Brexit: what do prediction markets tell us about its likelihood?

In the run-up to the EU referendum—and all significant political events—the results of opinion polls are closely followed by those seeking to track the likely direction of the vote. However, polls are far from being the only way to estimate the probability of an uncertain event; analysis of bookmakers’ odds suggest that there is currently a 23% chance1 of the UK public voting to leave the EU, suggesting that Brexit is perceived as more likely than Scotland voting for independence was at a comparable moment in the campaign. How can such prediction markets be used to draw inference about how their participants expect these events to unfold.

1 What are prediction markets?

Prediction markets allow the exchange of contracts where the financial return is dependent on the (currently unknown) outcome of future events. They exist in a wide range of forms. Perhaps the best-known examples of pure prediction markets are the election forecasting markets run by the University of Iowa.2 For instance, using this exchange, an investor can buy an asset for any of the potential US presidential election nominees of the Republican and Democrat parties. Should that potential nominee become the actual nominee, the asset pays out $1; otherwise it pays out nothing.3

Such markets are closely linked to other entities such as traditional bookmakers; however, subtle differences exist—prediction markets match traders willing to buy and sell outcome-dependent contracts, whereas a bookmaker sells contracts itself (the terms of the contract are the odds the bookmaker offers).

The prevailing prices in these markets can be interpreted as indicating information about the probability of that outcome occurring. For instance, the price of an asset paying $1 if Hillary Clinton becomes the Democrat nominee is approximately $0.84, over eight times the price of an asset conditioned upon Bernie Sanders becoming the nominee,4 representing her status as the most likely nominee.

Contact: David Jevons


[1] As at 1 June 2016.
[2]Wolfers, J. (2006), ‘Prediction Markets in Theory and Practice’, NBER Working Papers.
[3] Iowa Electronic Markets, available online at, accessed 22 May 2016.
[4] Iowa Ele