For asset managers today, the notion of embracing innovation has become almost a staple—a necessity in order to cope with fast-moving markets, an avalanche of information sources and the need to generate new return streams as avenues for capital deployment.
Two areas in which innovation is both growing and accelerating are the applications of data science and the tools and solutions to combat climate change. Both arenas involve not just the development and refining of new technologies but also enhancing the capabilities of human talent to solve increasingly complex problems of staggeringly large scope.
Data Science: Exploring the Next Frontier
Data science is arguably one of the most talked-about examples of innovation: tapping and manipulating big data to convey new capabilities to the traditional research process. There’s no simple recipe for translating oceans of data into investment advantages, but one ingredient is a must: a supportive culture. The willingness to try new things and ask the right questions is critical in getting the flywheel rolling. Data science isn’t a silver bullet—it’s a tactical scalpel that can help answer narrow questions.
One challenge for data scientists is that what was innovative in data science’s early days is now rapidly becoming table stakes, with processes such as web scraping, using alternative data and acquiring new data sets becoming common practice. One new frontier for innovation actually lies in aiming data science inward rather than outward: drawing insights from information generated and housed within investment firms themselves—in internal systems, collaboration tools and other locations.
For example, our ESIGHT ESG research and collaboration platform contains a large volume of information in the form of analysts’ written entries from company research and engagement, as well as numerical ESG scores assigned by some portfolio teams. Using natural language processing, we’re converting this information into sentiment scores that investors can use as new comparison points with their own formal ESG ratings—and even to highlight material differences in analyst sentiment versus third-party ESG ratings.
The processing of text-based information is an avenue that’s rife for innovation, as a growing universe of input is becoming accessible in that format. According to Cluster Computing, the amount of unstructured data has grown from roughly 10,000 exabytes (one exabyte equals one billion gigabytes) to nearly 50,000 in the past five years—much faster than structured-data growth.
Of course, numbers will always be a big focus when applying data science, but the ability of text-based insights to reveal not only what’s being said but how it’s being said will make text analysis a formidable tool for asset managers in the years ahead.
Earnings calls and transcripts, for example, are prized for the insights they provide about company executives’ sentiment and specific aspects of corporate strategy. But in what ways can an investor harness that intelligence to form a valuable part of the analyst’s mosaic beyond individual company engagements? One of the answers is to present data insights in ways that facilitate interaction. For example, an earnings call heat map enables analysts to assess sentiment indicators at the company, sector and industry levels: What segments are trending in the right direction? How does a CEO’s outlook for a firm compare with peers?
Equipping Organizations to Harness Big Data
Of course, no matter how promising data-science tools are, the real innovation comes in how effectively an organization can deploy data science as a differentiator. This goes back to the willingness to innovate, taking small incremental steps, logging early wins and making a quantifiable impact. Active managers are in the business of delivering differentiated returns to clients: it’s important for data scientists to work very closely with fundamental analysts, identify key questions and controversies, and collaborate to find the right data and tools to uncover answers. Asking the right question is as important as selecting the right data and tool. Once analysts and decision makers see impact examples, the flywheel is in motion.
Data scientists also must strive to keep things simple—somewhat counterintuitive in this arena. There’s no need to reflexively reach for the most sophisticated tools. The data scientist’s goal should be to take millions and millions of data rows and boil them down to 1,000, essentially—delivering something analysts can digest to supplement their own mosaic of fundamental insights. Distilling complicated datasets and techniques into key insights is analogous to qualitative analysts integrating diverse views to develop actionable ideas.
The data science toolkit must be highly accessible: browsing a full dashboard of data science tools not only helps analysts identify a tool that might work but can also inspire ideas for new tools—or even new questions. Firms can go even further, socializing tools, solutions and wins to stir the pot. One of our conduits is an internal newsletter summarizing ongoing projects and highlighting questions analysts are asking. “Shark tank”-style innovation days celebrate innovative analysts who’ve developed new and interesting tools.
So, in many ways, some of the central pillars of a new-school field like data science are actually old school: collaboration and communication.
Equipping Investors to Dimension Climate Risks and Opportunities
Climate change is another arena in which investment managers are not only innovating with new tools, technologies and solutions but also in how they equip human talent to do their jobs better. Tackling climate change is like dimensioning an iceberg—some elements are very obvious at the surface, but beneath the surface is a massive research challenge.
At the bottom-up level, innovation is being channeled into upgrading human skills—helping professional investors better understand the underlying climate science and translate it into investment implications. We should be striving for “climate analyst” 1.0 or 2.0 to advance to 3.0 and beyond. Our collaboration with Columbia Climate School and the Earth Institute is one example of how climate science is meeting investment science in the industry; more collaborations will certainly follow.
Of course, empowering with new tools is critical to helping analysts answer climate-related questions. When looking at commercial real estate, for example, data science can leverage precise geolocation data and existing climate-risk ratings for some properties to assign climate ratings to cohorts of nearby properties, an efficient way to assess a wide range of underlying assets in securitized structures.
Climate change’s wide-ranging impact is rivaled by its incredible depth, and we expect to see more innovative deep-dive research initiatives on important issues. Whether it’s exploring how to forecast the availability of alternative energy resources as climate evolves or diving deeper into the engineering of synthetic biology to help mitigate or adapt to climate change, there is no shortage of topics for asset managers to explore.
Scenario Analysis: Growing Pains, but a Needed Advancement
Pulling the lens back to the portfolio level, we expect innovation to accelerate in climate-change scenario analysis. This critical capability has the potential to help investment managers better understand the effects of climate change on physical assets, as well as to explore opportunities and challenges in transitioning to a lower-carbon economy.
It’s fair to say that this technology is going through some growing pains, with providers using divergent data and analytical approaches. In selecting a provider, we worked with Columbia to review a range of climate-scenario models, and also drew on experiences from our in-house climate model for a smaller universe of Australian equities. We’ve begun analyzing a pilot group of 21 investment strategies to understand their climate risks under several global-warming scenarios.
We expect climate-modeling efforts to converge over time, as the scrutiny from investors intensifies and regulators, clients, beneficiaries and others demand regular and comprehensive reporting. The next wave of advances should focus on critical areas including broader coverage of asset classes and markets, improved standards for source data, and more sophisticated and deeper modeling.
Combined with analysts’ depth of expertise, better models will ultimately yield better results as the industry ramps up its climate-change efforts.
A Growing Diversity of Climate Solutions for Investors
We’d be remiss in discussing climate-related innovation if we didn’t touch on solutions. As momentum from the investment community and the asset management industry coalesces around steering capital toward this goal, we’re seeing a diverse array of solutions focused exclusively—or in part—on climate change.
Sustainable strategies that derive their approaches and opportunity sets from the United Nations Sustainable Development Goals (UN SDGs), for example, have climate-related goals inherent within their mandates. Relevant SDGs include climate action, affordable and clean energy, and sustainable cities and communities.
The number of solutions solely focused on climate has also seen heavy growth in recent years. As of December 2020, Morningstar identified 400 mutual funds or exchange traded funds (ETFs) for self-described climate-related offerings globally with a collective $177 billion in assets. That amount has nearly tripled in the last three years, and includes low carbon, climate-conscious, green-bond and clean-energy solutions.
Investors’ due diligence is still critical in digging deeper into these solutions to understand how tightly linked actual strategies are to addressing climate change, but it’s clear that the solution set is growing. We expect innovation in climate strategies to continue, addressing different investment domains and return streams that provide investors with a lot of choices in targeting their capital.
