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Hydropower's ESG Paradox: Why the "Green" Asset Class Tops the Controversy Charts

August 20, 2026
5 mins read
Hydropower tops ESG controversy volume across 250,000+ projects, outranking coal. Why the greenest label in energy hides the heaviest social risk.

An analysis of over 250,000 infrastructure projects reveals that the sector most often filed under "clean energy" carries the heaviest environmental and social controversy footprint of any asset type assessed.

In the taxonomy of energy infrastructure, hydropower occupies a comfortable position. It is renewable, dispatchable, and long-lived, and it enters transition frameworks, green bond eligibility criteria, and net-zero roadmaps with minimal friction. Where coal is a legacy liability to be managed down and nuclear invites a specialized debate, hydropower is largely treated as settled. What these projects have actually done does not support that treatment.

Belo Monte, an 11,233 MW complex on the Xingu River in Pará, Brazil, is the sharpest test of the point, because it was built to answer this exact objection. Approved after decades of opposition to a far larger design, it was engineered as a run-of-river plant to minimize flooding, and its reservoirs cover 478 km², of which 274 km² was already river channel at high water, a 61% reduction compared with the 1980s proposal, according to the operator's own regulatory filing. The mitigation was designed from the start, and everything that follows happened regardless.

Biodiversity: the cost of a physical footprint

Environmental controversy across infrastructure concentrates on industrial accidents, water pollution, and biodiversity, and hydropower leads the third, outright, because dams require the permanent conversion of river systems and the land around them. Mexico's Federal Electricity Commission won environmental approval in September 2014 for the Las Cruces dam on the San Pedro Mezquital, upstream of Marismas Nacionales, a Ramsar-protected wetland, even though the project's own impact statement conceded that the damage to Indigenous ceremonial sites could not be mitigated. Along the Mekong River, river health and fish populations fell as dam construction spread through the basin. In Brazil, the Doce River carried a mass release of toxic material after an upstream failure. Elsewhere, the record includes violations of the Endangered Species Act and documented disruption to rainfall patterns.

At Belo Monte, the consequences have been measured rather than projected. The plant diverts water into a canal that bypasses a 130-kilometer stretch of the Xingu known as the Volta Grande, which has received less than 30% of its natural annual discharge since 2019, and some 86% of the stretch's seasonally flooded vegetation, 30,748 of 35,600 hectares, can no longer be inundated at all. The gap lies in the regulator's own file: IBAMA's technical staff called for 10,900 cubic meters per second in February, the historic peak month, compared with the 1,600 that the operating regime actually releases. Seven years of underwater video survey data published in Scientific Reports recorded total fish species richness falling from 62 to a post-operation average of 51, with the steepest losses near the dam and in the rocky rapids, which hold roughly 2.6 times as many species as sandy reaches. The zebra pleco, whose entire known range lies inside the dewatered stretch, now sits on Brazil's national list of threatened species as critically endangered.

None of this is an accident or a failure of operation. It is a structural consequence of the asset. A well-run dam still floods a valley, and a dam engineered specifically not to flood one still dewater the river below it.

When engineering fails: hydropower's physical risk profile

Coal mining leads infrastructure on industrial accidents, where the record is dominated by human tragedy and safety negligence: explosions, collapses, fires, and repeated, incremental failures. Hydropower ranks second, but its accidents take a different form, because in this sector, industrial failure means catastrophic engineering failure at scale. The record includes pipe ruptures causing severe land erosion, oil leaks, and dam collapses that killed and displaced people across whole regions, while PG&E's settlement over damages to the Middle Fork American River Hydroelectric Project and the litigation still running in Brazil after dam collapses give a sense of the exposure a single event can generate. For anyone underwriting these assets, the distinction is financial as much as physical: a coal mine's safety record is a rising cost curve, while a dam's structural integrity is a low-probability, near-unbounded loss.

At Belo Monte, that exposure has so far been financial. The project was budgeted at R$28.9 billion when Brazil's development bank approved a then-record R$22.5 billion loan in November 2012, and by late 2017, actual investment had reached R$38.6 billion, roughly 34% over. The operator owed R$28.3 billion to lenders and debenture holders at the end of 2024. Aliança Norte Energia Participações, the Vale and Cemig vehicle holding a stake in the project, discloses a possible loss of R$3.05 billion from a single construction-delay claim and describes the operator's liquidity as its principal point of attention and a source of investor alert. Neoenergia wrote off its own 10% holding by R$482 million in the fourth quarter of 2021.

The physical risk has been closer than the absence of a collapse suggests. In October 2019, the operator wrote to the national water regulator declaring an emergency, because reservoir levels had fallen far enough to expose an unprotected section of the Pimental dam's earthfill base to wind-driven wave erosion and, in the company's own words, structural damage. It cut outflow below the level agreed with the environmental regulator to protect the structure, and the letter surfaced only through investigative reporting.

Beyond the environment: displacement, water, and chronic corruption

Right to property

Hydropower ranks first among infrastructure sectors for property disputes, a direct function of the footprint a dam and reservoir require. The record shows land seizures, forced displacement, compensation that arrives short or not at all, communities never consulted before ground was broken, and blasting that cracked the foundations of nearby homes. Those affected are frequently the least equipped to hold an operator to account.

Fifteen years after Belo Monte broke ground there is still no audited count of who lost their homes. Estimates run from 20,000 to 40,000 depending on the definition used, against the operator's account of rehousing some 6,000 urban families. Landowners say expropriations are priced at unadjusted 2013 values while the project's own construction boom inflated the market, and as of 2025 none of the land required for the riverine resettlement program had been bought. A petition filed with the Inter-American Commission in 2011 still has no ruling.

Community health and safety

Hydropower sits alongside coal and nuclear as a leading source of community health disputes, but it arrives by a different route. Coal delivers PM2.5, nuclear delivers radioactive anxiety, and hydropower delivers water mismanagement: overconsumption that strips farmers of a livelihood, contaminated water reaching local crops. The grievance is agricultural rather than industrial, which widens the affected population considerably.

On the Volta Grande, catch per fisher fell from 11.1 kilograms a day between 2001 and 2008 to 4.53 kilograms between 2020 and 2023. A randomized household survey found 38.5% of residents in Belo Monte's resettlement neighborhoods living with moderate or severe food insecurity, against 28.3% across the surrounding city. In June 2026, federal prosecutors sought as interim relief for 635 families along the reduced-flow stretch the emergency delivery of three and a half to five liters of drinking water per person per day.

Corruption and bribery

Corruption and bribery accounts for close to 30% of governance controversy across infrastructure. What separates hydropower is the pattern. In airports, nuclear, and coal, corruption surfaces as discrete scandals: a probe opens, executives are charged, attention fades. In hydropower it keeps returning, tied repeatedly to falsified records and payments to local officials to secure land and water rights. Isolated scandals point to isolated actors. A pattern that recurs points to how these projects get permitted.

Brazilian prosecutors alleged that Belo Monte's construction contracts carried bribes worth 1% of their value, and three contractors admitted cartel conduct and kickbacks under leniency agreements that carried immunity. Everything after that was procedural closure rather than a finding of liability: the principal defendants were acquitted and the acquittal upheld on appeal in 2024, the competition authority archived its bid-rigging case in 2025, and no individual has been convicted in connection with the project. An investor screening for enforcement outcomes would have found a closed file. The costs landed elsewhere, in permitting delay, financing conditions, and a minority stake that has been for sale since 2022 without a buyer.

Hydropower's risk concentration: what this means

Hydropower's classification as clean energy is accurate on the metric it was designed to measure, because generation is low-carbon. But carbon intensity is one dimension of sustainability, and it is not the dimension that produces operational friction, legal exposure, or the loss of a social license.

What drew sustained opposition to these projects was water rights, displaced communities, cracked foundations, converted wetlands, and permits secured through local payments. None of it appears in a carbon accounting framework.

For investors, insurers, and lenders seeking transition-aligned infrastructure exposure, that is a material blind spot: an asset class that screens well on the primary criterion while carrying the heaviest social burden in the dataset, and carrying it on behalf of people who have no employment relationship with it. Belo Monte was engineered to avoid precisely that outcome and produced it regardless, which suggests the exposure is not a function of how a dam is built but of what a dam is.

The label is not wrong. It is simply measuring something other than risk.

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Alternative Data | Text Analysis | Sentiment Analysis

Alternative Data Trends: TotalEnergies

November 9, 2022
5 mins read

In October 2022, this multinational integrated energy and petroleum company found itself in the crosshairs of two non-government organizations (NGOs), accusing it of "exploiting a gas field used to manufacture kerosene used by Russian planes in their bombings in Ukraine," according to the French daily Le Monde. The accusations expressly point out the 16 March 2022 strike, which killed around 600 civilians taking shelter at a Mariupol theatre.

The company? TotalEnergies.

The NGOs? Razom We Stand (Ukraine) and Darwin Climax Coalitions (France).

Also, in October, TotalEnergies posted a third-quarter net profit amid these allegations. "The French group reported an adjusted net income of $9.86 billion, compared with $4.77 billion for the same period in 2021 and $9.8 billion in the second quarter of this year," per Reuters.

Should investors and asset and portfolio managers be concerned? Is this one instance of allegations a nothing burger, or is it a sign—one of many red flags—to evaluate? Let's find out in this edition of Alternative Data Trends: TotalEnergies.

Analyzing TotalEnergies's ESG and sentiment data

Company polarity and mentions volume

TotalEnergies polarity and web mentions chart

In the last year, mentions of TotalEnergies have gradually increased, spiking in March 2022. The spike in mentions coincides with a dip in the company's sentiment. These abrupt changes occurred when news broke about a lawsuit targeting TotalEnergies, claiming that the oil and gas group misled the public in its rebranding campaign, and a former French presidential candidate, Yannick Jadot, accused TotalEnergies of being complicit in war crimes.

After the spike in controversies in March 2022, TotalEnergies's web mentions continued to increase. And as a reflection of the mentions volume increases, the company's polarity decreases, moving in the direction of negative sentiment. News events triggering these movements include the legal case for allegedly fuelling Russian bombers and refinery strikes over wages.

ESG risks

TotalEnergies ESG risks chart

A screening of TotalEnergies's ESG risks uncovers a similar trend. For example, our platform detects an increase in social risks as early as January 2022. This signal coincides with the company's exit from Myanmar because of human rights abuses.

Examining TotalEnergies's environmental risks

Environmental-related mentions volume

TotalEnergies environmental mentions volume chart

Let's take a closer look at TotalEnergies's environmental risks, specifically for general environmental strategy, climate change and atmospheric pollution, and biodiversity. The topic of climate change and atmospheric pollution takes the largest share of mentions. Web data driving this volume include:

Studying TotalEnergies's social risks

Social-related mentions volume

TotalEnergies social mentions volume chart

TotalEnergies's social risks reveal even more controversies, looking at human rights, human capital, and customer relations topics. Human rights and human capital topics almost equally dominate topic mentions. Controversies highlighting the human rights topic include:

Human capital topic controversies include:

Gauging TotalEnergies's governance risks

Governance-related mentions volume

TotalEnergies governance mentions volume chart

Finally, let's look at TotalEnergies's governance risk, specifically within topics for influence strategy and communication, corruption, and board of directors (executive and company management). The influence strategy and communication topic dominates the mentions volume. These controversies relate to greenwashing, such as:

Recapping TotalEnergies's ESG risks and public perception

We only scratched the web data surface for information on TotalEnergies, but even with this overview, you can probably see a pattern. Should investors, asset managers, and portfolio managers be concerned about the latest allegations against TotalEnergies? Maybe. At the very least, the data shows you should dig into the research and ask more questions to mitigate any risks in your portfolio companies.

Reach out to SESAMm

SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms, by providing datasets or NLP capabilities to generate their own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more.

For questions, access to the full report, or to request a TextReveal® demo, contact us here:

Big Data | Text Analysis | Data Science

Bigger is Not Always Better: Forecasting Commodities with NLP Data

November 8, 2022
5 mins read

SESAMm has a large data lake of more than 20 billion articles (growing by 5–10 million a day) and 14 years of data in 100 languages. But its size alone is not what makes it good; it’s a refined process to find the exact data you want that makes it better.

Here’s an example to help explain the point. We’re sometimes asked for help researching data to forecast and monitor the commodities market, even by large companies with their own commodities desk of traders and quant researchers. Why would they seek help from outside their firm?

Simply put, traders want an edge. They want information advantages that others are likely to miss, so they look to alternative data from various sources, anything that adds value and is from different angles. And, as it turns out, commodities are a more challenging segment to analyze when it comes to alternative text data. Unlike for companies, commodity texts are scarcer and need more domain knowledge to unravel their implications. A simple sentiment analysis doesn’t bring enough relevant information.

For a more in-depth view,  join us as we discuss NLP-derived alternative data, its benefits, challenges for researchers, and why bigger isn’t always better in the world of data.

Read the full article on Quant Finance.

Alternative Data | Sentiment Analysis | Strategic Insights

NLP: A Better Way to Nowcast Macro and Commodities

November 4, 2022
5 mins read

The macroeconomic environment is moving quickly—inflationary pressures, war in Europe, political instability, and plenty of other topics to make a trader's head spin. While there’s an abundance of structured macro data, it's much more difficult to extract value from unstructured text on the web.

However, with the right partner and the right tools, it doesn't need to be difficult to rein in this complexity. But more on that later.

More data, more problems

We're obviously in the information age; we have more data within reach than ever before. And you'd think that more data would make it easier to find consistent relationships between the macro economy and price returns. The hard truth is that it doesn't.

Of course, you have access to comprehensive historical information, but developing economically intuitive and worthwhile systematic strategies from historical data alone is challenging. Despite having this data, it could still be incomplete, missing that bit of nuance for a theme you're examining.

Nowcasting for more complete, current data

Nowcasting—a contraction of the words now and forecasting—is the prediction of the present and the near future using data from the recent past as an economic indicator. Nowcasting models can be applied in real time as a proxy for official measures, such as monitoring the state of the economy, themes, or sectors: food, transportation, energy, and so on.

For example, you could look into what's being said about supply chain disruption for semiconductors. How is the topic trending across industries or the broader public? And how positively or negatively is that topic perceived over time? This information helps give financial data context and direction, a way to predict what happens next.
So where do you turn to for reliable, timely nowcasting data?

Nowcast-enhancing platform

At SESAMm, we have a flexible, adaptable, and modular platform to nowcast pretty much any macroeconomic theme: inflation, supply chains, unemployment, and everything in between. If you can gauge it, we can find data on it.

How do we do this? Our natural language processing (NLP) platform makes sense of all available news, articles, and forums on the web. Currently, there are more than 20 billion articles in our data lake, and it's growing by millions daily. And because we update our data lake multiple times a day, you can read nowcast macroeconomic indicators in near real time.

This flexible approach to building themes goes way beyond off-the-shelf sentiment feeds, and you can adapt to new, emerging factors on the fly.

Use case: inflation insights

With the TextReveal® API and Dashboards, you can generate custom proprietary inflation requests—or pull existing queries by country and sector—and use this data in your nowcasting models (Figure 1).

Dashboard view of TextReveal
Figure 1: TextReveal's dashboard highlights inflation topics on the web and associated sectors.

In this example extracted from our API (Figure 2), the number of sources mentioning inflation is relatively stable until 2021, when it starts to increase rapidly, in anticipation of actual inflation readings.

Source volume monthly evolution chart
Figure 2: The number of sources mentioning inflation increase in early 2021.

As you can see, you can track a topic and map it to various segments, creating a signal that accurately follows that theme over time. But that's not all. Macro teams can inject their expertise into building these queries, too. So if they have specific ideas on keywords and themes to capture—for example, inflation in Brazil—they have complete control over them.

Ultimately, you can break down the data by volume, sentiment, sector, language, or country. Do you want to know what the Japanese market makes of rising inflation in the U.S.? With SESAMm's platform, you can slice the data in different ways to find out.

Results with transparency

All that inform the results of your queries are available for scrutiny. Say you want to understand why a topic or theme is trending one way or the other, or maybe the sentiment isn't what you expect. You can drill down to the source articles to see why (figure 3).

An example of source articles affecting sentiment score
Figure 3: An example of source articles affecting sentiment score.

Use Case: predictive signals for macro factors and commodities

We worked with a client as part of an asset allocation strategy to build indicators reflecting the tone of the Fed fund rate to see what we could predict based on the indicators.

[figure 4]

Fed hawkish and dovish indicators chart
Figure 4: The Fed tone indicator successfully anticipated the major changes in the fed rates—a reduction during the COVID-19 crisis and a rise in 2022.

In Figure 4, the language we uncover becomes increasingly dovish, as indicated by the blue line, the aggregate of the hawkish and dovish indicators. It's proceeded by the fall in interest rates, the start of covid, and the recent inflationary period. Then, the indicators spike way before interest rates move up. Of course, it isn't the only factor, and it's not 100% predictive, but it does reflect future movements. Inflation is at an eight-year high right now, so it's indicative of continuing inflation returns and continuing rising interest rates.

Nowcasting and forecasting with TextReveal

With TextReveal, you can nowcast any macro theme by building expert-driven queries and predictive forecasting signals to get insights into volume, sentiment, and more.

If you want to find data relationships that accurately reflect economic trends and macro themes to what's happening online in near real time with a high degree of control, reach out for a demo.

Alternative Data | Risk Management | Sentiment Analysis

Alternative Data Trends: a16z, Flow, and the Public Web’s Sentiment

September 28, 2022
5 mins read

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)

a16z mention volume and polarity overview
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).

Polarity over time view
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?

a16z and Flow news clips
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

a16z ESG initiatives chart
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

 a16z portfolio ESG risks over time aggregate
Figure 5: a16z’s aggregated portfolio’s ESG risks over time.
a16z portfolio ESG risks over time by category
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.

a16z ESG risk analysis top 10 companies
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.

a16z SDG initiatives analysis
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.

Reach out to SESAMm

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

NLP | Alternative Data | AI

Summer Roundup: Our 10 Most-Read Blog Posts This Year (So Far)

September 7, 2022
5 mins read

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.

#10 What Investors Ought to Know About Natural Language Processing: A Quick Guide

What Investors Ought to Know About Natural Language Processing A Quick Guide

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.

#9 S&P 500 ESG Index Drops Tesla: This Analysis Supports the Decision

Illuminated Tesla sign at night

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.

#8 Alternative Data Trends: NLP Analysis on Commercial Real Estate

Commercial Real Estate

Read the takeaways of the current commercial real estate market we extracted using SESAMm’s NLP-powered engine to analyze web data.

#7 VIDEO: ESG Data Challenges and How AI and NLP Offer Solutions

sesamm-japan-investor-forum-title-slide-SESAMm

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.

#6 How Organizations Are Using NLP To Detect Greenwashing

Greenwashing_276x200

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.

#5 Alternative Data Trends: 5 Effects of the Failing Musk-Twitter Deal

Alternative Data Trends 5 Effects of the Failing Musk-Twitter Deal

Based on alternative data, discover how Elon Musk’s personal and related brands measure up to public sentiment following his failed acquisition of Twitter.

#4 What Investors Ought to Know About Data Lakes: A Quick Guide

What Investors Ought to Know About Data Lakes A Quick Guide

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.

#3 Gain Insights From Financial and ESG Data Using AI: A Comprehensive Guide

Gain Insights From Financial and ESG Data Using AI A Comprehensive Guide-1

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.

#2 What Investors Ought to Know About Knowledge Graphs: The Core of Text Analysis

Introducing Knowledge Graphs The Core of Text Analysis

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.

#1 Predicting stock price movements using news and social media data

Predicting stock price movements using news and social media data

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
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.

Learn more:Gain Insights From Financial and ESG Data Using AI: A Comprehensive Guide.”

Why are SDGs important to investors?

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.

dashboard view of Aker  Carbon Captures SDG performance
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.

Our AI and NLP platform, TextReveal:

  • 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

Alternative Data Trends: 5 Effects of the Failing Musk-Twitter Deal

August 17, 2022
5 mins read

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:

  1. Musk's fans were disappointed because they thought it would allow him to spread his message about sustainable energy sources further.
  2. 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.

Notes: Dates on the charts follow the day/month/year format. Additional timeline source: “A timeline of Elon Musk's tumultuous Twitter acquisition attempt,” ABCnews.com, July 13, 2022.

1. Merger and acquisition sentiment dropped from the beginning

Mention volume and polarity chart for M&A and Twitter
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

Mention volume and polarity chart for Elon Musk, Twitter, and M&A
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.

3. Musk's brands, Tesla and SpaceX, suffered, too

Tesla stock price and polarity chart for Musk, SpaceX, and Tesla sentiment
Figure 3: SpaceX sentiment polarity nearly matches Musk’s.

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

Twitter stock price and polarity chart for Twitter and Musk
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

Mention volume and polarity chart for the open source code topic
Figure 5: Musk possibly gained the open-source community’s favor, if the rise in polarity is an indication.

In late 2021 and early 2022, open-source sentiment polarity was dipping. It experienced its biggest dip after February 2022 when Twitter admitted it had mistakenly removed Ukraine open-source intelligence accounts.

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.

NLP | Alternative Data | AI

Predicting stock price movements using news and social media data

August 10, 2022
5 mins read

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.

Chart comparing U.S. news sentiment to Hang Seng Index and S&P 500 Index
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
SESAMm equity model performance and characteristics chart and tables
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.

U.S. High-Yield Total Return index monthly returns chart
Figure 3: NLP data can help provide a hedging signal by capturing deteriorating text sentiment.
High-yield model performance comparison chart
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.

A representation of the collaborative NLP alternative data model
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:

Alternative Data | Big Data | AI

What Investors Ought to Know About Data Lakes: A Quick Guide

July 27, 2022
5 mins read

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:

  1. Take advantage of advanced and sophisticated analytical techniques applied to complex and diverse data.
  2. 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:

  1. Broad and large
  2. Includes more than 100 languages
  3. Tuned to key indicators
  4. 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.

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