19 Jan 2021
2020 was the year ESG went mainstream. The confluence of a build-up in asset owner pressure, regulatory changes and the pandemic caused a rise in both operational and investment spending on ESG by investment managers.
But while fund-flows into ESG hit record highs and investors hired dedicated teams and procured data analytics to support ESG analysis, there remains a significant mismatch in expectations and outcomes. Many ESG investments lack specificity, overweighting specific “favourable” sectors such as tech, and there is a lack of consistency and quality across ESG data providers.
As a result, while investment in ESG has risen, integration has been sporadic and ad-hoc.
The reason for this mismatch is the mistaken assumption that ESG data should be treated in the same way as traditional accounting and financial datasets. As a result, managers tend to integrate ESG analytics as they would traditional investment data – using it to develop screeners, identify early risk indicators or as a quantitative score within an overall investment thesis.
However, ESG is not accounting data – it lacks the uniformity and the clarity to be used in this way – and this explains in part some of the oddities we see in adoption and outcome.
A better approach would be to treat ESG like an alternative dataset (alt data), which would bring a number of benefits.
Firstly, this would help bring to the fore the inconsistencies in metrics and assessment processes across providers. Because we think of ESG as financial data, we find these differences problematic – either overlooking them altogether or trying to mitigate through process or consensus. However, alt data expects and embraces such “inconsistency”, including essential steps to clean-up, normalise and strip data back to atomic and consistent series, which can be matched to investable assets.
Secondly, treating ESG as alt data would facilitate better integration into the investment process as each series and metric would be assessed objectively and without emotive baggage – in terms of its ability to explain or predict KPIs of interest. Thus focusing on financial metrics, such as revenue growth or profitability, or market metrics such as asset price performance and volatility. These “signals” could then be compared to other supporting research or data to develop an overall picture for an investment thesis or theme.
Finally, insights from ESG once structured, can be used qualitatively (as part of an asset allocation or thematic investment strategy), or quantitatively as an input into a model or set of trading rules to develop custom baskets, individual investment recommendations or portfolio strategies.
And of course, none of what has been described above precludes the use of ESG insights qualitatively, or as early warning indicators within a traditional research or investment process. But without clarity of meaning and materiality – what is a specific datapoint telling me and what action does it support from an investment perspective, it is impossible to see how ESG data can be used, other than as a box ticking exercise.
Trying to fit ESG data into the traditional investment process, akin to P/E ratios or earnings forecasts is likely to lead to frustration and inconsistent performance. Only then will ESG investing move from a talking point to actionable insight and truly become sustainable.
Invisage helps Investment Managers and Data Providers quickly and effectively integrate ESG into the investment process. Our Entropy platform helps normalise data, identify investment signals and develop portfolio strategies. If you would like to learn more about how we can help you improve your ESG ROI, contact us on firstname.lastname@example.org.