Working Paper: NBER ID: w10504
Authors: Justin Wolfers; Eric Zitzewitz
Abstract: We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.
Keywords: No keywords provided
JEL Codes: D7; D8; E3; E6; G1; H8; Q4
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| design of prediction markets (D47) | forecasting accuracy (C53) |
| trading in contracts based on geopolitical risks (G13) | market expectations about future events (D84) |
| use of prediction markets (G17) | improved predictive accuracy (C52) |
| conditional markets (D52) | market beliefs about regression coefficients (C29) |
| market-generated forecasts (G17) | accurate predictions of uncertain future events (C53) |