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Learning from Unrealized versus Realized Prices

Author

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  • Kathleen Ngangoué
  • Georg Weizsäcker
Abstract
Our market experiment investigates the extent to which traders learn from the price, differentiating between situations where orders are submitted before versus after the price has realized. When market participants have to submit their bids conditional on the price, they show a bias by reacting only to their private information and not to the hypothetical value of the price. In a sequential trading mechanism, where the price is known at the bid submission, bids react to price to an extent that is roughly consistent with the benchmark theory.

Suggested Citation

  • Kathleen Ngangoué & Georg Weizsäcker, 2015. "Learning from Unrealized versus Realized Prices," Discussion Papers of DIW Berlin 1487, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1487
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    References listed on IDEAS

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    Cited by:

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    2. Hubert Janos Kiss & Ismael Rodriguez-Lara & Alfonso Rosa-Garcia, 2019. "Does response time predict withdrawal decisions? Lessons from a bank-run experiment," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 12(3), pages 200-222, November.
    3. Moser, Johannes, 2017. "Hypothetical thinking and the winner's curse: An experimental investigation," University of Regensburg Working Papers in Business, Economics and Management Information Systems 36304, University of Regensburg, Department of Economics.
    4. Kai Barron & Steffen Huck & Philippe Jehiel, 2024. "Everyday Econometricians: Selection Neglect and Overoptimism When Learning from Others," American Economic Journal: Microeconomics, American Economic Association, vol. 16(3), pages 162-198, August.
    5. Antonio, Filippin & Marco, Mantovani, 2019. "Risk Aversion and Information Aggregation in Asset Markets," Working Papers 404, University of Milano-Bicocca, Department of Economics, revised Apr 2019.
    6. Hitoshi Matsushima, 2017. "Framing Game Theory," CARF F-Series CARF-F-425, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. Theo Offerman & Giorgia Romagnoli & Andreas Ziegler, 2022. "Why are open ascending auctions popular? The role of information aggregation and behavioral biases," Quantitative Economics, Econometric Society, vol. 13(2), pages 787-823, May.
    8. André Schmelzer, 2018. "Strategy-Proofness of Stochastic Assignment Mechanisms," The Journal of Mechanism and Institution Design, Society for the Promotion of Mechanism and Institution Design, University of York, vol. 3(1), pages 17-50, December.
    9. Evan M. Calford & Timothy N. Cason, 2024. "Contingent Reasoning and Dynamic Public Goods Provision," American Economic Journal: Microeconomics, American Economic Association, vol. 16(2), pages 236-266, May.
    10. Esponda, Ignacio & Vespa, Emanuel & Yuksel, Sevgi, 2024. "Mental Models and Learning: The Case of Base-Rate Neglect," University of California at San Diego, Economics Working Paper Series qt8cb387t8, Department of Economics, UC San Diego.
    11. Darius Schlangenotto & Wendelin Schnedler & Radovan Vadovič, 2020. "Against All Odds: Tentative Steps toward Efficient Information Sharing in Groups," Games, MDPI, vol. 11(3), pages 1-24, August.
    12. Shengwu Li, 2017. "Obviously Strategy-Proof Mechanisms," American Economic Review, American Economic Association, vol. 107(11), pages 3257-3287, November.
    13. Johannes Moser, 2017. "Hypothetical thinking and the winner's curse: An experimental investigation," Working Papers 176, Bavarian Graduate Program in Economics (BGPE).
    14. Wenner, Lukas M., 2018. "Do sellers exploit biased beliefs of buyers? An experiment," Games and Economic Behavior, Elsevier, vol. 110(C), pages 194-215.
    15. Niederle, Muriel & Vespa, Emanuel, 2023. "Cognitive Limitations: Failures of Contingent Thinking," University of California at San Diego, Economics Working Paper Series qt5q14p1np, Department of Economics, UC San Diego.
    16. Koch, Christian & Penczynski, Stefan P., 2018. "The winner's curse: Conditional reasoning and belief formation," Journal of Economic Theory, Elsevier, vol. 174(C), pages 57-102.
    17. Louis Golowich & Shengwu Li, 2021. "On the Computational Properties of Obviously Strategy-Proof Mechanisms," Papers 2101.05149, arXiv.org, revised Oct 2022.
    18. Moser, Johannes, 2018. "Hypothetical thinking and the winner's curse: An experimental investigation," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181506, Verein für Socialpolitik / German Economic Association.
    19. Antonio Filippin & Marco Mantovani, 2023. "Risk aversion and information aggregation in binary‐asset markets," Quantitative Economics, Econometric Society, vol. 14(2), pages 753-798, May.
    20. Anderson, David M. & Hoagland, Alex & Zhu, Ed, 2024. "Medical bill shock and imperfect moral hazard," Journal of Public Economics, Elsevier, vol. 236(C).

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    More about this item

    Keywords

    Naive expectations; asymmetric information; rational expectations; sequential markets;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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