Accuracy
Some academic research has focused on potential flaws with the prediction market concept. In particular, Dr. Charles F. Manski of Northwestern University published "Interpreting the Predictions of Prediction Markets", which attempts to show mathematically that under a wide range of assumptions the "predictions" of such markets do not closely correspond to the actual probability beliefs of the market participants unless the market probability is near either 0 or 1. Manski suggests that directly asking a group of participants to estimate probabilities may lead to better results.
However, Steven Gjerstad (Purdue) in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium," has shown that prediction market prices are very close to the mean belief of market participants if the agents are risk averse and the distribution of beliefs is spread out (as with a normal distribution, for example). Justin Wolfers (Wharton) and Eric Zitzewitz (Dartmouth) have obtained similar results, and also include some analysis of prediction market data, in their paper "Interpreting Prediction Market Prices as Probabilities." In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.
Douglas Hubbard has also conducted a sample of over 400 retired claims which showed that the probability of an event is close to its market price but, more importantly, significantly closer than the average single subjective estimate. However, he also shows that this benefit is partly offset if individuals first undergo calibrated probability assessment training so that they are good at assessing odds subjectively. The key benefit of the market, Hubbard claims, is that it mostly adjusts for uncalibrated estimates and, at the same time, incentivizes market participants to seek further information.
A series of laboratory experiments to compare the accuracy of prediction markets, traditional meetings, the Delphi method, and the nominal group technique on a quantitative judgment task, found only small differences between these four methods. Delphi was most accurate, followed by NGT and prediction markets. Meetings performed worst. The study also looked at participants' perceptions of the methods. Prediction markets were rated least favourable: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.
A common belief among economists and the financial community in general is that prediction markets based on play money cannot possibly generate credible predictions. However, the data collected so far disagrees. Analyzed data from the Hollywood Stock Exchange and the Foresight Exchange concluded that market prices predicted actual outcomes and/or outcome frequencies in the real world. Comparing an entire season's worth of NFL predictions from NewsFutures' play-money exchange to those of Tradesports, an equivalent real-money exchange based in Ireland, both exchanges performed equally well. In this case, using real money did not lead to better predictions.
Hollywood Stock Exchange creator Max Keiser suggests that not only are these markets no more predictive than their established counterparts such as the New York Stock Exchange and the London Stock Exchange, but that reducing the unpredictability of markets would mean reducing risk and, therefore, reducing the amount of speculative capital needed to keep markets open and liquid.
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