Report 1999-03: Do Agricultural Market
Advisory Services Beat the Market?Evidence
from the Corn and Soybean Markets Over 1995-1997
H. Irwin, Thomas E. Jackson and Darrel
1999 by Scott H. Irwin, Thomas E. Jackson and Darrel L. Good.
All rights reserved. Readers may make verbatim copies of this
document for non-commercial purposes by any means, provided that
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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
program. 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,
carrying charges and government programs
The purpose of this paper is to
address two basic performance questions for market advisory services:
1) Do market advisory services, on average, outperform an appropriate
market benchmark? and 2) Do market advisory services exhibit persistence
in their performance from year-to-year? Data on corn and soybean
net price received for advisory services, as reported by the AgMAS
Project, are available for the 1995, 1996 and 1997 marketing years.
Performance test results suggest that, on average, market advisory
services exhibit a small ability to "beat the market"
for the 1995 through 1997 corn and soybean crops. This conclusion
is somewhat sensitive to the type of performance test and market
benchmark considered. The predictability results provide little
evidence that future advisory service pricing performance can
be predicted from past performance. When services are grouped
by performance quantile, some evidence of predictability is found
for the poorest performing services, but not for top performing
Do Agricultural Market Advisory
Services Beat the Market? Evidence from the Corn and Soybean Markets
Price risk management is an important
business activity for US grain farmers. Using a survey of large-scale,
midwestern grain farmers, Patrick and Ullerich (1996) report that
price variability is the highest rated source of risk by crop
farmers. Given the dramatic fluctuations of grain prices in recent
years, it is likely that price variability will continue to be
a major source of risk for farmers.
Farmers view market advisory services
as a significant source of market information and advice in their
quest to manage price risks associated with grain marketing.
In a rating of seventeen risk management information sources,
Patrick and Ullerich (1996) report that the rank of computerized
information services and market advisors is surpassed only by
farm records. Patrick, Musser, and Eckman (1998) indicate that
35 and 38 percent of large-scale, midwestern grain farmers used
marketing consultants in 1993 and 1994, respectively. Schroeder,
Parcell, Kastens and Dhuyvetter (1998) surveyed Kansas crop farmers
and report that market advisory services and newsletters are the
highest ranked source of information used to formulate price expectations.
It is interesting to note that advisory services outranked even
futures markets in this latter survey.
Given the high value that farmers
place upon market advisory services, it is somewhat surprising
that only two academic studies investigate the pricing performance
of advisory services. The dearth of studies seems even
more anomalous in light of the large number of studies on grain
The lack of studies on market advisory services is most likely
due to the difficulty in obtaining data on the stream of recommendations
provided by services.
Gehrt and Good (1993) analyze the
performance of five advisory services for corn and soybeans over
1985 though 1989. Assuming a representative producer 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 percent of production at harvest. Average advisory service
revenue over the four years is larger than benchmark revenue for
both corn and soybeans.
While a useful starting point, the
two previous studies have important limitations. First, the sample
of advisory services is quite small, with the largest sample including
only six advisory services. Second, the results may be biased
due to the nature of the sample selection process. The literature
on the performance of mutual funds and investment newsletters
highlights the sample selection biases that plague many performance
results (e.g. Brown, Goetzmann, Ibbotson, and Ross, 1992; Jaffe
and Mahoney, 1999; Metrick, 1999). The most relevant bias for
previous studies of market advisory services is survivorship bias,
which results from tracking only advisory services that remain
in business at the end of a sample period.
The previous discussion suggests
the academic literature provides farmers with little basis for
evaluating and selecting advisory services. In 1994, the Agricultural
Market Advisory Service (AgMAS) Project was initiated, with the
goal of providing unbiased and rigorous evaluation of market advisory
services for farmers. Since its inception, the AgMAS Project
has collected marketing recommendations for about 25 market advisory
programs. The AgMAS Project subscribes to all of the services
that are followed, and as a result, "real-time" recommendations
are obtained. This prevents the data from being subject to survivorship
After the stream of recommendations
is collected for a given commodity in a particular marketing year,
the net price that would have been received by a producer that
precisely follows the set of marketing recommendations is computed.
This net price is the weighted average of the cash sale price
plus or minus gains/losses associated with futures and options
transactions. Brokerage costs are accounted for, as are the costs
of storing any portion of the crop beyond harvest. So far, the
AgMAS Project has reported corn and soybean results for the 1995,
1996 and 1997 marketing years. (Good, Irwin, Jackson, and Price,
1997; Jackson, Irwin, and Good, 1998; Jackson, Irwin, and Good,
The annual AgMAS comparison of net
price received for advisory services provides important information
that farmers can use in selecting a service. However, the comparisons
to date are descriptive only and do not rigorously address the
central questions regarding pricing performance. Following the
literature on mutual fund and investment newsletter performance
(e.g. Jaffe and Mahoney, 1999), two basic questions need to be
answered: 1) Do market advisory services, on average, outperform
an appropriate market benchmark? and 2) Do market advisory services
exhibit persistence in their performance from year-to-year?
The purpose of this report is to
address the previous two questions for corn and soybeans using
the net advisory prices reported by the AgMAS Project for the
1995, 1996 and 1997 marketing years. At least 21 advisory services
are included in the evaluations for each commodity and marketing
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. The availability of
only three marketing years is a limitation of the analysis, but
the time period considered does include years of rapidly increasing
and decreasing corn and soybean prices.
tests used to determine average performance of market advisory
services and predictability of performance through time have been
widely applied in the financial literature (e.g. Elton, Gruber,
and Rentzler, 1987; Lakonishok, Shleifer and Vishny, 1992; Irwin,
Zulauf, and Ward, 1994; Jaffe and Mahoney, 1999; Metrick, 1999).
Tests of performance relative to a benchmark are based on the
proportion of services exceeding the benchmark price and the average
percentage difference between the net price of services and the
benchmark price. Tests of predictability are based on the year-to-year
correlation of advisory service ranks, prices and percentage differences
from the benchmark. In addition, predictability is examined for
advisory services in different performance quantiles.
Data on Advisory Service Recommendations
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 a sample of services for the AgMAS Project, criteria
are developed to define an agricultural market advisory service
and a list of services is assembled.
The first criterion used to identify
services is that a service has to provide marketing advice to
farmers. 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 versus the use of futures and options for
"hedging" purposes are used for identification purposes
only. A discussion of what types of futures and options trading
activities constitute hedging, as opposed to speculating, is not
The second criterion is that specific
advice must be given for making cash sales of the commodity, in
addition to any futures or options hedging activities. In fact,
some marketing programs evaluated by the AgMAS Project do not
make any futures and options recommendations. However, marketing
programs 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.
The original sample of market advisory
services that met the two criteria were 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 (and remains)
generally representative of the majority of advisory services
available to farmers.
The original sample for 1995 includes
25 market advisory programs for both corn and soybeans. For a
variety of reasons, deletions and additions to the original sample
occur over time.
In 1996, the total number of advisory programs is 26 for corn
and 24 for soybeans, while in 1997 the total is 23 for corn and
21 for soybeans. The term “advisory program” is used because
several advisory services have more than one distinct marketing
program. A directory of the advisory services
included in the study can be found at the AgMAS Project website
As mentioned earlier, sample selection
biases may plague advisory service databases. The first form
is survival bias, which occurs if only advisory services that
remain in business at the end of a given period are included
in the sample. Survival bias significantly biases measures of
performance upwards since "survivors" typically have
higher performance than "non-survivors" (Brown, Goetzmann,
Ibbotson, and Ross, 1992). This form of bias should not be present
in the AgMAS database of advisory services because all services
ever tracked are included in the sample. The second and more
subtle form of bias is hindsight bias, which occurs if data from
prior periods are "back-filled" at the point in time
when an advisory service is added to the database. Statistically,
this has the same effect as survivorship bias because data from
surviving advisory services is back-filled. This form of bias
should not be present in the AgMAS database because recommendations
are not back-filled when an advisory service is added. Instead,
recommendations are collected only for the marketing year after
a decision has been made to add an advisory service to the database.
The actual daily process of collecting
recommendations for the sample of advisory services begins with
the purchase of subscriptions to each of the services. Staff
members of the AgMAS Project read the information provided by
each advisory service on a daily basis. The information is received
electronically, via DTN, web sites or email. For the services
that provide two daily updates, typically in the morning and at
noon, information is read in the morning and afternoon. In this
way, the actions of a farmer-subscriber are simulated in “real-time.”
The recommendations of each advisory
service are recorded separately. 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.
Several procedures are used to check
the recorded recommendations for accuracy and completeness. Whenever
possible, recorded recommendations are cross-checked against later
status reports provided by the relevant advisory service. Also,
at the completion of the marketing year, it is confirmed whether
cash sales total exactly 100%, all futures positions are offset,
and all options positions are offset or expire worthless.
Calculation of Net Advisory Service
At the end of a marketing year,
all of the (filled) recommendations are aligned in chronological
order. The advice for a given marketing year is considered to
be complete for each advisory program when cumulative cash sales
of the commodity reach 100%, all open futures positions covering
the crop are offset, all open option positions covering the crop
are either offset or expired, and the advisory program discontinues
giving advice for that crop year. The returns to each recommendation
are then calculated in order to arrive at a weighted-average net
price that would be received by a producer who precisely follows
the marketing advice (as recorded by the AgMAS Project).
In order to simulate a consistent
and comparable set of results across the different advisory services,
certain explicit assumptions are made. These assumptions are
intended to accurately depict marketing conditions for a representative,
central-Illinois farm. An overview of the simulation assumptions
is presented below. Complete details of the simulation assumptions
can be found in Jackson, Irwin and Good (1999).
A two-year marketing window, spanning
September of the year before harvest through August of the year
after harvest, is used in the analysis. For example, the 1997
marketing window is September 1, 1996 through August 31, 1998.
The beginning date is selected because advisory services in the
sample generally begin to make marketing recommendations around
this date. The ending date is selected to be consistent with
the ending date for corn and soybean marketing years as defined
by the US Department of Agriculture (USDA). There are a few exceptions
to the marketing window definition. Several advisory programs
have relatively small amounts (10% or less) of cash corn or soybeans
unsold as of the end of a window. One marketing program also
began pre-harvest hedges prior to September 1, 1996. In these
cases, the actual sales recommendations on the indicated dates
The cash price assigned to each
cash sale recommendation is the central-Illinois closing, or overnight,
bid. The central-Illinois price is the mid-point of the range
of bids by elevators in a 25-county area in central and east central-Illinois.
The bids are collected and reported by the Illinois Department
of Agriculture. The central-Illinois market also is used for
cash-forward contract transactions. Futures prices and options
premia are Chicago Board of Trade quotes.
Since most of the advisory program
recommendations are given in terms of the proportion of total
production (e.g., “sell 5% of 1997 crop today”), some assumption
must be made about the amount of production to be marketed. For
the purposes of this study, if the per-acre yield is assumed to
be 100 bushels, then a recommendation to sell 5% of the corn crop
translates into selling 5 bushels. When all of the advice for
the marketing year has been carried out, the final per-bushel
selling price is the average price for each transaction weighted
by the amount marketed in each transaction.
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 a function of yield in previous years. In this
study, the assumed yield prior to harvest is based on a linear
regression trend yield, while the actual reported yield is used
from the harvest period forward.
Brokerage costs are incurred when
farmers open or lift positions in futures and options markets.
For the purposes of this study, it is assumed that brokerage costs
are $50 per contract for a round-turn for futures transactions,
and $30 per contract to enter or exit an options position. Further,
it is assumed that CBOT corn and soybean futures are used, and
the contract size for each commodity is 5,000 bushels. Therefore,
per-bushel brokerage costs are 1 cent per bushel for a round-turn
futures transaction and 0.6 cents per bushel for each options
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 (carrying costs) consists
of two components: physical storage charges and the opportunity
cost incurred by foregoing sales when the crop is harvested.
Physical storage charges can apply to off-farm (commercial) storage,
on-farm storage, or some combination of the two. Opportunity
cost is the same regardless of the type of physical storage.
For the purposes of this study,
it is assumed that all storage occurs off-farm at commercial sites.
Carrying costs are assigned beginning at the ending of the estimated
ending points of the harvest windows. Physical storage charges
are assumed to be a flat 13 cents per bushel from the end of harvest
through December 31. After January 1, physical storage charges
are assumed to be 2 cents per month (per bushel), with this charge
pro-rated to the day when the cash sale is made. The storage
costs represent the typical storage charges quoted in a non-random
telephone survey of Central-Illinois elevators.
The interest charge for storing
grain is the interest rate compounded daily from the harvest mid-point
to the date of sale. The interest rate used is the average rate
for all commercial agricultural loans for the fourth quarter of
the harvest year and the first three quarters of the next calendar
year as reported in the Agricultural Finance Databook published
by the Board of Governors of the Federal Reserve Board. This
interest rate has been around 9% per year for the three years
of this study.
In addition to the storage and interest
costs, another charge is assigned to corn (but not soybeans).
This charge, referred to as a “shrink charge”, is commonly deducted
by commercial elevators on “dry” corn that is delivered to the
elevator to be stored, and reflects a charge for drying and volume
reduction (shrinkage) which occurs in drying the corn from (typically)
15% to 14% moisture. The charge for drying is a flat 2 cents
per bushel, while the charge for volume reduction is 1.3% per
bushel. The charge for this volume reduction is calculated as
1.3% times the average harvest-time cash price for each marketing
year. For example, for the 1997 crop the harvest-time cash price
was $2.65 per bushel, so the charge for volume reduction was 3.4
cents per bushel ($2.65*1.3%).
Simply comparing the net price received
across advisory services will not answer the question of whether
advisory services as a group enhance the income of farm subscribers.
Instead, a comparison to a benchmark price (or prices) is needed
to evaluate the performance of advisory services relative to pricing
opportunities offered by the market. In the stock market, mutual
funds are evaluated with respect to market benchmark performance
criteria (e.g., Bodie, Kane, and Marcus, 1989). These benchmarks
typically are indexes of stock market returns over the period
of evaluation, e.g., the Dow Jones Industrial Average and Standard
and Poor’s 500.
The selection of appropriate benchmarks
for advisory service performance evaluations is treated thoroughly
in a recent report by Good, Irwin and Jackson (1998). They argue
that, conceptually, a useful benchmark should: 1) be simple
to understand and to calculate; 2) represent the returns to a
marketing strategy that could be implemented by farmers;
3) be directly comparable to the net advisory price received
from following the recommendations of a market advisory service;
4) not be a function of the actual recommendations of the advisory
services or of the actual marketing behavior of farmers, but rather
should be external to their marketing activities; and 5)
be stable, so that it represents the range of prices made
available by the market throughout the marketing year instead
of representing the price during a small segment of the marketing
year. The market benchmark price that Good, Irwin and Jackson
argue is the most consistent with the above criteria is the average
cash price for corn and soybeans over the entire marketing horizon.
The marketing window used in the AgMAS project for a given crop
spans two calendar years, beginning on the first business day
of September in the year prior to harvest, and extends through
the last business day of August in the year after harvest. As
its name suggests, it is calculated as the average of the daily
central-Illinois cash grain bids available for the two-year marketing
window. Pre-harvest cash prices represent cash-forward bids for
harvest delivery in central-Illinois, while daily spot prices
for central-Illinois are used for the post-harvest period.
Two adjustments are made to the
daily cash prices to make the average cash price benchmark consistent
with the calculated net advisory prices for each marketing program.
First, instead of taking the simple average of the daily prices,
a weighted average price is calculated to account for changing
yield expectations. 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 to the daily cash
prices is to adjust the post-harvest cash prices to a harvest
equivalent by subtracting carrying charges. The daily carrying
charges are calculated in the same manner as those for the net
advisory price. Complete details of the construction of this benchmark
price can be found in Good, Irwin and Jackson (1998).
In order to test the sensitivity
of performance results to the choice of market benchmark, two
alternative versions of the previous average cash price benchmark
also are considered in the analysis. The first alternative benchmark
averages prices for the 20-month period starting in January of
the year of harvest and ending in August of the year after harvest.
The only difference between this alternative and the 24-month
benchmark is the exclusion of the pre-harvest period previous
to January. Hence, this alternative benchmark places more weight
on post-harvest prices than pre-harvest prices. The second alternative
benchmark averages prices only for a 12-month marketing year,
and includes only post-harvest prices in the averaging process.
Net Price Received Results for 1995
Net price received for the sample
of market advisory services for the 1995, 1996, and 1997 marketing
years is reported in Tables 1 and 2., 
Note that some of the marketing programs included in the table
are not evaluated for all three years. The three-year averages
are calculated only for the 19 marketing programs that are evaluated
for all three years.
As shown in Table
1, the average net advisory price for corn ranges from $2.32
per bushel in 1997 to $3.03 per bushel in 1995. The three-year
average for the 19 programs is $2.65 per bushel. The range of
average net advisory prices is large, with a low of $2.36 and
a high of $3.03. Not surprisingly, the range within the individual
years is even more substantial. 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,
such as 1997, the range in performance is just under $0.75 per
The three alternative market benchmark
prices for corn are shown at the bottom of Table
1. Three-year averages of the market benchmarks differ by
less than 10 cents per bushel. However, this masks large differences
within some of the years, particularly 1995. These data suggest
advisory service performance results for corn may be sensitive
to the selected benchmark.
The three-year results for soybeans
are listed in Table 2. The average
net advisory price for soybeans ranges from $6.40 per bushel in
1997 to $7.27 per bushel in 1996. The three-year average for
the 19 programs is $6.73 per bushel. Again, the range of average
net advisory prices is large, with a low of $6.37 and a high of
$7.27. As with corn, the range within the individual years is
even more substantial. The most dramatic example is 1995, where
the range in advisory prices exceeds two dollars per bushel.
Since many subscribers to market
advisory services produce both corn and soybeans, it also is of
interest to examine a combined measure of corn and soybean pricing
performance for each market advisory program. One way to aggregate
the results is to calculate the per-acre revenues implied by the
pricing performance results. The per-acre revenue for each
commodity is found by multiplying the net advisory price for each
market advisory program by the actual central-Illinois corn or
soybean yield for each year. A simple average of the two per
acre revenues is then taken to reflect a farm that uses a 50/50
rotation of corn and soybeans.
3 contains the combined corn and soybeans revenue results.
As with Tables 1 and 2, a three-year
average is calculated only for programs that were included in
the study for all three years. In addition, market advisory programs
that provide recommendations for corn but not soybeans (Ag Line
by Doane hedge and Allendale futures & options) are not included.
The three-year average revenue for all 19 market advisory programs
is $332 per acre. The three-year average for the individual programs
ranges from a low of $312 per acre to a high of $360 per acre.
Statistical Tests of Market Advisory
Service Pricing Performance
Two statistical tests are used to
test the null hypothesis that average market advisory service
pricing performance does not differ from that of the market benchmark.
The first test is based on the proportion of services exceeding
the benchmark price. This test is considered because it is not
influenced by extremely high or low advisory prices. The second
test is based on the average percentage difference between the
net price of services and the benchmark price. This test is useful
because it takes into account the average magnitude of differences
from the benchmark.
Independence of Observations
Before considering the statistical
test results, an important issue needs to be explored that may
have a substantial impact on the results. The issue is whether
the sample observations on net advisory price are independent,
both within and across years. The most likely form of dependence
is positive correlation, which, if ignored, would cause sample
standard deviation estimates across advisory services to be understated.
This in turn would cause the statistical significance of hypothesis
test results to be overstated.
There are several potential ways
that independence could be violated in the sample of market advisory
service prices. One potential violation is positive correlation
of corn pricing performance for a market advisory program in a
given year with its soybean pricing performance in the same year.
In other words, do services that do well in corn also tend to
do well in soybeans in the same year? If so, statistical tests
that pool pricing performance of services for corn and soybeans
may overstate the significance of positive or negative performance
because the standard deviation across the corn and soybean observations
would be understated.
The correlation results for market
advisory corn and soybean pricing performance within the same
marketing year are summarized in Table
4. Correlation in a given year is computed three ways. First,
the correlation of rank across corn and soybeans for a given year
is computed. To do this, the rank of each advisory service with
respect to the other services is calculated separately for corn
and soybeans. The services are ranked in descending order. For
example, the service with the highest net advisory price is ranked
number 1, and the service with the lowest net advisory price is
assigned a number equal to the total number of observations for
that commodity in the given year. The final step is to compute
the correlation of the corn and soybean ranks. Second, the simple
correlation between the net advisory corn and soybean price levels
is computed for a given year. Third, the correlation of advisory
service performance with respect to the 24-month market benchmark
price is calculated. The “return” to
market advice is calculated as the percentage difference between
the net advisory price and the 24-month market benchmark price
for the commodity. A graphical view of the rank and return correlations
is presented in Figure 1.
The results presented in Table
4 are similar across the different measures of correlation.
Significant positive correlation between corn and soybean pricing
results is found in 1995 and 1997, but not for 1996. This may
be due to the fact that the price patterns for corn and soybeans
were quite different for the 1996 crop year, while corn and soybean
prices moved (generally) in the same direction during the 1995
and 1997 marketing years. While market advisory programs do not
make exactly the same recommendations for corn and soybeans in
any given year, there often is a significantly positive correlation
in their corn and soybean pricing performance. This suggests
it is inappropriate to pool separate corn and soybean pricing
results when conducting statistical tests.
A second potential source of dependence
is correlation of net advisory prices through time for a given
service and commodity. This form of correlation may exist due
to persistence in the performance of advisory services through
time (winners continue to win, losers continue to lose). It may
also exist due to the overlapping nature of the marketing years;
each marketing year is two calendar years long, and each set of
contiguous marketing years overlaps by one year. If this correlation
through time exists, it would be inappropriate to pool samples
of net advisory prices across marketing years for the same reason
as discussed above. As will be shown in the following section,
this form of correlation generally is quite low, and therefore,
it is reasonable to pool net advisory prices across marketing
A third potential source of dependence
perhaps is less obvious. It is possible that net advisory prices
for a given commodity and marketing year are correlated because
of the existence of similar programs offered by the same market
advisory service. For example, AgriVisor offers four marketing
programs, which may not differ substantially in outcomes due to
similar methods of analysis and similar underlying strategies.
The potential impact of this form of correlation is examined by
creating one net advisory price for each of the market advisory
firms that offer multiple programs.
A single price is computed by averaging net advisory prices across
programs for a given year and commodity. Pricing performance
results are qualitatively similar to those using the full set
of disaggregated advisory prices, suggesting that net prices of
advisory programs for the same firm are uncorrelated or no more
correlated than net prices from different firms. Hence, use of
net advisory prices by program in tests of market performance
does not appear to be a substantive problem.
A formal test of the null hypothesis
that the proportion of advisory services "beating" the
market benchmark is insignificant requires the specification of
an appropriate test statistic. Anderson, Sweeney and Williams
(1996) show that the sample estimator of the proportion, , is distributed
binomially with an expected value of p and a standard error
of, where p
is the true value of the proportion in the population and n
is the number of sample observations. They also note that the
sampling distribution of is approximately
normal so long as and . Since both conditions
are met for all of the samples considered here, the normality
approximation is invoked. The form of the test statistic based
on the above assumptions is , where p0
is the assumed value of p under the null hypothesis.
The remaining issue is the expected proportion (p0)
under the null hypothesis. The efficient market hypothesis (Fama,
1970) implies that the expected probability of “beating the market”
is the same as the result of flipping a coin and showing heads,
or 0.5. Setting , the test statistic
A formal test of the
null hypothesis that the average percentage difference between
the net price of services and the benchmark price is zero also
requires the specification of an appropriate test statistic.
First, for a given marketing year and commodity, define the percentage
difference for the ith advisory service as , where NAPi
is the net advisory price for the ith advisory
service and BP is the market benchmark price for the same
commodity and marketing year. The sampling distribution of is well-known
and does not need to be described in detail here. The test statistic
for a null hypothesis of zero average percentage difference is
where is the estimated
standard deviation of the differences across the n advisory
services in the sample. The t-statistic follows a t-distribution
with n-1 degrees of freedom.
As noted earlier, ri
can be thought of as the “return” to following the recommendations
of a particular market advisory service. This raises the question
of whether the calculated “returns” are risk-adjusted. If one
is willing to assume that the average risk of advisory services
is equal to risk of the market benchmark, then market advisory
returns can be considered risk-adjusted returns. This type of
approach (risk-matching) is used frequently in studies of returns
to strategies in financial markets (e.g. Ritter, 1991). However,
since it is difficult to test the appropriateness of this assumption
over the short time period considered in this analysis, a risk-adjusted
interpretation of advisory returns should be treated with a good
bit of caution.
It is important to emphasize that
the tests discussed in this section address the pricing performance
of market advisory services as a group. In other words,
average pricing performance across all services is considered.
This is a different issue than the pricing performance of a particular
advisory service. It is possible that advisory services as a
group fail to beat the market, yet at the same time there exist
a small number of services that are exceptions to this outcome.
In the stock market, this argument is often made with respect
to the performance of the Fidelity Magellan Fund. Testing whether
an “exceptional” advisory service beats the market requires more
data than is available for this study and different statistical
methods (Marcus, 1990).
5 reports results of the proportional test of corn pricing
performance for each year and all three years pooled. Individual
year results are quite sensitive to the benchmark considered.
For example, the proportion of services above the 24-month benchmark
price in 1995 is 0.72 and statistically significant, while the
proportion of services above the 12-month benchmark is only 0.08.
This latter proportion is also statistically significant, but
in the opposite direction, indicating significantly inferior performance.
Despite the variation across benchmarks for individual years,
the overall proportions for the three years are similar across
the benchmarks, ranging only from 0.51 to 0.59. None of the three-year
proportions are significantly different from 0.5 at the five-
or ten-percent level, although the 12-month benchmark proportion
is quite close to significance at the ten-percent level.
6 shows the results of the proportional test of soybean pricing
performance for each year and all three years pooled. Like corn,
individual year results are sensitive to the benchmark considered.
The most dramatic contrast again can be found in 1995, where the
proportion of services above the 24-month benchmark price is 0.84
and statistically significant, while the proportion of services
above the 12-month benchmark is only 0.16. The overall proportions
for the three years range from 0.57 to 0.77. Two of the three-year
proportions (24-month and 20-month benchmarks) are significantly
greater than 0.5 at the one-percent level.
7 reports proportional test results for combined corn and
soybean revenue. Given the evidence of positive correlation between
the pricing performance of advisory programs for corn and soybeans
in the same year, it is inappropriate to simply pool the separate
net price observations for corn and soybeans to test combined
performance. Instead, corn and soybean net prices are aggregated
to form a single observation on per-acre revenue for each advisor
and year, and then proportions are computed. The per-acre combined
revenues are those first presented in Table
3. As would be expected, the proportions for revenue per
acre fall between the proportions for corn and soybean net advisory
prices and show a similar pattern of variation across the alternative
benchmarks in a given year. Combined corn and soybean performance
for the entire three-year period is less variable across the benchmarks,
with the proportion of programs above the benchmark ranging only
from 0.60 to 0.64. It is noteworthy that the three-year proportions
are significantly above 0.5 for all three benchmarks.
for the average return test of pricing performance are reported
in Tables 8, 9 and 10. Individual
year and three-year average test results for corn, shown in Table
8, are qualitatively the same as the proportional test results.
Point estimates of the three-year average returns range from –0.34
to 0.74 percent. However, none of the three-year average corn
returns are significantly different from zero. Individual year
and three-year average results for soybeans, reported in Table
9, are qualitatively similar to the proportional test results.
The only differences occur in 1997 for the 24-month and 20-month
benchmarks, where significance is detected for average soybean
returns but not the proportion of services above the market.
Point estimates of the three-year average soybean returns range
from 0.71 to 3.00 percent, substantially higher than for corn.
Two of the three-year average soybean returns are significantly
different from zero (24-month and 20-month benchmarks). Results
of the average return test for combined corn and soybean revenue,
found in Table 10, differ the most
from proportional test results. Three-year average revenue returns
are significant only for the 24-month benchmark, whereas three-year
proportions are significant for all three benchmarks. This divergence
in results appears to be due to large, negative returns in some
years (e.g. 1995, 12-month average benchmark) and relatively higher
variation in returns as compared to proportions. Point estimates
of the three-year average revenue returns range from -0.30 to
1.84 percent, which, as expected, is between the ranges for corn
In statistical terms, the pricing
performance test results presented in this section are fairly
clear. Little or no evidence is found regarding the ability of
market advisory services to consistently and significantly “beat
the market” for corn. There is substantial evidence that market
advisory services consistently and significantly “beat the market”
in soybeans. When corn and soybean net advisory prices are combined
into revenue per acre, some evidence also is found that market
advisory services significantly outperform the market. Tests
results for revenue are the most sensitive to the type of test
and benchmark considered. Overall, the statistical results suggest
that market advisory services have some ability to outperform
broad market benchmarks.
Given the statistical results summarized
above, a relevant question to ask is whether the pricing performance
of advisory services also is economically significant. While
"economic significance" is a vague concept, it is important
nonetheless. Perhaps the best perspective on this question is
gained by re-examining returns for corn and soybean revenue per
acre. Given the sensitivity of measured returns to the benchmark
considered, the best point estimate of revenue returns probably
is the simple average across the three benchmarks. This “grand
average” revenue return across all three marketing years is 0.74
percent, which translates into about $3 per acre above benchmark
While this level of return is probably best characterized as “small,”
it also appears to be non-trivial, particularly in comparison
to the cost of the services. Jackson, Irwin and Good (1999) report
that the average cost of the services is $279 per year. For a
1,000 acre corn and soybean farm, this translates into an average
cost of only 28 cents per acre. There are two important reasons
to be cautious about concluding that advisory returns generate
even a "small" level of economic significance: i) the
results are based on a limited sample of years, and ii) returns
are concentrated in only one market, soybeans.
The results of the analysis also
have implications for the ongoing debate about market efficiency
and risk management strategies in agriculture. One view is that
grain markets (cash, futures and options) are not efficient and,
therefore, provide opportunities for farmers to systematically
earn additional profits through marketing (e.g Wisner, Blue and
Baldwin, 1998). The other view is that grain markets are at least
efficient with respect to the type of strategies available to
farmers (e.g., Zulauf and Irwin, 1998). Since the return of advisory
services over 1995-1997 significantly exceeds transactions costs
in some cases, including the cost of the services, the results
potentially imply a rejection of market efficiency in the sense
of Grossman and Stiglitz (1980).
A firm conclusion cannot be reached due to the uncertainties pointed
out with respect to economic significance. In addition, there
is uncertainty about the appropriate adjustment for risk or a
complete accounting for the costs of implementing advisory service
recommendations. It may be that important costs are ignored,
such as search costs, monitoring costs and related management
costs. Nevertheless, the performance results suggest market advisory
services, at least to a modest extent, have some access to information
not available to other market participants and/or superior analytical
Finally, it is interesting to compare
the pricing performance results for market advisory services to
that of other investment professionals. According to Morningstar
Reports, only 16% of active mutual fund managers beat the returns
to a broad stock market average over the last decade (Clements,
1999). By comparison, the performance of agricultural market
advisory services is quite strong, with about half of the services
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 skillfulness of advisory services, or an
inappropriate adjustment for advisory service risk. Determining
which explanation is correct will be an important subject for
future research as more data on market advisory performance becomes
Predictability of Advisory Service
Even if, as a group, advisory services generate
positive returns, there is a wide range in performance for any
given year. For example, soybean net advisory prices for 1995
vary from $5.71 per bushel to $7.94 per bushel. While this example
probably is the most dramatic, the variation across advisors in
other cases is substantial. This raises the important question
of the predictability of advisory service performance from year-to-year.
In other words, is past performance indicative of future results?
This issue is addressed two ways: i) by calculating correlation
coefficients for measures of advisory service performance across
adjacent marketing years, and ii) determining the average performance
for services ranked by quantiles in a year subsequent to the initial
year. The testing procedures have been widely applied in studies
of financial investment performance (Elton, Gruber, and Rentzler,
1987; Irwin, Zulauf and Ward, 1994; Lakonishok, Shleifer and Vishny,
1992). Recent analysis by Brorsen and Townsend (1998) indicates
these methods are reasonably powerful in detecting performance
persistence if it exists.
The first test of predictability
is based on the correlation between performance measures of individual
market advisory programs across pairs of marketing years. The
first step in the analysis is to rank each advisory service based
on net price received. Then the services are sorted in descending
order. For example, the service with the highest net advisory
price is ranked number 1, and the service with the lowest net
advisory price is assigned a number equal to the total number
of observations for that commodity in the given year. Finally,
the correlation coefficient is computed between the sorted performance
measures for two adjacent marketing years. A significant correlation
indicates predictability in returns across years.
2 presents a graphical illustration of the correlation across
marketing years for corn, both in terms of advisory rank and percentage
return above the 24-month market benchmark price. Figure
3 shows the same relationships for soybeans, and Figure
4 for revenue.
Estimated correlation coefficients and tests of significance are
presented in Table 11. For corn, a significant
and moderately positive correlation is found in the net advisory
price and the percentage return above the 24-month benchmark between
the 1995 and 1996 marketing years. A positive correlation also
is found between the rank of the services in corn between 1995
and 1996, but it is not statistically significant. Nominally,
just the opposite situation occurs for the 1996 and 1997 marketing
years, where negative correlations are found for all three performance
measures. The net result is a small average correlation coefficient
across the two pairs of years, about 0.10. Hence, there does
not appear to be consistent pricing performance across time in
corn for individual advisory services.
Little evidence of predictability
is found for soybeans. All of the estimated correlation coefficients
are positive, but only one is significantly different from zero
(rank correlation, 1995 vs. 1996). When averaged across the two
pairs of marketing years, the correlation is only about 0.20.
Predictability results for revenue are similar to those found
for corn and soybeans. Overall, there does not appear to be evidence
of persistence in the pricing performance of market advisory services.
While the correlation analysis
does not appear to find predictability in advisory service performance
across all advisory services, it is possible that sub-groups of
advisory services may exhibit predictability. In particular,
predictability may only be found at the extremes of performance.
That is, only top-performing services in one year may tend to
perform well in the next year, or only poor-performing services
may perform poorly in the next year. To examine this form of
predictability, market advisory programs are grouped according
to performance in one marketing year, and their average performance
in the next marketing year is evaluated. Market advisory programs
are grouped into quantiles of thirds and fourths.
Quantile results for corn market
advisory programs in the 1996 marketing year based on performance
in 1995 are presented in Table 12.
When the programs are broken into three groups, the group in the
middle third of advisory performance in 1995 performs the best
in 1996 in terms of average price and average percentage return
above the market benchmark. The top third of advisory programs
in 1995 has a slightly better average rank in 1996. Similarly
mixed results are found among the top and middle groups when the
programs are broken into four groups. While statistical significance
is not assessed in this analysis, it appears that any real persistence
in performance is found in the bottom group – i.e., market advisory
programs that performed poorly in 1995 also perform poorly in
1996, both in terms of prices and rank.
The results of the 1996 and 1997
comparison for corn are presented in Table
13, and these results show a much more mixed picture. When
broken into three groups, advisory performance measures among
the groups are virtually identical in 1997. The quartile analysis
contains a rather odd statistical anomaly, in which the first
and third groups and the second and fourth groups in 1996 produce
identical average prices in 1997, as well as similar ranks. This
does not argue for overall persistence in performance among the
The soybean performance results
for 1995 versus 1996, shown in Table
14, present a very similar picture to the 1995 versus 1996
corn results. The main evidence of persistence in results is
that services that do poorly in 1995 also show worse pricing performance
in 1996. The soybean results for 1996 versus 1997 presented in
Table 15 also are similar to those
for corn over the same years, in that little evidence of persistence
16 presents two-year average results of the persistence measure
shown in Tables 12 through 15.
The advisory programs are grouped into quantiles each year (year
t) and the average result in the next year (year t+1) is calculated.
The two-year average results indicate that any persistence in
year-to-year performance is found only among the poorly performing
advisory programs. Based upon the results in Tables
12 through 15, it is obvious that the two-year results are
mostly a function of the 1995 versus 1996 results.
In general, the predictability results
reported in this section provide little evidence that future advisory
service pricing performance can be predicted from past performance.
The limited evidence in favor of predictability applies only to
the poorest performing services. This information may well be
of use to farmers as they make selection decisions. The similarity
between the results for advisory services and mutual funds is
striking. A number of studies find that mutual fund investment
performance is not predictable in general, but that mutual funds
ranked in the bottom tier in one year tend to remain in the bottom
tier in the future (e.g. Brown, Goetzmann, Ibbotson and Ross,
1992; Carhart, 1997). This has led researchers to search for
an explanation of why investors continue to invest in mutual funds
with predictably poor performance. One explanation is myopic
loss aversion on the part of mutual fund investors. (Odean, 1998).
Finally, even though past net price
performance does not appear to be useful in predicting future
net price performance, this does not mean it is impossible to
predict advisory service performance. There may be other variables
associated with performance that can be used for prediction.
For example, 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. Whether these types
of factors can predict advisory service performance is an interesting
question that awaits further research.
Farmers view market advisory services
as a significant source of market information and advice in their
quest to manage price risks associated with grain marketing.
Given the high value that farmers place upon market advisory services,
it is somewhat surprising that only two academic studies investigate
the pricing performance of advisory services. The lack of studies
is most likely due to the difficulty in obtaining data on the
stream of recommendations provided by services.
In 1994, the Agricultural Market
Advisory Service (AgMAS) Project was initiated, with the goal
of providing unbiased and rigorous evaluation of market advisory
services for crop farmers. Since its inception, the AgMAS Project
has been collecting marketing recommendations for about 25 market
advisory programs. The AgMAS Project subscribes to all of the
services that are followed, and as a result, "real-time"
recommendations are obtained. This prevents the data from being
subject to survivorship and hindsight biases.
The purpose of this paper is to
address two basic performance questions for corn and soybeans
using the net price received reported by the AgMAS Project for
the 1995, 1996 and 1997 marketing years. The two basic questions
are: 1) Do market advisory services, on average, outperform an
appropriate market benchmark? and 2) Do market advisory services
exhibit persistence in their performance from year-to-year? At
least 21 advisory services are included in the evaluations for
each commodity and marketing year. While the sample of advisory
services is non-random, it is constructed to be generally representative
of the majority of advisory services available to farmers. The
tests used to determine average performance of market advisory
services and predictability of performance through time have been
widely applied in the financial literature.
Tests of pricing performance relative
to a market benchmark are based on the proportion of services
exceeding the benchmark price and the average percentage difference
between the net price of services and the benchmark price. In
statistical terms, the pricing performance test results provide
little evidence that market advisory services consistently and
significantly “beat the market” in corn. There is substantial
evidence that market advisory services consistently and significantly
“beat the market” in soybeans. When corn and soybean net advisory
prices are combined into revenue per acre, some evidence also
is found that market advisory services significantly outperform
the market. Tests results for revenue are the most sensitive
to the type of test and benchmark considered. Overall, the statistical
results suggest that market advisory services have some ability
to outperform broad market benchmarks.
It is debatable whether the performance
of advisory services also is economically significant. Perhaps
the best perspective on this question is gained by examining returns
for corn and soybean revenue per acre. For all three marketing
years, returns averaged 0.74 percent above benchmark revenue,
which translates into about $3 per acre. While this level of
return is probably best characterized as “small,” it also appears
to be non-trivial, particularly in comparison to the cost of the
services. However, there are two important reasons to be cautious
about concluding that advisory returns generate even a "small"
level of economic significance: i) the results are based on a
small sample of years, and ii) returns are concentrated in only
one market, soybeans.
Tests of predictability are based
on the year-to-year correlation of advisory service ranks, prices
and percentage differences from the benchmark. In general, the
predictability results provide little evidence that advisory service
pricing performance can be predicted from year-to-year. The average
correlation coefficient relating performance from one year to
the next is about 0.10 to 0.20. When services are grouped by
performance quantile, some evidence of predictability is found
for the poorest performing services, but not for top performing
In conclusion, the results of this
study suggest that, on average, market advisory services exhibited
some ability to "beat the market" for the 1995 through
1997 corn and soybean crops. Possible explanations for this result
include: i) a unique time period in corn and soybean markets,
ii) inefficient commodity markets, iii) the skillfulness of advisory
services or iv) a return to risk. Determining which explanation
is correct will be an important subject for future research as
more data on market advisory performance becomes available.
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[*] Scott H. Irwin and Darrel
L. Good are Professors in the Department of Agricultural and
Consumer Economics at the University of Illinois at Urbana-Champaign.
Thomas E. Jackson is the AgMAS Project Manager in the Department
of Agricultural and Consumer Economics at the University of
Illinois at Urbana-Champaign. Funding for the AgMAS Project
is provided by the following organizations: American Farm Bureau
Foundation for Agriculture; Council for Food and Agricultural
Research (C-FAR); Cooperative State Research, Education, and
Extension Service, U.S. Department of Agriculture; Economic
Research Service, U.S. Department of Agriculture; and the Risk
Management Agency, U.S. Department of Agriculture. The authors
gratefully acknowledge the valuable comments of members of the
AgMAS Project Review Panel and participants at the 1999 NCR-134
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. Several
analyses 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. Each of these studies indicates the average price
generated by the services exceeds a benchmark price (e.g. selling
100 percent at harvest). More recent evaluations appear in Top
Producer magazine (e.g. Powers, 1993). In this case, evaluations
of corn, wheat, and soybean recommendations from advisory services
are reported on a regular basis. Kastens and Schroeder (1996)
examine futures trading profits based on the information reported
in Top Producer for the 1998-1996 crop years. They find
negative trading profits for wheat and positive trading profits
for corn and soybeans.
 See Zulauf and Irwin (1998) for a classification
and review of marketing strategy studies.
 When the AgMAS study began in 1994,
DTN and FarmDayta were separate companies. The two companies
merged in 1996.
 This assumption subsequently is relaxed
to reflect the growing importance of alternative means of electronic
delivery of market advisory services. Beginning in 1997, a
service that meets the original two criteria and is available
on a "real-time" basis electronically may be included
in the sample. Two examples are Utterback Marketing Service,
which is carried on a World Wide Web site, and Ag Review, which
is available via e-mail. Both are for-pay subscription services.
 Four services from the original sample
(Grain Field Report, Harris Weather/Elliott Advisory, North
American Ag, and Prosperous Farmer) are dropped in 1997 because
they no longer provide specific recommendations regarding cash
sales. Another service (Agri-Edge) included in the original
sample also is dropped in 1997 because the service was discontinued
during the 1997 crop year. After becoming aware of its availability,
one service (Progressive Ag) is added to the sample for 1996
and 1997. Another service (Utterback Marketing Services) is
included in 1997, but not 1995 or 1996 because its marketing
programs are not deemed to be clear enough to be followed by
the AgMAS Project during these years. Two programs for corn
only (Allendale futures & options and Ag Line by Doane hedge)
are introduced for the 1996 marketing year, and therefore, added
for 1996 and 1997. Finally, one service (Ag Alert for Ontario)
is added in 1996 but dropped in 1997 because their advice is
geared to Canadian farmers, and after review, is not deemed
to be generalizable to U.S. farmers.
 Four services (Agri-Edge, Brock Associates,
Pro Farmer, and Stewart-Peterson Advisory Services) each have
two distinct marketing programs, and one (Agri-Visor) has four
distinct marketing programs. Two services (Allendale and Ag
Line by Doane) both provide two distinct programs for corn but
only one for soybeans.
 Some of the programs that are depicted
as “cash-only” do in fact have some futures-related activity,
due to the use of hedge-to-arrive contracts, basis contracts,
and some use of options.
 There are a few instances where a service
clearly differentiates strategies based on the availability
of on-farm versus off-farm (commercial) storage. In these instances,
recorded recommendations reflect the off-farm storage strategy.
Otherwise, services do not differentiate strategies according
to the availability of on-farm storage.
 Technically, corn and soybean prices
during the month of September may be considered “pre-harvest”
in some marketing years for central Illinois. In the interest
of simplicity, September cash prices are treated as “post-harvest”
in the computation of the 12-month average cash price benchmark.
 These results originally are presented
in Jackson, Irwin and Good (1999). Complete details regarding
the components of the net prices (futures and options gains
and losses, net cash price, etc.) can be found in this study.
 From this point forward, the term
"marketing year" or "year" refers to the
marketing window for a particular crop year. This is done to
simplify the presentation of results. It is useful to remember
that a "marketing year" in the context of this research
actually represents a two-year marketing window.
 Note that return in this case refers
to return net of marketing costs but no other production costs.
 Return correlations based on the
20-month and 12-month average cash price benchmark are similar
to those based on the 24-month benchmark.
 These results are not presented in
due to space constraints, but are available from the authors
 This calculation ignores economies
of size that may accrue to larger farms implementing the recommendations.
It also ignores contract "lumpiness" problems that
may be significant for smaller farms.
 Adding the subscription cost of services
to the transactions costs considered in computing net advisory
prices does not alter the performance results. For a 1,000
acre farm, subscription costs amount to less than one-tenth
of one percent of the average corn and soybean revenue per acre.
 Return correlations also are calculated
for corn, soybeans and revenue using 20-month and 12-month benchmarks.
Results are similar to the 24-month benchmark return correlations
and are not presented due to space considerations.
 Bartlett’s approximation for the
standard error () of the Pearson
correlation coefficient (r)is employed. The test statistic
a standard, normal distribution.
 Given the similarity of revenue
correlation results to corn and soybean correlation results,
the quantile analysis is conducted only for corn and soybeans.
Even if year-to-year persistence in performance is found, it
may not be of much practical use to a farmer who wishes to use
the information to either subscribe to a service based upon
strong past performance or to avoid a service based upon poor
past performance. This is due to the fact that each marketing
window is two calendar years long, and each set of contiguous
marketing windows overlaps by one year. For example, the 1995
marketing window ends on August 31, 1996. Therefore, final
results for 1995-crop recommendations cannot be finalized until
after this time. However, by the end of August 1996 the 1996
marketing window had already ended its first year. Therefore,
a farmer who wishes to employ the 1995 performance results to
help select a market advisory service for the 1996 crop finds
that the information is available too late. The 1995 results
would, however, be available early in the 1997 marketing window.
In order to address this issue, 1995 pricing performance of
the advisory programs is compared with 1997 pricing performance.
For corn, a significantly negative correlation is found for
all three measures of pricing performance. For soybeans, correlation
is found to be very near zero for all three measures. Given
the results presented in the text, it is difficult to regard
the 1995/1997 results for corn as little more than a statistical
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