Why packaged, static ESG data sets don’t cut it—how to win at ESG investing amidst a throng of competitors and an ocean of generic ESG data.
Dating back to 1851, the America’s Cup is the premier international sailing competition. The odds to win favor the defending champions, who pick the location and even the boat type.
In 2013, Oracle Team USA was predicted to win over New Zealand. But the Americans found themselves down 0 to 6 on their home surf in the San Francisco Bay. Just three more losses with 12 races to go, and they would have to give up the cup to New Zealand. The Americans took stock. Their $12 million boat was capable of 48 knots, faster than any sailboat ever entered into competition. Their computer models, which analyzed tens of thousands of data points to project the fastest route, were unparalleled in sophistication and accuracy. They knew the course well. And yet, they were being crushed.
After hours of analyzing data, the Americans pulled up some choppy race footage. They saw that the Kiwis were sailing far off the wind at an angle that required the boat to cover much more distance but at a faster pace—something the Americans’ computer wasn't even programmed to allow. This insight catalyzed a comeback for the ages. With a slight change in tack, to go farther but faster, the Americans ended up winning the Cup, 9 to 8. Their initial reliance on “big data” had almost cost them the title.
Similarly, asset managers that rely on traditional ESG data sets to read the signals may miss important cues that would suggest taking a different tack to achieve the desired outcome. Commercial ESG data sets are all “programmed” the same way—serving up data organized into hard-wired frameworks that were designed ages ago, with a bias towards illuminating risk, not identifying opportunities.
Relying on traditional, commercial ESG data providers to inform your investment strategy is no different than leaving port without a compass and letting the wind and current determine your destination.
In sailing, you develop a strategy by considering a few key elements: the wind and weather, the tide and currents, geospatial characteristics of the course, and the design of the boat.
You can view an ESG investment strategy in an analogous way. Macroeconomic events like interest rates or inflation, and systemic risks like climate change and inequality, provide the big picture on the overall health of the market. Just like in sailing, you have to tell which way the wind is blowing.
Sectoral trends are the currents. Some industries will be uplifted and some will face headwinds as a result of a clean energy transition, in the way that oil and gas may be depressed and certain metals and mining stocks may experience gains.
Geospatial characteristics are risks or counterfactuals—confounding events that materialize along the way, requiring re-allocation or rebalancing. Often in ESG investing, these are driven by changing societal norms that affect the outlook for a particular group of stocks or industries, such as those affected by consumer preferences regarding plastics or the opioid debacle.
The attributes of the boat can be compared to idiosyncratic risks—specific to the company and its exposure to and management of ESG risks.
The consolidation of commercial data providers and their offerings, under the guise of standardization, means that investors will “tack when all the other boats tack”, causing them to buy high and sell low. Not a winning ESG strategy. The problem with efforts to “standardize” ESG data is the focus placed only on company-specific risks.
An effective investment strategy must be informed by many other factors. ESG asset managers who increasingly rely on data from a small set of commercial data providers will miss the larger signals that, in many cases, are the controlling factors driving growth, momentum, and impact.
When Jimmy Spithill, the skipper of Oracle Team USA, was on his way to sail the crucial ninth race that could have been his last in the match, he listened to one of his favorite songs: Rage Against the Machine's "Take the Power Back."
If ESG investors want to actually have an impact and capture success, they must find their momentum. They must propel themselves with an intentional and active strategy, one that doesn’t rely on stale ESG data.
To establish a clean energy transition investment strategy, investors would do well to look at “larger weather patterns” such as commodities prices, procurement patterns of companies, renewable portfolio targets of utilities, and government investment in infrastructure. Innovation in technology and disruption of entire industries will be needed.
Data about greenhouse gas emissions tells an investor nothing about whether a company is positioned to succeed in a clean economy or what trajectory they are on. A much better indicator is the investments a company is making towards their future. But this information is not found in traditional ESG data sets.
The good news for investors is that new technology using AI and natural language processing (NLP) enables the creation of data sets to support any investment thesis. Companies such as Amenity Analytics help investors write tailored taxonomies that key in on specific words and phrases within a particular context.
Writing a taxonomy structures the data from unstructured narratives across the entire investable universe and helps investors focus on themes that are important to them. Once the taxonomy is developed, it can be run frequently—continuously even—to identify emerging trends and anomalies well before they are represented in a standardized ESG rating. This minimizes “stale” ESG data, allowing investors to sail their own race.
Taxonomies can shed light on any ESG topic that is particularly significant for an investor, illuminating not only current performance, but whether a company is prioritizing climate initiatives or other ESG factors that may be more material to their core business. More powerful than researching and cataloguing carbon data is the insight regarding which companies are attending to climate adaptation and mitigation year round.
For example, in a recent engagement with a leading asset manager, Amenity was able to leverage their flexible taxonomy to drill into one of today’s emerging issues: the circular economy. The firm recognized that their analysts needed real-time, granular data into company actions across all functional areas of the circular economy, and that relying on traditional data sets for recycling data alone was not going to cut it.
Perhaps most importantly, this manager realized that their analysts were hungering for information and wanted to empower them to read through company actions in the most efficient way possible. By working with Amenity to develop this specific taxonomy and data feed of tagged articles, they took a significant step towards that goal.
As this investor realized, making progress on the transition to a clean economy requires more than reporting on a finite set of generic indicators. It requires an intentional strategy and an understanding of whether industries are actually shifting and companies are making good on their intentions over extended periods of time.
Traditional ESG data sets don’t help, but taxonomies utilizing NLP technology can be created and tailored to help investors find answers to specific questions, while thriving amidst shifting winds.
Investors shouldn’t completely ignore traditional ideas and data that have built the financial industry today.
However, ESG investing is an emerging topic that is largely still being defined and focuses on the long-term potential of companies. This requires fluid data sets such as the NLP offerings of Amenity Analytics, which change and adapt with the market while discovering what companies are doing today in order to prepare for an increasingly turbulent future. Just like team Oracle in America’s Cup, sometimes you must re-examine the difference between what you know and what you think you know in order to win the race.
Watch a recording of our special guests Jean Rogers, Founder of the Sustainability Accounting Standards Board (SASB) and one of the world’s leading ESG experts, alongside Bruno Bertocci, Managing Director of the Sustainable Equity Investors Team at UBS Asset Management. They discussed why it’s crucial for the analyst community to include next generation ESG data and second-order information as part of an effective investment strategy.
Request an ESG demo today to find out how you can analyze earnings call transcripts and other financial documents with our text analytics platform. Spot outliers, identify critical insights, and understand key drivers.
Amenity Analytics is the industry leader in providing insights from unstructured text by using Natural Language Processing (NLP) assisted by Artificial Intelligence (AI) and Machine Learning (ML). Amenity’s NLP system is a sector-agnostic, language-dependent tool for quantitative text analysis that is deployed across the financial services industry and beyond.
This communication does not represent investment advice. Transcript text provided by FACTSET and S&P Global Market Intelligence.
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