ESG Data Management: Opportunities and Challenges
The growing trend in ESG investing provides a new set of challenges to both firms who want to pursue a responsible investment policy, and the ESG data management systems they rely on to support their operations.
What is ESG Investing?
ESG investing takes environmental, social and governance considerations into account when deciding which companies to invest in. Historically, investment decisions were based on what would be perceived as the highest possible risk adjusted return, with no significant judgment calls on how those returns would be generated. Over the years, ESG has evolved to take a deeper view into an organisation’s behaviour, and its worthiness to attract investor’s money. In tandem, the ESG data management challenges have also evolved as companies look to incorporate ESG criteria in their investment decisions.
Environmental considerations cover aspects such as climate change – does the company rely on fossil fuels for instance? Is the company sourcing raw materials from diminishing stockpiles?
Social aspects look at the way the company treats their employees and develops products. It looks at the diversity of their employment strategy, how employees are treated, how a company treats their customers, and whether any animal cruelty exists in their product testing.
Governance covers the general management of the company. This ranges from the relationship between the C-suite and the board of directors, the values the company has towards staff, and ensuring that remuneration remains fair for all.
Why has ESG Investing Grown in Importance?
For fund management companies, the importance of ESG investing is undeniable and so too are requirements around ESG data management. It is estimated that investments taking ESG data into consideration have almost doubled over the course of the last four years, and now account for US$40.5 trillion of investor assets.
Moreover, inflows into ESG funds are accelerating. In the US, the first half of 2020 saw sustainable funds grow by $20.9 billion, nearly matching the total figure for 2019 of $21.4 billion. In Europe it is estimated that funds guided by ESG considerations will outnumber conventional funds by 2025.The net impact is that ESG data is being incorporated across a very large and growing proportion of the market and cannot be ignored. That is not just because investors have developed a social conscience, but also because ESG factors have shown to positively influence investment returns. One of the most comprehensive studies to date, aggregating results from approximately 2200 unique primary studies (Friede, Busch and Bassen), found a strong net positive correlation between ESG criteria and financial performance.
Why does ESG Data Management Pose Unique Challenges?
Conventional market data is relatively standardized. Although there may be subtle differences in the way different vendors name certain fields or calculate analytics, most of those differences can be normalized without significant difficulty. By contrast, the lack of standardization in ESG data vendors makes the normalization exercise much more challenging. A recent academic paper (Berg, Kölbel and Rigobon, 2019 – Aggregate Confusion: The Divergence of ESG Ratings) found there were 709 distinct indicators provided by six of the leading ESG data vendors – MSCI KLD(MSCI Stats), Sustainalytics, Moody’s (Vigeo Eiris), S&P Global (RobecoSAM), Refinitiv (Asset4) and MSCI – and these indicators could be grouped into 65 categories (where two or more indicators related to the same attribute).
In addition to dealing with data that is challenging to normalize, ESG data vendors also reach conclusions that can be highly divergent (see figure 1).
Figure 1: Normalized ratings for 25 firms with highest mean absolute distance to the average (sample size: 924 firms); Source: Berg, Kölbel and Rigobon
Given this high level of divergence in ESG ratings, Berg, Kölbel and Rigobon suggest that investment firms broadly face three options in looking to use this data more effectively:
- Option 1 would be to “include several ESG ratings in the analysis” in order to derive a consensus rating or index.
- Option 2 would be to “use one particular ESG rating to measure a specific” company characteristic – thus adopting a pick and mix approach to select specialists in each area and deriving your own composite indicator.
- Option 3 would be to “rely on verifiable and transparent measures” – which may involve directly evaluating data reported by companies. While this kind of data can also vary significantly, there are efforts underway both to standardize metrics and promote greater disclosure.
Irrespective of which option investment firms take, it is clear there are significant and unique challenges in the way ESG data needs to be aggregated, normalized and analysed.
What Technical Capabilities are Required for ESG Data Management?
Responsible investing requires a greater degree of data management to be truly successful. Many vendors now supply ESG data, but indicators are not well standardised and ratings can be highly divergent. The ability to import ESG data sets from multiple sources, to be able to validate, contrast, compare and analyse the sources to derive a gold copy standard allows investment companies to make informed, responsible and ethical decisions on behalf of their investors.
From a technical perspective, this may mean storing data not traditionally managed inside the investment data management system. The solution must be able to understand the fields from the various different data providers and fill gaps, allow a user to select the data they prefer from each source, or combine the sources into a master store. In this way, an enriched dataset enables the analyst to work with the data from a single vendor, or combined sources, or worst case, and so on – giving a broader picture of the investment opportunity.
In addition, the data model must have the ability to create new fields and properties, build relationships between different types and forms of data, and enable the analysis and comparison of the investment strategy through data visualisation and reports.
In-built analytical capabilities are also important, given that many firms may opt to derive their own consensus ratings or composite indicators. Equally useful is the ability to carry out time series analyses and correlate ESG indicators with other measures of financial performance.
We have written before about how older, more traditional data management systems built around reference and market data can struggle when asked to process data for new trading strategies. It can require significant development effort to include this new data as their existing data models are generally fixed towards the perceived ‘standard’ model for asset types such as an equity or a bond. The ideal here would be a data model that can be easily adapted and configured by business users – rather than IT specialists – to enable them to model the data to give them the answers they need.
As with any investment strategy, the quality or availability of the data on which decisions are made can be an enabler or a blocker to moving into new areas. ESG is no different and the flexibility of the data management platform that supplies the data can provide the keys to progressing in this market.