From Observer, March 20:
From soybean fields in Argentina to cocoa farms in West Africa, weather patterns are increasingly moving markets ahead of official data, exposing how much today’s price dynamics depend on signals that traditional financial models still fail to capture.
Commodity markets in 2026 are showing many signs of breaching historical patterns, and for a number of converging reasons. Price dynamics no longer line up neatly with the usual macro factors, such as economic cycles and interest rate narratives. As a result, inventories and demand forecasts are increasingly failing to produce satisfactory results based on past trends. Most importantly, the oil price surge driven by ongoing geopolitical tensions is creating a highly uncertain, difficult-to-model outlook.
While the World Bank projects stabilizing commodity prices in 2026, a “silent” risk is accumulating beneath the surface. The trouble here is not contained by oil itself, but easily spreads across the broad spectrum of other interdependent commodities. The ripple effects here have gone further than most existing models would suggest. For example, fertilizer markets have abruptly tightened, while agricultural inputs have become more expensive, and food markets are once again under pressure, even as many grains and soft commodities have yet to fully reflect the real stress they are absorbing.
At the same time, a series of seemingly disconnected events has taken hold across the soft commodities market. Argentine dryness has lifted parts of the soy complex despite uninspiring global demand. Brazil’s uneven rainfall patterns have injected volatility into coffee and sugar prices, often at odds with comfortable stock estimates. In the U.S., cold snaps have triggered sharp moves in natural gas even when storage data appeared reassuring. Wheat markets have reacted to weather headlines in the Black Sea before any confirmed production losses materialized.
Individually, each of these developments can be rationalized. But taken together, they point to something more fundamentally disruptive: markets are reacting to signals that traditional models routinely downplay, especially those designed to operate in real time, let alone automated ones.The rediscovered limits of financial models
The core problem here is not a lack of sophistication of the existing models. In fact, the majority of modern financial models are highly effective at processing monetary policy signals, earnings data and institutional balance sheet dynamics. Where they fall short is in handling physical variables that do not fit neatly into structured datasets.
Soil moisture, for example, does not appear on a central bank dashboard. Wind patterns are not part of quarterly earnings calls. Precipitation anomalies rarely make their way into consensus forecasts. And yet, these are precisely the variables now shaping supply in key commodity markets.
Traditional frameworks tend to react to confirmed data, such as crop reports, inventory updates or export statistics. By the time such information finds its way to official releases, the underlying conditions have often been in place for months. Markets, however, do not wait. They tend to move on expectation. As a result, a gap has opened up between what is happening on the ground and what is reflected in prices, and this discrepancy is becoming increasingly difficult to ignore.Weather as a market driver, not a footnote...
...MUCH MORE
On top of the increasing tendency of traders to (attempt to) anticipate the news, what the article refers to as "When the echo comes before the sound" we are seeing trades that are best explained as AI teasing-out patterns that are neither intuitive nor readily apparent.
Combine all the above with incredible amounts of liquidity, cash and credit, sloshing around the world and we are experiencing the frequency of large magnitude moves occurring at rates that would have been almost unthinkable twenty or even fifteen years ago.
