Information transparency in prediction markets

Prediction markets are designed and conducted for the primary purpose of aggregating information so that market prices forecast future events. In such markets, a group of traders buy and sell contracts and the payoff depends on unknown future events. Information is the key in a prediction market and the success of prediction markets depends on their design. In this paper, we theoretically develop and empirically test the effects of IT-enabled information transparency on prediction market performance (information aggregation efficiency and predictive accuracy) through traders' behavior (traders' participation activity and traders' dynamic interactions). We developed twelve prediction markets and empirically tested our hypotheses using a field experiment. The results suggest that improved information transparency (disclosure of different traders' buy and sell orders) can lead to higher levels of traders' dynamic interactions. Increases in traders' participation activity and dynamic interactions lead to higher information aggregation efficiency and greater market predictive accuracy. Interestingly, however, full disclosure of information and complete transparency do not necessarily further improve traders' activities. This paper is one of the first to take an information-based view to study prediction markets and highlights the importance of information transparency in the design of prediction markets. We further discuss the managerial implications, limitations and future research.

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