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Tech Traps: Four ESG Investing Traps Awaiting the Unwary
3 April 2020
Two comments from clients come to mind when I think about our approach to creating a solution for ESG investing - undoubtedly one of the biggest trends of our time. The first is, “Your system turns the Investment Policy Statement from a dead filed paper into a living document at the centre of the investment process.” The second is, “You give my client advisors freedom within a framework.” So, to avoid trap two, and to make the ESG investment process scalable and consistent, clients’ preferences should be treated as structured data within a framework, with clear documentation for the client on what these mean and how they will be applied. In addition, the impact on the investable universe of the client preferences should be made clear to the client. Trap 3: Data – poor understanding and transparency over what is being measured
As will be made clear, implementing mass customisation in ESG and impact investing is not easy. But it was the logical evolution of our applications, as well as being the right thing to do for the planet, clients and the wealth sector’s own interests. Staying relevant is crucial as the biggest intergenerational wealth transfer in history gets underway.
We interviewed clients and reviewed academic and industry literature, investment houses and activists on both sides of the Atlantic during our research and development into ESG investing. Here are some of the biggest traps we found to be lying in wait for wealth managers.
Trap 1: Taxonomy – not coming to a common understanding of what ESG means
ESG is specifically directed at Environmental, Social and Governance factors that measure how a company is run and performs its business. It is one form of sustainable investing, which also includes SRI (Socially Responsible Investing), product exclusion, impact and active investing. Often people will group these forms under one label with which they are familiar, such as “impact”.
There can be tens or hundreds of these factors, but usually significance is given to a smaller number of aggregate factors. So, to avoid trap one, make sure all parties understand what should be included in terms of preferences and controls.
Trap 2: Client preferences – allowing mandates to be either vague or over proscriptive
For decades clients have expressed preferences with respect to product involvement – the so called “sin stocks”. ESG extends the capability to measure a company’s attributes far beyond its industry sector. The danger is that each client will have their own ESG preferences and given free rein may be overly proscriptive about what can be included in a portfolio.
Equally, they could be quite vague. Clients’ preferences may range from “I want a greener portfolio than the benchmark”, through to them having a view on multiple product exclusions, ESG and impact factors.
My advice would be to define which preferences your firm can accommodate in an ESG Management Policy, together with how these are captured and delivered through your investment process.
There are many ESG data vendors. Original data capture is generally manual and then distilled into quantitative scores. Secondary data is the aggregation of this original data either to a product score, or across original data vendors to achieve a consensus view.
A firm needs to ask itself what it is trying to achieve with the data: an asset’s absolute score, sector relative score or score trend, or all three?
Next, decide how granular the data should be. Is it enough to say that the asset has a better ESG score than the benchmark? Or, do you need to know the ways in which it is better?
Here, the trap is that the asset can have an OK score overall, but with some really poor underlying scores. You must unpick how the poor scores relate to the client’s preferences. It is key to understand the asset’s ESG factors at least at the same level as client preferences. Scores can be aggregated, but the method should be transparent. Points really relevant to client preferences may be there in the detail, but hidden by aggregation, so avoid this trap by looking at the data in depth.
To avoid data traps, a firm should understand the data collection, aggregation and analysis as part of its investment due diligence process, so that it can explain and justify its views on assets to clients.
Trap 4: Analysis and portfolio construction – only carrying out superficial analysis
One client I spoke to said, “It’s like ordering a green salad, and it arrives with red peppers and chillies in it” (fine for some; unpalatable for others). Understanding the detail is key to avoiding issues. Portfolio managers must have visibility and clarity over the ESG attributes of each asset; how they contribute to the overall portfolio scores; and where any conflict with client preferences lie.
Of course, this becomes harder with composite assets such as mutual funds and unit trusts. Look-through gives the best answer as it will reveal the underlying detail, but then statistical significance needs to be considered. If the portfolio has 10 per cent in a fund with 100 holdings, the smallest will be at most 0.1 per cent of the portfolio. If such a holding conflicts with the client’s preferences, is it significant?
Typically, a firm’s ESG Management Policy would set out for the client how this is approached. The alternative is to use a published score for a fund, being conscious of potential underlying conflict. If the client is not willing to accept conflicts such as these, then it has to be questioned whether they should invest through composite assets.
To avoid the superficial analysis trap, use the data available and a good portfolio construction tool which incorporates that data to understand exposures. Always making the firm’s policy on significance clear to the client is also key to avoiding issues.
Doing good, well: your policy, your process, your systems
This brings me right back to the two client comments at the beginning of this piece. These covered making the client’s preferences a living document as part of the ongoing investment process, and setting a pragmatic framework for investment.
Combining the client’s ESG preferences as part of the suitability and investment management solution and monitoring the results, meets the objective of constantly delivering the investor’s needs. It is therefore difficult to envisage this as not being part of most wealth managers’ offerings looking ahead.
To do this well, an FI should have a clear ESG Management Policy setting out how ESG will be applied to client portfolio management, which preferences can be accommodated, how ESG data will be gathered and how exceptions to the process will be managed. This is key to setting the boundaries of preferences within a framework and collecting structured data which will together support mass customisation and ensure clients’ needs can be met.
Our mission has always been to help establish a common understanding between the client and their advisor of what portfolio risk means, as the foundation to a long-term relationship. We aim to achieve the same in the more complex area of sustainable investing.
This forms part of this publication’s latest research report, “Technology Traps Wealth Managers Must Avoid”. Download your free copy by completing the form below.
Two comments from clients come to mind when I think about our approach to creating a solution for ESG investing - undoubtedly one of the biggest trends of our time. The first is, “Your system turns the Investment Policy Statement from a dead filed paper into a living document at the centre of the investment process.” The second is, “You give my client advisors freedom within a framework.”
So, to avoid trap two, and to make the ESG investment process scalable and consistent, clients’ preferences should be treated as structured data within a framework, with clear documentation for the client on what these mean and how they will be applied. In addition, the impact on the investable universe of the client preferences should be made clear to the client.
Trap 3: Data – poor understanding and transparency over what is being measured