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).

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).

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.

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.

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.

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).

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.

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Environmental challenges, such as climate change, biodiversity loss, and resource depletion, rapidly increase daily. The urgency for a coherent and actionable framework to promote sustainable investments has never been more important. With all of the terms and claims surrounding sustainability, it is a struggle for investors, companies, and consumers to identify which activities and projects are genuinely beneficial for the environment. This is where the EU Taxonomy is a guiding instrument to illuminate the path toward a sustainable economy.
Introduced by the European Union, the EU Taxonomy is a green classification system designed to provide criteria for identifying environmentally sustainable economic activities. Offering a common language and set of standards, the Taxonomy helps mitigate the confusion surrounding sustainability claims and enhances the transparency needed for informed investment decisions.

Understanding the EU Taxonomy

What is the EU Taxonomy?

The EU Taxonomy is a systematic framework that translates the European Union's climate and environmental objectives into specific criteria for economic activities. It assists investors and companies in recognizing which investments qualify as “green,” ensuring that capital flows toward activities with a positive environmental impact.

Main Objectives

The EU Taxonomy is built upon several objectives aimed at transforming the economic landscape:

1. Supporting the transition to a sustainable economy: The Taxonomy enables businesses to align their operations with the EU's environmental goals by providing clear definitions and criteria for sustainable activities.
2. Mitigating market fragmentation: The Taxonomy aims to provide uniform standards across EU member states, reducing inconsistencies arising from different national interpretations of sustainability.
3. Protecting against greenwashing: In an environment where misleading claims about sustainability are prevalent, the Taxonomy offers a reliable benchmark, ensuring that companies cannot misrepresent their environmental practices.
4. Accelerating financing for sustainable projects: By identifying what constitutes a sustainable activity, the Taxonomy directs investments toward projects that contribute to the EU’s climate and environmental objectives, promoting the development of green technologies and practices.

The EU Taxonomy in Sustainable Finance

The EU Taxonomy is a key component of the broader sustainable finance agenda. By providing a systematic approach to sustainability, it helps guide financial flows toward investments that will foster real environmental progress. This initiative is particularly significant as global investors increasingly seek to align their portfolios with sustainability goals.

Understanding the EU Taxonomy

What is the EU Taxonomy?

The implementation of the EU Taxonomy is facilitated through the Taxonomy Climate Delegated Act. This act sets the criteria for activities concerning climate objectives, recognizing those contributing to achieving climate neutrality and enhancing climate change resilience. It represents the first step towards establishing a comprehensive set of criteria applicable to various sectors.

Sectors Covered

The Taxonomy initially focuses on sectors with significant contributions to greenhouse gas emissions. These sectors include:

• Energy: Emphasizing renewable energy production and energy efficiency improvements.
  
• Forestry: Promoting sustainable forest management.
• Manufacturing: Aiming for reduced emissions and efficient resource use in manufacturing processes.
• Transport: Encouraging the adoption of lower-emission transport options.
• Buildings: Focusing on energy-efficient design and construction methods.
  

Technical Screening Criteria

The Taxonomy includes strict technical screening criteria that define acceptable thresholds for sustainability. These criteria are based on scientific evidence and best practices, ensuring that activities meet established environmental standards.

Defining Green Economic Activities

The Six EU Environmental Objectives

At the core of the EU Taxonomy are six environmental objectives that guide the classification of economic activities:

1. Climate change mitigation: Activities that significantly reduce greenhouse gas emissions.
2. Climate change adaptation: Actions aimed at preparing for and adjusting to the impacts of climate change.
3. Sustainable use and protection of water and marine resources: Strategies to ensure long-term viability and health of water resources and marine ecosystems.
4. Transition to a circular economy: Practices that promote efficient resource use, waste reduction, and recycling.
5. Pollution prevention and control: Efforts focused on minimizing pollution and managing waste effectively.
6. Protection and restoration of biodiversity and ecosystems: Initiatives dedicated to conserving biodiversity and restoring ecological systems.

Conditions for Taxonomy-Aligned Activities

For economic activities to be recognized as taxonomy-aligned, they must fulfill four critical conditions:

1. Make a substantial contribution: The activity must significantly contribute to at least one of the environmental objectives.
2. Do no significant harm: It must not adversely impact any other environmental objective.
3. Comply with minimum social safeguards: Activities must meet criteria protecting social and labor rights.
4. Meet technical screening criteria: These are established through scientific methodologies, ensuring that activities genuinely contribute to sustainability goals.

Integrating the EU Taxonomy with Other Regulations

Corporate Sustainability Reporting Directive (CSRD)

The CSRD complements the EU Taxonomy by requiring companies to disclose comprehensive information about their environmental performance. This regulation aligns closely with the Taxonomy by mandating that organizations within its scope report on the extent to which their activities are taxonomy-aligned, promoting transparency and accountability in corporate sustainability efforts.

Sustainable Finance Disclosure Regulation (SFDR)

The SFDR mandates that financial market participants disclose how their financial products align with the Taxonomy standards. This regulatory framework aims to provide investors with insights into the sustainability impacts of their investment options, fostering greater trust and informed decision-making.

Implementation and Compliance

Corporate Mandatory vs. Voluntary Disclosure

• Mandatory Disclosure: Large companies and financial market participants are obligated to disclose the alignment of their activities with the Taxonomy. This requirement enhances transparency and ensures stakeholders have access to key information regarding corporate environmental performance.
• Voluntary Disclosure: Companies can also engage in voluntary reporting to highlight their sustainability strategies and progress. This allows businesses to strategically utilize Taxonomy criteria in their planning and investment decisions.

The Role of Member States and Financial Entities

Member States and the EU are expected to leverage the Taxonomy in their regulatory frameworks. This includes establishing public labels for green corporate bonds and financial products aligned with SFDR, thus fostering market acceptance and stimulating demand for sustainable investments.

Challenges and Opportunities

Market Fragmentation

While the EU Taxonomy aims to unify standards across the region, differences in implementation and interpretation among member states could lead to market fragmentation. The EU must maintain a cohesive approach and address any differences that may arise.

The Risk of Greenwashing

With the growing popularity of green investments, there is an increasing risk of greenwashing, where companies may exaggerate or misrepresent their sustainability claims. The EU Taxonomy provides a valuable tool to combat this risk by establishing clear and robust criteria for what constitutes a sustainable activity.

Benefits for Companies and Investors

For companies, engaging in taxonomy-aligned activities can attract institutional and retail investors, banks, and other financial entities that prioritize sustainability. Investors, on the other hand, benefit from improved clarity and assurance about the environmental impact of their investments, allowing them to align their portfolios with their sustainability values.

Future of the EU Taxonomy

Expansion of Coverage

The EU Taxonomy is not static; it is designed to evolve over time. While it currently focuses on sectors with the highest emissions, plans are in place to expand its coverage to include additional sectors and activities as the regulatory framework matures and new technologies emerge.

Adaptation to Technological Changes

As technological advancements grow, the EU Taxonomy must remain responsive to new developments in sustainability practices. This adaptability is crucial for ensuring that the criteria remain relevant and effective in guiding investments toward genuine environmental sustainability.

The Role of the EU Taxonomy in Achieving Sustainability Goals

The EU Taxonomy serves as a cornerstone for sustainable finance within the European Union. By providing clarity and consistency in defining what constitutes a sustainable activity, it empowers both companies and investors to make informed decisions that contribute to environmental preservation and climate goals.

Conclusion

The EU Taxonomy is an initiative aimed at redirecting investments toward environmentally sustainable activities. By establishing clear criteria and creating a common understanding of sustainability, the Taxonomy not only assists companies in navigating their transitions to greener practices but also safeguards the integrity of the environmental finance market.

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.

Financial and ESG insights begin with big data coupled with data science.

At SESAMm, our artificial intelligence (AI) and natural language processing (NLP) platform analyzes text in billions of web-based articles and messages. It generates investment insights and ESG analysis used in systematic trading, fundamental research, risk management, and sustainability analysis.

This technology enables a more quantitative approach to leveraging the value of web data that is less prone to human bias. It addresses a growing need in public and private investment sectors for robust, timely, and granular sentiment and environment, social, and governance (ESG) data.
This article will outline how the data is derived and illustrate its effectiveness and predictive value.

Content coverage and ESG data collection

The genesis of SESAMm’s process is the high-quality content that comprises its data lake, the source from which it draws its insights. SESAMm scans over four million data sources rigorously selected and curated to maximize coverage of both public and private companies. Three guiding criteria—quality, quantity, and frequency—ensure a consistently high input value.

Every day the system adds millions of articles to the 16 billion already in the data lake, going back to 2008. The coverage is global, with 40% of the sources in English (the U.S. and international) and 60% in multiple languages. The data lake, expanding every month, comprises over 4 million sources, including professional news sites, blogs, social media, and discussion forums.

The following tables illustrate SESAMm’s data lake distribution (Q1 2022):

Respect for personal privacy figures highly in the data gathering process. We don’t capture personal data, like personally identifiable information (PII), and respect all website terms of service and global data handling and privacy laws. SESAMm’s data also doesn’t contain any material non-public information (MNPI).

Deriving financial signals and ESG performance indicators

SESAMm’s new TextReveal® Streams platform applies NLP and AI expertise to process the premium quality content gathered in its data lake. This complex process involves named entity recognition (NER) and disambiguation (NED)—the process of identifying entities and distinguishing like-named entities using contextual analysis—and mapping the complex interrelationships between tens of thousands of public and private entities, connecting companies, products, and brands by supply chain, location, or competitive relationship.

Using SESAMm’s TextReveal Streams, this wealth of information is filtered to focus on four crucial contexts for systematic data processing, risk management, and alpha discovery:

• Sentiment covering major global indices: world equities (and Small Caps, Emerging), U.S. 3000, Europe 600, KOSPI 50, Japan 500, Japan 225
• Sentiment covering all assets and derivatives traded on the Euronext exchange
• Private company sentiment on more than 25,000 private companies
• ESG risks covering 90 major environmental, social, and governance risk categories for the entire company universe, which includes more than 10,000 public and more than 25,000 private companies with worldwide coverage

TextReveal Streams data sets and assessments are used by financial institutions, rating agencies, and the financial services sector, such as hedge funds (quantitative and fundamental) and asset managers, to optimize trade timing and identify new sustainable investment opportunities. Private equity deal and credit teams also use the data for deal sourcing and due diligence. Private equity ESG teams use it to manage initiatives like portfolio company environmental, social, and governance risk and reporting.

Methodology and technology for processing unstructured data

NLP workflow, from data extraction to granular insight aggregation

Data is continually extracted from an expanding universe of over four million sources daily. As it enters the system, it is time-stamped, tagged, indexed, and stored in our data lake to update a point-in-time history extending from 2008 to the present.
The source material is then transformed from raw, unstructured text data into conformed, interconnected, machine-readable data with a precise topic.

Mapping relationships between entities with the Knowledge Graph

At the heart of the text analytics process is SESAMm’s proprietary Knowledge Graph, a vast map connecting and integrating over 70 million related entities and their keywords. It’s essentially a cross-referenced dictionary of keywords, relating each organization to its brands, products, associated executives, names, nicknames, and their exchange identifiers in the case of public companies.

Entities within the Knowledge Graph are updated weekly and tagged to ensure changes are correctly tracked. The CEO of a company today, for example, may not be the CEO tomorrow, and brands may be bought and sold, changing the parent company with each sale. Weekly updates within the Knowledge Graph ensure the system is aware of these changes.

Named entity disambiguation (named entity recognition plus entity linking) is one of the NLP techniques used to identify named entities in text sources using the entities mapped within the Knowledge Graph universe.

At SESAMm, NED identifies named entities based on their context and usage. Text referencing “Elon,” for example, could refer indirectly to Tesla through its CEO or to a university in North Carolina. Only the context allows us to differentiate, and NED considers that context when classifying entities. This method is superior to simple pattern matching, limiting the number of possible matches, requiring frequent manual adjustments, and cannot distinguish homophones.

SESAMm uses three other NLP tools to identify entities and create actionable insights. These are lemmatization, embeddings, and similarity. Each is explained in more detail below.

Analyzing the morphology of words with lemmatization

News articles, blog posts, and social media discussions reference organizations and associated entities in various forms and functions. Lemmatization seeks to standardize these references so the system knows they mean the same thing.

For example, “Tesla,” “his firm,” “the company,” and “it” are all noun phrases that can appear in a single article and refer to a single entity. Even where the reference is apparent, it can take different forms. For example, “Tesla” and “Teslas” both refer to the same entity but have slightly different meanings (semantics) and shapes (morphology).

The lemmatization process standardizes reference shape (morphology) to facilitate identification and aggregation. Lemmatization is a more sophisticated process than stemming, which truncates words to their stem and sometimes deletes information.

Encoding context and meaning with word embedding

In NLP, embedding is a numerical representation of a word that enables its manifold contextual meanings to be calculated relationally. Embeddings are typically real-valued vectors with hundreds of dimensions that encode the contexts in which words appear and, thus, also encode their meanings.
Because they are vectors in a predefined vector space, they can be compared, scaled, added, and subtracted. An example of how this works is that the vector representations of king and queen bear the same relation to each other as the representations of man and woman once you subtract the vector that represents royal.

Using embedding is key to analyzing how words change meaning depending on context and understanding the subtle differences between words that refer to the same concept: synonyms. For example, the words business, company, enterprise, and firm can all refer to the same thing if the context is “organizations.” But they represent different things and even different parts of speech if the context changes.

In the phrase, “[Tesla] will be by far the largest firm by market value ever to join the S&P,” for example, one could replace the word firm with company or enterprise without affecting the meaning significantly. Contrast that with “a firm handshake,” where a similar substitution would render the phrase meaningless.

Also, words referring to the same concept can emphasize slightly different aspects of the concept or imply specific qualities. For example, an enterprise might be assumed to be larger or to have more components than a firm. Embeddings enable machines to make these subtle distinctions.

One advantage of using embedding is that it’s practical because it’s empirically testable. In other words, we can look at actual usage to determine what a word means.

Another advantage is that embeddings are computationally tractable. This understanding of a word’s definition allows us to transform words into computation objects to programmatically examine the contexts in which they appear and, thus, derive their meaning.

As lemmatization is an improvement on stemming, embeddings improve techniques such as one-hot encoding, which is close to the common conception of a definition as a single entry in a dictionary.

SESAMm uses the global vectors for word representation (GloVe) algorithm to generate embeddings. It’s an unsupervised learning algorithm that begins by examining how frequently each word in a text corpus co-occurs with other words in the same corpus. The result is an embedding that encapsulates the word and its context together, allowing SESAMm to identify specific words in a list and different forms of the listed words and unlisted synonyms.

GloVe is an extension of recent approaches to vector representation, combining the global statistics of matrix factorization techniques like latent semantic analysis (LSA) with the local context-based learning of word2vec. The result is an unsupervised algorithm that performs well at capturing meaning and demonstrating it on tasks like calculating analogies and identifying synonyms.

BERT is another algorithm used by SESAMm to generate embeddings. BERT produces word representations that are dynamically informed by the words around them. Google developed the technique, and it’s what’s known as a transformer-based machine learning technique, which means it doesn’t process an input sequence token by token but instead takes the entire sequence as input in one go. This technique is a significant improvement over sequential recurrent neural network (RNN) based models because it can be accelerated by graphics processing units (GPUs).

SESAMm uses BERT for multilingual NLP of its extensive foreign language text because it has been retained using an extensive library of unlabeled data extracted from Wikipedia in over 102 languages. BERT model was trained to predict words from context and next sentence prediction where it was trained to predict if a chosen following sentence was probable or not given the first sentence. As a result of this training process, BERT learned contextual embeddings for words. Due to this comprehensive pre-training, BERT can be finetuned with fewer resources on smaller datasets to optimize its performance on specific tasks.

Linking words, sentences, and topics with cosine similarity

Cosine similarity with centered means it’s identical to the correlation coefficient, which highlights another element of the computational tractability of the embeddings approach. It makes it easy to compare words and contexts for similarity.

Converting words to vector representations means we can quickly and easily compare word similarity by comparing the angle between two vectors. This angle is a function of the projection of one vector onto another. It can identify similar, opposite, or wholly unrelated vectors, which allows us to compute the similarity of the underlying word that the vector represents.

Two vectors aligned in the same orientation will have a similarity measurement of 1, while two orthogonal vectors have a similarity of 0. If two vectors are diametrically opposed, the similarity measurement is -1. In practice, negative similarities are rare, so we clip negative values to 0.

Cosine similarity measures whether two words, sentences, or corpora are close to one another in vector space or “about” the same thing in semantic space. To answer the question, “Is this sentence referencing company X?” we embed the sentence using the process described above and compute the cosine similarity between the sentence and the embedded company profile. Analogously, we compute similarities between sentences and the ESG topics SESAMm monitors by taking the maximum similarity between a sentence and each embedded keyword associated with an ESG topic.

These similarities allow us to identify whether a sentence references fraud, tax avoidance, pollution, or any other ESG risk topic among the more than 90 that SESAMm tracks across the web.

Similarities within ESG topics combine with word counts to resolve the recall and precision problem. Word counts are precise because if a word is identified within a context, then that context, by construction, references the topic.

The virtue of using these NLP techniques is that even if a given keyword list does not include every possible combination of words that a person might use to discuss a topic, relevant entities missed by the word-count process will be identified through vector similarity.

This is the power of SESAMm’s NLP expertise. We can scan many lifetimes’ worth of data in seconds to find the concepts you explicitly ask for and the concepts relevant to your search but that you did not think of yourself.

Sentiment analysis with deep learning and neural networks

Once we’ve identified the concepts and contexts of interest in all the forms they appear, we analyze the context to determine the speakers’ attitudes.

We use sentiment classification models to score a sentence with three possible outcomes: negative, neutral, or positive. The current classification models are based on deep learning AI technologies. Specifically, we stack convolutional neural networks with word embeddings and bayesian optimized hyperparameters—parameters not learned during training. This architecture improves the accuracy and enables fast shipping of production-ready models for a given language. We also produce state-of-the-art frameworks with architecture variations enabling multilingual capabilities, such as transformers and universal sentence encoders.

Condensing information and extracting insights with daily aggregation

Similarities, embedded word counts, and sentiment are state-of-the-art tools for processing unstructured text data. The same tools are effective cross-linguistically.

Once the information has been extracted from millions of data points, it’s aggregated and condensed into actionable insights.

All entities are referenced directly or indirectly within an article. Then, sentence-level references are aggregated to obtain an article-level perspective, and finally, all relevant articles are aggregated to gain an entity-level view of that day.

In this way, reams of data are compressed into several metrics to provide a daily aggregate view for each entity, highlighting trends at a sentence, article, and entity-level comparable over a multi-year history.

ESG analysis use cases

SESAMm’s TextReveal Streams is used in various investment domains, from asset selection to alpha generation and risk management. Systematic hedge funds track retail interest in real time to identify investment opportunities and protect their existing positions. In the Private Equity industry, equity and credit-deal teams use the data in various ways, from monitoring consumer perspectives via forums and customer reviews for evaluating deal prospects to estimating due diligence risks, all to help make investment decisions. Dedicated teams use our data for monitoring portfolio companies for ESG red flags that conventional ESG reporting might miss.

Below are two examples of how aggregated TextReveal Streams data can be used to help identify investment risk and opportunity.

LFIS CapitalL: ESG signals for equity trading

ESG controversies can significantly impact asset prices in the short term, and it’s now estimated that intangible assets, including a company’s ESG rating, account for 90% of its market value.

Working in partnership with LFIS Capital (LFIS), a quantitative asset manager and structured investment solutions provider, SESAMm developed machine learning and NLP algorithms that could analyze ESG keywords in articles, blogs, and social media, to generate a daily ESG score specific to each stock, which is part of the TextReveal Streams’ platform’s core functionality.

The results were promising when these scores were incorporated into a simulated strategy for trading stocks in the Stoxx600 ESG-X index.

A simulated long-only strategy running between 2015 and 2020, using the signals, delivered a 7.9% annualized return, 2.9% higher than the benchmark for similar annualized volatility (17.3% vs. 17.1%). The information ratio of the strategy was greater than 1, with a tracking error of 2.8%. Results for the previous three years were compelling, reflecting the growing interest and news flow around ESG themes.

Researchers also backtested a hypothetical long-short strategy for all stocks in the Stoxx600 ESG-X index with a market cap of over $7.5bn. This investment strategy delivered a Sharpe ratio of approximately 1 with annualized returns and volatility of 6.1% and 5.9%, respectively, between 2015 and 2020. Like the long-only strategy, returns were particularly robust over the three years up to 2020: +6.0% in 2018, +7.3% in 2019, and +11.3% in 2020.

Finally, a simulated “130/30” ESG strategy that combined 100% of the long-only ESG strategy and 30% of the long-short ESG strategy delivered a 10.8% annualized return, 5.8% higher than that of the Stoxx600 ESG-X index. Annualized volatility was similar at 16.9% vs. 17.1%. The strategy experienced a tracking error of 3.8% and an information ratio of over 1.5, with a consistent outperformance each year.

Disclaimer: Past performance is not an indicator of future results. Theoretical calculations are provided for illustrative purposes only. The investment theme illustrations presented herein do not represent transactions currently implemented in any fund or product managed by LFIS.

Read more: “How Alterntive Data Identifies Controversies Before Mainstream Sources With Examples”

Wirecard: ESG sentiment and volume as predictive indicators

The Wirecard scandal broke on June 21, 2020, when newswires carried the story that the major German payment processor had filed for bankruptcy after admitting that €1.9 billion ($2.3 billion) of purported escrow deposits did not exist.

Could SESAMm’s TextReveal Streams platform have provided investors with an early warning that the scandal was about to break?

The following chart derived from the platform shows how key ESG metrics, including ESG scores (volumes) and ESG scores (sentiment), reacted to the news.

An analysis of the charts pinpoints a shallow rise in the ESG scores (volumes) time series in the early part of June before the eruption on June 21.

The ESG scores (sentiment) metric also shows a steady increase in negative sentiment for governance, the most relevant of the three ESG factors regarding the scandal.

Additionally, before the crash, governance was the most negative of the three ESG factors most of the time. This was especially the case from late March to early April, and then before the scandal in early June, negative governance sentiment diverged higher from the other two.

The rate-of-change of negative governance sentiment as it rose and peaked in early June before the scandal broke was also extremely high, perhaps providing the basis for an early warning signal.

Portfolio managers who had been keeping an eye on the reputational slide in Governance for Wirecard may have decided the company was at high risk of a negative controversy emerging, giving them cause to drop the stock before the event.

In this way, it can be seen how while not providing a hard and fast early warning signal, SESAMm’s ESG scores can, nevertheless, be used as the basis for developing a data-driven, rules-based portfolio management approach that can help investors avoid high-risk candidates like Wirecard.

SESAMm takes on ESG data challenges

SESAMm’s NLP and AI tools analyze over four million data sources daily to identify thousands of public and private companies and their related products, brands, identifiers, and nicknames, turning reams of unstructured text into structured and actionable data.

SESAMm’s TextReveal Streams platform can be used in many quantitative, quantamental, and ESG investment use cases. TextReveal is a solution that allows you to fully leverage NLP-driven insights and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts.

Learn how SESAMm can support you in your investment decision-making and request a demo today.

To request a demo or for access to the full SESAMm Wirecard or LFIS reports, contact us here: