On 23rd September, our Singapore team including James Baker, Bruce Russell and Saurabh Kumar hosted our second Southeast Asia roundtable forum to discuss the common challenges faced by asset owners and asset managers in managing ESG data and reporting ESG specific analytics on their products.
The forum had a wide range of participants representing leading asset owners and asset managers in the region.
Here are five key takeaways from the panel discussion:
1. ESG is no longer a discretionary choice for buy-side firms: In the Asia Pacific region, ESG investing has gained substantial traction on the back of strong demand from millennial investors, focus on climate change, regulatory push, and reputational advantage. The massive magnitude of flows into responsible and ESG investment products in the last five years has proved that it’s not a seasonal investment theme that is going to fade away anytime soon. It is rather construed as a strong force to reckon with, and it will grow with time. This megatrend is also evident in the growth curve of PRI signatories. The number of PRI signatories have grown from 63 to 3826 in the last sixteen years. As of March 2021, these signatories represented a total AUM of 121.3 trillion USD. Please see the below exhibit for reference.
At the same time, the stakes with ESG investing have become high, with buy-side firms needing to tread their responsible and ESG investing paths carefully to avoid any potential greenwashing allegations in the future.
2. ESG datasets are fundamentally different from financial datasets: Of the various operational challenges emerging from ESG investing, we believe that one of the biggest challenges is related to the ESG data itself. ESG data is a type of alternative dataset – a unique dataset with its own taxonomy, which puts the onus on the end-users to manage it in a way that management of traditional financial datasets does not require. We have categorised the differences between ESG data and traditional financial data into five major categories. These are sources, regulation, coverage, frequency, and purpose. At a high level, traditional financial datasets tend to capture the tangible factors driving the direct performance of an issuer like its revenue growth, margin growth, profitability, assets, liabilities etc., whereas ESG datasets tend to capture intangible factors, which could be material to an issuer’s future performance. These may include brand value, health & safety record, board’s gender composition, social media reputation, GHG emissions etc. Please see the below exhibit for reference.
3. Lack of consistency, comparability, transparency, and integrity with ESG datasets is a hurdle in institutionalising ESG data operations: We all are aware of the lack of consistency, comparability, transparency, and integrity with ESG datasets provided by different ESG data vendors. Also, unlike credit ratings, correlations are much lower for ESG scores calculated for the same issuer by different ESG data vendors. Please see the below exhibit for reference.
There are two schools of thought on ESG ratings. One suggests that ESG rating will go through the same journey that credit ratings have gone through over time with vendor consolidation waves and consensus in reporting standards. If so, one could expect stronger rating correlations and consistency in the long run. The other camp considers ESG ratings as another form of sell-side research which, by design, offer facts embedded with opinions.
However, to avoid getting blindsided with ESG data anomalies, firms should try to familiarise themselves with the ESG scoring methodologies of their respective ESG data vendors. If possible, they should subscribe to data feeds from multiple vendors depending upon each vendor’s coverage strength to issuers across public and private markets, developed and emerging markets and the type of ESG indicators they publish. They should also try to overlay their proprietary scores on the issuers, which are part of their active investment universe.
4. In the current state, many asset owners and asset managers tend to exhibit idiosyncratic behaviour in their ESG client reporting obligations: Unlike traditional investment reporting, the ESG reporting metrics lack common industry standards. As a result, firms are presenting their ESG results in their own ways. Some firms are barely meeting the minimal reporting requirements by comparing their portfolio’s environmental, social and governance score against the benchmarks, while others are trying to come up with their own metrics and gain strategic mileage from their unique ESG insights. Some firms have opted to look before they leap and have taken a step back to do a thorough needs analysis to make sure their reporting deliverables are fit-for-purpose, both today and in the future.
While some reporting standards have already been introduced to cater to ESG portfolio disclosures (like SFDR), which is going to bring the much-needed clarity for reporting deliverables, firms should also look inward to assess what tangible goals can be achieved with their existing ESG product labels and try to extract insights accordingly.
5. ESG reporting needs to be streamlined with integrated workflows: Even before ESG, accurate and efficient client reporting, whether regulatory reporting, board reporting or fund factsheet production, has always been a challenge for many buy-side firms. While the industry has come a long way in the last decade with the proliferation of interactive BI platforms such as Tableau, Power BI, QlikView etc. and RPA tools like UiPath, Blue Prism, etc., it is still not uncommon to see front-to-back report production workflows that touch too many hands or require painful reconciliation between systems that aren’t integrated as well as they should be. ESG adds another layer of reporting complexity. For many asset managers and asset owners, ESG datasets sourced from external data providers end up either on Research Management Systems (RMS) used exclusively by front office teams or remain isolated in excel workbooks. In either case, they do not make it to the Enterprise Data Management (EDM) platforms where other investment datasets are stored. Due to the lack of cross-referencing of ESG datasets with golden copies of Security Master, IBOR, ABOR, Benchmark data etc., data from multiple systems must be manually stitched together to generate portfolio level ESG insights. This brings inefficiencies in the operational processes, weakens data governance principles and limits scalability in the reporting workflow. Please see the below exhibit for reference.
To overcome these barriers, firms should strive for an integrated data model which aligns ESG data with broader investment datasets. Reviewing the design limitations of their existing data management infrastructure could be a great starting point for asset managers to consider.
How Shoreline can help?
Our expertise in developing ESG data operating models combined with our deep understanding of leading market vendor’s offerings in the responsible and ESG investing world allows us to set you up for success in your ESG investment journey. With our unparalleled knowledge of investment firm’s operating models, system architecture, technology landscape and data service providers, we are able to assist both asset managers and asset owners with the maturity assessment of their ESG operating model at a programme level.