ESG stands for environmental, social, and governance and these three factors are being increasingly used by investors to assess a company. In the eyes of law, a company is treated as a person in many cases. For example residency of the company is tied to the place it operates in. Just like a responsible citizen, there are expectations from a company, too, to be a good corporate citizen.
The question is can a company continue to remain profitable if it is exploiting natural resources and causing environmental harm, can it work without the support of society or can it remain a respected company if governance standards are questioned? The answer is an emphatic no.
The idea of ESG is to add investments to companies that are doing this well and remove investments from those which don’t demonstrate competence in ESG reporting or management.
The risk of passive investment is that we can continue to invest in companies that are not following best practices from the point of view of ESG.
Are you comfortable putting money in companies that produce liquor, make weapons, enable gambling, produce tobacco or consume leather?
If not, can you shift your money to companies that more closely align with your values? ESG can help you do this.
ESG is not new. The coinage is. Any company that depends on natural resources like timber, minerals, water, land and labour is designed to profit from its use. The key is to distinguish between use and exploitation.
A company that uses the highways paid for by taxes from citizens must be in harmony with its neighboring societies to avoid conflict and sentiment of exploitation.
A board that oversees governance must respect the law of the land as it builds the business with the help of the management.
The role of ESG is to bring explicit focus on these softer aspects apart from balance sheet parameters like margin, EBIDTA, PAT, etc.
Fundamental analysis has always intrinsically taken this into account. What we call as environmental concerns can easily be looked as resource depletion risk or the ability to extract value out of the resource in question.
For example, while a company may have many blocks of coal, can it actually mine it and also find customers who will burn it to create energy? What if the customers switch to cleaner fuels?
Isn’t it the responsibility of the coal mining company to research cleaner ways to consume coal? Or for example, if it is a textile manufacturing company, it must ensure the safety of its labour.
If they are not paying minimum wage, employing child labour or underpaying women employees, they can be exposed to labour strikes or legal action. This will impact their ability to meet their order book apart from the irreparable reputational damage.
Things like this are always very hard to monitor manually except using forensic inspection at sites but can artificial intelligence (AI) help? Can we use alternative data to find adjacent indicators as a positive screen if not as a negative screen?
Let’s take the example of environmental concerns. How can we estimate with AI if a company is doing a good job? What are the source of alternate data? One of the factors could be to map resource consumption to their CSR (corporate social responsibility) activities.
We use NLP-based AI techniques to map their expenses from the bills of items of their CSR programmes. Apart from this, it is possible to evaluate the quality of their supply chain using AI as the suppliers are equally part of the brand.
Mining data from supplier legal entities can also expose their supply chain vulnerabilities. Similarly, we can map their CSR activities to the locations of their actual operations.
Does it really help if they support schools next to the address of their management whereas their factories are located far away? Is it in line with their business activities?
For example, if they are a mining company, are they helping residents displaced by mining to gain new ways of employment by reskilling them next to the location?
From a governance point of view, things are a little easier to monitor as data is easier to collect. We deploy AI to observe board compositions, compensation of management as a percentage of profits, capital expenditures in discretionary products like office decorations, cars, travel to name a few.
We can also observe the quality of their disclosures by extracting the text and applying basic NLP techniques on essay evaluation. How much effort are they putting to explain to their minority shareholders? Apart from this, we can learn a lot from their voting results.
Are they bringing in resolutions where only the dominant shareholder is voting in favour and most of the other shareholders are not voting in line with management expectations? This is always a red flag.
As of now, it is quite difficult for individual investors to observe this or monitor this but we look for small companies that are demonstrating a high level of corporate governance standards.
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