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Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first‐generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second‐generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd.

Event study methodology is a powerful procedure to quantify the impact of events and managerial decisions such as new product announcements on the value of a publicly traded company. However, for many events, appropriate financial data may not be available, either because suitable securities are not traded on financial markets or confounding effects limit the insights from financial data. In such instances, prediction markets could potentially provide the necessary data for an event study. Prediction markets are electronic markets where participants can trade stocks whose prices reflect the outcome of future events, e.g. election outcomes, sports results, new product sales or internal project deadlines. One key distinction between different prediction market applications is whether they require real money investments or play-money investment with non-monetary incentives for traders. Thus, the goal of this paper is to compare prediction markets’ ability to conduct event studies with respect to these two different incentive schemes. We empirically test the applicability of event study methodology in real-money vs. play-money prediction markets with two data sets. We show that event studies with prediction markets deliver robust and valid results with both incentive schemes.