Prediction Markets as institutional forecasting support systems

An attractive feature of Prediction Markets (PMs, also called information markets or virtual stock markets) is that they provide economic incentives for informants to share unique information, i.e., information that no other informants possess. This feature and the broad availability of the Internet have lead to applications of prediction markets in a number of fields. Most reported applications to date contain large numbers of informants, so it is unclear if PMs are appropriate for applications with few knowledgeable informants (i.e. small PMs) as is the case for most institutional forecasting tasks. Hence, we compare the performance of small PMs with more traditional judgment-based approaches which we call the Combined Judgmental Forecasts (CJF) approach, and the Key Informant (KI) approach. Our results show that forecasts from small PMs outperform more traditional approaches in settings of high information heterogeneity (i.e. where the amount of unique information possessed by informants is relatively high) and does no worse in settings of low information heterogeneity.