From News to Forecasts: How AI Makes Commodity Markets Predictable

Commodity prices, such as those for oil, gas, or metals, play a central role in the global economy. Companies and investors who can rely on precise predictions are better equipped to make strategic decisions, minimize risks, and seize opportunities. However, forecasting these prices is no simple task—it requires taking into account numerous factors, including political events, environmental changes, and economic data.

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The Challenge: Dynamic Markets and the Role of News


Commodity markets are complex and volatile, shaped by constantly shifting influences. A single geopolitical event, such as a natural disaster or an economic crisis, can abruptly affect prices. The problem? Information overload. News and reports from a multitude of sources must not only be read but also understood in their context and relevance to the markets. This is where artificial intelligence (AI) comes into play.

The AI Solution: How LLMs and Event Extraction Collaborate


Our system leverages large language models (LLMs) and combines them with an event extraction mechanism to process information in real-time and filter out relevant events.

Role of LLMs (Large Language Models) in Commodity Forecasting

LLMs can understand and analyze text in ways that were previously difficult to achieve manually. Trained on billions of words from diverse text sources, these models have developed a deep understanding of language. In practice, this means LLMs can interpret the significance of a complex news article—such as “The FAA Keeps Boeing’s 737 Max Production Cap in Place”—and deduce possible impacts on the commodity market.

By understanding specific technical language and recognizing complex events, an LLM can identify key indicators of market changes. For instance, when processing news about production restrictions at an aircraft manufacturer, the LLM might grasp the underlying implications for related markets such as aluminum or jet fuel. A downstream model uses this input to anticipate potential impacts and generate targeted forecasts about market movements. In this tandem system, the LLM acts as the initial analysis layer, while the second model provides a deeper evaluation of economic implications.

Event Extraction in Forecasting: The Key to Relevant Information

Event extraction takes this process a step further, allowing the system to pinpoint specific, market-relevant events within the text. This means the system doesn’t just “read” but also identifies which information is likely to cause price movements. The event extraction process involves three main steps:

  1. Entity Recognition: The system identifies key terms such as companies (e.g., Boeing), organizations (e.g., FAA), and specific products (e.g., 737 Max).
  2. Event Extraction: The model recognizes events, such as “Keeps Production Cap in Place”—the decision to maintain the production cap for the 737 Max model.
  3. Contextualization and Market Analysis: Finally, the system links the extracted events to commodity markets. In this case, the FAA’s decision would affect aluminum and fuel markets, as changes in production impact demand for certain raw materials.

The Benefits of Our Approach: Accuracy and Identifying Market Opportunities

Our solution offers several key advantages:

  • Enhanced Forecast Accuracy: By combining LLMs and event extraction in real-time, the AI can better predict short-term market movements, giving companies and investors a critical edge.
  • Real-Time Analysis: The system continuously processes current information, delivering up-to-date forecasts that businesses can immediately use for informed decision-making.
  • Identifying Market Opportunities: By identifying relevant events and trends early, businesses can respond to market opportunities strategically before competitors act.

Future Outlook: The Impact on Markets and Strategies

The possibilities unlocked by using AI for market forecasting are far-reaching. In the long term, this technology could fundamentally change how businesses respond to market changes. The integration of LLMs and event extraction enables a more precise link between news events and market shifts—not just in commodities. Future applications could extend to areas such as stock prices, exchange rates, or even consumer goods markets, which are similarly influenced by dynamic factors.

Summary

Our AI technology provides insights into the future of commodity markets by efficiently converting news events into forecasts through the combination of LLMs and event extraction. In an increasingly dynamic world, this technology opens new pathways to capitalize on opportunities and mitigate risks.

Discover how our solution can shape the future of price forecasting!

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