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Learning From Unrealized versus Realized Prices

Author

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  • Ngangoue, M. Kathleen

    (New York University)

  • Weizsäcker, Georg

    (Humboldt University Berlin)

Abstract

Our experiments investigate the extent to which traders learn from the price, differentiating between situations where orders are submitted before versus after the price has realized. In simultaneous markets with bids that are conditional on the price, traders neglect the information conveyed by the hypothetical value of the price. In sequential markets where the price is known prior to the bid submission, traders react to price to an extent that is roughly consistent with the benchmark theory. The difference\'s robustness to a number of variations provides insights about the drivers of this effect.

Suggested Citation

  • Ngangoue, M. Kathleen & Weizsäcker, Georg, 2018. "Learning From Unrealized versus Realized Prices," Rationality and Competition Discussion Paper Series 66, CRC TRR 190 Rationality and Competition.
    • Handle: RePEc:rco:dpaper:66
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Shengwu Li, 2017. "Obviously Strategy-Proof Mechanisms," American Economic Review, American Economic Association, vol. 107(11), pages 3257-3287, November.
    5. Wenner, Lukas M., 2018. "Do sellers exploit biased beliefs of buyers? An experiment," Games and Economic Behavior, Elsevier, vol. 110(C), pages 194-215.
    6. Barron, Kai & Huck, Steffen & Jehiel, Philippe, 2019. "Everyday econometricians: Selection neglect and overoptimism when learning from others," Discussion Papers, Research Unit: Economics of Change SP II 2019-301, WZB Berlin Social Science Center.
    7. 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.
    8. 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.
    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. 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.
    11. Johannes Moser, 2017. "Hypothetical thinking and the winner's curse: An experimental investigation," Working Papers 176, Bavarian Graduate Program in Economics (BGPE).
    12. Alex Hoagland & David M. Anderson & Ed Zhu, 2022. "Medical Bill Shock and Imperfect Moral Hazard," Papers 2211.01116, arXiv.org, revised Mar 2024.
    13. 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.
    14. 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.
    15. 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.
    16. Louis Golowich & Shengwu Li, 2021. "On the Computational Properties of Obviously Strategy-Proof Mechanisms," Papers 2101.05149, arXiv.org, revised Oct 2022.
    17. Marco Mantovani & Antonio Filippin, 2024. "When do prediction markets return average beliefs? Experimental evidence," Working Papers 532, University of Milano-Bicocca, Department of Economics.
    18. 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.
    19. 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.

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    More about this item

    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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