IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2604.24147.html

Price as Focal Point: Prediction Markets,Conditional Reflexivity, and the Politics of Common Knowledge

Author

Listed:
  • Maksym Nechepurenko

Abstract

Prediction markets are widely treated as forecasting devices that reveal collective expectations about uncertain futures. This article argues that under specifiable conditions they also function as coordination mechanisms: public probabilities that organize the behavior of voters, donors, journalists, traders, and institutions in ways that can be self-fulfilling or self-defeating. Most existing work asks whether prediction markets forecast accurately; this paper asks whether accurate forecasting is even the right criterion for a market that has become a public coordination device. Drawing on transaction-level evidence from the 2024 U.S. presidential election, we show that the social force of a market signal depends less on its size than on its persistence, the breadth of responding trader types, and cross-platform consensus. We introduce a Signal Credibility Index (SCI) -- combining the variance ratio VR(6), a two-sidedness diagnostic, and a trader-concentration adjustment -- as a microstructure-grounded criterion for predicting when price moves acquire behavioral traction. Applied to three major 2024 political shocks, the framework reveals that superficially similar events generated qualitatively distinct signal types with different implications for elite coordination. A cross-platform comparison establishes a systematic decoupling of social authority from epistemic robustness: the most visible market produced the least accurate forecasts. The framework carries direct implications for regulating prediction markets as democratic information infrastructure.

Suggested Citation

  • Maksym Nechepurenko, 2026. "Price as Focal Point: Prediction Markets,Conditional Reflexivity, and the Politics of Common Knowledge," Papers 2604.24147, arXiv.org.
  • Handle: RePEc:arx:papers:2604.24147
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2604.24147
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
    3. Clinton, Joshua D. & Huang, TzuFeng, 2025. "Prediction Markets? The Accuracy and Efficiency of $2.4 Billion in the 2024 Presidential Election," SocArXiv d5yx2_v1, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    2. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, Centre for Economic Policy Research.
    3. Dian Yu & Jianjun Gao & Weiping Wu & Zizhuo Wang, 2022. "Price Interpretability of Prediction Markets: A Convergence Analysis," Papers 2205.08913, arXiv.org, revised Nov 2023.
    4. Victor Tiberius & Christoph Rasche, 2011. "Prognosemärkte," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(4), pages 467-472, April.
    5. repec:grz:wpsses:2019-01 is not listed on IDEAS
    6. Coulomb, Renaud & Sangnier, Marc, 2014. "The impact of political majorities on firm value: Do electoral promises or friendship connections matter?," Journal of Public Economics, Elsevier, vol. 115(C), pages 158-170.
    7. Ho Cheung Brian Lee & Jan Stallaert & Ming Fan, 2020. "Anomalies in Probability Estimates for Event Forecasting on Prediction Markets," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2077-2095, September.
    8. Caleb Maresca, 2026. "Can Interest-Bearing Positions Solve the Long-Horizon Problem in Prediction Markets?," Papers 2602.21091, arXiv.org.
    9. Joyce E. Berg & John Geweke & Thomas A. Rietz, 2010. "Memoirs of an indifferent trader: Estimating forecast distributions from prediction markets," Quantitative Economics, Econometric Society, vol. 1(1), pages 163-186, July.
    10. Stefan Palan & Jürgen Huber & Larissa Senninger, 2020. "Aggregation mechanisms for crowd predictions," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 788-814, September.
    11. Jianjun Gao & Zizhuo Wang & Weiping Wu & Dian Yu, 2025. "Price Interpretability of Prediction Markets: A Convergence Analysis," Operations Research, INFORMS, vol. 73(1), pages 157-177, January.
    12. Shaw Dalen, 2026. "What Happens When Institutional Liquidity Enters Prediction Markets: Identification, Measurement, and a Synthetic Proof of Concept," Papers 2604.10005, arXiv.org, revised Apr 2026.
    13. Gustav Axén & Dominic Cortis, 2020. "Hedging on Betting Markets," Risks, MDPI, vol. 8(3), pages 1-14, August.
    14. Yu, Dian & Gao, Jianjun & Wang, Tongyao, 2022. "Betting market equilibrium with heterogeneous beliefs: A prospect theory-based model," European Journal of Operational Research, Elsevier, vol. 298(1), pages 137-151.
    15. Werner Antweiler, 2012. "Long-Term Prediction Markets," Journal of Prediction Markets, University of Buckingham Press, vol. 6(3), pages 43-61.
    16. Boulu-Reshef, Béatrice & Comeig, Irene & Donze, Robert & Weiss, Gregory D., 2016. "Risk aversion in prediction markets: A framed-field experiment," Journal of Business Research, Elsevier, vol. 69(11), pages 5071-5075.
    17. Agrawal, Shipra & Megiddo, Nimrod & Armbruster, Benjamin, 2010. "Equilibrium in prediction markets with buyers and sellers," Economics Letters, Elsevier, vol. 109(1), pages 46-49, October.
    18. Berg, Joyce E. & Rietz, Thomas A., 2019. "Longshots, overconfidence and efficiency on the Iowa Electronic Market," International Journal of Forecasting, Elsevier, vol. 35(1), pages 271-287.
    19. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    20. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting Prediction Market Prices as Probabilities," Working Paper Series 2006-11, Federal Reserve Bank of San Francisco.
    21. Albert N. Link & John T. Scott, 2013. "Private Investor Participation and Commercialization Rates for Government-sponsored Research and Development: Would a Prediction Market Improve the Performance of the SBIR Programme?," Chapters, in: Public Support of Innovation in Entrepreneurial Firms, chapter 11, pages 157-174, Edward Elgar Publishing.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2604.24147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.