The IMD Wrap: Blowing tires, engines and budgets in F1 and financial data

Speed, technology, complexity, regulations, and massive amounts of data streaming in real time: it’s easy to see why people like to compare market data to Formula One racing. And engineers, scientists, and consumers in the capital markets and in F1 share much in common.

Quants are just as at home constructing trading or risk models as they are crafting race strategies. It’s all about the data and the analytics: F1 cars are equipped with sensors that constantly monitor changing factors, such as speed, fuel weight, tire wear, aerodynamic effects, track conditions, and other teams’ movements.

And if you’ve ever seen the inside of an F1 team’s pit, team bus, or factory—where they design cars and model race strategy and potential outcomes before and during a race, analyzing tire degradation and fuel consumption on a lap-by-lap basis just as analysts price loan payments or asset depreciation—the similarities to a trading floor are obvious. The teams spend a fortune not just on drivers, top-notch designers, and expensive materials, but also on the enormous amounts of technology used to compete in F1.

Regular readers will know that I’ve been watching F1 since the era of not only Jacques Villeneuve but also his father, Gilles, and Nelson Piquet (Sr., not Jr.), and one of the most enjoyable IMD Awards evenings I ever hosted was when Leigh Diffey and former Benneton F1 pit crew member and author Steve Matchett joined us as guests to talk about F1 and present the awards. 

Even WatersTechnology editor-in-chief Anthony Malakian—who back in the day couldn’t tell Niki Lauda from Nicki Minaj or a hamburger from Gerhard Berger—has become enthralled with the sport in recent years, in part due to the success of Netflix’s behind-the-scenes series about F1, Drive to Survive. 

The author of the “It’s Pronounced Data” substack is also a fan, and references the series heavily in this week’s email, quoting Mercedes and Williams team bosses Toto Wolff and James Vowles talking about how important data is to the sport, then juxtaposing that with former Haas team boss Guenther Steiner saying that he ate a lot of Chipotle the day before. 

The 2024 race season got underway this past weekend in Bahrain, with Max Verstappen (and yes, I also remember his father, Jos Verstappen, racing) starting the season in his Red Bull car, which seems to have the edge on its rivals at the start of the season. 

Given the vast sums of money involved, different chassis designs, and engine manufacturers, and tire choices, and different drivers behind the wheel, it always amazes me that—even though one or some teams may end up rising above the rest of the field—all the teams end up just fractions of a second apart over one lap. 

But while teams can choose how they spend their money, no team can simply outspend the others. All teams are strictly limited to a budget of $135 million per year, enforced by the sport’s governing body, the FIA (not to be confused with the Futures Industry Association). Exceeding that can incur fines and fewer testing days, as Red Bull found when the team “misinterpreted” some clauses of the sport’s rules and overspent in 2021.

Funnily enough, those budgets aren’t too far off a medium-large financial firm’s annual spend on market data.

In the capital markets, though, the only penalty incurred for going over budget is the wrath of management. And the bodies that govern financial firms have a mixed approach to regulating the factors that impact those budgets, such as the cost of market data from exchanges or vendors. 

UK regulator the Financial Conduct Authority recently declined to step in and impose caps on market data fees, while the US Securities and Exchange Commission has the authority to approve or reject exchanges’ proposed fees for new data services. But in recent years the SEC has seen exchanges aggressively push back against that authority.

For trading firms, just like F1 teams, the ability to keep budgets in check is countered by FOMO, or the fear of missing out. Might using a more lightweight but more expensive material in manufacturing a chassis or bodywork make the car faster? Might using a similar material when building an engine make it lighter but more fragile and less reliable? Might a new dataset give a trader a new and unique point of view into a security’s performance or allow them to predict market conditions? All these extras come at a price, and you can be sure that the greater advantage they deliver, the higher the price will be.

So, how do data professionals maintain their essential spending on bread-and-butter datasets and find budget to explore new services, while staying within their prescribed limits? For all the current-day talk of organizations becoming data-driven, data is still all too often seen as a cost ripe for pruning. 

But there’s light at the end of the tunnel, and like seemingly every other conversation in 2024, it’s AI. Everyone and their dog now has—or is developing—all manner of AI tools to help surface data faster, or find more insightful correlations between data points, or cleanse data more thoroughly. 

But—as with every other function that relies on data—garbage inputs beget garbage outputs. For these new tools to operate effectively, the data on which they are trained and the data they use as inputs in production must be accurate and properly governed. Basically, to do all that sexy, new stuff, you’ve got to get the boring, old stuff right. The most aerodynamic race car won’t win anything with a shitty chassis.

And while the emergence of new data catalog systems may lead to an increase in data costs as consumers use them to discover new datasets, they may also—combined with inventory management systems—help reduce costs by identifying cheaper alternatives and eliminating instances of duplication and under-use.

Ultimately, the goal should be to increase spend on what delivers more value, and reduce spend on areas that don’t. I’ve never worked in F1 or in banking and technology, so I certainly don’t profess to have all the answers. But I know some people who do, and they’ll be speaking at this year’s North American Financial Information Summit in New York this coming May on an array of topics, including everything I’ve just mentioned, and more. You can find out more about the event here. It may seem a long way off, but time—like an F1 car—will go quickly, so register early, and we look forward to seeing you there.

Thoughts? Suggestions? Racing rants? Email me at max.bowie@infopro-digital.com.