FIFA World Cup Momentum Strategy

With the World Cup finals quickly approaching, I thought it would be interesting to develop a trading strategy involving World Cup data. Realistically, a national football team’s victory will not change the direction of its country’s economy, but maybe something could be said about the companies sponsoring the event. 

I decided to make an algorithm using QuantConnect’s data that would hold the companies involved in the 2018 FIFA World Cup. My reasoning to this theory is that as the most widely viewed event in history, the FIFA World Cup provides a marketing platform for sponsors like no other. Companies with the capital and growth potential will be attracted to the exposure, which will hopefully lead to more revenue, and consequently higher share prices. 

This algorithm considers top American sponsors of the FIFA organization. FIFA's commercial hierarchy comprises six FIFA Partners, eight FIFA World Cup Sponsors, and six National Supporters, a set of 14 companies to choose from. The companies are held while they work with FIFA. As positive as the results look, it is safe to say that correlation does not necessarily mean causation.

Here are a few of the links that got me started on this strategy: FIFA Partners official page, fact sheet on previous sponsors, massive bids for broadcasting rights and US Soccer sponsors.

Update Backtest

For fun, I mocked up a virtual stock elimination round for companies in the portfolio. The algorithm involved individual country portfolios that contained their national team sponsors. I stuck with countries whose national sponsors were publicly traded stocks that I could find data on. All the country portfolios then went into an equally-weighted portfolio. Each day, as the results of the games come in and teams get knocked out, the set of active teams in the World Cup decreases. The portfolio will then be rebalanced using only those active teams until there is a champion. As the national teams advance, their sponsors will receive relatively more exposure and if the theory tested above holds true, maybe the share prices will increase. The results below were disappointing, but understandably so. While World Cup data failed to provide trading insights in this case, finding, cleaning and determining use cases for external data was intriguing and something I plan doing more in the future.


Hi Gurumeher,

I like your idea! Have you traded it live? What was the result?




Update Backtest


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