Sports betting as a new asset class: Can a sports trader beat hedge fund managers from 2010- 2016?
The authors investigate whether sports traders who systematically invest in sports betting strategies can outperform hedge fund managers and the S&P 500 from 1st January 2010 to 7th January 2016. The authors take a simple betting strategy based on Horse races in the UK and invest consistently on laying (betting on the event not to occur) the 4 favourite horses (with the lowest odds) in each race. They find the following:
(1) this type of horse racing strategy provide uncorrelated returns to the market;
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(2) the strategy outperforms the Credit Suisse Hedge fund Index and S&P 500 Total returns on average for the last 6 years.
In conclusion, the authors find that Sports trading can provide an attractive option to investors as an alternative asset to generate excess returns which are uncorrelated to their existing portfolio.
We investigate whether sports betting can be used as an alternative asset class for investors to generate non-correlated excess returns. Sports betting as an asset class has only recently caught the attention of academics. In 2008, Gomber, Rohr and Schweickert, wrote the first paper discussing the market microstructure of sports betting and how it can develop into an alternative asset class which can provide alternative methods of generating alpha for investors. The authors discuss the need for a regulated market environment, an inter-bookmaker platform and a central counterparty clearing service for sports betting to truly become a new asset class in its own right. In 2013, Meers, Waters and Wortman, discuss how NFL (American football), betting can be used as an alternative asset class. The authors focus more on strategies on NFL and how one can generate positive alpha. In 2014, Pooler of the Financial Times wrote an article about sports betting as an asset class. Pooler discusses how traders can turn to sports betting as an alternative investments to achieve uncorrelated returns to their existing exposure and even outperform the market.
All our data is taken from the official Betfair Exchange website. We use historical data ranging from 1st January 2010 to 7th January 2016. The data contains the name of each horse, the event name, the date and time of the event, the winning horse, the starting odds of each horse, the weighted average odds of the odds from the start up until just before the horse race starts and the maximum and minimum odds which are traded before the race starts.
We create the following trading strategy. In the view of a trader who does not believe the favourite of the horse would win, and that the odds are misaligned, we invest a certain proportion of our portfolio (1%) laying the 4 horses which have the lowest starting odds. So if we invest 1% we would allocate 0.25% laying each horse. After each horse race we realize our profit/loss and reinvest our new capital and then again invest 1% of our new capital in laying the 4 favourites in the next horse race.
In our backtest over 6 years we traded in a total of 57000 horse races, with a total of 228,000 bets places.
We create a backtest similar to one seen in investment finance, where we create a daily Mark-to-market (MTM) for each strategy. Exhibit 1 shows the comparison in performance between a sports trader who consistently invests in the horse racing strategy and the S&P 500 total return index (S&P 500 TR) and the Credit Suisse Hedge Fund Index (CS Hedge Fund Index) from the 1st January 2010 to the 7th January 2016. Exhibit 2 shows the yearly returns shown in a table format.