# A Look Inside UBS's Quantitative Evidence & Data Science Team

## Led by Bryan Cross (pictured), the asset manager's QED team aims to blend quant and fundamental to find unique solutions to new problems.

#### Need to know

UBS Asset Management’s QED team is technically only 19 months old, but the seeds for this unit date back many more years inside the institution. Bryan Cross and Barry Gill explain the thinking behind the group, where it’s been successful, and where there’s room for growth. By Anthony Malakian with photos by Timothy Fadek

Bryan Cross grew up about an hour northwest of Chicago in the suburban town of Barrington, Illinois. His father would sometimes bring home six copies of that day’s New York Times crossword puzzle—one each for Bryan, his mom, dad, and three siblings: Will, Molly, and Anne—and it would be a race to see who could finish the fastest.

To this day, Cross still has a love for crosswords, but the experience also helped to inform his thinking for what would become UBS Asset Management’s Quantitative Evidence and Data Science (QED) unit. When it comes to QED staff, Cross believes in the concept of a liberal arts engineer: someone with intellectual flexibility—someone who is curious and competitive, but who can also work within the constructs of a team.

“Really good engineers like to deconstruct problems, and they’re able to recognize threads in that deconstruction that are similar to problems they’ve seen before elsewhere. They can then convert those deconstructed problems into business solutions that leverage that cross-domain expertise,” he says. “Much in the same way that you do a crossword, you say, ‘Oh, I’ve seen that pun before,’ or, ‘I’ve seen that type of clue before,’ or you’ve seen something related to it. You can then quickly pull that and search your database and find the solution for it.”

###### It was a startup within the organization. Nothing like this had ever been done. There was no blueprint.
Bryan Cross, UBS Asset Management

QED is UBS Asset Management’s foray into incorporating quant and data science principles into the traditional fundamental investment process. The internal group is a cross-asset effort that works alongside analysts, traders and portfolio managers across asset management and UBS O’Connor, the bank’s multi-strategy hedge fund. It uses artificial intelligence (AI)—from machine learning to deep learning, with a heavy emphasis on natural-language processing—to solve bespoke problems, and sources alternative datasets to drive excess returns for investors.

Since the group’s official formation in March 2018, there have been some rough patches, but QED is now starting to find its form. Users of QED come to the group for help—bespoke projects range from dashboards to web apps to email alerts, to more proactive and ambitious projects that help to analyze, say, the iBuyer market, or using disease modeling to decipher fads and trends in the fitness industry.

The aim is to be creative, curious and have some intellectual flexibility in order to create unique solutions to complex problems. But as with any new creation, there have been lessons learned along the way.

Infection Rates

After earning an economics degree from the University of Chicago in 2003, Cross enrolled in a partner program between UBS and his alma mater that would allow him to attain a master’s degree in financial mathematics while gaining real-world experience on the investment bank’s quantitative trading desk, which, at the time, was called program trading. His remit included principal and agency-portfolio trading, as well as ETF market making, automated ETF market-making, and a small statistical arbitrage book.

It was, as Cross puts it, a trial by fire into the quant domain. It forced him to think in terms of factors—such as valuation, growth, quality and momentum—at a portfolio level and quantitative risk management. They built innovative tools such as trade optimizers and what ended up being the early stages of what is now UBS’s central risk book at the investment bank. Those early experiences at the bank helped to inform the early elements of QED

Today, when you listen to Cross talk about QED, it sounds like he’s describing a fintech startup that’s ready to launch a Series A round of funding, rather than something that’s been nurtured inside one of the largest asset managers in the world. He calls his colleagues inside of Asset Management, “clients.” He uses a derivative of the now-common SaaS-tech delivery model: quant-and-data-science-as-a-service. There’s an earnestness in his voice similar to that of a startup CEO. He talks about the need to grow adoption among users. He knows that mistakes have been made—lessons are always meant to be learned when creating something completely new. He can see the problems that analysts and portfolio managers face, he knows QED can help, and he knows they have to walk before they can run.

“It was a startup within the organization. Nothing like this had ever been done. There was no blueprint,” he says. “The compliance aspect wasn’t set up. The vendor management wasn’t set up. Even the basics of, ‘OK, what do we do now?’ weren’t set up. So it was really exciting to put pen to paper and come up with the strategy for how we would attack this problem: how we think about driving adoption, our ultimate goals, and what are our key performance indicators.”

One example of how QED works revolves around the iBuyer market, a new term used to describe online real estate companies such as Zillow, which started as an online advertising platform but has morphed into an online marketplace for buying and selling homes. For the QED team, the big investment question was whether companies like Zillow—and others entering the market—would be able to ramp up this business as fast as they said they could. How is an individual company doing? Can that be measured relative to their plan? Relative to others in the market?

QED proactively sourced an alternative dataset from a vendor, combined that with an S-curve model—a statistical model for the adoption rate of innovations—to provide internal users with a probability estimate as to whether or not the company would hit its long-term guidance, which can be used as a measure of long-term fundamental health. (Not all alternative datasets are so informative, but more on that later.)

Another use case was for the fitness industry. An analyst had asked the QED team about a particular company in the space, which inspired them to think about fads and trends, which in turn led them to think about how disease modeling could potentially be used as an analysis tool. As Cross puts it, there is a period of time where you “convert” susceptible people (this is your technology acceptance model, or TAM) into infected people (the actual users of the technology). Many of those infected people then recover (this is your churn).

QED applied that to an unnamed fitness company where they could look back at other historical fitness trends and use data—from both public and private sources—from those trends to model the “infection” rate that occurred in the historical base classes, and then apply that to the current model to come up with a forecast for user growth for that particular current model.

“I’ve never read a sell-side report that looks at disease models as a proxy for fads,” Cross says. “I think that as a general observation about QED—and this goes back to the idea of liberal arts engineering—we’re really good at recognizing analogous situations across domains. That’s the value-add that we bring. We’re students of everything; we’re intellectually curious about everything. Someone from the team can read an article about modeling the spread of bird flu using a set of differential equations, and then she can realize that’s directly analogous to fitness trends, or adoption rates of streaming providers. From there, we can build something that works, is explainable, is sophisticated, and is better than guessing.”

Cross says sometimes users come to them with challenges, and sometimes they think of something and push it out to users. The key is using quant and data science principles to find innovative solutions. But sometimes, the process can be challenging.

If at First You Don’t Succeed

Barry Gill describes himself as such: “I am Irish. This is my 25th year at UBS. And I’m a dyed-in-the-wool stocks and markets guy.”

Today, Gill is the head of Active Equities at UBS Asset Management. [Editor’s note: Gill was promoted to head of investments on Nov. 1.] There has traditionally been a line drawn between fundamental investing and quantitative investing, but as passive investing has cut into active’s inflows and returns, there has been a merging of the two—which gave birth to the portmanteau “quantamental.”

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