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Ensuring sufficient liquidity is one of the key challenges for designers ofprediction markets. Various market making algorithms have been proposed in theliterature and deployed in practice, but there has been little effort toevaluate their benefits and disadvantages in a systematic manner. We introducea novel experimental design for comparing market structures in live tradingthat ensures fair comparison between two different microstructures with thesame trading population. Participants trade on outcomes related to atwo-dimensional random walk that they observe on their computer screens. Theycan simultaneously trade in two markets, corresponding to the independenthorizontal and vertical random walks. We use this experimental design tocompare the popular inventory-based logarithmic market scoring rule (LMSR)market maker and a new information based Bayesian market maker (BMM). Ourexperiments reveal that BMM can offer significant benefits in terms of pricestability and expected loss when controlling for liquidity; the caveat is that,unlike LMSR, BMM does not guarantee bounded loss. Our investigation alsoelucidates some general properties of market makers in prediction markets. Inparticular, there is an inherent tradeoff between adaptability to market shocksand convergence during market equilibrium.

Comparing Prediction Market Structures, With an Application to Market Making