PUBLIC INFORMATION BIAS AND PREDICTION MARKET ACCURACY

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Published: Dec 14, 2012
DOI: https://doi.org/10.5750/jpm.v1i3.430
Keywords:
prediction markets information aggregation election forecasting public information.

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Thomas S Gruca
Henry B. Tippie School of Business University of Iowa
Joyce E. Berg

Abstract

How do prediction markets achieve high levels of accuracy? We propose that, in some situations, traders in prediction markets improve upon publicly available information. Specifically, when there is a known bias in publicly available information, markets provide an incentive for traders to “de-bias” this information. In such a situation, a prediction market will provide a more accurate forecast than the public information available to traders. We test our conjecture using real-money prediction markets for seven local elections in the United States. We find that the prediction market forecasts are significantly more accurate than those generated using the pre-election polls.Previous versions of this paper were presented at the Workshop on The Growth of Gambling and Prediction Markets, the ISBM Conference on Prediction Markets in Marketing: Issues, Challenges and Research Opportunities, the DIMACS Workshop on Markets as Predictive Devices, and the 17th Annual AMA Advanced Research Techniques Forum. The authors thank the participants for their insights which have helped improve this research

Article Details

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Vol. 1 No. 3 (2007)
Section
Articles

References

Berg, Joyce, Robert Forsythe, Forrest Nelson, and Thomas A. Rietz (2007) ‘Results from a Decade of Election Futures Markets Research’, in Charles Plott and Vernon Smith (eds.) Handbook of Experimental Results, Elsevier Press (forthcoming).

Bruggelambert, Gregor (1999). Information and Efficiency in Political Stock Markets: An Electronic Market Experiment. Working paper, University of Essen.

Chen, Kay-Yut and Charles R. Plott (2001) ‘Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem’, working paper, California Institute of Technology.

Clemen, Robert T. (1989). Combining Forecasts: A review and annotated bibliography. International Journal of Forecasting, 5, 559-583.

Crespi, Irving (1988). Pre-Election Polling, New York: Russell Sage Foundation.

Cruzan, Alfred G., J.S. Armstrong and Randall Jones, Jr. (2005). Combining Methods to Forecast the 2004 Presidential Election. Working paper presented at the Southern Political Science Association.

Erikson, Robert and Christopher Wlezien (2007). Are Political Markets Really Superior to Polls as Election Predictors? Public Opinion Quarterly (forthcoming).

Forsythe, Robert, Forrest Nelson, George Neuman, and John Wright (1992) ‘Anatomy of a Political Stock Market’, American Economic Review, 82: pp. 1142-1161.

Forsythe, Robert, Thomas A. Rietz and Thomas W. Ross (1999) ‘Wishes, Expectation and Action: A Survey of Price Formation in Election Stock Markets’, Journal of Economic Behavior and Organization, 39: pp. 83-110.

Grossman, Sanford J. (1981). An Introduction to the Theory of Rational Expectations under Asymmetric Information. Review of Economic Studies 48, 541-559.

Gruca, Thomas S. (1994). The Incumbent Rule: An Update. The Polling Report, 10 (7): 1,7.

Gruca, Thomas S., Joyce Berg and Michael Cipriano (2005). Consensus and Differences of Opinion in Electronic Prediction Markets. Electronic Markets, 15 (1), 13-22.

Hulse, Carl (2003). Pentagon Prepares a Futures Market on Terror Attacks. New York Times, 28 July.

Kambil, Ajit and Eric Van Heck (2002). Making Markets, Cambridge, MA: Harvard Business School Press.

Kiviat, Barbara (2004). The End of Management? Time, 12 July.

Kou, Steven G. and Michael E. Sobel (2004). Forecasting the Vote: An Analytical Comparison of Election Markets and Public Opinion Polls, Political Analysis, 12, 277-295.

Lucas, Robert (1972). Expectations and the neutrality of money. Journal of Economic Theory 4, 103-124.

Oliven, Kenneth and Thomas A. Reitz (2003). Suckers are Born, but Markets are Made: Individual Rationality, Arbitrage and Market Efficiency on an Electronic Futures Market. Management Science, 50 (3), 336-351.

Panagakis, Nicholas (1989). Incumbent Races: Closer Than They Appear. The Polling Report, February 27, 1-3.

Pennock, David M., Steve Lawrence, C. Lee Giles and Finn Arup Nielsen (2000) ‘The Power of Play: Efficiency and Forecast Accuracy of Web Market Games’, NEC Research Institute Technical Report, 2000-168.

Plott, Charles R. (2000) ‘Markets as Information Gathering Tools’, Southern Economic Journal, 67: pp. 1-15.

Plott, Charles and Shyam Sunder (1982) ‘Efficiency of Experimental Security Markets with Insider Information: An Application of Rational Expectations Models’, Journal of Political Economy, 90: pp. 663-698.

Plott, Charles and Shyam Sunder (1988) ‘Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets’, Econometrica, 56: pp. 1085-1118.

Servan-Schreiber, Emile, Justin Wolfers, David Pennock, and Brian Galebach (2004) ‘Prediction Markets: Does Money Matter?’, Electronic Markets, 14: pp. 243-251.

Spann, Martin and Bernd Skiera (2003) ‘Internet-based Virtual Stock Markets for Business Forecasting’, Management Science, 49: pp. 1310-1326.

Sunder, Shyam (1995). Experimental Asset Markets: A Survey. In J.H. Kagel and A.E. Roth (eds.), The Handbook of Experimental Economics. Princeton University Press, 415-500.

Surowiecki, James (2004). The Wisdom of Crowds. New York: Doubleday.

Wolfers, Justin and Eric Zitzewitz (2004). Prediction Markets. Journal of Economic Perspectives, 18 (2), 107-126.