The purpose of this paper is to contribute to the growing body of research in prediction markets by using trading data as a means of characterizing trader behavior in these markets. Traders are placed in homogenous groups based on common Trading habits using clustering algorithms. Several behavioral themes are used to guide the analyses.
Several market experiments were run to collect trading data, which was then exported into a data warehouse. A secondary data analysis is performed on variables derived from the original trade data. In particular, k‐means clustering is used to form groups of traders that share common characteristics.
Participants can be classified into homogenous groups based on their trading behavior. Groups tend to differ based on the overall level of participation, how much of their trading activity is spent buying or selling, and how early they enter the market.
More research should be done using a variety of variables to further determine the impact of various types of trader behavior on prediction markets.
Using insights gained from work like this, the design of prediction markets can be fine tuned to encourage behavior that contributes to trader participation and the overall accuracy of the market predictions.
Little research has been done to evaluate the impact of trader behavior on the accuracy of prediction markets. The authors used a new prediction market implementation to collect detailed trading data. This data was then used to derive higher level trading attributes that can he used to characterize traders. The k‐means clustering algorithm was shown to be an effective means of defining groups of traders who share common market behaviors.
