ESG Analysis: Is judgment more important than data?
Over the past two years CFA Institute and the Principles for Responsible Investment have gone around the world to better understand the current state of environmental, social, and governance (ESG) integration. These meetings have resulted in the publication of four different papers aimed at helping investors better integrate ESG data in the investment process. One ESG topic that was raised wherever we went was the issue of ESG data, primarily the problem of obtaining quality data that is decision useful. Many of the investors and financial analysts we met said that the availability of meaningful and comparable ESG-related data is getting better but is not at the level it needs to be.
The availability of ESG-related data is growing and improving — just look at your Bloomberg terminal or whatever resource you use to collect ESG data. The amount of data available for ESG metrics has grown exponentially over the past decade, and for large multinational companies in developed markets, investors can find a lot of what they need. But gaps still exist.
If, however, you are trying to find ESG data for small- or mid-cap companies or companies in emerging markets, it is likely that the data does not yet exist or will take a great deal of digging to uncover.
In recent years, investors and analysts have been investing in ESG data from data providers and also developing the skills of their in-house teams to better understand ESG issues. These efforts will enable them to exercise better judgment even if they don’t have every piece of data they want.
Until the availability of data catches up with the demands of financial professionals, investors and analysts will have to use more judgment than data in some of their analysis and decision making surrounding ESG issues.
Is that so bad?
Not necessarily . . .
It’s called analysis.
After all, what we do as financial professionals is analysis. This often involves doing as much investigating and data analysis as we can so that we have a detailed, if not fully complete, picture of the companies in which we invest or are looking to invest. Although we can never have every piece of data we want, we can get enough of the story to make informed decisions. In the absence of all the data, we sometimes will have to use judgment.
One of our workshop participants summed up the question of data versus judgment well with the following observation:
I think it can be a false dichotomy to say that data is more important than judgment. I think that investing more generally, even outside of the ESG space, is always a combination of both. Obviously in the active management space, which is where we are, it’s a combination of both.
But I don’t think that there’s ever a space where you would you say that the data is somehow more important than the judgment. I think they go hand in hand. And I think when people get so focused on the quality of data in the ESG space, what they’re really saying is, “I would like to feel more confident in my judgment. I would like to feel like I had all of the information that I could have obtained that’s high quality and reliable so that I can make a better judgment using my qualitative interpretation of that quality data.”
Another workshop participant said that this isn’t a “data thing”; this is a “judgment thing.” What is the right incentive scheme for management? There isn’t a right or a wrong scheme because it depends on the company, sector, and country. Judgment is needed to determine what is appropriate to the profitability of the company, the industry, the state of development of the company, and so forth. Climate and water have different impacts on and materiality to a bank, to advertising, and to a chemical plant. We need to improve the data, but we also need to make judgments about how to best use the data and about what is relevant and material for the generation of a company’s underlying business.
Is data really a barrier?
Well, yes and no.
Yes, it can be in the short term, but less so in the long term. Yes, it can be for small- and mid-cap companies, but less so for larger companies. Yes, it can be for those who want data to drive decision making, but less so for those who are comfortable using judgment in their investment decision making. As the workshop participant quoted previously noted, data provides confidence when exercising judgment. Some investors are more comfortable doing this than others. And some investors think the current slate of data is just fine. In fact, they see it as an advantage.
Short term versus long term
Transparency and quality of data around ESG will improve to allow for better ESG analysis in the future. As a growing number of clients ask their existing and future investment managers about ESG integration practices, more money likely will be invested in the shares of companies with good ESG performance and high ESG scores. This trend could drive up share prices of companies with high ESG scores and drive down prices of companies with low ESG scores.
As ESG disclosure standards such as SASB (Sustainability Accounting Standards Board) and TFCD (Task Force on Climate-Related Financial Disclosures) gain traction with investors and issuers, the data supporting ESG data will only improve.
Small- and mid-cap companies
As standards around ESG data develop, small- and mid-cap companies and companies in emerging markets will feel more comfortable gathering ESG information. If something like SASB becomes the standard for ESG disclosure, then small- and mid-cap companies and companies in emerging markets will be able to focus on the handful of ESG metrics germane to their business and will not have to worry about hundreds of other ESG datapoints.
Currently, with so many ESG key performance indicators (KPIs) demanded by investors, practitioners are concerned about which ones to choose — a problem that standardization of data reporting would help. ESG integration requires understanding the ESG issues and selecting those that are material. Including every KPI in your analysis would result in a tiny weighting of each, with the most relevant KPIs being diluted.
Large companies have an advantage over small companies in reporting ESG data. Investors need comparable data and a significant data history, which are not available with small-cap firms. Because large companies have more resources in terms of personnel and budget, they also can spend more on ESG marketing, allocate more time to engaging with investors, and devote more resources to tracking ESG data.
Judgment versus data
ESG integration is just good analysis. Such analysis captures more of the risks and opportunities (including unknown risks and opportunities) that ESG integration techniques typically identify. Practitioners who fail to analyze ESG issues could miss alpha-generation opportunities, whereas investors who do analyze ESG are likely to outperform their peers over the long term simply because they are engaging in a more thorough analysis. Pristine, complete, and comparable ESG data are not always available. This is where judgment becomes essential. Being an analyst or portfolio manager means you have to understand the whole story of the companies in which you invest.
When complete data isn’t available, which is often the case with ESG analysis, analysts and portfolio managers really earn their money. By knowing the industries in which they invest and the companies they analyze, good analysts and portfolio managers exercise judgment to fill in the gaps when data isn’t available.
Good analysts track down data when they aren’t handed data in company filings. Analysts can do channel checking, kick the tires of a company’s supply chain, or do a deep dive into regulatory data (such as health and safety data in each market) that may not be included a company’s filings.
In our workshops held around the world over the past two years, we came across more than a few investors who admitted that ESG data needed to be improved, but they were in no hurry to see ESG data standards develop. These investors were confident that their judgment around ESG issues was a competitive advantage, and that if that data was standardized, their advantage might be taken away.
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