Online, Japan Investor Forum, July 2022.

Sylvain Forté, SESAMm's co-founder and CEO, discusses ESG data and its challenges. Further, he describes how to generate insights and reports on millions of companies, including micro-companies, using artificial intelligence and natural language processing.

Below is an approximation of this video’s audio content. Watch the video for a better view of graphs, charts, graphics, images, and quotes the presenter might be referring to in context.

About SESAMm

To give you a bit of context, I’m CEO of SESAMm, a French company of around 100 people that has been in business for eight years and that specializes in artificial intelligence for finance, especially with a focus on ESG.

So we work with some of the largest insurance companies in Japan, such as Tokio Marine, Asset Management One, or Japan Post Insurance. And we have seen the rise of ESG investing in the past few years, especially in the past four years in Europe and in the U.S. And we see now this trend also in Asia and in Japan, more specifically.

Primary uses of ESG data

The primary uses of ESG that we see are first complying with regulation. That is the key priority for most asset managers, but also improving performance. Many quantitative teams are seeing ESG also as a way to have new factors integrated that could qualify to generate alpha in investment funds. ESG is also used a lot in order to better manage risk in portfolio and, finally, to better analyze sustainable investment opportunities.

ESG use cases

So a couple of the main use cases are detecting ESC controversies. So purely from the perspective of generating risk alerts, excluding assets that are not well rated in portfolios, or creating portfolios that contain best-in-class assets, meaning most sustainable assets.

And finally, I want to mention that this trend is really global. So it's across both public assets, equities, and bonds, and also across private equity. And we see private equity reacting very quickly to the ESG trend.

Traditional ESG data challenges

So now, let's discuss in more detail some of the key challenges of ESG data. Traditionally, ESG data is created by teams of analysts that are looking at individual companies that are gathering data from each of the companies, and that are then reading the press in order to complement that information. This approach is relevant, but it is hard to scale, and it presents some difficulty. Traditional ESG ratings agencies are, for example, MSCI or system analytics.

The problem with a lot of traditional ratings is that they don't cover small companies very well. And this is one of the key challenges currently in ESG is the lack of coverage. So it is very difficult to cover small caps, microcaps, and also private companies. In particular, in Asia, the coverage is very poor right now for ESG, and that means that many portfolio companies may not be covered by ESG rating. In Japan specifically, even large companies are sometimes not covered by traditional ESG providers. So that creates a lot of data inefficiency in the industry.

Another key challenge that we see in ESG right now is the frequency of ESG ratings. So oftentimes, ESG ratings are updated only one time per year or just a few times per year. And when ESG ratings are used for risk management, obviously, the market is moving much more quickly than one time or a few times per year.

In addition to that, we see that ESG ratings mostly takes into account information that is reported by management and does not take as much into account information that is from outside of the company. For example, in the case of government scandals, such as fraud scandals, it is actually better to have information that is not reported by the company but that also has an external point of view.

Lastly, the last key challenge I want to mention in ESG data specifically, and one challenge that I'm sure you are aware of in market data and fundamental data is that ESG data is oftentime, not point-in-time. So that means that you don't have a continuous dataset that has not been modified over time. ESG agencies tend to modify their ratings after the fact, and so that means that the rating that you will receive now for a data point in 2020 will not be the same that the rating that you would actually have received in 2020 point-in-time. That creates a lot of problems when you want to back-test data because you cannot reproduce actual historical results.

So these are all of the key challenges that we have identified in ESG data currently, and there are challenges in order to address the needs that we described. But there are actually some solutions that exist.

The solution to ESG data challenges

And one of the key solutions right now that is merging in ESG is the use of artificial intelligence, in particular, what is called natural language processing, meaning text analysis.

What we do at SESAMm and what some other providers do is detecting ESG risks and positive impact with regards to sustainability by analyzing automatically billions of articles and messages in real time. So as an example, we have 18 billion articles and messages from common news websites, from social media, from blogs and forums, and from company reports. And we automatically detect ESG themes and risk and perform sentiment analysis in order to understand whether a company may be exposed to an ESG controversy or whether a company may have positive impact with regards to sustainability.

Advantages of AI for ESG data challenges

And the advantage of AI in that context is that it solves a lot of the challenges that we discussed before. So it helps access higher frequency data, it helps cover small companies, private companies, it helps also find information that is independent, that is public, and that is not necessarily just reported by management, and it also is point-in-time information that can easily be backlisted.

How SESAMm tackles ESG data challenges

So I'll mention a couple of use cases to illustrate that in more detail. But basically, at SESAMm, we create an ESG datasets in order to track more than 90 different ESG risks and also the 17 sustainable development goals in order to precisely identify positive impact. And we do that on millions of companies, not just large public companies but also small caps and also private companies.

SESAMm ESG data use cases

Some of the use cases that I wanted to illustrate for that is using artificial intelligence in order to perform ESG monitoring using alerts. What that means is that we automatically generate ESG alerts on portfolios, for example, of equities or bonds on a daily basis, including portfolios of Japanese equities. And this data is then used by quantitative analysts and also fundamental managers to systematically exclude companies that are exposed to controversies in a portfolio. And this is a very efficient approach to systematically exclude companies that are not sustainable that are exposed to them.

Secondly, we have companies generate ESG signals by combining market data and ESG AI data to generate alpha. So basically, we create long-only and long-term portfolios, and we incorporate these ESG signals in order to improve the alpha of these portfolios.

The two last examples I wanted to mention, one is positive impact. So there is a specific framework called the UNSDGs for sustainable development goals, which is well suited to automatically detecting positive impact actions by a company, such as implementing, for example, a new net zero carbon policy. And we automatically track these announcements and these positive actions that companies perform in order, again, to share this information in the form of alerts to help fundamental managers track the sustainability actions of their portfolio companies and automatically report on them without having to do manual research.

The last use case I wanted to illustrate, and it's going to be my last point, is due diligence in private equity. So this is not only applicable to public assets but also to private assets. As an example, we have the Carlyle Group, a very large private equity company in particular with the Japanese team, and we have them generate various kinds of analytics at the stage when they evaluate the company. And in particular, we help them monitor and track potential ESG risk and sustainability factors which are very important to assess potential private assets opportunities. So this is the last use case that I want to mention. And as you can see, there are many opportunities in a growing field in ESG that started in Europe and came out to Asia. But there are also a lot of the challenges which artificial intelligence can help solve in some cases and which are illustrated with some examples.

Thank you very much.

Paris, January 18, 2024 – SESAMm, a leader in AI-powered text analysis, is proud to announce its recognition in the Forrester report "ESG Data And Analytics Providers Landscape, Q1 2024."

Sylvain Forte, CEO of SESAMm, commented, “We are thrilled to be recognized by Forrester. Our AI-driven approach, particularly our flagship product, TextReveal®, positions us uniquely in the market. TextReveal® leverages a vast data lake and advanced natural language processing to provide comprehensive insights on public and private companies, a capability not commonly found in traditional ESG analytics.”

Alexandre Tiesset, Head of ESG at SESAMm, added, “Our technology's ability to process massive volumes of data, provide nuanced ESG insights and stakeholder feedback is a game changer. It significantly enhances our clients' ability to assess non-financial risks and monitor ESG-related events, offering them a more accurate and effective solution for their needs.”

SESAMm's AI technology sets it apart as a leader in ESG analytics, enabling more precise and timely insights into ESG controversies and positive events. This technology empowers private equity firms, asset managers, corporations, and financial institutions to monitor and identify company events and controversies, providing an edge in the rapidly evolving landscape of sustainable investment.

For more information about SESAMm and its innovative solutions, please visit www.sesamm.com.

Download the full Forrester report, The ESG Data And Analytics Providers Landscape Report for Q1 2024 (available to Forrester subscribers and for purchase).

About SESAMm

SESAMm is an innovative leader in AI and big data analytics for investment. The company specializes in using advanced natural language processing and machine learning techniques to provide comprehensive ESG and thematic insights, aiding clients in identifying potential risks and opportunities in their investment portfolios.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

May 2, 2022. The S&P 500 ousts Tesla, Inc. from the S&P 500 ESG Index. Tesla is widely recognized as the firm that ushered electric vehicle making into the mainstream. So the index’s move seems unreasonable or possibly made in error to many, raising some interesting questions:

• How does an environmentally-friendly corporation like Tesla get dropped from an ESG index?
• Why does a potentially non-environment-friendly company like Exxon make the ESG index and remain on it?
• What do these moves mean about the integrity and validity of ESG scores and ratings?

Before we go on, let’s bring some context.

Why did the S&P 500 ESG Index drop Tesla?

May 18, 2022. In an S&P blog post, "The (Re)Balancing Act of the S&P 500 ESG Index," a spokesperson announces and explains their decision. Here are the bullet points:

• Global industry group peers pushed Tesla’s S&P DJI ESG Score further down the ranks in the GICS industry group: Automobiles & Components.
• A decline in criteria level scores related to Tesla’s low carbon strategy and codes of business conduct contributed to its 2021 S&P DJI ESG Score.
• A media and stakeholder analysis identified "two separate events centered around claims of racial discrimination and poor working conditions at Tesla’s Fremont factory."
• The analysis also highlights "the handling of the NHTSA investigation after multiple deaths and injuries were linked to its autopilot vehicles, affecting the company’s S&P DJI ESG Score at the criteria level, and its overall score."

Companies, including Tesla, left out of the S&P 500 ESG Index post-rebalance. Image courtesy of Indexology Blog.

The S&P blog post summarizes their case about dropping Tesla, "While Tesla may be playing its part in taking fuel-powered cars off the road, it has fallen behind its peers when examined through a wider ESG lens." And in this statement lies the crux of why the index dropped Tesla and why others are still on.

Analyzing Tesla’s web data

SESAMm’s TextReveal® insights suggest that the S&P 500’s decision to remove Tesla could be justified based on increasing controversy levels concerning discrimination, ethical standards, and work health and safety. By analyzing text related to ESG topics across the web, we picked up trends for the following subtopics:

• climate_change_atmospheric_pollution
• ethical_standards
• discrimination_racism_sexism
• labor_standards
• health_and_safety_at_work
• general_environmental_impact

Tesla’s ESG scores (six subtopics)

Regarding the volume features (Figure 1), we observed a significant increase in the scores related to ethical standards, discrimination, and atmospheric pollution for Tesla before the controversy. The conclusions are mostly the same for ESG sentiment (negative) scores. An interesting note is that the negative score of health and safety at work slightly increased in the months before the removal of Tesla from the index.

Comparing Tesla’s sentiment with other S&P 500 ESG Index companies

To see how Tesla’s ESG sentiment scores compared with other companies, we must rescale them with respect to a large universe of companies. This process means that for a given company, we use percentiles of the distribution of each subtopic’s ESG score to do a rescaling to the S&P 500 ESG constituents list after the 2022 rebalancing. Rescaling allows us to compare the companies with each other because the rescaled score indicates how bad the company is compared to the others, according to a specific ESG subtopic.

The following graphs show different sets of subtopics, plotting the mean of the respective rescaled scores if several topics are considered. Here are the companies considered.

Companies removed from the index:

• Tesla
• Delta Air Lines
• Chevron Corporation

Companies that joined the index after the 2022 rebalancing:

• American International Group
• Expedia Group

Companies still part of the index:

• Exxon Mobil
• Apple
• Amazon

Tesla, Delta, Chevron, AIG, and Expedia compared

Apple, Amazon, and Exxon compared

The S&P 500’s choice is reasonable

Our analysis shows that the S&P 500’s decision to oust Tesla from the ESG index is reasonable. We found significant subtopic volumes and negative sentiment that support the S&P 500’s claims of racial discrimination, poor working conditions, and other controversies.

Thanks for reading this quick analysis. For a more detailed report, including Chevron’s and Delta’s ESG scores, reach out to a representative today.

SESAMm’s ready-to-use alternative data

Leverage our alternative data streams to incorporate systematic insights into your alpha signals or risk monitoring your entire portfolio. From tracking global sentiment to analyzing retail communities like WallStreetBets and integrating ESG alternative data into your systems, our solutions will make generating value from web insights easy.