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The Hidden Beauty of the Quadratic Market Scoring Rule: A Uniform Liquidity Market Maker, with Variations

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  • Michael Abramowicz

    (George Washington University)

Abstract

For some applications, prediction markets that rely entirely on voluntary transactions between individual participants may provide insufficient liquidity to aggregate information effectively, especially where the number of participants is small. A solution to this problem is to rely on an automated market maker, which allows participants to buy from or sell to the house. Robin Hanson has described a class of automated market makers called market scoring rules. This Article examines a member of this class that has received little attention, the quadratic market scoring rule. Its prime virtue is that it provides uniform liquidity across the probability or prediction spectrum. Market participants will thus have the same incentive to do research that is expected to produce an expected change in the market prediction, regardless of the current prediction. Formulas are provided for implementing the quadratic market scoring rule, as well as variations, for example to implement conditional markets.

Suggested Citation

  • Michael Abramowicz, 2007. "The Hidden Beauty of the Quadratic Market Scoring Rule: A Uniform Liquidity Market Maker, with Variations," Journal of Prediction Markets, University of Buckingham Press, vol. 1(2), pages 111-125, July.
    • Handle: RePEc:buc:jpredm:v:1:y:2007:i:2:p:111-125
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    Cited by:

    1. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2013. "Prediction Markets for Economic Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 657-687, Elsevier.
    2. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    3. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    4. Pankaj Pandey & Einar Snekkenes, 2016. "Using Financial Instruments to Transfer the Information Security Risks," Future Internet, MDPI, vol. 8(2), pages 1-62, May.

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