Financial analysts are siloing ESG. This must change
Most ESG issues are subtle, and financial models need to start reflecting this
TLDR: Analysts tend to look for direct links between ESG factors and financial performance, for example having to interrupt production at a factory due to a massive flood. Many effects of ESG factors are, however, more subtle than this. ESG performance is deeply integrated in business operations, which means that every ESG factor has a potential influence on financial performance. Both ESG risks and ESG opportunities play a role, and analysts need to consider not only the impact of ESG on a business, but also the impact of a business on ESG factors in its operative environment. Wangari aims to build financial models that link each ESG factor to relevant financial variables.
Financial analysts face immense pressure every day. They need to build financial models for many companies, dig deeper and write industry reports, maintain great relationships with their clients, and stay innovative enough to impress their managers while not straying too far from market consensus.
On top of all of this, over the past few years analysts have gotten a new item on their plates: ESG.
Many analysts have not been trained in using data with units like megatons instead of price per share. Trainings and dedicated certificates are now available, but studying for these demands extra time off an already stressful schedule.
To fix this situation, financial institutions have resorted to creating ESG teams. They are comprised of people who have a background in data science or natural sciences, and are thus better equipped to make sense of ESG reporting data. People on the ESG team work through ESG-related data, generate in-depth reports for clients, and deliver insights which they hope analysts can work with.
This, however, leads to another problem: The intersect between the respective competencies and goals of ESG people and financial analysts is often so small that they are not very involved with one another’s workflow. Financial analysts toil away at their models unless an ESG person has something really important to say, and ESG people make insightful sustainability reports which impress clients but only rarely find their way into financial models.
Financial analysts do not have the time to “do ESG.” ESG people do not have the time to do the analysts’ work.
Looking for subtle shifts instead of direct links
In the day-to-day reality of a financial analyst, ESG is a work department. Due to all the pressure they face from elsewhere than the ESG department, they only include ESG-related factors in their financial models if there is a clear and direct ESG-related impact on one of the financial variables.
As an example, if a massive flooding some years ago meant that production at a factory had to be interrupted for a significant amount of time, an analyst might adjust the financial forecast of that business for flood risks.
The problem with this approach is that it captures only some rare extreme events, while the majority of ESG-related considerations do not find their way into financial models. This is unfortunate because ESG-related considerations can have a significant impact on financial performance.
ESG is similar to AI in the sense that it is a very operational concept for businesses and impacts their financial performance in a variety of different ways. Using large AI models can increase costs; however, it can also increase labour efficiency and even drive revenue if customers appreciate AI-enhanced products.
Similarly, improving working conditions (this is a social factor and ties to the S in ESG) might increase costs; however, it can also increase productivity, drive revenue, and boost the reputation of a business.
ESG is hence much more integrated in business operations than financial analysts currently account for. Each change in an ESG performance variable might only result in a subtle shift of various financial variable. All together, however, they can significantly sway the result of a financial model.
And, unlike the case of AI, many businesses are obliged to report on their ESG performance. This gives financial institutions a unique window to glimpse what a business is really doing, and to fine-tune their models accordingly.
Finance needs a holistic view on ESG
Many businesses have understood the opportunity that ESG reporting represents to the financial industry, and have thus resorted to selling ESG-related data. This is a highly lucrative business model but has two shortcomings: First, they sell their data to ESG teams, which means that little, if any, of their data actually makes its way into financial models. Second, the sold data often uniquely focuses on ecological risk factors, which is only a small part of ESG.
The financial world deserves the full picture. This sounds simple but is actually a bucket list. In detail, getting the full ESG-picture to finance entails:
Covering all three letters of ESG,
Treating not only physical but also transitional aspects,
Thinking about risks, opportunities, and feedback loops,
Understanding which ESG factors are easy and which are hard to quantify, and
Providing links between every ESG factor and financial variables that appear in analysts’ models.
To truly bring ESG to the financial world, one cannot skimp on any of these aspects. Covering all of this is precisely what Wangari is aiming to do.
ESG is three letters
When it comes to ecological concerns, a lot of work has been done. Many frameworks exist in order to help businesses quantify their impact on nature and on the climate.
Social concerns are another matter entirely. Not many frameworks include social aspects at all. Some interesting approaches regarding the S in ESG, however, have been developed in the context of multi-capital-accounting. Bringing more social aspects into financial modeling is therefore a challenge, but one that Wangari is willing to face.
Governance-related aspects are often overlooked. This, however, might be with good reason. Many financial analysts understand fundamental management theory and how businesses operate very well. The G of ESG is therefore the only letter that routinely finds its way into financial models these days.
Transitional risks and opportunities are important
Physical climate risks are easy enough to grasp: A factory owner would not want a massive flooding to touch their assets, lest they need to interrupt production. A farmer would rather not face drought during planting season. A city would rather not be touched by a hurricane.
Quantifying these risks and predicting extreme weather events is an extremely challenging task that many companies, notably insurers and reinsurers, master very well. This, however, is only a small piece of the puzzle.
Physical aspects like weather phenomena are important, but there is another category: transitional aspects. This is an umbrella term that includes four sub-categories: reputational, regulatory or legal, operational, and market-related aspects. All these aspects are ESG-related risks and opportunities, too, and must be factored in to form the bigger picture.
Risks, opportunities, and feedback loops
It is not just about risks. ESG-related issues can be an opportunity, too. A farmer in Canada might soon be able to grow crops that only grew in the southern US a decade before. Climate-conscious consumer goods can achieve high margins because consumers are willing to pay a premium. Due to the changing regulatory environment, the financial sector has an ESG-opportunity to use more ESG data in their models.
Analysts must also be able to scrutinize ESG factors that happen to a business, versus ESG factors that originate from a business. A factory flooding happens to a business, but water pollution through chemicals originates from some businesses.
The distinction is important because it uncovers interesting dynamics and feedback loops. A business that pollutes a river might face legal repercussions or consumer boycotts. The pollution is a physical nature-related misbehavior that originated from the business; the result are S- and G-related factors that happen to the business.
Such feedback loops and other dynamics must be understood in detail and applied to financial models on a sector- or subsector-wide basis.
Quantifiable and not-so-quantifiable factors
One additional difficulty is that many ESG factors are not easy to quantify. Calculating carbon emissions is a difficult task; nevertheless, carbon is a relatively quantifiable factor because there are established frameworks and scientific methodologies for this.
Health and safety, on the other hand, is quite difficult to quantify these days. There are not many universal frameworks for it, and gathering data on this issue is challenging.
Analysts must custom-build methodologies (or use those that Wangari is setting out to develop) in order to quantify ESG factors. Only then can they find a robust and traceable way into existing financial models.
Linking ESG factors and financial variables
Having considered all of the above, analysts can finally link ESG factors and financial variables. If a company discloses its plans to buy more energy efficient equipment (this represents an E-related opportunity), then it might face more capital expenditures at first, but decreased operational expenses moving forward because the energy bill will be lower.
If a company lets its workers conduct their work in particularly stuffy or noisy conditions (this is an S-related risk), then this might increase its operational expenses because of increased sick days and an elevated turnover rate. It might also decrease its revenues as workers in suboptimal conditions struggle to achieve peak productivity levels.
If a company is subject to many cybersecurity attacks (this is a G-related risk), then it might face increased capital and operational expenditures as it must invest in and maintain data security systems and pay for any business interruption that ensued from an attack.
How to make a gargantuan job easier for analysts
With their already existing workload, analysts cannot be expected to accomplish all the tasks we listed above. ESG teams are part of the solution; however, they often lack the in-depth understanding that is needed to bridge the gap between themselves and financial analysts.
Cross-functional teams are a solution; however, this means recruiting a lot more ESG people because each analyst team will need some of them. This approach is further complicated by the fact that ESG people often have specializations, for example regarding climate-related risks or governance-related opportunities. Hiring a full ESG team for each team of analysts is not feasible for most financial institutions.
Luckily, however, much of the workload to bridge the gap between ESG and financial modeling can be automated. This is precisely what Wangari is working on.
We aim to provide all ESG-related insights to analysts by taking into consideration all aspects described in this article. All these insights will come via a software, such as an Excel plug-in. This way, analysts will be able to do what they do best, while not needing to worry about ESG-related shifts.
ESG teams in financial institutions will not be automated away. Their work is needed to understand things in more detail, and provide human-generated context where software alone is not sufficient.
What Wangari does is bridging the gap between teams and breaking the silo that ESG is currently in. This way, everyone will thrive: analysts and ESG teams, financial institutions and their clients, and the planet.