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Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution

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  • Lian Jian

    (Annenberg School of Communication, University of Southern California, Los Angeles, California 90089)

  • Rahul Sami

    (School of Information, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

We conduct laboratory experiments on variants of market scoring rule prediction markets, under different information distribution patterns, to evaluate the efficiency and speed of information aggregation, as well as test recent theoretical results on manipulative behavior by traders. We find that markets structured to have a fixed sequence of trades exhibit greater accuracy of information aggregation than the typical form that has unstructured trade. In comparing two commonly used mechanisms, we find no significant difference between the performance of the direct probability-report form and the indirect security-trading form of the market scoring rule. In the case of the markets with a structured order, we find evidence supporting the theoretical prediction that information aggregation is slower when information is complementary. In structured markets, the theoretical prediction that there will be more delayed trading in complementary markets is supported, but we find no support for the prediction that there will be more bluffing in complementary markets. However, the theoretical predictions are not borne out in the unstructured markets. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.

Suggested Citation

  • Lian Jian & Rahul Sami, 2012. "Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution," Management Science, INFORMS, vol. 58(1), pages 123-140, January.
  • Handle: RePEc:inm:ormnsc:v:58:y:2012:i:1:p:123-140
    DOI: 10.1287/mnsc.1110.1404
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    Cited by:

    1. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.
    2. 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.
    3. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    4. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    5. Krishnamurthy Iyer & Ramesh Johari & Ciamac C. Moallemi, 2014. "Information Aggregation and Allocative Efficiency in Smooth Markets," Management Science, INFORMS, vol. 60(10), pages 2509-2524, October.
    6. 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.
    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. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.
    9. Florian Teschner & Henner Gimpel, 2018. "Crowd Labor Markets as Platform for Group Decision and Negotiation Research: A Comparison to Laboratory Experiments," Group Decision and Negotiation, Springer, vol. 27(2), pages 197-214, April.

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