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Polls to probabilities: Comparing prediction markets and opinion polls

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  • Reade, J. James
  • Vaughan Williams, Leighton

Abstract

The forecasting of election outcomes is a hugely popular activity, and not without reason: the outcomes can have significant economic impacts, for example on stock prices. As such, it is economically important, as well as of academic interest, to determine the forecasting methods that have historically performed best. However, the forecasts are often incompatible, as some are in terms of vote shares while others are probabilistic outcome forecasts. This paper sets out an empirical method for transforming opinion poll vote shares into probabilistic forecasts, and then evaluates the performances of prediction markets and opinion polls. We make comparisons along two dimensions, bias and precision, and find that converted opinion polls perform well in terms of bias, while prediction markets are good for precision.

Suggested Citation

  • Reade, J. James & Vaughan Williams, Leighton, 2019. "Polls to probabilities: Comparing prediction markets and opinion polls," International Journal of Forecasting, Elsevier, vol. 35(1), pages 336-350.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:336-350
    DOI: 10.1016/j.ijforecast.2018.04.001
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    References listed on IDEAS

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    Cited by:

    1. Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.

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