Funds of funds become data analysis machines

Fund of funds managers can digest data more easily than LPs and GPs – and have access to plenty.

Limited partners are increasingly demanding ever-more granular data on their portfolios in a quest for transparency. But for a significant proportion, once received, they’re unable to do anything useful with this mountain of information – either they don’t have the resources, or they don’t know how.

That’s where funds of funds can come in. The majority have been collecting data on the hundreds of funds they have diligenced and invested in since they launched – after all, they see a far larger swathe of the industry than your average LP. But over the last few years, several players have been quietly turning themselves into data analysing machines. They’ve hired data scientists, built programmes and created mechanisms to ingest, process, analyse and serve up data in whatever form their clients desire.

Hamilton Lane, an early mover in this space, has more than 4,200 funds and more than 12,500 individual deals in its database. Says vice-chairman and head of strategic initiatives Erik Hirsch: “[We’re] making sure we can provide to our clients on as real time basis as possible information and total transparency about what they own and how it’s performing, what they’ve paid for, what the expenses are, what their exposures are, such that any time [a client] can ask you, ‘Where is my money?’ and you’re providing them as granular a level of information as they find sufficient and acceptable.”

Schroder Adveq is using artificial intelligence, alongside its investment teams, to rank managers as part of creating investment longlists and shortlists.

Lee Gardella Schroder Adveq
Lee Gardella

“If I’m going to Seattle, for example, and I want to meet with a manager as well as a few clients, we’d pull up the intelligence longlist and say, ‘Who is of quality in the Pacific Northwest?’” says Lee Gardella, head of investment risk and monitoring and investment committee member. “[Perhaps] we haven’t been talking to them recently because our team may be focused on one thing [while] our app is telling us something different. So maybe it’s somebody we ought to talk to.”

Adams Street’s data cache has allowed it to see market opportunities where perhaps others have not. For example, in its global multi-strategy fund of funds, the firm has calculated that a 25-35 percent exposure to venture capital – often perceived as a riskier strategy than buyout – can diversify risk in the overall portfolio.

“It gives the investor exposure to different sources of return, different patterns of cashflows and different J-curve profiles of companies,” says partner Tobias True.

Proving sophistication

While thus far the focus has been on accessing and collecting the data, the next five years will bring greater focus on how it’s used, True predicts. Today, for example, the firm is working on ways to estimate market beta versus alpha on immature portfolios, analysing how much risk is coming from the market and how much from individual investments.

“You can’t just come up with that based on data reported from the manager. But it’s something where once you have this broad data set and you have data scientists who can look cross-sectionally and run analysis on that, then you can actually get to some really interesting and insightful observations, which could have a big impact in the industry as far as how people think about risk.”

In the future, Gardella sees technology playing an even greater role in portfolio construction, particularly with regard to specific risk profiles. What’s more, the ability to handle data will become increasingly attractive to potential clients.

“Data and its presentation to the client and within the organisation is going to change private equity investing. It’s going to be one of the ways you’re assessed and judged by your prospective clients,” Gardella says, adding data capabilities will be “one way of proving how sophisticated you are as an institution.”