A dynamic pari-mutuel market for hedging, wagering, and information aggregation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
The effects of market-making on price dynamics
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Automated market-making in the large: the gates hillman prediction market
Proceedings of the 11th ACM conference on Electronic commerce
A practical liquidity-sensitive automated market maker
Proceedings of the 11th ACM conference on Electronic commerce
Cost function market makers for measurable spaces
Proceedings of the fourteenth ACM conference on Electronic commerce
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Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 0-1) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. 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.