MIT Startup Flyberry Capital Emerges with Big-Data Hedge Fund
In the good old days—say, the early ‘90s—math and physics PhDs who wanted to make lots of money became quantitative analysts, or quants, on Wall Street. Now, they just start their own hedge funds.
It isn’t that simple, of course, but here’s a case in point: Flyberry Capital, a one-year-old startup in Cambridge, MA, founded by a team that includes MIT electrical engineering and computer science PhDs and an MIT Sloan MBA. Flyberry is using—can you believe it?—“big data” techniques to draw insights from huge streams of information, and applying them to finance. In particular, to inform trading decisions for its early stage hedge fund.
Flyberry manages what you might call a micro-hedge fund, soon to be on the order of $1 million, though it could be a lot more than that (see below). The firm uses its “global intelligence system” to gather information from hundreds of sources—such as sensor networks, weather patterns, earthquake detectors, government sites, news sites, blogs, and social media—and develops models of how markets react to various events. Then, based on those insights, the team makes trades in commodity futures, market futures, and other common types of trades.
“As much as we make use of a big data toolkit, we’re not a quant shop. We don’t do high-frequency trades,” says Zeid Barakat, Flyberry’s co-founder and chief strategy officer. “We don’t stay in the market, we just trade based on discrete events.”
If there’s a weather-related crop issue—like this year’s drought in the U.S. and its impact on corn—Flyberry tries to anticipate the market reaction. Or, with Hurricane Isaac bearing down on Louisiana, the firm could analyze years of historical data on storms (e.g., Katrina) and come up with some hypotheses. The same idea applies to any kind of event—say, the release of a consumer index report. Flyberry tries to deduce a “mathematical relationship between that event and the market,” says co-founder and CEO Michael Chang. “We gather a lot of information from the world, and try to test if this hypothesis is correct.”
Chang, who did his PhD at MIT in modeling and analytics, emphasizes that Flyberry’s technology platform is comprehensive. On the front end, it uses natural language processing to make sense of text-based data. On the back end, it’s optimized to test hundreds of hypotheses and develop multiple trading models so as to automatically generate a trading strategy. “It’s not a single algorithm or process,” Chang says. “It’s an end-to-end trade machine.” (As compared to most quant shops and funds, which focus on a few strategies, he says, “They have a fish. We have the boat and net.”)
And finance is just the beginning. If the team’s technology proves itself out over time, then it could also be used to help manufacturers manage their supply shops and predict plant failures, for example, or to create toolkits for risk management in investment funds, the firm says.
Of course, there’s plenty of skepticism to go around. Hedge funds in general haven’t performed well in recent years. Investors are wary of new financial strategies. And any firm can say it has some black-box system that can predict the market. Barakat, an MIT Sloan School grad and entrepreneur, shuns the black-box approach, saying Flyberry only uses trading strategies that it understands. “At the end of the day, it’s all about your returns and how successful you are,” he says. “People don’t have to take our word on this. They can see our trade results.”
So far, things are looking good. Flyberry has advanced to the finals of a hedge fund competition this summer sponsored by Lion’s Path Capital. (The other finalist is SLCM Capital, a New York startup.) In doing so, Flyberry is likely to secure $1 million in trading funds, and has shown gross returns of 6 percent over the month-long competition. (The company has been doing real trades since April but, for legal reasons, is unable to broadly disclose its trade results. “Believe me, we would like to,” says Barakat.) The winner of the final round could receive $25 million to work with down the road, at which point Lion’s Path would take an equity stake.
Flyberry currently has eight full-time employees and has raised about $500,000 in angel funding. The startup has some themes in common with other tech companies like Recorded Future (Web analytics and predictions), Fina Technologies (data-based trading algorithms), Quant5 (analytics for marketing), Lexalytics (text analysis), and Bluefin Labs (social media around TV).
So how big could Flyberry get? Chang is pretty bullish on the opportunity, not surprisingly. He expects the company will have more than $100 million under management within about a year, and over $1 billion within five years. Those are big numbers. He also wants to establish a big brand presence in Asia.
To get there, of course, the Flyberry team will have to adjust its trading models if they’re not working, and continue to develop new models to stay ahead of the curve. And it will probably need to find new revenue streams. But that’s where the firm’s multiple-model approach should pay off, says Barakat.
Still, he admits, “nobody knows what’s going to happen in the future…but we like our odds.”