Prof. Louis Cheng is currently the Dr. S H Ho Endowed Professor of Banking and Finance and Director of the Research Institute for Business at the Hang Seng University of Hong Kong. He is a member of the Investment Committee of the Hospital Authority Provident Fund Scheme in which he leads the effort in promoting ESG integration for an investment portfolio of about USD 8.74bil (as of March 2020). Dr. Cheng actively engages in ESG research and knowledge transfer activities. Dr. Cheng discusses the state of ESG in Hong Kong, and the sustainable landscape at large in this blog.
The Environmental, Social, and Governance (ESG) concept was developed in 2006 by The United Nations Principles for Responsible Investment (UNPRI). In recent years, ESG has been a hot topic for both academic research and industry practice in the areas of finance and accounting.
One of the main challenges on ESG integration in investments and ESG reporting is the measurement issue. As ESG is non-financial information, quantifying ESG data into a composite measure that is acceptable by all stakeholders is extremely difficult. The recent work on “Aggregate Confusion” by the MIT team is a good example aiming to understand the divergence of various ESG ratings provided by the data providers.
We should recognize the importance of measuring ESG efforts of corporations from the perspectives of various stakeholders, namely, asset owners and analysts, accounting professionals, consumers, and institutional investors.
Which criteria should be used for ESG fund selection?
In the past few years, on behalf of a large pension fund in Hong Kong, I have the opportunity to participate in the selection and evaluation of portfolio managers for their ESG integration. I have also conducted a lot of research related to ESG integration and ESG intelligence for equity portfolios.
My overall impression is that, at the top level (asset managers with large AUM, say USD800 bil or more), ESG integration is relatively mature with unique inhouse solutions supported by external ESG data providers. For this level, we tend to focus more on evidence related to alpha generation and/or risk mitigation. Since the approach to ESG integration vary substantially even at the top level, I conclude that it is the corporate culture and passion of the lead managers in determinate the specific path to engage ESG. For instance, asset managers with strong tradition in corporate governance and engagement such as Blackrock, the ESG analysis tend to extend their tradition of CSR and channel their ESG efforts through top management and the board.
For the others who are new to ESG integration with no prior CSR presence, they tend to adopt a more scientific approach in evaluating E, and S aspects of the investment choices with a quantitative model. In both cases, we are happy with asset managers that show clear understanding and conviction of risk mitigation with some potential for alpha enhancement.
For medium size asset managers, the ESG effort and performance are less obvious and more confusing. The right jargons appear to be in place in their presentations but the procedure and coverage appear to be superficial. I believe that their ESG effort may be mainly driven by client demand and regulations. However, the inhouse research team are not up to the standard and expectation to perform meaningful ESG integration due to lack of resources.
What are the most pressing issues on ESG integration and the possible solutions
The Key Challenges for Better ESG Integration:
- Quantify Social Return using scientific or systematic KPIs.
- Integrate Social and Financial Return into a composite performance indicator.
- Large scale research to profile ESG preference in terms of utility function is needed to form a scientific database to construct benchmarks related to ESG investments.
Based on my research and interaction with buy-side managers, there is a clear consensus on the need and demand for better ESG intelligence. So, in my view, the most pressing challenge is to navigate through the ESG data confusion and identify better ESG rating through better intelligence.
The well-known “Aggregate Confusion” phenomenon by the MIT team demonstrates the main divergence of ESG rating comes from 3 aspects: Scope, Rater, and Weighting differences. My proposed solution is to create better intelligence. In other words, the ESG integration in asset management should consider the additional intelligence reflected by the divergence.
If the investment choice (equity and fixed income) receives a very dispersed set of ESG scores from data providers, then such a divergence is telling us there exists some potential bias or uncertainty in a given rating, leading to a less trust-worthy appraisal outcome for the investment choice.
We should recognize the importance to adopt ESG integration go beyond social returns but include risk mitigation and even seeking alpha. Future ESG performance evaluation and ESG integration should make use of this ESG divergence in selecting investments in the portfolios. In other words, I am proposing a divergence-adjusted ESG rating for examining risk mitigation and alpha generation for buy-side and asset owners.
What should asset managers and fund houses do?
As mentioned earlier, I propose a divergence-adjusted ESG rating for examining risk mitigation and alpha generation for buy-side and asset owners. I also suggest that asset managers should prepare an ESG successful story among the investment choices to showcase their ESG approach.
Products of fund houses should find a way in identifying the ESG profile of the asset owners and tailormade their products to their ESG preference. For instance, some products focus on E only and others on S only. A combination of E and S with different weighting should also be available for clients. Of course, the challenge is to identify the ESG profile of the asset owners first.
Berg, Florian and Kölbel, Julian and Rigobon, Roberto, Aggregate Confusion: The Divergence of ESG Ratings (May 17, 2020). Available at SSRN: https://ssrn.com/abstract=3438533 or http://dx.doi.org/10.2139/ssrn.3438533