Report 2004-01: Advisory Service Marketing Profiles for Corn in 2001
Evelyn V. Colino,
Silvina M. Cabrini,
Scott H. Irwin,
Darrel L. Good,
and Joao Martines-Filho
2004 by Evelyn V. Colino, Silvina M. Cabrini, Scott
H. Irwin, Darrel L. Good, and Joao Martines-Filho
rights reserved. Readers may make verbatim copies of this document
for non-commercial purposes by any means, provided that this copyright
notice appears on all such copies.
The advisory service marketing
recommendations used in this research represent the best efforts
of the AgMAS Project staff to accurately and fairly interpret the
information made available by each advisory service. In cases where
a recommendation is vague or unclear, some judgment is exercised
as to whether or not to include that particular recommendation or
how to implement the recommendation. Given that some recommendations
are subject to interpretation, the possibility is acknowledged that
the AgMAS track record of recommendations for a given program may
differ from that stated by the advisory service, or from that recorded
by another subscriber.
material is based upon work supported by the Cooperative State
Research, Education and Extension Service, U.S. Department of
Agriculture, under Project Nos. 98-EXCA-3-0606 and 00-52101-9626.
Any opinions, findings, conclusions, or recommendations expressed
in this publication are those of the authors and do not necessarily
reflect the view of the U.S. Department of Agriculture. Additional
funding for the AgMAS Project has been provided by the American
Farm Bureau Foundation for Agriculture and Illinois Council on
Food and Agricultural Research.
Marketing decisions are an important
part of farm business management. Farmers are interested in the
possibility of enhancing farm income and reducing income variability
when marketing crops. There are many tools to assist farmers in
such marketing decisions. Several surveys, including Patrick, Musser
and Eckman (1998) and Schroeder et al. (1998), report that farmers
specifically viewed one of these tools, professional market advisory
services, as an important source of marketing information and advice.
It is often thought that advisory services can process market information
more rapidly and efficiently than farmers to determine the most
appropriate marketing decisions, but limited research has been conducted
in the area.
In 1994, the Agricultural Market Advisory Service (AgMAS) Project
was initiated at the University of Illinois with the goal of providing
unbiased and rigorous evaluation of advisory services for producers.
Since its inception, the AgMAS Project has collected real-time marketing
recommendations for about 25 market advisory services and analyzed
the performance of these services. In a recent publication, Irwin,
Martines-Filho and Good (2003) evaluate corn and soybean advisory
services over 1995-2001 and their results show that, when both average
price and risk are considered, only a small fraction of services
for corn and a moderate fraction for soybeans outperform market
benchmarks. On the other hand, a majority of the services outperform
a farmer benchmark for both crops.
AgMAS comparisons of net price received among advisory services
are an important source of information for farmers in selecting
an advisory service. However, pricing performance is not the only
relevant aspect in the evaluation of advisory services. Pennings
et al. (2004) show that the nature of the recommendations made by
advisory services also is an important factor in the way farmers
evaluate services. They suggest that the nature of the recommendations
can be thought of as the "marketing philosophy" or "marketing
style" of an advisory service. Marketing style is defined by
the tools that a service recommends and the complexity of the recommended
marketing strategies. For example, recommendations may differ as
to whether or not futures and options contracts are used, frequency
of transactions and average amount per transaction. Farmers and
other market observers are familiar with the idea that advisory
services have different marketing styles. Williams (2001) identifies
the marketing styles of five prominent advisors, labeled somewhat
colorfully, as the banker, race car driver, astronaut, sprinter
and insurance agent.
It is reasonable, then, to assert that farmers will prefer to follow
a service with a style that matches their personal approach to marketing.
However, objective information about advisory service marketing
style has been quite difficult for farmers to obtain in the past.
The research found in several AgMAS reports provides a useful starting
point. Bertoli et al. (1999) examine corn and soybean marketing
style from two perspectives for the services evaluated by the AgMAS
Project in 1995. The first is the construction of a detailed "menu"
of the tools and strategies used by each of the advisory services
in 1995. The menu describes the type of pricing tool, frequency
of transactions and magnitude of transactions. The second is the
development of a daily index of the net amount sold by each market
advisory service. To construct such an index, the various futures,
options and cash positions recommended for a service on a given
day are weighted by the respective position "delta." Daily
values of the index are plotted for the entire 1995 crop year, generating
the marketing "profile" for a service. Martines-Filho
et al. (2003a, 2003b) extend Bertoli's original research by constructing
corn and soybean marketing profiles and loan deficiency payment/marketing
loan gain (LDP/MLG) profiles for all advisory programs tracked by
the AgMAS project for the 1995-2000 crop years.
The purpose of this report is to present marketing profiles and
loan deficiency payment/marketing loan gain profiles for the advisory
services followed by the AgMAS Project for the 2001 corn crop. In
addition, the average profiles for 1995-2000 found in Martines-Filho
(2003a) are updated through the 2001 crop year. As noted above,
marketing profiles are constructed by plotting the cumulative net
amount priced under each service's set of recommendations throughout
the crop year. LDP/MLG profiles are constructed by plotting the
cumulative percentage of the crop on which the LDP/MLG was claimed
during the crop year. Finally, note that this report is not intended
to be a complete analysis of advisory service marketing style in
corn. Further analysis is required to categorize services by the
types of tools and strategies used, as well as their typical marketing
profile. Ultimately, the goal is to determine style categories for
advisory services based on objective, quantitative factors. Previous
studies of mutual fund and hedge fund style provide useful models
for this effort (e.g., Sharpe, 1992; Brown and Goetzmann, 1997;
Brown and Goetzmann, 2001).
The remainder of this report is organized as follows. First, the
data collection procedures and assumptions employed by the AgMAS
Project to evaluate advisory services' recommendations are presented.
Second, the construction of marketing and LDP/MLG profiles is explained.
Finally, the individual crop year profiles for the advisory services
in corn for 2001 are presented, along with average, maximum and
minimum profiles across 1995-2001.
The marketing profiles presented
in this report are based on data generated by the AgMAS Project.
This section describes briefly the AgMAS data collection procedure.
For a more complete explanation, refer to Irwin, Martines-Filho
and Good (2003).
The market advisory services evaluated by the AgMAS Project do not
comprise the population or a random sample of market advisory services
available to farmers. Neither approach is feasible because no public
agency or trade group assembles a list of advisory services that
could be considered the "population." To assemble the
sample of services for the AgMAS Project, five criteria were developed
to define an agricultural market advisory service and a list of
services was assembled.
The first criterion is that marketing recommendations from an advisory
service must be received electronically in real-time, in the form
of satellite-delivered pages, Internet web pages or e-mail messages.
Services delivered electronically generally ensure that recommendations
are made available to the AgMAS Project at the same time as farm
The second criterion used to identify services is that a service
has to provide marketing recommendations to farmers rather than
(or in addition to) speculators or traders. Some of the services
tracked by the AgMAS Project do provide speculative trading advice,
but that advice must be clearly differentiated from marketing advice
to farmers for the service to be included.
The third criterion is that marketing recommendations from an advisory
service must be in a form suitable for application to a representative
farmer. That is, the recommendations have to specify the percentage
of the crop involved in each transaction and the price or date at
which each transaction is to be implemented.
The fourth criterion is that advisory services must provide "one-size
fits all" marketing recommendations so there is no uncertainty
about implementation. While different programs for basic types of
subscribers may be tracked for an advisory service (e.g., a cash
only program versus a futures and options hedging and cash program),
it is not feasible to track services that provide "customized"
recommendations for individual clients.
The fifth criterion addresses the issue of whether a candidate
service is a viable, commercial business. This issue has arisen
due to the extremely low cost and ease of distributing information
over the Internet, either via e-mail or a website. It is possible
for an individual with little actual experience and no paying subscribers
to start a "market advisory service" by using the Internet.
The specific criterion used is that a candidate advisory service
must have provided recommendations to paying subscribers for a minimum
of two marketing years before the service can be included in the
Having assembled a sample of advisory services, the process of collecting
recommendations begins with the purchase of subscriptions to each
of the services. The information is received electronically, via
satellite, websites or e-mail. Staff members of the AgMAS Project
record the information provided by each advisory service on a daily
basis. For the services that provide multiple daily updates, information
is recorded as it is provided through the day.
Some advisory services offer two or more distinct marketing programs.
This typically takes the form of one set of advice for marketers
who are willing to use futures and options, and a separate set of
advice for farmers who only wish to make cash sales. In this situation,
recommendations under each program are recorded and treated individually
as distinct strategies to be evaluated.
At the end of the marketing period, all of the (filled) recommendations
are aligned in chronological order. The advice for a given crop
year is considered complete for each advisory program when cumulative
cash sales of the commodity reach 100%, all futures positions covering
the crop are offset, all option positions covering the crop are
either offset or expire, and the advisory program discontinues giving
advice for that crop year.
The final set of recommendations attributed to each advisory program
represents the best efforts of the AgMAS Project staff to accurately
and fairly interpret the information made available by each advisory
program. In cases where a recommendation is considered vague or
unclear, some judgment is exercised as to whether or not to include
that particular recommendation or how to implement the recommendation.
Given that some recommendations are subject to interpretation, the
possibility is acknowledged that the AgMAS track record of recommendations
for a given program may differ from that stated by the advisory
program, or from that recorded by another subscriber.
In order to evaluate the advisory
services' recommendations certain explicit assumptions need to be
made. The assumptions are intended to accurately depict "real-world"
marketing conditions facing a representative central Illinois corn
and soybean farmer. Key assumptions are explained in this section.
Complete details on all assumptions can be found in Irwin, Martines-Filho
and Good (2003).
First, a two-year marketing window, from September 1st of the year
previous to harvest through August 31st of the year after the harvest,
is used in the analysis. Note that throughout the remainder of this
report, the term "crop year" is used to represent the
two-year marketing window.
Second, since most of the advisory program recommendations are
given in terms of the proportion of total production (e.g., "sell
5% of 2000 crop today"), some assumption must be made about
the amount of production to be marketed. When making transactions
prior to harvest, the actual yield is unknown, and the expected
yield is employed to compute the bushel amount for each transaction.
The expected yield for each year is based upon a log-linear trend
regression model of actual yields. It is assumed that after harvest
begins farmers have a reasonable idea of actual realized yield.
The assumed actual yield corresponds to the Central Illinois Crop
Reporting District yield.
Since harvest occurs at different dates each year, estimates of
harvest progress as reported for central Illinois are used. Harvest
progress estimates typically are not made available soon enough
to identify precisely the beginning of harvest, so an estimate is
made based upon available data. Specifically, the date on which
50% of the crop is harvested is defined as the mid-point of harvest.
The entire harvest period then is defined as a five-week window,
beginning two and one-half weeks before the harvest mid-point, and
ending two and one-half weeks after the harvest mid-point. To compute
the bushel amount for each transaction, the percentage recommended
is multiplied by the expected yield, if the position is opened before
the first day of harvest, or by the actual yield, if the position
is opened after the first day of harvest. This procedure implicitly
assumes that the "lumpiness" of futures and/or options
contracts is not an issue. Lumpiness is caused by the fact that
futures contracts are for specific amounts, such as 5,000 bushels
per CBOT corn futures contract. For large-scale farmers, it is unlikely
that this assumption adversely affects the accuracy of the results.
This may not be the case for small- to intermediate-scale farmers,
who are less able to sell in 5,000-bushel increments.
In some cases, the AgMAS Project stopped following a program, either
because the program went out of business or it stopped making recommendations
for farmers. In such cases, it is assumed that cash bushels after
the date of discontinuation are sold in equal amounts over the remaining
days of the marketing window. Any futures or options positions that
remain open on the date of discontinuation are closed on that date
using settlement futures prices or options premiums.
Construction of Marketing Profiles
The marketing profile of an advisory
program for a given crop year is constructed by plotting the cumulative
net amount priced during the marketing season. The amount priced
depends on the various positions recommended by the program. It
is necessary to weight the different recommended transactions in
some way to compute a daily index of the amount priced.
The computation of the percentage of the crop priced from cash,
forward contract or futures positions is straightforward. Specifically,
the percentage of the crop sold under cash, forward contracts or
short futures can be added to compute total percentage priced. Likewise,
the percentage of grain owned under long futures positions is subtracted.
For example, on a given pre-harvest day, assume that since the beginning
of the crop year a service has recommended selling futures for 30%
of expected production, cash forward contracting another 20% and,
later, buying futures for 10% of the expected production. The value
of the index on that day would be 40% (30% + 20% - 10%).
On the other hand, put and call options represent a more complicated
situation since they are not straightforward purchases or sales
of grain. To compute the percentage of the crop priced from positions
in options markets, a measure of option risk, called "delta,"
is employed. The option delta indicates how much the option price
will change per unit change in the price of the underlying asset,
in this case, the futures price. The next section explains how deltas
for calls and puts are computed and used in the computation of the
daily index of the amount priced.
Option deltas are computed using
Black's model (Black, 1976), which is a valuation model for futures
options. Black's model computes the premium for calls and puts on
futures as a function of the risk-free interest rate, time to expiration,
and the relationship between the option strike price and the price
of the underlying futures contract:
The formulas to compute the options delta are as follows:
In this study, a two-step procedure
is used to estimate options deltas. First, equation (1) or (2) is
employed to compute the "implied" volatility of the underlying
futures prices. Option premiums and futures prices are obtained
from the Chicago Board of Trade for each day that an option position
is opened. The risk-free interest rate employed is the three-month
Treasury bill rate, obtained from the Federal Reserve Bank of St.
Louis. Implied volatility is computed by solving equations (1) or
(2) for the volatility that equates the observed market premium
with the model value. Since it is not possible to invert equations
(1) and (2) to express volatility as a function
of the rest of the parameters, an iterative search is applied to
find the implied volatility values. Then, the estimated volatilities
are used in formulas (5) and (6) to obtain the delta values for
the recommended option positions.
The delta for option contracts changes every daily, since the futures
price will likely change from one day to the next. Time-to-expiration
will, of course, decrease as time passes and even volatility may
change with time. Therefore, deltas employed in the construction
of the marketing profiles are updated on a daily basis.
Long calls have positive delta values, since they represent the
right to buy the underlying asset in the future at the pre-agreed
price, and therefore, become more valuable as the futures price
increases. The deltas for call options must take values between
0 and 1. Calls that are deep-in-the-money have deltas close to one,
and those which are deep out-of-the money have deltas close to zero.
Near-the-money calls have deltas close to 0.5. Long puts have negative
deltas values, since they represent the right to sell the underlying
asset at the strike price, and hence, the position becomes more
valuable as the futures price decreases. Deltas for put options
must fall between -1 and 0. Deep-in-the-money puts have deltas near
-1 and deep-out-of-money puts have deltas of 0. Near-the-money puts
have deltas close to -0.5. The deltas for short calls and puts are
just the negative of the delta values for the corresponding long
As mentioned earlier, delta indicates approximately how much the
option price will change per unit of change in the price of the
underlying asset. For example, if the delta for a December corn
futures call is 0.8, a $0.10/bushel increase in the December corn
futures price will increase the option value by $0.08/bushel. Options
deltas can also be interpreted as the equivalent position in the
underlying asset in terms of price action sensitivity. For example,
if an individual holds a long call on a corn futures contract for
5,000 bushels, a call delta of 0.5 indicates that the call position
is equivalent, in terms of price action sensitivity, to a long position
in the futures contract for 2,500 bushels of corn. If the price
of December corn futures increases by $0.10/bushel, both the value
of the call contract and the position in long futures increase by
$250, indicating that they are equivalent in terms of price risk.
This notion of delta is used to compute the cumulative net amount
priced from positions in options markets. The equivalent long futures
position is obtained by multiplying the size of the option position
by its delta and the negative of this amount corresponds to the
amount priced from that specific option. The next section presents
the details of the computation of the index of the cumulative amount
priced, where deltas are employed to convert an option position
into the equivalent amount priced by futures positions.
Computation of the Cumulative
Net Amount Priced
Option deltas allow all positions
in cash, forward and futures and options markets recommended by
a program to be combined into an index of the cumulative percentage
of a crop priced for each day in the marketing window. The index
value for an advisory program on day t is based on the transactions
recommended by that program since the beginning of the crop year
up to day t. For the pre-harvest period, the index reflects the
amount priced as a percentage of the expected yield. Equation (7)
presents the index computation for the pre-harvest period (for t
between the first day of the marketing window and the day before
the first day of harvest):
It is assumed that farmers learn the actual yield on the first
day on harvest. At this time, the total production is known and
so, the percentage of grain priced before harvest is adjusted. For
example, suppose that the expected yield for a certain crop year
is 100 bushel/acre and the pre-harvest percentage priced based on
this yield is 50%. Suppose that harvest arrives and the actual yield
turns out to be 125 bushel/acre. The amount priced on the first
day of harvest becomes 40% (50%*100/125). Hence, for the period
after harvest, the index considers positions opened before harvest
as based on actual yield. Equation (8) shows the computation of
the index in the post-harvest period (for t between the first day
of harvest and the last day in the marketing window):
The treatment of three other
types of contracts should be mentioned as special cases. First,
percentages of the crop sold through basis contracts are recorded
on the date the cash price is determined (by setting the futures
price). This results in basis contracts being treated the same as
forward contracts, except that the percentages are not recorded
when the basis contract is first entered, but when the final cash
price is established. Second, percentages of the crop sold through
hedge-to-arrive contracts (HTA) are recorded on the date the futures
price is set. Thus, HTA contracts are treated the same as selling
futures contracts on the same date. Third, percentages of the crop
sold through delayed pricing contracts are recorded on the date
the cash price is established, which typically occurs after delivery.
Cross-hedging is a marketing
tool that can be recommended by an advisory program, and occurs
when a program includes within the set of recommendations for one
commodity a transaction in another commodity market. For example,
on August 2nd, 2001 one service recommended cross-hedging corn production
in November 2001 soybean futures contracts. This type of positions
is based on the fact that prices for different commodities are correlated,
that is, they move together. Advisory programs made only a few cross-hedge
recommendations during the years considered in this study. In the
cases where a cross-hedge is recommended, the percentage priced
from such a position in futures or options markets is computed as:
Example of Marketing Profile Construction
A simple example of the construction
of marketing profiles is considered in this section to facilitate
understanding of the procedures used to develop actual marketing
profiles for advisory services. The example is based on the following
hypothetical set of corn recommendations for the 2001 crop year:
1 presents the marketing profile for this set of recommendations.
Since the first transaction was made on April 25th, the net amount
priced from the beginning of the crop year to this date equals 0%.
On April 25th the profile line in Figure
1 makes the first step, and the quantity priced becomes 30%,
since short corn futures have been recommended for 30% of expected
production. The index computation according to equation (7) for
April 25th is:
The index value is the same until
June 26th when long puts are recommended for 50% of the expected
production. Note in Figure
1 that on June 26th the profile line has the second step, and
on the dates following, the line takes values lower than 80% (30%
+ 50%). This happens because the absolute value of the put delta
is always lower than one. For example, on the date that the put
position is opened, the December corn futures price is $2.0375/bushel,
which is higher than the strike price of $1.90/bushel, and therefore,
the option is out of-the-money. The option delta on June 26th is
-0.28, indicating the position is equivalent to a 14% (0.28*50%=
14%) short position for expected production. For June 26th the value
of the index is computed as:
For the period of time when the
put option position is open, the line becomes irregular, reflecting
the fact that option delta changes every day.
The cumulative percentage changes substantially on July 27th, when
there is a step down in the marketing profile line. On this date,
the futures position is closed by buying futures, and hence, the
amount priced decreased by 30%. From this date to August 20th the
line represents the amount priced only from the long put option
position on 50% of the expected production. The value of the index
on July 27th is computed as:
On August 20th the put position
is closed and 50% of the expected production is sold under forward
contracts, so the amount priced becomes 50%:
For the 2001 corn crop, September
17th is the first day of harvest, and therefore, on this date the
percentage priced is adjusted to reflect actual yield. The expected
yield for 2001 is 152.4 bushel/acre and the actual yield is 157
bushel/acre. Since the actual yield is higher than expected, the
proportion priced decreases on the first day of harvest to reflect
this adjustment. Note in Figure
1 that there is a small step down on the first day of harvest,
and the value of the index, according to Equation (8), becomes 48.54%:
The last recommendation in this
example occurs on March 19th, 2002, when remaining production (51.46
%) is sold in the cash market and the amount priced becomes 100%:
There are three additional issues
associated with interpretation of the marketing profiles that should
be noted. The first is related to the use of option deltas to compute
the net amount priced for option positions. Technically, delta is
valid only for "infinitesimal" price changes, which means
that delta may be an imprecise measure when large price changes
are considered. For example, if an option position for 50% of the
crop with a delta of 0.28 is recommended, it will be equivalent,
in terms of price sensitivity, to a long position in the underlying
futures contract for 14% (50%*0.28) of the crop. This equivalence,
though, strictly holds only for small futures price changes. There
is no hard and fast rule for what constitutes "small"
versus "large" futures price changes. The key point is
that the approximation becomes systematically less reliable the
larger the price change considered. Please note that the approximation
is not likely to be a significant concern since option delta estimates
are updated daily and corn and futures price changes usually are
constrained by daily price limits.
The second interpretation issue is associated with basis risk,
which is uncertainty associated with the difference between the
local cash price and the futures price. In constructing marketing
profiles, the amount priced under futures contracts is treated the
same as a forward contracts, even though pricing under futures contracts
is subject to basis variability whereas this is not the case for
pricing under forward contracts. This does not create a problem
in constructing marketing profiles because the profiles are based
on quantity priced, not on price levels, and hence, basis risk is
not a consideration. However, when interpreting marketing profiles,
it is important to recognize that different forms of pricing may
be reflected in the same marketing profile at different points in
The third interpretation issue is associated with spread risk, defined
as uncertainty about the price difference between futures contracts
with different expiration dates. Spread risk is a consideration
when a hedging strategy involves two transactions: first selling
futures with a nearby expiration date and later rolling-over the
position to another contract with expiration closer to the delivery
date of the grain. When constructing marketing profiles, the futures
positions are treated separately as one-transaction hedges. This
does not create a problem in constructing marketing profiles because
the profiles are based on quantity priced, not on price levels,
and hence, spread risk is not a consideration. Once again, when
interpreting marketing profiles, it is important to recognize that
different forms of pricing may be reflected in the same marketing
profile at different points in time.
Construction of LDP/MLG Profiles
The 1996 "Freedom-to-Farm"
Act established a loan deficiency payment program for several agricultural
commodities, including corn. Under this program, if market prices
are below a Commodity Credit Corporation loan rate, farmers can
receive payments from the US government for the difference between
the loan rate and the market price. Since there is considerable
flexibility in the way the loan payment can be claimed by the farmer,
there is the opportunity for advisory programs to give recommendations
for the implementation of this program. In those years when the
market price is lower than the loan rate, the use of the loan program
is an important part of marketing strategies, since loan programs
recommendations can have a big effect on the net price received.
Furthermore, most of the advisory programs evaluated in the AgMAS
Project make recommendations about loan deficiency payments and
marketing loan gain (LDP/MLG) when market prices drop below the
loan rates. To provide information about the ways that advisory
services recommend claiming the deficiency payments, LDP/MLG profiles
are developed for 2001. Averages LDP/MLG profiles across programs
are also developed for the years 1998-2001. Only in these crop years
are corn prices below loan rates during part of the marketing window.
The "LDP/MLG profile" for each advisory service is constructed
by plotting the cumulative percentage of the crop on which the LDP/MLG
is reclaimed along the marketing window. The construction of these
profiles is simpler than the construction of marketing profiles
described in the previous section, but some explanation is needed
about the computations.
Specific decision rules are needed regarding pre-harvest forward
contracts because it is possible for an advisory program to recommend
taking the LDP on those sales before the grain is actually harvested
and available for delivery in central Illinois. To begin, it is
assumed that amounts sold for harvest delivery with pre-harvest
forward contracts are delivered first during harvest. Since LDPs
must be taken when title to the grain changes hands, LDPs are assigned
as these "forward contract" quantities are harvested and
delivered. This requires assumptions regarding the timing and speed
of harvest. Earlier it was noted that a five-week harvest window
is used to define harvest. This window is centered on the day nearest
to the mid-point of harvest progress in central Illinois as reported
by NASS. Various assumptions could be implemented regarding harvest
progress during this window. Lacking more precise data, a reasonable
assumption is that harvest progress for an individual representative
farm is a linear function of time. Then, it is assumed that, starting
on the first day of harvest, grain becomes available for delivery
in equal amounts per day along the five-week harvest period. When
forward cash sales have been made, the grain that becomes available
is assumed to be delivered to cover these contracts and LDP/MLGs
are assumed to be claimed at the delivery time. Other assumptions
regarding the claim of LDP/MLGs for grain priced under futures and
option contracts can be found in Irwin, Martines-Filho and Good
Summary of Marketing and LDP/MLG Profiles for Corn, 1995 - 2001 Crop Years
The figures in this report present
marketing and LDP/MLG profiles from each advisory program followed
in 2001 by the AgMAS Project for corn and their respective average
profiles between 1995 and 2001. In certain cases the average profiles
are presented for some, but not all seven crop years, because the
program began to be tracked after the 1995 crop year. Table
1 presents a list of the programs whose 2001 marketing and LDP/MLG
profiles are presented in this study. The reason why some programs
are not included in all years over 1995-2001 also is listed in the
"comments" column of this table.
2.1 through 28.4
present the marketing and LDP/MLG profiles for individual programs
in alphabetical order. For the programs that were tracked for more
than two years, the average, maximum and minimum amount priced is
computed and presented as a chart after the individual crop year
The scale for the vertical axis of the figures generally runs from
a negative 25% to a positive 125%, since, for the majority of the
programs, the net amount priced varies between these two levels.
However, a few programs have more extreme values of the percentage
priced. Note that the amount priced is a measure of within-crop
year price risk, as the higher the proportion of a crop priced,
the lower the sensitivity of the value of the farmer's position
to crop price changes. When 100% of the crop is priced there is
no price sensitivity, which means that changes in price do not affect
the value of the farmer's position. At the other extreme, when the
amount priced is 0%, the value of the farmer's position will vary
in the same proportion as the change in price, that is, if corn
price increases by 5%, the value of the farmer's position will also
increase by 5%. A proportion of grain sold higher than 100% is called
over-hedging, and is actually an overall short position in the corn
market. In this case, price changes have the opposite effect on
the farmer's position value. If corn price increases, the value
of the farmer's position decreases and vice versa. For some programs
it is possible to find a negative amount priced, indicating a net
long position greater than total production. This can be interpreted
as the farmer owning even more grain than expected or actual production.
In this case, price sensitivity is even greater than with 0% of
grain priced. For example, if the proportion of grain sold is
-50%, when corn prices decrease by 10%, the value of the farmer's
position decreases 15%.
The marketing profiles also provide other useful information. The
number of steps in the profile lines and the location of these steps
along the marketing season provide information about timing, frequency
and size of recommended transactions. It is also possible to determine
from the figures how intensely a program uses options markets, since,
because deltas change daily, the profile line is irregular when
options positions are open. In the same way, LDP/MLG profiles provide
information about the size and timing of LDP/MLG claims.
29.1 through 37.2
contain the averages, maximums and minimums for marketing and LDP/MLG
profiles across all advisory programs tracked in each crop year
from 1995 to 2001. Figure
36.1 contains the marketing profile grand average, maximum and
minimum across all services over the 1995-2001 crop years. Figure
36.2 compares the grand average to 24- and 20-month market benchmark
profiles. Market benchmarks are those employed by the AgMAS project
in the advisory services performance evaluation, and they measure
the average price offered by the market to farmers during the marketing
window. Under the 24-month market benchmark, the crop is sold in
approximately equal amounts each day along the two-year marketing
window beginning on September 1st of the year before harvest and
ending on August 31st of the year after harvest. Under the 20-month
benchmark the crop is sold in approximately equal amounts every
day during the period that begins on January 1st of the year of
harvest and ends on August 31st of the year after harvest. Figure
37.1 contains the LDP/MLG profile grand average, maximum and
minimum across all services over the 1998 - 2001 crop years. Finally,
37.2 compares the LDP/MLG grand average, to the 24 and 20-months
market benchmark LDP/MLG profiles. Note that those figures where
average marketing profiles and LDP/MLG profiles are developed the
first day of harvest shown as an average of the first day of harvest
across the set of years included in the chart.
Bertoli, R., C. Zulauf, S. H.
Irwin, T. E. Jackson and D. L. Good. "The Marketing Style of
Advisory Services for Corn and Soybeans in 1995." AgMAS Project
Research Report 1999-02, Department of Agricultural and Consumer
Economics, University of Illinois at Urbana-Champaign, August 1999.
Brown, S.J. and W.N. Goetzmann.
"Mutual Fund Styles." Journal of Financial Economics,
Brown, S.J. and W.N. Goetzmann. "Hedge Funds With Style."
Working Paper No. 00-29, Yale International Center for Finance,
Yale University, February 2001.
Black F. "The Pricing of
Commodity Contracts." Journal of Financial Economics,
Irwin, S.H., J. Martines-Filho and D.L. Good. "The Pricing
Performance of Market Advisory Services In Corn and Soybeans Over
1995-2001." AgMAS Project Research Report 2003-05, Department
of Agricultural and Consumer Economics, University of Illinois at
Urbana-Champaign, June 2003.
Martines-Filho, J., Irwin, S.H., Good, D. L., Cabrini, S.M., Stark,
B.G., Shi, W., Webber, R.L., Hagedorn, L.A., Williams, S.L. "Advisory
Service Marketing Profiles for Corn Over 1995-2000". AgMAS
Project Research Report 2003-03, Department of Agricultural and
Consumer Economics, University of Illinois at Urbana-Champaign,
Martines-Filho, J., Irwin, S.H., Good, D.L., Cabrini, S.M., Stark,
B.G., Shi, W., Webber, R.L., Hagedorn, L.A., Williams, S.L. "Advisory
Service Marketing Profiles for Soybeans Over 1995-2000". AgMAS
Project Research Report 2003-04, Department of Agricultural and
Consumer Economics, University of Illinois at Urbana-Champaign,
McNew, K. and W.N. Musser. "Farmer
Forward Pricing Behavior: Evidence from Marketing Clubs." Agricultural
and Resource Economics Review, 31(2002):200-210.
Pennings, J.M.E., Irwin S.H., Good D.L., and Isengildina O. "Heterogeneity
in the Likelihood of Market Advisory Service Use by U.S. Crop Producers."
Working Paper, Department of Agricultural and Consumer Economics,
University of Illinois at Urbana-Champaign, March 2004.
Patrick, G.F., W.N. Musser and
D.T. Eckman. "Forward Marketing Practices and Attitudes of
Large-Scale Midwestern Grain Farmers." Review of Agricultural
Sharpe, W.F. "Asset Allocation:
Management Style and Performance Measurement." Journal of
Portfolio Management, 19(1992):7-19.
Williams, E. "The Compatibility
Quotient: Before You Hire a Pro, Match Your Marketing Style."
Top Producer, November 2001, pp. 14-17.
Evelyn V. Colino and Silvina M. Cabrini are Graduate Research
Assistants for the AgMAS Project in the Department of
Agricultural and Consumer Economics at the University
of Illinois at Urbana-Champaign. Scott H. Irwin is the
Laurence J. Norton Professor of Agricultural Marketing.
Darrel L. Good is Professor in the Department of Agricultural
and Consumer Economics at the University of Illinois at
Urbana-Champaign and Joao Martines-Filho is former Manager
of the AgMAS and farmdoc Projects in the Department of
Agricultural and Consumer Economics at the University
of Illinois at Urbana-Champaign.
This terminology is adapted from the financial industry,
where investments such as mutual funds and hedge funds
typically are grouped by investment objective or "style."
 In a related study, McNew and Musser (2002) study
the pre-harvest pricing behavior of farmer marketing clubs
in Maryland over 1994-1998. They find that farmers tend
to forward price significantly less than that predicted
by risk minimization hedging models and that the amount
hedged varies substantially across marketing years.
Some of the programs that are depicted as "cash
only" have some futures-related activity, due to
the use of hedge-to-arrive contracts, basis contracts
 Short refers to a "sell" position in the
market. Long refers to a "buy" position in the
Delta formulas are formally derived by taking
the partial derivative of the value function (equations
1 and 2) with respect to the futures price (F0).
Implied volatility is estimated using Fincad
here for Order Form