Quant Empire Forged Through Netflix Ascends the Hedge Fund Heap

  • QIM’s Woodriff and Voloridge’s Vogel sit atop hedge-fund world
  • Small shops use ‘hunter-gatherer’ ethic to topple the big guys

What Is the Quant Advantage?

Like so many relationships nowadays, this one was cemented through Netflix.

It was October 2006 and Jaffray Woodriff was having trouble concentrating on his quantitative hedge-fund business. The programmer kept finding himself obsessing instead over a million-dollar contest that Netflix Inc. was running. It was part of the movie service’s push to improve its viewer-preference algorithm. The money would go to the person who wrote code that gave it the biggest lift.

Jaffray Woodriff

Photographer: Andrew Harrer/Bloomberg

Lots of quants were caught up in it. Figuring out how one movie choice influences the next is a classic machine-learning puzzle and the Netflix Prize became a cause celebre for math elites, who furiously tracked each other’s progress on a blog that showed who was winning. After pulling eight all-nighters to get to fourth place, Woodriff noticed that nothing he did could dislodge the guy in front of him, a Florida-based data scientist named David Vogel.

He had a pitch. Let’s combine forces.

So began a collaboration that over the last decade has helped cement Woodriff and Vogel as two of the most successful hedge fund managers in the world, overseers of a burgeoning quant empire that has quietly thrust them onto the plane occupied by giants such as Renaissance Technologies and Two Sigma. Woodriff’s Quantitative Investment Management and Vogel’s firm have funds that regularly post annual returns in excess of 20 percent, with QIM’s long-short version up close to 50 percent this year.

After pushing their Netflix ranking as high as second, Woodriff brought Vogel on as a consultant at QIM, convincing him that his skills could translate into markets. Two years after, Vogel launched Voloridge Investment Management, with QIM later buying a 25 percent stake in the company.

“It’s a sign of potentially being a great trading quant if you’re good at machine learning, no matter the domain or discipline,” said Woodriff, now 47 years old. “That’s why I was so confident that David would be good at this.”

Few managers have demonstrated the mastery of Woodriff and Vogel. QIM’s flagship program returned 20.8 percent last year, making it the 10th best hedge fund in the world managing more than $1 billion, according to data compiled by Bloomberg. The market-neutral fund at Vogel’s $1.5 billion Voloridge boasts a 29 percent three-year annualized return, good for the third best in a recent Barron’s ranking.

While the pair aren’t household names, the ease with which they spun math skills into gold is a big reason traditional managers are turning over rocks looking for quants to rev up their strategies. Computer-driven shops with a few billion in assets are so trouncing the titans of Wall Street that everyone from Paul Tudor Jones to Credit Suisse Group AG are raiding the talent pool.

Compared with Renaissance or Two Sigma, QIM and Voloridge are small. Vogel opened his firm in the seaside town of Jupiter, Florida. In a modest sized office with just six computer screens on its main level, QIM calls Charlottesville, Virginia, home. Both have fewer than 50 employees -- a size Woodriff says he likes because it resembles the “hunter-gatherer” societies of early humanity.

Of the two, Woodriff is the more voluble, happy to chronicle a lifetime of mathematical obsession, handed down from his grandparents, that began sitting for hours while a boy on his father’s Virginia farm, rolling dice. By nine he was forecasting population statistics.

“I got very obsessed with calculating probability and trying to understand why. Really, deeply why,” Woodriff said. “Well, maybe it’s not that deep, but as a seven-year-old it is kind of deep.”

Trying to explain how his programs work, Woodriff says he assembles over 10,000 mathematical formulas through machine learning, each with flavors of favorite quant inputs, orthogonal to data points like volatility and price momentum. There’s a pattern to it all, but it’s so intricate that he has a hard time putting it into words.

Vogel is harder to reach. Searching for a quantitative breakthrough this summer, he packed up and flew to Hawaii in order to be alone with his research for four weeks. Asked what lessons from outside of finance influence what he does today, the 43-year-old, with degrees from Massachusetts Institute of Technology and New York University, doesn’t exactly wax poetic.

“It is important to understand that just because a pattern repeated itself historically, it will not necessarily repeat itself in the future,” Vogel says. “Hence, we need to be very careful of this in finance and leverage my experience in machine learning to make decisions on which models to implement for trading.”

Repeating patterns are everything to Woodriff and Vogel, from Netflix ratings to financial markets. Like all quant managers, their success stands against the random walk theory of finance, evidence that you can teach computers to spot patterns in asset prices and adapt fast enough to anticipate how those patterns will change.

Proving the efficient market hypothesis wrong was on Woodriff’s mind when he graduated from the University of Virginia in 1991 and holed up in the school’s computer lab for the summer. He commandeered 20 PCs, set out on a three-day, no-sleep bender analyzing stock data. Fueled by hourly doses of Pepsi and hypnotized by spreadsheets, he didn’t stop until discovering a formula he now calls “the third way”: neither trend following nor mean reversion, but a new pattern of factors entirely his own.

Factor investing, the circuitry of smart beta ETFs and one of the guiding philosophies of quantitative investing, has become something of a rage two decades after Woodriff’s breakthrough. It’s a way of unlocking hidden returns in markets by letting mathematical rules dictate which stocks you own -- high momentum, or low volatility, for instance.

Woodriff says he uncovered a completely new combination of factors during his library camp-out that no one else knows about. With a few tweaks, it’s the backbone of QIM’s trading programs today, which buy and sell about 1,500 individual stocks and ETFs. Whatever it is -- he’s not saying -- it’s helped propel QIM to become one of the top hedge funds in the world. QIM’s long-short program has returned 46 percent so far this year and its managed futures program is up 9.2 percent. The S&P 500 gained 6.2 percent while equity hedge funds fell almost 2 percent over the same period, according to a Hedge Fund Research index.

“As it turned out, what I was doing in ’91 was my own machine learning method. Who knew?” Woodriff said, referring to the computerized version of the scientific method quants use today to test millions of trading strategies to optimize holding periods and stock selection. “It’s more of a style that identifies patterns in the market that is not related to any other factor.”

A commitment to that process unites Vogel and Woodriff.

“My career background is almost entirely based on machine learning,” Vogel said by e-mail. “I have applied machine learning in many other data intensive industries and in the quant space seemed like a good application.”

Vogel first came to programming as a teenager, enrolling in coding courses during high school. Rather than in finance, his first years out of college were spent using predictive machine learning models in health-care.

He never lost his taste for improving global health through data. Before Hawaii, Vogel spent another month this summer digging into non-profit research, far from the reach of financial markets. His work included cancer studies for University of Washington’s protein lab and Paul Tudor Jones’s charity for fighting corporate greed. Vogel also sifted through climate change data and worked on his family’s private foundation.

The pair haven’t always delivered market-beating returns. From 2010 to 2014, QIM’s managed futures program hit a wall, dropping as much as 12 percent in 2014. Woodriff’s been working on redeeming the dry spell since, and says that keeping his firm’s size in check has helped QIM regain its footing the past two years.

“That stretch was because we got too big, and that cost us significantly,” he said. “We’re going to be really careful with that going forward.”

One of QIM’s key selling points to investors has been its lack of correlation to anything else, Woodriff said. Add a QIM fund to your holdings, and you’ll find a strategy that delivers actual diversification. The same goes for Voloridge funds, he said.

“Voloridge and QIM got an incredible amount of benefit growing because of our lack of correlation to other people. We’re not correlated to trend followers and we’re not correlated to the stock market,” Woodriff said. “That means when you run our simulated results against a portfolio you already have, it wants to allocate a lot of money to us.”

The pair are as confident in their investment process as they are in other facets of money management. For compensation, they take a percentage of their own returns and forgo the flat management fee that other hedge funds receive. Less than 2 percent of all funds omit the stipend, according to a Credit Suisse estimate.

The two have collaborated on nearly every aspect of their business. Employees from both firms know each other, and their bosses have assigned them counterparts at the other company to talk to -- whenever -- by phone or e-mail. The staff have even traveled to Florida or Virginia to visit one another.

The cooperation ends when it comes to alpha, though. That’s “the one thing off-limits,” says Woodriff.

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