ESG Investing, Data & Graph
What is ESG?
ESG Investing is the consideration of environmental (E), social (S) and governance (G) factors alongside the typical financial factors in an investment decision-making process. In its philosophy, it is closely aligned with ‘responsible investing’, ‘impact investing’ and ‘sustainable investing’ but here we are focused on the data requirements and standards critical to the successful delivery of this rapidly increasing branch of the investment world.
To give some context of the growth of ESG, we find that:
- US investment flows into ESG grew by over 250% in 2019
- Similarly, the total assets in European ESG reached their highest ever level of €668bn 
- Even in these turbulent markets, flows into passive ESG products have been at least 3 times greater than the traditional Exchange Traded Funds (ETFs) 
The relative merits of ESG investment performance is a hotly debated topic, even though in Q1 2020, 60% of European ESG ETFs outperformed the broader European market. And given that, on the 20th April, the price of US oil went negative for the first time ever, we are in very new, very challenging investment conditions.
However, it’s also clear that the responses to the COVID-19 pandemic is refocusing the minds of asset owners, regulators and big corporates to longer term sustainability and ESG . The Principles for Responsible Investment, supported by the UN, stresses that “it’s time for asset owners sitting at the apex of the investment chain to lead the financial sector through this crisis” .
How do you know if you’re ESG positive? What does good look like for an investment manager?
As you would expect, there are an increasing number of data vendors offering ESG Ratings for companies and countries, with detailed analysis and ESG Indices too. Many of the larger asset managers have also launched their own set of ESG ratings and research. This results in a market that often reaches different ESG conclusions for the same company.
In a paper published on the 3rd March 2020, the Institute of International Finance (IIF), which has 450 members from banks to central banks, is calling for greater international alignment of sustainable finance policies and regulation to better support the transition to a sustainable economy .
I believe that this is a hugely important initiative and would result in a material improvement in achieving the macro ESG goals but that’s a much deeper discussion for another time!
So, let’s turn our attention instead to the way an individual investment firm can improve its own ESG analysis, scoring and investment process. Before analysis, we have data — which can easily cover over 1,000 metrics from Environmental (e.g. climate change, sustainability), Social (e.g. diversity, human rights) and Governance (e.g. corporate behaviour, employee relations). Assuming, for now, that we have a clearly defined ESG investment strategy, we need to source our quality information.
What data do we need and where do we get it from?
We want good quality, timely information to inform our ESG investment process . We’ve already mentioned the availability of a wide variety of vendor sources and they can present huge discrepancies in ratings. For example, on an ESG scale ranging from -3 (weak) to +3 (strong), the FT highlights Banco Santander can merit a rating anywhere from just over 0 to 2.5 depending upon rating vendor. Even Google ranks from -1 to 2 which present diametrically opposing points of view on one of the most influential firms in the world
This is unsurprising when you realise that the vast majority of the ESG ratings are based on self-reported data (allowing firms to only release data when it’s positive) which is un-audited. So, if you really want to promote faith in your much-vaunted ESG process, an investment manager has to combine these vendor ratings with specific, qualified external verification.
Step 1 of this verification starts with researching and capturing all publicly available data. For example, filings at Companies House for the UK, global company data from OpenCorporates or deploying web scrapers to pull data (legally) from the target firms themselves. This should be done regularly and, ideally, in real-time to provide the latest and greatest data.
Steps 2, 3 and beyond continue with really rolling up your sleeves in sourcing data. We’ve all seen the stories about accessing satellite imagery to monitor lorry deliveries, or shadows or predicting pregnancies and, yes, all of those could theoretically help in adding ever more granular detail & transparency to an ESG rating. But, for brevity, I’ll stick with Step 1.
Where do we put it & how do we best analyse this data?
So, let’s assume that you’ve managed to start collecting your numerous data sets. Where do you put it so that you can start your analysis?
- We know that the data will be in different formats, different update frequencies and probably different languages too. We will need a flexible schema to handle that variety.
- We know that more data will produce better, more qualified trends and insights, and we want to be able to monitor or model dependencies between data too. We will need high performance on complex transactional data.
- Finally, we know that our questions of the data, our hypotheses to test, will be extensive and evolve and need to be ahead of our competition. This is where we need to demonstrate our expertise and our commercial value. We will need the ability to run very deep, extensive analytics.
Given these three demands, we can’t use traditional relational databases (inflexible schema, poor analytic performance) or key-value databases (poor performance for complex transactions and deep analytics) either. You have to move to graph technology.
The ESG data problem we’ve set out is handled best through the interrogation of large, connected data sets which is a graph-shaped problem.
How can we use Graph?
Graph databases have been around for a long time and have been steadily improving in storage, performance, update frequency and privacy. High profile examples sit at the heart of LinkedIn, Twitter, Google & Amazon and power their ability to recommend possible connections, products or solutions.
Closer to home, graph technology is helping to solve problems in fraud detection, AML, risk assessment, customer 360 and product & service marketing. It’s increasing revenues and improving operational efficiency in many sectors, not just financial services. Given a solid graph foundation, deployed on the cloud, firms are also able to leverage AI and machine learning in truly automating their processes and thinking.
We’re now at Graph 3.0 and fully capable of handling the ESG opportunity.
We wholeheartedly recommend learning more about graph and trying it out for yourself. There are free & easy ways to start building your first graph in a safe & secure way and we’d be happy to point you in the right direction.
Originally published at https://6point6.co.uk.