(Bloomberg) -- The insatiable demand for unique market data is putting banks in a tricky situation.
Quantitative investors, starved for trading signals that can be spun into gold, are pressuring the finance firms they work with to grant them access to proprietary information. It’s easy to see why. In a world where every kernel of publicly available intelligence is quickly processed and acted upon, investing advantages can evaporate quickly. In quant speak, the alpha gets arbitraged away.
So, to gain a competitive advantage, some systematic investors are experimenting with peculiar sources, like satellite images or credit card transactions. Others, however, see an untapped resource on Wall Street in obscure data sets that already exist but may not be readily available -- like the number of clients who read research reports.
As the cries for valuable new information become deafening, banks are struggling with a balance. How to offer useful tidbits without giving away anything sensitive?
“The buy-side has been after this data for a long time, but now the drive is getting even more pervasive across the entire industry as everything gets arbitraged away,” said Benjamin Dunn, president of the portfolio consulting practice at Alpha Theory LLC, which works with managers overseeing about $200 billion. “There’s a willingness on the research side to give the data up, but it’s the compliance and legal side that’s the hold up.”
‘Extra Sensitive’At Credit Suisse, striking that balance has in part fallen to the new head of quantitative equity research, Matthew Rothman. Credit Suisse hired Rothman from Acadian Asset Management last year to serve its growing quant clientele.
No stranger to the appeal of untapped data, Rothman and his team are exploring the possibility of monetizing useful data that clients are requesting. But they’re still in the early stages, moving slowly as they evaluate what information would be appropriate to share, he said.
“We understand why clients would be interested in this data, but we’re extraordinarily sensitive to all of our client concerns about what is appropriate to share,” Rothman said in an interview at Bloomberg headquarters in New York. “We agree there’s potential alpha in there, but the question is, who does that alpha belong to. We’re trying to be helpful while being extra sensitive."
For many quantitative investors, the holy grail of automated investing is uncovering a piece of data that can foreshadow an asset’s rise or fall, giving the fund an undiscovered edge. Steve Cohen’s Point72 Asset Management, for example, purchased information on credit card transactions a few years ago, while Acadian struck a deal earlier this year to use big data gathered by Microsoft’s search engine. And investors regularly use a variety of sources to assess markets such as energy, mining and real estate development.
Protecting ClientsData collected by banks and brokers can be even more appealing: It’s less exotic, with a clearer application. For example, if Credit Suisse were to make available the number of clicks its research reports receive, a quant might see an item on an undervalued company gaining traction and bet the stock will rise because of the increasing interest.
To protect some clients and serve others, banks are looking for ways to make their data sets anonymous without rendering them useless. Options include aggregating figures or publishing them with a lag. But that may not be good enough for some investors.
“There are certainly banks giving some data, but getting down to security level detail is something we usually don’t see,” Dunn said. “In meeting with a couple of them, they have interesting ownership and flow data, but they’re trying to figure out what the right level of aggregation is.”
Language ProcessingFor Credit Suisse, it’s not just about proprietary data, but also about new technology. For example, Rothman says his researchers are looking into natural language processing to capture the tone of reports for more nuanced signals.
The 51-year-old’s deliberate pace is no mistake. Rothman knows what it looks like when risky bets go wrong, having overseen quantitative strategy at Lehman Brothers during the quant crisis of August 2007, when levered funds tanked amid a wave of forced selling. Rothman emerged as one of the lead voices to dissect and explain what happened.
Today, he champions data less quirky than some of the other offerings that providers are pushing. For data to be useful, he said, it should address an existing problem. It can’t just be random figures that investors have to blindly stumble through.
“There’s become this fascination with data for the sake of data,” Rothman said. “Teams are hired to gather this data, approaching funds who are not quite buying into it and are very unsure with what to do with it. It’s the cart leading the horse.”
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