Prediction Markets

We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of unknown future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market’s expectations about not only 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. The authors would like to thank David Pennock, Emile Servan-Schreiber of NewsFutures, David Dempsey and John Delaney of Tradesports, David Siegel and Scott Hereld of Trackmaster, and George Neumann of IEM for help with data. Thanks to Kay-Yut Chen, Andrew Leigh, Rohan Wolfers, Betsey Stevenson, Tim Taylor, Hal Varian and Craig Yee for stimulating discussions. Doug Geyser, Chris Lion, Paul Reist, Eric Snowberg, and Ravi Pillai provided outstanding research assistance.