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September 2007 Iowa State University Regis Lefaucheur

New Generation Grain Contracts Decision Contracts. September 2007 Iowa State University Regis Lefaucheur. Decision Commodities. Based in Ames, IA Provides innovative forward contracts to grain producers to help them take the emotion, stress and guesswork out of grain pricing

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September 2007 Iowa State University Regis Lefaucheur

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  1. New Generation Grain ContractsDecision Contracts September 2007Iowa State University Regis Lefaucheur

  2. Decision Commodities • Based in Ames, IA • Provides innovative forward contracts to grain producers to help them take the emotion, stress and guesswork out of grain pricing • Customer base: Iowa, Illinois, Minnesota, North Dakota, Wisconsin, Missouri • Decision Contracts enable producers to put discipline into grain marketing

  3. Decision Commodities • What does Decision Commodities do? • Take the EMOTION out of selling • Take the GUESSWORK out of selling • Consistently outguessing or outperforming the market is impossible.

  4. What are producers saying? “ forward contracts are always terrible ways to market…20% of the time I sold too soon, and 80% of the time I didn’t market enough…” “ I only sell grain when prices are in the top third of the market…”

  5. Grain Industry Vocabulary • Definitions: • Futures price • Basis • Cash Price • Spread • Relationships September 17, 2007 Corn Basis

  6. Producer Hedging Producer Goal: Lock in an attractive price (higher is better) – Protection against lower prices

  7. Elevator Hedging Elevator Goal: Lock in the price of the grain he bought to protect his margins (storage, drying, basis, hedging)

  8. End User: feed mill, ethanol End User Goal: Lock in an attractive price (lower is better) – Protection against higher prices

  9. Decision Contracts Risk Management Continuum Insurance against low prices Insurance against high prices Put Option Contract Futures Contract Call Option Contract Elevator Margin Dependent End-User Price Sensitive Farmers Price Sensitive

  10. Relationship Between Futures, Basis And Cash Price See: http://www.hoic.com/grain/bids.asp

  11. Example

  12. BASIS What is BASIS? Chicago Board of Trade - Corn Futures Price = $3.50 Processor Bid - $3.25 Warehouse Bid - $3.20 Producer Bid - $3.10 BASIS = “0.25 under” BASIS = “0.30 under” BASIS = “0.40 under” How can a user increase or decrease the movement of grain locally if they can’t change the price on the Chicago Board of Trade?

  13. BASIS What factors contribute to determining the BASIS? Feed Ethanol River Processing Processing Processing

  14. BASIS What factors contribute to determining the BASIS? Industrial Export Dairy Ethanol Feed Poultry Export • What impact does a BASIS change in one area have on other areas? • Which markets have the greatest influence on basis, spreads and the futures market?

  15. SPREAD Spread = price difference between various futures reference months The Spread can be Carry or Inverted Dec July Spread= - $ 2.79 - / Inverted July Sept Spread = + $ 0.04 + / Carry CBOT WHEAT

  16. STORE or SELL ? What decision should a producer make? STORE SELL Basis Narrow Wide Spread Carry Inverted Futures price High Low Result: Decrease in the movement of grain Result: Increase in the movement of grain

  17. EFFICIENT MARKET HYPOTHESIS RANDOMNESS

  18. Efficient Market Hypothesis • According to Fama, an efficient market is one that accurately incorporates all known information in determining price. This is know as the efficient market hypothesis. • Although there is considerable disagreement about the degree to which EMH holds, it has become the dominant paradigm used by economists to understand and investigate the behavior of financial and commodity markets. • Markets for commodities and products that are widely traded in agriculture and the food industry are a model of efficiency. The compile all the information and knowledge of traders, businesses and producers, and express this data in the form of a price.

  19. Efficient Market Hypothesis • The daily pricing process in the market is usually referred to as a random walk. A commonly used analogy of a random walk is the flipping of a fair coin. Overtime, the expected change is price is zero. • The market is all-knowing. Consistently outguessing or outperforming it is very difficult (if not impossible)

  20. Randomness • What does randomness looks like?

  21. Randomness and Unpredictability CBOT Corn December Futures 2007 Randomness It is easy to see patterns in random events

  22. SOYBEAN – Up/Down days

  23. SOYBEAN – Up/Down days Nov 01 to Oct 31 - 1997-2007 - NOV Soybean CBOT

  24. Randomness and Unpredictability • Another way to view markets is that while prices can’t be predicted, prices aren’t all that random either. • While we can’t guarantee that the “pattern” depicted below will be repeated in the future, this 25 year frequency histogram tends to suggest that there is some central tendency around $2.15 to $2.95 range.

  25. BEHAVIORAL FINANCE AND EMOTIONS

  26. Behavioral Finance and Emotions 2004 December Corn Futures 06-2003 to 09-2004

  27. Behavioral Finance • Regret: Regret management is probably a more accurate term for risk management.There is a very natural tendency to avoid regret. In grain marketing, this is often embodied into a decision not to decide. Regret is the more powerful behavioral issue. Obviously, regret can significantly impair our ability to make rational choices. • Risk aversion: Most people are risk averse and seek to avoid risk. However, there tends to be identifiable biases in human behavior that lead to irrational decision making. One of these biases is the tendency to accept increases risk over a guaranteed loss. It reflects a tendency to underestimate the chances of “extreme” market situation occurring. In grain marketing, this tendency leads some to accept price risk rather than pay for insurance – such as a put option purchase or guaranteed minimum price contract. Why don’t producers want to pay 5 cents to protect 50 cents?

  28. Long Term Corn Prices 2007 December Corn Futures 07-2006 to 09-2007 Reference price anchoring: It is the tendency for a person to “fix” a specific figure in their mind as the perceived value. • An example of driving with eyes fixed on the rear view mirror is establishing a target price based on last year’s market price. “Bull” or “bear” forecasts coming from newsletters, magazines, market advisors, radio shows… • Reality takes a long time to soak in, and when it does, it is usually too late as opportunities are long gone.

  29. BehavioralFinance • Escalation:too much invested to quit.A situation where a person enters a transaction hoping for a favorable outcome but after circumstances change to unfavorable, the person finds it difficult to escape or even adds to it. • Endowment: Associated with the fear of giving up something. It is more painful to give up an asset than it is pleasurable to obtain. Ownership as a positive feeling. Producers have a tendency to store grain beyond economic justification, because when it is sold, there is no opportunity to gain anymore. For grain under storage, the perceived value increases with the duration of ownership.

  30. Behavioral Finance • Which one of the following would you choose? • A. Winning a guaranteed $3,000 • B. Taking an 80% chance of winning $4,000 • Now consider this option • A. Lose $3,000 • B. Take an 80% chance of losing $4,000

  31. Behavioral Finance Results: • You are not alone if you said you would take the $3,000 but roll the dice and risk losing the $4,000 • As human being, we don’t deal with losses the same way we do with gains • We will ride a loss to the bitter end while cutting a gain short • Contributing to this mindset is a condition known as the Gambler’s fallacy, which is the belief that a successful outcome is due after a run of back luck. But chance – or the randomness of an efficient market – is not self-correcting. Flipping 10 consecutive heads does not increase the chance that the eleventh toss will yield a tails, any more than a trend of lower grain prices ensures an up- or downswing in tomorrow’s market.

  32. Agricultural Market Advisory Services • AGMAS • Agmas study (U of I Urbana): provide a neutral evaluation of the performance of market advisory services for corn, soybean and wheat. • Tracking about 25-35 advisory programs per year since 1994 • Paid subscriptions obtained for each services • Recommendations recorded in “real-time” • Two important issues:. Market advisory service performance relative to appropriate benchmarks. Predictability of market advisory service from year-to-year • Result data available for the period 1995-2003

  33. Agricultural Market Advisory Services • Benchmarks: • average of two-year marketing window (24 months) : price offered by the market • USDA producer average : an “indicator” of marketing performance of farmers

  34. Performance relative to Benchmark

  35. Predictability from year-to-year

  36. Agricultural Market Advisory Services • Limited evidence that advisory services outperform market benchmarks, particularly after taking risk into account • Substantial evidence that advisory services outperform farmer benchmarks, even after taking risk into account • Little evidence that past performance can be used to predict future performance

  37. Decision Contracts Farmers continue to identify price and income risk as their greatest management challenge

  38. Setting goals Upper Third AveragePrice Lower Third Price series for CZ contract : Average Price = $2.50 Upper Third = $ 2.51 and above Lower Third = $ 2.31 and below

  39. Daily Average Corn Prices – 1998-2005 • Based on December Corn (CZ) • In the last 23 years for corn: • Marketing at harvest was high price point = 4 years (93, 95, 02, 06) • High price point was spring/summer = 7 years (84, 87, 88, 90, 94, 96,04) • High price point was before spring = 12 years (85,86,89,92,94,97,98,99,00,01,03,05)

  40. Corn - High Low Average Historical Prices

  41. Decision Contracts • Decision Commodities automated pricing models : • Index • Rally • Accelerator • Topper

  42. Decision Contracts • ...a tool that delivers execution discipline without major time requirements • Example • You want to pre-harvest market 20,000 bushels of corn for Fall delivery, starting in January. • The Index pricing model can, for example, price an even increment of bushels every day between January and September, achieving an average price for that time period. • Assuming 100 (market) days between January and September, 200 bushels will be priced every day at the price of the underlying futures contract. • You sign a forward contract with local grain elevator (specifying Decision Contracts as pricing mechanism), and deliver grain in same manner as usual.

  43. Decision Contracts Producer Cash Contract Grain ElevatorEthanol Plant Forward futures price Basis Decision Contracts provides pricing mechanism for the futures price

  44. Decision Contracts – “Index” Pricing Model Index: Average Price Contract • Prices an even increment of bushels each day for a given pricing period. • Producer settings: • Pricing period beginning • Pricing Period end • Delivery Period • Bushels Example: 10,000 bu 100 days 100 bu would be priced each day 2007 Soybeans The Index price model will result in the average price for the specified time pricing period.

  45. Decision Contracts – “Rally” Pricing Model Rally: Bushels price on days during the pricing period when:1) the day’s closing price is above the floor price and 2) the one day price change is less than the sensitivity level. Producer Settings: • Pricing period beginning • Pricing period end • Bushels • Price Floor • UpPoint • Throttle Bushels price if one day price change less than UpPoint Bushels to Price = Remaining Bu X Throttle Remaining Days Floor If UpPoint = 0 pricing occurs when price goes down.

  46. Rally Example 10,000 bu = 100 X 5 (Throttle) = 500 bu 100 days 500 bu on the first day

  47. Decision Contracts – “Accelerator” Pricing Model Accelerator: • The daily amount of bushels priced “accelerates” as the futures prices increase, and vice versa • Bushels price on days during the pricing period when:1) the day’s closing price is above the floor price 2) pricing factor increases (decreases) as futures prices go up (or go down) • Producer Settings: • Pricing period beginning • Pricing period end • Bushels • Floor and Pivot Bu priced = (remaining bushels) X Throttleremaining days

  48. Decision Contracts – “Accelerator” Pricing Model • Throttle acts as a multiplier of daily bushels priced Pivot Floor

  49. Decision Contracts – “Accelerator” Pricing Model December Corn 2003 Accelerator (red) = $2.41 Bu priced = 90.37 % Floor price (purple) = $2.30 Daily bushels sold increase when futures prices move into upper ranges, and vice versa

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