisy the clue-asset relationship is, the closer trade comes to being eliminated entirely.

Suggested Citation

  • Angrisani Marco & Guarino Antonio & Huck Steffen & Larson Nathan C, 2011. "No-Trade in the Laboratory," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 11(1), pages 1-58, April.
    • Handle: RePEc:bpj:bejtec:v:11:y:2011:i:1:n:9
    DOI: 10.2202/1935-1704.1745
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    Cited by:

    1. Philippe Aghion & Ernst Fehr & Richard Holden & Tom Wilkening, 2018. "The Role of Bounded Rationality and Imperfect Information in Subgame Perfect Implementation—An Empirical Investigation," Journal of the European Economic Association, European Economic Association, vol. 16(1), pages 232-274.
    2. Andrea Morone & Simone Nuzzo, 2019. "Market efficiency, trading institutions and information mirages: evidence from a laboratory asset market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 317-344, June.
    3. Morone, Andrea & Nuzzo, Simone, 2016. "Do Markets (Institutions) Drive Out Lemmings or Vice Versa?," MPRA Paper 74322, University Library of Munich, Germany.
    4. Carrillo, Juan D. & Palfrey, Thomas R., 2011. "No trade," Games and Economic Behavior, Elsevier, vol. 71(1), pages 66-87, January.
    5. Hoppe, Eva I. & Schmitz, Patrick W., 2015. "Do sellers offer menus of contracts to separate buyer types? An experimental test of adverse selection theory," Games and Economic Behavior, Elsevier, vol. 89(C), pages 17-33.
    6. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    7. Kleinlercher, Daniel & Stöckl, Thomas, 2021. "Thou shalt not trade—An analysis of the violations of no-trade predictions in experimental asset markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    8. Morone, Andrea & Nuzzo, Simone, 2015. "Market Efficiency, Trading Institutions and Information Mirages: evidence from an experimental asset market," MPRA Paper 67448, University Library of Munich, Germany.
    9. Wenner, Lukas M., 2018. "Do sellers exploit biased beliefs of buyers? An experiment," Games and Economic Behavior, Elsevier, vol. 110(C), pages 194-215.
    10. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
    11. Ruiz-Buforn, Alba & Alfarano, Simone & Camacho-Cuena, Eva & Morone, Andrea, 2018. "Crowding out effect and traders' overreliance on public information in financial markets: a lesson from the lab," MPRA Paper 88866, University Library of Munich, Germany.

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    More about this item

    Keywords

    no-trade theorem; laboratory experiment;

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • 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

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