Forced labor is often assumed to be a problem of distant supply chains. The case of Packers Sanitation Services Inc. (PSSI) dismantles that assumption entirely.
Forced labor is often assumed to be a problem of distant supply chains. The case of Packers Sanitation Services Inc. (PSSI) dismantles that assumption entirely.
PSSI was a leading U.S. industrial cleaning contractor, servicing major meatpacking plants and backed by a top-tier private equity firm. Yet between 2022 and 2024, it became the center of one of the most significant child labor scandals in the U.S., one that had been quietly signaling its risks for years. SESAMm's controversy monitoring platform captured those early signals long before regulators intervened.
The Scandal
In November 2022, the U.S. Department of Labor discovered that PSSI had employed minors as young as 13 in hazardous overnight roles across 13 locations in 8 states. A federal investigation confirmed 102 children had been illegally employed, many handling dangerous chemicals and machinery. Three years earlier, in 2019, PSSI had already been sued for wage violations. The signal was there. It went unheeded.
The Fallout
The consequences were swift. A $1.5 million DOL fine. Contract terminations by Cargill and JBS. A DHS trafficking investigation. A replaced CEO. By late 2024, PSSI had shut its corporate office entirely. Even the private equity owner, Blackstone, faced direct scrutiny from pension funds, a reminder that labor violations travel up the ownership chain.
The Lesson
Every warning sign in this case was publicly visible before the crisis broke out. Wage lawsuits, labor complaints, and media coverage are all available in the public domain. Real-time controversy monitoring can surface these signals early, giving companies and investors the chance to act before exposure becomes unavoidable.
Forced labor is not only a humanitarian crisis. It is a material risk that demands better data, earlier detection, and stronger accountability.
Download the full case study infographic to see the complete timeline of events and key takeaways
Wednesday, September 14, 2022, a16z (Andreessen Horowitz), a large, well-known VC firm, funded Flow, a new startup led by a seemingly scandalous entrepreneur, Adam Neumann, the founder infamously known to have been ousted as WeWork CEO.
Why did a16z invest in Flow and, by proxy, Adam Neumann?
In his blog post about “Investing in Flow,” Andreessen acknowledges the U.S. housing crisis in the first sentence, and here’s what he has to say about Neumann: “Adam is a visionary leader who revolutionized the second largest asset class in the world—commercial real estate—by bringing community and brand to an industry in which neither existed before.” Andreessen continues, “[I]t’s often underappreciated that only one person has fundamentally redesigned the office experience and led a paradigm-changing global company in the process.”
So that gives us a clue as to what Andreessen thinks. But what does the public web have to say, and what is its overall sentiment?
In this edition of Alternative Data Trends, we dig into public web data before, during, and after a16z announced that it would fund Flow. Does the public web agree with Andreesen’s view? If not, how does it differ? And how can this information inform an investor and other VC firms?
Let’s find out.
a16z web mention volume and polarity (Nov. 2015 to Jun. 2022)
Figure 1: Andreessen Horowitz mention volume and polarity chart.
Mention volumes spike in mid-June 2021
TextReveal® uncovered 181,620 articles and messages from SESAMm’s data lake about Andreessen Horowitz (Figure 1). Mention volume remains consistent until late 2020, at which time a16z invests in a bunch of new companies and startups, such as:
Beacons
Clubhouse
Dapper Labs
Eco
Helium
Labster
Maven
Nansen
OpenSea
Skydio
SpotOn
Tackle.io
Valon
Zus Health
a16z also focused on the NFT market and, as a result, launched the world’s biggest crypto-fund valued at $2.2 Billion in June 2021. Moreover, Andreessen Horowitz launched its own media property, Future.com, in mid-2021.
Andreessen Horowitz web mentions further spike after it doubles down, announcing $4.5B crypto fund IV in May 2022. Additional news increased mention volume because of its investment in Neumann’s new startups, Flowcarbon and Flow.
Polarity (positive and negative sentiment) dips
Sentiment toward a16z remained relatively stable over time with only minor dips until mid-2021, when it began falling, a trend driven by mentions of Flow investments news, the Uniswap related lawsuit, and suspected CoinSwitch Forex law violations (Figure 2).
Figure 2: Uniswap and CoinSwitch events affected a16z’s polarity as early as July 2022. As it rebounded, Flow began influencing polarity negatively by mid-August.
Why was Flow affecting a16z’s polarity so much?
Figure 3: Newsclips about a16z investing in Flow.
Despite Andreessen’s reasons for giving Flow and Neumann a chance, the public’s opinion seems to disagree, leaning toward a negative sentiment (Figure 3). Overall, the public doesn’t seem to trust that Neumann is worth a second chance and that his choices are beyond forgiving. Moreover, the public criticizes a16z’s choice to overlook women and people of color. This The Guardian article highlights tweets of these differences in opinion:
In summary, TextReveal’s web data analysis tells us that it’s essential to keep an eye on the latent ESG risks this investment could bring to a16z’s portfolio, particularly on the social side.
Andreessen Horowitz, from an ESG perspective
a16z ESG initiatives
Figure 4: a16z’s governance initiatives exceed environmental and social.
From a mention volume perspective, a16z’s ESG initiative numbers remain stable (Figure 4). Andreessen Horowitz has a good share of ESG initiatives shares with the highest percentage for governance driven by partnerships and collaborations, followed by the environmental aspect that has been increasing over the last two years.
ESG risks, from a portfolio perspective
Figure 5: a16z’s aggregated portfolio’s ESG risks over time.
Figure 6: a16z’s portfolio’s social risk spikes in January 2020.
Figures 5 and 6 cover 160 companies in Andreessen Horowitz’s portfolio in the venture and growth stage. Overall, a16z’s portfolio represents a lower ESG risk (<15%) over time, except for the occasional moderately higher ESG risks score (<35%) indicated by two prominent spikes, one at the end of 2016 (Q4) and the second at the beginning of the year 2020 during the pandemic (Q1). The first spike is mainly a governance risk related to Soylent’s products being recalled and supply-chain-shortage risks. The spike is also caused by another top executive resigning from Magic Leap. In contrast, the second spike is a social risk driven by Instacart’s employees’ strike upon working conditions and safety concerns during the Covid-19 pandemic.
Note: Very low risk is <5%, low risk is <15%, moderate risk is <=35%, high risk is <=50%, and very high risk is >50%. Also, note that this scale is for demonstration only and does not indicate actual risk values.
Figure 7: A deeper look into the top companies in a16z’s portfolio generating mention volumes shows Instacart and MakerDAO in the moderate risk range. In contrast, the others are low to very low in risk in comparison.
Does the public’s view of a16z’s investment of Flow have merit?
Maybe, maybe not.
Looking at Andreessen Horowitz’s company and portfolio through the lens of web data, it is, if anything, consistent with its ESG initiatives and has experienced very few controversies. Should investors ignore the potential red flags that come with Flow and Adam Neumann? Of course not. But they should feel assured that a16z has exhibited a pattern of making sound investments. For example, if we compare the firm’s SDG initiatives to those in its portfolio (Figure 8), they are almost identical.
Figure 8: Andreessen Horowitz portfolio companies are focusing on the Sustainable Development Goals with specific attention toward goal 9: Industry, Innovation, and Infrastructure and Goal 17: Partnerships for the Goals, followed by Goal 4: Quality Education and Goal 15: Life On Land.
It’s possible that maybe Marc Andreessen and a16z et al. see something in Flow that the general public does not. After all, it’s why they’re a successful venture capital firm that consistently “backs bold entrepreneurs building the future through technology,” controversies and all.
Summer is almost over for us in the northern hemisphere. (We know. It's sad for us, too.) And with this seasonal shift comes back-to-school and back-to-work activities, including taking a last-minute vacation. And vacations mean time for reading, right?
While they may not be beach reads, we think we have some great choices. These are the posts that have been most popular on SESAMm's blog in the past five months. Let's get started with SESAMm's most-read blog posts since this spring, starting with number 10.
Read this quick guide about what natural language processing is, how it’s used, why it's important to uncover financial alternative data. Bonus: Get an overview of how NLP works at SESAMm.
Review SESAMm's analysis based on its ready-to-use data streams, revealing red flags that support the decision to oust Tesla, Inc. from the S&P 500 ESG Index.
Watch CEO Sylvain Forté at Japan Investor Forum, discussing ESG data, its challenges, and how to use AI and NLP to generate insights on millions of companies.
See how we apply our NLP capabilities to identify companies likely to engage in greenwashing practices by analyzing text in billions of web-based articles.
Based on alternative data, discover how Elon Musk’s personal and related brands measure up to public sentiment following his failed acquisition of Twitter.
Discover why SESAMm’s data lake is ideal for investment research and other basics like what a data lake is, why it’s important, what it does, and how it works.
Learn how SESAMm’s AI and NLP platform is used to gain financial and ESG insights from alternative data for systematic trading, fundamental research, and more.
Learn what SESAMm’s Knowledge Graph is, what it does, and how it’s used in text analysis for financial research, such as in private equity and hedge funds.
Tokio Marine & Nichido Fire Insurance Company and SESAMm work together to predict stock price movements using NLP-generated data from news and social media.
Thank you for reading through our Summer Roundup: the 10 most-read blog posts this year.
Which is your favorite? How would you rate these posts? Let us know what you think on Twitter or LinkedIn.
The modern world is in a peculiar place right now. We’ve got the technology and resources to improve our planet, but we often don’t know how to use them despite our best intentions. Or, at the very least, we don’t know where to put our efforts. Consequently, some investors are looking into Sustainable Development Goals (SDGs). Not only do they want their investments to earn more, but they also want them to do good. If you’re also interested in doing good with your investments, it’s essential to understand the SDGs and their meaning for your portfolio. In this article, we’ll break down the SDG basics, SDG scores, their relevance to investing, and how SESAMm can help you get and read SDG metrics. But first, a quick review of SDGs.
What SDG means
SDGs, or Sustainable Development Goals, are a set of 17 goals that the United Nations set in 2015 to be achieved by the year 2030, a framework that “provides a shared blueprint for peace and prosperity for people and the planet, now and into the future.” The global goals and the 2030 Agenda for Sustainable Development cover issues such as human rights, poverty, health, education, gender equality, and environmental sustainability, and they were designed to be universal across countries and continents worldwide. Here are the 17 UN Sustainable Development Goals:
SDG 1: No Poverty: Striving to end poverty in all its forms everywhere. This goal underscores the importance of equitable resource distribution and access to basic needs.
SDG 2: Zero Hunger: Aiming to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture, thereby ensuring that everyone, everywhere, has enough quality food to lead a healthy life.
SDG 3: Good Health and Well-being: It emphasizes the need for universal healthcare access, including reproductive, maternal, and child healthcare, and combats health threats by supporting research and development of vaccines and medicines.
SDG 4: Quality Education: Envisioning inclusive and equitable quality education and lifelong learning opportunities for all, this goal recognizes education as the foundation of empowerment and prosperity.
SDG 5: Gender Equality: Achieving gender equality and empowering all women and girls to participate fully in societal, economic, and political spheres
SDG 6: Clean Water and Sanitation: This goal aims to ensure the availability and sustainable management of water and sanitation for all, recognizing the essential role of water resources in sustaining life and ecosystems.
SDG 7: Affordable and Clean Energy: Promoting access to affordable, reliable, sustainable, and modern energy for all; this goal underscores the critical nature of energy in achieving other SDGs and the transition towards renewable energy sources to combat climate change.
SDG 8: Decent Work and Economic Growth: It focuses on promoting sustained, inclusive economic growth, full and productive employment, and decent work for all, highlighting the role of the private sector in initiating impactful initiatives.
SDG 9: Industry, Innovation, and Infrastructure: Aiming to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation, this goal recognizes the importance of a robust infrastructure and an innovative ecosystem as drivers of economic growth and development.
SDG 10: Reduced Inequalities: This goal seeks to reduce inequality within and among countries, focusing on policies designed to achieve greater equity and involve stakeholders from all sectors of society in decision-making processes.
SDG 11: Sustainable Cities and Communities: It aims to make cities and human settlements inclusive, safe, resilient, and sustainable, emphasizing the need for green public spaces, improved urban planning, and sustainable construction practices.
SDG 12: Responsible Consumption and Production: Focusing on promoting resource and energy efficiency, sustainable infrastructure, and providing access to a better quality of life for all, this goal underscores the importance of adopting sustainable practices and reducing waste.
SDG 13: Climate Action: Taking urgent action to combat climate change and its impacts, this goal underscores the necessity for countries, stakeholders, and the private sector to collaborate in reducing emissions and enhancing renewable energy usage.
SDG 14: Life Below Water: Aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development, this goal addresses the critical importance of our aquatic ecosystems.
SDG 15: Life on Land: Protecting, restoring, and promoting sustainable use of terrestrial ecosystems, sustainably managing forests, combating desertification, halting and reversing land degradation, and halting biodiversity loss.
SDG 16: Peace, Justice, and Strong Institutions: Promoting peaceful and inclusive societies for sustainable development, providing access to justice for all, and building effective, accountable, and inclusive institutions at all levels.
SDG 17: Partnerships for the Goals: This goal recognizes the importance of revitalizing the global partnership for sustainable development and the role of strong partnerships in achieving the SDGs, involving governments, the private sector, civil society, and others.
The UN’s 17 Sustainable Development Goals. Image courtesy of UN.org.
What are SDG scores?
Each Sustainable Development Goal has specific targets or indicators that help measure progress toward achieving those targets over time. SDG scores are numerical values given to each entity (country, company, person, etc.) based on their performance in meeting specific targets or indicators for each particular goal. Incorporating these evaluations into the decision-making process is crucial for stakeholders across various sectors, including the private sector, healthcare, financial services, and more. These stakeholders can leverage insights from SDG scores to prioritize initiatives that address critical issues like climate change, emissions reduction, and ecosystem preservation.
How do SDGs relate to ESG?
The environmental, social, and governance (ESG) framework is a tool to achieve and comply with the SDG goals. From a company’s perspective, ESG and SDG frameworks emphasize the importance of measuring and reporting progress. Companies incorporating ESG criteria into their operations often report on their sustainability performance, which can directly show their contribution towards achieving specific SDGs. For investors, ESG metrics provide a tangible way to evaluate companies' potential risks and opportunities related to sustainability, which can also align with the broader objectives of the SDGs.
The SDGs primarily focus on global challenges such as poverty, inequality, climate change, and environmental degradation, which represent the environmental and social pillars of ESG.
Within the same principles, several of these goals directly relate to the governance pillar of ESG. On the one hand, goal 16 aims to reduce corruption and bribery, develop effective and transparent institutions, and ensure inclusive and representative decision-making. On the other hand, goal 17 strives to enhance international cooperation, encourage effective public, public-private, and civil society partnerships, and ensure that policies are coherent and integrated, all of which are governance-related issues.
While the SDGs might not explicitly label these aspects as 'governance' in the way the ESG framework and regulatory landscapes do, the inclusion of these goals demonstrates a clear recognition of the importance of governance in achieving sustainable development. SDGs and ESG also have different purposes. ESG measures companies’ environmental, social, and governance performance risks and initiatives, while SDGs evaluate any entity’s performance in reaching its goals. Put another way, SDGs represent the goals, while ESG concerns methodology and processes.
At the company level, SDGs help align corporate strategy with society’s needs. Because the UN designed SDGs to be measurable, countries, companies, and people can hold themselves accountable for progress toward achieving them. And because the goals are measurable, we can score a company’s efforts, giving you an indicator to invest responsibly by aligning your portfolios with SDGs.
According to a publication by McKinsey & Company, sustainable investing appears to have a positive effect, if any, on returns. In other words, investors care about SDGs not only because they benefit society but also because they measurably support better investment decisions. For example, by incorporating SDGs into company assessments, investors can identify well-run businesses that are better positioned to benefit from the positive effects of improved social and economic conditions. SDGs also allow investors to make better-informed decisions within a defined investment time horizon by focusing on a company’s business exposure toward them. Investors can thus better measure and track a company’s opportunity exposure as a result of its achievement of the SDGs.
How to measure an entity’s SDG score
There are tools available to measure progress toward each goal—and those tools will play an essential role in helping investors decide which entities they want to invest in and which ones they don’t want to support. For example, SESAMm’s platform, TextReveal®, can analyze web data to generate SDG scores for virtually any entity in our data lake.
How SESAMm provides SDG scores
SESAMm provides SDG scores through its platform, TextReveal, a platform that allows investors to gain insights into companies, people, or topics. Specifically, we use artificial intelligence (AI) to track entities’ contributions toward SDGs, including public and private companies.
We track the 17 Sustainable Development Goals and the 169 underlying targets to detect negative news and positive events, using a similar algorithm we use for ESG alerts and gathering alternative data. Each UN SDG item displays a score from 0 to 5 to show the intensity of the company’s positive impact. Then, we translate the information into multiple languages.
This dashboard view example shows some SDG scores for Aker Carbon Capture.
We queried the Norwegian carbon capture company, Aker Carbon Capture, using our SDG positive impact dashboard over the past three years. As you might notice, Aker contributes to the goals associated with Partnerships, Climate Action, Clean Energy, and Sustainability. Maybe they could do more regarding Decent Work and Economic Growth, and Responsible Consumption and Production, but overall, the company’s online data shows a positive contribution.
See how SESAMm can help you with your SDG research
SESAMm is the leading provider of AI solutions and analytics for investment firms and corporations.
Analyzes text in billions of web-based articles and messages
Generates investment insights, ESG and SDG analysis used in systematic trading, fundamental research, risk management, and sustainability analysis
Enables a more quantitative approach to leveraging the value of web data that’s less prone to human bias
Addresses a growing need in public and private investment sectors for robust, timely, and granular sentiment and SDG data
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.
Alternative Data | Text Analysis | Sentiment Analysis
On April 24, 2022, Elon Musk, CEO of Tesla, Space X, The Boring Company, and Neuralink—and one of the most popular people on Twitter with one of the largest followings—reached an agreement to buy Twitter for roughly 44 billion dollars. On July 8, 2022, the deal failed to materialize after Musk withdrew from the negotiations due to his concerns about the company's alleged overabundance of fake Twitter user accounts, aka bots. As a result, the Twitter stock price plummeted by 15% after the announcement.
Now that his deal to buy Twitter has failed and culminated in a legal battle, Musk's public sentiment has reached all-time lows. The public sentiment for Twitter has also taken a hit. In general, public sentiment surrounding this deal was largely negative from both sides:
Musk's fans were disappointed because they thought it would allow him to spread his message about sustainable energy sources further.
Twitter's users were happy because they believed his involvement would have led to changes that would have made the platform less accessible than ever before.
But how exactly was public sentiment affected by the fallout of Elon Musk's failed Twitter acquisition? Let's find out. Here are five effects of the failed Musk-Twitter deal.
1. Merger and acquisition sentiment dropped from the beginning
Figure 1: Twitter M&A sentiment took a hit at key events during Musk’s evaluation period.
Musk had been exploring the possibility of purchasing Twitter as early as January 2022 when he began increasing his positions in Twitter stock. By March 14, Musk became the largest shareholder in the company, according to a securities filing. And that's when the sentiment toward the acquisition began to drop.
M&A sentiment experienced a further drop when Musk officially announced his offer to purchase the Twitter company on April 14, 2022. On Reddit, for example, members of the r/Economics community posted and engaged with the following: Elon Musk Launches $43 Billion Hostile Takeover of Twitter, a post that since has been removed but represents one of many sources feeding sentiment toward the topic.
In May 2022, Musk announced a hold on the deal, pushing M&A sentiment even farther down. And more recently, in late June and early July when Twitter sued Musk for breaching the M&A agreement, M&A sentiment fell deeper into the negative space.
2. Sentiment for Elon Musk and Twitter declined likewise
Figure 2: Overall, Musk’s sentiment polarity suffers the most.
But how do Elon Musk's and Twitter's sentiments evolve with M&A mentions?
In measuring and analyzing M&A mentions in web data, we found that Twitter's brand suffered but not nearly as much as Musk's. Both of their sentiments dropped in April when Musk announced his offer. However, Musk's sentiment suffered more when he put the deal on hold in May and again in June when Twitter filed a lawsuit against him.
Figure 2 shows two additional drops in Musk's sentiment for July. These correspond to news events regarding the trial, including news about the trial's start date in October.
Unfortunately for Musk, his other brands also experienced a drop in sentiment. For example, Tesla's sentiment experienced corresponding declines compared to Musk's, but not nearly as much as SpaceX's (Figure 3). One reason for this disparity could be the open letter SpaceX's workers wrote. The workers voiced their concern about Musk's behavior in this letter, stating, "Elon's behavior in the public sphere is a frequent source of distraction and embarrassment for us."
Further, in Figure 3, we track Tesla's stock performance. Initial data shows a possible correlation between Tesla's stock price and sentiment. However, further analysis and backtesting are needed to confirm this correlation.
4. Musk's sentiment suffered more than Twitter's
Figure 4: Twitter’s sentiment polarity isn’t as affected as Musk’s.
Twitter's sentiment remained relatively stable, seeing only a minor drop when Musk became the largest shareholder. Even Twitter's stock price remained stable, experiencing a temporary increase when Musk purchased Twitter stock but settling after. It's worth noting that Twitter's stock price was declining before January 2022, which might have influenced Musk's decision to buy.
In contrast, Musk's sentiment took a huge hit when he became the largest shareholder.
5. It’s not only about Musk and Twitter
Figure 5: Musk possibly gained the open-source community’s favor, if the rise in polarity is an indication.
However, in April 2022, Musk said that one of the ways he wanted to improve Twitter was to make its algorithms open source to increase trust. How did the open-source community take the news? According to the chart (Figure 5), well. Open-source sentiment polarity jumped back up.
Analyzing the M&A sentiments
Overall, Elon Musk’s sentiment polarity reached lower levels than those of Twitter and his other brands—although SpaceX took a significant hit, too. Whether because of his brash public statements or his employees criticizing his focus and intentions, data shows that netizens were not supportive of his attempted acquisition. And with the Twitter v. Musk court battle scheduled and looming, his sentiment doesn’t seem like it will be improving anytime soon.
Reach out to SESAMm
To learn more about how we 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 and discovered two key findings:
NLP data from news and social networking websites can have strong relationships with investor behavior. Thus, it’s possible to forecast investors’ rational reactions to changes in data and price movements based on those relationships.
NLP data proved to help anticipate tail events. For example, given the macroeconomic environment of the last 10 years, the stock market performed well. So in this context, investors are sensitive to negative narratives in times of uncertainty, such as the 2015 market sell-off, the U.S.-China trade war, the coronavirus pandemic, and the start of the Ukraine-Russian war, and post their concerns online.
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 text sentiment from news data with a neural network.” – Tokio Marine & Nichido Fire Insurance Co. Ltd representative
Extract 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 impact of news and social networking data on investor behavior for stock and bond markets, transforming text information into knowledge usable by TMNF. For instance, when the model detects a negative narrative raising uncertainty in the market, investors can use this signal to reduce their risk exposure.
Predicting future stock price movements from news and social media data
Tokio Marine Nichido and SESAMm’s joint research found that natural language data from news and social networking sites effectively predict future stock price movements. In the case involving the pandemic, for example, there was a time lag of as long as a month between the time COVID-19 became news and the time it affected the U.S. stock market (Figure 1). By using SESAMm’s technology to analyze news data during this period, the team found that US news and social networking sentiment had already deteriorated sharply before stock prices reacted. This sentiment deterioration is due to the fear of the coronavirus-spread effect on the global economy. In an all-time high S&P 500, U.S. investors did not initially consider this risk. In comparison, HSI companies were closer to the coronavirus spread risk, resulting in HSI investors reacting ahead of those in the U.S.
Figure 1: In 2020, U.S. news sentiment falls ahead of the stock market in response to COVID-19 concerns.
The model can calculate sentiment for each company by analyzing the news of individual companies. It’s also possible to create a composite to measure the sentiment related to a stock index. The sentiment data also helps management and investor relations because it provides a quantitative means of understanding the extent to which investors are concerned about certain news about their company.
Verifying the results
Verification using Japanese has revealed that the timing of bottoming and ceiling of text sentiment precedes those of stock prices. The collaborating team compared the performance of:
A model that uses only orthodox financial and economic data as inputs
A model that considers NLP and financial and economic data, confirming that the latter could generate higher alpha
Figure 2: Back-testing confirms that SESAMm’s equity model can predict a market downturn, capturing changes in text sentiment and reducing positions ahead of market crashes.
Since measuring sentiment is mean reversionary by nature, the TMNF team believes it provides good support for position management during rallies and crashes. It’s also valuable for avoiding forced loss-cut at the bottom when liquidity temporarily evaporates and the market crashes.
Expanding the research to other use cases
In addition to analyzing the stock market, Tokio Marine Nichido also expanded the scope of the research to include R&D on using natural language data in trading U.S. high-yield bonds. Research shows that NLP data can help provide a hedging signal for the negatively skewed high-yield market (Figure 3) by capturing deteriorating text sentiment (Figure 5). For example, these signals can inform investors to reduce positions before market reactions.
Figure 3: NLP data can help provide a hedging signal by capturing deteriorating text sentiment.
Figure 4: An NLP-informed high-yield strategy can outperform the U.S. high-yield total return index and a strategy without NLP. Same volatility level for the three back-tests.
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 is 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.
Figure 6: The joint Tokio Marine Nichido and SESAMm NLP alternative data model: Simple yet robust.
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 here:
If you’ve taken a basic computer course, you might have learned this famous phrase: Garbage in. Garbage out. It’s become so popular that people use it in other references, like diet and exercise and video or audio signal flow. But I digress.
What does the garbage in, garbage out phrase have to do with data lakes? Think of it this way, if you were to build an ideal lake for leisure, would you pump in any water? Probably not. My guess is that you’d want the cleanest, bluest, purest water you could find that would provide an ideal place for swimming, fishing, or whatever activity you like to do at a lake. So similar to the reason to pump good water into an actual lake for an ideal relaxing vacation spot, for example, we want to pump good data into a data lake because it yields ideal results.
Before we discuss SESAMm’s data lake, we’ll cover a few of these basics:
What is a data lake?
Why is a data lake needed?
How does a data lake work?
What is a data lake?
Data lakes are centralized repositories organizations use to store large amounts of unstructured, semi-structured, and structured data.
Data lake vs. data warehouse
The main differences between a data lake and a data warehouse are how they store your data and how the data is used. For example, data warehouses typically store hierarchically structured data in files or folders. In contrast, data lakes use flat architecture and object storage. Also, with a data lake, the data is raw with no specific purpose. But with a data warehouse, the information is structured, filtered, and processed for a particular purpose.
Why is a data lake needed?
Organizations like SESAMm employ a data lake for two main reasons:
Take advantage of advanced and sophisticated analytical techniques applied to complex and diverse data.
Perform data access and retrieval activities more efficiently and easily.
More specifically, companies employ data lakes for simple data management, to store and catalog data securely, and to conduct data analytics. For instance, data lakes allow you to import any data amount from multiple sources in their original format.
They also allow various roles within your organization—business analysts, data developers, and data scientists—to access data sets. Moreover, they can use their preferred frameworks and tools, such as Apache Hadoop, Spark, and Presto, to name a few, without moving data to a separate analytics system.
Furthermore, data lakes allow companies to generate various insights, from reporting on historical data to forecasting likely outcomes through incorporating AI and machine learning models, practices that can prescribe suggested actions to achieve better results.
Benefits of a data lake
The biggest benefit of a data lake is that you can ingest your raw data in its native format. This raw unstructured format allows you to use the data in various applications and understand the data from multiple perspectives, running different types of analytics from dashboards and visualizations to big data processing and machine learning. However, if you have a specific intent for your data lake, including applying AI and machine learning, structured data input is ideal.
Another benefit to a data lake is because, according to AWS, “Organizations that successfully generate business value from their data will outperform their peers.” AWS further explains, “An Aberdeen survey saw organizations who implemented a data lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet-connected devices stored in the data lake. This [ability] helped them to identify and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.”
How a data lake works (not technical)
As an investor, you probably won’t be building your own data lake because that’s what companies like SESAMm are for, but this section will give you a quick overview of how a data lake works.
You only need a few elements to make a data lake work without getting too technical. First, you need to source data. Sources can include:
Binary data (audio, images, and video)
Semi-structured data (CSV, JSON, logs, and XML)
Structured data from relational databases (columns and rows)
Unstructured data (documents, emails, and PDFs)
Second, you need reliable, secure, and fast data storage for your sourced data. Cloud storage providers could provide better scalability and affordability compared to on-premises solutions. Third, you need an analytics platform to access and analyze your sourced data. There are many open source and commercial platforms to choose from should creating a data lake be of interest to you, but we won’t get into the details here.
Last, you need to store the data in an open format like object storage. Object storage stores data with metadata tags, identifiers that make it easier to locate and retrieve data across regions. Overall, object storage and similar open formats enable many apps to take advantage of the data inexpensively while improving performance.
Four reasons SESAMm's data lake provides a unique foundation for data scientists' and investors' use cases
What makes SESAMm’s data lake unique and ideal for investment research and advanced analytics? SESAMm’s data lake is:
Broad and large
Includes more than 100 languages
Tuned to key indicators
Updated in near real time
Including data since 2008, the data lake consists of more than four million data sources made up of more than 20 billion articles, forums, and messages, such as professional news sites, blogs, and social media, increasing by an average of six million per day.
Moreover, the coverage is global, with 40% of the sources in English (the U.S. and international) and 60% in multiple languages. We select and curate these sources to maximize coverage of both public and private companies, focusing on quality, quantity, and frequency to ensure a consistently high input value.
SESAMm’s developers also tune the machine learning algorithms for key indicators such as mention volume, sentiment and emotion, ESG, and SDG. Additionally, they optimize the structure and schema for optimized SQL queries. The data lake is also updated hourly to give investors near real-time insights into their investment interests.
To learn how you can generate alternative data from text using NLP algorithms on our industry-leading, ready-to-use data lake, request a demo today.
Housing and construction fees have skyrocketed over the past few years. This increase goes back to multiple factors: economic unrest, raw materials disruption, and labor shortage, to name a few. What does web data have to say about all this?
In this week’s “Alternative Data Trends” issue, we’ll talk about commercial real estate, unveiling the industry’s ESG and SDG conformity and the effects of COVID-19 on the supply chain and labor.
Commercial real estate volume of mentions
While analyzing web data dealing with commercial real estate, we detected an evident increase in the industry’s volume of mentions. This trend spiked in April 2020 and was initially hindered by the COVID pandemic, which resulted in a drop in sentiment polarity. Still, it witnessed a rapid recovery leveraging digitalization and e-solutions (Figure 1).
Figure 1: Commercial real estate market mentions Feb 2015 to Mar 2022.
Case study: Unibail-Rodmaco-Westfield
To further understand the commercial real estate industry, we studied Unibail-Rodamco-Westfield and its competitors. Unibail-Rodmaco, a French commercial real estate company, acquired Westfield, a U.S. company, in December 2017. This acquisition accentuated its market share and grew its web voice share compared to its competitors (Figure 2).
Figure 2: Unibail volume of mentions compared to the market.
The chart in Figure 3 shows that the company’s volume of mentions has been increasing ever since the acquisition occurred. However, a negative sentiment polarity has been steadily increasing due to social ESG risks related to collective health crises during COVID and security-disrupting threats. In addition, the company faced difficulties collecting rent from retailers leading to lawsuits.
The arrows in this chart indicate Unibail ESG risks in time. The first arrow points to the social risks generated by security threats, in 2016, and the second arrow points to the issue of unpaid rent and lawsuits filed regarding the matter, in 2020.
Figure 3: Unibail ESG risks.
According to web data, Unibail has the second highest volume of sustainability mentions among analyzed groups. The company was notably related to sustainable development goals number 8* and number 12**. This volume is manifested in their initiatives to help unemployed people and maintain sustainable ethics and practices when launching their malls and shopping centers (Figure 4).
* Social development goal for decent work and economic growth.
** Social development goal for responsible consumption and production.
Figure 4: Unibail SDG volume of mentions compared to the market.
The impact of COVID on the emerging commercial real estate market
As previously mentioned, COVID had several effects on the industry, both negative and positive. Furthermore, it reshaped the market and its work policies. Some companies, as well, chose to switch to remote work and digitalization. In Figure 5, we can see that sentiment related to remote work policies has steadily improved since the pandemic started. However, in the last few months, we’ve seen a sharp decline, potentially signaling a negative reaction to some companies requiring employees back to their offices.
Figure 5: Remote work policies’ volume of mentions.
In addition, the pandemic has resulted in labor shortage and supply chain disruption, eventually leading to tremendous inflationary pressure. Raw materials prices, including oil, gas, iron, and wood, have witnessed a drastic increase and a disequilibrium between the volume of demand and the quantity available (Figure 6).
Figure 6: Labor shortage and supply chain disruption Feb 2015 - Dec 2021.
Data source
To produce this analysis, we combined natural language processing with billions of textual web data related to the real estate market, commercial real estate in particular. Using NLP-powered models gives us an edge as we can extract ESG, SDG, and financial insights that aren’t necessarily obvious or easy to detect. These insights help investors make better investment decisions.
SESAMm leverages artificial intelligence and machine learning to help you decipher and understand timely sentiments, trends, and ESG metrics on a wide range of public and private companies.
Stay in touch with SESAMm
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In this issue of the "what investors ought to know about…" series, we'll cover natural language processing (NLP), a tool that draws from the computer science and computational linguistics disciplines. In the last topic, we discussed knowledge graphs as the core of text analysis. And if knowledge graphs are the core of the data’s context, NLP is the transition to understanding the data.
What is natural language processing?
Natural language processing is an artificial intelligence (AI) technology that automates the data analysis of mined textual, unstructured data to include natural language understanding and natural language generation to simulate a human's ability to create language. It combines computational linguistics with machine learning and deep learning models, performing a special linguistic analysis by algorithms so a machine can "read" text.
Where is natural language processing used?
Today, various industries use NLP, from email filters to virtual assistants and search engines to chatbots. Here's a list of common ways natural language processing is used:
Chatbots: Chatbots are computer programs that use NLP. They simulate human conversation by identifying a sentence's intent, determining suitable topics, keywords, and emotions, and calculating the best response based on the data's interpretation.
Email filters: Email filters apply machine learning using many data samples to sort emails into the right inbox.
Machine translation: Translation software like Google Translate or Microsoft Translator use NLP to translate text from one language to another, such as English to French.
Natural language generation (NLG): NLG, a subfield of NLP, builds applications or computer systems that can automatically produce natural language texts of various types by using a semantic representation as input. Applications of NLG include question answering and text summarization.
Predicting and autocorrecting text: Predictive text and autocorrect use NLP to recognize and recall commonly used words and names to make text suggestions and correct common errors.
Search engines: Search engines like Google search use NLP machine learning to interpret a searcher's intent and provide relevant results. It can even suggest subjects and topics related to the query the searcher might be interested in.
Virtual and voice assistants: Virtual assistants like Apple's Siri or Amazon's Alexa use NLP technology to understand and respond to voice requests. Speech-to-text can dictate messages and notes, and speech recognition can control everything from smartphone apps and smart speakers to thermostats and home security systems.
Web sentiment analysis: Sentiment analysis automates classifying opinions in a text as positive, negative, or neutral. It's a method companies like SESAMm use to monitor sentiments like a brand's sentiment on the web and social media.
Why natural language processing is important to uncover financial-related alternative data
NLP is important because it helps resolve human language ambiguity in big datasets (big data). Languages are complex, diverse, and expressed in unlimited ways, from speaking hundreds of languages and dialects to having a unique set of grammar and syntax rules, slang, and terms for each. In text form, these variables are unstructured text. But with NLP, we can transform unstructured data into structured data and make sense of it.
Because of NLP's power, investors can research and analyze unstructured data from the web to gain insights into financial and ESG data. You can use this wealth of information to focus on systematic data processing, risk management, and alpha discovery through contexts, such as:
Major global indices sentiment
Euronext exchange sentiment
Private company sentiment
ESG risks for public and private companies worldwide
A quick overview of how natural language processing works at SESAMm
At SESAMm, we use named entity recognition (NER), which extracts the names of people, places, and other entities from text, and then named entity disambiguation (NED) to identify named entities based on their context and usage. For example, text referencing "Elon" could refer indirectly to Tesla through its CEO or a university in North Carolina. NED considers the context when classifying entities for an accurate match. Compared to simple pattern matching, which limits the number of possible matches, requires frequent manual adjustments, and can't distinguish homophones, NED is superior.
Process representation for NER and NED.
When identifying entities and creating actionable insights, SESAMm uses three other NLP tools: lemmatization and stemming, embeddings, and similarity. The lemmatization process normalizes a word into its base form (morphology) to help identify and aggregate entities. Embedding assigns the entity a numerical value to help analyze how words change meaning depending on context and understand the subtle differences between words that refer to the same concept—similarity measures whether two words, sentences, or objects are close to one another in meaning.
Representation of nodes in a knowledge graph.
Of course, NLP couldn't function without the core of the text analytics process: knowledge graphs. A knowledge graph is a digital representation of a network of real-world entities, the foundation of a search engine or question-answering service. This structured data model puts the schema in context through semantic metadata and linking, providing a framework for analytics, data integration, sharing, and unification. In other words, it's like a map and legend, with the legend labeling the concepts, entities, and events and the map connecting and identifying their relationships. These details are stored in a graph database and visualized as a graph representation, hence the term knowledge graph.
SESAMm's natural language processing platform for investment research and analysis
SESAMm is the leading provider of natural language processing and machine learning solutions and analytics for investment firms and corporations.
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?
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)
Figure 1: Tesla ESG scores for volumes and sentiments (1-year moving average), all source types.
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.
Figure 2: Tesla ESG scores for volumes and sentiments (1-year moving average), all source types, select subtopics.
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
Figure 3: Six-subtopic rescaled scores for Tesla, Delta, Chevron, AIG, and Expedia.
Apple, Amazon, and Exxon compared
Figure 4: Six-subtopic rescaled scores for Apple, Amazon, and Exxon.
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.
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