Abstract
Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest. An arising question is how to design incentive schemes and feedback mechanisms to motivate participants to contribute to such an information exchange. We design a prediction market for economic derivatives that aggregates macro-economic information. We show that the level of participation is mainly driven by a weekly newsletter which acts as a reminder. In public goods projects participation feedback has been found to increase participants' contributions. We find that the induced competitiveness of market environments seem to superpose classical feedback mechanisms. We show that forecast errors fall over the prediction horizon. The market generated forecasts compare well to the Bloomberg-survey forecasts, the industry standard. Additionally we can predict community forecast error by using an implicit market measure.
Recommended Citation
Teschner, Florian; Mazarakis, Athanasios; Riordan, Ryan; and Weinhardt, Christof, "Participation, Feedback & Incentives in a Competitive Forecasting Community" (2011). ICIS 2011 Proceedings. 16.
https://aisel.aisnet.org/icis2011/proceedings/onlinecommunity/16
Participation, Feedback & Incentives in a Competitive Forecasting Community
Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest. An arising question is how to design incentive schemes and feedback mechanisms to motivate participants to contribute to such an information exchange. We design a prediction market for economic derivatives that aggregates macro-economic information. We show that the level of participation is mainly driven by a weekly newsletter which acts as a reminder. In public goods projects participation feedback has been found to increase participants' contributions. We find that the induced competitiveness of market environments seem to superpose classical feedback mechanisms. We show that forecast errors fall over the prediction horizon. The market generated forecasts compare well to the Bloomberg-survey forecasts, the industry standard. Additionally we can predict community forecast error by using an implicit market measure.