Prediction Markets: How Do Incentive Schemes Affect Prediction Accuracy?

Abstract

Prediction markets are a promising approach for forecasting future events. The basic idea of a prediction market is to trade virtual stocks whose final value is tied to the outcome of uncertain future events. Market prices can then be interpreted as predictions of the likelihood of those future events. In information efficient markets, prices represent all available information about the participants’ valuations at any time. 

The results of recent studies on prediction markets are encouraging. Prior experience in this field demonstrates that real money as well as play money markets predicted future events at a remarkable accuracy. Experimental economists most probably would insist that performance-related payment is required in order to obtain valid conclusions about economic behavior. Payments based on the participants' performance are usually intended to provide incentives for rational – or at least well considered – decision making. On the other hand, there is evidence that monetary incentives do not necessarily increase performance. 

We study the impact of different monetary incentives on prediction markets in a field experiment. In order to do so, we compare three groups of users, corresponding to three treatments with different incentive schemes, in a prediction market for the FIFA World Cup 2006. The subjects of the first group are paid a fixed amount. To subjects in the second group we promise a payment which linearly depends on their deposit value in the prediction market. In the third group, individuals are paid according to their ordinal rank. 

We study the predictive power of markets depending on the incentive scheme. The goal of our work thus is to analyze the impact of different incentive schemes on the market quality and the predictive power of markets. Based on these results we want to give advice on engineering incentive schemes for future prediction markets.