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Comparing Prediction Market Structures, With an Application to Market Making

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  • Aseem Brahma
  • Sanmay Das
  • Malik Magdon-Ismail

Abstract

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.

Suggested Citation

  • Aseem Brahma & Sanmay Das & Malik Magdon-Ismail, 2010. "Comparing Prediction Market Structures, With an Application to Market Making," Papers 1009.1446, arXiv.org.
    • Handle: RePEc:arx:papers:1009.1446
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    References listed on IDEAS

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    1. Sanmay Das, 2005. "A learning market-maker in the Glosten-Milgrom model," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 169-180.
    2. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
    3. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    4. Robin Hanson, 2009. "On Market Maker Functions," Journal of Prediction Markets, University of Buckingham Press, vol. 3(1), pages 61-63, April.
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    Cited by:

    1. Amos Storkey, 2011. "Machine Learning Markets," Papers 1106.4509, arXiv.org.

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