Thanks to local giants like Facebook and Uber, Silicon Valley is the epicenter of the new, technology-driven era. That prowess has caught the attention of British hedge fund billionaire David Harding, whose Winton Capital Management is mining the Bay Area for scientists to help it make better investment decisions.
“It’s been pleasantly surprising to speak to engineers and people in data science on the West Coast who seem to intrinsically understand the logic of investing systematically,” Harding — founder, executive chair and CEO of $34.5 billion Winton — tells Institutional Investor at this week’s Milken Institute Global Conference in Beverly Hills, California.
Quantitative commodity trading adviser Winton recently opened a San Francisco location that will serve as a data science center. The office, which has been up and running for about three months, houses six scientists focused on expanding Winton’s proprietary data sets and exploring new applications for its expertise in pattern recognition and statistical inference. This outpost will also support Winton Ventures, the London-based firm’s venture capital arm, which invests in data and other tech start-ups. Over time, Winton says, the team will probably grow to between 30 and 40 data scientists as the firm takes advantage of Silicon Valley’s talent pool.
The San Francisco office represents the next wave for Harding when it comes to capturing new data and finding new ways to refine Winton’s research and investment programs. The work done there will complement research and strategy development in London.
Harding, 54, who launched Winton in 1997, is something of a data scientist himself. After earning a physics degree from the University of Cambridge in 1982, he started his career at London-headquartered Sabre Fund Management, one of the first futures traders, where he designed trading systems and began to hone his research-driven approach to systematic investing. In 1987 he founded Adam, Harding & Lueck, a London-based quant CTA, with Michael Adam and Martin Lueck. AHL’s 1994 acquisition by U.K. alternative-investment titan Man Group led to the creation of systematic-trading firm Man AHL. Keen to freely pursue his own investment methods, Harding left Man in 1996 to start Winton.
The firm now has offices in six countries, including Australia, China, Japan and Switzerland. Winton runs three core strategies: the flagship Winton Diversified Program, an $18.5 billion long-short vehicle that invests in global futures, forwards and cash equities; the $13.3 billion Winton Futures Program, a long-short global futures and forwards strategy; and the $2.2 billion Winton Long-Only Equity Program.
“Data interests me because a lot of it is incorrect, and once we are able to put it together we are able to look at a range of different research premises that can impact how we allocate capital,” says Harding, whose eyes light up at the prospect of finding new ways to evaluate companies and markets.
Rather than cast about for already created data sets, Winton wants to develop its own to track metrics that may not yet be assembled. “For example, I’ve always been interested in the eternal debate over organic growth versus acquisitions,” he says. “It’s not just the research question that matters. There is a lot of money at stake too.”
By creating a database that looks at career histories, for instance, Winton could extrapolate a handful of research questions to assess companies. Among the possible queries: whether gender diversity among senior management has an impact, how compensation relates to performance and if companies benefit from splitting the CEO and chair roles.
Compiling the data could be the key to determining how any of these single factors affects a business, Harding contends: “Increasingly, instead of the idea coming first and the data second, we gather the data first and let the ideas flow from that.”
Harding knows that his firm can tackle those research questions at greater speed and depth than those that might be working from a more academic perspective or as part of the rash of financial technology start-ups that are trying to monetize data. “We have over 100 people on our data team,” he says. “Academic research in this field inevitably isn’t able to bring to bear the level of resources that a successful corporation can. For them it is a bit like going up against the U.S. Army with a peashooter.”