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

Abstract

We study the behaviour 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. We employ 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 results are announced. We find that although both markets appear to be inefficient in absorbing the new information contained in vote outcomes, the betting market is apparently less inefficient than the FX market. The different rates of convergence to fundamental value between the two markets leads to highly profitable arbitrage opportunities.

Forecasting mid-price movement of Bitcoin futures using machine learning

“…The second is based on the development of cryptocurrencies with an emphasis on futures pricing behaviour, while finally, the third area through which we develop our work is based on several pieces that have examined the predictability of cryptocurrency spot prices. Primarily, machine learning has been used across a variety of areas such as that of stock markets (Wittkemper and Steiner 1996;Ntakaris et al 2018;Sirignano 2019;Huck 2019;Sirignano and Cont 2019;Huang and Liu 2020;Philip 2020); currency markets during crises (El Shazly and El Shazly 1999;Zimmermann et al 2001;Auld and Linton 2019); energy markets such as West Texas Intermediate (Chai et al 2018), crude oil markets (Fan et al 2016), Cushing oil and gasoline markets (Wang et al 2018), gold markets (Chen et al 2020); gas markets (Ftiti et al 2020), agricultural futures (Fang et al 2020); copper markets (Sánchez Lasheras et al 2015); and coal markets (Matyjaszek et al 2019;Alameer et al 2020); cryptocurrency spot markets Chowdhury et al 2020;Chen et al 2021) options markets (Lajbcygier 2004;De Spiegeleer et al 2018); and futures markets (Kim et al 2020).…”

Exchange Rates and Political Uncertainty: The Brexit Case

“…Betfair supplied us with the odds implicit in the contract prices, which are observed from 5/27/2015 to 6/24/2016 at different time-intervals (often of 1 second), each week day for a total of 143,290 observations. This data source was used in recent papers, e.g., Auld and Linton (2019). We complement this with a second source for betting odds, the New Zealand-based company PredictIt, which launched a market on the Brexit vote on November 3, 2014 and caters mainly US-based investors.…”

“…We focus on ten currencies, which are representative of the major British partners: Euro, US Dollar, Japanese Yen, Swiss Franc, Canadian Dollar, Danish Krone, Swedish Krone, Norwegian Krone, Australian Dollar, New Zealand Dollar.5 This has also been documented in recent papers that focus on exchange rate predictability and Brexit (e.g., Korus and Celebi (2018), Hanke, Poulsen and Weissensteiner (2018),Auld and Linton (2019) andClark and Amen (2017)). None of these papers considers second moments-so their findings are unrelated to political uncertainty-or builds a model to interpret the result 'structurally'.…”

Efficiency of Tanzania's foreign exchange market

“…The strong form efficiency states that the spot rate reflects all information from historical, public and private sources, so that no economic agent can realize the abnormal rate of return. Similar studies such as Auld and Linton (2019), Nwosu et al (2014), Stershic and Gujral (2020), and Titan (2015), also discussed these three forms of market efficiency.…”