Posted on : August 12th 2021
Posted by : Sanjeev Kumar Jain
Investment in ESG has been multiplying for several years now. The amount of professionally managed portfolios that have used vital elements of ESG is more than $17.5 trillion, as per this 2018 Global Sustainable Investment Alliance report. According to the Organization for Economic Cooperation and Development (OECD) report, ESG-related traded investment products available to institutional and retail investors today go beyond $1 trillion and continue to grow. We can see signs of this through the emergence of responsible investing firms and a focus from large firms on improving ESG benchmarking, reporting, and tracking.
Socially conscious or sustainable investing has spread across the world. It has a long-term financial implication in increasing a firm's chances to survive and thrive by anticipating and resolving surprises arising from ESG failures. Several companies are making efforts to project themselves as socially responsible or ‘sustainable’ companies. According to a Harvard Law School Forum report on Corporate Governance, the percentage of institutional and retail investors that apply ESG principles to at least a quarter of their portfolios rose to 75% in 2019 from 48% in 2017.
To have a strong ESG investing strategy, an enterprise must first have a strong ESG data strategy to drive investment operations. One of the key hurdles to this is the qualitative nature and variability of data. Unlike other financial datasets, ESG measures more than just qualitative number-crunching statistics and relies on understanding a company's actions and intentions. Therefore, investor firms spend a considerable amount of resources trying to normalize and understand the data, thus, slowing them down.
Human-based approaches tend to rely on analysts, which introduces human bias into your data, skewing ESG scores.
Let’s look at some challenges owing to the qualitative nature of ESG investing and how to deal with them.
The main goal of ESG data is to capture a firm’s performance on a given ESG issue as accurately as possible. Without this, firms do not have an accurate understanding of potential acquisitions ESG performance.
When measuring ESG scores, there has to be an understanding of incidents/events that have occurred and policies and decisions taking place that affect these incidents. Measuring these factors are often not binary measurements but a full-scale understanding of the scale and thoroughness of these policies. This data can be used in multiple ways, including internally measuring one’s firm on ESG. The same holds for
The second biggest challenge with ESG data points is that the metrics used to measure ESG, especially the qualitative metrics, are not standard across enterprises. Each data point assesses a material ESG issue; whether the assessment is viewed positively or negatively is entirely dependent on how an investor interprets it. For example, a metric on companies conducting ethical audits can have mixed results depending on how an investor defines ethical audits. Each investment firm has a different threshold for a company to meet when looking at ESG data points.
Due to a lack of standardization, investors cannot assess the performance of companies' ESG criteria appropriately and identify material risks, creating challenges in even some of the most mature ESG data strategies. Therefore, there are calls for having a common, mandatory standard for ESG definitions.
There are primarily two reasons for the lack of ESG standardization:
Rapid Growth: The rapid interest in ESG and slow movement of compliance has created a gap in industry standards
Data differences: Different ESG scores and ratings, as well as methodologies across the different rating providers such as Bloomberg, MSCI, and Refinitiv, leading to varying scores for the same company.
Investment firms looking at getting a competitive edge in ESG investment know that technology is a necessity. While technology solutions are available for ESG data crunching, it is still to catch up with the speed at which ESG investment is growing. According to research by the CFA Institute, technology or the lack of it has more to do with a company’s culture as many companies still have siloed and fragmented structures. Due to this separation, there are diverse views of technology, limiting investment firms’ technology capabilities or capacity.
The first step before collecting ESG data is to remember that you need ESG data to make an informed decision about the correct data, sourcing, and use. Your ESG data strategy should match the investment operations strategy.
Every investor uses ESG data in different ways to fit their personal/ corporate strategy. To get the best from an ESG investment, Investment managers/analysts should ensure that they match their ESG framework to the first step. This framework should seamlessly fit into your investment operations. If required, look for guidance from SASB and SFDR frameworks.
ESG data can be overwhelming, but to get the correct information, investors will have to define each data point in close alignment with their strategy, thus, reducing open-ended data points.
In the final step, use AI/ML and automation technologies to get the data you want. These technologies will help eliminate analyst bias if you outsource the data collection to an outsider or need more from the existing data that an analyst might not help you with. While you will be able to buy most quantitative and binary data from providers, make sure to be innovative when extracting qualitative datasets defined by your investment strategy.
As per a survey by consulting firm bfinance, 84% of global institutional investors face the challenge of replicating the deep knowledge of any region, sector, or company in ESG data to create high-quality data.
ESG data is often qualitative. Investors often spend a disproportionate amount of time interpreting non-standard data, thus, slowing down the investment. As mentioned above, several reasons like lack of standardized reporting, inconsistent data, and different data points have all contributed to a chaotic ESG data system. However, with the right technology and processes, one can improve the data strategy to better overall investments.
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