The study, The behaviour of betting and currency markets on the night of the EU referendum, conducted by researchers from the Faculty of Economics at the University of Cambridge, England, used big data and machine learning technology to show how financial markets lagged behind in getting the right results on European Union (EU) referendum night --also called the Brexit night.
Researchers from the Faculty of Economics at the University of Cambridge studied the behaviors of the Betfair betting market and compared it to the sterling-U.S. dollar on the night the United Kingdom voted on the Leave or Remain in the European Union referendum in June 2016.
The researchers say the study findings support the idea that prediction markets --or gambling-- might provide a better forecast of election outcomes than international financial experts, or even polls.
"It looks like the gamblers had a better sense that Leave could win, or that it could at least go either way." -Tom Auld, lead researcher, University of Cambridge
"Our findings suggest that participants across both markets suffered a behavioral bias as the results unfolded Initially, both traders and gamblers could not believe the U.K. was voting to leave the EU, but this disbelief lingered far longer in the City," said Dr. Tom Auld, lead author of the study.
According to Dr. Auld and his co-author, Professor Oliver Linton, they used the data that was publicly available prior to the referendum to create a forecasting model.
"According to theories such as the 'efficient market hypothesis', the markets discount all publicly available information, so you cannot get an edge on the market with data already out there," said Dr. Auld.
"However, using data publicly available at the time, we show that the financial markets were very inefficient, and should have predicted Brexit possibly over two hours before they actually did."
According to the study published in the International Journal of Forecasting, the researchers' model would have predicted the final result by 1:30 a.m. that night, adding that the betting market moved to a Leave result around 3 a.m., by which time Brexit odds were 1 to 10.
Meanwhile, the foreign exchange market did not fully understand the outcome until around 4 a.m. The BBC finally predicted a Leave victory at 4:40 a.m.
Dr. Auld said that if there were a second referendum, the vote should be better understood by markets and primed to profit from any inefficiencies.
If only there were a Brexit second referendum . . .
To put the situation into perspective, let's watch how Financial Times economics editor Chris Giles explains in a very clever way his almost foolproof method for finding the will of the people in a vote between 'no deal', Theresa May's deal, and Remain.
In terms of future scenarios, according to Dr. Tom Auld, prediction markets could be used to help value or price financial assets during events such as major votes. This means that the scientific community could have found a positive side to gambling.
This conception of useful applications of prediction markets could be a game changer for some Artificial Intelligence (AI) technology applications.
For example, for IBM's Project Debater technology: Speech by Crowd, the AI-driven platform that crowdsources points of view on both sides of an argument, a change in supporting arguments' percentage would change the speech outcome.