Participation, Feedback & Incentives in a Competitive Forecasting Community

Abstract

Macroeconomic forecasts are used extensively in industry and government despite the lack of accuracy and reliability. Prediction markets as a community forecasting method have begun to gain interest in academia industry alike. An open question is how to design incentive schemes and feedback mechanisms to motivate online communities to contribute and thereby increase the predictive power of the market. We design a prediction market for macroeconomic variables that aggregates information from a cross-section of participants. We analyze participation and feedback in this online community. We show that a weekly newsletter that acts as a reminder drives participation. In public goods projects participation feedback has been found to increase participants' contributions. We find that the competition inherent in markets appears to dominate classical feedback mechanisms. We show that forecast errors fall over the prediction horizon. The market-generated forecasts compare well with the Bloomberg- survey forecasts, the industry standard. Additionally we can predict community forecast error using an implicit market measure.