By Igor Isaev
In the era where ESG and CVaR models define financial strategies, a critical asset as weather data often remains underappreciated. However, commodity trading is nowhere without taking into consideration the important factor of weather conditions forecasting.
This gap is surprising because climate has a huge impact on commodity markets. It affects everything from crop production and energy supply to transportation. What if we could react to market-moving weather events before the headlines even hit? Accurately understanding and predicting its changes can position many financial companies as leaders in the sector.
Yet, many trading strategies still don’t fully use this valuable resource. In this article, we’ll delve into why real-time weather analytics isn’t just a helpful tool but key for staying competitive and making better predictions.
Why is weather data underestimated in commodity trading?
Most market participants have access to weather data today, but their correct interpretation remains a serious problem. Additionally, there is no universal system where weather data can be fixed in a consolidated way. The problem becomes even more serious due to the fact that weather patterns are variable and context-dependent, requiring careful and timely interpretation.
Also, it is not enough to simply forecast changes; it is important to define how the particular weather will affect the commodities. This can vary across different regions, and understanding this relationship requires not only data but also proper analytics and the establishment of causal relationships.
Furthermore, there is a problem with speed. Weather affects commodity prices much faster than is often assumed. Frosts, or hurricanes, begin to influence the market long before information about it gets into the media. This delay in the dissemination of data creates great opportunities for traders who can react quickly to changes in the weather but also serious risks for those who rely on late reports from news channels.
Imagine a situation where farmers begin to notice frosts on their plantations and report this to journalists. The process of collecting information, verifying it, and distributing it on news channels can take several days. However, the market is not waiting: rumors about weather anomalies begin to circulate at the same moment, provoking price fluctuations that are based not on accurate data but on assumptions.
For traders, such a delay poses a serious threat. Relying only on news channels means missing out on precious time when it would be possible to make informed decisions based on real data. This not only deprives them of the opportunity to take advantage of the market situation, but also increases the likelihood of failure due to reactions to outdated or fake information.
How can the weather data provide a competitive advantage?
As I have already mentioned, the ability to integrate weather data into commodity trading is the opportunity to get ahead of the whole market. Traders who use real-time weather data analysis models gain a significant advantage over competitors and slow news channels. They can react to changes even before the information becomes publicly available, which helps them to predict market movements more accurately.
For example, this summer, there were rumors about frosts in countries where coffee grows. Considering that it is a plant intolerant to low temperatures, this has led to a spike in coffee prices. In this case, traders, depending on their analytical data, were able to quickly verify that these rumors were unfounded. This allowed them to gain advantageous positions in the market while competitors reacted to unconfirmed information and highly probably lost their money to some extent.
The use of satellite images and local weather forecasts can also be priceless. Modern technologies give an opportunity to integrate data from satellites into analytical models, creating the most accurate forecasts. As a result of this approach, financiers can take into account even small local anomalies that can significantly affect the prices of raw materials.
The information can also be analyzed on a multi-level basis from satellites and hydrometeorological stations. If necessary, more detailed information from a network of regional ground stations and enterprise metering devices can be integrated.
Another bright example is the recent hurricane in the Mexican Gulf. When it threatened oil and gas platforms in the region, thanks to weather models, the impact on LNG production was predicted in advance. Traders with this approach were able to adjust their positions and minimize risks, while many competitors faced losses due to unpreparedness.
Why is weather analytics as relevant as never before?
The ongoing climate change increases the frequency and intensity of extreme weather events such as droughts, hurricanes, and floods. In turn, this raises uncertainty in commodity markets, making them more unpredictable. The entire production infrastructure is designed to work under certain loads, including wind and temperature. Recently, natural phenomena have periodically exceeded these limits, which leads to equipment failures.
The ability to take these changes into account in real-time is becoming critical for traders, especially for those seeking to minimize risks and remain competitive. Moreover, considering these real-life examples also leaves no doubt that weather data is invaluable for commodity trading.
In conditions of growing climate volatility, the speed of decision-making is becoming even more important. Weather analytics allows traders to react to events almost instantly. They can use the time between their actual occurrence and reflection in news reports. This time window provides the protection of positions and the benefit of market opportunities.
Now, more than ever before, the use of weather analytics is becoming not just an impressive tool but a necessary tool for successful commodity trading.
About the Author
Igor Isaev is the Head of Analytics Center at Mind Money. He is a financial markets analytics expert, boasting over 20 years of hands-on experience in the stock and commodity market analytics and trading strategies development.
