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Making Faster, Better Investment Decisions

Investment data science enables asset managers and allocators to better understand why investment decisions are good or not so good – and to improve future decision making in the process.

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Northern Trust

Northern Trust

It’s a fact that technology is central to modern investment decision making for asset owners and the asset managers who serve them. Computers and data existed long before they were incorporated into investing, of course, but the rise and establishment of electronic trading – and the order management systems that evolved to perpetuate it – unequivocally changed the investment decision making process. Today, order management systems are relied on to such a degree that many market participants would posit they represent the top of the investment management value chain. That’s a view that may soon change, however, as new innovations exert their influence upon investors and asset managers.

An emerging trend likely to replace order management systems at the top of the investment management value chain is investment data science, particularly as outsourced trading continues to grow. With the execution of trades moved out of the house, decision making will focus more and more on alpha generation and the tools that will be used to support the decision-making process. In this likely scenario, the digitization of the investment process will emerge as the dominant force in the value offered by asset managers to their clients.

“Institutional investment teams need to drive efficiency, transparency, and data-driven feedback into their decision-making without disrupting the way their team members think and their individual processes,” says Marc Mallett, Director of Strategy for Asset Servicing, Americas, at Northern Trust. “Investment data science enables that, and that’s why we view it as the top of the decision-making value chain.”

Mark Mallet

Mark Mallet

Achieving behavioral alpha

As much as technology has influenced investment decision making to date, the decision makers still operate in a largely analog world and make their stock picks and other choices based on self-identified idiosyncratic formulas they believe to be the secret sauce, so to speak. The idea behind investment data science is not to undermine the knowledge and expertise that an individual manager or entire team leverage in making decisions – but rather to up their game even more.

Investment behavioral analytics – which are analogous to Moneyball-type performance analytics – are a new capability that Mallett and his colleagues are focused on providing asset managers and asset owners as part of a larger cloud-based solutions platform.

“With behavioral analytics capabilities, asset management firms and their teams can run analysis on historical trading activity and identify where a portfolio manager is adding, or even decreasing, value,” says Mallett. “A simple way of thinking of it is that you can ask the platform questions it can answer based on investment data science. For example, is a particular PM or the team of PMs as a whole consistently driving value through their stock picking, adjusting the size of their investments, or adding to an existing position? Does the data reveal unconscious bias in decision making, or perhaps more herding into a particular position than the manager would like to think? Are they concerned about loss aversion? Do they ultimately hold positions too long, even though the data would tell them to get out of the position? Even a short list of behavioral analytics questions brings the concept of behavioral alpha clearly into view, and the results of such ongoing analysis can have a major positive impact on decision making.”

Mallett suggests another way of thinking about behavioral analytics is in the same manner one might consider the game film review done by coaches and athletes in nearly every sport these days. “What it can reveal, for example, is that you were in the right position but not for the right reasons – you were just lucky,” says Mallett. “There’s nothing wrong with a stroke of good luck, but it doesn’t mean you made a sound decision. Behavioral analysis tells you if the process works, which can lead to better decisions because when you have a solid process and you consistently apply it, the results are going to come.”

Mark Mallet

Mark Mallet

An always-on, 24/7 investment committee meeting

Investment data science and behavioral alpha are concepts that have been enabled by the democratization of technology. Investment analytics and processing capabilities available today are vastly improved compared to just a few years ago. As mentioned earlier, the access Northern Trust clients have to such capabilities is part of a larger platform that incorporates these new innovations to address the challenges of a complex investment lifecycle. That lifecycle kicks off with idea generation and screening, often inspired or validated by deep research and analysis, then optimizing the opportunity within whatever constraints exist, and extending to implementation, performance tracking, and reporting. The information generated during that sequence can be leveraged to improve future investment decisions and transparency across the investment team and with clients, provided it is captured and effectively analyzed.

“Investment decision makers are looking for solutions that enhance their existing process, not change it,” says Mallett. “That’s understandable, and it’s possible to do that and also make sure that the best thinking at a firm – the investment thesis with true conviction behind it, the deepest analysis – is available on enterprise platforms so the rest of the firm is aware and can see the rationale behind conviction in a particular stock, for example.”

For years now service providers have been driving improvements to the operations side at asset owners and managers. The solutions that emerged have helped asset owners address shortcomings in resources, and aided asset managers in the challenge of fee compression. Until now, however, very little time has been spent injecting efficiency and scalability into the investment process itself. Synthesizing the investment decision making process and feeding it with the right stuff to improve outcomes is the focus of what Mallett and his colleagues at Northern Trust are doing.

“What each team of fundamental investors and each individual within that team does is unique to them,” says Mallett. “Our new capabilities allow them to put that unique information seamlessly into their existing workflow and to organize their data to formalize, measure, and refine their investment process.”

In the new cloud-based platform Mallett describes, all of the unique signals and precious information surfaced by portfolio managers and other investment professionals is made available for time series analysis so everyone on the platform can see how analysis evolves. More transparency for each individual involved in the process, the investment team overall, and the CIO is just one result of accessing this powerful new set of tools. All of that information matters because in many cases members of an investment team know as much or more about what makes an investable company successful than the company itself. They meet with management, analyze supply chains, and know the challenges the company must overcome. As they accumulate information, they develop a view on what will drive value at a company – looking across all of the varied signals and identifying a price target is what generates conviction, the roots of which can be shared across the platform. All of this is achieved without the information being lost in a forest of spreadsheets and piles of notebooks.

Here’s one way of looking at the impact the new capabilities can have: a healthy investment decision-making process should incorporate lively and well-informed debate about why a particular stock might be added to the portfolio, or a long-held name dropped. Typically, with information tucked away in the minds and notes of the best thinkers, it would take days or weeks to pull together all of the relevant points for discussion. Instead, says Mallett, with the platform “it’s an always on, 24/7 investment committee meeting. All the data needed to facilitate informed dialogue across the team is always available and ready to be discussed because the process of creating that information is embedded into the tools each team member uses to create their view.”

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