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).

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.

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).

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]

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.

Luxury brand Loro Piana, owned by LVMH, has been placed under a one-year judicial administration by an Italian court after a labor exploitation investigation uncovered serious abuses within its supply chain. According to Reuters, workers at a subcontracted factory were paid as little as €4 per hour and subjected to 90-hour workweeks, often living inside the premises. One worker was reportedly attacked after requesting unpaid wages, requiring 45 days of medical treatment.
The case highlights the growing scrutiny of labor conditions in Italy’s fashion manufacturing sector, especially among high-end labels. Loro Piana is now the fifth luxury brand, joining Dior, Armani, Valentino, and Alviero Martini, under court supervision due to supplier-related violations.

A Complicated Web of Subcontracting

What sets this case apart is the complexity of the supply chain. Loro Piana did not contract directly with the workshop where the violations occurred. Instead, it worked through two front companies, both of which lacked actual manufacturing capacity. These intermediaries then subcontracted the work to a network of unregistered or poorly monitored producers. All the firms involved in this chain have been swept up in the investigation.

This multi-tier outsourcing structure made it difficult to detect violations and raises questions about accountability. The Milan court noted that Loro Piana "culpably failed" to supervise its partners, prioritizing cost and output over due diligence.

Why It Matters

Luxury brands trade on trust and exclusivity. Consumers expect not just quality, but integrity, especially regarding sourcing. When serious labor violations are revealed, the reputational risks extend far beyond one product or supplier. They affect brand credibility, investor confidence, and long-term consumer loyalty.

This incident also reinforces a trend: regulators are increasingly willing to intervene when voluntary monitoring fails. Judicial administration isn’t just symbolic; it’s a legally binding oversight mechanism aimed at forcing systemic change.

The Path Forward

For fashion brands, this is a clear signal that supply chain governance must go deeper. That includes mapping indirect suppliers, improving transparency around subcontracting, and enforcing ethical standards at every level. Simply trusting the next link in the chain is no longer enough.

In a sector built on craftsmanship and heritage, safeguarding those values behind the scenes is just as important as what ends up on the runway.

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.

We recently held an insightful webinar co-hosted by Charlotte Salmon of Indefi and Alejandro Plaza of SESAMm, titled "AI in ESG Due Diligence: Best Practices from Indefi and SESAMm." The session explored the innovative application of artificial intelligence in the ESG risk assessment process.

During the webinar, Charlotte and Alejandro discussed how Indefi's proven strategies, when combined with SESAMm's cutting-edge AI platform, can significantly enhance ESG risk management and the selection of target companies.
One of the key highlights was a detailed case study on major delivery apps, including Glovo, Uber Eats, DoorDash, Deliveroo, Grubhub, and Just Eat. This segment covered industry and competitive analysis, sentiment analysis, and a deep dive into ESG controversies and positive SDG impacts associated with these companies.

Key Topics Covered:

• Due diligence process for ESG risk assessment
• AI integration in ESG risk management
• Selection of target companies
• Delivery apps case study: industry and competitive analysis, sentiment analysis, and deep dives into ESG controversies and SDG positive impacts

Don't miss out on this opportunity to learn from industry leaders about the future of ESG due diligence. Watch the full webinar replay: