Research Reports

Report 2005-02: The Pricing Performance of Market Advisory Services in
Corn and Soybeans Over 1995-2003: A Non-Technical Summary

March 2005

Scott H. Irwin, Darrel L. Good, Joao Martines-Filho and Lewis A. Hagedorn [1]

Copyright 2005 by Scott H. Irwin, Darrel L. Good, Joao Martines-Filho and Lewis A. Hagedorn . 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.

Introduction

Farmers in the US consistently identify price and income risk as one of the greatest management challenges they face. Surveys suggest that numerous farmers view professional market advisory services as an important tool in managing price and income risk. As a result, there is a need to develop an ongoing "track record" of the performance of market advisory services to assist farmers in identifying successful alternatives for marketing and price risk management. The Agricultural Market Advisory Service (AgMAS) Project was initiated in 1994 with the goal of providing such information.

The purpose of this research report is to summarize the pricing performance of professional market advisory services for the 1995-2003 corn and soybean crops. The results for 1995-2001 were released in earlier AgMAS research reports, while the results for the 2002 and 2003 crop years are new. Complete details on data collection, computation of net advisory prices and benchmarks and pricing performance tests can be found in the full AgMAS research report by Irwin et al. (2005).

At least 23 advisory programs are included in the evaluations for each commodity and 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. Two indicators of pricing performance are presented. The first indicator is the proportion of advisory programs that beat benchmark prices. The second indicator is the average price of advisory programs relative to benchmarks. Both market and farmer benchmarks are considered in the evaluations.

At the outset, it is important to point out that only nine 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 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.

Computing the Returns to Marketing Advice

In order to evaluate the returns to the marketing advice generated by advisory services, the AgMAS Project purchases a subscription to each of the programs offered by a service[2]. The information is received electronically via websites, e-mail or satellite service (DTN). Staff members of the AgMAS Project read the daily information provided by each advisory program. As a result, "real-time" recommendations are obtained.

After AgMAS staff collects the stream of recommendations for a particular crop year, 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. 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 1995-2003 corn and soybean crops. 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 next step in evaluating pricing performance is specification of objective standards of performance. These objective standards typically are referred to as "benchmarks." It is commonplace to compare performance to benchmarks in other economic contexts, such as financial investments. Some of the best-known stock investment benchmarks are the Dow-Jones Industrials Index, S&P 500 Index and the Wilshire 5000 Index.

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 pricing 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. The first market benchmark averages cash price over the entire 24-month marketing window, which begins on September 1 of the year prior to harvest and ends on August 31 of the year after harvest. The second market benchmark is computed by simply deleting the first four months of the 24-month pricing-window from the computations of the average market price. Behavioral market theory suggests that the average return actually achieved by market participants is 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. Note that the same simulation assumptions applied to advisory service track records (e.g., storage costs) are applied to the market and farmer benchmarks.

Net Advisory Prices and Benchmarks for 1995 - 2003

Net advisory prices and benchmarks for the 1995-2003 crop years are reported in Tables 1 and 2. In order to obtain a consistent set of net advisory prices and benchmarks for the entire sample period, commercial storage costs are assumed. It is not possible to present parallel results assuming on-farm variable costs of storage, because the AgMAS Project first computed net advisory prices and benchmarks under this alternative storage cost assumption for the 2000 crop year. See the previously mentioned AgMAS research report by Irwin et al. (2005) for 2000-2003 crop year results that assume on-farm variable costs of storage. Also note that some of the market advisory services included in the tables are not evaluated for all nine crop years.

Table 1 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 1. 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 2, the average advisory price for soybeans ranged from $5.24 per bushel in 2002 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 2003, where the range in advisory prices is just under $4 per bushel. The three alternative benchmark prices for soybeans are shown at the bottom of Table 2. 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.

Since many subscribers to market advisory services produce both corn and soybeans, it is relevant 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 service 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.

Table 3 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 nine crop years (1995, 1999 and 2000) the range in advisory revenue exceeds $100 per acre.

Advisory Service Pricing Performance Over 1995-2003

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, marketing information and market analysis are the two highest rated uses of market advisory programs by farmer-subscribers (Pennings et al., 2004). 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 1995-2003 is presented in Table 4. Note that average proportions for 1995-2003 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 4), 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 nine crop years. The average proportion for 1995-2003 is 50% versus the 24-month benchmark and 59% 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 equals or exceeds 50% each crop year. The average proportion above the farmer benchmark over 1995-2003 is 68%. This is larger than the average proportions versus the market benchmarks and indicates a better chance of market advisory programs generating net prices higher than the farmer benchmark. However, there has been a noticeable downtrend in proportions versus the farmer benchmark since 1998.

Moving to soybeans (Panel B: Table 4), 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 proportions for the two market benchmarks is 45 and 36 percentage points, respectively. No clear trend is apparent for the proportions versus either market benchmark. Despite these differences for individual crop years, the average proportions for 1995-2003, 65% versus the 24-month benchmark and 72% versus the 20-month benchmark, both indicate a better than average chance of advisory prices beating market benchmark prices in soybeans. The average proportion above the farmer benchmark over 1995-2003 is 54%. This indicates a small chance of market advisory programs generating net prices in soybeans higher than the farmer benchmark. In addition, there has been a sharp downtrend in proportions versus the farmer benchmark since 1998.

Given the combined nature of 50/50 advisory revenue, it is not surprising that revenue proportions (Panel C: Table 4) typically are between those of corn and soybeans. The average proportion for 1995-2003 is 59% versus the 24-month benchmark and 68% versus the 20-month benchmark, indicating a marginal to better than average chance of advisory revenue beating market benchmark revenue. The average proportion above the farmer benchmark over 1995-2003 is 62%. This indicates a moderate chance of advisory revenue beating farmer benchmark revenue. Mirroring the results for corn and soybeans, a sharp downtrend is observed in proportions versus the farmer benchmark since 1998. 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 farmer benchmark in one commodity that more than offset the losses below the benchmark in the other commodity.

Overall, the directional performance results over 1995-2003 suggest several key findings. First, advisory programs in corn do not consistently beat market benchmarks, but tend to consistently beat the farmer benchmark. Second, advisory programs in soybeans exhibit just the opposite pattern, consistently beating the market benchmarks but not the farmer benchmark. Third, in terms of 50/50 revenue, advisory programs show marginal consistency in beating both the market benchmarks and the farmer benchmark. So, the results provide mixed performance evidence with respect to both the market benchmarks and the farmer benchmark.

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 5 and 6 basically tell the same story as those based on the proportion beating the benchmarks. Average differences from market benchmarks for corn over 1995-2003 (panel A: Table 5) are small, ranging from 1 to 3¢ cents per bushel.[3] At 8¢ cents per bushel, the average difference from the farmer benchmark for corn is larger. Average differences from market benchmarks for soybeans over 1995-2003 (panel B: Table 5) are substantial, ranging from 14 to 16¢ per bushel. In contrast, the average difference from the farmer benchmark for soybeans is -1¢ per bushel. Average differences for 50/50 advisory revenue range from $4 to 7 per acre for market benchmarks over 1995-2003 (Table 6). The average revenue difference versus the farmer benchmark is similar at $7 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 2003 for soybeans (Panel B: Table 5), where the average difference from the 24-month market benchmark is +27¢ per bushel, while the average difference from the farmer benchmark is -105¢ per bushel. [4].

An important consideration is the size of the average differences versus the farmer benchmark from an economic decision-making perspective. The average advisory return relative to the farmer benchmark is $7 per acre, or about two percent of average farmer benchmark revenue. Even though this return is small and entirely from corn, it nonetheless represents a non-trivial 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, 2004). The comparison does not account for yearly subscription costs, which is not a major problem because subscription costs are quite small relative to revenue. Subscription costs are only 18¢ per acre for a 2,000 acre farm and 72¢ per acre 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).

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. Price patterns represent one potential source of bias in the performance results. It turns out there are systematic patterns in corn and soybean price movements during the sample period that may have an important impact on the tests results. Figure 1 shows the average pattern of corn and soybean prices over the 24-month marketing window for the 1995-2003 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 prices over the 24-month window is substantial, with the high in pre-harvest prices about 55¢ per bushel higher than the post-harvest low (net of storage costs). A marketing strategy in corn 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. The price pattern in soybeans is noticeably different, with prices roughly flat for the pre-harvest period and then rising sharply through the post-harvest period before dropping off sharply. In this case, a marketing strategy that systematically priced more heavily in the first two-thirds of the post-harvest period would perform the best.

Next, consider the average "marketing profiles" found in Figure 2 for corn and soybeans over the 1995-2001 crop years.[5] The marketing profiles show the average amount of corn and soybean crops priced (sold) by market benchmarks, advisory programs and farmers on a cumulative basis, each day over the two-year period beginning in September of the year before harvest and ending August of the year after harvest. Since USDA marketing weights represent grain purchases, which are not necessarily the same as pricing weights due to farmers' use of forward contracts, the marketing profile for farmers is only hypothetical. 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. In light of the downward price trend in corn, the marketing profiles reveal why market benchmarks and advisory programs in corn generated higher average prices than the farmer benchmark over the last nine crop years. More than likely, farmers priced much less of the corn crop in the pre-harvest period than the market benchmarks or advisory programs. In contrast, the price trends in soybeans favored the marketing pattern of farmers, allowing them to perform about the same as advisory programs and actually outperform the market benchmarks.

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 nine crop years provides a reliable guide for the future. The persistence of downward price trends frequently observed over 1995-2003 in corn is an especially hotly debated issue. Further study is needed to determine whether the price patterns observed over 1995-2003 are representative of patterns in the long-run. This information would help to clarify whether market conditions during 1995-2003 bias performance comparisons in any particular direction.

Please note that the AgMAS research report by Irwin et al. (2005) contains complete pricing performance results. In particular, additional results show that consideration of risk weakens performance results in some cases and that it is difficult to predict the pricing performance of advisory programs from past performance.

Summary and Conclusions

The purpose of this research report is to summarize the pricing performance of professional market advisory services for the 1995-2003 corn and soybean crops. The first indicator of performance presented in the report is the proportion of advisory programs that beat benchmark prices. Between 50 and 59% of the programs in corn have net advisory prices above market benchmarks over 1995-2003, indicating a zero to marginal chance of advisory prices in corn beating market benchmark prices. In contrast, 68% of the programs have prices above the farmer benchmark in corn. Between 65 and 72% of advisory programs in soybeans have advisory prices above the market benchmarks over 1995-2003, suggesting a better than average chance of advisory prices beating market benchmark prices in soybeans. The proportion of advisory programs above the farmer benchmark in soybeans is only 54%, indicating a small chance of programs generating net prices in soybeans higher than the farmer benchmark. Between 59 and 68% of advisory programs have revenue above the market benchmarks over 1995-2003, while 62% have revenue above the farmer benchmark. This indicates a moderate chance of advisory revenue beating farmer benchmark revenue. Overall, the directional test results provide mixed performance evidence with respect to the market benchmarks and 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 1995-2003 are small, ranging from one to three cents per bushel. At 8¢ per bushel, the average difference versus the farmer benchmark for corn is larger. Average differences from market benchmarks for soybeans over 1995-2003 are substantial, ranging from 14 to 16¢ per bushel. In contrast, the average difference from the farmer benchmark for soybeans is -1¢ per bushel. Average differences for advisory revenue range from $4 to 7 per acre for market benchmarks over 1995-2003. The average revenue difference versus the farmer benchmark is $7 per acre.

Overall, the performance results over 1995-2003 suggest several key findings. First, advisory programs in corn do not consistently beat market benchmarks, but tend to consistently beat the farmer benchmark. Second, advisory programs in soybeans exhibit just the opposite pattern, consistently beating the market benchmarks but not the farmer benchmark. Third, in terms of 50/50 revenue, advisory programs show marginal consistency in beating both the market benchmarks and the farmer benchmark. So, the results provide mixed performance evidence with respect to both the market benchmarks and the farmer benchmark.

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 over the view that grain markets are inefficient and provide substantial opportunities for farmers to gain additional profits through marketing. The evidence is more positive with respect to the farmer benchmark, even after taking risk into account. This raises the possibility that even though advisory services do not "beat the market," they nonetheless provide the opportunity for some farmers to improve performance relative to the market. Mirroring debates about stock investing, 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. The rising interest in these new marketing contracts suggests the potential for historic changes in the approach farmers' use to market crops.

References

Anderson, J.R. "Sparse Data, Estimational Reliability, and Risk-Efficient Decisions." American Journal of Agricultural Economics, 55(1974): 564-572.

Colino, E.V., S.M. Cabrini, S.H. Irwin, D.L. Good and J.Martines-Filho. "Advisory Service Marketing Profiles for Soybeans in 2001." AgMAS Project Research Report 2004-02, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, April 2004. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Colino, E.V., S.M. Cabrini, S.H. Irwin, D.L. Good and J.Martines-Filho. "Advisory Service Marketing Profiles for Corn in 2001." AgMAS Project Research Report 2004-01, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, April 2004. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Irwin, S.H., D.L. Good., J. Martines-Filho, and L.A. Hagedorn. "The Pricing Performance of Market Advisory Services in Corn and Soybeans Over 1995-2003." AgMAS Project Research Report 2005-01, University of Illinois at Urbana-Champaign, March 2005. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Lattz, D.H., C.E. Cagley and D.D. Raab. Summary of Illinois Farm Business Records for 2001, Circular 1384, University of Illinois Extension, 2002.

Martines-Filho, J., S.H. Irwin, D.L. Good, S.M. Cabrini, B.G. Stark, W. Shi, R.L. Webber, L.A. Hagedorn and S.L. Williams. "Advisory Service Marketing Profiles for Corn Over 1995-2000," AgMAS Project Research Report 2003-03, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, June 2003a. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Martines-Filho, J., S.H. Irwin, D.L. Good, S.M. Cabrini, B.G. Stark, W. Shi, R.L. Webber, L.A. Hagedorn and S.L. Williams. "Advisory Service Marketing Profiles for Soybeans Over 1995-1999," AgMAS Project Research Report 2003-04, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, June 2003b. (http://www.farmdoc.uiuc.edu/agmas/reports/index.html)

Pennings, J.M.E., O. Isengildina, S.H. Irwin and D.L. Good. "Modeling the Impact of Market Advisory Services on Producers' Decisions." Journal of Agricultural and Resource Economics, 29(2004):308-327.

Tomek, W.G. and H.H. Peterson. "Risk Management in Agricultural Markets: A Review." Journal of Futures Markets, 21(2001):853-985.

Endnotes