Universality in Prediction Markets

In a prediction market (PM), investors buy and sell contracts tied to the outcome of a real-world event such as “Will Donald Trump be Re-elected President in 2020?”. Those who guess correctly receive a fixed payout, while everyone else receives nothing.  Contract prices are driven by supply and demand as investors react to new information. As such, a contract’s price captures the crowd’s estimation of the event’s likelihood over time. A large body of research has focused on the accuracy of PMs in predicting final results, with little attention given to the dynamics that drive markets to (in)accurate predictions. Here, we analyze 2859 contracts from a popular online PM – PredictIt – covering events such as elections, bills, and politicians career milestones. We find striking universal statistical laws governing the distribution of price fluctuations, the size of trade volume over time, and the likelihood of different price levels over a contract’s lifetime. What’s more, we find that (up to rescaling), PM time series cluster naturally into only a handful of characteristic shapes. Our findings suggest that the complex human interactions driving PM dynamics can be embedded in a low-dimensional space of variables, opening the door to the mechanistic modelling of these social systems.