Research

Basic

Several studies explain the concept of prediction markets and provide useful summaries of the method, e.g.

• Spann, M. & Skiera, B. (2003). Internet-based Virtual Stock Markets for Business Forecasting, Management Science, 49, 1310-1326. [Full text]
• Wolfers, J. & Zitzewitz, E. (2006). Prediction Markets in Theory and Practice, New Palgrave Dictionary of Economics and the Law (in press). [Full text]
• Wolfers, J. & Zitzewitz, E. (2004). Prediction Markets, Journal of Economic Perspectives, 18, 107-126. [Full text]

An overview and classification of 152 studies on prediction markets, published between 1991 and 2006, is provided by

• Tziralis, G. & Tatsiopoulos (2007). Prediction Markets: An Extended Literature Review, Journal of Prediction Markets, 1, 75-91. [Full text]

Evidence on the accuracy of prediction markets

This section summarizes research that analyzes the relative performance of prediction markets and other forecasting methods.

• Markets vs. polls (election forecasting)

  • Berg, J., Nelson, F. & Rietz, T. (2008). Prediction Market Accuracy in the Long Run, International Journal of Forecasting, 24, 283-298. [full text]
  • Erikson R. S. & Wlezien C. (2007). Are Political Markets Really Superior to Polls as Election Predictors? Public Opinion Quarterly, forthcoming.[full text]
  • Stix, G. (2008): When Markets Beat the Polls, Scientific American Magazine, March 2008. [Abstract]

Evidence on the relative performance of prediction markets and polls for predicting elections is mixed. While the Iowa Electronic Markets have shown to outperform raw polls 74% of the time (Berg et al. 2008), a more sophisticated interpretation of polls found these "damped polls" to be more accurate than both the IEM's vote-share and winner-take-all markets (Erikson & Wlezien 2007). For discussion see Stix, G. (2008) or click here. For a list of research employing IEM data click here.

• Markets vs. unaided experts and groups

  • Pennock, D. M., Lawrence, S., Giles, C.L. & Nielsen, F.A. (2000). The Power of Play: Efficiency and Forecast Accuracy in Web Market Games, Technical Report 2000-168, NEC Research Institute. [full text]

    For predicting Oscar Award winners, Pennock et al. (2000) compared prices of the Hollywood Stock exchange to expert judgments of five movie columnists. On the day the experts revealed their forecasts, only one of them was better than the market predictions. From the day after, the market outperformed all experts as well as the expert consensus.

  • Servan-Schreiber, E. J., Wolfers, J., Pennock, D. M. & Galebach, B. (2004). Prediction Markets: Does Money Matter? Electronic Markets, 14, 243-251. [full text]

    For predicting the results of NFL games, Servan-Schreiber et al. (2004) compared the forecasts of two markets to those of 1,947 self-selected individuals. At the end of the season, the markets ranked 6th and 8th compared to the individuals. The human average – which would be the outcome of a classical survey – ranked 39th.

• Markets vs. other forecasting methods

  • Chen, K. Y., Plott, C. R. (2002). Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem, Social Science Working Paper No.1131, California Institute of Technology, Pasadena. [full text]

    For forecasting sales figures, Chen and Plott (2002) reported on an internal market at Hewlett-Packard that beat the official forecasts of the company in 6 out of 8 events.

  • Jones Jr., R. J. (2008). The state of presidential election forecasting - The 2004 experience, International Journal of Forecasting, 24, 308-319.[Abstract]

  Jones (2008) analyzed the forecasts of IEM's vote-share market for the 2004 election and compared them to traditional polls, a Delphi expert survey, regression models and a combination of all four approaches, the Pollyvote. He concludes that in comparison with most methods of forecasting the popular vote, the IEM was the superior performer.

  • Green, K. C., Armstrong, J. S. & Graefe, A. (2007). Methods to elicit forecasts from groups. Delphi and prediction markets compared. SSRN Working paper. [full text]

  • Spann, M. & Skiera, B. (2003). Internet-based Virtual Stock Markets for Business Forecasting, Management Science, 49, 1310-1326. [Full text]

    Spann and Skiera (2003) compared forecast accuracy of an internal market at a large German mobile phone operator. They found that the market forecasts outperformed were more accurate than four extrapolation models (arithmetic mean, geometric mean, linear trend and exponential trend).

Corporate Markets

• Chen, K.-Y. & Plott, C. R. (2002). Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem. Social Science Working Paper No.1131, California Institute of Technology, Pasadena. [Full text]
• Cowgill, B., Wolfers, J. & Zitzewitz, E. (2008). Using prediction markets to Track Information Flows: Evidence from Google, working paper. [Full text]
• Ortner, G. (1997). Forecasting Markets - An Industrial Application: Part I, working paper, TU Vienna. [Full text]
• Spann, M. & Skiera, B. (2003). Internet-based Virtual Stock Markets for Business Forecasting, Management Science, 49, 1310-1326. [Full text]

Decision Markets

• Hanson, R. (1999). Decision Markets, IEEE Intelligent Systems, 14, 16-19.

Manipulation

Expect of Hansen et al. (1998), most empirical studies report that manipulative attacks on result accuracy have not been successful historically (Rhode and Strumpf 2006), in the laboratory (Hanson et al. 2006), and in the field (Camerer 1998).

• Camerer, C. (1998): Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting, Journal of Political Economy, 106(3), 457-482.[Abstract]
• Hansen, J., Schmidt, C. & Strobel, M. (2004). Manipulation in Political Stock Markets - Preconditions and Evidence, Applied Economics Letters, 11, 459-463. [Abstract]
• Hanson, R., Oprea, R. & Porter, D. (2006). Information Aggregation and Manipulation in an Experimental Market, Journal of Economic Behavior & Organization, 60, 449-459. [full text]
• Rhode, P. W., and Strumpf, K. S. (2006). Manipulating Political Stock Markets: A Field Experiment and a Century of Observational Data, Working Paper, University of North Carolina(2006). [full text]

Market Regulation

• Kenneth J. Arrow et al. (2008). The Promise of Prediction Markets, Science Magazine, 320, 877-878. [Full text]