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Determining Sustainable Performance – How ESG Data Can Be Used

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Determining Sustainable Performance – How ESG Data Can Be Used 1

By Stef Nielen, Director of Strategic Business Development, Asset Control

The environmental, social and governance (ESG) data market has been growing fast over recent years. In total, management consultancy, Opimas estimates the ESG data market was US$617 million in 2019. With an expected annual growth rate of 20% for ESG data and 35% for ESG indices, the market could approach US$1 billion by 2021.

There is growing evidence to indicate that when integrated into investment analysis and portfolio construction, ESG data may offer asset managers the opportunity to achieve competitive edge. ESG data is an emerging field and there are no global standards. However, asset managers are looking to differentiate themselves by being ahead of the curve in this area when it comes to sourcing, analysing and reporting on ESG information.

For many asset managers, getting access to ESG data and operationalising its use is key in helping them to find more alpha, in other words the excess performance on an investment against a market index or benchmark.

Hunting for Alpha

The main idea behind gathering ESG data, after all, is to analyse and discover whether there are any potential market externalities to be found behind all the investments – where an externality is deemed to be a consequence of an industrial or commercial activity which affects other parties without this being reflected in market price. Investors need to know whether their data management processes cater for their ESG sourcing, analysing, investment decision-making and reporting.

Ultimately, effectively using and combining the wide range of available ESG data sources is a key part of the day asset manager’s attempt to capitalise on these market externalities. And so, yes, the search of the sustainable positive externalities or continuous ‘spill over benefits’, represents the latest chapter in alpha hunting that the buy-side has been after for decades. Apart from using ESG data in the investment process, ESG benchmarks play an increasing role in external reporting – both to customers and regulators.

ESG data, therefore, has potentially huge value to asset managers in terms of helping to drive sustainable business performance. But before it can deliver this value and become actionable, due to the absence of standards and the wide range of data sources, the data will need to be normalised and cleaned. That will be key before asset managers can use it to construct a portfolio, manage a fund or create an index.

Many fund managers are just now starting to figure out how to combine and clean the essential time-series and reference data from their different sources. Now they are suddenly also confronted with the challenge of consolidating and normalising ESG-data from different additional vendors and (in-house) sources at their disposal. But with so much ESG data coming in from all directions, how can one see the wood for the trees? How can one squeeze true asset allocation and investment decision information out of all of that, rather than merely accumulating data?

Getting to the solution

Finding that real alpha, always required one to sift through (and clean up) a lot of data from different vendors and sources first. Nothing new there. For example, carbon data is relatively easy to get from several providers when it concerns major equity allocations nowadays. But is it always rightly historically dated? If not, perhaps you might want to make some corrections here and there and align with other sources – and keep track of those while you are at it – before they become part of a final asset allocation decision.

And how does one map a company’s carbon information readily to the exposures that their bonds are giving you? Does that carbon allocation apply there too? All such reference data would need to be systematically mapped and cross-referenced before you could make sense of your overall fixed-income investments.

A final green example of creating ESG information before you can see the wood for the trees is normalising ESG ratings that are sourced from the several different providers. To do this properly and create your own in-house overarching combined rating cleaned by sector or industry classes, you would indeed require an algorithmic approach. And by extension that approach would need a proper system to manage all the information and track its data (overrides and changes).

So, having your own data sourcing, cross-referencing and aggregation capabilities is key, especially when you want this data to be centrally administered and disseminated to every possible discipline in the buy-side house such that one can manage the funds and the assets and reflect them properly against the applicable benchmarks.

The cheaper option would be to just use data from a single source, but you would most likely lose out on a lot of combined information that way. In contrast, an enterprise data management system empowers you to decode, normalise, and operationalise the combined reference and ESG data from many different sources and create genuine investment decision information that helps drive business advantage and competitive edge

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