Designing Prediction Markets to Achieve Convergence Speed

Abstract

The aim of this paper is twofold: to propose the model of artificial prediction markets that capture the characteristics of real prediction markets and to study the impact of key parameters on the performance of the proposed markets. In the experiments, the artificial markets are implemented and the market performance in terms of convergence speed is measured. Our experimental results show that the number of traders and the mean value of initial belief have no significant impact on the convergence speed. However, the trader’s memory size impacts negatively on the convergence because of its delay in adjusting to the true value. Finally, the external information transmission rate and the ratio of smart traders have positive impacts on the convergence of the prediction markets. The insights can assist a market maker in designing and constructing more efficient prediction markets.