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Agricultural Price Discovery and Forecasting in the United States
2019 India Agricultural Outlook Forum
Michael K. Adjemian
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What I will talk about
• Factors that affect agricultural commodity prices
• How commodity prices are discovered and shared in the United States
– What role does USDA play?
• Price forecasting methods
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Factors that affect commodity prices
• Macroeconomic shocks
– Population growth, per-capita income, exchange rates, financial shocks
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Factors that affect commodity prices
• Seasonality of supply
– Weather fluctuations
– Production response is lagged to market signals
• Storability, trade, and transfer costs
• Derived demand
• Inelasticity of supply and demand
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Price discovery
• Prices clear the market… if they are allowed to
• New information alters price expectations & behavior among market participants – They revise their planting/production/investment
choices
• Examples of info: poor weather forecasts, embargoes, trade war news, animal disease concerns
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Quality of price discovery related to market structure
• Number of traders -> liquidity – Increased competition
• Transparency
– Reduce information asymmetries and potential for manipulation
• Ease of arbitrage
– Permits prices to find natural equilibria over space and time
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Price discovery in the United States • Major grains, softs, cattle, pigs, & milk
– Futures and cash markets
• Minor commodities, poultry, and specialty crops
– Cash markets only
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U.S. cash markets
• Found all over the nation – Major mkt prices reported by
USDA-AMS
• Immediate transfer
• Some are more competitive than others – Export terminals, processing areas
– Just two purchasers of in-shell
peanuts in Georgia
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U.S. futures markets • Rules are meant to foster liquidity
– Homogeneous assets: quality, transaction date, location
– Only negotiable is price
– Represent just a few cash markets
• Most are highly competitive – Prices reflect all public news; they represent equilibrium
difference of opinion about expected mkt. fundamentals
• Prices discovered there used as cash mkt. benchmarks – Cash market prices often quoted in terms of basis
– Basis= Pcash – Pfutures
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Cash - futures over space
• Avg. corn basis in August, 2016-2018
Proximity to cheap/river
transport
Dairy & poultry- feeding
operations
Cattle feedlots
Distant from demand centers
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Market transparency • Crucial to efficiency & robustness
– Support planning and hedging / transfer of risk
– Insufficient or asymmetric information reduces liquidity and economic activity
• Three principal sources of commodity market information in the United States – Futures markets
– Private news services
– The USDA (strong reputation for objectivity) • Why? Levels the playing field, enhances transparency, and
portrays current and expected market conditions
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How the USDA shares information • Routine, scheduled publications
– Highly anticipated by traders due to their objectivity & comprehensive nature
– Relied on for guidance about market conditions/expectations
• Department organizes, collects, and/or reviews
– Farm surveys from the National Agricultural Statistics Service (NASS)
– Trade data – Foreign attache reports – Satellite imagery – Weather forecasts – Govt. program participation
• Publishes domestic and international crop and livestock
production, supply, use, inventories, & prices
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U.S. farm commodity data • Supply side
– Farmers surveyed for planting intentions (March), and acreage choices (June)
– Crop yield and condition is observed by field enumerators; they identify and monitor random sample plots (throughout growing season)
• Demand side
– On- and off-farm stocks are monitored via survey – Feed use depends on herd size, makeup, and location,
which are identified and reported by USDA – Food & industrial use is modeled by analysts – Export sales and inspections are reviewed
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How the USDA prepares reports • Interagency group
– Agricultural Marketing Service (AMS), Economic Research Service (ERS), Foreign Agricultural Service (FAS), and the Farm Service Agency (FSA)
• Meet the morning ahead of publication, in sealed, “lockup” conditions
• Revise projections using… – Carefully-guarded NASS data and other information
– Economic models and statistical analysis
– Expert judgement
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Markets react to USDA news • USDA publishes most reports at noon in the U.S.
Eastern time zone; 10:30pm in New Delhi
• Reactions can be substantial – Forward & current prices adjust to reflect new expected
supply and demand equilibrium – Options premia fall as market uncertainty about crop
conditions is reduced – Evidence that confidential preparation process works
• Markets fully absorb USDA news within a few
minutes…
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Avg. corn futures market reaction to USDA news, 2009-2019
Scal
ed V
ola
tilit
y; 1
= “
no
rmal
”
Source: Adjemian and Irwin (2019)
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World cotton market • Three largest producers: India (23%), China(22%),
U.S. (18%)
• Three largest users: China (33%), India (20%), Pakistan (8.7%)
• U.S. only uses 2.5% of the world cotton production; it is a net exporter
• U.S. cotton prices are affected by the price of substitutes (e.g., wool and synthetics) and regional supply and demand conditions/ trading patterns
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Cotton price discovery in the U.S. • ICE futures No.2 contract delivers in SC/TX/TN
– Mar, May, Jul, Oct, and Dec expiries
• USDA-AMS issues cash prices & quality bulletins in 7 major production areas – Dumas, AR; Visalia, CA; Rayville, LA; Florence, SC;
Memphis, TN; Corpus Christi, TX; Lamesa, TX
– Fiber color, strength, length, etc.
• Regular USDA reports & other market developments inform prices
New crop Old crop
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Forecasting commodity prices
• Structural supply and demand model
• Time series approach
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Structural supply and demand model
• Inputs – Price and quantity data
– Supply and demand elasticites (flexibilities)
• Can be cumbersome – How partial/general do you want to make the
model?
• Misspecification errors can be costly
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Simple structural model
Dcotton
S2USDA
S1USDA
Qcotton
Pcotton
ΔP
ΔUSDA Production Forecast
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Time series approach
• Many choices – Simple vs. sophisticated
• Condition price on fundamentals – For example: year-end stocks value summarizes supply and
demand conditions – High expected stocks/expected -> lower prices – Low expected stocks/use -> higher prices – Effect is asymmetric when storage is possible
• Price forecast generated by inputting fundamentals updates – Incorporate uncertainty in forecasts?
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Corn Prices vs Ending Stocks
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Corn Prices vs Ending Stocks
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Corn Prices vs Ending Stocks
*R2 increases to 73% when including a single price lag
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Forecast Corn Prices
*Include other factors to improve forecast precision…
Model performance improves post-RFS
Except for period of very low stocks
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Forecast Corn Prices
Source: Adjemian, Bruno, and Robe (2019)
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USDA Forecasting Process
Source: Liefert (2019)