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Barcelona, QuantMinds International, November 2022

CEO Sylvain Forté joins QuantMinds correspondent Joanna Simpson in an interview highlighting the use of AI in ESG Investing and how we use it to detect greenwashing practices.

Below is an approximation of this video’s audio content. Watch the video for a clearer understanding of the topics discussed during the interview.

Joanna:  I'm Joanna Simpson here at QuantMinds International in Barcelona. Joining me now is Sylvain Forté, CEO of SESAMm. Thank you very much for being here.

Sylvain: Thank you.

Joanna: Tell me, how does it feel to be here at QuantMinds International?

Sylvain:  It feels very good, actually. We've been to the conference a couple of times already, so it's not our first year, and this time we brought several people from our team. We're all here together, presenting our technology and discussing some of the novelties in the space. It's very exciting and personalized.

Joanna: Great. And what role does artificial intelligence have to play in the future of ESG and ESG investing, in particular?

Sylvain:  ESG is a massive trend in the industry right now, not just in asset management and the quant space but also in private equity, in corporate space like tracking suppliers, clients, etc. And one of the key problematic themes that we see is data gaps. There's a lack of data in terms of coverage; small caps, mid caps, or even private firms are not well covered. The frequency of information tends to be lagging. There's a very low frequency, like quarterly updates or so. There's also a lack of transparency and the like.

So, I believe that AI is primarily a tool that can help build that information gap and, for example, cover millions of companies instead of just a few tens of thousands of companies manually. What we do at SESAMm is leverage a technology called natural language processing (NLP), where we screen text automatically to understand potential ESG controversies or positive impact events. This leads us to have a coverage of around 5 million companies, meaning every publicly listed company out there and private firms that no one else would cover otherwise. This enables many use cases.

There's also frequency; you can generate indicators every single day, more like a quantitative time series that people are used to, and this enables clients to get access to information even locally, like Raiffeisen, one of our clients, is tracking clients in Poland, in Austria, in Germany, or in Ukraine using NLP which would not be possible with traditional ESG metrics. I think that the key topic of AI is expanding the use, expanding the coverage in terms of ESG data, and making sure that data is systematic, follows a good process, and is transparent.

Joanna: What examples are there of ESG investing being enhanced by AI?

Sylvain:  We see two primary use cases.

The first one is more quantitative, where people are looking to leverage ESG NLP data in their systematic trading process. It's either for alpha generation; for example, we work with LFIS, an asset manager in France that created a fund based on ESG NLP data. Their primary goal is to enhance their strategy to generate outperformance, which is really a good use case in that space. This is the quantitative use case where you can use higher frequency data like daily data to leverage ESG like any other kind of alternative dataset and derive superior returns.

Then we have more discretionary use cases where we see large asset managers or private equity shops which are looking to perform risk management tasks or help their team prioritize the scoring of assets. Say they have a team that does their own proprietary scoring on assets with regards to ESG, but how do I prioritize? I have 3000 assets to follow, I need some kind of alert on that whole universe to make sure that I focus on the assets that could be most controversial today. That's one of the things that we provide; daily alerts using natural language processing where people can say okay, there is a massive shift right now; as an ESG analyst, I'm going to make a decision to look at this asset specifically to help cover it and update the score.

Joanna:  Can AI help with greenwashing in ESG investing, and if so, how?

Sylvain:  Yes, it's one of the other kinds of problems that you have in ESG is the lack of transparency on the methodology creates some anomalies in some cases. And one of the big anomalies is that there's this averaging effect where a firm that has both positive actions and negative topics is going to be, on average, neutral, which is really problematic.

We had a big example like this in France recently with Orpea, a listed company of nursing homes exposed to a massive scandal with regards to mistreating patients—so more like social washing than greenwashing. And the problem is their scores were pretty high because, at the same time, they had some positive impact. They were implementing new diversity policies and the like, so it was averaging up.

At SESAMm, we leverage NLP to completely differentiate positive and negative topics. So if a firm is doing good stuff that is aligned with SFDR, and they have positive actions, etc., great! That's going to be one score. But if, at the same time, they have very negative topics, there are a lot of risks we're going to still detect that's not going to be averaged. It's going to be very specifically focused on.

Joanna: Sylvain Forté, thank you for your time.

Sylvain: Thank you very much.

To learn more about how SESAMm uses Text Reveal to find ESG data, contact a representative today.

With summer here, travelers are turning to vacation rental platforms more than ever to plan their getaways. As these platforms grow, it’s important to understand the ESG controversies they face, impacting users, hosts, and local communities worldwide.

Vacation rental platforms like Airbnb, Booking.com, Expedia, and Tripadvisor have faced several ESG challenges recently, including regulatory issues, safety and privacy concerns, and social controversies related to housing affordability and community displacement.
Different companies face varied risks: Airbnb deals with global restrictions and legal issues, Booking.com faces antitrust fines and tax disputes, Expedia contends with operational challenges, and Tripadvisor grapples with reputational concerns. All, however, must balance growth with ethical practices.

What are the most pressing ESG challenges currently facing the vacation rental sector?
Read to find out.

Booking.com has encountered various ESG risks due to regulatory scrutiny and legal issues. The company faced a €413 million fine in Spain for abusing market dominance with price parity clauses and a €94 million tax settlement in Italy for VAT compliance. Additionally, Russia penalized it for antitrust violations, while hotel operators in Japan filed lawsuits over unpaid fees. Legal scrutiny also surrounds its listings in the occupied West Bank, with investigations by Dutch prosecutors. In the U.S., a court ruled that Booking.com illegally scraped Ryanair’s website. Finally, a significant data breach exposed millions of guests’ sensitive information, raising cybersecurity concerns, and planned workforce reductions highlight ongoing operational risks for stakeholders globally.

Key Controversies:

• Booking.com fined 413 million euros by the Spanish authority CNMC for unfair competition
• ‍Booking.com to pay 94 million euros to settle Italian tax dispute
• Firms in Japan sue Booking.com in Tokyo over unpaid deposits‍
• Booking.com's listing in West Bank examined by Dutch prosecutor‍
• Booking.com aims to lay off 25% of its workforce

Airbnb: Global Crackdowns and Controversies Rise

Airbnb is facing significant regulatory and legal challenges globally, including Spain's order to remove over 65,000 listings, Italy's €576 million tax settlement, and stricter rental rules in Greece and France. In the US, cities like New York have imposed tight short-term rental limits, while Airbnb is dealing with class-action lawsuits in Canada and pricing accusations in Australia. Safety and privacy issues also plague the platform, including lawsuits related to guest deaths. Furthermore, Airbnb has been criticized for its listings on occupied Palestinian land and its impact on housing affordability. In 2023, the company cut 1,900 jobs, or about 25% of its workforce, increasing its ESG risks amidst evolving pressures.

Key Controversies:

• Spain orders removal of over 65,000 Airbnb rentals for regulatory violations
• Italy bans Airbnb self-check-ins
• Airbnb fined 300,000 euros for lack of consumer information
• Short-term rental restrictions hit Airbnb in New York
• Airbnb accused by Australian regulator of misleading pricing

Expedia: Governance Gaps and Growing Legal Risks

Expedia Group faces several ESG risks, though generally less severe than its larger peers. Key challenges include a $33 million penalty in Australia for misleading hotel rates, 1,500 job cuts, antitrust investigations in Europe, and a $29.8 million payment under the Helms-Burton Act. Legal issues also involve unpaid commissions, tax avoidance claims, and COVID-19 flight refund disputes. Governance problems include executive departures and a reverse racism lawsuit, along with data breaches affecting millions. Overall, these risks are significant but less severe than those of competitors.

Key Controversies:

• Australia fines Expedia Group's Trivago $33 mln on misleading hotel room rates
• Expedia is cutting 1,500 jobs
• US court orders Expedia to pay 29.8 million in the Helms-Burton application
• White candidate sues Expedia alleging reverse racism in hiring process
• Expedia faces class action over COVID-19 flight refund refusals

Conclusion

In conclusion, the vacation rentals sector faces significant ESG challenges that threaten its growth and credibility. Major players like Booking.com, Airbnb, and Expedia must address regulatory scrutiny, safety concerns, and social issues to meet the evolving expectations of travelers. By prioritizing transparency, community engagement, and compliance, these platforms can rebuild trust and promote responsible tourism. Embracing these changes not only mitigates risks but also positions them to lead in sustainable travel and reshape the future of vacation rentals.

Reach out to SESAMm

TextReveal’s web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.

Tokio Marine & Nichido Fire Insurance Co., Ltd. (TMNF) tapped SESAMm for a joint research venture to predict future stock price movements. SESAMm provided various NLP indicators, such as digital sentiment calculated for single stocks or indices (seen as an entity), as well as its experience in machine learning to work on this task.

These studies concluded with two key findings:

1. Relationships exist between NLP data from news and social networking sites and investor behavior under specific circumstances. Researchers and investors can use the “digital sentiment” as an indicator of investor sentiment to anticipate price changes. They can then use this anticipation for a specific company or, more generally, any entity that can be isolated in a text (like an index).
   
   
2. By focusing on more stressed situations, like the 2015 market sell-off, the U.S.-China trade war, the coronavirus pandemic, and the start of the Ukrainian crisis, we could show that digital sentiment is beneficial in times of significant stress in the market. Digital sentiment more accurately reflects the stress level in these complicated situations. It, therefore, helps to predict stock price movements more accurately in these stressed cases, providing a tail hedge. It’s not biased by an excess of confidence linked to the “central banks put” for instance.

Providing safety and security since 1879

Tokio Marine Insurance Company was first established in 1879. Over the years, it has added products and services, acquired other businesses, and merged with other companies to eventually become Tokio Marine & Nichido Fire Insurance Co., Ltd. Commonly called Tokio Marine Nichido today, the company is a property and casualty insurance subsidiary of Tokio Marine Holdings, the largest non-mutual private insurance group in Japan. Its products and services provide safety and security to its clients and partners, contributing to more fulfilling lifestyles and business development.

One of the company’s philosophies is to be a good corporate citizen and fulfill its social responsibilities, including protecting the global environment, promoting human rights, creating a responsible working environment, and contributing to society and individual local communities. Recently, the Emperor of Japan awarded Tokio Marine Holdings, Inc. the Medal with Dark Blue Ribbon for donating to the Japan Student Services Organization to support students who face financial difficulty during the

COVID-19 pandemic. Individuals, corporations, or organizations are awarded the Medal with Dark Blue Ribbon for their outstanding contributions to the public.

Transforming and accepting the challenge to grow

According to TMNF, “The business environment surrounding the insurance industry is changing at a faster pace than ever due to changes in demographics, advances in technologies, such as autonomous driving and AI, and longer-term trends, such as the intensification and frequent occurrence of natural disasters, as well as further progress in digitalization due to the COVID-19 pandemic.”

“The business environment surrounding the insurance industry is changing at a faster pace than ever…”

“While these changes in the business environment pose a threat, we consider them to be excellent opportunities for transformation and the creation of new value.” So they’ve adopted the concept, “Transformation (“X”) and Challenge to Growth 2023: Aiming to be the company most chosen for quality and its passion.” Ultimately, it strives to support customers and local communities in times of need while contributing to social responsibility. Five social issues that it will prioritize are:

• Global climate change and the increase in natural disasters
• The increased burden of long-term care and healthcare due to the aging of society and advances in medical technology
• Technological innovation and its effects on the environment
• Symbiotic society and responding to the novel coronavirus
• Industrial infrastructure and how it supports economic growth and innovation

Leveraging a partner with the right technology

To secure and protect its clients’ assets while elevating social issues, Tokio Marine Nichido sought out an edge in the stock market. Under these circumstances, it was fortunate that TMNF discovered SESAMm in 2020 through the Plug and Play Japan program, a platform with an event that connects Japan to markets abroad. SESAMm had presented its NLP alternative data solution, TextReveal®, to which TMNF considered the platform for access to alternative data and sought collaboration with the SESAMm team for a research project.

“SESAMm has the technology to extract sentiment from news data with a neural network.”
– Tokio Marine & Nichido Fire Insurance Co. Ltd representative

Extracting relations between NLP data and the financial market

In 2021, Tokio Marine Nichido Insurance began collaborating with SESAMm to develop an AI analytics model for alternative data. It models the effect of news and social networking data on investor behavior for stock and bond markets. In other words, it structures text information into knowledge usable by TMNF.

Monitor risks and topics

NLP data can improve the understanding of the market’s behavior by exhibiting the most important topics over time, with a direct indication of the importance of the topics through the text volume (Figure 1).

Researchers can also use it to focus on a specific topic or a certain period. For instance, a short analysis of the most frequent keywords in the press, which preceded the market fall during the COVID-19 pandemic, showed the significant predominance of pandemic-related terms (Figure 2).

Focusing on the equity market

NLP tools provide specific data, like sentiment, to get more detailed information at the company level and for many underlyings. Indicators for equity indices, for instance, can be calculated and provide a clean sentiment to monitor markets.

In many situations of stress over recent years, such sentiment proved to be an early indicator of the market’s future degradation. For example, there was a time lag of as long as a month between the time COVID-19 became the main news focus and the time it affected the U.S. stock market. By using SESAMm’s technology to analyze news data during this period, the team found that the U.S. digital sentiment had already deteriorated sharply before stock prices reacted (Figure 3).

This sentiment deterioration occurred because of the fear of the coronavirus’s spread’s effect on the global economy (see Figure 2). Even with an all-time high S&P 500, U.S. investors didn’t initially consider this risk. In comparison, HSI companies were closer to the coronavirus spread risk. So as a result, HSI investors reacted ahead of their U.S. counterparts. In other words, by using natural language data, it was possible to capture a risk overlooked by U.S. investors but related in the publicly available texts and take action ahead of the market deleveraging.

Generalizing the results to the credit market

Tokio Marine Nichido also expanded the scope of the research to U.S. high-yield bonds index trade. In the credit market, a high yield has a high beta, which makes its risk comparable to the equity market.

Research shows that, on a risk-adjusted basis, the NLP-data-built signal has a positive and consistent performance through the timeline compared to the U.S. HY T.R. index benchmark (Figure 4). Its performance has a low correlation with the index (Figure 5), so the sentiment is diversifying. It not only acts as a diversifier but delivers higher returns than the benchmark when the U.S. High Yield market sold off (Figure 6). As such, the NLP signal diversifies, hedges, and protects against adverse periods. It provides a mechanical pick-up in risk-adjusted return when running alongside traditional strategy.

The NLP signal outperforms the index in realistic backtest conditions, including long allocation only, turnover constraints, and trading fees (Figure 7). The quantitative model integrates some macro indicators, but the previous NLP signal induces the main source of outperformance and risk mitigation.

TMNF is also applying the research to estimate the Fed’s stance—hawkish or dovish—using natural language data, too. It hypothesizes that the market will be focused on the Fed’s stance on interest rate hikes in the next few years.

“The model developed in collaboration with SESAMm is simple in structure, yet, it’s an orthodox and robust model that uses valid data as input.”

Summarizing the collaboration

In developing models, Tokio Marine Nichido believes it’s essential to consider “what data to consider” and to keep it simple. And TMNF achieved these tenets. The model developed in collaboration with SESAMm is simple in structure, yet, it’s an orthodox and robust model that uses valid data as input which is preferable to a risky over-fitting by increasing complexity.

Get in touch with SESAMm

To learn more about Tokio Marine Nichido’s case study or to request a TextReveal demo, reach out to us.