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:

1. 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.
   
   
2. 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.

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

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.

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.

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:

Discussions around nuclear weapons and defense have recently highlighted how differently investors interpret weapons exclusions. In particular, Russia’s invasion of Ukraine has brought security considerations back into focus across Europe and prompted some investors to revisit long-standing exclusion policies.

At the same time, regulatory frameworks such as the Sustainable Finance Disclosure Regulation (SFDR) encourage investors to screen portfolios for controversial activities and disclose how those risks are managed. However, these frameworks do not impose a single global definition of weapons exposure. As a result, policies can vary widely between institutions.

Controversial Weapons

What Do We Mean by Controversial Weapons?

In responsible investment policies, the term “controversial weapons” has a relatively clear meaning. It refers to weapons that are prohibited or heavily restricted under international conventions because of their indiscriminate or humanitarian impacts.
Typical examples include:

• cluster munitions
• anti-personnel landmines
• chemical weapons
• biological weapons
• nuclear warheads

Because these weapons are banned or widely condemned under international treaties, investors usually apply strict zero-tolerance exclusions. Any company involved in producing these weapons, or supplying critical components for them, is typically excluded from ESG-focused portfolios.

What Counts as Weapons Exposure?

The broader category of weapons exposure is more complex and is where investor interpretations often begin to diverge. Recent discussions across Europe’s sustainable finance community have focused on whether defense companies should remain excluded from ESG portfolios, particularly in light of renewed security concerns following Russia’s invasion of Ukraine.

Many exclusion frameworks distinguish between controversial weapons and other forms of military-related activity. Companies may be involved in conventional weapons manufacturing, such as firearms, missiles, bombs, or military electronics. Others produce defense systems and equipment, including radar, communications technology, or aircraft components. Civilian firearms are also frequently treated as a separate category within exclusion policies.

In these cases, investors often rely on revenue thresholds rather than absolute bans. A company may be excluded if more than five to ten percent of its revenue comes from weapons manufacturing, while smaller or indirect exposure may still be permitted depending on the investor’s mandate.

A key challenge in these screenings is that a company’s weapons exposure is not always obvious from its core business description. A firm may supply components, software, or materials used in weapons systems or operate as part of a broader defense supply chain. This is particularly difficult to identify in private markets, where companies are not required to disclose detailed segment revenues or defense-related contracts.

As a result, defining and detecting weapons exposure requires clear policy definitions and structured screening logic. What counts as weapons involvement, and where the exclusion threshold lies, ultimately depends on each investor’s mandate and risk tolerance.

What's Different Now?

These questions are no longer abstract. Since Russia's invasion of Ukraine, major asset managers have visibly shifted their positions. Allianz Global Investors, for instance, updated its Article 8 fund policies in 2025 to allow defense companies. Global Trading UBS and Franklin Templeton made similar moves, each removing revenue-based weapons thresholds that had been standard practice for years. At the regulatory level, Hortense Bioy, Head of Sustainable Investing Research at Morningstar Sustainalytics, noted that "since the start of the war in Ukraine in 2022, it has become increasingly clear that geopolitics plays a more significant role in shaping the boundaries of sustainable investing than ethics." What these shifts share is a common thread: the thresholds and definitions that once felt settled are now being redrawn, which is precisely why screening frameworks need to be flexible enough to reflect each investor's current policy, whatever that may be.

Customizable Screening

Because exclusion policies are defined at different levels, it’s rarely as simple as establishing a generic exclusion list. Limited partners often impose their own restrictions or revenue thresholds, which general partners must apply alongside the fund’s internal ESG policy and regulatory restraints. In some cases, LP requirements may further restrict or override the fund’s baseline approach.

As a result, acceptable levels of exposure to activities such as conventional weapons can vary significantly across portfolios.

Screening frameworks, therefore, need to adapt to the investor’s policy rather than forcing the policy to adapt to the tool.

In this case, SESAMm’s AI-generated exclusion screening report can be customized to match each investor’s requirements. Threshold-based classifications help identify different levels of involvement, allowing investors to distinguish between companies with no exposure, limited exposure, or significant involvement in controversial activities. Each classification is supported by underlying evidence and source documentation, allowing analysts to verify the reasoning behind the flag.

This approach makes it possible to apply a consistent methodology across both public and private companies while remaining aligned with the investor’s specific exclusion framework.

Discussions around defense and responsible investment will continue to evolve as geopolitical and regulatory contexts shift. Recent debates around nuclear deterrence and defense participation illustrate how differently investors can interpret weapons exclusions, even when they operate under the same regulatory frameworks.

For investors, the challenge is therefore not only defining exclusion policies but ensuring that those policies can be applied consistently and transparently across portfolios. As definitions of weapons exposure vary, and as supply chains and private market structures add further complexity, screening frameworks must be capable of translating policy into clear, operational rules.

Secondaries investors evaluate large, diversified portfolios under compressed timelines, with the level of detail and underlying company visibility differing by transaction type.

In this context, screening is embedded in the underwriting workflow, not a one-off exercise: it helps apply investment guidelines, support LP opt-outs, prioritize follow-up diligence, and enable ongoing monitoring over the life of the investment.

Watch this webinar replay to hear Jessica Huang, Private Equity and Secondaries ESG Lead at Ares Management, and Sylvain Forté, CEO at SESAMm, discuss:

• The operational and data challenges secondaries teams face
• How screening is applied in secondaries investing in practice
• How AI helps teams scale screening and support ongoing monitoring workflows