Interpreting Prediction Market Prices as Probabilities
While most empirical analysis of prediction markets treats prices of binary options as predictions of the probability of future events, Manski (2004) has recently argued that there is little existing theory supporting this practice. We provide relevant analytic foundations, describing sufficient conditions under which prediction markets prices correspond with mean beliefs. Beyond these specific sufficient conditions, we show that for a broad class of models prediction market prices are usually close to the mean beliefs of traders. The key parameters driving trading behavior in prediction markets are the degree of risk aversion and the distribution on beliefs, and we provide some novel data on the distribution of beliefs in a couple of interesting contexts. We find that prediction markets prices typically provide useful (albeit sometimes biased) estimates of average beliefs about the probability an event occurs.
|Date of creation:||Apr 2006|
|Date of revision:|
|Publication status:||published in: Robert Hahn and Paul Tetlock (eds), Information Markets: A New Way of Making Decisions in the Public and Private Sectors, AEI-Brookings Press, 2006|
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Justin Wolfers & Eric Zitzewitz, 2004.
03-025, Stanford Institute for Economic Policy Research.
- Charles F. Manski, 2004.
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- repec:reg:rpubli:259 is not listed on IDEAS
- Jeff Dominitz & Charles F. Manski, 2004. "How Should We Measure Consumer Confidence?," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 51-66, Spring.
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