A new breed of hedge fund has become popular thanks to big data -- but it's 'less sexy' than we're used to

Photo: d TIMOTHY A. CLARY/AFP/Getty Images

The hedge fund world is a little like a dog show.

Breeds are distinct, and judged separately from each other: you can be a macro guy, a fundamental type, a quant…

And like with dog breeds, certain kinds of funds can come in and out of fashion. Occasionally, a cross-breed emerges that captures the imagination and becomes the latest fad.

These days, the new hybrid breed everyone is into is something called “quantamentals.”

And yes, like a cockapoo, not everyone is fond of this name, either.

Quantamental managers combine the bottom-up stock-picking skills of fundamental investors with the use of computing power and big data sets to test their hypotheses. For example, while a look at financial statements and a visit to a retailer’s outlets might help predict future profits or ability to repay its debt, a portfolio manager could also test a theory with algorithms that crunch through data on millions of credit card accounts.

“Big data and quantitative analysis can help identify those items, but so can, and so will, fundamental security analysis,” Steve Einhorn, vice chairman at Omega Advisors, told Business Insider last month. “There will always be, in my opinion, an important place for the traditional analyst and portfolio manager, but over time it will certainly be complemented by these quantitative approaches.”

(Einhorn’s comments were made prior to Omega’s founder Leon Cooperman being charged with insider trading. Einhorn hasn’t been accused of any wrongdoing).

Quantamental investing has been driven in part by the rise of so-called alternative data. Fund managers can now study everything from social-media data to predict footfall in a location or sentiment around a new movie, to the number of cars in a mall parking lot.

The amount of data sources doesn’t stop there; it’s endless, with new possibilities coming out all the time, and making the constant influx unlikely to become commoditized.

That said, a lot of the new data doesn’t have a long history or enough info points that computers alone can draw from.

While that may hurt pure quant strategies, those that use a human element to parse through the info will have better luck, some say.

“The main skillset that human can bring to the table, which quantitative trading strategies can’t really perform, is the ability to reason based on small datasets,” Manoj Narang said at The Trading Show conference in New York on Thursday.

Narang, who made his name in high speed trading before launching hedge fund firm Mana Partners, called the mix of quant and discretionary strategies “one of the most exciting growth areas.”

New ‘masters of the universe’

Even if 10 analysts were to parse through the same datasets, they’d all come up with different predictions based on it, according to Gene Ekster, who advises hedge funds on how to use the fresh data, and believes he was the first to coin the term “alternative data” two years ago.

“Your kings of the universe are no longer the folks wearing suits and going to galas. It’s the folks that are crunching Python [a programming language] and going to meet ups,” Ekster said. “These are becoming the new masters of the universe.”

Getting the staffers to crunch through the numbers is one thing — sometimes this requires data scientists who command a premium salary. Gathering the data is another, and grows more expensive depending on how niche the request.

“This alternative data approach is in a way making the hedge fund industry less sexy, not more sexy,” Ekster said. “It’s decreasing the margins of the bigger operations.”

At the same time, there’s a disconnect for the types of people needed for these jobs and the applicants in the market. Quants — those that use computer-driven models to trade — have become a hot commodity in the hedge fund world, recruiters say.

And some legendary hedge funders, like Paul Tudor Jones and Steve Cohen, are expanding units focusing on algorithmic trading.

Business schools haven’t caught on yet as much as needed.

“Data scientists and computer science students are learning data science but not about Wall Street or investing,” said Michael Gantcher, head of sales at RS Metrics, which sells data based on satellite and aerial photograph, among other things.

At Steve Cohen’s multibillion shop, Point72 Asset Management, recent recruits are getting schooled in programming alongside fundamental stock picking. Older analysts are getting classes on data science and stats that they can weave into traditional financial modelling, Matthew Granade, Point72’s chief market intelligence officer, told Business Insider.

A decade from now, a hedge fund portfolio manager is going to look very different, with a background in data and computer science, Granade said.

“In a seven to 10 year-time frame, the portfolio managers at the top of these things are going to be trained in all the different pieces,” he said.

That doesn’t mean that everyone is going to be a genius in all the subsets of skills needed, though.

“You’re probably not going to have a person on top of this who is a whiz at programming and a whiz at Excel modelling,” he said. “But I do think in the timeline we’re talking about, they can have a deep appreciation of company fundamentals coupled with an understanding of how this data works and how statistics work and how to think of all these data sets.”

Ekster, the hedge fund consultant, said he was recently contacted by a University of Michigan professor looking to revamp the curriculum, a sign of a slowly shifting tide. For the analysts at the fundamental hedge funds, it will mean doing the same job and learning how to weave in the fresh data into models.

“I wouldn’t say that it’s taking away from the fundamental approach,” added Erik Haines, director of data and analytics at Guidepoint, which provides funds with data. “It’s just becoming another tool within the toolkit.”

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