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Prediction Markets: Practical Experiments in Small Markets and Behaviours Observed

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  • Jed D. Christiansen

Abstract

This paper discusses a series of prediction markets created and operated in the summer of 2006 to measure calibration and behaviour of small-scale prediction markets. The research finds that small markets are very well calibrated and determines a potential minimum threshold of participation to ensure well-calibrated results. The results also established the markets as very efficient at predicting small probabilities. Behavioural aspects of markets are also examined. Trader behavioural types are assessed and categorised; while a small group of traders were extremely active, over half of all traders rarely traded. Market manipulation is examined and found to be occasionally effective, though only in very small markets. Finally, incentives to trade are discussed; these markets were effective with no incentives for trading at all.

Suggested Citation

  • Jed D. Christiansen, 2007. "Prediction Markets: Practical Experiments in Small Markets and Behaviours Observed," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 17-41, February.
    • Handle: RePEc:buc:jpredm:v:1:y:2007:i:1:p:17-41
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    Cited by:

    1. Strijbis, Oliver & Arnesen, Sveinung, 2019. "Explaining variance in the accuracy of prediction markets," International Journal of Forecasting, Elsevier, vol. 35(1), pages 408-419.
    2. Graefe, Andreas & Armstrong, J. Scott, 2011. "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task," International Journal of Forecasting, Elsevier, vol. 27(1), pages 183-195, January.
    3. Edoardo Gaffeo, 2013. "Using information markets in grantmaking. An assessment of the issues involved and an application to Italian banking foundations," DEM Discussion Papers 2013/08, Department of Economics and Management.
    4. John Garvey & Patrick Buckley, 2011. "Using Technology to Encourage Critical Thinking and Optimal Decision Making in Risk Management Education," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 14(2), pages 299-309, September.
    5. Lennart Sjöberg, 2009. "Are all crowds equally wise? a comparison of political election forecasts by experts and the public," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 1-18.
    6. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    7. Tobias Kranz & Florian Teschner & Christof Weinhardt, 2015. "Beware of Performance Indicators," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(6), pages 349-361, December.
    8. repec:cup:judgdm:v:12:y:2017:i:4:p:328-343 is not listed on IDEAS
    9. Michael D. Lee & Megan N. Lee, 2017. "The relationship between crowd majority and accuracy for binary decisions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(4), pages 328-343, July.
    10. Bin-Tzong Chie & Chih-Hwa Yang, 2021. "Efficiency of the Experimental Prediction Market: Public Information, Belief Evolution, and Personality Traits," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(4), pages 1-3.
    11. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.
    12. Bundzel, Marek & Kasanický, Tomáš & Pinčák, Richard, 2016. "Using string invariants for prediction searching for optimal parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 680-688.
    13. Buckley, Patrick, 2016. "Harnessing the wisdom of crowds: Decision spaces for prediction markets," Business Horizons, Elsevier, vol. 59(1), pages 85-94.

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