Why fundamental analysis differs for commodities
Technical analysis assumes price contains all relevant information, which holds for equity indices where thousands of participants continuously incorporate data. Commodity markets operate differently. Physical supply constraints, storage costs, transportation bottlenecks, and geopolitical disruptions create pricing dynamics that chart patterns cannot capture. A refinery outage or OPEC production cut changes the physical barrel count available to the market. These are structural changes to supply that persist until new capacity comes online, not sentiment shifts that mean-revert. Fundamental commodity analysis therefore begins with the physical balance sheet: how many barrels, bushels, or tonnes exist relative to consumption.
Oil market fundamentals: supply, demand, and inventories
Crude oil supply is driven by OPEC spare capacity, US shale production (which responds to price with a 4 to 6 month lag), and non-OPEC production from Brazil, Guyana, and Canada. Demand depends on global economic activity measured through industrial production, PMI surveys, and refinery utilization rates. Seasonal patterns matter: US driving season lifts gasoline demand while Northern Hemisphere winter increases heating oil consumption. Inventories serve as the buffer, with the EIA Weekly Petroleum Status Report providing US commercial crude stocks and Cushing hub levels. When inventories fall below their five-year seasonal average, the market tightens and prices respond with upward pressure.
Geopolitical risk and commodity pricing
Geopolitical events affect commodities through two channels: actual supply disruption and risk premium. A pipeline shutdown in Libya removes physical barrels immediately, while tensions in the Strait of Hormuz add a risk premium without necessarily altering supply. Roughly 20% of global oil transits through the Strait of Hormuz. Quantifying geopolitical risk requires distinguishing between events that change the physical balance sheet and events that temporarily elevate implied volatility. The former justifies sustained price moves; the latter tends to fade within days or weeks as the threat recedes.
The US dollar and commodity prices
Most globally traded commodities are denominated in US dollars, so when the dollar strengthens, commodities become more expensive in local currency terms for non-US buyers, suppressing demand at the margin. The inverse relationship between the Dollar Index (DXY) and broad commodity indices has been empirically persistent, though the correlation varies by commodity and time period. Energy markets show weaker dollar sensitivity than metals because oil demand is relatively price-inelastic in the short run. A systematic model should incorporate dollar momentum as a secondary factor rather than a primary signal.
Contango, backwardation, and the futures curve
The shape of the futures curve encodes the market's view of supply-demand balance over time. Backwardation (near-term contracts priced above deferred) signals tight current supply and incentivizes inventory drawdowns. Contango (deferred above near-term) indicates ample supply and rewards storage. The spread between the first and second month futures contract is one of the most information-rich signals in commodity markets. Persistent backwardation historically correlates with inventory depletion and rising spot prices, while contango coincides with oversupply.
Data sources for energy market analysis
Three institutions publish core energy fundamental data. The US Energy Information Administration (EIA) provides weekly inventory reports, monthly production data, and the Short-Term Energy Outlook. The International Energy Agency (IEA) publishes monthly Oil Market Reports covering global supply, demand, and OECD inventories. OPEC's Monthly Oil Market Report includes member production data and demand forecasts. Cross-referencing these sourcess consensus and divergence in supply-demand projections. The Baker Hughes rig count offers a leading signal for future US production changes.
Systematic vs. discretionary fundamental models
A discretionary analyst reads reports, forms a view, and trades on conviction. A systematic fundamental model translates the same inputs into quantifiable signals with defined rules, enabling consistency and backtesting against historical data. The systematic approach requires identifying which data series carry predictive information, determining lookback windows, and establishing signal combination methods. The limitation is that qualitative geopolitical assessments and policy shifts resist quantification. Effective fundamental models combine hard data such as inventories and production with structured rules for softer information like OPEC meeting outcomes or sanctions announcements.
