Applied Economic Perspectives and Policy (2010) volume 32, number 1, pp. 77–94.

doi:10.1093/aepp/ppp006

Submitted Article
The Adequacy of Speculation in Agricultural
Futures Markets: Too Much of a Good Thing?
Dwight R. Sanders*, Scott H. Irwin, and Robert P. Merrin

Dwight R. Sanders, Department of Agribusiness Economics, Southern Illinois

University; Scott H. Irwin, Department of Agricultural and Consumer Economics,
University of Illinois at Urbana-Champaign; Robert P. Merrin, Department of
Finance, Universiteit Maastricht, the Netherlands
*Correspondence to be sent to: E-mail: dwights@siu.edu.

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Submitted 3 July 2008; revised 16 December 2008; accepted 22 December 2008.

Abstract This paper revisits the “adequacy of speculation” debate in agricultural

futures markets using the positions held by index funds in the Commitment of
Traders reports. Index fund positions were a relatively stable percentage of total
open interest from 2006 – 2008. Traditional speculative measures do not show any
material shifts over the sample period. Even after adjusting speculative indices for
commodity index fund positions, values are within the historical ranges reported
in prior research. One implication is that long-only index funds may be beneficial
in markets traditionally dominated by short hedging.
Key words: Commitment of Traders, index funds, commodity futures
markets.
JEL Codes: G13, Q11, Q13.

Introduction
In a series of classic papers, Working (1953, 1954, 1960, 1962) argued
that agricultural futures markets are primarily hedging markets and that
speculation tends to follow hedging volume. However, the nature and
structure of futures markets has changed dramatically since Working’s
pioneering research. Fueled by academic evidence showing that commod-
ity futures portfolios can generate returns comparable to equities (e.g.,
Gorton and Rouwenhorst 2006), the investment industry has developed
products that allow individuals and institutions to “invest” in commod-
ities through over-the-counter (OTC) swaps, exchange-traded funds
(ETFs), and exchange-traded notes (ETNs), all of which are linked to
popular commodity indices such as the Goldman Sachs Commodity Index
(Acworth 2005; Engelke and Yuen 2008). Domanski and Heath (2007) term
this the “financialization” of commodity markets.

# The Author(s) 2010. Published by Oxford University Press, on behalf of the Agricultural and
Applied Economics Association. All rights reserved. For permissions, please email:
journals.permissions@oxfordjournals.org

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Applied Economic Perspectives and Policy

The rapid rise of this new class of speculators has led many to argue
that today’s speculative trade in agricultural futures markets is the prover-
bial tail wagging the dog. In other words, speculation is viewed as driving
the increase in overall market participation; this is a reversal of the tra-
ditional view that speculation follows hedging volume. Some also claim
that these new speculators, especially long-only commodity index funds,
create “price distortions” and potentially disrupt traditional cash-futures
convergence patterns (Morrison 2006; Henriques 2008).
Several previous studies have investigated the role of speculation in
agricultural futures markets. The conventional method of monitoring
speculative positions in futures markets is accomplished with the
Commodity Futures Trading Commission’s (CFTC) Commitments of Traders
(COT) reports. Based on this data, Working (1960) developed a speculative
index to measure the adequacy of speculative positions to “balance” the

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hedging positions held by commercial traders. Working’s index reflects
his view that the level of speculation is meaningful only when it is con-
sidered relative to the level of hedging in the market. Working (1960),
Nathan Associates (1967), Labys and Granger (1970), Peck (1980, 1981),
and Leuthold (1983) use Working’s speculative index to examine whether
speculative activity in grain and livestock futures markets is adequate.
Generally, these academic studies concluded that speculation in agricul-
tural futures markets is not excessive. For example, Peck (1980) found that
“ . . . wheat, corn, and soybean markets are characterized by very low rela-
tive levels of speculation” (1040), and Leuthold (1983) found no “ . . . evi-
dence to indicate that the levels of speculation in livestock have led to
increased price variability as often alleged in the popular press” (133). It is
interesting to note that a common concern expressed in these studies was
the inadequacy of speculation on agricultural futures markets relative to
hedging pressure.
Given the allegations about the size and impact of speculators in agri-
cultural futures markets that have again arisen within industry (Sjerven
2008), government (CHSGA 2008), and academia (AFPC 2008), additional
research efforts are needed to better understand the market participation
of speculators in general and long-only index funds in particular. The
objective of this article is to revisit the “adequacy of speculation” debate
in agricultural futures markets, bringing new data to the task. Specifically,
COT data - including positions held by long-only index funds as reported
in the Commodity Index Trader (CIT) report - will be closely examined to
better characterize the nature of speculation in grain and livestock futures
markets.

Data
Traditional COT Report
The traditional COT report provides a breakdown of each Tuesday’s
open interest for markets in which 20 or more traders hold positions equal
to or above the reporting levels established by the CFTC.1 Two versions of

1
See Hieronymus (1971), McDonald and Freund (1983), and Fenton and Martinaitas (2005) for
extensive discussions of the history and evolution of the COT report. CFTC (2008b) contains a detailed
explanation of current COT reports.

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 The Adequacy of Speculation in Agricultural Futures Markets

the report are released: the Futures-Only Commitments of Traders report

includes futures market open interest only, while the Futures-and-Options-
Combined Commitments of Traders report aggregates futures market open
interest and “delta-weighted” options market open interest. Open interest
for a given market is aggregated across all contract expiration months in
both reports.
Noncommercial open interest is divided into long, short, and spreading;
whereas, commercial and nonreporting open interest is simply divided
into long or short. The following relation explains how the market’s total
open interest (TOI) is disaggregated:

½NCL þ NCS þ 2ðNCSPÞ þ ½CL þ CS þ ½NRL þ NRS ¼ 2ðTOIÞ ð1Þ

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl}
Reporting Nonreporting

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where NCL, NCS, and NCSP are noncommercial long, short, and spread-
ing positions, respectively. CL (CS) represents commercial long (short)
positions, and NRL (NRS) are long (short) positions held by nonreporting
traders. Reporting and nonreporting positions must sum to the market’s
total open interest (TOI), and the number of longs must equal the number
of short positions.
A frequent complaint about the traditional COT data is that the trader
designations may be somewhat inaccurate (e.g., Peck 1982; Ederington
and Lee 2002). For speculators, there may be an incentive to self-classify
their activity as commercial hedging to circumvent speculative position
limits. In contrast, there is little incentive for traders to desire the
noncommercial designation. So it is often thought that the noncommercial
category is a relatively pure subset of reporting speculators (Sanders,
Boris, and Manfredo 2004). Available evidence on the composition of non-
reporting traders is dated (Working 1960; Larson 1961; Rutledge 1977;
Peck 1982), so little is known about this group other than their position
size is less than reporting levels. The data set is further limited because it
is purely a classification system and provides no insight regarding either
the motives or the complex issues that underlie trading decisions
(Williams 2001).
While there may be some incentive for reporting traders to desire the
commercial designation, the CFTC implements a fairly rigorous process—
including statements of cash positions in the underlying commodity—to
ensure that commercial traders have an underlying risk associated with
their futures positions. However, in recent years industry participants
have begun to suspect that these data were “contaminated” because the
underlying risk for many reporting commercials were not positions in the
actual physical commodity (CFTC 2006a,b). Rather, the reporting commer-
cials were banks and other swap dealers hedging risk associated with
OTC derivative positions.
For example, a commercial bank may take the opposite side of a long
commodity swap position desired by a customer (Hull 2000, 121). The
commercial bank, not wanting the market risk, will then buy commodity
futures contracts to mitigate their market exposure associated with the
swap position. Technically, the bank’s position is a bona fide hedge against
an underlying risk in the swap market. Yet, the bank clearly is not a
traditional commercial hedger who deals with the underlying physical
commodity; rather, the bank has paper or swap risk that may or may not

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Applied Economic Perspectives and Policy

emanate from the physical market. Indeed, the third party or bank custo-
mer who initiated the position may be hedging or speculating; their
motives are not necessarily known, even to the swap dealer. However, the
OTC swap positions that can be easily identified are those “ . . . seeking
exposure to a broad index of commodity prices as an asset class in an unle-
veraged and passively-managed manner” (CFTC, 2008a). In this instance,
the bank customer is essentially long a commodity index such as the
Goldman Sachs Commodity Index (GSCI) via a swap with the bank. The
bank then mitigates their long GSCI exposure by hedging each commodity
component (e.g., crude oil, corn, and live cattle) in the respective individ-
ual futures markets. Because the banks and swap dealers can easily ident-
ify swaps associated with commodity indices, the CFTC can further
segregate the reporting trader categories to include “index traders.”

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Commodity Index Traders (CIT) Report
Starting in 2007—in response to complaints by traditional traders about
the rapid increase in long-only index money flowing into the markets—
the CFTC released supplemental reports which break out the positions of
index traders for 12 agricultural markets. According to the CFTC (2008a),
the index trader positions reflect both pension funds that would have pre-
viously been classified as noncommercials, as well as swap dealers who
would have previously been classified as commercials hedging OTC trans-
actions involving commodity indices.
The CFTC readily admits that this classification procedure has flaws
and that “ . . . some traders assigned to the Index Traders category are
engaged in other futures activity that could not be disaggregated . . .
Likewise, the Index Traders category will not include some traders who
are engaged in index trading, but for whom it does not represent a sub-
stantial part of their overall trading activity” (CFTC 2008a). Regardless,
the data are an improvement over the more heavily aggregated traditional
COT classifications, and they should provide some new insights as to
trader activity.

Summary Statistics and Trends

In this section, summary statistics and trends are presented for various
measures of market participation and activity. Data for the traditional
COT trader positions are available for each week from March 21, 1995
through April 15, 2008 (683 observations), while CIT data are only avail-
able for the period covering January 3, 2006 through April 15, 2008 (120
observations). Both reports reflect combined futures and options positions,
where options are adjusted to the delta-equivalent futures position. The
reports show traders’ holdings as of Tuesday’s market close. Wherever
possible, the entire data set from 1995 onward is used. However, in some
instances, the focus is on the period for which the CIT data are available—
January 2006 through April 2008. Markets included in the analysis are as
follows: corn, soybeans, soybean oil, Chicago Board of Trade (CBOT)
wheat, Kansas City Board of Trade (KBOT) wheat, cotton, live cattle,
feeder cattle, and lean hogs.2
2
The Chicago Board of Trade is now part of the CME Group, Inc.

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Changes in Market Activity

Agricultural futures markets experienced rapid growth beginning in
late 2004. For example, open interest for CBOT wheat futures increased
275% from June 2004 to June 2006 (Sanders, Irwin, and Merrin 2008). The
increase in open interest may be attributed to easier market access and
lower trading costs associated with electronic trading, an inflationary
environment for many commodity markets, and, potentially, an increase
in the use of commodity futures as an investment tool and inflation
hedge. The motives that drive the level of trading activity are varied and
complex, and it is difficult to attribute activity in commodity futures to
any single element (Williams 2001).
Using the data from the traditional COT report, the positions of the
trader groups—as measured by their percentage of total open interest—
are examined for the 1995 – 2005 and 2006 – 2008 periods. As shown in

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table 1, the relative size of nonreporting traders has declined uniformly
across all markets. For example, nonreporting traders in CBOT wheat com-
prised, on average, 22% of the open interest from 1995 – 2005, but only
10% of the open interest from 2006– 2008. On the contrary, noncommer-
cials increased their share of open interest in every market, and the com-
mercials’ share of the open interest increased in all but three markets
(KCBOT wheat, soybeans, and cotton).

Index Trader Activity Based on CIT Data

The sample period from January 2006 through April 2008 is available
for analyzing the CIT data. The CIT data are first compared to the original
COT classifications to determine from which traditional COT category the
index positions are extracted. As expected, index trader positions are pri-
marily aggregated within the commercial long positions. Across markets,
roughly 85% of the index trader positions were previously contained in
the long commercial category of the traditional COT reports; the remaining
15% comes primarily from the long noncommercial category. This
suggests that the majority of long-only commodity index positions are
initially established in the OTC markets, then the underlying position is
transmitted to the futures market by the swap dealers (including both
commercial and investment banks) hedging OTC exposure.
A detailed view of position size as a percentage of total open interest is
provided in table 2, panel A. Over the sample period, index traders do
make up a fairly large portion of certain markets. In particular, index
traders comprise over 20% of the open interest in live cattle, lean hogs and
CBOT wheat. In all other markets, index trader positions tend to be
between 10% and 15%. While this is not an insignificant share of open
interest, in no market is the index share larger than either the noncommer-
cial or commercial categories. Rather, the index share of open interest
tends to be closer to that of the nonreporting traders.
Importantly, the data show that the percentage of total open interest
attributable to index traders has been relatively stable over the sample
period. For instance, wheat index traders’ share of the market has fluctu-
ated in a fairly narrow range between 17% and 26%. Similarly, index
funds’ percentage of open interest in corn and soybeans were stable in the
11% – 13% range for most of 2007 – 2008, and have not exceeded 15% since
mid-2006.

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 Applied Economic Perspectives and Policy
Table 1 Percent of Open Interest held by Trader Category, COT Reports, 1995–2008

1995–2005 2006– 2008

Market Noncommercial Commercial Nonreporting Noncommercial Commercial Nonreporting

Corn 28% 47% 25% 39% 46% 15%

Soybeans 33% 42% 25% 40% 44% 16%
82

Soybean oil 31% 51% 18% 34% 58% 8%

CBOT wheat 35% 42% 22% 42% 48% 10%
KCBOT wheat 20% 55% 25% 32% 48% 20%
Cotton 34% 53% 13% 41% 52% 7%
Live cattle 30% 41% 29% 40% 44% 16%
Feeder cattle 32% 24% 43% 42% 27% 32%
Lean hogs 34% 36% 30% 40% 45% 15%

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 The Adequacy of Speculation in Agricultural Futures Markets

Table 2 Positions held by COT Groups, 2006–2008

Market Noncommercial Commercial Index Nonreporting

Panel A: Percent of total open interest

Corn 37% 36% 12% 15%
Soybeans 38% 33% 13% 16%
Soybean oil 33% 46% 12% 8%
CBOT wheat 39% 29% 21% 10%
KCBOT wheat 30% 40% 10% 20%
Cotton 40% 37% 16% 7%
Live cattle 37% 28% 20% 16%
Feeder cattle 37% 19% 12% 32%
Lean hogs 37% 26% 22% 15%
Panel B: Percent net long
Corn 48% 94%

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237% 219%
Soybeans 28% 235% 94% 218%
Soybean oil 46% 242% 95% 22%
CBOT wheat 26% 254% 90% 225%
KCBOT wheat 66% 247% 98% 211%
Cotton 15% 247% 96% 28%
Live cattle 21% 257% 97% 239%
Feeder cattle 33% 220% 98% 245%
Lean hogs 27% 265% 98% 224%
Panel C: Percent of long positions
Corn 42% 23% 23% 12%
Soybeans 41% 21% 25% 13%
Soybean oil 39% 27% 24% 10%
CBOT wheat 39% 14% 40% 8%
KCBOT wheat 41% 21% 20% 18%
Cotton 40% 20% 31% 9%
Live cattle 40% 12% 39% 10%
Feeder cattle 44% 16% 23% 17%
Lean hogs 36% 9% 44% 12%
Panel D: Percent of short positions
Corn 33% 49% 1% 18%
Soybeans 36% 45% 1% 19%
Soybean oil 27% 66% 1% 7%
CBOT wheat 40% 45% 2% 13%
KCBOT wheat 19% 58% 0% 22%
Cotton 39% 55% 1% 5%
Live cattle 34% 44% 0% 22%
Feeder cattle 31% 23% 0% 46%
Lean hogs 38% 42% 0% 19%

An additional criticism of index funds is their disproportionate presence

on the long side of the market, stemming from the fact they are “long-
only.” To examine this idea more closely, we first examine the percent net
long position held by each trader category in the CIT data. The percent net
long position is simply defined by trader category as the net position
(long positions minus the short positions) divided by the total positions
held (Sanders, Manfredo, and Boris 2004).
The percentage of net long for each trader group is calculated over the
sample period and presented in table 2 ( panel B). As expected, index

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Applied Economic Perspectives and Policy

traders are 90% to 98% net long in each market. Likewise, the commercial
category is 20% to 65% net short, reflecting the traditional short hedging
of producers, first handlers, and warehouses. Interestingly, for two of the
markets with high levels of index participation—CBOT wheat and lean
hogs—index funds are the only category that held a net long position over
the sample period.
To more closely examine each side of the market, the relative size of
the long and short side of the markets is presented in table 2, panels C
and D, for each trader category. Since index funds are almost exclusively
long, their percentage of the market roughly doubles when only consid-
ering the long side of the market, as opposed to total positions (long þ
short positions). For example, when considering CBOT wheat, index
funds are 21% of the total positions, but 40% of the long positions.
Across markets, index funds range from 20% (KCBOT wheat) to 44%

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(lean hogs) of the long positions in the market. In both CBOT wheat and
lean hogs, index funds held a larger portion of long positions than any
other trader group. While this attests to the sheer size of the market par-
ticipation of index funds, it does not necessarily imply that the positions
are excessive.

Traders and Position Size Based on CIT Data

The CIT data (as well as the COT data) include information on the
number of reporting traders in each category. From this, we can determine
the number of reporting index traders in each market, as well as the
average trader’s position size relative to the other trader categories.
Table 3, panel A shows the average number of reporting traders over
the sample period from January, 2006 to April, 2008; it is apparent that
there are relatively few reporting index traders. The corn, CBOT wheat,
and soybean futures markets have 24 long index traders with reportable
positions, while the KCBOT wheat futures market has 15 reporting long
index traders. There are a few index traders with short positions, but these
most likely reflect some of the positions held by long index traders that
the CFTC could not disaggregate. Across most of the markets, the
numbers of reporting commercial and noncommercial traders are of
similar magnitudes.
The average reporting trader’s position size by category over the sample
period is displayed in table 3, panel B. The average position size is simply
calculated as the positions held by that category, divided by the number
of reporting traders in that category. Because a trader may appear in more
than one category, the calculated average position size is likely lower than
the actual. Still, index trader positions are relatively large. For example, in
the corn futures market, the average long index trader has a position of
16,805 contracts, which is more than 10 times the size of the average long
position held by either commercials or noncommercials. In fact, index
traders in CBOT wheat have an average position size larger than the
CFTC position limit (6,500), which provides some indirect evidence that
speculators or investors are able to use OTC instruments and commercial
hedge exemptions to surpass speculative position limits. However, since
index funds are long-only and not known for rapid-fire trading, it is not
clear that this presents a problem.

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 The Adequacy of Speculation in Agricultural Futures Markets

Table 3 Traders and Positions by COT Trader Category, 2006–2008

Noncommercial Commercial Index

Market Long Short Spread Long Short Long Short

Panel A: Average number of traders

Corn 203 133 234 275 331 24 10
Soybeans 138 111 166 113 152 24 7
Soybean oil 64 38 55 49 57 16 3
CBOT wheat 102 118 142 65 101 24 9
KCBOT wheat 57 24 37 50 72 15 2
Cotton 112 78 87 63 62 21 4
Live cattle 79 68 87 80 137 23 3
Feeder cattle 35 29 29 35 51 16 1
Lean hogs 57 68 80 24 43 21 2

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Panel B: Average position size (contracts)
Corn 1,218 644 2,062 1,422 2,542 16,805 1,176
Soybeans 616 398 955 1,081 1,743 6,123 691
Soybean oil 861 483 1,123 1,647 3,527 4,550 688
CBOT wheat 573 553 981 1,091 2,297 8,597 1,092
KCBOT wheat 723 349 549 626 1,223 1,948 180
Cotton 382 393 891 921 2,652 4,104 361
Live cattle 566 434 722 408 864 4,569 462
Feeder cattle 267 152 203 153 153 473 62
Lean hogs 401 394 565 754 1,885 3,853 212

Speculative Index
As noted in the introduction, Working (1953, 1954, 1960, 1962) argued
that futures markets are primarily hedging markets, and that speculation
tends to follow hedging volume. Therefore, speculation can only be con-
sidered ‘excessive’ or ‘inadequate’ relative to the level of hedging activity
in the market. Peck (1979, 339) provides a succinct re-statement of the
arguments found in Working’s papers:
“Taken together, these analyses reaffirm the fundamental importance of hedging
to futures markets and dependence of total activity upon hedging needs. The
results also lend support to the Working definition of an appropriate measure of
hedger demands upon a market. Net hedging is not the most useful view of the
demands commercial users make on a market. Speculation is needed to offset
both long hedging and short hedging. Only coincidentally are long and short
hedgers sufficiently alike in date and amount to be offsetting, although increased
balance increases the probability of such correspondence and differences in seaso-
nal needs between long and short hedgers decreases this probability. The appro-
priate measure of minimum required speculation must at least begin with total
hedging demand.”

Working (1960) developed a mathematical index of speculation based on

this view of the functioning of futures markets. Indeed, his speculative
index has been used in several studies to examine grain and livestock
futures markets for adequate speculative activity (Working 1960; Nathan
Associates 1967; Labys and Granger 1970; Peck 1980, 1981; Leuthold 1983).
Nearly all prior research is concerned with a lack of sufficient speculative
activity to support hedging demands in the marketplace. While this
notion seems at odds with the current market environment, Working’s
T still provides an objective measure of speculative activity.

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Applied Economic Perspectives and Policy

Working’s speculative “T” index is easily calculated using the tra-

ditional COT trader categories:

T ¼ 1 þ SS=ðHL þ HSÞ if ðHS  HLÞ ð2aÞ

or

T ¼ 1 þ SL=ðHL þ HSÞ if ðHL . HSÞ; ð2bÞ

where open interest held by speculators (noncommercials) and hedgers

(commercials) is denoted as follows:
SS ¼ Speculation, Short SL ¼ Speculation, Long
HL ¼ Hedging, Long HS ¼ Hedging, Short.
Peck (1980, 1037) notes that the speculative index “ . . . reflects the extent

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by which the level of speculation exceeds the minimum necessary to
absorb long and short hedging, recognizing that long and short hedging
positions could not always be expected to offset each other even in
markets where these positions were of comparable magnitudes.” Working
(1960, 197) is careful to point out that what may be “technically an ‘excess’
of speculation is economically necessary” for a well-functioning market.
As a highly simplified example of the calculation and interpretation of
Working’s speculative T index, consider the intuitive case where HL ¼ 0;
then, T¼ SL/HS ¼ 1þ (SS/HS).3 It follows, if long speculation (SL) just
equals short hedging (HS), then T equals its minimum value of 1.00,
where the level of speculation is just sufficient to offset hedging needs.
Now consider if HL ¼ 0, HS ¼ 100, SL ¼ 110, and SS ¼ 10, then T equals
1.10, or there is 10% speculation in excess of that which is necessary to
meet short hedging needs.
As noted by several authors (e.g., Leuthold 1983), Working’s T suffers
from the problem of how to classify the nonreporting traders.
Nonreporting traders can be classified as speculators, thus creating an
upper bound on the speculative index; or they can be classified as
hedgers, creating a lower bound on the index. With either of these
approaches, however, the index will be impacted through time if the pro-
portion of nonreporting traders in a market changes. As shown in table 1,
diminishing levels of nonreporting trader positions is clearly a problem
over our sample period. Thus, we follow the advice of Rutledge (1977)
and initially allocate the nonreporting traders’ positions to the commercial,
noncommercial, and index trader categories in the same proportion as that
which is observed for reporting traders.
The averages of the weekly values for Working’s T are presented in
table 4 for a number of sub-periods using the traditional COT data. The
speculative indices reported do not seem extraordinarily high in any sub-
period from 1995 through 2008 using the traditional COT data. Corn
futures average 1.08, which suggests that there is only 8% more specu-
lation than the minimum needed to offset short and long hedging needs.
The highest speculative index within the grains is CBOT wheat, at 1.15,
and for livestock this value is 1.38, recorded for feeder cattle. Average
index values across the nine markets range from 1.12 to 1.14 for the
3
Note that SS þ HS ¼ SL þ HL must hold in a zero sum futures market if all positions are categor-
ized as speculative or hedging. If HL ¼ 0, the identity reduces to SS þ HS ¼ SL. Dividing by HS and
then rearranging yields T ¼ SL/HS ¼ 1 þ (SS/HS).

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 The Adequacy of Speculation in Agricultural Futures Markets

Table 4 Working’s Speculative Index, 1996–2008

COT COT COT COT CIT

Market 1995– 2001 2002–2003 2004–2005 2006–2008 Adjusted 2006–2008

Corn 1.06 1.09 1.10 1.07 1.13

Soybeans 1.08 1.08 1.10 1.09 1.21
Soybean oil 1.07 1.07 1.07 1.06 1.09
CBOT wheat 1.13 1.15 1.15 1.14 1.31
KCBOT wheat 1.05 1.05 1.09 1.09 1.14
Cotton 1.05 1.05 1.09 1.10 1.20
Live cattle 1.12 1.13 1.11 1.15 1.30
Feeder cattle 1.28 1.31 1.26 1.38 1.67
Lean hogs 1.23 1.15 1.13 1.16 1.39
Average 1.12 1.12 1.12 1.14 1.27

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different sub-periods, implying that speculation is barely large enough to
meet total hedging demands. In addition, there is no discernable trend in
the indexes across the different sub-periods.
In the last column of table 4, Working’s T is re-calculated for the 2006 –
2008 sample period by re-classifying index traders as speculators using
CIT data. Because commercial hedgers are predominantly net short in
each market, they require long speculators to “carry” their hedging. Thus,
by recategorizing the long-only index funds into the noncommercial cat-
egory, Working’s “T” is essentially shifted upward in each market. Even
with this adjustment, the average speculative index for the nine markets
only increases from 1.14 to 1.27. The largest increase, from 1.38 to 1.67,
occurs in feeder cattle.
Further perspective is provided by comparing Working’s speculative
index in recent periods with those reported by other researchers for earlier
periods. Table 5 presents the historical estimates from four previous
studies (Working 1960; Labys and Granger 1970; Peck 1980; Leuthold
1983), along with the upper and lower bounds for the CIT adjusted data
from 2006 – 2008.4 The upper (lower) range results from assuming that
nonreporting traders are speculators (hedgers).5 Nonreporting traders are
a proportionately smaller part of the market than they have been histori-
cally (see table 1), resulting in a smaller range of T values than recorded
in previous work. Therefore, the calculation of Working’s T in recent years
is not particularly sensitive to the speculator or hedger classification
imposed on nonreporting traders. The exception is feeder cattle, where
nonreporting traders still represent over 30% of the total open interest.
Comparing the historical estimates in table 5 with our results using the
traditional COT data in table 4, one is struck by the relatively low levels of
speculation from 1995– 2008. The average values for the speculative
indexes range from 1.12 to 1.14 across the different subperiods,

4
The 1967 Nathan Associates study reported speculative indexes in graphical form rather than tabular
form; hence, results from this study are not included in table 10. The data sample for Peck’s 1981
study is a sub-sample of the data from her 1980 study.
5
Note that CIT adjusted speculative indexes for 2006– 2008 in table 5 allocate nonreporting traders’
positions to the commercial, noncommercial, and index trader categories in the same proportion as that
which is observed for reporting traders. Consequently, the estimates in table 5 fall between the reported
ranges in table 6.

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 Applied Economic Perspectives and Policy
Table 5 Working’s Speculative Index Reported in Prior Research

Workinga Labys & Grangerb Peckc Peckc Leutholdd CIT Adjustede

Market 1954 –1958 1950–1965 1947–1971 1972–1977 1969– 1980 2006– ‘08

Corn 1.16 1.19 1.263–1.609 1.045–1.204 1.06– 1.34

Soybeans 1.28 1.31 1.329–1.946 1.061–1.310 1.10– 1.45
Soybean oil 1.14 1.18 1.07– 1.15
CBOT wheat 1.22 1.19 1.355–1.891 1.094–1.323 1.19– 1.49
KCBOT wheat 1.081–1.264 1.009–1.045 1.05– 1.36
Cotton 1.27 1.16– 1.27
Live cattle 1.568–2.173 1.05– 2.34 1.13– 1.60
88

Feeder cattle 1.08– 3.80 1.14– 2.61

Lean hogs 1.10– 8.69 1.18– 1.68
Average 1.21 1.22 1.257–1.678 1.155–1.411 1.08– 4.94 1.12– 1.55
a
Working (1960), Table 3, 194. Nonreporting traders are allocated to hedging or speculating based on the levels of hedging and speculating in reported positions (see Working’s technical
appendix 2, 214 –216).
b
Labys and Ganger (1970), Table 5 –6, 127. Nonreporting traders are allocated to hedging or speculating based on the levels of hedging and speculating in reported positions following the
method of Working (1960).
c
Peck (1980), Table 1, 1039 and Table 2, 1042. Peck estimates an upper (lower) bound by assuming all nonreporting traders are speculators (hedgers). The date range represents the most
inclusive time period over which the index was calculated across the markets.
d
Leuthold (1983), Table VI, 131. Leuthold estimates an upper (lower) bound by assuming all nonreporting traders are speculators (hedgers). The date range represents the most inclusive time
period over which the index was calculated across the markets.
e
Upper (lower) range results from assuming that nonreporting traders are speculators (hedgers).

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 The Adequacy of Speculation in Agricultural Futures Markets

approximately equal to or smaller than the averages from the historical

estimates spanning the late 1940s to the late 1970s, with the exception of
Leuthold’s upper bound estimates in livestock futures markets. As noted
earlier, a common concern expressed in previous studies was that specu-
lation on futures markets was not large enough to accommodate hedging
pressure. The results for 1995– 2008 are wholly consistent with this histori-
cal concern regarding agricultural futures markets. Peck’s (1980) con-
clusions are especially relevant in this regard. For example, she was
concerned about the inadequacy of speculation in CBOT wheat from 1972 –
1977 after finding a speculative index ranging from 1.094 to 1.323. Peck
(1980) viewed this level of speculation as inadequate when compared to
an index of 1.355 to 1.891 from 1947 – 1971, a period that “would hardly be
characterized as speculative” (Peck 1980, 1041). Likewise, Peck (1980)
reports KCBOT wheat had a speculative index ranging from 1.009 to

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1.045, which was “manifestly inadequate” (Peck 1980, 1043). Across the
sub-periods from 1995– 2008, CBOT wheat had an average speculative
index of 1.14 and KCBOT wheat averaged 1.07.
The final column of table 5 shows upper and lower bounds for
Working’s T from 2006– 2008 using the CIT adjusted data. Again, there is
little evidence that current index levels are deviating from historical
norms, even after accounting for index trader positions. For instance, the
range reported for live cattle futures (1.13– 1.60) is generally lower than
the 1.568 – 2.173 reported by Peck (1980) and the 1.05– 2.34 range recorded
by Leuthold (1983). Interestingly, the values reported for cotton (1.27) and
soybean oil (1.14) by Working for the 1954 – 1958 period are generally at
the upper end of the recent range reported for 2006 – 2008. There is no per-
vasive evidence that current speculative levels, even after accounting for
index trader positions, are in excess of those recorded historically for agri-
cultural futures markets.
It is somewhat surprising and counter-intuitive that Working’s T has
remained at or below historical levels, given the large increase in both
noncommercial positions and long-only index participation in the
markets. As demonstrated by Working (1960), and carefully explained by
Peck (1981), the subtleties of Working’s speculative index require close
study, and the index can be impacted by shifts in any trader category.
Consider a base case of equation (2a), where HL ¼ 0, HS ¼ 100, SL ¼ 150,
and SS ¼ 50; then, T ¼ 1 þ (50/100) ¼ 1.50, indicating that there is 50%
more speculation than technically necessary to satisfy commercial hedging
needs.
Now consider two alternative cases. First, assume that there is a large
increase in long speculation, accompanied by an equal increase in short
hedging positions, such that HL ¼ 0, HS ¼ 200, SL ¼ 250, and SS ¼50,
then T ¼ 1 þ (50/200) ¼ 1.25. That is, the speculative index actually
declines because all of the increase in speculation was met by hedging,
and the “excess” speculative positions are now actually a smaller pro-
portion of the total hedging demand. In the second alternative case,
assume that the increase in long speculation is met by other short specu-
lators, such that HL ¼ 0, HS ¼ 100, SL ¼ 250, and SS ¼ 150, then T ¼ 1 þ
(150/100) ¼ 2.50. Here, the T index increases quite dramatically because
speculators traded with speculators and there is no commercial hedging
need for this additional speculation. While there are many other scenarios
under which Working’s T can increase or decrease, these two cases are

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Applied Economic Perspectives and Policy

illustrative of what would commonly be considered necessary and exces-

sive speculation, respectively.
In table 6, the hedging and speculative positions used to calculate
Working’s T are presented for the first three months of 2006 and 2008
using the CIT adjusted data. With a few exceptions, the data in table 6
suggest that the first alternative case above is fairly descriptive of the
changes experienced in the commodity markets over this interval. In the
corn market, there was a large increase in long speculative (SL) positions
(þ233,768). However, this is not enough to absorb the 525,471-contract
increase in short hedging; thus, Working’s T declines. A similar story
holds for soybeans, soybean oil, and cotton. Feeder cattle provide an excel-
lent example of the second alternative case provided above. In this
market, HS and HL decline by very similar amounts, while there are also
parallel increases in SL and SS positions—that is, speculators are trading

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with each other. As a result, Working’s T increases fairly dramatically,
from 1.374 to 1.917. Live cattle show a similar increase in speculative
trade, but the increase in Working’s T (0.200) is of a smaller magnitude
than seen in feeder cattle (0.543). In the other markets, there is some com-
bination of these two impacts that result in fairly minor shifts in
Working’s T index.
Given the relatively small shifts in Working’s T documented in this
research, it is apparent that the much publicized increase in long-only
speculative positions is largely accompanied by a comparable increase in
short hedging. While the increase in long-only speculation has received
the most publicity, the increase in the size of short hedging positions is
equally interesting. For example, the average short hedging position for
corn during the first quarter of 2008 is slightly less than 6 billion bushels,
and for soybeans a little over 2 billion bushels. Of course, what is not
clear is the validity of Working’s classical paradigm: speculation follows
hedging. Did long speculation increase to meet short hedging needs as
assumed by Working? Or is the tail wagging the dog?
Unlike traditional speculators in Working’s day—who were regarded as
scalpers, day traders, or position traders and were responsive to hedging
needs in the market—long-only index funds appear to be more mechanical
and less responsive to hedging demands. While this does not alter the cal-
culation of the speculative index, it does bring into question Working’s
assumption about the nature of speculation in today’s markets. It is also
possible that the commercial category still contains “contamination”, both
from hedgers who are really speculating and swap dealers who are
hedging OTC swaps not used for commodity index investments. The
degree of this contamination is unknown, and it is unclear whether it
would lead to over- or under-estimating long or short positions. Therefore,
the potential directional impact on Working’s T is difficult to discern.
In sum, agricultural futures markets do not have a historically high level
of speculative activity based on Working’s speculative T index. Working
and others strongly maintained that futures markets were hedging
markets, where speculators enter the market in response to hedging press-
ures. For example, Peck (1979– 80, 329) unequivocally states that “Taken
together, the historical evidence is clear: futures markets reflect commercial
needs.” The rise of long-only index funds in agricultural futures markets
opens this basic tenet to debate and may bring into question the appropri-
ateness of traditional measures of speculative market balance.

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 The Adequacy of Speculation in Agricultural Futures Markets

Table 6 Speculative and Hedging Positions for Working’s T, First Quarter of 2006
and First Quarter of 2008

Market HL HS SL SS Working’s T

Corn
2006a 328,362 654,461 558,600 208,043 1.212
2008 598,790 1,179,932 792,368 182,291 1.102
Change 270,428 525,471 233,768 225,752 20.109
Soybeans
2006 126,832 192,218 183,105 107,221 1.336
2008 175,973 440,793 351,379 74,844 1.121
Change 49,141 248,575 168,274 232,377 20.215
Soybean oil
2006 66,636 124,134 92,515 35,599 1.187
2008 121,196 228,515 128,546 25,844 1.074

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Change 54,560 104,381 36,032 29,755 20.113
CBOT wheat
2006 57,942 213,278 251,926 92,148 1.340
2008 70,084 240,864 300,880 121,578 1.391
Change 12,141 27,585 48,954 29,430 0.051
KCBT wheat
2006 43,993 110,601 80,158 13,560 1.088
2008 46,459 96,556 67,827 15,767 1.110
Change 2,466 214,045 212,330 2,207 0.023
Cotton
2006 41,582 108,085 86,777 21,824 1.146
2008 107,826 296,434 200,773 18,918 1.047
Change 66,244 188,349 113,995 22,906 20.099
Live cattle
2006 54,549 128,951 129,786 45,305 1.247
2008 34,970 144,549 198,211 80,303 1.447
Change 219,579 15,599 68,425 34,998 0.200
Feeder cattle
2006 10,707 17,725 20,769 10,632 1.374
2008 6,310 13,435 28,284 18,111 1.917
Change 24,397 24,290 7,515 7,479 0.543
Lean hogs
2006 15,949 65,438 93,522 40,036 1.492
2008 36,825 113,971 149,415 69,055 1.458
Change 20,876 48,533 55,893 29,019 20.034

Note: HL ¼ Hedging, Long; HS ¼ Hedging, Short; SL ¼ Speculating, Long; SS ¼ Speculating, Short

a
The data reflect average positions in the first calendar quarter of 2006 and 2008, respectively.

Summary and Conclusions

This paper revisits the “adequacy of speculation” debate in agricultural
futures markets, bringing new data to the task. Specifically, this research
examines the size and activity of trader categories in the traditional
Commodity Futures Trading Commission’s COT and CIT reports.
The data are first closely examined for potential shifts or changes in
trader activity. Regarding the relative size of the index funds, they usually
comprise 10% to 20% of the total open positions within most markets.
However, because the indexes are almost exclusively long, they tend to
make up 20% to 40% of the long-side of the market. In some markets

91
Applied Economic Perspectives and Policy

(i.e., lean hogs, CBOT wheat), index funds are the predominant long
position holder. The agricultural markets averaged fewer than 25 report-
ing long index traders over the 2006 – 2008 sample period. However, the
long index traders have average positions that are more than 10 times the
size of the typical noncommercial trader.
Several notable trends or shifts in market participation are observed in
the data. First, agricultural commodity futures markets have experienced a
rapid increase in open interest that started in late 2004 and continues into
2008 for many markets. For most markets, the index funds’ percentage of
open interest peaked in 2006 and has since stabilized. Second, traditional
speculative measures do not show any material changes or shifts over the
sample period. In most markets, the increase in long speculative positions
was equaled or surpassed by an increase in short hedging. Thus, even
after adjusting speculative indices for index fund positions, values are

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within the historical ranges reported in prior research.
While the analysis in this report does not test directly for price impacts,
it does provide some pertinent evidence in this regard. First, the stabiliz-
ation of the index funds’ percentage of total open interest may suggest
that other traders have adjusted their strategies to better cope with this
relatively new market participant. Second, Working’s speculative index
suggests that long-only index funds may in fact be beneficial in markets
dominated by short hedging pressure. That is, they improve the adequacy
of speculation by helping the market to “carry” unbalanced short
hedging. The relatively normal level of speculation over the sample period
raises some doubt as to whether index funds are behind recent commodity
price increases.
Much like in the last major episode of structural change in commodity
markets from 1972 –1975, some blame speculators for the recent increase
in commodity prices. Proposals are once again surfacing to curb
“harmful” speculation in futures markets. Such policy decisions aimed at
curbing speculation may well be counterproductive in terms of price
levels or market volatility. In particular, these policy initiatives could
severely compromise the ability of futures markets to accommodate
hedgers and facilitate the transfer of risk.
There is certainly a need for additional research on several fronts. Early
research on futures markets stressed that an understanding of the size and
motivation of various market participants was a crucial first step in under-
standing other, more advanced market performance issues. For instance,
the concept of short hedgers paying a risk premium to speculators may
need to be revisited. The activity of all trader groups, especially index
traders, should be investigated using more disaggregated data (e.g., daily
by contract maturity). Patterns in index trading—such as the rolling of
positions from one contract maturity to another—may have impacts on
market liquidity and short-term volatility. The price impact of trader
groups also needs to be carefully examined, as do the incentives for
various market participants. Finally, the fundamental question of whether
agricultural futures markets are still the sort of hedging markets defined
by Working should be re-examined.

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 The Adequacy of Speculation in Agricultural Futures Markets

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