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Prediction Market Performance in the 2016 U.S. Presidential Election


  • Andreas Graefe


The 2016 U.S. presidential election was a particularly bad case for prediction markets, as was the Brexit vote in the UK. In theory, these markets should be very effective in aggregating the information of individual forecasters into an overall market forecast. Because the individual participants must put "skin in the game," they are expected to be more diligent about making use of relevant information than participants in surveys who are simply asked what they think will happen. In this article, Foresight's Prediction Markets Editor Andreas Graefe reviews the recent performance of prediction markets to explain why the theoretical benefits of this approach to forecasting have not always stood up in practice. He raises the possibilities of market manipulation, participant misunderstanding, and bettors' systematic bias. Copyright International Institute of Forecasters, 2017

Suggested Citation

  • Andreas Graefe, 2017. "Prediction Market Performance in the 2016 U.S. Presidential Election," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 45, pages 38-42, Spring.
  • Handle: RePEc:for:ijafaa:y:2017:i:45:p:38-42

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    Cited by:

    1. Aliakbari, Elmira & McKitrick, Ross, 2018. "Information aggregation in a prediction market for climate outcomes," Energy Economics, Elsevier, vol. 74(C), pages 97-106.
    2. Graefe, Andreas, 2023. "Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)," International Journal of Forecasting, Elsevier, vol. 39(1), pages 170-177.
    3. Graefe, Andreas, 2019. "Accuracy of German federal election forecasts, 2013 & 2017," International Journal of Forecasting, Elsevier, vol. 35(3), pages 868-877.
    4. Rami Zeedan, 2019. "The 2016 US Presidential Elections: What Went Wrong in Pre-Election Polls? Demographics Help to Explain," J, MDPI, vol. 2(1), pages 1-18, March.

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