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Report 2003-05:The Pricing Performance of Market Advisory
Services in Corn and Soybeans Over 1995-2001
June,
2003 
Scott
H. Irwin, Joao Martines-Filho,
and Darrel L. Good
Copyright 2003 by Scott H. Irwin,
Joao Martines-Filho and Darrel L. Good. All 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.
DISCLAIMER
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. In
addition, the net advisory prices presented in this report may differ
substantially from those computed by an advisory service or another
subscriber due to differences in simulation assumptions, particularly with
respect to the geographic location of production, cash and forward
contract prices, expected and actual yields, storage charges and
government programs.
Introduction
Farmers in the US
consistently identify price and income risk as one of the greatest
management challenges they face. The roller coaster movement of corn and
soybean prices over the last decade is ample evidence of the uncertainty
and risk facing grain farmers. Surveys suggest that numerous farmers view
market advisory services as an important tool in managing price and income
risk (e.g., Sogn and Kraner, 1977; Smith, 1989; Patrick and Ullerich,
1996; Patrick, Musser and Eckman; 1998; Schroeder et al., 1998; Norvell
and Lattz, 1999; Pennings et al., 2001). Furthermore, Davis and Patrick
(2000) find that the use of market advisory services has a significant
influence on the use of forward pricing by farmers.
A limited number of
academic studies investigate the pricing performance of market advisory
services.
In the earliest study, Marquardt and McGann (1975) evaluate the accuracy
of cash price predictions for 10 private and public outlook newsletters in
corn, soybeans, wheat, cattle and hogs over 1970-1973. They find that
futures prices generally are a more accurate source of forecasts than
either the private or public newsletters. Gehrt and Good (1993) analyze
the performance of five advisory services for corn and soybeans over the
1985 through 1989 crop years.
Assuming a representative farmer follows the hedging and cash market
recommendations for each advisory service; a net price received for each
year is computed and compared to a benchmark price. They generally find
that corn and soybean farmers obtained a higher price by following the
marketing recommendations of advisory services. Martines-Filho (1996)
examines the pre-harvest corn and soybean marketing recommendations of six
market advisory services over 1991 through 1994. He computes the harvest
time revenue that results from a representative farmer following the
pre-harvest futures and options hedging recommendations and selling 100%
of production at harvest. Average advisory service revenue over the four
years is larger than benchmark revenue for both corn and soybeans.
Kastens and Schroeder (1996) examine the futures trading profits of seven
to ten market advisory services for the 1988-1996 crop years. They report
negative gross trading profits for wheat and positive gross trading
profits for corn and soybeans. The authors indicate that incorporating
brokerage commissions and subscription costs would have substantially
diminished trading returns.
While a useful starting
point, previous studies have important limitations. First, the
cross-section of advisory services tracked for each crop year is quite
small, with the largest sample including only ten advisory services.
Second, the results may be subject to survivorship bias, a consequence of
tracking only advisory services that remain in business at the end of a
sample period. The literature on the performance of mutual funds, hedge
funds and commodity trading advisors provides ample evidence of the upward
bias in performance results that can result from survivorship bias (e.g.,
Brown et al., 1992; Schneeweis, McCarthy and Spurgin, 1996; Brown,
Goetzmann and Ibbotson, 1999). Third, the results may be subject to
hindsight bias if advisory service recommendations were not collected on a
“real-time” basis (Jaffe and Mahoney, 1999). Hindsight bias is the
tendency to collect or record profitable recommendations and ignore or
minimize unprofitable recommendations after the fact.
This discussion suggests
the academic literature provides farmers with a limited basis for
evaluating the performance of market advisory services. The Agricultural
Market Advisory Service (AgMAS) Project was initiated in 1994 with the
goal of providing unbiased and rigorous evaluation of market advisory
services.
The AgMAS Project has collected marketing
recommendations for no fewer than 23 market advisory programs each crop
year since the project was initiated. While the sample of advisory
services is non-random, it is constructed to be generally representative
of the majority of advisory services offered to farmers. Further, the
sample of advisory services includes all programs tracked by the AgMAS
Project over the study period, so pricing performance results should not
be plagued by survivorship bias. Finally, the AgMAS Project subscribes to
all of the services that are followed and records recommendations on a
real-time basis. This should prevent the pricing performance results from
being subject to hindsight bias.
The purpose of this
research report is to evaluate the pricing performance of market advisory
services for the 1995-2001 corn and soybean crops. The results for
1995-2000 were released in earlier AgMAS research reports (e.g., Irwin,
Martines-Filho and Good, 2002), while results for the 2001 crop year are
new. Following the literature on mutual fund and investment newsletter
performance (e.g., Metrick, 1999; Jaffe and Mahoney, 1999), two basic
questions will be addressed in the report: 1) Do market advisory services,
on average, outperform appropriate benchmarks? and 2) Do market advisory
services exhibit persistence in their performance from year-to-year?
Certain explicit assumptions are made to produce a consistent and
comparable set of results across the different advisory programs.
These assumptions are intended to accurately depict
“real-world” marketing conditions facing a
representative central Illinois corn and soybean farmer. Several key
assumptions are: i) with a few exceptions, the marketing window for a crop
year runs from September before harvest through August after harvest, ii)
on-farm or commercial physical storage costs, as well as interest
opportunity costs, are charged to post-harvest sales, iii) brokerage costs
are subtracted for all futures and options transactions and iv) Commodity
Credit Corporation (CCC) marketing loan recommendations made by advisory
programs are followed wherever feasible. Based on these and other
assumptions, the net price received by a subscriber to a market advisory
program is calculated for the 1995-2001 corn and soybean crops.
Four basic
indicators of performance are applied to advisory program prices and
revenues over 1995-2001. The first indicator is the proportion of
advisory programs that beat benchmark prices. The second indicator is the
difference between the average price of advisory programs and benchmarks.
The third indicator is the average price and risk of advisory programs
relative to the average price and risk of benchmarks. The fourth
indicator is the predictability of advisory program performance from
year-to-year. Both market and farmer benchmarks are developed for the
evaluations. All benchmarks are computed using the same assumptions
applied to advisory service track records.
At the outset, it
is important to point out that only seven crop years are available to
analyze market advisory service pricing performance. From a purely
statistical standpoint, samples with ten or fewer observations typically
are considered “sparse.” On the surface, this suggests the sample may not
contain enough information to draw conclusions about advisory service
pricing performance. There are several reasons why this may not be the
case. First, Anderson (1974) explored the reliability of agricultural
return-risk estimates based on sparse data sets and found the surprising
result that even as few as three or four observations can be very useful.
Second, even though the number of crop years is limited, at least 23
advisory programs are tracked for each crop year. This has the potential
to substantially increase the information provided by the sample. Third,
from a practical, decision-making standpoint, samples with seven
observations often are considered adequate to reach conclusions. The
results of university crop yield trials represent a well-known example. A
typical presentation of the results includes only current year yields and
two-year or three-year averages. In many cases, even the two-year and
three-year averages cannot be presented because of turnover in the
varieties tested from year-to-year.
Despite the limitations, this type of yield trial data is widely used by
farmers in making variety selections. On balance, then, it seems
reasonable to argue that the seven years of data currently available on
advisory service pricing performance may be used to make some careful
conclusions. Caution obviously is in order given the possibility of
results being due to random chance in a relatively small sample of crop
years.
This report has been
reviewed by members of the AgMAS Review Panel, which provides independent,
peer-review of AgMAS Project research. The members who reviewed this
report are: T. Randall Fortenbery, Associate Professor in the Department
of Agricultural and Applied Economics at University of Wisconsin-Madison
and Diana Klemme, Vice President, Director – Grain Division, Grain Service
Corporation, Atlanta, Georgia.
The next section of the
report describes the procedures used to collect the data on market
advisory service recommendations. The second section describes the
methods and assumptions used to calculate the returns to advisory service
marketing advice. The third section presents the methods and assumptions
used to compute benchmark prices. The fourth section of the report
presents 2001 pricing results for corn and soybeans. The fifth section
presents a summary of the combined results for the 1995-2001 crop years.
The sixth section discusses the performance evaluation results for
1995-2001. The final section presents a summary and conclusions.
Data
Collection
The market advisory services included in
this evaluation do not comprise the population of market advisory services
available to farmers. The included services also are not a random sample
of the population of market advisory services. Neither approach is
feasible because no public agency or trade group assembles a list of
advisory services that could be considered the "population." Furthermore,
there is not a generally agreed upon definition of an agricultural market
advisory service. To assemble the sample of services for the AgMAS
Project, criteria were developed to define an agricultural market advisory
service and a list of services was assembled.
Five criteria are used to determine which
advisory services are included in the AgMAS study. First, marketing
recommendations from an advisory service must be received electronically
in real time. The recommendations may come 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. This form of delivery also ensures that recommendations are
received in “real-time.” This avoids the problem of recommendations being
delivered after the date of implementation intended by an advisory
service. Such a problem could occur frequently with recommendations
delivered via the postal service.
The second criterion 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 terms "speculative" trading
of futures and options and “hedging” use of futures and options are only
used to identify whether a service is focused on speculators or farmers.
Within a clearly defined farm marketing program, a distinction between
speculative and hedging use of futures and options is not necessary.
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 --cash, futures or options-- and the price or date at
which each transaction is to be implemented. It is also helpful if
advisory services make specific recommendations about implementation of
the marketing loan program, but that is not required. Note that some
advisory services evaluated by the AgMAS Project do not make any futures
and options recommendations, so it is not necessary to make such
recommendation to be included in the study. Services that make futures
and options hedging recommendations, but fail to clearly state when cash
sales should be made, or the amount to be sold, are not considered for
inclusion.
The fourth criterion is
that advisory services must provide “blanket” or “one-size fits all”
marketing recommendations so there is no uncertainty about
implementation. While different programs 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.
A 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. Hence, there is a need to exclude firms that are not viable
commercial concerns. At the same time, any filter in this regard should
not be so restrictive that newer and smaller advisory services are
excluded from the AgMAS study for an unreasonably long period of time.
This same issue is prevalent when evaluating the performance of other
types of professional investment advisors, such as commodity trading
advisors. In these cases, it is not unusual to screen firms by the length
of track record and amount of funds under management.
An analogous screen for market advisory services can be based on the
length of time the service has provided recommendations and the number of
paying subscribers. 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. This criterion should exclude non-viable services, while
at the same time providing a relatively low hurdle for new and legitimate
market advisory services.
The original sample of
market advisory services was drawn from the list of Premium Services
available from the two major agricultural satellite networks, Data
Transmission Network (DTN) and FarmDayta, in the summer of 1994.
While the list of advisory services available from these networks was by
no means exhaustive, it did have the considerable merit of meeting a
market test. Presumably, the services offered by the networks were those
most in demand by farm subscribers to the networks. In addition, the list
of available services was cross-checked with other farm publications to
confirm that widely followed advisory firms were included in the sample.
It seems reasonable to argue that the resulting sample of services was
generally representative of the majority of advisory services available to
farmers.
Additions and deletions
to the sample of advisory services have occurred over time. Additions
largely have been due to the increasing availability of market advisory
services via alternative means of electronic delivery, in particular,
websites and e-mail. Deletions have occurred for a variety of reasons. A
total of 39 and 38 advisory service programs for corn and soybeans,
respectively, have been included in the sample at some point in time.
Table 1 contains the complete list of advisory programs and includes a
brief explanation why each program not included for all crop years was
added or deleted from the sample. The term “advisory program” is used
because several advisory services have more than one distinct marketing
program. For example, AgLine by Doane, Brock, Pro Farmer and
Stewart-Peterson Advisory Services each have two distinct marketing
programs, Risk Management Group has three distinct marketing programs and
AgriVisor has four distinct marketing programs. Allendale provides two
distinct programs for corn, but only one for soybeans.
The total number of
advisory programs evaluated for the 2001 crop year is 27 for corn and 26
for soybeans. Three new programs were added for the 2001 crop year: Ag
Financial Strategies, Grain Field Marketing and Northstar Commodity. One
program, Agri-Mark, was deleted from the sample for the 2001 crop year.
This service stopped providing specific recommendations regarding cash
sales.
Three forms of
survivorship bias may be potential problems when assembling an advisory
program database. Survival bias significantly biases measures of
performance upwards since "survivors" typically have higher performance
than "non-survivors" (e.g., Brown et al., 1992; Schneeweis, McCarthy and
Spurgin, 1996; Brown, Goetzmann and Ibbotson, 1999).
The first and most direct form of survivorship bias occurs if only
advisory programs that remain in business at the end of a given sample
period are included in the sample. This form of bias should not be
present in the AgMAS database of advisory programs because all programs
that have been tracked over the entire time period of the study are
included in the sample. The second form of survivorship bias occurs if
discontinued advisory programs are deleted from the sample for the year
when they are discontinued. This is a form of survivorship bias because
only survivors for the full crop year are tracked. The AgMAS database of
advisory programs should not be subject to this form of bias because
programs discontinued during a crop year remain in the sample for that
crop year. The
third and most subtle form of survivorship bias occurs if data from prior
periods are "back-filled" at the point in time when an advisory program is
added to the database. This is a form of survivorship bias because data
from surviving advisory programs are back-filled. The AgMAS database
should not be subject to this form of bias because recommendations are not
back-filled when an advisory program is added. Instead, recommendations
are collected only for the crop year after a decision has been made to add
an advisory program to the database.
Another important
consideration when assembling a database on advisory program
recommendations is hindsight bias (Jaffe and Mahoney, 1999). This is the
tendency to collect or record profitable recommendations and ignore or
minimize unprofitable recommendations after the fact. Since the AgMAS
Project subscribes to all of the services that are followed and records
recommendations on a real-time basis, the database of recommendations
should not be subject to hindsight bias. The information is received
electronically, via DTN, website or e-mail. For the programs that provide
multiple daily updates, information is recorded for all updates. In this
way, the actions of a farmer-subscriber are simulated in real-time.
When recording
recommendations of each advisory program, specific attention is paid to
which year’s crop is being sold, (e.g., 2001 crop year), the amount of the
commodity to be sold, which futures or options contract is to be used
(where applicable) and any price targets that are mentioned (e.g., sell
cash corn when March 2002 futures reaches $2.40). If a price target is
given and not immediately filled, such as a stop order in the futures
market, the recommendation is noted until the order is either filled or
canceled. Recommendations for farm marketing programs are not screened
for "speculative" versus "hedging" uses of futures and options.
Consequently, all futures and options trades presented to farmers as a
part of marketing recommendations are included.
As noted above, 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 (although futures and options are not
always used) and a separate set of advice for farmers who only wish to
make cash sales.
In this situation, both strategies are recorded and treated as distinct
strategies to be evaluated. Some programs also differentiate advice based
on the availability of on-farm storage. In the past, when a service
clearly differentiated strategies based on the availability of on-farm
versus off-farm (commercial) storage, only the off-farm storage strategy
was tracked. Starting with the 2000 corn and soybean crops, if a service
clearly differentiates on-farm and off-farm storage strategies at or
before harvest, both strategies are recorded.
Several procedures are
used to check the recorded recommendations for accuracy and completeness.
Whenever possible, recorded recommendations are crosschecked against later
status reports provided by the relevant advisory program. Also, at the
completion of the crop year, it is confirmed whether cash sales total
exactly 100%, all futures positions are offset and all options positions
are offset or expire.
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
Calculating
the Returns to Marketing Advice
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 to be
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. In order
to produce a consistent and comparable set of results across the different
advisory programs, certain explicit assumptions are made. The assumptions
are intended to accurately depict “real-world” marketing conditions facing
a representative central Illinois corn and soybean farmer. Based on these
assumptions, the returns to each recommendation are then calculated in
order to arrive at a weighted average net price that would be received by
a farmer who precisely follows the marketing advice (as recorded by the
AgMAS Project). It should be interpreted as the harvest-equivalent net
price received by a farmer because post-harvest sales are adjusted for
physical storage and interest opportunity costs.
The discussion about
marketing assumptions in the following sections centers on the 2001 crop
year. It is important to note that some assumptions have changed over
time. Specific information on assumptions for the 1995-2000 crop years can
be found in earlier AgMAS pricing reports (e.g., Martines-Filho, Irwin and
Good, 2000). Assumed values for key variables used in the simulation of
advisory service performance over the 1995-2001 crop years can be found in
Appendix A.
Geographic
Location
The simulation is
designed to reflect conditions facing a representative central Illinois
corn and soybean farmer. Whenever possible, data are collected for the
Central Crop Reporting District in Illinois as defined by the National
Agricultural Statistics Service (NASS) of the US Department of Agriculture
(USDA). The eleven counties (DeWitt, Logan, McLean, Marshall, Macon,
Mason, Menard, Peoria, Stark, Tazewell and Woodford) that make up this
District are highlighted in Figure 1.
Caution should be used
when applying the results to other areas of the US, because yields and
basis patterns may be quite different from those of central Illinois.
Differences in yields and basis patterns could have a substantial impact
on prices computed for farmers or advisory services in another area. The
resulting change could be either up or down relative to AgMAS advisory
prices and benchmarks, depending on local conditions. Appendix B to this
report, entitled “A Cautionary Note on the Use of AgMAS Net Advisory
Prices and Benchmarks,” contains further discussion on this point.
Marketing
Window
The time period over which a farmer
normally makes pricing decisions for a particular crop is termed the
“marketing window.” It also can be referred to as the pricing
“decision-horizon” or “timeline” of a farmer. A marketing window does not
necessarily equal the time period of observed market activity. The reason
is that not taking action (e.g., not hedging pre-harvest) is one type of
decision that can be made during a marketing window.
In the present context, the objective is to
define the normal marketing window of a representative farmer who
subscribes to the advisory programs tracked by the AgMAS Project. Good,
Hieronymus and Hinton (1980) provide a useful starting point. They define
the marketing window for an Illinois grain farmer as the period extending
from the initial production planning time until the end of the storage
season. First production decisions in Illinois normally occur in October
through November of the year preceding planting (e.g., fall tillage and
application of fertilizer), while the storage season typically extends
through July or August of the year following harvest. This results in a
marketing window between 21 and 23 months in length. Chafin and Hoepner
(2002) reach a similar conclusion in their text on commodity marketing:
In building an integrated marketing plan, crop
producers must keep in mind the fact that pricing decisions on a single
crop span a two-year period: the growing year and the storage year.
The first stage of a crop “marketing year” begins in November as
production plans are being made for the new crop and continues throughout
the growing season until the end of harvest. During the second stage of
the “marketing year,” pricing of the harvested (old) crop begins at
the end of the 12-month “growing” year and continues for the next 12-month
storage year. Thus, the pricing of a single crop spans 730 days-the
“growing year” plus the “storage year.” (p. 326)
The actual pricing
pattern of advisory programs included in the AgMAS study provides further
information for defining the relevant marketing window. As noted earlier,
observed market positions cannot directly reveal the intended pricing
window of a representative farmer following advisory program
recommendations. However, averages over time and advisors should be
suggestive as to the typical starting and ending points used to make
recommendations for a crop. Figure 2 presents the average “marketing
profile” of advisory programs in corn and soybeans over the 1995-2000 crop
years.
The marketing profiles show the average amount of corn and soybean crops
priced (sold) by advisory programs, on a cumulative basis, each day over
the two-year period beginning in September of the year before harvest and
ending in August of the year after harvest. The profiles suggest that a
farmer following the recommendations of market advisory programs included
in the AgMAS study, on average, will begin making significant marketing
decisions (pricing more than one percent) in September of the year before
harvest and will not complete marketing until August of the year after
harvest.
Overall, this discussion
indicates it is reasonable to assume a 24-month marketing window for a
representative farmer subscribing to advisory programs. In the case of
the 2001 crop, the marketing window is then defined as the two-year period
beginning September 1, 2000 and ending on August 31, 2002. Two further
issues need to be discussed with respect to the market window. The first
issue is exceptions to the specific definition. For example, one program
in corn started its first hedging position for the 2001 crop year in the
middle of July 2000. One other advisory service had a relatively small
amount (10%) of cash corn and soybeans unsold in its programs as of August
31, 2002. These bushels were sold in the spot cash market by October 23,
2002. Given that the marketing window is defined as the “normal” window,
it is argued that a representative farmer would approach the marketing
window with some flexibility, particularly for recommendations that do not
extend too far outside the limits of the marketing window. Since the
transactions in question for the 2001 crop do not extend much outside the
limits of the marketing window, they are included in the relevant advisory
program’s track record.
The second issue is the definition of business days within the marketing
window. This issue arises because different entities in the agricultural
sector have different policies with respect to holidays. For the purposes
of this study, an “official” business day within the marketing window is
defined as a business day where the Chicago Board of Trade is open and
cash prices are reported by the Illinois Department of Ag Market News.
Finally, note that throughout the remainder of this report the term "crop
year" is used to represent the two-year marketing window.
Prices
The
price assigned to each cash sale recommendation is the central Illinois
closing, or overnight, bid. The data are collected and reported by the
Illinois Department of Ag Market News.The central Illinois price is the mid-point of
the range of bids by elevators in the North Central and South Central
Price Reporting Districts, as defined by the Illinois Department of Ag
Market News. The North and South Central Illinois Price Reporting
Districts are highlighted in Figure 3. Prices in this 35-county area best
reflect prices for the assumed geographic location of the representative
central Illinois farmer (Central Illinois Crop Reporting District).
Pre-harvest cash forward
contract prices for fall delivery are also needed. Pre-harvest bids
collected by the Illinois Department of Ag Market News are used when
available. The central Illinois pre-harvest price is the mid-point of the
daily range of pre-harvest bids by elevators in the North Central and
South Central Price Reporting Districts, again, as defined by the Illinois
Department of Ag Market News. Pre-harvest forward prices are available
from this source for the 2001 corn and soybeans crops during February 1,
2001 to August 31, 2001.
Since the marketing
window for the 2001 corn and soybean crops begins in September 2000 and
the Illinois Department of Ag Market News did not begin to report actual
cash forward bids until February 1, 2001, pre-harvest prices need to be
estimated for the first few months of the marketing window. For a date
between September 1, 2000 and January 31, 2001, a two-step estimation
procedure is adopted. First, the forward basis for the period in question
is estimated by the average forward basis for the first five days the
Illinois Department of Ag Market News reports actual forward contract bids
(February 1-7, 2001)
.
Second, the estimated forward basis is added to the settlement price of
the Chicago Board of Trade (CBOT) 2001 December corn futures contract or
2001 November soybean futures contract between September 1, 2000 and
January 31, 2001. This estimation procedure is expected to be a
reasonably accurate reflection of actual forward prices for the early
period of the marketing window, as the actual price of the harvest futures
contract is used and only the forward basis is estimated. In addition,
the estimation procedure is typically applied to a relatively small number
of transactions. The average net amount sold before February 1st
over 1995-2000 is only 13% for corn and 10% for soybeans, and many of
these transactions are in futures or options contracts rather than forward
contracts.>
Some market
advisory programs recommended the use of post-harvest forward contracts to
sell part of the 2001 corn and soybean crops. The Illinois Department of
Ag Market News reported post-harvest bids for January 2002 delivery from
September 4, 2001 to November 30, 2001. Post-harvest bids also were
reported for March 2002 delivery from December 3, 2001 to February 1,
2002. These central Illinois bids are used wherever applicable. For the
2001 crop year, forward bids are available to match all advisory program
recommendations.
In the future, if
the positions recommended by advisory programs either do not match the
delivery periods reported by the Illinois Department of Ag Market News or
are made after the Illinois Department of Ag Market News stops reporting
post-harvest forward contract prices, the following procedure will be used
to estimate the post-harvest forward contract prices needed in the
analysis. First, three elevators in central Illinois agreed to supply
data on spot and forward contract prices on the dates when advisors made
such recommendations. Each of these elevators is in a different county in
the Central Illinois Crop Reporting District (Logan, McClean, DeWitt).
Second, the spread between each elevator’s forward price and spot price
will be calculated for the relevant date. Third, the forward spread will
be averaged across the three elevators for the same date. Fourth, the
average forward spread from the three elevators will be added to the
central Illinois cash price (discussed at the beginning of the section) to
arrive at an estimated post-harvest forward contract price for central
Illinois. This procedure was used in a few cases for the 1998 and 1999
crop years.
The fill prices for
futures and options transactions generally are the prices reported by the
programs. In cases where a program did not report a specific fill price,
the settlement price for the day is used. This method does not account
for liquidity costs in executing futures and options transactions.
Quantity
Sold
When making hedging or forward contracting
decisions prior to harvest, the actual yield is unknown. Hence, an
assumption regarding the amount of expected production per acre is
necessary to accurately reflect the returns to marketing advice. Prior to
harvest, the best estimate of the current year’s expected yield is likely
to be a function of yield in previous years. In this study, the assumed
yield prior to harvest is the calculated trend yield, while the actual
reported yield is used from the harvest period forward. The expected
yield for 2001 is based upon a log-linear regression trend model of actual
yields from 1972 through 2000 for the Central Illinois Crop Reporting
District. Previous research suggests this type of trend model provides a
reasonable fit to corn and soybean yield data (Fackler, Young and Carlson,
1993; Zanini, 2001).
In central Illinois, the
expected 2001 yield for corn is calculated to be 152.4 bushels per acre.
Therefore, recommendations regarding the marketing quantity made prior to
harvest are based on yields of 152.4 bushels per acre. For example, a
recommendation to forward contract 20% of expected 2001 production
translates into a recommendation to contract 30.5 bushels per acre (20% of
152.4). The actual reported corn yield in central Illinois in 2001 is 157
bushels per acre. The same approach is used for soybean evaluations. The
calculated 2001 trend yield for soybeans in central Illinois is 48.8
bushels per acre and the actual yield in 2001 is 48 bushels per acre.
It is assumed that after
harvest begins, farmers can make reasonably accurate projections of
realized yields. Therefore, recommendations made after the start of
harvest are assumed to be based on actual yields instead of expected
yields. Since harvest does not occur during the same exact period each
year, data on harvest progress are needed to establish the relevant
harvest window, and in particular, the date that harvest begins. Harvest
progress data are reported by NASS for the central Illinois Crop Reporting
District; however, the reports typically are not made available soon
enough to identify precisely the beginning of harvest. Consequently, the
exact “location” of the harvest window cannot be identified based upon
available data. The following alternative procedure is used to estimate
the harvest window each year. First, the business day nearest to 50%
completion of harvest is defined as the mid-point of harvest. Second, the
entire harvest period is defined as a five-week window, beginning twelve
business days before the mid-point of harvest, and ending twelve business
days after the mid-point of harvest (a total of 25 business days, or five
weeks). In most years, the five-week window includes at least 80% of the
harvest.
Since NASS harvest
progress reports are made weekly, the exact date of the harvest mid-point
is not known. However, it is possible to estimate the date of the
mid-point using the weekly progress numbers of the two reports that
encompass 50% harvest progress. For example, the NASS estimate of corn
harvest progress in central Illinois is 40% on September 30, 2001.
Harvest progress is estimated to be 67% in the next report on October 7,
2001. A daily progress estimate for this week can be constructed by
taking the difference of these estimates and dividing the result by seven;
in this example, harvest progressed at rate of approximately 3.86% per
day. Counting forward from 40% at a rate of 3.86% per day, the business
day closest to 50% progress is October 3, 2001. This mid-point is used to
construct the harvest window for corn by counting backwards and forwards
twelve business days. The same procedure is used to determine the harvest
window for soybeans.
For 2001, the harvest
period for corn is defined as September 17, 2001 through October 19,
2001. For soybeans, the harvest period is September 14, 2001 through
October 18, 2001. Therefore, recommendations for corn made after
September 16th are applied on the basis of the actual yield of
157 bushels per acre. For soybeans, recommendations made after September
13th are applied on the basis of the actual yield of 48 bushels
per acre.
The issue of changing
yield expectations typically is not dealt with in the recommendations of
the advisory programs. For the purpose of this study, the actual harvest
yield must exactly equal total cash sales of the crop at the end of the
marketing time frame. Hence, an adjustment in yield assumptions from
expected to actual levels must be applied to cash transactions at some
point in time. In this analysis, an adjustment is made in the amount of
the first cash sale made after the beginning of the harvest period. For
example, if a program advises forward contracting 50% of the corn crop
prior to harvest, this translates into sales of 76.2 bushels per acre (50%
of 152.4). However, when the actual yield is applied to the analysis,
sales-to-date of 76.2 bushels per acre imply that only 48.54% of the
actual crop has been contracted. In order to compensate, the amount of
the next cash sale is adjusted to align the amount sold. In this example,
if the next cash sale recommendation is for a 10% increment of the 2001
crop, making the total recommended sales 60% of the crop, the
recommendation is adjusted to 11.46% of the actual yield (18 bushels), so
that the total crop sold to date is 60% of 157 bushels per acre (76.2 + 18
= 94.2 = 0.6*157). After this initial adjustment, subsequent
recommendations are taken as percentages of the 157 bushels per acre
actual yield, so that sales of 100% of the crop equal sales of 157 bushels
per acre.
While the amount of cash
sales is adjusted to reflect the change in yield information, a similar
adjustment is not made for futures or options positions that are already
in place. For example, assume that a short futures hedge is placed in the
December 2001 corn futures contract for 25% of the 2001 crop prior to
harvest. Since the amount hedged is based on the trend yield assumption
of 152.4 bushels per acre, the futures position is 38.1 bushels per acre
(25% of 152.4). After the yield assumption is changed, this amount
represents a short hedge of 24.3% (38.1/157). The amount of the futures
position is not adjusted to move the position to 25% of the new yield
figure. However, any futures (or options) positions recommended after the
beginning of harvest are implemented as a percentage of the actual yield.
If actual yield is
substantially below trend, and forward pricing obligations are based on
trend yields, a farmer may have difficulty meeting such obligations. This
raises the issue of updating yield expectations in “short” crop years to
minimize the chance of defaulting on forward pricing obligations. While
not yet encountered in the AgMAS evaluations of corn and soybeans, this
situation has arisen in the evaluation of wheat (Jirik, Irwin, Good,
Jackson and Martines-Filho, 2000).
As in wheat, a relatively
simple procedure will be used to update yield expectations in any future
corn or soybean short crop years. First, trend yield will be used as the
expected yield until the August USDA Crop Production Report is
released, typically around August 10th. Second, if the USDA
corn or soybean yield estimate for the Central Illinois Crop Reporting
District is 20% (or more) lower than trend yield, a “reasonable” farmer is
assumed to change yield expectations to the lower USDA estimate. Third,
as with normal crop years, the adjustment to actual yield is assumed to
occur on the first day of harvest.
The 20% threshold is
intentionally relatively large for at least three reasons. First, it is
desirable to make adjustments to the trend yield expectation on a limited
number of occasions. Given the large variability in annual yields, a
small threshold could result in frequent adjustments. Second, it is not
uncommon for early yield estimates to deviate significantly from the final
estimate. A small threshold could result in unnecessary adjustments prior
to harvest. Third, yield shortfalls of less than 20% are unlikely to
create delivery problems for a farmer.
Brokerage
Costs
Brokerage costs are incurred when farmers
open or close positions in futures and options markets. For the purposes
of this study, it is assumed that brokerage costs are $50 per contract for
round-turn futures transactions and $30 per contract to enter or exit an
options position. Further, it is assumed that CBOT corn and soybean
futures and options contracts are used, which have a contract size of
5,000 bushels. Therefore, per-bushel brokerage costs are one cent per
bushel for a round-turn futures transaction and 0.6¢ per bushel for each
options transaction.
LDP
and Marketing Assistance Loan Payments
While the 1996 “Freedom-to-Farm” Act did away with government
set-aside and target price programs, price protection for farmers in
program crops such as corn and soybeans was not eliminated entirely.
Minimum prices are established through a “loan” program. Specifically, if
market prices are below the Commodity Credit Corporation (CCC) loan rate
for corn or soybeans, farmers can receive payments from the
US government that make
up the difference between the loan rate and the lower market price.
There is considerable flexibility in the way the loan program can be
implemented by farmers. This flexibility presents the opportunity for
advisory programs to make specific recommendations for the implementation
of the loan program. Additionally, the prices of both corn and soybeans
were below the loan rate during significant periods of time in the
2001-2002 marketing year, so that use of the loan program was an important
part of marketing strategies. As a result, net advisory program prices
may be substantially impacted by the way the provisions of the loan
program are implemented. Finally, all of the advisory programs tracked by
the AgMAS project for the 2001 crop year make specific recommendations
regarding the timing and method of implementing the loan program for the
entire corn and soybean crops.
Before describing the decision rules, it is useful to provide
a brief overview of the loan program mechanics. Then, the rules developed
to implement the loan program in the absence of specific recommendations
can be described more effectively.
Program Mechanics
There are two mechanisms for implementing the price protection benefits of
the loan program. The first mechanism is the loan deficiency payment
(LDP) program. LDPs are computed as the difference between the loan rate
for a given county and the posted county price (PCP) for a particular
day. PCPs are computed by the USDA and change each day in order to
reflect the average market price that exists in the county. For example,
if the county loan rate for corn is $2.00 per bushel and the PCP for a
given day is $1.50 per bushel, then the LDP is $0.50 per bushel. If the
PCP increases to $1.60 per bushel, the LDP will decrease to $0.40 per
bushel. Conversely, if the PCP decreases to $1.40 per bushel, the LDP
will increase to $0.60 per bushel.
LDPs are made available to farmers over the period beginning
with corn or soybean harvest and ending May 31st of the
calendar year following harvest. Farmers have flexibility with regard to
taking the LDP, because they may simply elect to take the payment when the
crop is sold in a spot market transaction (before the end of May in the
particular marketing year), or choose to take the LDP before the crop is
delivered and sold. Note that LDPs cannot be taken after a crop has been
delivered and title has changed hands.
The second mechanism is the non-recourse marketing assistance
loan program. A loan cannot be taken on any portion of the crop for which
an LDP has been received. Under this program, farmers may store the crop
(on the farm or commercially), maintain beneficial interest, and receive a
loan from the CCC using the stored crop as collateral. The loan rate is
the established rate in the county where the crop is stored and the
interest rate is established at the time of loan entry. Corn and soybean
crops can be placed under loan anytime after the crop is stored through
May 31st of the following calendar year. The loan matures on
the last day of the ninth month following the month in which the loan was
made.
Farmers may settle outstanding loans in two ways: i) repaying
the loan during the 9-month loan period, or ii) forfeiting the crop to the
CCC at maturity of the loan. Under the first alternative, the loan
repayment rate is the lower of the county loan rate plus accrued interest
or the marketing loan repayment rate, which is the PCP. If the PCP is
below the county loan rate, the economic incentive is to repay the loan at
the posted county price. The difference between the loan rate and the
repayment rate is a marketing loan gain (MLG). If the PCP is higher than
the loan rate, but lower than the loan rate plus accrued interest, the
incentive is also to repay the loan at the PCP. In this case only,
interest is charged on the difference between the PCP and the loan rate.
If the PCP is higher than the loan rate plus accrued interest, the
incentive is to repay the loan at the loan rate plus interest. In this
latter case, interest is based on the loan rate.
Under the second alternative, the farmer stores the crop to
loan maturity and then transfers title to the CCC. The farmer retains the
proceeds from the initial loan. This was generally not an attractive
alternative in the 2001 marketing year since the PCP was often below the
cash price of corn and soybeans. Repaying the loan at the PCP and selling
the crop at the higher cash price was economically superior to forfeiture.
The non-recourse loan program establishes the county loan
rate as a minimum price for the farmer, as does the LDP program. For the
2001 crop, the sum of LDPs plus marketing loan gains was subject to a
payment limitation of $150,000 per person. Forfeiture on the loans
provided the mechanism for receiving a minimum of the loan rate on bushels
in excess of the payment limitation.
The average loan rates for the 2001 corn and soybean crops
across the eleven counties in the Central Illinois Crop Reporting District
are $1.95 and $5.41 per bushel, respectively. Spot cash prices fell below
these loan rates for almost all of the 2001 post-harvest period for corn
and for soybeans. This is reflected in Figure 4, which shows corn and
soybean LDP or MLG rates for central
Illinois
during the 2001 post-harvest period.,
For corn and soybeans, LDPs or MLGs are relatively high during harvest,
varying from $0.10 to $0.23 per bushel for corn and from $0.80 to $1.37
per bushel for soybeans. Then fall to zero or near zero by the end of
2001 crop year. As cash corn and soybean prices increase during the
summer of 2002, corn and soybeans MLGs decrease to zero at the beginning
of July 2002.
Decision Rules for
Programs with a Complete Set of Loan Recommendations
If an advisory program
makes a complete set of loan recommendations, the specific advice is
implemented wherever feasible. However, specific decision rules are still
needed regarding pre-harvest forward contracts because it is possible for
an advisory program to recommend taking the LDP on those sales before it
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 necessitates 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 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.
Tables 2 and 3 summarize the information used to assign LDPs
to pre-harvest forward contracts. The second column shows the amount
harvested assuming a linear model. The third column shows the LDP
available on each date of the harvest window and the fourth column
presents the average LDP through each harvest date. An example will help
illustrate use of the tables. Assume that an advisory program recommends,
at some point before harvest, that a farmer forward contract 50% of
expected soybean production. This translates into 24.4 bushels per acre
when the percentage is applied to expected production (0.50*48.8 = 24.4).
Next, convert the bushels per acre to a percentage of actual production,
which is 50.8% (24.4/48 = 0.508). To determine the LDP payment on the
50.8% of actual production forward contracted, simply read down Table 3 to
October 2, 2001,
which is the date when 50.8% of harvest is assumed to be complete. The
average LDP up to that date (September 14, 2001- October 2, 2001) is $0.91
per bushel; the last column of Table 3. This is the LDP amount assigned
to the forward contract bushels.
Note that LDPs for any sales (spot, forward contracts,
futures or options) recommended during harvest are taken only after all
forward contract obligations are fulfilled. Grain industry practices may
actually offer more flexibility in establishing LDPs than is assumed
here. In addition, so long as prices remain below the loan rate, crops
placed under loan by an advisory program do not accumulate interest
opportunity costs because proceeds from the loan can be used to offset
interest costs that otherwise would accumulate.
Decision Rules for
Programs with a Partial Set of Loan Recommendations Or No Loan Recommendations
If an advisory program
makes a partial set of loan recommendations, the available advice is
implemented wherever feasible. In the absence of specific
recommendations, it is assumed that crops priced before May 31, 2002 are
not placed under loan. Those crops receive program benefits through LDPs.
After May 31, 2002, eligible crops (unpriced crops for which program
benefits have not yet been collected) are assumed to be under loan until
priced.
In the absence of
specific recommendations, rules for assigning LDPs and MLGs are developed
under the assumption that loan benefits are established when the crop is
priced or as soon after pricing that is allowed under the rules of the
program. This principle is consistent with the intent of the loan program
to fix a minimum price when pricing decisions are made. Two rules are
most important in the implementation of this principle. First, LDPs on
pre-harvest sales (forward contracts, futures or options) are established
as the crop is harvested. Second, if the LDP or MLG is zero on the
pricing date, or the first date of eligibility to receive a loan benefit,
those values are assigned on the first date when a positive value is
observed, assuming a beneficial interest in that portion of the crop has
been maintained. Specific rules for particular marketing tools and
situations follow:
1)
Pre-harvest forward contracts.
The same decision rules
are applied as discussed in the previous section. Specifically, it is
assumed that amounts sold for harvest delivery with pre-harvest forward
contracts are delivered first during harvest, although not all buyers
require that forward contract bushels be delivered first. LDPs, if
positive, are assigned as these “forward contract” quantities are
harvested and delivered. This necessitates assumptions regarding the
timing and speed of harvest. A linear model of harvest progress is
assumed in the five-week harvest window. The specific information used to
assign LDPs to pre-harvest forward contracts is again found in Tables 2
and 3. As a final point, note that LDPs for any other sales (spot,
futures or options) recommended during harvest are taken only after all
pre-harvest forward pricing obligations are fulfilled.
2)
Pre-harvest
short futures.
The use of futures contracts to price during the pre-harvest seasons is
treated in the same manner as pre-harvest forward contracts. LDPs are
assigned on open futures positions as the crop is harvested, or as soon as
a positive LDP is available, if the futures position is still in place and
cash sales have not yet been made. These are assigned after forward
contracts have been satisfied. If the underlying crop is sold before
there is a positive LDP, then that portion of the crop receives a zero
LDP. During the harvest window, if the futures position is offset before
a positive LDP is available and the crop has not yet been sold in the cash
market, that portion of the crop is eligible for loan benefits on the next
pricing recommendation.
3)
Pre-harvest
put option purchases.
Long put option positions, which establish a minimum
futures price, are
treated in the same manner as pre-harvest short futures.
4)
Post-harvest forward contracts. The main issue with respect to post-harvest forward
contracts is when to assign the LDPs or MLGs. Those can be established on
the date the contract is initiated, on the delivery date of the contract,
or anytime in between. Following the general principle outlined earlier,
LDPs and MLGs for post-harvest contracts are assigned on the date the
contract is initiated or the first day with positive benefits prior to
delivery on the contract.
5)
Post-harvest short futures.
As with post-harvest forward contracts, the main issue with post-harvest
short futures positions is when to assign loan benefits. These are
assigned when the short futures position is initiated or as soon as a
positive benefit is available if the futures position is still in place
and cash sales have not been made. If the underlying crop is sold before
a positive LDP is available, that portion of the crop receives a zero
LDP. If the short futures position is offset before a positive LDP is
available and the cash crop has not yet been sold, that portion of the
crop is eligible for loan benefits on the next pricing recommendation.
6)
Post-harvest long put positions.
Long put option positions established after the crop is harvested are
treated in the same manner as post-harvest short futures.
7)
Spot sales before May 31, 2002.
If a spot cash sale of corn or soybeans is recommended before May 31,
2002, it is assumed that the LDP, if positive, is established that same
day.
8)
Loan program after May 31, 2002. Since LDPs are not available after
May 31, 2002, it is
assumed that any corn or soybeans in storage and not priced as of this
date, for which loan benefits have not been established, are entered in
the loan program on that date. This is a reasonable assumption since spot
prices are below the loan rate for soybeans and near the loan rate for
corn in central Illinois on May 31, 2002 and a prudent farmer would take
advantage of the price protection offered by the loan program.
When the crops are subsequently priced (cash sale, forward contract, short
futures, or long put option), the marketing loan gain, if positive, is
assigned on that day. Forfeiture is not an issue for these bushels
because all cash sales were made before the end of the nine-month loan
period. Note also that the $150,000 payment limitation is not considered
in the analysis, as production is based on one acre of corn and/or
soybeans.
Storage
Costs
An important element
in assessing returns to an advisory program is the economic cost
associated with storing grain instead of selling grain immediately at
harvest. The cost of storing grain after harvest consists of two
components: physical storage costs and the opportunity cost incurred by
foregoing sales when the crop is harvested. Physical storage costs depend
on the type of storage available and the horizon used by a farmer to make
storage decisions. From a representative farmer’s perspective, there are
four relevant physical storage scenarios: i) on-farm storage using a
short-run decision-horizon, ii) off-farm (commercial) storage using a
short-run decision-horizon, iii) on-farm storage using a long-run
decision-horizon and iv) off-farm (commercial) storage using a long-run
decision-horizon. Short-run in this context is defined to be one storage
season, usually the ten-month period after the harvest of a particular
crop. Long-run is defined to be any decision-horizon longer than one
storage season. In each of the previous scenarios, the physical storage
charge should be the relevant marginal cost of physical storage (Williams
and Wright, 1991). In contrast, opportunity cost should be the same
regardless of the type of physical storage used or whether a short- or
long-run decision-horizon is considered.
Early AgMAS pricing reports consider only one scenario: commercial storage
using a short-run decision-horizon. Starting with the 2000 crop year, net
advisory prices and benchmarks are computed using physical storage costs
applicable to each of the four storage scenarios. In all cases, storage
and interest charges are assigned beginning October 22, 2001 for corn and
October 19, 2001 for soybeans, the first dates after the end of the
respective 2001 harvest windows. It should be noted that the cost of
drying corn to 15% moisture and the cost of drying soybeans to storable
moisture are not included in the calculations. This cost is incurred
whether the grain is stored or sold at harvest, or whether the grain is
stored on-farm or off-farm. Therefore, this cost is irrelevant to the
analysis and excluded.
The first scenario
considered is on-farm storage and a short-run decision-horizon. Because
pre-existing storage facilities are assumed to be available on-farm, the
marginal cost of physical storage equals the on-farm variable cost of
physical storage. Estimates of the on-farm variable cost of physical
storage are drawn from a recent study conducted at Kansas State University
(Dhuyvetter, Hamman and Harner, 2000). The estimates assume storage
occurs in a 25,000 bushel round metal bin, the “medium-sized” storage
capacity examined in the
Kansas State
study. The first component of on-farm physical storage is a flat charge
of 6.7¢ per bushel for conveyance, aeration, insecticide and repairs. The
flat charge is applied to both corn and soybeans and reflects the fact
that most physical costs of on-farm storage are “one-time” in nature.
That is, once the decision is made to store, most costs are pre-determined
and do not vary with the length of storage.
The second component of
on-farm physical storage is shrinkage. Corn shrinkage is assumed in the
Kansas State study to start at one-percent per bushel for the first month
of storage and increase at a rate of one-tenth of one percent for each
month stored thereafter. For example, if corn is stored six months, the
total shrinkage is assumed to be 1.5% per bushel. Agricultural
engineering specialists at the University of Illinois and Purdue
University indicated that the on-farm shrink schedule for corn used in the
Kansas State study is reasonable. In addition, the schedule is consistent
with published research about shrinkage of corn stored on-farm (Hurburgh,
Bern, Wilcke and Anderson, 1983). Given that the harvest-time cash price
of corn in central Illinois for 2001 is $1.87 per bushel, the shrink
charge assigned to corn stored on-farm for one-month is 1.87¢ per bushel
($1.87*0.01*100). The shrink charge is increased 0.19¢ per bushel
($1.87*0.001*100) for each additional month of storage.
Since the Kansas State study did
not estimate shrinkage costs for soybeans, the same agricultural
engineering specialists noted above were consulted for a reasonable
estimate. This turned out to be a constant 0.25% per bushel shrink
factor. Given that the harvest-time cash price of soybeans in central
Illinois
for 2001 is $4.33 per bushel, the flat shrink charge assigned to soybeans
is 1.08¢ per bushel ($4.33*0.0025*100).
As noted earlier, storage costs include the physical cost of
storage and interest opportunity costs. Interest cost is computed using
the 2001 harvest cash price and an annual interest rate of 7.4%.
Specifically, the interest charge for storing grain on-farm is computed as
the harvest price times the interest rate compounded daily from the end of
harvest to the date of sale.
The interest rate is the average rate for all other farm operating loans
for Seventh Federal Reserve District agricultural banks in the fourth
quarter of 2001 as reported in the Agricultural Finance Databook,
which is published by the Board of Governors of the Federal Reserve Board.
Interest rates for the fourth quarter are assumed to most accurately
reflect actual opportunity costs on agricultural loans related to storage.
The
second scenario considered is storage off-farm at commercial facilities
and a short-run decision-horizon. The marginal cost of physical storage
in this case is the sum of commercial storage, drying and shrinkage
charges. As in the past, storage costs at commercial elevators in 2001
are drawn from an informal telephone survey of nine central Illinois
elevators.
Based on this information, physical commercial storage charges are assumed
to be a flat 13¢ per bushel from the end of harvest through December 31.
After January 1, physical storage charges are assumed to be 2¢ per month
(per bushel), with this charge pro-rated to the day when the cash sale is
made. The drying charge to reduce corn moisture from 15% to 14% is a flat
2¢ per bushel, while the charge for shrinkage is 1.3% per bushel.
The cost of commercial shrinkage is based on the harvest price (no
shrinkage is assumed for soybeans in commercial storage). Given that the
harvest-time cash price of corn in central Illinois for 2001 is $1.87 per
bushel, the charge for volume reduction is 2.43¢ per bushel
($1.87*0.013*100). Therefore, the flat shrink and drying charge assigned
to all stored corn is 4.43¢ per bushel.
Interest opportunity cost is computed using the same procedures and
assumptions as outlined above for on-farm storage.
The third and fourth
scenarios shift to a long-run decision-horizon, where the on-farm scenario
is applicable to a farmer considering the construction of new on-farm
storage facilities and the commercial scenario is applicable to a farmer
that plans on using commercial storage facilities over the long-run.
Since all costs are variable in the long-run, the relevant marginal
physical storage cost in both of these scenarios is the total cost.
Dhuyvetter, Hamman and Harner (2000) estimate the on-farm fixed cost of
physical storage for a 25,000 bushel round, metal bin to be 14.6¢ per
year. This fixed cost can be added to the on-farm variable cost estimate
discussed earlier to compute the total physical cost of on-farm storage.
Presumably, commercial physical storage charges paid by farmers reflect
total variable and fixed costs of storage at commercial facilities.
Consequently, the commercial storage costs discussed earlier in the
context of short-run decisions also represent long-run commercial physical
costs.
A
comparison of the estimated costs of storage for corn and soybeans in the
2001 crop year is found in Tables 4 and 5, respectively. The first item
of note is that the on-farm variable cost of physical storage changes
little for corn as the storage length increases and is constant for
soybeans as the storage length increases. The reason is the previously
mentioned “one-time” nature of most physical costs of on-farm storage. As
shown in Figure 5, this results in a “non-linear” relationship between
on-farm variable costs of storage per month and the length of storage.
For example, the on-farm variable cost for corn stored two months after
harvest is about 4.5¢ per month. This can be compared to the on-farm
variable cost of corn stored six months after harvest of about 1.6¢ per
month. The second item of note is the much lower level of on-farm
variable costs versus commercial storage costs. Of course, this is not
surprising given that variable on-farm storage costs do not include fixed
costs, while commercial storage costs presumably reflect total variable
and fixed storage costs at commercial facilities. The third item of note
is the similar level of total on-farm costs (variable plus fixed) and
total commercial costs for all but the shortest and longest storage
lengths. Figure 5 illustrates this finding on a per month basis. This
result is not surprising assuming reasonably competitive conditions in the
market for storage. If total on-farm storage costs were substantially
less than total commercial costs, this would encourage a rapid expansion
of on-farm storage and vice versa. In fact, the proportion of
on-farm versus off-farm storage capacity in Illinois has been roughly
equal for a number of years.
This is consistent with a basic equilibrium in the storage market where
total on-farm costs and commercial costs are about the same.
Given the information presented in Tables 4 and 5, it is
possible to compute net advisory prices and benchmarks under each of the
four storage scenarios described at the beginning of this section. It
turns out that only two sets of storage costs are necessary to represent
all four scenarios. Most obviously, on-farm storage costs in the
short-run are estimated by on-farm variable storage costs (fourth column
in Tables 4 and 5). Commercial storage costs in the short-run and
long-run can be estimated by commercial storage costs (last column in
Tables 4 and 5). Based on the equilibrium argument made above, on-farm
storage costs in the long-run can also be estimated based on commercial
storage costs. Therefore, in the remainder of this report, reference will
be made only to on-farm variable storage costs and commercial storage
costs.
The calculation of
storage charges may be impacted by an advisory program’s loan
recommendations and/or the decision rules discussed in the previous
section. Specifically, during the period corn or soybeans are placed
under loan, interest costs are not accumulated, as the proceeds from the
loan can be used to offset interest opportunity costs that otherwise would
accumulate. This most commonly occurs after May 31, 2002, when it is
assumed that all un-priced grain, for which loan benefits have not been
collected, is placed under loan until priced.
If a crop is priced (forward contracts, futures or options) while under
loan but stored beyond the time of pricing, interest opportunity costs are
accumulated from the day of pricing until the time storage ceases (since
it is assumed the loan is repaid on the date of pricing).
It could be argued that
interest opportunity costs should be charged based on the LDP available at
harvest but not taken by an advisory program. This adjustment is not made
because it would not substantially impact the results due to the small
interest opportunity costs involved.
Benchmark
Prices
The essential concept underlying
performance evaluation of market advisory programs is fairly simple: the
comparison of the net prices generated by advisory programs with prices
that could have been obtained by a farmer through one or more appropriate
alternative strategies (Sharpe, Alexander and Bailey, 1999, p. 829). The
comparison strategies are commonly referred to as benchmarks because they
serve as objective standards of performance, much like a yardstick
provides an objective measurement of distance. Within this broad
framework, two basic types of performance evaluation can be applied to
market advisory programs. The first type is based on comparison to
“peer-group” benchmarks, whereby net advisory prices are compared to each
other or the average price across all advisory programs. The second type
is based on comparison to “external” benchmarks, whereby net advisory
prices are compared to prices from strategies that do not depend upon
market advisory program behavior. In financial markets, it is commonplace
to compare investment performance to external benchmarks, such as the
Dow-Jones Industrials Index, S&P 500 Index and Wilshire 5000 Index.
The AgMAS study focuses
on performance evaluation using external benchmarks. While peer-group
evaluation provides useful information about the rank of advisory
programs, it cannot answer the question of whether performance of advisory
programs as a group or an individual advisory program is “superior” or
“inferior” in an absolute economic sense. To answer this question,
external benchmarks must be specified based on theories of market pricing.
The first class of
external benchmarks is based on the theory of efficient markets. This
theory assumes that market participants are rational and that competition
instantaneously eliminates all profitable arbitrage opportunities. In its
strongest form, efficient market theory predicts that market prices always
fully reflect available public and private information (Fama, 1970). The
practical implication is that no trading strategy can consistently beat
the return offered by the market (e.g., Brorsen and Anderson, 1994;
Brorsen and Irwin, 1996; Zulauf and Irwin, 1998). Hence, the return
offered by the market becomes the relevant benchmark. In the context of
the AgMAS study, a market benchmark should measure the average price
offered by the market over the marketing window of a representative farmer
who follows advisory program recommendations. The average price is
computed in order to reflect the returns to a naïve, “no-information”
strategy of marketing equal amounts of grain each day during the marketing
window. The difference between advisory prices and the market benchmark
measures the value of advisory service information. The theory of
efficient markets predicts this difference, on average, will equal zero.
If all market
participants are rational in the way efficient market theory assumes, then
the only interesting external benchmarks are market benchmarks. However,
there is growing evidence that many market participants may not be fully
rational in the efficient market sense. Hirshleifer (2001) provides a
comprehensive review of the judgment and decision biases that appear to
affect securities market investors, such as framing effects, mental
accounting, anchoring and overconfidence. He also provides an exhaustive
review of empirical studies that attempt to measure the potential impact
of such biases on securities prices and investment returns. As an
example, Barber and Odean (2000) find that individual stock investors
under-perform the market by an average of one-and-a-half percentage points
per year, an economically significant amount, particularly when viewed
over long investment horizons. They argue that a combination of
overconfidence and excessive trading explains this finding. Brorsen and
Anderson (2001) provide an illuminating discussion of how judgment and
decision biases may impact farm marketing. Finally, new “behavioral”
theories of market pricing have been developed based on the assumption
that market participants are subject to judgment and decision biases
(e.g., Daniel, Hirshleifer and Subrahmanyam, 1998).
Behavioral market theory
suggests that the average return actually achieved by many market
participants may be less than that predicted by efficient market theory,
due to the judgment and decision biases that plague most participants. As
a result, the average return actually received by market participants
becomes an appropriate external benchmark. In the context of the AgMAS
study, a behavioral benchmark should measure the average price actually
received by farmers for a crop. The difference between net advisory
prices and a farmer benchmark should then measure the value of market
advisory service information relative to the information used by farmers.
Behavioral market theory does not predict a specific value for this
difference. It may be positive, negative or zero, depending on the impact
of judgment and decision biases on advisory programs versus farmers.
Finally, it is important to emphasize that the farmer benchmark should be
based on the pricing performance of farmers who do not follow the advisory
programs tracked by the AgMAS Project, otherwise, the value of market
advisory service information relative to the information used by farmers
cannot be “cleanly” disentangled.
It is important to
re-iterate that market and farmer benchmarks convey quite different
information about the performance of market advisory programs, even though
both are forms of a relative benchmark. This should be carefully
considered when making performance comparisons based on the two types of
benchmarks. In addition, there are some desirable properties from a
practical perspective that both types of benchmarks should possess: i)
they should be relatively simple to understand and to calculate; ii) they
should represent the returns to a marketing strategy that can be
implemented by farmers; and iii) they should be directly comparable to net
advisory prices (Good, Irwin and Jackson, 1998).
Market
Benchmarks
As pointed
out in the previous section, a market benchmark is designed to measure the
average price offered by the market to farmers. The appropriate time
period for computing the average price is the marketing window of a farmer
who follows the recommendations of the advisory programs included in the
AgMAS study. This window was defined earlier (see the “Marketing Window”
section) as the 24-month period that begins on September 1st of
the year before harvest and ends on August 31st of the year
after harvest. A 24-month market benchmark is simply computed as the
average price over the two-year marketing window. It should be noted that
this specification of a market benchmark is substantially different than
common practice of using the average harvest price as a market benchmark.
The analysis found later in this section implies that using the average
price during a relatively short time period, such as harvest, may
introduce excessive year-to-year variation in the benchmark.
Figure 6 presents average
marketing profiles for market benchmarks and advisory programs in corn and
soybeans over the 1995-2000 crop years. For comparison purposes, average
marketing profiles for 24- and 20-month market benchmarks are included.
The 20-month benchmark simply deletes the first four months of the
24-month marketing window from the computations of the average market
price. As a result, this benchmark is based on the average price over the
period that begins on January 1 of the year of harvest and ends on August
31 of the year after harvest. For both corn and soybeans, the market
benchmarks appear to provide a surprisingly good “fit” to the average
profile of the advisory programs. More specifically, if a simple linear
trend regression is fit to the average profile of the advisory programs
(not shown), the estimated trend line is remarkably close to the 24-month
benchmark for corn and the 20-month benchmark for soybeans.
The results discussed in
the previous paragraph suggest there is some uncertainty about
specification of the most appropriate market benchmark for corn and
soybean performance evaluations. Leamer (1983) argues persuasively (and
famously) that in this type of situation it is crucial to understand the
“fragility” of results when key assumptions are changed. Consequently,
both a 24-month and a 20-month market benchmark will be used in
comparisons to net advisory prices. Cash forward prices for central
Illinois are used during the pre-harvest period, while daily spot prices
for central Illinois are used for the post-harvest period. The same
forward and spot price series applied to advisory program recommendations
are used to construct both market benchmarks. Details on the forward and
cash price series can be found in the earlier “Prices” section of this
report.
Three adjustments are
made to the daily cash prices to make the 24-month and 20-month average
cash price benchmarks consistent with the calculated net advisory prices
for each marketing program. The first is to take a weighted-average
price, to account for changing yield expectations, instead of taking the
simple average of daily prices. This adjustment is consistent with the
procedure described previously in the "Yields and Harvest Definition"
section. The daily weighting factors for pre-harvest prices are based on
the calculated trend yield, while the weighting of the post-harvest prices
is based on the actual reported yield for central Illinois. The second
adjustment is to compute post-harvest cash prices on a harvest equivalent
basis, which is done by subtracting on-farm variable or commercial storage
costs (physical storage, shrinkage and interest) from post-harvest spot
cash prices. The daily storage charges are calculated in the same manner
as those for net advisory prices. The third adjustment is made with
respect to the loan program. In the context of evaluating advisory program
recommendations, it was argued earlier that a “prudent” or “rational”
farmer would take advantage of the price protection offered by the loan
program, even in the absence of specific advice from an advisory program.
This same logic suggests that a “prudent” or “rational” farmer will take
advantage of the price protection offered by the loan program when
following the benchmark average price strategy. Based on this argument,
the 24-month and 20-month average cash price benchmarks are adjusted by
the addition of LDPs and MLGs. Bushels marketed in the pre-harvest period
according to the benchmark strategy are treated as forward contracts, with
the LDPs assigned at harvest. Bushels marketed each day in the
post-harvest period are awarded the LDP or MLG in existence for that
particular day. Finally, just as in the case with comparable advisory
program recommendations, it is assumed that all un-priced grain on May 31,
2002 is placed under loan. Interest opportunity costs are not charged to
the benchmark after this date if cash prices on the date of loan
redemption are below the CCC loan rate.
While the 24- and
20-month market benchmark prices can obviously differ for a given crop
year, averages of the two benchmark prices across crop years are not
expected to differ substantially. First, the difference in the marketing
windows for the two benchmarks is relatively small, as the 20-month
benchmark reduces the 24-month marketing window by only about 17%.
Second, given a sufficiently large sample of crop years and efficient corn
and soybean markets (cash, futures and options), the law of one price
implies that annual averages of different average price benchmarks should
be equal when stated on a harvest equivalent basis (Brorsen and Anderson,
1994). Of course, if corn and soybean markets are inefficient, the
equivalence would not hold. In particular, if pre-harvest prices contain a
“drought premium” as some argue (e.g., Wisner, Baldwin and Blue, 1998),
then the 24-month benchmark price may be consistently higher or lower than
the 20-month benchmark price, depending on the evolution of the drought
premium.
In contrast to averages,
the variation of 24- and 20-month market benchmark prices across crop
years is expected to differ. The reason for the difference is the
well-known result in statistics that the sampling variation of the mean
(average) is inversely related to the sample size used to compute the
average (e.g., Griffiths, Hill and Judge, 1993, p.82). Since the sample
of daily prices used in computing the 24-month benchmark is larger than
the sample for the 20-month benchmark, the variation of the 24-month
benchmark should be smaller than variation of the 20-month benchmark.
A practical concern with
the market benchmarks is that a farmer may not be able to implement the
benchmark strategies since they involve marketing a small portion of the
crop every day. There are two reasons to believe this concern is not
overly serious. First, a number of companies have developed and offer
grain “index” contracts that allow farmers to receive the average market
price over a pre-specified time interval. An extensive discussion of
these new contracts can be found in the AgMAS Research Report by Hagedorn
et al. (2003). Second, a strategy of routinely selling at less frequent
intervals closely approximates the market benchmark prices. For example,
a farmer might consider alternative “tracking” strategies of marketing
only once a month or once every other month over the 24-month window.
Using mid-month prices, a tracking strategy of
marketing only once a month (24 times) generates average prices over
1995-2001 that are quite close to 24-month market benchmark prices. The
average difference is only three cents per bushel for corn and two cents
per bushel for soybeans, and the maximum difference for any particular
crop year is eight cents per bushel in corn and five cents per bushel in
soybeans. A tracking strategy of marketing once every other month (12
times) also generates average prices over 1995-2001 that are quite close
to 24-month market benchmark prices. The average difference is only three
cents per bushel for corn and five cents per bushel for soybeans.
The average difference
results for the benchmark tracking strategies should not be a surprise
given the previous argument about averages of different benchmark prices
in efficient markets. More surprising is the result that the variation of
the tracking strategies across crop years is only two to four cents per
bushel (four to nine percent) more than the 24-month benchmark over
1995-2001. This is surprising because the tracking strategies are based
on dramatically smaller samples, 12 or 24 observations compared to about
500 observations for the 24-month benchmark, but have only a marginally
higher variation across crop years. The most likely explanation is that
corn and soybean price patterns were dominated by downward trends over the
1995-2001 crop years, and the tracking strategies “captured” this effect
almost as well as the 24-month benchmark because transactions for the
tracking strategies were equally spaced across the entire marketing
window. Further research is needed to fully understand the behavior of
tracking strategies under different price scenarios.
Farmer
Benchmarks
As noted earlier, a farmer benchmark is designed to measure the average
price received by farmers for a crop. This type of benchmark should
reflect the actual behavior of farmers in marketing grain, and include all
of the transactions (e.g., cash, forward, futures and options) that
farmers employ in this regard. In addition, the farmer benchmark should
be based on the pricing performance of farmers who do not follow the
advisory programs tracked by the AgMAS Project. In theory, such a farmer
benchmark should not be difficult to calculate. First, a representative
sample of grain farmers in the relevant geographic area who do not follow
the programs in the AgMAS Project would be drawn (randomly). Next, the
average price received by each farmer would be computed (using the same
assumptions as in the computation of net advisory prices and market
benchmarks). Last, the farmer benchmark would be computed as the
weighted-average price received by all farmers in the sample, with the
weights equal to the sample proportion of the crop produced by each
farmer.
In practice, the detailed type of data needed to construct a
valid farmer benchmark is not available, so an approximation must be
used. The only known approximation is the USDA average price received
series. In Illinois, this series is based on information collected in
monthly mail and telephone surveys of about 200 grain dealers, processors
and elevators that actively purchase grain from farmers (Harden, 2003).
The survey is conducted by the Illinois Agricultural Statistics Service,
the state office for the National Agricultural Statistics Service of the
USDA.
Surveyed firms report total quantities and gross value for grain
purchased directly from farmers (USDA, NASS, 2002). Total quantities are
reported on a dry, or shrunk, basis at the standard moisture content for
the commodity. Total gross value is the value of bushels purchased from
farmers after deducting price discounts and adding premiums for quality
factors and moisture content and adding premiums for direct delivery to
mill, processor, river terminal or rail terminal. Check-off fees and
charges for drying, cleaning, storing or grading are not deducted. The
general principle used to determine the timing of transactions is the
month when grain is purchased, that is, when cash changes hand between the
firm and farmers. Hence, cash sales, forward contracts and deferred
payment contracts are reported for the month of delivery. Basis, minimum
price, option and hedge-to-arrive contracts also are reported for the
month of delivery. Alternatively, delayed pricing contracts are reported
in the month when the grain is priced, which typically occurs after
delivery. The average price received estimate for a month is the total
gross value across all surveyed firms divided by total quantities summed
across all surveyed firms. This estimate may incorporate statistical
adjustments that reflect size differences across reporting firms and other
factors.
The USDA price received series has both strengths and
weaknesses with respect to measuring the average price received by
(unadvised) farmers. On the positive side, the USDA series reflects the
actual pattern of cash grain marketing transactions by farmers, and thus,
incorporates the marketing windows and timing strategies actually used by
farmers; includes forward contract transactions for both the pre-harvest
and post-harvest periods, with the transactions recorded at the forward
price, not the spot price at the time of delivery; and grain sales are
adjusted to industry standards for moisture. On the negative side, the
USDA series is only available in the form of a state average; includes
cash transactions for different grades and quality of grain sold by
farmers; does not include futures and options trading profits/losses of
farmers; reflects a mix of old and new crop sales by farmers; and is based
on the pricing behavior of both unadvised and advised farmers.
Fortunately, none of the problems mentioned above appear to
be prohibitive with respect to the use of the USDA series as a measure of
the average price received by farmers. Consider first the state average
nature of the series. It is straightforward to adjust the USDA series to
an alternative geographic location, since spatial basis patterns are
relatively stable. This type of adjustment turns out not to be necessary
for AgMAS performance evaluations because central
Illinois prices closely
mirror the average price for the entire state of Illinois. Based on an
analysis of weekly prices, the average cash price for central Illinois
over January 1995 - December 2001 differs from the state average price by
about one-half cent and one cent, respectively, for corn and soybeans
(state average lower for both corn and soybeans). The correlation of
weekly prices for central Illinois and the state is 0.96 for corn and 0.99
for soybeans. Hence, from a statistical standpoint, central Illinois and
state average prices are nearly equivalent.
While it is not possible to adjust the USDA series to a
constant grade and quality, to reflect futures and options trading
profits/losses of farmers or to only reflect new crop sales, because the
data simply are not available, the resulting biases probably are small and
some may work in opposite directions. Examining the grade and quality
issue first, it is well known that some fraction of the corn crop is
discounted relative to the standard number two yellow corn grade. This is
also true for the soybean crop relative to the standard number one yellow
soybean grade, but likely to a smaller extent than corn. As a result, the
USDA average price received reflects a weighted-average of both
undiscounted and discounted grain sales. The weights are unknown, but the
direction of the bias relative to average prices for the standard grade is
clearly downward. In other words, when compared to the average price at
the standard grade, the USDA average price received should be adjusted
upwards to reflect the impact of discounts.
A key question, of course, is the magnitude of the grade and
quality bias discussed above. An extensive search of the literature was
conducted and no previous study was uncovered that directly measured the
proportion of corn and soybeans sold at a discount or the average
magnitude of price discounts in central
Illinois (or other
Midwestern US areas). The Federal Grain Inspection Service of the US
Department of Agriculture (FGIS) was contacted and staff indicated that
FGIS does not have an historical series of this type. One older study was
located that contained some information on the issue. Hill, Kunda and
Rehtmeyer (1983) reported the results of a 1982 survey of grain elevator
operators in Illinois.
One question in this survey asked elevator operators to estimate the
percentage of corn and soybean receipts at country elevators that
typically exceed grade factors. Unfortunately, the results were not
netted across grade factors, so it is not possible to estimate the typical
proportion of the crop sold at a discount (if a lot is over one grade
limit it will have a higher than average chance of being over the grade
limit for other factors). In addition, the average magnitude that grade
factors were exceeded is not reported, so it is impossible to estimate the
dollar value of the average discount. Nonetheless, the results provide
some perspective on the quality issue. For corn delivered in the fall,
the percentage typically above a grade factor ranged from 0.2 to 7.5% of
deliveries. For soybeans delivered in the fall, the percentages were
about the same, except for foreign material, where over 30% of the bushels
delivered typically exceeded the grade factor. When winter and summer
delivery was considered, the percentages increased somewhat for corn and
decreased for soybeans. Other than foreign material for soybeans, this
evidence suggests that less than 10% of the corn and soybean crops in the
early 1980s were sold at a discount to the standard grade.
To provide more recent evidence on quality, the nine central
Illinois elevators surveyed annually for commercial storage costs were
queried in December 2001 about the average quality of corn and soybean
crops. The most frequent response from the elevator managers in this
informal survey was that less than one percent of corn and soybeans is
sold at a discount relative to the standard grade. The range was from
zero to less than five percent. The largest estimate of the average
dollar value of discounts was two to three cents per bushel. These
figures provide enough information to make a very rough estimate of
maximum quality bias in the USDA average price received series. Using the
maximum proportion of five percent and the maximum average discount value
of three cents from the informal survey, the downward bias relative to the
standard grade would be only 0.15¢ per bushel (0.05*3). Furthermore, if
the average discount is three cents, then one-third of the crop would have
to be sold at a discount to induce a downward bias even as large as one
cent (0.33*3 = 1). In sum, while the evidence is limited and sketchy, it
does suggest that any downward quality bias in the USDA average price
received series, at least for corn and soybeans in central Illinois, is
quite small.
Now, consider the potential bias from omission of futures and
options profits/losses. If a farmer uses futures and options exclusively
for “pure” hedging purposes, they will consistently take short positions
at about the same points in the marketing window each year.
Unless futures prices are biased upwards or downwards, this type of
hedging will not result in large profits or losses, as the price changes
from upward and downward price trends should roughly offset over time.
If a farmer uses futures and options to engage in “selective” hedging,
they may have large profits or losses related to the timing of trading.
While no direct evidence on the profits or losses of farmers is available
in this context, there is convincing evidence that small traders in
general consistently lose money in futures and options markets.
It seems reasonable to assume that farmers engaged in selective hedging
are similar to other small traders, and hence, selective farmer hedging in
futures and options markets results in aggregate trading losses.
Given that, in aggregate, pure hedging is expected to yield zero profits
on average and selective hedging is expected to yield losses on average,
the net effect of the two types of futures and options trading by farmers
should be negative. In this case, when compared to average prices at the
standard grade, the USDA average price received should be adjusted
downward to reflect the impact of net trading losses.
As before, the key question is the potential magnitude of the
bias from omission of futures and options losses. The key piece of
evidence in this regard is the limited scale of farmer trading in futures
and options markets. Surveys have consistently reported that relatively
few farmers directly use futures and options contracts on a regular basis
(e.g., Patrick, Musser and Eckman, 1998). Given this information, it is
reasonable to argue that the magnitude of farmers’ net losses from futures
and options trading, in aggregate, should be small. As a result, the
upward bias in the USDA average price received from the omission of
futures and options net losses should be small.
Next, consider the potential bias from mixing old crop and
new crop sales during the 12-month marketing year used to compute the USDA
average price received. The first step is to determine the potential
magnitude of the problem. Fortunately, bounds for the “shifting” of old
crop sales into the next marketing year can be computed by dividing ending
stocks for a marketing year by crop production for the same marketing year
(e.g., September 1, 2000 soybean stocks divided by 1999 soybean
production). Over the 1995/1996 through 2001/2002 marketing years,
on-farm ending stocks in Illinois averaged four percent of statewide corn
production and three percent of statewide soybean production. These
percentages are the lower bounds on shifting because farmers presumably
own on-farm stocks and sales of these stocks will be shifted to the next
marketing year. Over the 1995/1996 through 2001/2002 marketing years,
total ending stocks (on-farm and off-farm) in Illinois averaged 13% of
statewide corn production and 8% of statewide soybean production. These
percentages are the upper bounds on shifting; assuming farmers own all of
the stocks in off-farm storage facilities. Clearly, this assumption is
unrealistic, as commercials own some, if not most, of the stocks in
off-farm facilities at the end of a marketing year. The bottom-line is
that shifting of old crop sales into the next marketing year, on average,
is somewhere between 4 and 13% of corn production and 3 and 8% of soybean
production. This suggests the magnitude of shifting from one crop year to
the next probably is not large.
The second step is to determine the impact shifting old crop
sales will have on the USDA average price received. Consider the simplest
case where old crop sales in the next marketing year are made at spot
prices for the new crop and the same proportion is shifted every year.
The same price received would result as in the no shifting case. Only to
the degree that the proportion shifted varies from year-to-year will the
average price received differ from the no-shifting case. The proportion
does vary from year-to-year, but not by a substantial amount. For
example, on-farm ending stocks in Illinois varied from only two to six
percent of corn production over the 1995/1996 through 2001/2002 marketing
years. The impact of this variability on average price received will
depend on farmers’ ability to time shifts to take advantage of favorable
spreads between old crop and new crop prices. If farmers as a group have
timing ability in this context, then the USDA average price received will
be biased upwards relative to the average price at the standard grade.
However, given the difficulty of predicting old crop-new crop price
spreads (Lence and Hayenga, 2001) and the small absolute magnitude of
actual shifting of sales, it seems reasonable to argue that the bias in
average price received from shifting old crop sales across marketing years
is quite small.
The last issue to consider is that the USDA average price
received series reflects the pricing behavior of unadvised and advised
farmers, where advised refers to the programs tracked by the AgMAS
Project. As pointed out earlier, this means it may not be possible to
“cleanly” disentangle the value of market advisory service information
relative to the information used by farmers, as the USDA series already
reflects the impact of market advisory program information to some
degree. A recent national survey of advisory service subscribers by the
AgMAS Project provides some perspective on the dimensions of this problem
(Pennings et al., 2001). While only 11% of the survey respondents said
they followed market advisory service recommendations closely, two-thirds
indicated they followed the recommendations loosely. Further, when asked
to rate the impact of advisory service recommendations on their marketing,
subscribers gave an average rating of six on a nine-point scale, with a
one indicating no impact at all and a nine indicating great impact. To
the extent that farmers subscribe to market advisory services, these
results suggest that the average price received by farmers for a crop is
influenced by the marketing advice of advisory services.
This discussion suggests that a key unknown is the proportion
of farmers that subscribe to advisory services. Unfortunately, this
information is proprietary, so it is not possible to provide exact figures
for the programs tracked by the AgMAS Project. Several studies have
reported survey evidence on the use of advisory services, marketing
newsletters and marketing consultants (defined generically), with
estimates ranging widely from 21.1 percent of Illinois farmers (Norvell
and Lattz, 1998) to 66 percent of farmers nationwide (Smith, 1989). It
is uncertain what these estimates imply for the proportion of farmers that
subscribe to the programs tracked by the AgMAS Project. On one hand, the
programs tracked by the AgMAS Project are among the most popular and
widely-followed. On the other hand, the same programs clearly are a
subset of all advisory services, marketing newsletters and marketing
consultants offered to farmers. While the available evidence is sketchy
and uncertain, it nonetheless does suggest that a non-trivial proportion
of Illinois farmers likely subscribe to the advisory programs tracked by
the AgMAS Project. It therefore can be reasonably concluded that the
average price received by Illinois farmers for corn and soybeans is
impacted to some degree by the information provided by these same
programs. The direction of the resulting bias depends on the pricing
performance of unadvised versus advised farmers. Patrick, Musser and
Eckman (1998) survey large-scale Midwestern grain farmers and find that
farmers using marketing consultants generally received higher prices than
those that did not. While this evidence cannot be generalized to all
farmers because of the skewed size distribution of the sample, it does
nevertheless seem to be a plausible outcome. If it is accepted that
advised farmers outperform unadvised farmers, then the USDA average price
received series will be biased upward relative to the price received by
unadvised farmers. Regrettably, there is nothing that can be done about
this problem without other sources of data on farmer pricing performance.
The USDA average price received is probably best viewed as an estimate of
the upper bound for the average price received by unadvised farmers.
To summarize, the evidence and arguments discussed above
suggest that the net systematic bias in the USDA average price received
due to spatial, quality, futures/options and old/new crop factors is
small, at least for corn and soybeans in central Illinois. It is
difficult to construct a scenario where the overall level of bias from
these factors would materially effect performance evaluation of market
advisory programs. A more difficult problem is presented by the mixture
of unadvised and advised farmers that the USDA average price received
reflects. This “mixing” likely biases the USDA price received series
upward relative to the price received by unadvised farmers. Given the
limited evidence on the extent that Illinois farmers use the programs
tracked by the AgMAS Project and the precise impact of their
recommendations, it is difficult to assess the magnitude of the bias.
Overall, the USDA average price received should be viewed as only an
approximation of the “true” average price received by unadvised farmers.
For this reason, comparison of advisory program pricing performance to a
USDA average price received benchmark is not likely to be as precise as
that offered by the market benchmarks.
Several adjustments are made to the USDA average price
received data for the state of Illinois in order to make the computed
farmer benchmark consistent with net advisory prices. To begin, mid-month
on-farm or commercial storage charges are applied to the monthly average
price received in the 12-month marketing year (September through August).
Next, the annual weighted-average price received is computed using the
percentage of the crop marketed in each calendar month as the weights.
Finally, actual state average LDPs and MLG’s are added for the 1998, 1999,
2000 and 2001 crops.
Given the uncertainties involved in measuring the average
price received by farmers, it would be useful to specify alternative
farmer benchmarks. Unfortunately, as the discussion in this section has
detailed, there simply is no alternative measure that reflects the actual
marketing behavior of farmers. The inability to provide information on
the sensitivity of performance comparisons to alternative farmer
benchmarks is a limitation of the analysis and should be kept in mind when
viewing the results.
Finally, it is interesting to consider arguments about the
expected difference in averages and variation between the farmer benchmark
and the market benchmarks. If corn and soybean markets are efficient and
farmers are rational, then the average price across crop years for the
farmer and market benchmarks should be similar. Under these assumptions,
the variation in farmer benchmark prices across crop years could be
smaller or larger than the variation in market benchmark prices, depending
on the length of the marketing window used by farmers and the exact nature
of the marketing strategies implemented by farmers.
Unfortunately, it is not possible to determine the average
marketing window or the pricing pattern of farmers using USDA monthly
marketing weights. For perspective, average monthly USDA marketing
weights for corn and soybeans in Illinois over 1995-2000 are presented in
Figure 7. These weights reflect the pattern of grain purchases by
commercial facilities from farmers over the 12-month marketing year.
Grain purchases, as defined by the USDA, do not necessarily reflect the
pricing pattern of farmers due to the use of forward pricing instruments.
There is ample survey evidence that many farmers use pre-harvest forward
contracts to price a portion of their crops, and that post-harvest forward
contracts are commonly used, particularly for January delivery (e.g.,
Patrick, Musser and Eckman, 1998; Coble et al., 1999; Pennings et al.,
2001). The evidence on the magnitude of forward contracting by farmers is
more limited.
Two surveys provide the best evidence that is available on
the magnitude of forward contracting, as a large number of farmers are
randomly sampled. The first, by Coble et al. (1999), asked farmers in
four states a number of questions regarding risk management, including the
percent of crop production in 1998 priced before harvest. Based on the
responses reported in the study, it can be estimated that farmers in
Indiana and Nebraska (the closest states to Illinois) priced an average of
15.7% of corn and 14.0% of soybeans pre-harvest. The second is the 2001
Agricultural Resource Management Study (ARMS) by the USDA. This national
survey asked farmers about their use of marketing contracts for the 2001
crop (USDA/NASS, 2003). It was reported that farmers in the Corn Belt
region (Illinois, Indiana, Iowa, Missouri and Ohio) marketed 10.1% of corn
and 9.0% of soybeans through marketing contracts that included forward
contracts, price setting based on grade and yield formulas and pre-harvest
pooling arrangements. Given the broad definition of marketing contracts,
the USDA estimates are upper bounds on the amount of forward pricing for
the 2001 corn and soybean crops. The estimates from the two studies
suggest that the magnitude of forward pricing is modest, but nonetheless,
large enough to make the USDA monthly marketing weights potentially
misleading indicators of the true pricing pattern of farmers. It is also
important to emphasize that the estimates discussed here pertain to only
two crop years and there may be considerable variation in the magnitude of
forward pricing across other crop years. For example, Coble et al. (1999)
also asked farmers how much of their 1999 production they expected
to price before harvest. The responses indicate that farmers in Indiana
and Nebraska expected to price an average of 26.9% of corn and 23.1% of
soybeans pre-harvest in 1999.
A further difficulty is that almost no concrete evidence
exists on the exact length of the typical marketing window of farmers.
The two studies discussed above only investigated the magnitude of
forward pricing, not the timing of such decisions. Without evidence to
the contrary, it seems reasonable to argue that many farmers use a
marketing window not unlike the 24-month and 20-month windows assumed for
the market benchmarks, but the amount of pre-harvest forward pricing is
far less than is assumed for the market benchmarks. The two surveys
suggest that pre-harvest forward pricing by farmers typically is in the
range of 10 to 20%, compared to an average of 53 and 43% for 24-month and
20-month benchmarks, respectively, over 1995-2001. All else equal, this
would lead to the expectation that the variation of farmer benchmark
prices would exceed that for the market benchmarks.
Under rationality, it is still possible for the variation of
farmer benchmark prices to be smaller than for market benchmarks if
farmers employ market-timing strategies that successfully reduce price
variation. Alternatively, if farmers are subject to the same judgment and
decision biases as appears to be the case for participants in other
markets, then it would be reasonable to expect the farmer benchmark to
have a lower average price and higher variation than the market
benchmarks. Which of the above scenarios is correct can only be
determined empirically.
Net Advisory Prices and Benchmarks for 2001
Net
advisory prices and benchmarks for the 2001 corn and soybean crops are
presented in Tables 6 through 11. These results are new and add to the
sample of net advisory prices and benchmarks previously available for
analysis. For a specific example of how marketing recommendations are
translated into a final net advisory price that incorporates the
simulation assumptions, see Jackson, Irwin and Good (1996). It is
important to emphasize that all of the net advisory prices and benchmarks
presented in Tables 6 through 11 are stated on a harvest equivalent basis
using either on-farm variable or commercial storage costs.
Net
advisory prices and benchmarks for corn in 2001 assuming on-farm variable
storage costs are presented in Table 6. In addition, this table shows the
components of the advisory prices and benchmarks. The 2001 average net
advisory price for all 27 corn programs is $2.09 per bushel under the
assumption of on-farm variable costs. It is computed as the unadjusted
cash sales price ($2.04 per bushel) minus storage charges ($0.11 per
bushel) plus futures and options gain ($0.03 per bushel) minus brokerage
costs ($0.01 per bushel) plus LDP/MLG gain ($0.16 per bushel). The range
of net advisory prices for corn in 2001 assuming on-farm variable storage
costs is $1.78 to $2.68 per bushel. Corresponding benchmark prices range
from $2.02 per bushel (20-month average market benchmark) to $2.07 per
bushel (24-month average market benchmark and farmer benchmark).
Net
advisory prices and benchmarks for soybeans in 2001 assuming on-farm
variable storage costs are presented in Table 7. The 2001 average net
advisory price for all 26 soybean programs is $5.50 per bushel under the
assumption of on-farm variable costs. It is computed as the unadjusted
cash sales price ($4.35 per bushel) minus storage charges ($0.11 per
bushel) plus futures and options gain ($0.02 per bushel) minus brokerage
costs ($0.02 per bushel) plus LDP/MLG gain ($1.25 per bushel). The range
of net advisory prices for soybeans in 2001 assuming on-farm variable
storage costs is $4.92 to $5.85 per bushel. Corresponding benchmark
prices range from $5.27 per bushel (20-month average market benchmark) to
$5.63 per bushel (farmer benchmark).
Since many Corn Belt
farmers grow both corn and soybeans, it also is useful to examine a
combination of the results for the corn and soybean marketing programs.
In order to do this, gross revenue is calculated for a central Illinois
farmer who follows both the corn and soybean marketing advice of a given
program. It is assumed that the representative farmer splits acreage
equally (50/50) between corn and soybeans and achieves corn and soybean
yields equal to the actual yield for the area in 2001. The 50/50 advisory
revenues are computed on a per acre basis and compared with the revenue a
central Illinois farmer could have received based on benchmark prices for
both corn and soybeans. Advisory revenue per acre is calculated only for
those programs that offer both corn and soybean marketing advice.
Advisory program revenues and benchmarks in 2001 assuming on-farm variable
storage costs are presented in Table 8. The average revenue achieved by
following both the corn and soybean programs offered by an advisory
program is $296 per acre. The range of 50/50 advisory revenue in 2001
assuming on-farm variable storage costs is $278 to $351 per acre.
Corresponding benchmark revenues range from $285 per acre (20-month
average market benchmark) to $297 per acre (farmer benchmark).
For comparison purposes,
the annual subscription cost of each advisory program also is listed in
the last column of Table 8. Subscription costs average $353 per program,
a level that does not appear to be large relative to total farm revenue,
whether a large or small farm is considered. For a 2,000 acre farm,
subscription costs average less than one-tenth of one percent of total
advisory revenue. For a 500 acre farm, subscription costs average about
two-tenths of one percent of total advisory revenue.
Net advisory prices and
benchmarks for corn in 2001 assuming commercial storage costs are
presented in Table 9. The 2001 average net advisory price for all 27 corn
programs is $1.99 per bushel under the assumption of commercial storage
costs. It is computed as the unadjusted cash sales price ($2.04 per
bushel) minus storage charges ($0.23 per bushel) plus futures and options
gain ($0.03 per bushel) minus brokerage costs ($0.01 per bushel) plus LDP/MLG
gain ($0.16 per bushel). The range of net advisory prices for corn in
2001 assuming commercial storage costs is $1.61 to $2.48 per bushel.
Corresponding benchmark prices range from $1.94 per bushel (20-month
average market benchmark) to $2.00 per bushel (24-month average market
benchmark).
Net
advisory prices and benchmarks for soybeans in 2001 assuming commercial
storage costs are presented in Table 10. The 2001 average net advisory
price for all 26 soybean programs is $5.45 per bushel under the assumption
of commercial storage costs. It is computed as the unadjusted cash sales
price ($4.35 per bushel) minus storage charges ($0.16 per bushel) plus
futures and options gain ($0.02 per bushel) minus brokerage costs ($0.02
per bushel) plus LDP/MLG gain ($1.25 per bushel). The range of net
advisory prices for soybeans in 2001 assuming commercial storage costs is
$4.89 to $5.82 per bushel. Corresponding benchmark prices range from
$5.21 per bushel (20-month average market benchmark) to $5.55 per bushel
(farmer benchmark).
Advisory program revenues and benchmarks in 2001 assuming commercial
storage costs are presented in Table 11. The average revenue achieved by
following both the corn and soybean programs offered by an advisory
program is $287 per acre when commercial storage costs are assumed. The
range of 50/50 advisory revenue in 2001 assuming commercial storage costs
is $264 to $334 per acre. Corresponding benchmark revenues range from
$277 per acre (20-month average market benchmark) to $286 per acre (farmer
benchmark).
Figures 8 and 9 show the pattern of corn prices for the 2001
crop year based on on-farm variable and commercial storage costs,
respectively. The top chart shows daily cash prices from September 1,
2000 through August 31, 2002. The pre-harvest prices are the cash forward
contract prices for harvest delivery. The middle chart is a repeat of the
top chart with daily LDP or MLG added to the daily price. For the
pre-harvest period, the LDP is the average LDP available at harvest time.
The third chart offers a different perspective, in that post-harvest daily
cash prices are adjusted for cumulative storage costs (interest, physical
storage and shrinkage charges). The chart illustrates the pattern of
harvest equivalent prices plus LDP or MLG.
Pre-harvest corn prices for the 2001 crop year are above the
CCC loan rate most of the time. Prices decline into harvest as average
yields and total production exceed expectations, but make a significant
post-harvest recovery in August 2002 when prices reach $2.50 per bushel.
The price pattern for the 2001 crop year is typical of a large crop year
followed by a year of weather-reduced production.
Figures 10 and 11 show the pattern of soybean prices for the
2001 crop year based on on-farm variable and commercial storage costs,
respectively. The three charts are the same as for corn, depicting daily
cash prices, cash prices plus LDP/MLG and cash prices plus LDP/MLG minus
storage charges. Soybean prices for the 2001 crop follow a similar
pattern to that for corn. Cash prices are more volatile in the
pre-harvest period, maintaining below the CCC loan rate all the
pre-harvest and most of the post-harvest seasons. Prices rallied
beginning in May 2002 and peaked at $5.90 per bushel in the middle of
July. That is the only time the cash price is above the loan rate in the
entire crop year. The price pattern for the 2001 crop year reflects dual
harvest periods, the US in the fall months and South America in the spring
months, and the impact of weather-reduced US production in 2002. The
largest LDPs/MLGs occur during the harvest season of the 2001 crop.
Net Advisory Prices and Benchmarks for 1995-2001
Net advisory
prices, revenue and benchmarks for the 2000-2001 crop years, assuming
on-farm variable storage costs, are reported in Tables 12 through 14.
Results are not presented for earlier crop years because the AgMAS
Project first computed net advisory prices and benchmarks under on-farm
variable storage costs for the 2000 crop year. Net advisory prices,
revenue and benchmarks for the 1995-2001 crop years, assuming commercial
storage costs, are reported in Tables 15 through 17. In both sets of
results, please note that some of the market advisory programs included in
the tables are not evaluated for all crop years. Finally, in order to
obtain a consistent set of net advisory prices and benchmarks for the
entire sample period, the following discussion focuses on the net advisory
prices, revenue and benchmarks where commercial storage costs are assumed.
Table 15 shows the
average advisory price for corn ranges between $1.99 per bushel in 2001
and $3.03 per bushel in 1995 (based on commercial storage costs). Range
statistics reveal that net advisory prices for corn vary substantially
within individual crop years. The most dramatic example is 1995, where
the minimum is $2.29 per bushel and the maximum is $3.90 per bushel. Even
in years with less market price volatility, it is not unusual for the
range of prices across advisory programs to be near a dollar per bushel.
The three alternative benchmark prices for corn are shown at the bottom of
Table 15. The variation in benchmark prices from year-to-year is similar
to that of average net advisory prices. However, there can be substantial
differences in benchmark prices for a particular crop year. For example,
the 24-month market benchmark in 1998 is $2.24 per bushel, while the
farmer benchmark is only $1.97 per bushel. These data suggest performance
results for corn may be sensitive to the selected benchmark.
As reported in Table 16,
the average advisory price for soybeans ranged from $5.44 per bushel in
2000 to $7.27 per bushel in 1996 (based on commercial storage costs).
Similar to corn, the range of individual net advisory prices within a crop
year is substantial. The most dramatic example is 1999, where the range
in advisory prices approaches $2.50 per bushel. The three alternative
benchmark prices for soybeans are shown at the bottom of Table 16. The
variation in soybean benchmark prices from year-to-year is similar to that
of average net advisory prices. Once again, there can be substantial
differences in benchmark prices for a particular crop year.
Table 17 contains the
combined corn and soybeans revenue results (based on commercial storage
costs). The lowest average advisory revenue, $287 per acre, occurred in
2001, while the highest average advisory revenue, $369 per acre, occurred
in 1996. Given the results for corn and soybeans, the large range of
individual advisory revenues within a crop year is not surprising.
Nonetheless, it is startling to see the possible economic impact of
following the best versus the worst performer in a given crop year. For
example, in three of the seven crop years (1995, 1999 and 2000) the range
in advisory revenue exceeds $100 per acre.
For the reader’s
convenience, Tables 18 through 20 report the most recent two-year averages
(2000-2001), three-year averages (1999-2001), four-year averages
(1998-2001), five-year averages (1997-2001), six-year
averages (1996-2001)and seven-year averages (1995-2001) of net
advisory prices, revenues and benchmarks (based on commercial storage
costs).
The averages are computed in these tables only for
the advisory programs active in each of the indicated crop years. The
reported averages may reflect survivorship bias as a result of this
assumption, which should be considered when viewing the averages.
Finally, note that the average, minimum and maximum reported for each
column in the Tables 18 through 20 are computed across the advisory
program averages in each column.
Information on the
sources of the differences between net advisory prices and benchmarks in
corn and soybeans is found in Table 21. Panel A shows average net
advisory prices and benchmarks broken out by component. Panel B presents
the average difference in the components between advisory programs and the
benchmarks. All of the averages in the table assume commercial storage
costs. In cases where the average net advisory price is above the average
benchmark price (e.g., net advisory price in corn versus the farmer
benchmark) the difference is largely explained by the higher net cash
sales price of advisory programs. The average net futures and options
gain of advisory programs is relatively small, as is the difference in
LDP/MLGs between advisors and the benchmarks.
Performance Evaluation Results for 1995-2001
Four basic indicators of
performance are applied to advisory program prices and revenues over
1995-2001. The first indicator is the proportion of advisory programs
that beat benchmark prices. A valuable feature of this directional
indicator is that it is not influenced by extremely high or low advisory
prices. The second indicator is the difference between the average price
of advisory programs and benchmarks. This indicator is useful because it
takes into account both the direction and magnitude of differences from
benchmark prices. The third indicator is the average price and risk of
advisory programs relative to the average price and risk of the
benchmarks. Evaluations based on this indicator are important because
risk is incorporated into the performance comparisons. The fourth
indicator is the predictability of advisory program performance from
year-to-year. This indicator provides information on the value of past
pricing performance in predicting future performance.
Before considering the
performance evaluation results, two important issues need to be
discussed. First, the results presented in this section of the report
address the performance of market advisory programs as a group. In
other words, average pricing performance across all programs is
considered. This is a different issue than the pricing performance of a
particular advisory program.
Simply put, it is inappropriate to make performance inferences for an
individual advisory program based on aggregate results. Second, farmers
subscribe to market advisory programs for a variety of reasons. For
example, Pennings et al. (2001) survey farmer-subscribers and find that
the two highest rated uses of market advisory programs are marketing
information and market analysis. While the quality of marketing
information and market analysis is likely to be positively correlated with
the returns to marketing recommendations, this does not necessarily have
to be the case. It is possible that advisory programs provide valuable
information and analysis to farmer-subscribers, yet fail to exhibit
superior pricing performance.
Directional
Performance
The first, and simplest,
indicator of pricing performance is the proportion of advisory programs
that beat the market or farmer benchmarks. Positive performance is
indicated if the proportion of advisory programs beating a benchmark
exceeds 50%, the proportion one would observe if advisory performance is
random, like flipping a fair coin. A noteworthy feature of this
“directional” indicator is that it is not influenced by extremely high or
low advisory prices or revenue.
The proportion of
advisory programs in corn, soybeans and 50/50 advisory revenue above the
benchmarks over 1995-2001 is presented in Table 22. Note that average
proportions for 1995-2001 are computed over the full set of advisory
programs, and therefore, do not necessarily equal the average of the
individual crop year proportions. This “grand” average equally weights
each of the net advisory prices or revenues in the sample, whereas an
average of the individual crop year averages would equally weight the crop
years. The first average is preferred for the present purpose as it
implies an equal probability of selecting an individual advisory program
across the entire sample.
Considering corn first
(Panel A: Table 22), there is some variation in the proportion of net
advisory prices above the two market benchmarks for individual crop years,
particularly 1998, but the patterns are similar overall. There also does
not appear to be any discernable trend in the proportions for either
benchmark over the seven crop years. The average proportion for 1995-2001
is 49% versus the 24-month benchmark and 60% versus the 20-month
benchmark, indicating a zero to marginal chance of advisory prices in corn
beating market benchmark prices. In contrast, the proportion of net
advisory prices above the farmer benchmark exceeds 50% each crop year.
The average proportion above the farmer benchmark over 1995-2001 is 73%.
This is substantially higher than the average proportions versus the
market benchmarks and indicates a sizeable chance of market advisory
programs generating net prices higher than the farmer benchmark.
Moving to soybeans (Panel
B: Table 22), there is more variation in the proportion of net advisory
prices above the two market benchmarks for individual crop years.
Particularly sharp differences are observed in 1998 and 1999, where the
spread between the proportions is between 26 and 45 percentage points. No
clear trend is apparent for the proportions versus either market
benchmark. Despite these differences for individual crop years, the
average proportions for 1995-2001, 63% versus the 24-month benchmark and
74% versus the 20-month benchmark, both indicate a better than average
chance of advisory prices beating market benchmark prices in soybeans.
The proportions above the farmer benchmark are all above 50%, except the
2001 crop when only 27% of the programs were able to beat the farmer
benchmark. The average proportion above the farmer benchmark over
1995-2001 is 67%. This indicates a reasonable chance of market advisory
programs generating net prices in soybeans higher than the farmer
benchmark.
Given the combined nature
of 50/50 advisory revenue, it is not surprising that revenue proportions
(Panel C: Table 22) typically are between those of corn and soybeans. The
average proportion for 1995-2001 is 56% versus the 24-month benchmark and
70% versus the 20-month benchmark, indicating a marginal to better than
average chance of advisory revenue beating market benchmark revenue. The
proportion of advisory revenues above the farmer benchmark exceeds 50%
each crop year, except for 2001, and averages 71% over 1995-2001. This
indicates a sizable chance of advisory revenue beating farmer benchmark
revenue. It is interesting to note that 100% of the advisory programs in
1998 generated revenue that exceeded the farmer benchmark, despite the
fact that less than 100% did so in corn and soybeans. This simply
reflects a situation where some programs had gains above the farm
benchmark in one commodity that more than offset the losses below the
benchmark in the other commodity.
Overall, the directional
performance results over 1995-2001 suggest several key findings. First,
advisory programs in corn do not consistently beat market benchmarks, but
they do consistently beat the farmer benchmark. Second, advisory programs
in soybeans tend to beat both market and farmer benchmarks. Third, in
terms of 50/50 revenue, advisory programs only marginally beat market
benchmarks, but consistently beat the farmer benchmark. So, the results
provide mixed performance evidence with respect to market benchmarks and
consistently positive evidence with respect to the farmer benchmark.
Finally, it is interesting to compare the directional pricing
performance results for market advisory programs to that of other
investment professionals. Malkiel (1999) reports a typical estimate of
the proportion of active mutual funds managers that beat the stock
market. Specifically, he shows that only 33% of active mutual fund
managers generate returns higher than the S&P 500 stock index over
1974-1998. By comparison, market advisory programs perform better, with
about half of the programs beating the market in corn and about two-thirds
beating the market in soybeans. This divergence may simply reflect a
unique time period in corn and soybean markets, relatively less efficient
commodity markets, the skill of advisory programs, or a return to risk.
Average
Price Performance
The second
indicator of pricing performance is the difference between the average
price of advisory programs and the market or farmer benchmarks. This
indicator takes into account both the direction and magnitude of
differences from the benchmarks. The results found in Tables 23 and 24
basically tell the same story as those based on the proportion beating the
benchmarks. Average differences from market benchmarks for corn over
1995-2001 (panel A: Table 23) are small, ranging from zero to three cents
per bushel.
At 10¢ cents per bushel, the average difference from the farmer benchmark
for corn is larger. Average differences for soybeans over 1995-2001
(panel B: Table 20) are even larger for both types of benchmarks, ranging
from 11 to 18¢ per bushel versus market benchmarks and 17¢ per bushel
versus the farmer benchmark. Average differences for 50/50 advisory
revenue range from three to seven dollars per acre for market benchmarks
over 1995-2001 (Table 24). The average revenue difference versus the
farmer benchmark is $12 per acre. Note that the average differences can
mask considerable variability across the benchmarks within a crop year and
across crop years. A dramatic example of this occurred in 1998 for
soybeans (Panel B: Table 23), where the average difference from the
24-month market benchmark is –4¢ per bushel, while the average difference
from the farmer benchmark is +64¢ per bushel.
It should be pointed out
that average differences versus the farmer benchmark appear to be
non-trivial from an economic decision-making perspective. For example,
the average advisory return relative to the farmer benchmark ($12 per
acre) is nearly four percent of average farmer benchmark revenue. This
represents a substantial increase in net farm income (defined as returns
to farm operator management, labor and capital), typically about $50 per
acre for grain farms in Illinois (Lattz, Cagley and Raab, 2002). The
comparison does not account for yearly subscription costs, which is not a
major problem because subscription costs are quite small relative to
revenue. As noted earlier, subscription costs are less than one-tenth of
one percent of average farmer benchmark revenue for a 2,000 acre farm and
about two-tenths of one percent for a 500 acre farm. A more serious issue
is fully accounting for the cost of implementing, monitoring and managing
the marketing strategies recommended by advisory programs. Such costs are
difficult to measure, but may well be substantial (Tomek and Peterson,
2001).
At this juncture,
the findings should be considered only suggestive. The reason is that the
statistical significance of the results has not been investigated. In
other words, are the returns to marketing advice simply the result of
random chance or do they reflect truly positive pricing performance? A
number of different statistical tests can be used to determine the
significance of observed differences in sample means. In the present
context, it is critical to recognize that there is a “natural” pairing in
the sample data that can be used to increase the power of statistical
tests (Snedecor and Cochran, 1989). More specifically, net advisory
prices and benchmark prices for the same crop year are paired, in the
sense that the same crop year receives different “treatments” from
advisory programs and benchmarks. The treatments correspond to the
differing marketing strategies used by advisory programs and benchmarks.
Given that the sample data are paired, the appropriate test of the null
hypothesis of zero difference between the mean of net advisory and
benchmark prices is the paired t-test.
Application of
the paired t-test to average pricing performance is complicated by the
fact that net prices across programs are positively related. This type of
statistical test assumes that sample differences are generated
independently (Snedecor and Cochran, 1989, pp. 101).[51]
It should come as no surprise that this assumption is violated for market
advisory programs. Many of the programs appear to use similar methods of
analysis and all make heavy use of similar supply and demand information
(primarily from the USDA). Furthermore, alternative programs offered by
the same advisory service are likely to generate similar pricing results.
Statisticians call this an “implicit factor” problem.
Correlation
coefficients estimated across net advisory prices most directly provide
evidence on the magnitude of the dependence problem. However, the sample
is not large enough to independently estimate all possible pair-wise
correlations.[52]
Useful evidence can be generated by estimating “market model” regressions
for each commodity. This entails simply regressing net advisory prices
(or revenue) for a given program on a market benchmark. If net advisory
prices share a common “market factor” the explanatory power of the
regressions will be high. In order to maximize the number of time-series
observations available for each program, the sample for this analysis is
limited to the 15 programs active in all seven crop years. The
explanatory power of the market model regressions turns out to be quite
substantial, with an average  of 0.79 in corn, 0.82 in soybeans and 0.72 for revenue,
and the regressions all have positive slope estimates.[53]
The high level of
dependence across net advisory prices and revenue basically creates an
information problem in the sample. Take the case of corn. There are 179
computed net advisory prices across all programs and crop years. However,
the 179 net advisory prices are not independent, due to the strong
positive correlation across programs. The key question is the amount of
independent information contained in the sample of 179 net advisory
prices. It is not possible to precisely estimate the true number of
independent observations, but it is certainly far less than 179. Similar
logic holds for soybeans and 50/50 advisory revenue.
The bottom-line
from this discussion is that an assumption of independence for advisory
prices and revenue will overstate the reliability of sample estimates.
This in turn will bias statistical tests towards a conclusion that pricing
performance is significantly positive. The approach taken here to deal
with the problem is “conservative.” Specifically, statistical tests
assume the minimum possible number of independent observations in the
sample. This minimum is six observations, one for each crop year. The
tests are conservative since conclusions are based on the minimal possible
assumption about the amount of information in the sample. If test results
based on this conservative assumption indicate statistical significance,
then a high degree of confidence can be placed on conclusions. The cost
of this approach is an increased probability that positive pricing
performance is mistakenly attributed to chance.
Implementing the
conservative testing approach is straightforward.[54]
First, the average net advisory price or revenue is computed across all
programs active in a crop year, and it is considered the return for an
“average” advisory program. Second, the averaging process is repeated for
each of the crop years to form a sample of seven observations for the
average advisory program. These averages can be found in Tables 15
through 17 under the “Descriptive Statistics” heading. Third, benchmark
prices are subtracted from each of the average advisory prices or
revenues. Fourth, a paired t-test is applied to the seven difference
observations to determine if average price performance is statistically
significant.
Differences from
the benchmarks each crop year and statistical test results for an average
advisory program are presented in Table 25. Note that the average
differences reported in Table 25 are nearly identical to those reported in
Tables 23 and 24. This outcome is not surprising. The average
differences in Table 25 assume an equal weighting of the seven crop years,
while the average differences in Tables 23 and 24 assume an equal
weighting of each net advisory price or revenue in the sample. The two
types of averages differ only because the number of advisory programs
changes across crop years. Since this change is quite small across crop
years, the difference in the two types of averages is negligible.
The impact of the
conservative approach to testing the significance of average differences
is reflected in the standard error estimates. This statistic measures the
“typical” error, without regard to sign, in estimating the average
difference between advisory programs and a particular benchmark (Mirer,
1995, p. 238).[55]
For example, the standard error estimate for the average difference in
soybeans versus the 24-month market benchmark indicates that the typical
error in estimating the true difference, without regard to sign, is five
cents per bushel. A measure of reliability is needed because a sample is
being used to make an inference about the “true” population difference,
and the sample will not perfectly reflect the characteristics of the
population. This is the essence of the role of random chance in
estimation. The key point in this regard is that standard error estimates
vary inversely with sample size.[56]
As a result, standard error estimates (typical estimation errors) will be
much larger if it is assumed that seven independent observations are
available as opposed to, say, 179 independent observations.
With this
background, the statistical test results in Table 25 can be considered.
The relevant information in the sample for testing statistical
significance is summarized by the t-statistic, which is just the ratio of
the average difference estimate to the standard error estimate. The
two-tail p-value indicates the probability of observing a value of the
t-statistic (or higher in absolute value) across many random samples. It
is usually argued that p-values must be equal to or smaller than 0.05 to
confidently conclude that average differences do not equal zero
(Griffiths, Hill and Judge, 1993, p. 134). Stated differently, there
should be less than a 1 out of 20 chance that the wrong conclusion is
reached. In corn, the p-values for average differences versus both market
benchmarks are substantially larger than 0.05, so it can be concluded that
average differences are insignificantly different from zero. Just the
opposite conclusion is reached versus the farmer benchmark. The p-value
of 0.02 indicates the average difference of 10¢ per bushel in corn is
highly significant. In soybeans, the p-values for average differences
versus both market benchmarks are smaller than 0.05, so it can be
concluded that average differences are significantly different from zero.
In contrast to the results for corn, the average difference of 18¢ per
bushel in soybeans versus the farmer benchmark is insignificantly
different from zero, although the p-value indicates the difference is
marginally significant. Test results for 50/50 advisory revenue show
mixed results. With the market benchmarks, results show statistical
significance for the average difference from the 20-month benchmark, but
not from the 24-month benchmark. The average difference of $12 per acre
versus the farmer benchmark is significantly different from zero.
Overall, the test results with respect to market benchmarks indicate no
evidence of statistically significant average price performance in corn,
consistent evidence of significant performance in soybeans and mixed
evidence for 50/50 advisory revenue. The test results with respect to the
farmer benchmark indicate statistically significant performance in corn,
marginally significant performance in soybeans and significant performance
for 50/50 advisory revenue.
When viewing
statistical test results, it is always important to assess whether the
nature of the sample information or the comparisons bias the results in
one direction or the other. There is in fact a systematic trend in corn
and soybean price movements during the sample period that has an important
impact on the tests results. Figure 12 shows the average pattern of corn
and soybean prices over the 24-month marketing window for the 1995-2001
crop years. These charts are based on the same harvest equivalent forward
and spot cash prices (including LDP/MLGs) used to compute net advisory
prices and the market benchmarks. The downward trend in corn and soybean
prices over the 24-month window is substantial, with pre-harvest highs in
corn and soybean prices about 60¢ and 80¢ per bushel, respectively, higher
than post-harvest lows. A marketing strategy that systematically priced
more heavily in the pre-harvest period relative to the post-harvest period
would have generated much higher returns than a strategy that did not.
Now consider the
average marketing profiles for corn and soybeans shown in Figure 13. The
market benchmark and advisory program profiles were presented earlier in
Figure 6 and the USDA marketing weights were presented in Figure 7. As
noted earlier in the “Farmer Benchmark” section, USDA marketing weights
represent grain purchases, which are not necessarily the same as pricing
weights due to farmers’ use of forward contracts. Only a hypothetical
marketing profile for farmers is presented (labeled “Farmers ?”) as a
result. It is based on a similar marketing window as the market
benchmarks and advisory programs, but reflects substantially less pricing
in the pre-harvest period.[57]
In light of the downward price trends, the marketing profiles make it is
easy to understand why market benchmarks and advisory programs generated
higher average prices than the farmer benchmark over the last six crop
years.
The key question
is whether the price trends and marketing patterns of the last seven years
provide a reliable picture of the future. Scenario analysis is helpful in
illustrating the range of possible outcomes. Consider first a scenario
where future upward price trends offset the downward price movements of
the last seven crop years and advisors and farmers do not significantly
change their marketing behavior. Future performance results under this
scenario will be just the opposite of those for the last seven crop years
because farmers will benefit relatively more than advisors from the upward
price trends. Of course, it is possible for advisory programs to
outperform farmers in an environment of rising prices if they time
strategy changes better than farmers. Consider an alternative scenario
where downward price trends continue to be the norm and advisors and
farmers do not significantly change their marketing behavior. Future
performance results basically will be the same as those observed over the
1995-2001 sample period. Farmers could equal the performance of advisors
under a downward price trend scenario if they systematically increase
pre-harvest pricing. These scenarios show that future performance
differences could range from complete reversal to no change, depending on
future price trends.
In sum, pricing
performance depends on a complex set of variables that include corn and
soybean price behavior, advisory program strategies and the marketing
behavior of farmers. It is on open question whether the behavior of these
variables in the last seven crop years provides a reliable guide for the
future. The persistence of downward price trends generally observed over
1995-2001 is an especially hotly debated issue. While the results clearly
provide some evidence on the pricing performance of advisory programs,
there is simply no replacement for a larger sample of crop years when
attempting to reach firm conclusions. In particular, more observations
are needed on crop years with rising prices. Longer-term evidence on the
performance of farmers versus the market would also be helpful.
Average
Price and Risk Performance
Comparison of
average advisory prices or revenues to benchmarks is an important
indicator of performance. However, average price or revenue comparisons
may not provide a complete picture of performance. For example, two
advisory programs can generate the same average advisory price, but the
risk of the programs may differ substantially. The difference in risk may
be the result of using different pricing tools (cash, forward, futures or
options), different timing of sales and variation in the implementation of
marketing strategies.
A number of
theoretical frameworks have been developed to analyze decision-making
under risk. One of the simplest and most popular is the mean-variance (EV)
model, which uses variance as a measure of risk. The basic idea in this
case is to look at risk as the chance farmers will fail to achieve the net
price they expect based on following an advisory program. This approach
to quantifying risk does not measure the possibility of loss alone. Risk
is seen as uncertainty: the likelihood that what is expected will fail to
happen, whether the outcome is better or worse than expected. So an
unexpected return on the upside or the downside – a net price of $2.50 or
$1.50 per bushel when a net price of $2.00 per bushel is expected – counts
in determining the risk of an advisory program. Thus, an advisory program
whose net price does not depart much from its expected (mean) price is
said to carry little risk. In contrast, an advisory program whose net
price is quite volatile from year-to-year, often departing from expected
net price, is said to be quite risky.
To apply the EV model to
a particular decision, either distributions of outcomes must be normal or
decision-makers must have quadratic utility functions (Hardaker, Huirne
and Anderson, 1997, p.141). If either or both of these conditions hold,
then risky choices can be divided into efficient and inefficient sets
based on the famous EV efficiency rule: if the mean of choice A is greater
than or equal to the mean of choice B and the variance of A is less than
or equal to the variance of B, with at least one strict inequality
holding, then A is preferred to B by all risk-averse decision makers.
Since quadratic utility has the unlikely characteristic that absolute risk
aversion increases with the level of the outcome, application of the EV
model usually is based upon an assumption of normally distributed
outcomes. This presents a potential problem in the case of market
advisory programs that employ options strategies. Such strategies are
designed to create non-normal price distributions by truncating
undesirable prices, either on the downside or the upside, or both.
Fortunately, simulation analysis suggests that the EV model produces
reasonably accurate results even in cases where options strategies are
employed (Hanson and Ladd, 1991; Ladd and Hanson, 1991; Garcia, Adam and
Hauser, 1994).
The basic data needed for
assessing market advisory pricing performance in an EV framework are
presented in Table 26. For each of the 15 advisory programs tracked in
all seven crop years of the AgMAS study, the seven-year average net
advisory price or revenue and standard deviation of net advisory price or
revenue is reported. The average price and standard deviation of the
three benchmarks also are reported. Standard deviation is substituted for
variance as the measure of risk because it easier to understand.
Performance results are the same whether standard deviation or variance is
used to measure risk (Hardaker, Huirne and Anderson, 1997, p.143), hence
the use of the simpler measure. Standard deviation estimates can be
thought of as the “typical” variation in net advisory prices from
year-to-year. The larger the standard deviation for an advisory program,
the less likely a farmer is to get exactly the net price expected, though
it is possible by chance to get a higher price instead of a lower one for
any particular time period.
The sample of advisory
programs for the EV analysis is limited to those which are tracked all
seven crop years in order to maximize the number of observations available
to estimate risk (standard deviation).
Even with this restriction, seven observations would appear to be a
relatively small sample for estimating the risks of market advisory
programs. However, as noted in the introduction, Anderson (1974) explored
the reliability of agricultural return-risk estimates based on limited
data and found the surprising result that even as few as three or four
observations can be very useful. Nonetheless, the standard deviations
reported in Table 26 may be somewhat inaccurate estimators of the true
risks of advisory programs. With that in mind, the standard deviations
suggest that the risk of advisory programs varies substantially. In corn,
the standard deviations range from a low of $0.20 per bushel to a high of
$0.75 per bushel. In soybeans, the standard deviations range from a low
of $0.50 per bushel to a high of $0.96 per bushel. Finally, revenue
standard deviations for the 15 programs range from a low of $17 per acre
to a high of $53 per acre. Standard deviations of the benchmark prices
tend to be near the average standard deviation of the 15 advisory programs
for corn, soybeans and 50/50 advisory revenue.
The average price and
risk (standard deviation) for individual advisory programs and the
benchmarks are plotted in Figures 14 through 16. Panel A in each of the
figures is divided into four quadrants based on the average price (or
revenue) and standard deviation of the 24-month market benchmark, while
panel B is divided into four quadrants based on the average price (or
revenue) and standard deviation of the farmer benchmark. Advisory
programs in the upper left quadrant of each chart have a higher average
price (or revenue) and less risk than the benchmark, which is the most
desirable outcome from a farmer’s perspective. According to the EV
efficiency rule introduced earlier, advisory programs in this quadrant are
said to “dominate” the 24-month market benchmark or the farmer benchmark.
Advisory programs in the lower right quadrant have a lower price and more
risk than the benchmark, which is the least desirable outcome from a
farmer’s perspective. The 24-month market benchmark or the farmer
benchmark dominates an advisory program located in this quadrant. The two
remaining quadrants reflect a higher price and more risk than the
benchmarks or a lower price and less risk than the benchmarks. A farmer
may prefer an advisory program to the benchmark in either of these two
quadrants, but this depends on personal preference for risk relative to
average price.
The data plotted in panel A of Figure 14 show
that only 1 of the 15 advisory programs in corn dominates the 24-month
market benchmark (upper left quadrant). Six advisory programs are
dominated by the 24-month market benchmark (lower right quadrant). In
contrast, panel B in Figure 14 indicates stronger performance, with 9 of
the 15 advisory programs in corn dominating the farmer benchmark (upper
left quadrant). Only one program in corn is dominated by the farmer
benchmark (lower right quadrant).
The data plotted in panel
A of Figure 15 indicate that 4 of the 15 advisory programs in soybeans
dominate the 24-month market benchmark (upper left quadrant), while only
three advisory programs are dominated by this market benchmark (lower
right quadrant). Panel B in Figure 15 again suggests stronger
performance, with 10 of the 15 advisory programs dominating the farmer
benchmark (upper left quadrant). Only one program in soybeans is
dominated by the farmer benchmark (lower right quadrant).
Similar patterns are
evident for 50/50 advisory revenue. Panel A of Figure 16 shows that in
terms of revenue only 2 of the 15 advisory programs dominates the 24-month
market benchmark (upper left quadrant), while 6 of the 15 are dominated by
this market benchmark (lower right quadrant). Panel B in Figure 16 shows
that 6 of the 15 programs dominate the farmer benchmark (upper left
quadrant) and no program is dominated by the farmer benchmark (lower right
quadrant).
A key motivation for this
analysis is to determine whether consideration of risk alters performance
conclusions based only upon average price. This is most easily assessed
by comparing the proportion of advisory programs that beat the benchmarks
in terms of price in Table 22 with the proportion of programs that
dominate the benchmarks in terms of average price and risk (upper left
quadrant proportions in Figures 14-16). For corn, 49% of the advisory
programs beat the 24-month market benchmark based on price alone over
1995-2001. This drops to 7% when risk is considered. The same
proportions for the farmer benchmark in corn drop from 73 to 60%. For
soybeans, 63% of the advisory programs beat the 24-month market benchmark
based on price alone over 1995-2001, while only 27% do so when risk is
considered. The proportion for the farmer benchmark in soybeans is
unchanged, at 67%, when risk is considered. For 50/50 advisory revenue,
56% of the advisory programs beat the 24-month market benchmark based on
revenue alone over 1995-2001 and only 13% doing so when risk is
considered. The proportions for the farmer benchmark in terms of advisory
revenue decrease from 71 to 40%. Overall, the results indicate that
consideration of risk tends to weaken conclusions about the performance of
advisory programs.
Two other issues with
respect to risk need to be considered. The first is the sensitivity of EV
comparisons to the alternative market benchmarks. Comparing the results
for the 24-month and 20-month market benchmarks, the proportion of
programs in the upper-left quadrant increases from 7 to 40% for corn
(panel A: Figure 14), from 27 to 60% for soybeans (panel A: Figure 15) and
from 13 to 40% for 50/50 advisory revenue (panel A: Figure 16). These
comparisons suggest EV performance results are somewhat sensitive to
changing the market benchmark specification. Nonetheless, the qualitative
implications of the EV comparisons are similar for the two market
benchmarks. The second issue is the statistical significance of EV
performance differences. Paralleling the argument in the previous
section, it is possible that positive performance of advisory programs in
an EV context is due to random chance. Bradley and Blackwood (1989) have
developed a simultaneous statistical test of the equivalence of means and
variances for paired data. With only seven observations to estimate both
the mean and variance (or standard deviation), the power of this
particular test to detect positive performance may be relatively low. In
addition, the test is fairly technical in nature. Application of the test
therefore is left to future research.
Finally, the mean-variance evaluation
presented in this section can be extended to portfolios of advisory
programs. For example, a soybean portfolio might consist of 50% marketed
by advisory program #1 and 50% marketed by advisory program #2. The
potential improvement in performance by following a combination of
programs depends on the degree that net advisory prices or revenues are
uncorrelated. A recent AgMAS Research Report by Stark et al. (2003)
analyzes the potential risk reduction among market advisory programs for
corn and soybeans. Under the assumption that programs are
equally-weighted and randomly-selected (naïve diversification), results
from this study show that increasing the number of programs reduces
portfolio expected risk, but the marginal decrease in risk from adding a
new program decreases rapidly with portfolio size. The risk reduction
benefit from this type of diversification among advisory programs is
relatively small because advisory prices, on average, are highly
correlated. A one service portfolio has only a 20%, 16% and 32% higher
standard deviation than the minimum risk portfolio for corn, soybeans and
50 /50 revenue, respectively. Most risk reduction benefits are achieved
with small portfolios. For instance, a four service portfolio has only
5%, 4% and 9% higher risk than the minimum risk portfolio for corn,
soybeans and 50/50 revenue, respectively. Based on these results, there
does not appear to be strong justification for farmers adopting portfolios
with a large number of advisory programs.
For a more complete
analysis of the possible benefits from diversification among advisory
programs, it is necessary to evaluate portfolios constructed using modern
portfolio theory (MPT). Under this approach, an efficient set of optimal
portfolios of market advisory programs is constructed by minimizing
portfolio variance for each level of expected price or revenue. The
resulting optimal portfolios generally will not be equally-weighted across
programs. It is possible for an optimal portfolio of advisory programs to
generate higher prices and less risk than a benchmark, even if individual
advisory programs that make up the portfolio do not. The main difficulty
in generating optimal portfolios is obtaining accurate estimates of the
means, variance and correlations for individual programs from the
available data. Application of MPT to market advisory programs represents
an interesting area of future research.
Predictability
of Performance
Even if, as a group, advisory programs generate
positive marketing returns, there is a wide range in performance for any
given year. For example, soybean net advisory prices for 1995 vary from
$5.66 per bushel to $7.94 per bushel (see Table 16). While this example
is one of the most dramatic, the variation across advisors in other cases
is substantial. This raises the important question of the predictability
of advisory program performance from year-to-year. In other words, is
past performance indicative of future performance? Three types of
predictability tests are used to answer this question: i) the
predictability of “winner” and “loser” categories across crop years, ii)
the correlation of advisory program ranks across crop years and iii) the
differences between prices for “top” and “bottom” performing advisory
programs across crop years. The testing procedures have been widely
applied in studies of financial investment performance (e.g., Elton,
Gruber and Rentzler, 1987; Irwin, Zulauf and Ward, 1994; Lakonishok,
Shleifer and Vishny, 1992; Malkiel, 1995).
The first test of
predictability is based on placing advisory programs into “winner” and
“loser” categories across adjacent crop years. This non-parametric test
is robust to outliers, which is important when analyzing predictability
across all advisory programs. For a given commodity, the first step in
this testing procedure is to form the sample of all advisory programs that
are active in adjacent crop years. The second step is to rank each
advisory program in the first year of the pair (e.g., t = 1997)
based on net advisory price. For example, the program with the highest
net advisory price is ranked number one and the program with the lowest
net advisory price is assigned a rank equal to the total number of
programs for that commodity in the given crop year. Then the programs are
sorted in descending rank order. The third step is to form two groups of
programs in the first year of the pair: winners are those programs in the
top half of the rankings and losers are programs in the bottom half. The
fourth step is to rank each advisory program in the second year of the
pair (e.g., t +1 = 1998) based on net advisory price and once again
form winner and loser groups of programs. The fifth step is to compute
the following counts for the advisory programs in the pair of crop years:
winner t-winner t+1, winner t-loser t+1, loser
t-winner t+1, loser t-loser t+1. If advisory
program performance is unpredictable, approximately the same counts will
be found in each of the four combinations. The appropriate statistical
test in this case is known as Fisher’s Exact Test (Conover, 1999,
pp.188-189).
Results of the
winner and loser predictability test are shown in Table 27. Winner and
loser counts for individual crop years indicate a modest difference, at
best, in the chance of a winner or loser in one period being a winner or
loser in the subsequent period. As an example, consider the results for
corn in 1997 and 1998. Of the eleven winners (top half) in 1997, six are
winners in 1998 and five are losers (bottom half). Of the twelve losers
in 1997, five are winners in 1998 and seven are losers. In other words,
the conditional probability of a winner from 1997 repeating in 1998 is 55%
(6/11) and the conditional probability of a loser from 1997 repeating in
1998 is 56% (7/12). Averaged across all comparisons, the conditional
probability of a winner (loser) repeating is 54% (57%) for corn, 58% (60%)
for soybeans and 55% (57%) for 50/50 revenue. These probabilities are
only slighter higher than what would result from flipping a coin
(randomness). There is only one case (50/50 revenue, 1999 vs. 2000) where
individual year counts are significantly different from the equal
distribution expected under an assumption of no predictability. Even in
this case, caution should be used when considering the reported p-value,
because it is likely overstated due to the observed dependence across
advisory programs.
Overall, these results imply that the performance of winning and losing
advisory programs is not predictable through time.
While
predictability may be limited or non-existent across all advisory
programs, it is possible for sub-groups of advisory programs to exhibit
predictability. Specifically, predictability may be found only at the
extremes of performance. That is, only top-performing programs in one
year may tend to perform well in the next year, or only poor-performing
programs may perform poorly in the next year, or both. This is the
motivation for the second test of predictability, which is based on the
correlation between ranks of all advisory programs active in adjacent
pairs of crop years. For a given commodity, the first step in this
testing procedure is to once again form the sample of all advisory
programs that are active in both adjacent crop years. The second step is
to rank each advisory program in the first year of the pair (e.g., t
= 1997) based on net advisory price. Then the programs are sorted in
descending rank order. The third step is to sort and rank the sample of
programs in the second year of the pair (e.g., t + 1 = 1998). The
fourth step is to compute the correlation coefficient between ranks for
the two adjacent crop years. If advisory program performance is
unpredictable, the estimated correlation will be near zero. Assuming the
standard error of the correlation coefficient is approximately equal to
 , the appropriate statistical test is a Z-test.
Results of the rank correlation predictability
test are presented in Table 28. Rank correlation coefficients for corn
range from of -0.12 to 0.53. Statistically significant correlations are
found for three of the six comparisons in corn. The range of rank
correlation coefficients for soybeans, 0.03 to 0.65, is similar to the
range for corn. However, statistically significant correlations are found
for only one of the six comparisons in soybeans. Rank correlation
coefficients for 50/50 revenue have the widest range, from -0.17 to 0.72.
Statistically significant correlations are found for two of the six
revenue comparisons. Once again, caution should be used when considering
the reported p-values, as they likely overstate the significance of
the rank correlation estimates due to the dependence across advisory
programs. Average rank correlation coefficients across the six
comparisons are nearly identical for corn, soybeans and 50/50 advisory
revenue. With values of either 0.27 or 0.28, the average rank
correlations suggest marginal predictability in the pricing performance of
top- and bottom-performing market advisory programs.
The rank correlation
tests results suggest it is useful to determine the magnitude of
predictability in top- and bottom-performing advisory programs. Hence,
the third test of predictability is based on the difference between net
advisory prices for top- and bottom-performing advisory programs across
adjacent crop years. For a given commodity, the first step in this
testing procedure is to sort programs by net advisory price in the first
year of the pair and group programs by quantiles (thirds and fourths).
The second step is to compute the average net advisory price for the
quantiles in the second year of the pair. Note that the same programs
make up the quantiles in the first and second year of the pair. For
example, the average price of the top fourth quantile formed in 1995 is
computed for 1996. The third step is to compute the difference in average
price for the top- and bottom-performing quantiles. If performance for
the top- and bottom-performing quantiles is the same, the difference will
equal zero. The appropriate statistical test in this case is a paired
t-test of the difference in the means of the top- and
bottom-performing quantiles. There are a total of six comparisons (1995
vs. 1996, 1996 vs. 1997, 1997 vs. 1998, 1998 vs. 1999, 1999 vs. 2000 and
2000 vs. 2001), so there are five degrees of freedom for the t-test.
Since differences are computed for an “average” advisory program in top-
and bottom-performing quantiles, dependence across individual advisory
programs is not an issue, and p-values for the t-test are
unbiased. Carpenter and Lynch (1999) recommend this test because it is
well-specified and among the most powerful in their comparison of several
predictability tests for mutual funds.
Results for the t-test of
predictability are shown in Table 29. The first column under each
commodity heading shows the average price of the different quantiles in
the first year of the comparisons (six in total). The average price for
the first year is “in-sample” because this is the formation year for the
quantiles. The second column under each heading reports the average price
of the same quantiles in the second year of the comparisons. The average
price for the second year is “out-of-sample” because this is the year
after formation of the quantiles. In all cases, the average price or
revenue of the top quantile relative to the bottom quantile declines
substantially from the first to the second year of the comparisons.
Nonetheless, the average difference between top- and bottom-performing
quantiles for the second year of the pair is consistently positive. For
example, programs in the top third beat the bottom third in the second
year by an average of 11¢ per bushel in corn, 25¢ per bushel in soybeans
and $11 per acre for revenue. Average differences are statistically
different from zero for corn when a five percent level of significance is
applied. However, the results for soybeans and 50/50 revenue are
marginally significant. Average prices for the top quantile out-of-sample
also exceed benchmark prices for the same period (1996-2001). Top third
returns beat the 24-month market benchmark by an average of 3¢ per bushel
in corn, 23¢ per bushel in soybeans and $8 per acre for 50/50 revenue.
Top fourth returns beat the 24-month market benchmark by an average of 6¢
per bushel in corn, 28¢ per bushel in soybeans and $10 per acre for 50/50
revenue.
The quantile results
provide some evidence that the performance of top- and bottom-performing
market advisory programs can be predicted across adjacent crop years.
However, the evidence is not sufficient to conclude that performance
predictability is useful from an economic standpoint, due to the
overlapping nature of the marketing windows for each crop year. To see
the point, consider the case of a farmer who uses 1995 performance results
to select a top-performing advisory program. Since the 1995 marketing
window ends on August 31, 1996, halfway through the 1996 marketing window
and one day before the beginning of the 1997 marketing window, the farmer
could not implement their selection of an advisory program until the 1997
crop year. Performance would have to persist across three crop years,
1995, 1996 and 1997, for a farmer to benefit from the predictability.
Quantile results for
non-overlapping crop years are shown in Table 30. The testing procedure
is the same as before, except there are only five comparisons (1995 vs.
1997, 1996 vs. 1998, 1997 vs. 1999, 1998 vs. 2000 and 1999 vs 2001) and
four degrees of freedom for the paired t-test. The results are
strikingly different than the previous results for overlapping crop
years. The difference between top- and bottom-performing quantiles in the
second year of the pair is near zero for corn, positive for soybeans and
zero for 50/50 revenue. All of the average differences are
insignificantly different from zero. These results indicate
predictability of pricing performance for top and bottom advisory programs
is short-lived, in the sense that performance does not persist long enough
to be taken advantage of by farmers.
The predictability
results presented so far are all based on individual crop year
comparisons. It is possible for performance to be predictable over long
time horizons, but unpredictable over short horizons due to a large amount
of “noise” in performance from year-to-year (e.g., Summers, 1986). This
is consistent with the argument that over the long-term “cream rises to
the top” in terms of performance. To assess long-term predictability, the
sample is limited to the 15 programs active in all seven crop years of the
study. Next, net advisory prices are averaged for each of the 15 programs
using two different sample splits: the first three
crop years (1995-1997) versus the second four crop years (1998-2001)
and the first four crop years (1995-1998) versus the second three crop
years (1999-2001). The three tests of predictability are then applied to
the two sets of averages for each sample split. The results are striking,
in that virtually no evidence of predictability is found for any of the
tests. Winner-loser counts are quite close to what is expected under
randomness, rank correlations are all insignificantly different from zero
and the average difference between top- and bottom-performing programs
tends to be very small or negative.
These results clearly show that advisory program performance is
unpredictable over longer time horizons.
The test results
presented in this section provide little evidence that the pricing
performance of advisory programs can be usefully predicted from past
performance. This conclusion does not mean it is impossible to predict
advisory program performance. There may be other variables that are useful
for predicting performance. Chevalier and Ellison (1999) study whether
mutual fund performance is related to characteristics of fund managers
that indicate ability, knowledge or effort and find that managers who
attended higher-SAT undergraduate institutions generate systematically
higher returns. Barber and Odean (2000) examine the trading records of
individual stock investors and report that frequent trading substantially
depresses investment returns. Similar factors, such as education of
advisors, cash only programs versus futures and options programs,
frequency of futures and options trading, or storage costs, may be useful
in predicting the performance of market advisory programs.
Summary
and Conclusions
Surveys suggest that
farmers view market advisory services as an important tool in managing
price and income risk. As a result, farmers need information on the
performance “track record” of market advisory services to help them
identify successful alternatives for marketing and price risk management.
The Agricultural Market Advisory Service (AgMAS) Project was initiated in
1994 with the goal of providing unbiased and rigorous evaluation of market
advisory services.
The purpose of this
research report is to evaluate the pricing performance of market advisory
services for the 1995-2001 corn and soybean crops. No fewer than 23
market advisory programs are available for each crop year. While the
sample of advisory services is non-random, it is constructed to be
generally representative of the majority of advisory services offered to
farmers. Further, the sample of advisory services includes all programs
tracked by the AgMAS Project over the study period, so pricing performance
results should not be plagued by survivorship bias. The AgMAS Project
subscribes to all of the services that are followed and records
recommendations on a real-time basis, which should prevent pricing
performance results from being subject to hindsight bias.
Certain explicit
assumptions are made to produce a consistent and comparable set of results
across the different advisory programs. These assumptions are intended to
accurately depict “real-world” marketing conditions facing a
representative central Illinois corn and soybean farmer. Several key
assumptions are: i) with a few exceptions, the marketing window for a crop
year runs from September before harvest through August after harvest, ii)
on-farm or commercial physical storage costs, as well as interest
opportunity costs, are charged to post-harvest sales, iii) brokerage costs
are subtracted for all futures and options transactions and iv) Commodity
Credit Corporation (CCC) marketing loan recommendations made by advisory
programs are followed wherever feasible. Based on these and other
assumptions, the net price received by a subscriber to a market advisory
program is calculated for the 1995-2001 corn and soybean crops.
Two different types of
benchmarks are developed for the performance evaluations. Efficient market
theory implies that the return offered by the market is the relevant
benchmark. In the context of this study, a market benchmark should
measure the average price offered by the market over the marketing window
of a representative farmer who follows advisory program recommendations.
Both a 24-month and a 20-month market benchmark are specified in order to
test the fragility of performance results to different market benchmark
assumptions. Behavioral market theory suggests that the average return
actually achieved by market participants as an appropriate benchmark. In
the context of the present study, a behavioral benchmark should measure
the average price actually received by farmers for a crop. A farmer
benchmark is specified based upon the USDA average price received series
for corn and soybeans in Illinois. All benchmarks are computed using the
same assumptions applied to advisory program track records.
Four basic indicators of
performance are applied to advisory program prices and revenues over
1995-2001. The first indicator is the proportion of advisory programs
that beat benchmark prices. Between 49 and 60% of the programs in corn
have net advisory prices above market benchmarks over 1995-2001, while 73%
of the programs have prices above the farmer benchmark. Performance is
stronger in soybeans. Between 63 and 74% of advisory programs in soybeans
have advisory prices above the market benchmarks over 1995-2001 and 67%
are above the farmer benchmarks. Between 56 and 70% of advisory programs
have revenue above the market benchmarks over 1995-2001, while 71% have
revenue above the farmer benchmark. The results provide mixed performance
evidence with respect to market benchmarks and consistently positive
evidence with respect to the farmer benchmark.
The second indicator is the difference between
the average price of advisory programs and the market or farmer
benchmarks. The results basically tell the same story as those based on
the proportion beating the benchmarks. Average differences from market
benchmarks for corn over 1995-2001 are small, ranging from zero to three
cents per bushel. At 10¢ per bushel, the average difference from the
farmer benchmark for corn is larger. Average differences for soybeans
over 1995-2001 are even larger for both types of benchmarks, ranging from
11 to 18¢ per bushel versus market benchmarks and equaling 17¢ per bushel
versus the farmer benchmark. Average differences for advisory revenue
range from three to seven dollars per acre for market benchmarks over
1995-2001. The average revenue difference versus the farmer benchmark is
$12 per acre.
Statistical test results with respect to
market benchmarks indicate no evidence of significant average price
performance in corn, consistent evidence of significant performance in
soybeans and mixed evidence for 50/50 advisory revenue. The test results
with respect to the farmer benchmark indicate statistically significant
performance in corn, marginally significant performance in soybeans and
significant performance for 50/50 advisory revenue. Caution should be
used when considering the results, due to the relatively small sample of
crop years available for analysis. In particular, the presence of sharp
downward price trends in most crop years suggests the possibility that the
1995-2001 sample period may not provide a reliable guide to future
differences in pricing performance.
The third indicator is
the average price and risk of advisory programs relative to benchmarks.
Few advisory programs in corn generate a combination of average price and
risk superior to market benchmarks over 1995-2001. In contrast, a
majority of programs in corn generate a combination of average price and
risk superior to the farmer benchmark. A small number of programs in
soybeans generate a combination of average price and risk superior to
market benchmarks, while most programs generate a combination superior to
the farmer benchmark. Relatively few advisory programs generate a
combination of revenue and risk superior to market benchmarks. A moderate
number of programs produce a revenue combination superior to the farmer
benchmark. The results indicate that consideration of risk tends to
weaken performance results based only upon average price.
The fourth indicator is
the predictability of advisory program performance from year-to-year.
“Winner” and “loser” predictability results are similar for corn, soybeans
and advisory revenue. The conditional probability of winner and loser
programs (top half and bottom half) repeating are only slighter higher
than what would result from flipping a coin (randomness) and provide
little evidence that pricing performance for all advisory programs can be
predicted from past performance. The performance of top- and
bottom-performing programs does not appear to be predictable in a useful
sense either. For example, comparisons of non-overlapping crop years show
that programs in the top quantile beat the bottom quantile only in
soybeans and none of the average differences are significantly different
from zero. Overall, there is little evidence that advisory programs with
superior performance can be usefully selected based on past performance.
In conclusion,
the results of this study provide an interesting picture of the
performance of market advisory programs in corn and soybeans. There is
limited evidence that advisory programs as a group outperform market
benchmarks, particularly after considering risk. This supports the view
that grain markets (cash, futures and options) are efficient with respect
to the types of marketing strategies available to farmers (e.g., Zulauf
and Irwin, 1998) over the view that grain markets are inefficient and
provide substantial opportunities for farmers to gain additional profits
through marketing (e.g., Wisner, Blue and Baldwin, 1998). In contrast,
there is more evidence that advisory programs as a group outperform the
farmer benchmark, even after taking risk into account. This raises the
intriguing possibility that even though advisory services do not “beat the
market,” they nonetheless provide an opportunity for farmers to improve
marketing performance because farmers under-perform the market. Mirroring
debates about stock investing (e.g., Damato, 2001), the relevant issue is
then whether farmers can most effectively improve marketing performance by
pursuing “active” strategies, like those recommended by advisory services,
or “passive” strategies, which involve routinely spreading sales across
the marketing window. Recently, a number of grain companies began
offering “averaging” or “indexing” contracts that allow farmers to easily
implement a passive approach to marketing (Smith, 2001). The rising
interest in these “new generation” marketing contracts suggests the
potential for historic changes in farmers’ approach to grain marketing.
Future research that provides a better understanding of the costs and
benefits of active versus passive approaches to marketing will be
especially valuable.
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Endnotes
[1] Scott H. Irwin is a Professor in the Department of Agricultural
and Consumer Economics at the University of Illinois at Urbana-Champaign.
Joao Martines-Filho is Manager of the AgMAS and farmdoc Projects
in the Department of Agricultural and Consumer Economics at the University
of Illinois at Urbana-Champaign. Darrel L. Good is Interim Head and
Professor in the Department of Agricultural and Consumer Economics at
the University of Illinois at Urbana-Champaign. The authors gratefully
acknowledge the research assistance of Wei Shi, Brian Stark and Rick
Webber, graduate students in the Department of Agricultural and Consumer
Economics at the University of Illinois at Urbana-Champaign. Invaluable
assistance with estimating on-farm storage costs was provided by Kevin
Dhuyvetter, Department of Agricultural Economics, Kansas State University,
Lowell Hill, Department of Agricultural and Consumer Economics at the
University of Illinois at Urbana-Champaign, Marvin Paulsen, Department
of Agricultural Engineering at the University of Illinois at Urbana-Champaign,
and Dirk Maier, Department of Agricultural and Biological Engineering,
Purdue University. Helpful comments on this research report were received
from members of the AgMAS Project Review Panel.
King, Lev and Nefstad (1995) examine the corn and soybean
recommendations of two market advisory services for a single year.
The focus of their study is not pricing performance, but a
demonstration of the market accounting program Market Tools.
Some analyses also have appeared in the popular farm press. Marten
(1984) examines the performance of six advisory services for corn and
soybeans over 1981 through 1983. Otte (1986) investigates the
performance of three services for corn over the period 1980 through
1984. Both studies indicate the average price generated by services
exceeds a benchmark price. Top Producer magazine has provided
evaluations of advisory services in corn, soybeans and wheat for a
number of years (e.g., Powers, 1993).
Throughout this report, the term "crop year" refers to the marketing
window for a particular crop. This is done to simplify the
presentation and discussion of market advisory service performance
results. A “crop year” is more than twelve calendar months in length
and includes pre-harvest and post-harvest marketing periods.
Dr. Darrel L. Good and Dr. Scott H. Irwin of the University of
Illinois at Urbana-Champaign jointly direct the Project. Correspondence with
the AgMAS Project should be directed to: AgMAS Project Manager, 406
Mumford Hall, 1301 West Gregory Drive, University of Illinois at
Urbana-Champaign, Urbana, IL 61801; voice: (217)333-2792; fax:
(217)333-5538; e-mail: agmas@uiuc.edu. The AgMAS Project also has a
website that can be found at the following address:
http://www.farmdoc.uiuc.edu/agmas/.
Funding for the AgMAS project is provided by the following
organizations: Illinois Council on Food and Agricultural Research;
Cooperative State Research, Education, and Extension Service, US
Department of Agriculture; Economic Research Service, US Department of
Agriculture; Risk Management Agency, US Department of Agriculture; and
Initiative for Future Agriculture and Food Systems, US Department of
Agriculture.
The University of Illinois Variety Testing program
is a well-known example of this type of yield trial. The results of
this research program can be found at
http://www.cropsci.uiuc.edu/vt/.
For example, Managed Accounts Reports (MAR), a well-known provider of
performance information for hedge funds and commodity trading
advisors, requires that commodity trading advisors have a 12-month
record of trading actual client accounts and a minimum of $500,000
under management to be tracked in their database. More specific
details can be found at MAR’s website (http://www.marhedge.com).
When the AgMAS study began in 1994, DTN and FarmDayta were separate
companies. The two companies merged in 1996.
Five programs were discontinued within the 1995 – 2001 crop years: Ag
Profit by Hjort, Agri-Edge (cash only), Agri-Edge (hedge), Cash Grain
and Stewart-Peterson Strictly Cash. Excluding these programs from the
sample could result in a form of selection bias, particularly if
discontinuation is related to poor performance. Including a
discontinued program for a crop year does require an assumption about
marketing the cash positions remaining after the discontinuation date.
A similar issue has been treated extensively in the literature on the
performance of commodity funds and commodity trading advisors (e.g.,
Elton, Gruber and Rentzler, 1987). In this literature, if a commodity
fund or trading advisor is discontinued before the end of a calendar
year, some form of benchmark returns are substituted for the missing
returns after the discontinuation date. Following this logic, the cash
positions that remained after the date of discontinuation were sold
using the same strategy as the market benchmarks utilized for this study
(the details of the construction of these benchmarks are given in the
“Benchmark Prices” section). In effect, this simply means that cash
bushels after the date of discontinuation are sold in equal amounts over
the remaining days of the crop year. Finally,
note that 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.
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 and/or options
It turns out that only one program in 2000 and no program in 2001 met
this requirement for differentiating on-farm and off-farm strategies.
Consequently, except for one program in 2000, performance results for
on-farm and off-farm storage costs are based on the same set of
recommendations.
As with advisory programs, different procedures are used for computing
interest opportunity costs on days when the cash price is below the
loan rate and vice versa. Refer to footnote 31 for specific
details on the computations.
The different forms of averaging will produce equal estimates only if a
time-series cross-section data set is “balanced.” That is, the number of
programs is the same for each crop year and there are no missing observations.
This clearly is not the case here. It turns out that, after rounding, the two
different methods of averaging produce the same estimates of the average
proportion.
For example, one possibility is that advisory programs as a group fail to
beat market benchmarks, yet at the same time some programs have
“exceptional” performance. Testing whether performance is exceptional for
a particular advisory program requires different statistical tests than
the ones used here (Marcus, 1990).
[49]
Differences are calculated as advisory price minus benchmark price.
So, a positive difference indicates an advisory price above the
benchmark price and vice versa.
[50]
To facilitate direct comparisons across corn, soybeans and 50/50
revenue, average differences for 1995-2001 also are computed on a
percentage basis:
|
|
Average
Difference Between Advisory Programs and Benchmark |
|
|
24-Month Market |
20-Month Market |
Farmer |
|
Corn |
-0.1% |
+1.7% |
+4.8% |
|
Soybeans |
+2.0% |
+3.2% |
+3.3% |
|
50/50
Revenue |
+0.9% |
+2.4% |
+4.1% |
It is
interesting to note that the percentage difference versus the farmer
benchmark is larger for corn than soybeans, just the reverse of the
results on a cents per bushel basis.
[51]
See Appendix C for presentation of the statistical model underlying
this discussion.
[52]
Assume 25 advisory programs are included in each crop year over
1995-2001. Then, a total of 300 pair-wise correlation coefficients
would have to be estimated. However, the sample only contains 175
observations. There simply is not enough information (degrees of
freedom) to estimate each correlation independently.
[53]
The full set of regression results is available from the authors upon
request.
[54]
This test was first proposed by Fama and MacBeth (1973) and it has
been widely applied in studies of stock market returns.
[55]
In more formal terms, “typical” means one can be 95% confident the
true value of the difference will be contained in an interval about
two standard errors above and below the average difference estimate.
[56]
The standard error of the average difference is estimated as , where  is the standard deviation of differences across
crop years and T is the sample size (seven in this case).
[57]
The amount priced by farmers in the pre-harvest period is assumed to
be about 18%, near the upper end of the 10% to 20% range suggested by
the Coble et al. (1999) and USDA ARMS (2003) surveys. Readers should
note that the marketing profile for farmers is subjectively
determined, and therefore, should be viewed cautiously. In the
section on farmer benchmark prices, it was noted that almost no
concrete evidence exists on the exact length of the typical marketing
window of farmers or the precise pattern of forward pricing.
where T is the number of crop years in the sample, yt
is the advisory program’s net price for the tth crop
year and  is the average net advisory price over the T
crop years.
Fisher’s Exact Test is the appropriate statistical test because both row
and column totals are pre-determined in the 2 x 2 contingency table
formed on the basis of winner and loser counts.
Fisher’s Exact Test assumes sample observations are independent. As
discussed in the section on average price performance, this clearly is
not the case, and therefore, the p-values for such tests likely
overstate the true significance of the results. Pooled test results for
1995-2001 are not reported for the same reason.
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