Prediction Markets

28 Pages Posted: 31 May 2004 Last revised: 15 Sep 2010

See all articles by Justin Wolfers

Justin Wolfers

University of Michigan at Ann Arbor - Department of Economics; University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy; The University of Sydney - Discipline of Economics; Brookings Institution - Economic Studies Program; Peter G. Peterson Institute for International Economics; National Bureau of Economic Research (NBER); IZA Institute of Labor Economics; Centre for Economic Policy Research (CEPR); CESifo (Center for Economic Studies and Ifo Institute); Kiel Institute for the World Economy

Eric Zitzewitz

Dartmouth College; NBER

Multiple version iconThere are 2 versions of this paper

Date Written: May 2004

Abstract

We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.

Suggested Citation

Wolfers, Justin and Zitzewitz, Eric W., Prediction Markets (May 2004). NBER Working Paper No. w10504. Available at SSRN: https://ssrn.com/abstract=552109

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