My review of the literature and case studies (that have been published) indicates that prediction markets have improved the accuracy of forecasts, but the improvements have not been great enough to encourage widespread (or even minimal) acceptance. Furthermore, these studies like to average their results over a number of markets, disguising the fact that some markets improve forecasts, while others fail to do so. Some studies look at average absolute errors, covering up the fact that some predictions were underestimating the true outcome and others overestimating it. This means the real errors are as much as twice as large as those reported. Few, if any, explanations for the failures are ever presented. This raises the issue of consistency. In case studies such as these, where there is no clear under- or over-estimation tendency, for which a correction may be made, the prediction errors are just too great.

Clearly, if similar prediction markets do not provide consistently accurate forecasts, they will not be relied upon for any important business decisions.