Your sustainability objectives invite a significant and evolving challenge: ESG data. No matter the investment style, proper ESG integration requires reliable and current data to both construct ESG portfolios and confirm they’ve succeeded in meeting investors’ ESG objectives. For quantitative investment methodologies, ESG data must also be comprehensive and have an extensive history. What are the requirements for useful ESG data? Where are their pitfalls?
Unreliable
Data reliability means that the data sources must be trustworthy. ESG data today has high error frequency and low internal consistency (e.g., overall ESG scores reflecting pillar score changes). Ideally, the metrics should also be clearly defined such that the figures can be independently verified, at least in principle. However, at both the simplest and more demanding levels, ESG data present severe challenges, including: consistency, harmony and subjectivity.
First, data providers regularly introduce new sources, as investors’ interest in ESG is booming while older databases continue to evolve. This often negatively impacts the consistency of the data.
Also, unlike standard company fundamentals which have been reported and analyzed for many decades, there’s still little consensus on how to define or quantify ESG data. Even within the dataset of any single vendor, there are often variations due to changes in the raw-data sources, calibration methodologies, or ad hoc manual adjustments.
Finally, ESG data are unavoidably subjective to a large degree: all three pillars (Environmental, Social, and Governance) reflect implicit value systems, and their quantification depends on each provider’s judgement regarding which issues to consider, how to calibrate the rating scale, or how to distill the underlying ratings to a small number of figures. This opens the door to further inconsistencies at each step of this manual process.
Lagged
For non-ESG data, the time interval between news or company announcements and when they are accurately reflected by data vendors has been generally compressed to a matter of days, even hours. This is definitely not the case for ESG data, partly because of the amount of manual labor involved in procuring the data, cleaning them up, or curating them in other ways; also because the impact of news can be hard to ascertain at the time it becomes public knowledge.
These data management issues result in lagged ESG data – weekly or monthly, at best. And even at this low frequency, it’s often the case that analysts revisit only a fraction of the companies in the database at each update, so that many months (sometimes more than a year) are required for a full refresh of the database.
Incomplete
You might be surprised to learn that ESG data are difficult to access at high quality for broad index universes. Ratings for typical multidimensional ratings models are very resource intensive for this many securities because of the lack of similarity in the raw data sources (e.g., company filings, news, regulatory action, etc.) and the thorough cleaning required. Usually, only larger data vendors can afford to support the large teams that need to be employed for such a task.
What’s more, normalization of the ESG data poses special challenges, and it can have unintended consequences. For example, if normalization does not include an industry adjustment, specific industries will often have a uniform and persistently poor rating, and there will be little incentive for companies within these industries to improve. Also, if normalization does not result in a compact range of scores, then a few companies will exhibit such extremely high or extremely low scores that including or excluding them respectively can be used to “greenwash” portfolios through quite small tweaks in the holdings that don’t materially affect the ESG profile, but which nevertheless result in big shifts in the headline figures.
Limited
It’s important for investors to recognize that the broader investment community has only recently considered ESG factors. Compared to other datasets, there is a lack of historical ESG data that you can reliably use to extend the present rating systems far into the past.
Subjective aspects of the data, which require a high degree of manual intervention, exacerbate this challenge which is both costly to apply over large datasets and hard to do in a way that ensures internal consistency.
What’s more, ESG considerations by their very nature tend to evolve considerably over time. For example, the rating of a company’s environmental credentials is unavoidably affected by the prevailing consensus at the time as to what issues materially affect the environment, by technological progress, and by regulations that can dramatically shift the boundaries of what is legally permissible, etc.
Clear Challenges, But What’s the Answer?
ESG data challenges abound, but there’s a practical way to mitigate their effects on your sustainability goals. In our latest eBook, “Can Taking a Big Picture View of ESG Bypass ESG Data Pitfalls?,” you can see a closer examination of ESG data from a leading data provider and how you can use these insights in pursuit of more stable ESG characteristics and portfolio-level outcomes consistent with your expectations. Download it today.
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