Ensuring sufficient liquidity is one of the key challenges for designers of
prediction markets. Various market making algorithms have been proposed in the
literature and deployed in practice, but there has been little effort to
evaluate their benefits and disadvantages in a systematic manner. We introduce
a novel experimental design for comparing market structures in live trading
that ensures fair comparison between two different microstructures with the
same trading population. Participants trade on outcomes related to a
two-dimensional random walk that they observe on their computer screens. They
can simultaneously trade in two markets, corresponding to the independent
horizontal and vertical random walks. We use this experimental design to
compare the popular inventory-based logarithmic market scoring rule (LMSR)
market maker and a new information based Bayesian market maker (BMM). Our
experiments reveal that BMM can offer significant benefits in terms of price
stability and expected loss when controlling for liquidity; the caveat is that,
unlike LMSR, BMM does not guarantee bounded loss. Our investigation also
elucidates some general properties of market makers in prediction markets. In
particular, there is an inherent tradeoff between adaptability to market shocks
and convergence during market equilibrium.