(ESG — stands for environmental, social and governance)
I was looking at a graph on the Visual Capitalist website recently, on a page that depicted where investors put their money in 2018 — graphically represented by Jeff Desjardins and published on February 20th, 2019. Pictures are worth a thousand words…
Specifically, what caught my attention was the outflow of money from mutual funds into Exchange Traded Funds or ETFs. Note that the outflows from mutual funds in 2018 was $91.3 billion and the inflows to ETFs was $238.4 billion. Mutual funds were still the larger pool of investments at $16.3 trillion against the paltry $3.4 trillion of ETFs. Mr. Desjardins’ latest publication of January 21st, 2020 shows the ETF universe has grown to $5.75 trillion AuM in 2019. Not a small increase, by any measure, but only $51 billion are in thematic ETFs, which is where the socially responsible stuff is counted. We’ll overlook the fact that many ESG-labelled ETFs are still in high-carbon industries…
“So what?”, you might think. Especially if you’re not convinced that ESG has a future. Sounds like moving from an investment where you have to pay someone big bucks to pick stocks (mutual funds), in favor of a completely passive index or collection of companies with a micro-fee (ETFs) is a good idea. Especially if the active stock-pickers are rarely beating the ETFs that track the benchmarks or indices which the mutual fund managers are also tracking. Why wouldn’t you do it? Aside from creating a revenue problem for investment managers with eroding margins, this all sounds like great news for asset owners and investors.
Essentially, you would expect “good” companies to go up and “bad” companies to go down as defined by the financial criteria that you choose to predict the stock price. Most of these price movements are anticipatory of the actual results, since the stock market tries to stay ahead of the present, which is already old news.
There are so many variables implicit in stock market forecasting (or private equity forecasting) that there are as many theories about predicting the movement of prices as there are gurus in the stock market. Their overall results are pretty meh. No one has a crystal ball (but there are some baffling outliers in the hedge fund industry). Last I checked, professional fund or active asset managers were collectively losing to their indices or benchmarks 64.5% of the time (Source: CNBC — March 15th 2019). This might partially explain the move from mutual funds to ETFs. And this goes back, like 9 years, so not a flash in the pan.
But there is another wave of change coming. According to TruValue Labs, a leading ESG AI-research firm, 87% of company value comes from intangible, non-financial, or ESG data (shout-out to Andre Shepley at TruValue Labs for his informative research and who presented these numbers in a recent webinar). Let that sink in for a moment…The vast majority of financial investment professionals have been primarily focusing on what amounts to no more than 13% of the information that will determine the price of a stock or company value? No wonder those managed funds are having performance issues! To be fair, holders of professional investment credentials are not prohibited from looking beyond the figures alone, but that’s a relatively new development in an industry that hasn’t changed much in the last 100 years (think Benjamin Graham) and that were mostly convinced until recently (kudos to the CFA Institute and others for getting with the program) that 10-Ks, 10-Qs and private company audited financial reports and disclosures were the primary source of valuation inputs.
The use of artificial intelligence (AI) and machine learning (ML) to sift through thousands of data points contained in a variety of research reports, news and documents in order to evaluate tendencies, threats and changes to the “market” perception of companies is becoming widespread. The information, or the perceptible changes in the information translates into possible or probable variations in quoted share prices or unquoted company valuations. Using heuristics and algorithms, information can be sifted through at near light-speed in order to capture the slightest changes of sentiment, material issues and behavior of companies of interest. Better than the eyeballs of an analyst, glued to a monitor with thousands of news feeds in which a full workday won’t suffice to read.
Hedge funds, especially the quantitative variety have been doing some of this for some time. Now that it is quickly being generalized to the overall investment management industry, we can expect some major shifts in how money is managed by the big asset managers. When everyone has the same information, self-fulfilling prophecies have a tendency to —self-fulfill. What this means is that everyone will eventually be getting the data and acting on it with more or less conviction in the same direction. The danger is that they will be thinking that they’ve got the ESG-thing sorted out by just capturing the data, when the real objective is to shift investments towards a survivable future.
The data-crunching just got a dose of serious amphetamines, what with graph-database technology and the application of machine learning to reading documents in real-time now available beyond the secretive hedge-fund trading desks. No longer limited to the number of investment analysts generating primary research and pouring over secondary documents, the machines can parse the information and display the results on custom-designed dashboards and maybe even place a trade directly, based on the information sought and obtained.
What the investment decision-makers (human or computer) do with this new superpower is what will probably differentiate the women from the girls (wishful thinking). What seems to be lacking in the new approach is a cultural, or human-intelligence framework that will make such analysis meaningful. I admire the data scientists and programmers that have fine-tuned their heuristics and algorithms to properly sift through the noise and the unstructured data. But what will the investment professionals do with their new-found insights? Probably buy, hold or sell as they did before. This would be a waste of an urgent opportunity.
It underlines the importance of an ESG investment strategy as the first step in creating meaningful and value-aligned investments. If all of the technology is just being used as a complex alert system for companies falling afoul of their non-financial, intangible and ESG criteria, identifying potential risks on their dashboards and radars, then we are missing the crucial purpose of responsible investing in critical times. Namely the opportunity to use investment dollars to back companies that truly are trying to tackle some of the most urgent problems of our current and future generations and that threaten our very existence.
Now would be the time to use the superpower of AI and ML to source investments that transition from the extinction economy and avoid all of the injustices and inequalities which are the consequences of culture-blind (-bound) short-term profit oriented companies, maintained by those who still hold the belief that financial gains make everything right and stakeholders are really shareholders with a slight conscience. We are beyond best-in-class investing. It’s time to make new benchmarks and indices that reflect the world in which our children survive.
Thanks to AI and ML, (ESG) investors could now get back to focusing on values, culture and meaning (the long-term wins) and use the heuristics and algorithms to align and monitor their investments with the big-picture issues. This requires a cultural change that is unfortunately not keeping up with the machines. Or perhaps some investment professionals are having trouble keeping up with societal changes or the science of global warming. Either way, complacency will have grave consequences for everyone.
Originally published on Medium February 13th, 2020 https://medium.com/@jonathanjenny1/esg-ai-ml-no-substitute-for-values-culture-and-meaning-83c838a05a87