Digital banking: Institutions lag hedge funds in big data use

But for many large, and some of the most successful, hedge funds, Twitter and indeed every other form of social media is informing and guiding investment decisions and trading strategies.

Bridgewater Associates, for example, one of the world’s largest and most successful hedge funds, has been using “everything that is available” to help it better model global economic activity in real-time, according to Greg Jensen, chief investment officer and one of its three CEOs.

“From Twitter and Facebook (and so on) we can capture every time somebody is saying they bought a new car. We could add those up and can compare that to the stats and be really on the pulse of what’s going on with something like auto sales or, similarly, with home sales,” says Jensen. There are multiple examples of other hedge funds using unstructured data in the pursuit of greater market intelligence to enhance returns.

But broadly speaking, and much the same as in banking, big data strategies haven’t yet been embraced wholesale by longer-term institutional investors such as sovereign wealth funds, asset managers, insurers and pension funds.

Indeed, in a survey of 400 global institutional investors conducted last year by State Street in collaboration with the Economist Intelligence Unit, two-thirds of executives said data and analytics capabilities will be among their most important competitive priorities in the future, while less than a third said they believe they are gaining a competitive advantage from their data.

Basil Qunibi, founder and CEO of Novus, a portfolio analytics and intelligence platform for institutional investors, says big data is still not particularly well understood in the world of finance but that its power, if harnessed in the right way, can be huge.

He founded Novus in 2007 to help institutional investors such as SWFs and endowments and pension funds make better and more informed decisions about which hedge funds or money managers they should invest their money with, using big data analytical techniques.

“What we’ve built over the last couple of years is a system that aggregates millions of data points across public and private data through connections with administrators, custodians, investment managers, regulatory agencies and other sources. More importantly, we’ve built an analytical framework on that infrastructure that helps clients generate significant, predictable insights on their managers and portfolios in a highly intuitive way,” says Qunibi.

They have built a combination of hedge fund expertise with Silicon Valley engineering, designed to help investors make better investment decisions, monitor portfolio risks and opportunities, and generate higher returns.

“That’s really about understanding the underlying DNA of their managers from a fundamental, predictive perspective,” says Qunibi. “How did managers generate alpha? Not as an error term to beta but based on the fundamental activities like market timing, position sizing, trading, security selection, etc. Do managers have one home run in their track record or do they exhibit persistent singles and doubles? What are the underlying fundamentals and environments that support these skill-sets?”