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Manipulation And (Mis)Trust In Prediction Markets

Author

Listed:
  • Lawrence Choo

    (Nuremberg Germany)

  • Todd R. Kaplan

    (Haifa)

  • Ro’i Zultan

    (BGU)

Abstract

Markets are increasingly used as information aggregation mechanisms to predict future events. If policy makers make use markets, parties may attempt to manipulate the market in order to influence decisions. We experimentally find that policymakers could still benefit from following information contained in market prices. Nonetheless, manipulation is detrimental. First, manipulators affect market prices, making them less informative. Second, when there are manipulators, policy makers often ignore - or even act against - the information revealed in market prices. Finally, mere suspicion of manipulation erodes trust in the market, leading to the implementation of suboptimal policies - even without actual manipulation.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2020. "Manipulation And (Mis)Trust In Prediction Markets," Working Papers 2012, Ben-Gurion University of the Negev, Department of Economics.
  • Handle: RePEc:bgu:wpaper:2012
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    References listed on IDEAS

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    1. Jan Hansen & Carsten Schmidt & Martin Strobel, 2004. "Manipulation in political stock markets - preconditions and evidence," Applied Economics Letters, Taylor & Francis Journals, vol. 11(7), pages 459-463.
    2. Hellwig, Martin F., 1982. "Rational expectations equilibrium with conditioning on past prices: A mean-variance example," Journal of Economic Theory, Elsevier, vol. 26(2), pages 279-312, April.
    3. Colin F. Camerer, 1998. "Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 457-482, June.
    4. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    5. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    6. Dubey, Pradeep & Geanakoplos, John & Shubik, Martin, 1987. "The revelation of information in strategic market games : A critique of rational expectations equilibrium," Journal of Mathematical Economics, Elsevier, vol. 16(2), pages 105-137, April.
    7. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    8. Deck, Cary & Lin, Shengle & Porter, David, 2013. "Affecting policy by manipulating prediction markets: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 48-62.
    9. Radner, Roy, 1979. "Rational Expectations Equilibrium: Generic Existence and the Information Revealed by Prices," Econometrica, Econometric Society, vol. 47(3), pages 655-678, May.
    10. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    11. Brice Corgnet & Mark DeSantis & David Porter, 2015. "Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency," Working Papers 15-15, Chapman University, Economic Science Institute.
    12. Veiga, Helena & Vorsatz, Marc, 2009. "Price manipulation in an experimental asset market," European Economic Review, Elsevier, vol. 53(3), pages 327-342, April.
    13. Benjamin J. Gillen & Charles R. Plott & Matthew Shum, 2017. "A Pari-Mutuel-Like Mechanism for Information Aggregation: A Field Test inside Intel," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1075-1099.
    14. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    15. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    16. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    17. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    18. 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.
    19. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
    20. Colin Camerer, 1998. "Can asset markets be manipulated? A field experiment with racetrack betting," Natural Field Experiments 00222, The Field Experiments Website.
    21. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    More about this item

    Keywords

    prediction markets; policy; experiment;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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