Can machine learning give IFA clients an ESG investment selection edge?

17 May 2021

Our government is a firm believer in the power of investors to push the companies they invest in towards behaviours that will be kind to the planet. And of course, the investors with the most muscle are those with the most cash to invest.

So, it was entirely logical for the government to take powers in Pensions Act 2021 to mandate a new climate-friendly approach by the big pension schemes - those with more than £5bn of assets and the 38 authorised master trusts. From October 2021, they must have an effective governance process for dealing with the climate-related risks to their portfolio. Furthermore, by the end of next year, just as UK is hosting COP26, they must report in line with the Taskforce on Climate-related Financial Disclosure (TCFD) standards.

For them, climate change has been springboarded into the ‘legislative must-do' camp and we will now witness rapid change in the institutional markets. Understanding the difficulties that institutional investors face, and the things they may get wrong is key to providing retail clients with a clear path through the ESG maze.

The clamour for investments that do good is already underway. Consultancy the lang cat's latest report on ESG, 'Crossing the ESG Event Horizon' noted that in the UK alone assets under management in ‘Responsible Investment' funds grew 89% between January 2019 and June 2020 (Source: Investment Association).

Looking across the Channel, PwC has estimated that the share of European assets held in ESG investments is set to leap from 15% in 2020 to 57% of all tradeable assets in the region by 2025, putting downward pressure on assets that do not make the ESG grade.

Dunstan Thomas decided to interview one of the many players in the world of ESG rating and building ESG-led investment strategies which reflect objective research in order to rate companies' progress on ESG.

We asked Dr Daniel Philps, Head of Rothko Investment Strategies, to explain to us how firms are being ESG rated today and how ESG measurement is likely to mature fast, aided and abetted by powerful machine learning algorithms analysing rich data sets.

ESG Ground Truths?

One of the problems with setting up an ESG index or rating system today is, in machine learning parlance, the universal ‘ground truths' are not yet set firm. Institutional asset owners, pension consultants, asset managers and pension trustees often have different views of what socially or environmentally-responsible investing is and therefore what an ESG index should measure. Only segregated accounts, of the sort reserved for large institutions, allow that sort of purity of definition. However, artificial intelligence (AI) can help us establish an objective truth faster and more consistently once those ground truths are agreed.

Investors are already discovering that the components of ESG may be in conflict with each other and not the holy trinity originally envisaged. Take Cambodia's recent appeal for foreign investment to help develop its new oil discovery. My ‘E' antenna says, ‘avoid this black carbon-heavy fossil fuel'. But my ‘S' conscience has its heartstrings tugged by the plans of one of the world's poorest countries to use oil revenues to develop health and education systems. As for ‘G', well, Cambodia has come a long way since the despotic years of rule by Pol Pot and the Khmer Rouge.

What does AI bring to the world of ESG rating?

How then can AI algorithms do a better job in spotting and objectively analysing ESG risk? First, given the poor availability of ESG data (and the danger or misreporting), AI offers a powerful approach to data acquisition and validation. It's capable of scouring thought leaders' comments on social media, influencers such as Amnesty International's red-flags, as well as news flow on events and sentiments. You can therefore eliminate a reliance on either what a company says about itself or what its management tells a human analyst.

Second, ratings are often criticised for inconsistencies over time, sector, country and more. Machines are inherently objective and can be scaled across vast investment universes. This makes AI-led ESG risk assessment a compelling solution in principle.

Algorithms will, in the future, be capable of being run over all the available structured and unstructured information linked to tens of thousands of listed businesses, tirelessly uncovering developments worthy of analysing and scoring, all in a consistent and objective manner. While there are huge challenges to developing AI in this area, the current alternative (human analysts) are expensive, inconsistent, and necessarily subjective.

ESG Risk or Responsibility?

Let's look at a typical approach of one major US investor today, the Californian State Teachers Retirement System (CalSTRS). CalSTRS is the second-largest pension fund in the US with AUM of £283bn. Last year, it decided that a small proportion of its investments must be net-zero. They have established a roadmap for their investment managers whereby that percentage must increase steadily year on year.

So not only must the investor's investment managers meet that percentage target, but they would also be wise to increase weightings in companies that have set fairly aggressive net zero emissions targets. However, this narrow focus on Green House Gas (GHG) emissions may not extend to covering other legitimate ESG targets such as over-extraction of water in manufacturing or microplastics pollution.

However, if as an investment manager you go beyond these highly-focused remits into other ESG-related targets that are not a priority for the asset owner and that are perceived to limit investment returns, you risk losing your mandate. It's a balancing act for asset managers - you cannot afford to make too many changes too quickly.

Asset managers must move at the pace of the investors, working with their changing ESG priorities. However, it's critical to be able to tailor ESG measurement systems to investors' bespoke demands and focus areas.

What this means is that, over the next decade or so, there will be less and less capital moving to companies that don't change their behaviour in line with the Paris Accord and other ESG targets. The key will be to ascertain which risky areas to divest from this year, and which need to be tackled next year and so on. A divestment road maps might need to be built by investment managers covering the next 10 years or so, and this must be signed off by the investors.

Saints or Sinners: All relative?

ESG risk analysis will need to consider also whether it's about the level of GHG emissions that a company is putting out today or the degree of reduction intended or achieved. One also has to consider how material emissions are to the business a company operates in.

For some companies like BP or Shell, the scale of the task is clearly massive. So, arguably they should be rewarded for meeting or exceeding their emission reduction targets and diverting resource into clean energy projects successfully. These are arguably the companies whose efforts to go green will yield the most positive results in terms of global GHG emission reductions because their impacts are the most material. But are GHG emissions an absolute sin or a relative one?

Investors will need to decide therefore if they are puritans or pragmatists. In other words, do you decarbonise your portfolio of investments aggressively, come what may, potentially leaving some of the worst offenders with no direct incentive to complete their emission reduction programmes?

Or, as a pragmatist do you instead accept that some businesses are materially worse than others and engage with them to support their transition for the good of all? Or, thornier still, is whether you levy the same punishment on an emerging market (EMs) energy company as a European one, given that tighter regulations will likely take longer to be enforced in EMs.

Every IFA client is different and special

Stopping to pause, I am amazed at how far we have come already. It was 30 years ago that I worked on the launch of what we called an Ethical Index fund. It proved to be a hot favourite with IFA clients at the time, and it was built on a simple exclusion approach - it was the FTSE All Share Index with a couple of hundred ‘offending' companies excluded altogether.

But today's clients no longer want a ‘one size fits all' approach like this. There are a thousand different ways to save the planet, and we both can and should listen to investors' preferences. Nuclear energy is wonderfully clean yet terribly dangerous. Wind turbines harness an unendingly renewable energy source but can extract a terrible toll on sea bird populations.

‘Know Your Customer' is the mantra of every good adviser. AI tools mean we can extend this into the field of Responsible Investing in the retail sector in a way that is impossible for institutions. The trustees of a pension fund with thousands of investors will never get beyond guessing what the majority of them think is responsible investing.

Yet a carefully worked questionnaire can help elicit what's personally important to an IFA's client, recording this and playing back their answers for confirmation, before feeding these results into an AI ESG algorithm to drive investment selection. Please use a computer-based questionnaire rather than paper, they are much better for personalisation and create much more engaging customer journeys.

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by Adrian Boulding, Director of Retirement Strategy at Dunstan Thomas
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Follow Adrian Boulding on Twitter: @adrianboulding or read Adrian's previous article here.