analyzing-commodity-markets

name	analyzing-commodity-markets
description	Structures commodity market analysis with supply/demand balances, inventory dynamics, and price driver attribution. Use when analyzing commodities, evaluating supply/demand, or forecasting commodity prices.
tags	["analysis","economic-analysis"]
metadata	{"author":"casemark","practice_areas":["Economic Research","Macroeconomics","Policy Analysis"],"document_types":["Analysis Report"],"skill_modes":["Analysis"]}

Analyzing Commodity Markets

Structures commodity market analysis with supply/demand balances, inventory dynamics, and price driver attribution.

When To Use

• Building a supply/demand balance for a specific commodity (e.g., crude oil, copper, wheat, natural gas)
• Attributing recent price moves to fundamental, technical, or macro drivers
• Evaluating inventory dynamics — draws, builds, days-of-supply coverage
• Assessing the impact of policy changes (tariffs, export bans, sanctions, subsidies) on commodity flows
• Comparing forward curve structure (contango/backwardation) against physical market signals
• Preparing commodity-focused sections of macro research notes or investment committee materials

Inputs To Gather

• Commodity and timeframe: Specific commodity (or commodity complex) and the analysis horizon (spot, quarterly, annual)
• Production data: Global and regional output figures, capacity utilization, rig counts, planted acreage, or mine throughput as applicable
• Consumption/demand data: Sectoral demand breakdown (industrial, transport, power generation, feed/food), regional demand estimates
• Inventory levels: Exchange-reported stocks (LME, COMEX, SHFE), commercial and strategic reserve levels, floating storage or in-transit volumes
• Price series: Spot, front-month futures, and relevant spreads (crack spreads, crush margins, locational basis)
• Policy/event context: Sanctions, OPEC+ decisions, weather events, trade policy shifts, regulatory changes [VERIFY current policy status]
• Forward curve and positioning: Futures term structure, CFTC/COT managed-money positioning, options open interest

Workflow

1. Define scope and commodity taxonomy

   • Identify whether the analysis covers a single commodity, a complex (e.g., energy, base metals, agriculture), or a cross-commodity theme
   • Set the time horizon: near-term (spot to 3 months), medium-term (1–4 quarters), or structural (multi-year)

2. Construct the supply/demand balance

   • Build a table with production, consumption, net trade, and implied stock change by period
   • Separate known data periods from forecast periods; label forecasts clearly
   • Identify the marginal source of supply (swing producer, marginal cost curve position)
   • Note any supply disruptions, maintenance schedules, or ramp-ups in new capacity [VERIFY production figures against latest reporting agency data — EIA, IEA, USDA, ICSG, etc.]

3. Analyze inventory dynamics

   • Calculate days-of-supply coverage (inventories / daily consumption)
   • Compare current stocks to 5-year seasonal range and identify whether levels are above, below, or within normal bands
   • Assess visible vs. estimated invisible inventories (e.g., Chinese bonded warehouse stocks, floating storage)
   • Note rate of change — whether stocks are drawing or building, and at what pace relative to seasonal norms

4. Attribute price drivers

   • Decompose recent price action into categories:
     • Fundamental: supply outage, demand surprise, inventory report
     • Macro: USD moves, rate expectations, GDP revisions, risk appetite
     • Technical/positioning: speculative positioning extremes, options expiry, trend-following signals
     • Policy/geopolitical: sanctions, tariffs, weather, conflict disruption
   • Rank drivers by estimated magnitude of price impact

5. Evaluate forward curve structure

   • Characterize the curve as contango, backwardation, or flat and note the degree ($/unit, % annualized)
   • Interpret the curve signal: backwardation typically signals tight physical markets; contango suggests ample supply or weak spot demand
   • Compare curve shape to inventory trajectory — divergences may flag mispricing or hidden stock shifts

6. Assess risks and scenarios

   • Identify the top 2–3 upside and downside risks to the base case balance
   • Quantify scenario impact where possible (e.g., "loss of 1 mb/d Libyan supply would shift the balance to a 0.5 mb/d deficit")

Output

Structure the deliverable as:

• Executive summary: 3–5 sentence overview of the commodity's current state, balance trajectory, and price view
• Supply/demand balance table: Quarterly or annual, with production, consumption, stock change, and price assumptions
• Inventory analysis: Current levels, seasonal context, days-of-supply, trajectory
• Price driver attribution: Ranked list of factors moving the market, with directional impact
• Forward curve commentary: Structure description, interpretation, and any notable spread trades
• Risk matrix: Upside/downside scenarios with estimated probability and price impact
• Key data releases calendar: Upcoming reports that could shift the view (e.g., EIA weekly, USDA WASDE, OPEC MOMR)

Quality Checks

• Supply/demand balance must arithmetically reconcile (production - consumption = stock change +/- net trade adjustments)
• All data sources and vintages are cited; no undated or unsourced figures
• Forecast assumptions are separated from reported actuals and clearly labeled
• Inventory comparisons use consistent units (barrels, tonnes, bushels) and seasonal adjustment methodology
• Price driver attribution avoids circular reasoning (don't attribute a price rise solely to "buying pressure" without identifying the catalyst)
• Forward curve analysis references actual curve data rather than generic statements
• [VERIFY] any referenced government policy, sanction, or trade restriction against current status — these change frequently
• [VERIFY] production quotas (OPEC+, mining country export limits) against most recent official announcements