Anderson Bror Sen 2005

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MARKETING PERFORMANCE OF OKLAHOMA

FARMERS

KIM B. ANDERSON AND B. WADE BRORSEN

Relative to marketing most agricultural com-modities, the dominant academic paradigm isthe efficient market hypothesis that suggeststhat little profits can be made by trying to timewhen to sell wheat. The paradigm says thatfarmers will, on average, receive an averageprice.

A view that has been expounded at Coop-erative Extension meetings and in farm pressarticles is that most farmers receive a belowaverage market price. For example, at a re-cent Farm Bureau meeting, the master of cer-emonies made the statement, producers tendtosell onthe bottomthird of the market. Also,in a mid-March 2001 issue of the ProgressiveFarmer, Dr. G.A. Barnaby from Kansas StateUniversity wasquotedas saying,Maybe thatswhy most farmers sell in the bottom third ofthe market (Batchelor, p. 25).1 The researchquestion then is do farmers really sell in the

bottom third of the market or is this just aclever marketing gimmick by market advisoryservices?

The idea of farmers selling in the bottomthird of the market is not without theoreticalfoundation. Results from behavioral financefind that people have systematic psychologi-cal biases (Brorsen and Anderson). These bi-ases could result in herding behavior in such away that the majority could always be wrong.A study by Slusher, based on a narrow sur-

vey of Indiana corn farmers and conducted in1986, is the only one that we know of that hasaddressed the issue of actual farmer perfor-

Kim B. Anderson is professor and Cooperative Extensioneconomist. B. Wade Brorsen is a regents professor and Jean andPatsy Neustadt Chair, Department of Agricultural Economics,Oklahoma State University.

This project was funded by a grant from the Southern RegionRisk Management Education Center, USDA. Helpful commentswere provided by Scott Irwin and Terry Kastens.

This article was presented in a principal paper session at theAAEA annual meeting (Providence, Rhode Island, July 2005).

The articles in these sessions are not subjected to the journalsstandard refereeing process.

1 Dr. Barnaby may or may not have been quoted out of context.The point is that the quote producers tend to sell in the bottomthird of the market is generally believed and expounded by pro-ducers and mass media professionals.

mance. There is a need for rigorous researchto either challenge or support the claim thatfarmers sell in the bottom third of the market.

The general objective of this project will beto determine the actual pricing performance ofOklahoma wheat producers. The two specificobjectives are to: (a) determine if most Okla-homa wheat producers sell in the bottom one-third of the market, and (b) determine if thereis any short-run pattern in when Oklahomawheat producers tend to sell.

Granger causality tests are used to deter-mine the pattern of when producers sell. Suchtests are tests of whether producers are noisetraders (Black; Sanders, Irwin, and Leuthold).Noise traders trade on noise as if it were in-formation. Positive feedback noise traders buyafter price increases while negative feedbacktraders sell. Sanders, Irwin, and Leuthold de-fined a feedback trader as having long memory

if more than the most recent periods return in-fluenced decisions.

Data

Daily wheat purchases and actual prices paidwereobtained fromthree Oklahoma elevators,one each, located in the southern, central, andnorthern parts of the wheat-producing region.

The data were entered and screened for er-rors. Daily reported prices for each locationwere also obtained from the Oklahoma CropReporting Service.

Data were collected for the period June 1992through May 2001. The South, Central, andNorth elevators purchased an average of 1.2,1.25, and2.3million bushels per year with aver-age transactions of 1,572, 789, and 837 per yearfor the South, Central, and North elevators, re-spectively. There were 14,148 transactions for

the South elevator, 7,101 for the Central ele-vator, and 7,533 for the North elevator.Oklahoma is closer to the Gulf than most

U.S. wheat-producing regions and the U.S.wheat harvest starts near southern Oklahoma

Amer. J. Agr. Econ. 87 (Number 5, 2005): 12651270Copyright 2005 American Agricultural Economics Association


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1266 Number 5, 2005 Amer. J. Agr. Econ.

and progresses to North Dakota. Thus, harvestprices in Oklahoma tend to be higher than innorthern states.

Benirschka and Binkley argued that grainclosest to the market would be stored theshortest period. Thus, it would be expected

that most of Oklahomas wheat would be soldearly in the crop year. Indeed, the OklahomaState University Cooperative Extension rec-ommendation is to sell all wheat by January ofeach year. Oklahomas harvest is in June butseasonal price lows are normally in late-July.Thus, Oklahoma producers, particularly in thesouth, have a strong incentive to sell at harvest.Therefore, Oklahoma data should provide astrong test of whether farmers sell in the lowerthird of the market.

Producers tend to compare the prices theyreceived to the market prices offered betweenharvests. Thus selling in the lower third ofthe market was defined relative to a twelve-month cash-marketing window or benchmark.Another benchmark that producers use is themarket price offered during harvest.

Jirik et al. use a twenty-four month windowin their analyses of market advisory servicesand 50% of sales are before harvest. Whilepreharvest sales may be common for corn andsoybeans in Illinois, they are not common forOklahoma wheat.

Procedures

InOklahoma,about90%ofthewheatisstoredin commercial elevators (USDA) and wheatstored on farm is mostly used for seed. Ac-tual storage charges were provided for wheatstored in the South elevator. Storage costs

of $0.00085 per day were used for the Cen-tral and North elevators. Net prices receivedwere calculated by subtracting storage costand interest cost of the prime rate plus 2%from the actual price received. Net prices werecalculated for each transaction. The averagenet price received was the average transactionprice.

For each location, daily market prices wereobtained from the Crop Reporting Service,USDA and were adjusted for storage and in-

terest costs for each day of the marketing year.These net prices were used to define the bot-tom third, bottom half, and upper third of thedistribution of net prices offered to farmers ateach location. The percentage of bushels soldin the bottom third, bottom half, and upper

third of the net prices offered that year wascalculated and used to determine if two-thirdsof Oklahoma farmers sold in the bottom thirdof the price distribution.

For farmers to consistently receive a be-low average price, farmers must follow a

pattern for selling wheat. Behavioral financetheory (Brorsen and Anderson) suggests pat-terns that may exist. Important patterns to testare whether farmers are more likely to sellas prices increase or as prices decrease. Theframework to do such tests is Granger causal-ity (Sanders et al.). Granger causality is usedbecause spurious results can be obtained byregressing one autocorrelated series upon an-other. This approach will also be used to deter-mine if sales vary by day of the week or time

of the month.The two variables in the bivariate autore-gression are the frequency of sales on day t(dayt) and the change in reported prices ondayt(dpricet). Daily bushels sold was also con-sidered as an alternative dependent variable tofrequency of sales, but was not used because ofseverenon-normality created dueto some verylarge sales. Results using bushels also showedprices leading sales when a square-root trans-formationwasused forthedependentvariable,even though the regression had a severe lossof power due to the non-normality. Only theprices for the North elevator were used in thebivariate regression to avoid any possible in-fluence of sales being clustered at harvest forthe other locations.

Seasonality was modeled with sine and co-sine functions of frequencies of one year,six months, and four months. Day ofthe week was modeled with dummy vari-ables. Friday was omitted so the coefficientsshould be interpreted as the difference from

Friday.The frequency of sales is a count variable.

The two traditional ways of modeling a countvariable are (1) ordinary least squares with asquare-root transformation of the dependentvariable, and (2) a Poisson regression model.The square-root model failed to provide resid-uals that were normally distributed so the Pois-son model was used instead. Both approachesgave similar answers regarding prices leadingsales, but the Poisson did show considerably

more statistical significance. An asymmetricresponse was also considered. Producers wereslightly moreresponsive to price increases thanprice decreases. No new insight was gainedfrom the more complex model so the simplermodel is reported here.


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Anderson and Brorsen Marketing Performance of Oklahoma Farmers 1267

The model for change in price is estimatedwith ordinary least squares. No unit-root testswere conducted since the variable of interestis the change in price. Literature has shownthat high-frequency cash prices can be wellmodeled as unit-root processes even though

low-frequency cash prices are clearly meanreverting.

The lengths of lag were selected using se-quential F-tests with a maximum lag length ofeight. The length of lag was allowed to dif-fer across the two models, but not within amodel. Two lags were selected for change inpriceandeightlagswereselectedforfrequencyof sales. Neither model passes all tests. There issome remaining autocorrelation in both mod-els and thus the hypothesis tests suffer from

some asymptotic bias.

Results

Table 1 shows the proportions of sales occur-ring each month. The South elevator has 56%of sales during June harvest. The southern areais one of the first to harvest wheat and pro-ducers tend to sell before prices reach harvestlows. Producers at the North elevator tend to

harvest three to four weeks later and sell only9% of their wheat in June. Northern produc-ers apparently receive a return to storage withOctober being the month with the most wheatmarketed.

The 29% of sales in March, April, and Mayat the North elevator is higher than expected.Some producers may be waiting for high pricesand then selling before harvesting the nextcrop. This behavior is consistent with myopic

Table 1. Percentage of Bushels That Okla-homa Wheat Producers Sold Each Month

South Central NorthMonth Elevator Elevator Elevator

June 56 25 8July 9 15 7August 3 6 7September 2 6 8October 3 8 16November 3 5 6

December 3 7 4January 3 9 5February 2 6 9March 5 6 10April 2 3 8May 9 4 11

loss aversion and could be due to what Kahne-man and Riepe (p. 60) call the disposition ef-fect (the reluctance of investors to realize theirlosses).

There is a difference between markets be-ing efficient and producers making efficient

decisions. Future research should investigatewhether some producers need to be encour-aged to sell earlier in the crop year. Andersonrecommends thatOklahoma producers sell be-fore the end of the calendar year. Certainly itdoes appear that most producers are respond-ing to market signals to sell their wheat early inthe marketingyear. However, someOklahomaproducers appear to be making inefficient de-cisions (Anderson and Brorsen 2004b).

Table 2 presents the test of the primary hy-

pothesis of this study: do farmers sell in thelower one-third of the market? The hypothesisis soundly rejected. Not only do most farmersnot sell in the lower third, but they also sellnearly half in the upper third. This is true evenat the North elevator where harvest sales arelight.

The argument that most farmers sell in thebottom one-third of the market has been usedto argue that farmers should use market advi-sory services or marketing pools that seek toobtain an average price. Since this study showsthat most farmers do not sell in the bottomone-third, most producers do not need tochange the way that they time sales. The resultsdo not imply that producers have an ability topredict price. It may simply imply that produc-ers are receiving a return from responding tomarket signals of whether to store or sell.

The returns that producers received abovea simple twelve-month average are presentedin table 3. The returns are substantially largerat the South elevator. But, even at the North

elevator, producers beat the twelve-monthaverage by 11c/ per bushel.

Elevators in some parts of the countryhave begun to offer contracts that achieve the

Table 2. Percent of Sales by OklahomaWheat Producers in Bottom 1/3, Bottom 1/2,or Upper 1/3 of the Net Prices Availablein Each Year

South Central North

Elevator Elevator ElevatorPercentile (%) (%) (%)

Bottom 1/3 24 20 20Bottom 1/2 37 33 34Upper 1/3 53 49 49


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Table 3. Average Price Offered and Average Price Received by Oklahoma WheatProducers: June 1992 through May 2001

12-Month Harvest Producer 12-Month HarvestElevator Price ($) Price ($) Price ($) Difference Difference

South 3.03 3.28 3.31 +28c/ +3c/

Central 2.96 3.05 3.05 +09c/ 0North 3.02 3.18 3.13 +11c/ 5c/

Table 4. Poisson Regression of Number ofSales ( freqt) at a North Oklahoma WheatElevator

IndependentVariable Estimate P-Value

Intercept 0.247


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Anderson and Brorsen Marketing Performance of Oklahoma Farmers 1269

than the first lag of price changes is sta-tistically significant. Seasonality and day ofthe week are also significant with the mostsales occurring on Monday and the least onThursday.

Lagged sales are not statistically significant

in predicting price changes (tables 5). There-fore, producers are noise traders in the shortrun. The negative coefficients on both lags ofprice changes do suggest some potential returnto providing liquidity through negative feed-back trading. The negative coefficient on thesecond lag of sales is significant at the 10%level, which is mildly suggestive that produc-ers may be selling at good times. Seasonal-ity and day of the week are not statisticallysignificant.

Conclusions

The hypothesis, wheat producers sell in thebottom one-third of the market, was not sup-ported by this research. Nearly two-thirds ofthe market transactions were in the top one-half of the market price offered. This impliesthat timing of about one-third of the sales needto be changed.

Oklahoma has a strong price incentive tosell early in the marketing year. Regions thatharvest later in the marketing year can be ex-pected to get greater returns to storage thanOklahoma producers. Thus, while a twelve-month pool may work poorly in Oklahoma,it might be fine in other states.

In the short run, farmers are negative feed-back traders since they sell after price increasesandtend to hold after price decreases. They arealso noise traders since their sales did not pre-

dict price changes. Thus, most producers havebeen successful in deciding whether to sell inJune or in March, but they have had little orno success in deciding whether to sell today ortomorrow.

Cooperative Extension-marketing pro-grams that ignored day-to-day movementsand concentrated on how to respond tomarket signals of when to time sales still mayprovide the best guidance to farmers. Thereis some indication of producers storing grain

longer than is economical, so there is benefitfrom a Cooperative Extension program thatencourages farmers to sell before January 1.

Inaction can result from the psycholog-ical mistake of being reluctant to realizelosses. The results also imply that produc-

ers are doing a relatively good job of mar-keting and they do not need to invest morein marketing. Producers should spend scarcemanagement resources concentrating on be-ing a low-cost producer or on marketing activ-ities that addvalue ratherthan trying to predict

what the price is going to be tomorrow or nextweek.

References

Anderson, K.B., and B.W. Brorsen. Price RiskManagement: What to ExpectMechanicalMarketingStrategies. OklahomaCooperativeExtension Service F-592. Oklahoma State Uni-

versity, April 2004a.. Price Risk Management: What to ExpectProof that Oklahoma Wheat Producers Do aGood Job of Marketing. Oklahoma Coopera-tive Extension Service F-593. Oklahoma StateUniversity, April 2004b.

Batchelor, C. Grain Marketing Made Simple.Progressive Farmer(2001):246.

Benirschka, M., and J.K. Binkley. Optimal Stor-age and Marketing over Space and Time.American Journal of Agricultural Economics

77(1997):51224.Black, F. Noise. Journal of Finance 41(1986):529

43.Brorsen, B.W., and K.B. Anderson. Implica-

tions of Behavioral Finance for FarmerMarket Strategy Recommendations. Paperpresented at the NCR-134 Conference onApplied Commodity Price Analysis, Fore-casting, and Market Risk Management St.Louis, Missouri, 2324 April, 2001. Avail-able at http://agecon.lib.umn.edu/cgi-bin/pdfview.pl?paperid=2862&ftype=.pdf.

De Bondt, W.F.M. Betting on Trends: IntuitiveForecasts of Financial Risk and Return. Inter-national Journal of Forecasting 9(1993):35571.

Jirik, M., S.H. Irwin, D.L. Good, J. Martines-Filho,and T.E. Jackson. Do Agricultural MarketAdvisory Services Beat the Market? Evidencefrom the Wheat Market over 199598. Ag-MAS Project Research Report, University ofIllinois, March 2001.

Kahneman, D., and M.W. Riepe. Aspects of In-vestor Psychology: Beliefs, Preferences, and

Biases Investment Advisors Should KnowAbout. Journal of Portfolio Management24(1998):5265.

National Agricultural Statistics Service, Agricul-tural Statistics Board, USDA. Grain Stocks.September 2001 and other issues.


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Sanders, D.R., S.H. Irwin, and R.M. Leuthold.Noise Trader Demand in Futures Mar-kets. OFOR paper number 9602, Univer-sity of Illinois, June 1996. Available at http://agecon.lib.umn.edu/.

Slusher, D.W. The Relationship of Soybean

Price Received to Marketing Alternative

Selection and Farmer Characteristics forNorthwest Indiana Farmers. UnpublishedMS thesis, Purdue University, August1987.

Solt, M.E., and M. Statman. How Useful Is theSentiment Index? Financial Analyst Journal

44(1988):4555.

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