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The behaviour of betting and currency markets on the night of the EU referendum

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  • Auld, Tom
  • Linton, Oliver

Abstract

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.

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  • Auld, Tom & Linton, Oliver, 2019. "The behaviour of betting and currency markets on the night of the EU referendum," International Journal of Forecasting, Elsevier, vol. 35(1), pages 371-389.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:371-389
    DOI: 10.1016/j.ijforecast.2018.07.014
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    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    3. Peng Ding, 2016. "On the Conditional Distribution of the Multivariate Distribution," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 293-295, July.
    4. Ricardo J. Caballero & Alp Simsek, 2020. "A Model of Fickle Capital Flows and Retrenchment," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2288-2328.
    5. Christian Franz Horn & Bjoern Sven Ivens & Michael Ohneberg & Alexander Brem, 2014. "Ideas Markets: Prediction Markets – A literature review 2014," Journal of Prediction Markets, University of Buckingham Press, vol. 8(2), pages 89-126.
    6. Hirshleifer, David & Hsu, Po-Hsuan & Li, Dongmei, 2013. "Innovative efficiency and stock returns," Journal of Financial Economics, Elsevier, vol. 107(3), pages 632-654.
    7. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    8. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    11. repec:pri:cepsud:91malkiel is not listed on IDEAS
    12. Ke WU & Spencer WHEATLEY & Didier SORNETTE, 2017. "The British Pound on Brexit night: a natural experiment of market efficiency and real-time predictability," Swiss Finance Institute Research Paper Series 17-12, Swiss Finance Institute.
    13. Stefano Dellavigna & Joshua M. Pollet, 2009. "Investor Inattention and Friday Earnings Announcements," Journal of Finance, American Finance Association, vol. 64(2), pages 709-749, April.
    14. David Hirshleifer & Sonya Seongyeon Lim & Siew Hong Teoh, 2009. "Driven to Distraction: Extraneous Events and Underreaction to Earnings News," Journal of Finance, American Finance Association, vol. 64(5), pages 2289-2325, October.
    15. Jim Yuh Huang & Joseph C.P. Shieh & Yu-Cheng Kao, 2016. "Starting points for a new researcher in behavioral finance," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 12(1), pages 92-103, February.
    16. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    17. Ravija Badarinathi & Ladd Kochman, 1996. "Football Betting and the Efficient Market Hypothesis," The American Economist, Sage Publications, vol. 40(2), pages 52-55, October.
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    2. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    3. Wiśniowski, Arkadiusz & Bijak, Jakub & Forster, Jonathan J. & Smith, Peter W.F., 2019. "Hierarchical model for forecasting the outcomes of binary referenda," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 90-103.
    4. Auld, T., 2022. "Betting and financial markets are cointegrated on election night," Cambridge Working Papers in Economics 2263, Faculty of Economics, University of Cambridge.
    5. Paolo Manasse & Graziano Moramarco & Giulio Trigilia, 2024. "Exchange rates and political uncertainty: the Brexit case," Economica, London School of Economics and Political Science, vol. 91(362), pages 621-652, April.
    6. Wael Bousselmi & Patrick Sentis & Marc Willinger, 2018. "Impact of the Brexit vote announcement on long-run market performance," Working Papers hal-01954920, HAL.
    7. Facundo Albornoz & Jake Bradley & Silvia Sonderegger, 2020. "The Brexit referendum and the rise in hate crime; conforming to the new norm," Discussion Papers 2020-12, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    8. Facundo Albornoz & Jake Bradley & Silvia Sonderegger, 2022. "Updating the Social Norm: the Case of Hate Crime after the Brexit Referendum," Working Papers 203, Red Nacional de Investigadores en Economía (RedNIE).

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