Affiliation:
1. Carnegie Mellon University
2. Microsoft Research
3. Beeminder
Abstract
Automated market makers are algorithmic agents that enable participation and information elicitation in electronic markets. They have been widely and successfully applied in artificial-money settings, like some Internet prediction markets. Automated market makers from the literature suffer from two problems that contribute to their impracticality and impair their use beyond artificial-money settings: first, they are unable to adapt to liquidity, so that trades cause prices to move the same amount in both heavily and lightly traded markets, and second, in typical circumstances, they run at a deficit. In this article, we construct a market maker that is both sensitive to liquidity and can run at a profit. Our market maker has bounded loss for any initial level of liquidity and, as the initial level of liquidity approaches zero, worst-case loss approaches zero. For any level of initial liquidity we can establish a boundary in market state space such that, if the market terminates within that boundary, the market maker books a profit regardless of the realized outcome.
Funder
Division of Information and Intelligent Systems
Google
Division of Computing and Communication Foundations
Publisher
Association for Computing Machinery (ACM)
Subject
Computational Mathematics,Marketing,Economics and Econometrics,Statistics and Probability,Computer Science (miscellaneous)
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