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

Lian Jian () and Rahul Sami ()
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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

Management Science, 2012, vol. 58, issue 1, 123-140

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.

Keywords: prediction markets; experiments; market scoring rule (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (14)

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