ASSET ALLOCATOR SPOTLIGHT

 

Cutting-edge data analysis solutions are more accessible and cost effective than many asset managers realize.

 

By Gary Paulin, Clare Flynn Levy, Greg McCall | May 2022

Using data analytics to improve decision making has grown significantly over the past decade in many industries, including asset management. But even as technology continues to improve and make data more accessible, leveraging data science and analysis can still be seen as expensive and time consuming.

However, cutting-edge data analysis solutions are more accessible and cost effective than many asset managers realize. To discuss how to optimize the investment decision-making process, we gathered Gary Paulin, Head of Global Strategic Solutions at Northern Trust; Clare Flynn Levy, Founder and CEO of Essentia Analytics and Greg McCall, President and Co-Founder of Equity Data Science (EDS).

Gary Paulin: Fund managers consume and produce a lot of data in the investment process. Some firms have the capacity to build internal data science functions to not only capture data but also extract value from it through analytics. The vast majority, however, don’t have the resources to build out this capacity internally and so being able to access these solutions as a service, and on a variable cost basis, is appealing.

Data science providers are a relatively recent phenomenon. Demand for these services should accelerate as more firms understand the importance of codifying their process to improve scalability, solve for succession and help match the growing sophistication of investor diligence  ̶ which is becoming more data driven.

Greg McCall: My background is like that of our asset manager clients, so I have walked a mile in their shoes. I was a fundamental portfolio manager and analyst following the technology sector for more than 25 years. During those days I was always data driven, trying to better understand where we succeeded and failed and how we could be more productive. Yet I was consistently coming up short at finding an automated solution to our challenges to drive continuous improvement in our investment process. Obstacles I came up against included things like poor use of our intelligence (due to siloed data/content and teams), non-existent measurement or feedback and limited collaboration tools.

Regarding how I leveraged my experiences in creating solutions, I often quote Andy Grove, the former CEO of Intel, who says it best: “What we have learned from decades of rapid development of information technology is that the key is relentless focus on ‘better, faster, cheaper’ – in everything,” Grove said. “The best results are achieved through the cooperative efforts of different disciplines, all aimed at the same objective.”

Equity Data Science (EDS) follows this formula. We have a strong focus on creating better, faster, less expensive solutions. In addition, EDS was launched by a multi-disciplinary group of software developers, data scientists and fundamental investors, each with different perspectives yet with a single goal of improving accessibility to investment data analytics. 

Clare Flynn Levy: Like Greg, I started Essentia to solve a problem born out of my experience as an equity fund manager. A fund manager’s job is to make decisions, all day, every day. Some of those decisions result in trades and some don’t. To know whether a fund manager is good at what they do and for that fund manager to make even better decisions on a go-forward basis, they need visibility into which types of decisions, and in which circumstances, they tend to do well, and which tend to destroy value. They need to be able to look in the mirror at their own behavior and their own biases and then correct them, just like professional athletes do. That’s Essentia.

Essentia’s analytics and the fundamental questions they answer are designed from a portfolio manager’s point of view, started by leveraging my own expertise. Eight years later, I am one of 10 former portfolio managers on the Essentia team. We have an enormous amount of domain expertise baked into our product.

That’s evident in our unique approach. We don’t just offer analytics; we offer fund managers the means to put data insights to work daily through “nudges”. Without giving too much away, the way we do nudges is informed by having been fund managers ourselves. It’s about asking the right questions and using the right language at the right points in time. 

Greg McCall: With an analog process, managers reduce their probability of successful outcomes, not because their investment process is flawed, but because of the difficulty in optimizing the available information and intelligence – even if we solely just look within their own walls and forget the outside world. If we further assume that we live in a more competitive world with more data and with less time-to-decision, the current analog process breaks down even faster.

The keys to successful investment management, and to all enterprises, are improving productivity, continuous innovation, competitive differentiation and maximizing intelligence. The most successful funds today have been leveraging technology (digital transformation) to become more accurate (data driven) for more than a decade. EDS makes that digital investment process easier to implement for everybody, big and small. The “equity” in EDS is about equitable access to data science for all firms, regardless of size. 

Clare Flynn Levy: Traditional performance analytics involve looking at outcomes and trying to work backward to explain those outcomes. That’s because, historically, the people doing the analysis only had access to quarterly or monthly holdings data from the underlying managers.

But the world has changed. Today all asset management companies are capturing more granular data on their trading and performance. Asset owners increasingly have access to more granular data from their underlying managers. Custodians and fund administrators like Northern Trust are making it easier for them to leverage that data. By offering Essentia, Northern Trust makes it possible not only for their asset manager clients to prove, and continuously improve their own decision-making skills, but also makes it possible for asset owners to answer questions like “what is this manager’s strongest skill?” and “does this manager do what they say they do?” 

Greg McCall: Driving improvement in any organization requires continuous self-reflection and feedback, identifying where you succeed and where the blind spots are. EDS has made that much easier with a powerful, configurable and cost-effective investment process management platform.

The EDS mission is to provide the best possible technology to achieve three goals – building, operating and sustaining a repeatable investment process. If we do this successfully, any and every piece of information that can be useful in the decision-making investment process is optimized  ̶  both before and after an investment is made.

A few examples of how EDS helps managers leverage data to drive continuous improvement:

1. Prioritizing research: The most valuable asset an investor has is his or her time. EDS helps them maximize their time by automating the research and analysis process.
2. Determining the accuracy of their financial models and forecasts: All investors live by the success/failure of their performance and EDS helps identify which inputs into their investment process, and which stocks, sectors, team members, and datasets, are helping them succeed and where the blind spots are.
3. Lastly, fundamental investing is both qualitative (talking to people) and quantitative (numbers and math). EDS is the only platform that brings them both together in one system and across the entire investment lifecycle.

Clare Flynn Levy: At the end of the day, active fund management has been disrupted, albeit very slowly, by index funds. That’s because the average active fund manager hasn’t been outperforming index funds, net of fees. As a result, we’re seeing lots of consolidation in the industry and the number of active fund managers is decreasing.

In order to improve the odds of surviving, all active fund managers need to raise their game and take advantage of a data-driven feedback loop on the quality of their decision making. Logical as that is, every organization has some people who are very resistant to actually doing it. That’s natural but it’s a great example of how emotionally-driven decision making can hold back performance. Fortunately more and more managers are starting to realize that it’s worth pushing through the fear. Meanwhile, the next generation of managers, who are now rising up in their organizations, are very comfortable with data-driven feedback so they take to the idea naturally.

At the same time, asset allocators are noticing certain managers using behavioral analysis to differentiate in the market, which is leading them to ask other managers how they are mitigating their own behavioral biases. As a result of all of this, we expect behavioral analytics to become a standard part of the way the industry measures itself in the future.

Gary Paulin: Data science solves a number of critical challenges facing fund managers. First, it can help streamline and harmonize multiple linear workstreams into one dynamic platform and bring transparency to what’s often hidden in spreadsheets or inside the mind of a portfolio manager  ̶  unlocking a manager’s unique dataset, their own decision making. The ability to codify a process helps distribution efforts and provides investors with the clarity and evidence they desire when making allocation decisions. It can also remove key person risks (replacing people with process) and help with succession.

Second, it can help to improve alpha generation by enhancing portfolio construction and position sizing, ensuring that not only is there an objective way to rank conviction but that such conviction matches with position sizing in the portfolio itself. Too often we find fund managers generating significant alpha through their highest conviction ideas to only see this alpha eroded due to lower conviction positions underperforming.

Finally, data science makes it easier for investors and regulators to review past decisions and perform due diligence. It also improves an investor’s ability to identify the investment skill of the manager which should lead to better outcomes over time. 

Now all these challenges can be solved with variable cost fintech solutions like EDS and Essentia. Our partnerships with these firms form a key aspect of the Northern Trust Whole Office strategy, which facilitates access to new technologies and capabilities that address all our clients’ challenges, not just those focused on the back and middle office. For Northern Trust, Whole Office goes beyond data and into problem solving and decision making.

 

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