Public Information Bias and Prediction Market Accuracy
Thomas Gruca, Joyce E. Berg and Michael Cipriano
The Journal of Prediction Markets
Volume 1, Number 3, pages 219-231, 2007 (December)
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.