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Leveraging Data to Stay Ahead of the ESG Curve

As more managers and investors target ESG goals, relying on data to make decisions and to communicate those decisions to stakeholders will be key.

Across the industry, we’ve seen an increase in the number of asset managers who have committed to ESG mandates. In fact, according to a Northern Trust and WBR survey of global asset managers, “Driving Growth in Asset Management: The Next Chapter” conducted in the second quarter of 2022, 61% of respondents plan on launching or increasing their ESG options.

As more managers and investors target ESG goals, the demand for data has grown. Transparency and accountability are the foundation of ESG investing, and managers feel the pressure – whether based on regional regulations or investor demands – to demonstrate that their products are, in fact, green, and not greenwashed.

While today there are multiple vendors that offer access to ESG data, there is still a lack of shared standards around what datasets are tracked, how they are tracked, and how to draw out and act on insights from that data. Consider this finding from “ESG’s Imprint on Institutional Investing,” a 2022 market study of global institutional investor clients, conducted by PwC Strategy& on behalf of Northern Trust: 89% of asset managers and 78% of asset owner survey respondents cited that collecting and analyzing data was a top challenge in incorporating ESG into their investment process.

The Northern Trust ESG study further confirms the need for better data and analysis for ESG investing. When respondents were asked what their greatest ESG needs are for their investment practices, 76% of asset managers cited internal reporting and data, while 40% of asset owners agreed.  Also of significance were analytical service needs (59% and 64%), and data solutions (47% and 40%).

Without a shared industry standard of analysis and reporting, committing to ESG investing mandates has been more challenging for institutional investors, and many questions have arisen as a result. How can they make their small sustainability investment teams work smarter? Can they access the data needed to drive portfolio-level decisions? What signals can they derive from the data that will help guide their decisions?

Five applications for the broad, non-standardized world of ESG data analytics

Armed with the right data, responsible investing teams must know how to apply it to produce actionable insights and are seeking solutions to help them get there. Some may choose to partner with a single data provider, while others may keep the function in-house, supported by direct data relationships. No matter which data management model firms select, a properly supported strategy will allow institutional investors to leverage the following applications to meet their ESG goals.

  • Screening – Screening securities for performance is likely the most common and straightforward way of using ESG data, involving analysis of certain factors of a company before deciding if it’s a fit for a portfolio. However, managers should be sure they have the capacity to go deep on their screening, including comparing multiple securities to understand their impacts, especially across the different segments of environmental, social and governance, and across industries. For example, comparing the ESG score of a credit card company with an electric car company may feel like trying to compare apples and oranges at the surface. Clearly, the environmental factors of the car company weigh heavier on its score than that of a credit card company that makes social elements of ESG a priority for their structure and operations. The right data solution should allow investors to analyze and weigh these different factors on a level playing field to understand which one truly is the better fit.
  • Understanding risk and performance drivers – As asset owners begin defining their ESG goals and benchmarking against them, they will need to grasp how their asset managers’ decisions align. Asset managers have been and will continue to feel pressure from investors that ask for customized ESG reporting on specific investment angles and considerations. For example, one investor may put special emphasis on gender equality risk while another cares primarily about carbon emission risk. Being able to quickly pull custom data views on different holdings will be crucial to gaining investors’ trust and allaying their concerns of greenwashing.
  • Portfolio optimization and simulation – When managers and self-managed asset owners bring new ESG focus to their strategies, understanding how changes may affect their portfolios before the trade is crucial. Firms should seek data tools and feeds which allow them to simulate changes to their portfolio and understand how they affect different ESG scores surrounding their portfolios.
  • Tracking change within specific holdings – For managers or self-managed owners who take a stake in an issuer and hope to see it improve not only in value but on an ESG front, the ability to access relevant ESG metrics for the organization as well as track change over time will allow them to hold management accountable to the ESG-focused promises they make to their stakeholders.
  • Reporting and benchmarking – A post-trade view on ESG investment decisions assists investors in taking a big-picture, hindsight look at how they’re adhering to their ESG goals and how they progress over time. With the right reporting approach, they can view rates of return or contribution by E, S and G ratings or view aggregate scoring, as well as track relative performance of each ESG pillar over time to understand the effectiveness of high vs low scoring allocations. Having this kind of big picture view allows them to adjust their investment decisions to bring them closer to achieving those goals.

To keep up with the many pressures that are emerging as ESG becomes a bigger consideration, relying on data to make decisions and to communicate those decisions to stakeholders will be key.

As ESG mandates continue to grow, regulatory frameworks are likely to develop, and investors will be ahead of the curve if they focus on how to put ESG data to work now. The right solutions can help them get there.

Paul Fahey portrait

Paul Fahey

Head of Investment Data Science, Asset Servicing, Americas
As Head of Investment Data Science, Paul works with asset managers and allocators to better understand the investment decision process and to improve future decision making. Partnering with expert fintech firms to leverage the power of data, behavioral analytics, investment analytics, and Northern Trust’s peer universe data to drive meaningful improvement to their investment strategy.

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