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A Manipulator Can Aid Prediction Market Accuracy

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  • ROBIN HANSON
  • RYAN OPREA

Abstract

Prediction markets are low volume speculative markets whose prices offer informative forecasts on particular policy topics. Observers worry that traders may attempt to mislead decision makers by manipulating prices. We adapt a Kyle‐style market microstructure model to this case, adding a manipulator with an additional quadratic preference regarding the price. In this model, when other traders are uncertain about the manipulator's target price, the mean target price has no effect on prices, and raising the variance of the target price can increase average price accuracy, by boosting the returns to informed trading and thereby incentives for traders to become informed.

Suggested Citation

  • Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    • Handle: RePEc:bla:econom:v:76:y:2009:i:302:p:304-314
    DOI: 10.1111/j.1468-0335.2008.00734.x
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    2. Corgnet, Brice & Deck, Cary & DeSantis, Mark & Porter, David, 2018. "Information (non)aggregation in markets with costly signal acquisition," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 286-320.
    3. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2013. "Prediction Markets for Economic Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 657-687, Elsevier.
    4. Karimi, Majid & Zaerpour, Nima, 2022. "Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1035-1049.
    5. Veiga, Helena & Vorsatz, Marc, 2008. "Aggregation and dissemination of information in experimental asset markets in the presence of a manipulator," DES - Working Papers. Statistics and Econometrics. WS ws084110, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Veiga, Helena & Vorsatz, Marc, 2009. "Price manipulation in an experimental asset market," European Economic Review, Elsevier, vol. 53(3), pages 327-342, April.
    7. Helena Veiga & Marc Vorsatz, 2010. "Information aggregation in experimental asset markets in the presence of a manipulator," Experimental Economics, Springer;Economic Science Association, vol. 13(4), pages 379-398, December.
    8. Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.
    9. 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.
    10. Chau, Ching & Aspris, Angelo & Foley, Sean & Malloch, Hamish, 2021. "Quote-Based manipulation of illiquid securities," Finance Research Letters, Elsevier, vol. 39(C).
    11. Reinstein, David & Song, Joon, 2014. "Listen to the Market, Hear the Best Policy Decision, but Don't Always Choose it," Economics Discussion Papers 10008, University of Essex, Department of Economics.
    12. Boleslavsky, Raphael & Kelly, David L. & Taylor, Curtis R., 2017. "Selloffs, bailouts, and feedback: Can asset markets inform policy?," Journal of Economic Theory, Elsevier, vol. 169(C), pages 294-343.
    13. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.
    14. Siemroth, Christoph, 2014. "Why prediction markets work : the role of information acquisition and endogenous weighting," Working Papers 14-29, University of Mannheim, Department of Economics.
    15. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    16. Boris Maciejovsky & David V. Budescu, 2020. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets," Organization Science, INFORMS, vol. 31(6), pages 1497-1514, November.
    17. Mohammad Zare & Omid Naghshineh A. & Erfan Salavati & Adel Mohammadpour, 2021. "An agent-based model and detect price manipulation based on intraday transaction data with simulation," Applied Economics, Taylor & Francis Journals, vol. 53(43), pages 4931-4949, September.
    18. Brown, Alasdair & Reade, J. James & Vaughan Williams, Leighton, 2019. "When are prediction market prices most informative?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 420-428.

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