Market-generated forecasts are typically accurate
Prediction Markets
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.
Justin Wolfers (Stanford University)
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