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Sebastian Deimer, Joaquin Poblete


Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures).

This paper contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This paper finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R² than was expected by formed hypotheses.

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Arrow, K., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J., et al. (2008). The Promise of Prediction Markets. Science 16. pp. 877-878.

Berg, J., Forsythe, R., & Rietz, T. (1996). What Makes Markets Predict Well? Evidence from the IOWA Electronic Markets. In W. G. W Albers, Understanding Strategic Interaction: Essay in Honor of Reinhard Selten (pp. 444-463). New York: Springer.

Berg, J. E., R. Forsythe, F. D., Nelson, & T. A. Rietz .(2008a). Results from a Dozen Years of Election Futures Markets Research. In: C. R. Plott und V. L. Smith (ed.): The Handbook of Experimental Economics Results, Volume 1, pp. 742-751. North Holland.

Berg, J. E., Nelson, F. D., & Rietz, T. A. (2008a). Prediction Market Accuracy in the Long Run. International Journal of Forecasting. 24 (2) pp. 285-295.

Chen, K.-Y., & Plott, C. (2002). Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem. California Institute of Technology Social Science Working Paper No. 1131.

Cowgill, B., Wolfers, J., & Zitzewitz, E. (2008). Using Prediction Markets to Track Information Flows: Evidence from Google. Derived from (accessed 1 August 2010)

Fama, E. F, 1970. Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association. 25(2) pp. 383-417.

Forsythe, R., Rietz, T., & Ross, T. (1999). Wishes, Expectations and Actions: A Survey on Price Formation in Election Stock Markets. Journal of Economic Behavior & Organization. 39 pp. 83–110.

Grossman, J., S., & Stiglitz, J. E. (1976). Information and Competetive Price Systems. The American Economic Review. 66 (2) pp. 246-253.

Hanson, R. (2002). Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation. Derived from (accessed 1 August 2010)

Hanson, R. (2003). Combinatorial Information Market Design. Information Systems Frontiers. 5(1) pp. 107-119.

Hayek, F. A. (1945). The Use of Knowledge in Society. The American Review. 35 (4) pp. 519-530

Ivanow, A. (2009). Using Prediction Markets to Harness Collective Wisdom for Forecasting. The Journal of Business Forecasting, 28 (3) pp. 9-14.

Jacobs, V. (2009). Prediction Markets: How They Work and how Well They Work. Master paper, Katholieke Universiteit, Leuven.

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica. 47 (2) pp. 263-292.

Looney, R. (2003). DARPA’s Policy Analysis Market for Intelligence: Outside the Box or Off the Wall. Strategic Insights, September, 2:9.

Luckner, S. (2008). Prediction Markets: Fundamentals, Key Design Elements, and Applications. The 21st Bled eConference, eCollaboration: Overcoming Boundaries Through Multi-Channel Interaction. Bled, Slovenia.

Manski, C. (2004). Interpreting the Predictions of Prediction Markets. NBER Working Paper 10359. March 2004.

Pennock, D., Larence, S., Nielsen, F., & Giles, C. (2001). The Real Power of Artifical Markets. Science. 291 (5506) pp. 987-988.

Polk, C., Hanson, R., Ledyard, J. , & Ishikida, T. (2003). Policy Analysis Market: An Electronic Commerce Application of a Combinatorial Information Market. In

Proceedings of the Fourth ACM Conference on Electronic Commerce, 272–3.

Rosenbloom, E., & Notz, W. (2006). Statistical Tests of Real-Money Versus Play-Money Prediction Markets. Eletronic Markets. 16 (1) pp. 63-69.

Rothschild, D., & Wolfers, J. (2008). Market Manipulation Muddies Election Outlook. Derived from (accessed 1 August 2010)

Servan-Schreiber, E., Wolfers, J., Pennock, D., & Galebach, B. (2004). Prediction Markets: Does Money Matter? Electronic Markets. 14 (3) pp. 243-251.

Smith, V. L. (1982). Markets as Economizer of Information: Experimental Examination of the Hayek-Hypopaper. Economic Inquiry. 20 pp. 165-179.

Spann, M., & Skiera, B. (2009). Sports Forecasting: A Comparison of the Forecast

Accuracy of Prediction Markets, Betting Odds and Tipsters. Journal of Forecasting. 28 (1) pp. 55-72.

Surowiecki, James (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Doubleday, New York.

Tziralis, G.,& Tatsiopoulos, I. (2007), Prediction Markets: An Extended Literature Review, Journal of Prediction Markets. 1(1) pp. 75-91.

Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. Journal of Economic Perspectives. 18 (2) pp. 107-126.

Wolfers, J., & Zitzewitz, E. (2006a). Interpreting Prediction Market Prices as Probabilities. Discussion Paper Series IZA DP No. 2092.

Wolfers, J., & Zitzewitz, E. (2006b). Prediction Markets in Theory and Practice. Discussion Paper Series IZA DP No. 1991.



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