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Data Science Tech Is Giving Asset Owners a More Powerful Voice
As institutional investors gain access to advanced analytical tools, they are demanding more from their asset managers.
There’s an ongoing shift in the dynamic between institutional investors and their managers today. Asset owners are enjoying increasing access to progressively more powerful analytical technology, giving them greater ability to more granularly oversee their managers’ performance. Simultaneously, funds continue to proliferate in the market to give institutions more investment choices.
As a result, asset owners have a more powerful voice than ever before – and this is changing the power dynamic between them and their managers. Put simply, the days when asset owners were mere price takers are gone. They are now price makers, and they are demanding greater transparency and more value for their money when assessing fund managers.
We spoke with Paul Fahey, Head of Investment Data Science for Northern Trust, and Alex Pappas, Relationship Manager for Venn, the investment analysis platform by Two Sigma, about how both allocators and managers can best navigate the progressing democratization of data analysis and the changing power dynamic taking place in institutional investing.
Institutional investors can now perform far more advanced data analysis than they could even a decade ago. What factors have driven this evolution?
Paul Fahey: Clearly advances in technology have improved the calculation, consumption, and analytics around large data sets, which seem to be growing by the day in both size and number. More recently, we’re also seeing a greater number of digital natives – or individuals that are more conversant and comfortable with technology. Frankly, they’re demanding the use of advanced data science tools; they’re not prepared to work in an analog world anymore. So, I think the technology advances have facilitated some of the large investment data science activities that we’re seeing today, and you also have a demographic that’s very comfortable in that space.
Alex Pappas: Increasing transparency requirements also come to mind as a factor in driving the democratization of data analysis. Secondly, many traditional custodians have long had this wealth of data in their systems, but it required technology advances to make it more digestible for their clients. Powerful data science tools and APIs that are simple to use are making that access easier than ever.
How has this changed the dynamic between asset allocators and managers?
Paul Fahey: Asset owners and allocators have a heightened level of interrogation of their managers. They’re more demanding and want to get under the covers more; they not only want to know what the manager delivered, but also understand how they got there. Managers that embrace this new level of transparency are going to get more airtime, because they can have more open, two-way conversations and gain more opportunities to talk about the alpha they’ve generated. And I think this leads to a better relationship between the allocator and the manager.
Also, asset owners have more investment choices than ever before. You’ll hear, “there are more funds in existence today than there are actual securities.” This larger menu forces managers to really vie for the allocator’s capital. Again, as allocators demand more access to data and transparency, managers that best respond to this will have successful relationships.
Alex Pappas: Today, asset owners can ask managers more pointed questions, such as, “what was your thought process during this period of underperformance? Why did your exposures shift to different factors than you’ve had historically?” and so forth. And in sourcing new managers, increased access to data is making it easier to find those with proven track records and screen out those that don’t meet specified criteria for new mandates.
For example, an investment committee can say, “We want to compare these three managers, show us the rolling correlations between them, or show us the ability for each of these three managers to produce that idiosyncratic, alpha-like residual return.” That’s a pretty big ask, but clients can now do that relatively quickly with a tool like Venn, which is our platform, and package it into a clear reporting element. After uploading your managers’ returns, it literally requires clicking a couple of buttons.
Yet many organizations still use spreadsheets…
Paul Fahey: Yes. We recently did a survey of 300 global asset management firms1, and 52% said their organizations are still using spreadsheets to aggregate internal and fundamental data. The “tyranny of spreadsheets,” to borrow an expression from the book, The Technologized Investor, causes analysts to spend time on data acquisition, aggregation, and compilation – not true analytics. There are horror stories of individuals printing a report before they walk into an investment committee meeting to present – and invariably, their response to questions is “let me get back to you on that.”
Data science tools can practically eliminate data compilation and aggregation labor, so analysts can do true analytics work and provide timely insights. This need is becoming more urgent as investment data grow more complex. Our survey also found that 66% of global asset managers said they were leveraging five to eight data sources, and I expect that number to rise. So consuming large amounts of data is increasingly vital to managers. Without data science technology, you simply cannot analyze the large data sets required to surface the right signals and key insights you need to make better investment decisions.
Alex Pappas: The dynamic nature of data science technology is also invaluable. Rather than using a static PowerPoint presentation, the tools allow analysts to quickly paint a picture of a portfolio’s current exposures for the investment team in a clear and concise way – and show different scenarios in real time. You can say, “if we change these weights, what would that do? What happens if the S&P drops by 20%?” and display the result immediately. That’s powerful, because it enables you to tell a strong story while optimizing for time – and investment teams are almost always understaffed.
Where are we in the adoption of data science technology?
Paul Fahey: In our recent survey I mentioned previously, 98% of asset managers said that they’re either using or are planning to use data science/decision-support tools in the next two years. So almost all firms know they need to incorporate this technology.
Importantly, we should think about data science technology as not artificial intelligence, but augmented intelligence. We still have human beings applying intelligence and expertise, but these portfolio managers are using data and technology to be better at what they do. The technology is helping to surface the signals to assist in their process, and that’s the ultimate outcome.
I think modern chess provides a good analogy. Gary Kasparov’s Advanced or Centaur Chess (human and computer working together) is nearly undefeated when playing against either a human being or a computer. I see that playing out in what we’re doing with portfolio managers.
How can Northern Trust help investors advance their use of data science technology?
Paul Fahey: We really want to perform that role for them, and we can help on a number of fronts. Clearly we have capabilities that we’ve developed in-house, but we also work with a growing list of fintechs that allow us to provide a better solution set for our clients. One key relationship is Two Sigma Venn, of course, and we also have relationships with Equity Data Science and Essentia Analytics; their technologies are focused on asset managers’ front office investment decision making processes.
Tell us more about the capabilities that the Venn solution offers?
Alex Pappas: We purpose-built Venn for the allocator market. A few years ago, we realized that even some of the largest and most sophisticated asset allocators lacked the technology and analytical tools that asset managers have had for years. These allocators had AUM in the billions, and sometimes trillions, but lacked a modern toolkit for analytics, due diligence, and portfolio construction.
This was our impetus for building Venn. It’s a returns-based tool that lets allocators quickly evaluate manager performance and understand risk sources across multiple asset classes. It uses a spectrum of risk metrics and factors – such as equity, credit, value, small cap, or residual – to expose sources of risk and return. In minutes, allocators can use Venn to construct portfolios to see how diversified they are from a factor viewpoint. Often, they’re surprised to see how much exposure they have to elements like equity beta.
Importantly, allocators can upload their investment data from a spreadsheet or PDF to construct their portfolio on Venn. We also have a large library of mutual funds, ETFs, indices, and underlying stocks, so they don’t necessarily even need to upload data to begin using the platform to make data-driven investment decisions.
What advantages does Northern Trust offer in helping allocators incorporate data science technology?
Paul Fahey: We are providing a solution, not selling a product. That’s really part of our DNA here at Northern Trust. We’re consultative. We won’t show up at a client’s office with a tidy product and say, “Here you go.” First and foremost, we have a conversation about the client’s strategy; what they want to accomplish and how our platform can help them execute that specific strategy. And then we collaborate to create the right solution to help them to be successful today and in the future.
Alex Pappas: This is one of several reasons we wanted to partner with Northern Trust. We were aligned with their focus on being a solutions provider, and we continue to be aligned in how both of our firms are thinking about this evolving landscape of embracing technology and data science – with the goal of giving clients more powerful tools, like Venn, to make better investment decisions.
Regarding the future, what do you see coming next for data science technology?
Paul Fahey: There’s a saying: things have never moved this fast before and they’ll never be this slow again. That’s certainly true for data science technology, and our abilities are progressing so quickly that it’s difficult to make any predictions four or five years out. That said, I do think the data needs being driven by escalating ESG concerns are going to occupy us for the foreseeable future, and naturally advanced data science tools will be imperative to meet these needs in any meaningful way. While we’re already seeing data science technology being heavily used for ESG purposes, centered around both regulatory and investor requirements, I expect these tools to play a much greater role in the ESG space soon.
For one reason, remember that ESG is becoming far more data-driven as it evolves. Investors won’t settle for any anecdotal evidence from managers regarding ESG capabilities in the near future, for example. This means all parties will be demanding comprehensive ESG data. But building that data set is extremely challenging, as it requires dealing with multiple data providers both in the traditional and alternative sides — and then processing vast amounts of data to create digestible, actionable information. While, again, advanced data science technology is quickly becoming a basic necessity across the spectrum of sophisticated investing, I see the increasing data needs for ESG as one significant factor speeding its adoption among allocators and managers.
Learn more about using data science tools to improve investment decisions
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