Report 2006-04: Advisory Service Marketing Profiles for Corn over 2002-2004
Evelyn V. Colino,
Silvina M. Cabrini,
Nicole M. Aulerich,
Tracy L. Brandenberger,
Robert P. Merrin,
Scott H. Irwin,
Darrel L. Good,
and Joao Martines-Filho
2006 by Evelyn V. Colino, Silvina M. Cabrini, Nicole M. Aulerich, Tracy L. Brandenberger, Robert P. Merrin, Wei Shi, 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
This 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, Illinois Council on Food and Agricultural
Research and Aurene T. Norton Trust.
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 at least 23 market advisory services each year and analyzed the
performance of these services. In a recent publication, Irwin et al. (2006) evaluate corn and
soybean advisory services over 1995-2004 and the results provide limited evidence that advisory
programs as a group outperform market benchmarks, particularly after considering risk. The
evidence about performance is more positive with respect to farmer benchmarks even after
taking risk into account. For example, the average advisory return relative to farmer benchmarks
is $8 to $12 per acre with only marginal increase in risk.
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, 2005) show that the nature of the recommendations made by advisory services also is an
important factor in the way farmers evaluate services. This research suggests that the nature of
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
1 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.”
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), and Colino et al. (2004a, 2004b) 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-
2001 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 2002 through 2004 corn crops. In addition, the average profiles for 1995-2001 found in
Colino et al. (2004a) are updated through the 2004 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 2002,
2003 and 2004 are presented, along with average, maximum and minimum profiles across 1995-
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 et al. (2006).
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 subscribers.
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 AgMAS study.
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
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 et al.
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
Second, since most of the advisory program recommendations are given in terms of the
proportion of total production (e.g., “sell 5% of 2004 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
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 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
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 exercise price, and therefore, become more valuable as the futures price
increases. Deltas for call options must take values between 0 and 1. Calls that are deep-in-themoney
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 exercise 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 positions.
As mentioned earlier, deltas indicate approximately how much option prices 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
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 of harvest. At this time,
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 bushels/acre and the preharvest
percentage priced based on this yield is 50%. Suppose that harvest arrives and the actual
yield turns out to be 125 bushels/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
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 July 22, 2004 one service
recommended cross-hedging 2004 corn production in January 2005 soybean futures contracts.
This type of position 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 2004 crop year:
Figure 1 presents the marketing profile for this set of recommendations. Since the first
transaction was made on April 5th, the net amount priced from the beginning of the crop year to
this date equals 0%. On April 5th 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 5th is:
The index value is the same until June 30th when long puts are recommended for 50% of
the expected production. Note in Figure 1 that on June 30th 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 $2.67/bushel, which is higher
than the strike price of $2.40/bushel, and therefore, the option is out of-the-money. The option
delta on June 30th is -0.22, indicating the position is equivalent to a 11 % (0.22*50%= 11 %)
short position for expected production. For June 30th 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 16th, 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 4th 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 14th is computed as:
On August 4th the put position is closed and 50% of the expected production is sold under
forward contracts, so the amount priced becomes 50%:
For the 2004 corn crop, September 14th is the first day of harvest, and therefore, on this
date the percentage priced is adjusted to reflect actual yield. The expected yield for 2004 is
161.3 bushel/acre and the actual yield is 186 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 43.36%:
The last recommendation in this example occurs on March 18th, 2005, when remaining
production (56.64 %) 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, deltas are valid only for “infinitesimal” price changes,
which mean that deltas may be imprecise measures 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 time.
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 the 2003 and 2004 crop years. LDP/MLG profiles are not
considered for the 2002 crop year because central Illinois corn prices were below loan rates only
briefly late in the marketing year, after the vast majority of the corn crop had already been
marketed. Average LDP/MLG profiles across programs are also developed for the 1998-2001
and 2003-2004 crop years. The “LDP/MLG profile” for each advisory service is constructed by
plotting the cumulative percentage of the crop on which the LDP/MLG is claimed 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
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 et al. (2006).
Summary of Marketing and LDP/MLG Profiles for Corn, 1995 – 2004 Crop Years
The figures in this report present marketing and LDP/MLG profiles from each advisory
program followed in 2002, 2003, and 2004 by the AgMAS Project for corn and their respective
average profiles between 1995 and 2004. In certain cases the average profiles are presented for
some, but not all 10 crop years, because the program began to be tracked after the 1995 crop
year. Table 1 presents a list of the programs whose marketing and LDP/MLG profiles are
presented in this study. The reason why some programs are not included in all years over 1995-
2004 also is listed in the “Comments” column of this table.
Figures 2.1 through 30.7 present the marketing and LDP/MLG profiles for individual
programs in alphabetical order for the 2002 through 2004 crop years. 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 figures.
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 scale for the horizontal axis of the figures corresponds to the two-year marketing
window, that is, from September 1st of the year previous to harvest through August 31st of the
year after harvest. However, a few programs begin their marketing recommendations over a
particular crop year earlier than September 1st, and in these cases, the figures start with a positive
net percentage priced. Similarly, a few programs continue their marketing recommendations for
a period longer than the end of the two-year marketing window. In these cases, the net amount
priced at the end of the graph is less than 100%.
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.
Figures 31.1 through 40.4 contain the averages, maximums and minimums for marketing
and LDP/MLG profiles across all advisory programs tracked in each crop year from 1995 to
2004 as well as the comparisons between those averages and 24- and 20-month market
benchmark profiles for each crop year. Figure 41.1 contains the marketing profile grand average,
maximum and minimum across all services over the 1995–2004 crop years. Figure 41.2
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 41.3 contains the LDP/MLG profile grand average, maximum and minimum across all
services over the 1998-2001 and 2003-2004 crop years. Finally, Figure 41.4 compares the
LDP/MLG grand average to the 24- and 20-month market benchmark LDP/MLG profiles. Note
that those figures where average marketing profiles and LDP/MLG profiles are developed the
first day of harvest is an average of the first day of harvest across the set of years included in the
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,
Colino, E. V., S.M. Cabrini, S.H. Irwin, D.L. Good and J. Martines-Filho. "Advisory Service
Marketing Profiles for Corn in 2001." AgMAS Project Research Report 2004-02,
Department of Agricultural and Consumer Economics, University of Illinois at Urbana-
Champaign, April 2004.
Colino, E. V., S.M. Cabrini, S.H. Irwin, D.L. Good and J. Martines-Filho. "Advisory Service
Marketing Profiles for Soybeans in 2001." AgMAS Project Research Report 2004-02,
Department of Agricultural and Consumer Economics, University of Illinois at Urbana-
Champaign, April 2004.
Irwin, S.H., D.L. Good, J. Martines-Filho and R.M. Batts. "The Pricing Performance of Market
Advisory Services In Corn and Soybeans Over 1995-2004." AgMAS Project Research
Report 2006-02, Department of Agricultural and Consumer Economics, University of
Illinois at Urbana-Champaign, April 2006.
Martines-Filho, J., S.M. Cabrini, B.G. Stark, S.H. Irwin, D.L. Good, W. Shi, R.L. Webber, L.A.
Hagedorn and S.L. Williams."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, March 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 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., O. Isengildina, S.H. Irwin and D.L. Good. "The Impact of Market Advisory
Service Recommendations on Producers’ Marketing Decisions."
Journal of Agricultural and Resource Economics,29(2004):308-327.
Pennings, J.M.E., S.H. Irwin, D.L. Good and O. Isengildina."Heterogeneity in the Likelihood of
Market Advisory Service Use by U.S. Crop Producers." Agribusiness: An International Journal,, 21(2005):109-128..
Patrick, G.F., W.N. Musser, and D.T. Eckman."Forward Marketing Practices and Attitudes of
Large-Scale Midwestern Grain Farmers." Review of Agricultural Economics,, 20(1998):38-53.
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."
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Evelyn V. Colino, Silvina M. Cabrini, Nicole M. Aulerich, Tracy L. Brandenberger, Robert P. Merrin, and Wei Shi
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, and Darrel L. Good is Professor in the Department of Agricultural and Consumer
Economics at the University of Illinois at Urbana-Champaign. 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 and is Professor in the Escola Superior de Agricultura Luiz de Queiroz (ESALQ) at
the University of São Paulo, Brazil.
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 hedgeto-
arrive contracts, basis contracts and options.
 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