An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets

In this paper, we employ simulation-based methods to study the role of a market maker in improving price discovery in a prediction market. In our model, traders receive a lagged signal of a ground truth, which is based on real price data from prediction markets on NBA games in the 2014–2015 season. We employ empirical game-theoretic analysis to identify equilibria under different settings of market maker liquidity and spread. We study two settings: one in which traders only enter the market once, and one in which traders have the option to reenter to trade later. We evaluate welfare and the profits accrued by traders, and we characterize the conditions under which the market maker promotes price discovery in both settings.