The Adequacy of Speculation in Agricultural Futures

Markets: Too Much of a Good Thing?

by

Dwight R. Sanders, Scott H. Irwin, and Robert P. Merrin

Suggested citation format:

Sanders, D. R., S. H. Irwin, and R. P. Merrin. “The Adequacy of Speculation 

in Agricultural Futures Markets: Too Much of a Good Thing?” Marketing 

and Outlook Research Report 2008-02, Department of Agricultural and

Consumer Economics, University of Illinois at Urbana-Champaign, June

2008. [http://www.farmdoc.uiuc.edu/marketing/morr/morr_archive.html]
The Adequacy of Speculation in Agricultural Futures Markets: 

 Too Much of a Good Thing? 

by 

Dwight R. Sanders, Scott H. Irwin, and Robert P. Merrin*

June 2008 

Marketing and Outlook Research Report 2008-02 

 

*

 Dwight R. Sanders is an Associate Professor of Agribusiness Economics at Southern Illinois University, 

Carbondale, Illinois. Scott H. Irwin is the Laurence J. Norton Chair of Agricultural and Consumer Economics at the 

University of Illinois, Urbana, Illinois. Robert P. Merrin is a Ph.D. student in the Department of Finance at 

Universiteit Maastricht, Netherlands. A version of this report was presented at the 2008 NCCC-134 Conference on 

Applied Commodity Price Analysis, Forecasting, and Market Risk Management. The authors appreciate the 

comments and suggestions provided by the Conference participants. 
i

The Adequacy of Speculation in Agricultural Futures Markets: 

 Too Much of a Good Thing? 

Abstract 

The objective of this report is to re-visit the “adequacy of speculation” debate in agricultural 

futures markets. The Commodity Futures Trading Commission makes available the positions 

held by index funds and other large traders in their Commitment of Traders reports. The results 

suggest that after an initial surge from early 2004 through mid-2005, index fund positions have 

stabilized as a percent of total open interest. 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. So, even after 

adjusting speculative indices for 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. Attempts to curb speculation through 

regulatory means should be weighed carefully against the potential benefits provided by this 

class of speculators. 

Key Words: Commitment’s of Traders, index funds, commodity futures markets 
ii

The Adequacy of Speculation in Agricultural Futures Markets: 

 Too Much of a Good Thing? 

Table of Contents 

Introduction..................................................................................................................................... 1 

Data................................................................................................................................................. 2 

Traditional COT Report............................................................................................................... 2 

Commodity Index Traders (CIT) Report..................................................................................... 4 

Bank Participation (BP) Report................................................................................................... 4 

Summary Statistics and Trends....................................................................................................... 5 

Changes in Market Activity......................................................................................................... 5 

Index Trader Activity Based on CIT Data................................................................................... 6 

Traders and Position Size Based on CIT Data ............................................................................ 7 

Index Trader Activity Based on Bank Participation (BP) Data................................................... 8 

Speculative Index.......................................................................................................................... 10 

Policy Implications ....................................................................................................................... 14 

Summary and Conclusions ........................................................................................................... 17 

References..................................................................................................................................... 19 

Tables and Figures...........................................................................................23
The Adequacy of Speculation in Agricultural Futures Markets: 

 Too Much of a Good Thing? 

Introduction 

In a series of classic papers, Working (1953, 1954, 1960, and 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 the pioneering 

research of Working. The days of the dentist-speculator from Dubuque dabbling in the soybean 

futures market are long gone. Fueled by academic evidence showing that commodity 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 

commodities through over-the-counter swaps and structured notes that are linked to popular 

commodity indices, such as the Goldman Sachs Commodity Index (Acworth, 2005). Domanski and 

Heath (2007) term this the “financialisation” of commodity markets. 

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 proverbial tail wagging the dog. In other words, 

speculation is viewed as driving the increase in overall market participation, a reversal of the 

traditional 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 

through 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 hedging positions held by commercial traders. The index 

reflects Working’s view that the level of speculation is meaningful only when it is considered 

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 the speculative index to examine 

whether speculative activity in grain and livestock futures markets is adequate. Generally, these 

academic studies concluded that speculation in agricultural futures markets is not excessive. For 

example, Peck (1980) found that “...wheat, corn, and soybean markets are characterized by very 

low relative levels of speculation” (p. 1040) and Leuthold (1983) found no “...evidence to indicate 

that the levels of speculation in livestock have led to increased price variability as often alleged in 

the popular press” (p. 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 agricultural futures markets 

that have once again arisen within industry (Sjerven, 2008), government (CHSGA, 2008), and the 

academy (AFPC, 2008), additional research efforts are needed to better understand the role and 

potential impact of speculators in general and long-only index funds in particular. The objective of 

this report is to re-visit 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 
2

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. A separate CFTC 

report—Banker Participation in Futures Markets— will also be used to provide a unique look at the 

increase in long-only index activity. The first section of the report will provide an overview of the 

relative trading activity of commercial, non-commercial, and non-reporting traders. Particular 

attention will be given to recent trends indicative of a shift in speculative activity, perhaps 

associated with the growth in long-only commodity funds. The second section will use Working’s 

speculative index to examine the adequacy of speculation relative to hedging demands in 

agricultural futures markets. Trends in index values since the mid-1990s will be examined to 

determine whether speculation has actually risen relative to hedging demands. To provide further 

historical perspective, index levels also will be compared to levels reported in previous studies of 

agricultural futures markets, some extending back as far as the late 1940s. The final section of the 

report explores the policy implications of the analysis. Throughout the report, the focus is on 

summarizing the data and highlighting recent trends to provide a more objective assessment of 

speculative activity. 

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 the report are released. The Futures-Only Commitments of Traders report 

includes futures market open interest only. 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 versions of the report. The weekly reports are released every Friday at 3:30 p.m. Eastern 

Standard Time. 

 Reports also are available in both a short and long format. The short report shows open 

interest separately by reportable and non-reportable positions. For reportable positions, additional 

data are provided for commercial and non-commercial holdings, spreading, changes from the 

previous report, percentage of open interest by category, and number of traders. The long report, in 

addition to the information in the short report, also groups the data by crop year, where appropriate, 

and shows the concentration of positions held by the largest four and eight traders. 

 

 Using the information in the short report, non-commercial open interest is divided into long, 

short, and spreading; whereas, commercial and non-reporting open interest is simply divided into 

long or short. The following relation explains how the market’s total open interest (TOI) is 

disaggregated: 

(1) [ 2( )] [ ] [ ] 2( )

Reporting Non Reporting

NCL + NCS + NCSP + CL + CS + NRL + NRS = TOI

−

 	   
   	  
 

 

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 (2008) contains a detailed explanation of current COT reports. 
3

where, NCL, NCS, and NCSP are non-commercial long, short, and spreading positions, 

respectively. CL (CS) represents commercial long (short) positions, and NRL (NRS) are long 

(short) positions held by non-reporting traders. Reporting and non-reporting 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 non-commercial designation. So, 

it is often thought that the non-commercial category is a relatively pure subset of reporting 

speculators (Sanders, Boris, and Manfredo, 2004). The available evidence about the composition of 

non-reporting traders is dated (Working, 1960; Larson, 1961; Rutledge, 1977-78; Peck, 1982), so 

little is known about this group other than their position size is less than reporting levels. 

 

 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 began 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 commercials 

were banks and other swap dealers hedging risk associated with over-the-counter (OTC) derivative 

positions. 

 

 For example, a commercial bank may take the opposite side of a long commodity swap 

position desired by a customer.2 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 commercial hedger in the traditional sense. Indeed, the third party or 

bank customer 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 unleveraged 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 individual 

 

2

 According to Hull (2000, p. 121), “A swap is an agreement between two companies to exchange cash flows in the 

future. The agreement defines the dates when the cash flows are to be paid and way that they are to be calculated. 

Usually the calculation of the cash flows involves the future values of one or more market variables.” A cash forward 

contract is a simple example of a swap in commodities markets. Suppose a farmer enters into a forward contract with a 

grain merchant today to deliver 10,000 bushels of soybeans on October 1, 2008 at $12 per bushel. Since the grain 

merchant can sell the grain as soon as it is delivered by the farmer, the forward contract is equivalent to a “swap” 

agreement where the grain merchant will pay a cash flow of $120,000 on October 1, 2008, and in return, will receive a 

cash flow of 10,000 x S, where S is the spot price of soybeans on October 1, 2008. Hull notes that swap agreements 

typically have cash flows on more than one date, whereas the forward contract “swap” has cash flows on a single date. 
4

futures markets. Because the banks and swap dealers can easily identify swaps associated with 

commodity indices, it allows the CFTC to further segregate the reporting trader categories to 

include “index traders.” 

Commodity Index Traders (CIT) Report 

Starting in 2007—in response to complaints by traditional traders about the rapid increase in longonly 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, the index 

trader positions reflect both pension funds that would have previously been classified as noncommercials as well as swap dealers who would have previously been classified as commercials 

hedging OTC transactions 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 substantial 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. 

 

The CIT data are released in conjunction with the traditional COT report showing combined 

futures and options positions. The index trader positions are simply removed from their prior 

categories and presented as a new category of reporting traders. The CIT data include the long and 

short positions held by commercials (less index traders), non-commercials (less index traders), 

index traders, and non-reporting traders. 

 

Bank Participation (BP) Report 

Well before there were concerns about long-only index funds, the U.S. Federal Reserve Board 

desired to monitor the participation of commercial banks in futures markets (Huhman, 2006). In 

response, the CFTC compiled the Bank Participation in Futures Markets report (BP). The BP

report contains the positions held by commercial banks in futures markets where there are at least 

five banks with open positions. A Bank Participation in Options Markets report is also compiled, 

but it does not adjust positions to a delta-equivalent basis, reducing its usefulness. The BP reports 

are released monthly on a reporting date that is aligned with the COT reports. However, the BP

reports are independent and they are not technically part of the COT reports. 

 The BP report started to sporadically show positions in the grain markets by mid-2003, 

indicating that at least five commercial banks were participating in the grain futures markets during 

some months in that year. By late 2004, there are consistent data available for the primary grain 

markets representing on-going participation by at least five commercial banks each month. 

Consistent participation by commercial banks in the livestock futures markets began in mid-to-late2005. 
5

 Importantly, positions shown in the BP report are a pure subset of the traditional COT

commercial category. Moreover, it is generally thought that the commercial bank positions are 

mostly hedges against OTC swaps on commodity indices (Huhman, 2006). In which case, there 

should be a strong correspondence between BP data and the CIT data. Because the BP data actually 

have a longer available history than the CIT data, the BP data will be used to supplement the 

analysis of index trader positions for certain markets. 

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). CIT data are only available 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 forward is used. However, in some instances, the focus is on the period 

for which the CIT data are available—January 2006 through April 2008. Monthly BP data are 

collected for January 2006 through April 2008. Markets included in the analysis are as follows: 

corn, soybeans, soybean oil, CBOT wheat, KCBOT wheat, cotton, live cattle, feeder cattle, and lean 

hogs.3

Changes in Market Activity 

Agricultural futures markets experienced rapid growth starting in late 2004. The open interest for 

many agricultural futures markets doubled or even tripled from late 2004 through 2006. For 

example, Figure 1 shows that open interest for CBOT wheat futures increased 275% from June 

2004 to June 2006. As illustrated in Figure 2, other markets, such as soybeans, have continued to 

see rapid growth in open interest into 2008. The increase in open interest may be attributed to 

electronic trading and easier market access, an inflationary environment for many commodity 

markets, and, potentially, an increase in the use of commodity futures as an investment tool and 

inflation hedge. 

 Using the data from the traditional COT report, the positions of the trader groups—as 

measured by their percent of total open interest—are examined for the 1995-2005 and 2006-2008 

periods. As shown in Table 1, uniformly across all markets, the relative size of non-reporting 

traders has declined. For example, non-reporting traders in CBOT wheat comprised, on average, 

22% of the open interest from 1995-2005, but only 10% of the open interest from 2006-2008. On 

the flipside, non-commercials increased their share of open interest in every market, and the 

commercials’ share of the open interest increased in all but three markets (KCBOT wheat, 

soybeans, and cotton). The specific trends by COT category for CBOT wheat are shown in Figure 

3. 

 Clearly, the agricultural futures markets have changed since 2004. First, the total level of 

open interest has increased markedly. Second, the relative role of smaller (non-reporting) traders 

 

3

 CBOT denotes Chicago Board of Trade (now CME Group, Inc.), KCBOT denotes Kansas City Board of Trade, and 

MGEX denotes Minneapolis Grain Exchange. 
6

has diminished, while there has been a relative increase in market positions held by reporting 

traders—especially in the commercial category. In the next section, we use the CIT data to gain 

further insights into the market composition. 

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. The results are shown in Table 2. 

 As expected, index trader positions are primarily 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 other 15% is primarily from the 

long non-commercial 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. 

 

 One of the primary concerns expressed by the industry is the magnitude of the commodity 

index activity. In a recent Barron’s article, one analyst quipped that “index funds account for 40% 

of all bullish bets on commodities...the index funds hold about $211 billion worth of bets on the 

buy side of U.S. markets” (Epstein, 2008). Presumably, the sheer size of the index fund positions 

may allow them to distort prices or price relationships across markets. 

 

 A detailed view of position size as a percent of total open interest is provided in Table 3. 

Over the sample period, index traders do make up a surprisingly large portion of certain markets. In 

particular, index traders are 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 the open interest, in no market is the index share larger than either the noncommercial or commercial categories. Rather, the index share of open interest tends to be closer to 

that of the non-reporting traders. 

 

 Importantly, the data show that the percent of total open interest attributable to index traders 

has been relatively stable over the sample period. This is demonstrated for CBOT wheat in Figure 

4, where the index traders’ share of the market has fluctuated in a fairly narrow range between 17% 

and 26%. As shown in Figure 5, the same is true for both corn and soybeans, where the index funds 

percent of open interest has not exceeded 15% since 2006. Similarly, Figure 6 indicates that index 

fund positions as a percent of total open interest in livestock futures markets have been relatively 

stable over the sample period. The exception seems to be live cattle which trended higher in mid2007 and then stabilized around 23%. 

 

 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). For 
7

example, a long position of 1,000 and a short position of 2,000 would yield a percent net long of -

33% ((1,000-2000)/3000), indicating that the net position held by that trader category is 33% short. 

 The percent net long for each trader group is calculated over the sample period and 

presented in Table 4. As expected, index 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 5 for each trader category. Since index funds are almost 

exclusively long, their percent of the market roughly doubles when only considering the long side 

of the market as opposed to total positions (long + short positions). For example, index funds are 

21% of the total positions in CBOT wheat, but they are 40% of the long positions in CBOT wheat. 

Across markets, index funds range from 20% (KCBOT wheat) to 44% (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. With the exception of feeder cattle, the short-side of the 

market is mostly dominated by commercial hedgers. 

 

 The CIT data clearly show that index funds are a large portion of the open interest in some 

agricultural commodity markets. In lean hogs and CBOT wheat, the index funds tend to be the 

largest category on the long-side of the market. While this attests to the sheer size of their market 

participation, it alone does not necessarily imply that the positions are excessive or detrimental to 

the market. 

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 6 shows the average number of reporting traders over the sample period from January, 

2006 to April, 2008. The total number of reporting traders for the market is shown as well as the 

number in each trader category. A reporting trader can appear in more than one category; therefore, 

the sum of each category need not equal the total reporting traders in that market. 

 

From the data in Table 6, it is immediately 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, there does not seem to be any glaring disparities between the number of reporting 

commercial and non-commercial traders. 

 
8

 Table 7 shows the average reporting trader’s position size by category over the sample 

period. 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. 

 

 Not surprisingly, given the fairly large percent of open positions held by index traders 

(Table 3) and the relatively few number of reporting index traders (Table 6), the average index 

trader’s position size is 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 non-commercials. This finding is consistent: long index 

traders have the largest average position size. 

 

 For comparison, Table 7 also shows the reporting levels and speculative limits for each 

market. Clearly, the average position size of all reporting trader categories is larger than the 

required reporting level. However, the average position size held by non-commercials generally 

falls far short of the speculative limits. This suggests that among non-commercial reporting traders 

there is likely a wide variation in position sizes, where some non-commercial positions must 

approach the speculative limits. 

 

 Commercial traders who are bona fide hedgers are not subject to the positions limits. Yet, in 

no market does the average position size held within the commercial category exceed the 

speculative limit. However, the index traders—who are largely categorized as commercial hedgers 

in the original COT data—come much closer to the speculative limits on average. In fact, the 

average long index trader in CBOT wheat holds 2,097 contracts in excess of the speculative limit of 

6,500. While this is legal, it does provide some indirect evidence that speculators or investors are 

able to use OTC instruments and commercial hedge exemptions to surpass speculative position 

limits. 

 

Based on the information in Tables 6 and 7, it is clear that index traders number relatively 

few; but, they hold very large long positions. The average position size for an index trader can be 

more than 10 times the size of the average position held by other trader groups. Since index funds 

are long-only and not known for rapid-fire trading, it is not clear that this presents a problem. 

Although, it does support the notion that when the index funds “roll” positions to a new contract 

month it is a much anticipated event (e.g., the “Goldman roll”) and associated with considerable 

trading volume. 

Index Trader Activity Based on Bank Participation (BP) Data 

In a monthly report separate from the COT and CIT reports, the CFTC reveals the participation of 

commercial banks in the commodity futures markets. There must be at least five commercial banks 

participating in the market on the “as of” date for that market to be included in the Bank 

Participation in Futures Markets (BP) report. Most agricultural futures markets begin to show up 

sporadically in the monthly BP reports in 2003 and then more consistently from 2005 forward. It is 

generally thought that the bank participation reflects hedging OTC commodity index swaps. So, to 

facilitate comparison to the CIT data, the BP summary statistics are initially calculated over the 

same sub-sample from January 2006 through April 2008. 
9

 As shown in Table 8, the average number of banks participating in the agricultural 

commodity markets ranges from the minimum reporting level of 5 in feeder cattle to 14 in CBOT 

wheat. However, much like the CIT, the banks have a surprisingly large portion of open interest, 

holding 9% of the futures-only open interest in CBOT wheat. Likewise, the banks individually hold 

very large long positions. For instance, the average commercial bank’s long position in CBOT 

wheat was 4,970 contracts, second in size only to the 8,597 average for all CIT traders (see Table 

7). 

 

 To further compare the BP traders to the CIT. We calculate the net position (long positions 

–short positions) for both the BP and CIT data. As shown in Figure 7 for the corn futures market, 

the net positions of these groups are closely related. This is generally true across all markets (not 

shown). So, it would appear that the BP traders are a representative sample or subset of the CIT

data. In the final column in Table 8, we calculate the percent of the CIT positions that would be 

held by the commercial banks included in the BP data. The percent of CIT positions that are likely 

commercial banks range from a low of 18% (feeder cattle) to a high of 35% (CBOT wheat). 

Because the BP data are futures-only and the CIT data are combined futures and options, the 

position sizes and percent of CIT open interest reported in Table 8 represent a lower bound on the 

estimates. 

 The relationship between the BP data and the CIT data is important because it indicates 

whether it is reasonable to use the BP data as a proxy for index trader activity in the period prior to 

2006. Three markets—CBOT wheat, corn, and soybeans—have at least some BP data available for 

2003. Any missing observations in 2003 and early 2004 are replaced by the positions reported in 

the prior month. Then, the banks’ percent of futures-only open interest is calculated for each 

market and plotted from 2003-2008. If the BP positions are a representative sample of the overall 

CIT category, then the general trends in the BP data can provide evidence in regards to the growth 

pattern of overall index fund activity. 

 The percent of futures-only open interest held by the commercial banks is plotted in Figure 

8. It is immediately clear that the most rapid increase in index participation appears to have 

occurred prior to the start of the CIT data history (prior to 2006). In the case of wheat, BP positions 

grew from 2% at the end of 2003 to almost 12% of the total futures open interest by May of 2005. 

For all three of these markets, the rapid increase in bank positions—and presumably all index 

positions—occurred during the 18 month period from early 2004 to mid-2005. Over that time 

period, BP positions in corn futures climbed from less than 1% of futures-only open interest to over 

6%. 

 The BP data suggest that index activity showed the greatest growth (as a percent of the 

market) from early 2004 through mid-2005. It seems that this time period, if any, would be most 

prone to a market impact by index traders. In fact, as shown in Figure 9 for soybeans, the peak in 

the absolute position size comes well after the peak in the percent of open interest. This tendency is 

confirmed by looking at the actual CIT data over 2006-2008. As shown in Figure 10, the absolute 

index position in soybeans increased steadily through 2007, while the percent of open interest was 

fairly stable at around 13%. Likewise, as Figure 11 reveals for lean hogs, the absolute position size 

held by index funds increased from 40,000 contracts to over 60,000 from late 2007 into early 2008. 
10

While there was some increase in the percent of open interest held by index traders, it did not 

exceed the 25% level seen in early 2006. This may indicate that the market is doing a better job of 

adjusting to index trader buying. As a result, index traders’ percent of total open interest is 

relatively stable even though their absolute position size continues to increase. 

 The BP positions are highly correlated with those of the CIT positions; thus, the BP

positions provide a useful proxy for index trader activity prior to 2006. The BP data suggest that 

index activity—as reflected by commercial banks—started in the grain futures markets in 2003 and 

grew rapidly from early 2004 to mid-2005. In most markets, the percent of open interest 

attributable to index traders’ peaked in early-2006, even though absolute position sizes continued to 

increase. These data suggest that the futures market may have went through a period of adjustment 

as this new type of trader entered the marketplace. 

Speculative Index 

As noted in the introduction, Working (1953, 1954, 1960, and 1962) argued that futures markets are 

primarily hedging markets and that speculation tends to follow hedging volume. Therefore, 

speculation can only be considered ‘excessive’ or ‘inadequate’ relative to the level of hedging 

activity in the market. Peck (1979-80, p. 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 

seasonal needs between long and short hedgers decreases this probability. The appropriate 

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. 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 about 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. 

 Working’s speculative “T” index is easily calculated using the traditional COT trader 

categories: 

(2a) T = 1+ SS / (HL + HS) if (HS ≥ HL) 

 or 

(2b) T = 1 + SL / (HL + HS) if (HL > HS) 
11

where open interest held by speculators (non-commercials) and hedgers (commercials) is denoted as 

follows, 

 SS = Speculation, Short SL = Speculation, Long 

 HL = Hedging, Long HS = Hedging, Short 

Peck (1980, p. 1037) notes that the speculative index, “...reflects the extent 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 is careful to point out that what 

may be “technically an ‘excess’ of speculation is economically necessary” for a well-functioning 

market (1960, p. 197). 

As a highly simplified example of the calculation and interpretation of Working’s 

speculative T index, consider the intuitive case where HL=0; then, T= SL/HS = 1+ (SS/HS).4

 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 off-set hedging needs. Now, consider if 

HL=0, HS=100, SL=110, and SS=10, then T equals 1.10 or there is 10% speculation in excess of 

that 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 non-reporting traders. Non-reporting traders can be classified as speculators, 

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 proportion of non-reporting traders in a market changes. As shown in Table 1 

and Figure 3, diminishing levels of non-reporting trader positions is clearly a problem over our 

sample period. So, we follow the advice of Rutledge (1977-78) and initially allocate the nonreporting traders’ positions to the commercial, non-commercial, and index trader categories in the 

same proportion as that which is observed for reporting traders. 

 The values for Working’s T are presented in Table 9 for a number of sub-periods using the 

traditional COT data. The speculative indices reported do not seem extraordinarily high in any subperiod from 1995 through 2008 using the traditional COT data. Corn futures average 1.08 which 

suggests that there is only 8% more speculation 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 it is 1.38 recorded for feeder cattle. Average index values across the nine markets range 

from 1.12 to 1.14 for the 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. This is demonstrated in more detail in Figure 12 using the traditional COT

trader classifications for CBOT wheat. 

 

4

 Note that SS + HS = SL + HL must hold in a zero sum futures market if all positions are categorized as speculative or 

hedging. If HL = 0, the identity reduces to SS + HS = SL. Dividing through by HS and re-arranging yields T = SL/HS 

= 1 + (SS/HS). 
12

 In the last column of Table 9, Working’s T is re-calculated for the 2006-2008 sample period 

by re-classifying index traders as speculators using CIT data.5

 Because commercial hedgers are 

predominantly net short in each market, they require long speculators to “carry” their hedging. So, 

by re-categorizing the long-only index funds into the non-commercial category, Working’s T 

essentially is 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 10 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 2006-2008.6

 

The upper (lower) range results from assuming that non-reporting traders are speculators (hedgers).7

 

Non-reporting traders are a proportionately smaller part of the market than they have been 

historically (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 non-reporting traders. The exception is feeder cattle, 

where non-reporting traders still represent over 30% of the total open interest. 

 Comparing the historical estimates in Table 10 with our results using the traditional COT

data in Table 9, one is struck by the relatively low levels of speculation throughout 1995-2008. The 

average values for the speculative indices range from 1.12 to 1.14 across the different sub-periods, 

about 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 speculation on futures 

markets was not large enough to accommodate hedging pressure. The results for 1995-2008 are 

wholly consistent with this historical concern regarding agricultural futures markets. Peck’s (1980) 

conclusions 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 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” (p. 1041). Likewise, Peck reports KCBOT wheat had a speculative index ranging 

from 1.009 to 1.045 which was “manifestly inadequate” (p. 1043). Across the sub-periods over 

 

5

 While it was noted in the previous section that bank participation data is a useful proxy for index trader positions 

previous to 2006, it is not used here for three reasons. First, bank participation data is available only for futures 

positions, whereas CIT data are reported for combined futures and options positions. Second, we do not want to 

introduce further measurement errors into the position data before 2006. Third, the basic findings reported in this 

section would be unchanged by using the bank participation data to adjust data previous to 2006. 

6

 The 1967 Nathan Associates study reported speculative indexes in graphical form rather than tabular form, and 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.

7

 Note that CIT adjusted speculative indexes for 2006-2008 in Table 8 allocate non-reporting traders’ positions to the 

commercial, non-commercial, and index trader categories in the same proportion as that which is observed for reporting 

traders. Consequently, the estimates in Table 8 fall between the reported ranges in Table 9. 
13

1995-2008, CBOT wheat had an average speculative index of 1.14 and KCBOT wheat averaged 

1.07. 

 

 The final column of Table 10 shows upper and lower bounds for Working’s T over 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 those reported by Peck 

(1.568-2.173) and Leuthold (1.05-2.34). 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 pervasive evidence that current speculative levels, even 

after accounting for index trader positions, are in excess of those recorded historically for 

agricultural 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 non-commercial 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=0, 

HS=100, SL=150, and SS=50, then T = 1 + (50/100) = 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 that is accompanied by an equal increase in short hedging positions, such that HL = 0, 

HS = 200, SL = 250, and SS =50, then T = 1 + (50/200) = 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 proportion of the total hedging demand. In the 

second alternative case, assume that the increase in long speculation is met by other short 

speculators, such that HL=0, HS = 100, SL = 250, and SS=150, then T = 1 + (150/100) = 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 illustrative of 

what would commonly be considered necessary and excessive speculation, respectively. 

 In Table 11, 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 11 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 (+233,768), 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 excellent 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 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 combination of 

these two impacts that result in fairly minor shifts in Working’s T index. 
14

 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. This is clearly illustrated in Figure 13 for soybeans, where 

Working’s T initially declines and then stabilizes as long speculation just offsets short hedging by 

commercials. 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 during the first quarter of 2008 in corn is slightly less than 6 billion bushels and in 

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 who 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 calculation of the speculative index, it does bring into question Working’s maintained 

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 plus it is unclear whether it would lead to over- or underestimation of long or short positions. So, 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 

pressures. For example, Peck (1979-80, p. 329) unequivocally states, “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 tenant to debate and may bring into question 

the appropriateness of traditional measures of speculative market balance. 

 

Policy Implications 

The impact of speculators—especially index funds—on commodity prices is currently being hotly 

debated in the popular press and political circles (e.g., Masters, 2008). It is commonly asserted that 

speculative positions create a “bubble,” where market prices far exceed fundamental values. 

Proposals have recently surfaced in the U.S. Congress to sharply increase futures margins in an 

effort to curb this damaging speculation (CHSGA, 2008). Indeed, recent political wrangling has 

spurred the CFTC to announce new initiatives for monitoring speculative traders in agricultural 

futures markets (CFTC, 2008b). When observing this debate, one is struck by the paucity of hard 

evidence typically used to justify positions, mostly negative, on the impact of speculation. 

Several recent academic studies actually provide evidence relevant to the current debate 

about the price impacts of speculation in agricultural futures markets. Bryant, Bessler, and Haigh 

(2006), Gorton, Hayashi, and Rouwenhorst (2007), and Sanders, Irwin, and Merrin (2007) 

rigorously test whether there is a causal relationship between COT position data and the movement 
15

of agricultural futures prices, finding very little evidence of a relationship. Only Wang (2001, 

2003) finds some linkages between large trader positions and market returns.8

 While none of these 

studies directly assess the price impact of long-only index funds, the weight of the evidence 

suggests such an impact is unlikely. 

 

There are additional reasons to be skeptical about the assertion that speculation has led to 

bubbles in agricultural futures prices over the last two years. First, the research presented in this 

report shows that the level of speculation in agricultural futures markets—as measured by 

Working’s T—is not outside of historical norms. If speculation is driving prices above fundamental 

values, it is not obvious in the level of speculation relative to hedging. Second, recent price 

increases do not neatly fit a bubble explanation. As shown in Figure 14, price increases are 

concentrated in the grain and oilseed markets. Yet, the highest concentration of long-only 

speculative positions is often in the livestock futures markets (Table 3), which generally did not 

participate in the price increases and for which index funds are rarely mentioned as problematic. It 

is difficult to rationalize why speculation by index funds would only impact particular agricultural 

futures markets. Third, very high prices have been observed for commodities without futures 

markets (e.g., durum wheat and edible beans) and in agricultural futures markets that are not 

included in popular commodity indices (e.g., rice and fluid milk). 9

 To assert that the commodity 

markets are being driven higher by a speculative bubble ignores historically low world grain 

inventories and other market fundamentals that are broadly driving commodity prices higher. 

Fourth, as Hamilton (2008) points out, if speculators create a bubble in futures prices for storable 

commodities, this also creates an incentive to store commodities because prices in the future exceed 

levels normally required to compensate inventory holders for storage. We should therefore observe 

an increase in inventories when a bubble is present. In fact, inventories for grains and oilseeds have 

fallen sharply over the last two years. 

 

Finally, for any trader group to push prices away from fundamental value requires that the 

unpredictability of their market opinions serve as an effective deterrent to arbitrage (De Long et al., 

1990). This notion seems unlikely given the ease with which other large non-commercial traders 

should be able to trade against index fund positions. Index funds do not attempt to hide their 

current position or their next move. Generally, funds that track a popular commodity index (e.g., 

GSCI) publish their mechanical procedures for rolling to new contract months. Moreover, they 

usually indicate desired market weightings when the index is re-balanced. So, the only uncertainty 

in their trading patterns may stem from the overall in-flow or out-flow of monies associated with 

the underlying investment vehicle (e.g., mutual fund). It seems unlikely that more traditional 

 

8

 These studies focus on the relationship between trader positions and the direction and magnitude of futures price 

changes. A related but different set of studies examine the relationship between trader positions and the volatility of 

futures prices (e.g., Streeter and Tomek, 1992; Goodwin and Schnepf, 2000). Changes in trader positions may be 

related to the volatility of futures prices but not the direction of futures price changes. 

9

Over the period from January 3, 2006 through April 15, 2008, durum wheat prices increased from $3.18 to $12.99 per 

bushel (http://www.mgex.com/history/historical_new.cfm); nearby futures prices for rough rice increased from $8.27 

per hundred pounds to $22.17; nearby class III fluid milk futures increased from $12.65 per hundredweight to $17.29 

(http://www.cmegroup.com/CmeWeb/ ftp.wrap/bulletin); and the monthly average price received by farmers for dry 

edible bean prices increased from $19.30 per hundred pounds to $34.40 from January 2006 to April 2008 

(http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1002). 
16

speculators would allow index funds to push prices away from fundamental values. Moreover, the 

finite horizon of futures contracts plus the general ease of creating “substitute portfolios” in futures 

and options markets further diminishes the likelihood that speculative arbitrage is limited (Shleifer 

and Summers, 1990). 

When considering current policy debates about speculation in agricultural futures markets, it 

is also important to consider lessons from comparable episodes in the past. We submit that 1972-

1975 is the last time period with comparable levels of structural change in commodity markets. 

U.S. and international commodity markets experienced a period of rapid price increases from 1972-

1975, setting new all-time highs across a broad range of markets. In hindsight, economists generally 

consider this a period marked by rapid structural shifts such as oil embargoes, Russian grain 

imports, and the collapse of the Bretton Woods fixed exchange rate system (Cooper and Lawrence, 

1975). 

 

 It is particularly interesting to note that commodity price increases over 1972-1975 were 

often blamed on speculative behavior associated with the “tremendous expansion of trading in 

futures in a wide range of commodities” (Cooper and Lawrence, 1975, p. 702). In fact, Labys and 

Thomas (1975, p. 287) motivate their research with words that could have been written in 2008 

instead of 1975: 

“This paper analyses the instability of primary commodity prices during the recent period of 

economic upheaval, and determines the extent to which this instability was amplified by the 

substantial increase in futures speculation which also occurred. Of particular interest is the 

degree to which this speculation rose and fell with the switch of speculative funds away 

from traditional asset placements and towards commodity futures contracts.” 

Perhaps not surprisingly, Labys and Thomas found only a weak relationship between speculative 

activity and price instability. Still, public pressure to curb speculation resulted in a number of 

regulatory proposals and the upward adjustment of margin requirements (Rainbolt, 1977; Tomek, 

1985; Fishe and Goldberg, 1986). The only consistent impact of higher margin requirements in the 

past seems to be a decline in futures trading volume. The evidence that higher futures margins 

lowered price variability is mixed, and there is no evidence that higher futures margins actually 

lowered price levels (Fishe and Goldberg, 1986; Peck and Budge, 1987; Haradouvelis and Kim, 

1996). So, while higher margins may have reduced speculation—through lower open interest and 

reduced volume of trade—it seems to do little to cure the “problems” of high prices or price 

variability. 

 Like the 1972-1975 period, the 2006-2008 period has experienced a rapid increase in 

commodity price levels. Demand growth from developing nations, the diversion of row crops to 

biofuel production, and U.S. monetary policy are some of the economic fundamentals thought to 

underlie recent commodity price increases (Trostle, 2008). The complex interplay between these 

factors and how they impact price expectations is often difficult to grasp in real-time. So, much like 

the mid-1970’s, the scapegoat for commodity price increases seems to have become the speculator. 

The present research suggests that current levels of speculation—given hedging needs—are at 

historically normal levels. Indeed, Working’s T in many agricultural futures markets is at levels 

associated with “inadequate” speculation in the past. If this is the case, then policy decisions aimed 
17

at curbing speculation may well be counter-productive in terms of price levels and market volatility. 

In particular, these policy initiatives could severely compromise the ability of futures markets to 

accommodate hedgers and facilitate the transfer of risk. 

Summary and Conclusions 

In a series of classic papers, Working (1953, 1954, 1960, and 1962) argued that agricultural futures 

markets are primarily hedging markets and that speculation tends to follow hedging volume. 

However, the rapid growth of new types of speculators has led many to argue that today’s 

speculative trade in agricultural futures markets is the proverbial tail wagging the dog. 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). 

The objective of this report is to re-visit 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 (CFTC) 

Commitments of Traders (COT) reports, the Commodity Index Trader (CIT) reports, and the Bank 

Participation (BP) reports. Data for the traditional COT trader positions are available for each 

week from March 21, 1995 through April 15, 2008 (683 observations). CIT data are only available 

for the period covering January 3, 2006 through April 15, 2008 (120 observations). Monthly BP

data are collected for January 2006 through April 2008. Markets included in the analysis are: corn, 

soybeans, soybean oil, CBOT wheat, KCBOT wheat, cotton, live cattle, feeder cattle, and lean hogs. 

The data are first closely examined for potential shifts or changes in trader activity. In 

regards to the relative size of the index funds, they usually comprise 10% to 20% of the total open 

positions within most markets. However, because they are almost exclusively long, they tend to 

make up 20% to 40% of the long-side of the market. In some markets (i.e., lean hogs, CBOT 

wheat), index funds are the predominant long position holder. The agricultural markets averaged 

fewer than 25 reporting 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. For CBOT wheat, the average index position size exceeds the speculative limit. 

 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. Second, from early 2005 to mid2006 there was a dramatic increase in index funds’ percent of total open interest (as shown by the 

BP data). For most markets, the index funds’ percent of open interest peaked in 2006 and has since 

stabilized, even though absolute position size continues to grow. Third, 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. So, 

even after adjusting speculative indices for index fund positions, values are within the historical 

ranges reported in prior research. 

 While the analysis in this report does not test for directly for price impacts, it does provide 

some pertinent evidence in this regard. First, if there is a market impact from index fund activity, it 
18

seems likely that it would have occurred during the period of most rapid growth: 2004-2005. 

Second, the stabilization of the index funds’ percent of total open interest may suggest that other 

traders have adjusted their strategies to better cope with this relatively new market participant. 

Third, 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. However, the traditional notion that 

hedging begets speculating may need to be re-visited. 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 in 1972-

1975, some are blaming speculators for the recent increase in commodity prices. Proposals are once 

again surfacing to increase margins in an effort to curb “harmful” speculation in futures markets. 

Such policy decisions aimed at curbing speculation may well be counter-productive 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 understanding other, more advanced, market performance issues. Hence, the 

activity of all trader groups, especially index traders, should be investigated using more 

disaggregated data (e.g., daily by contract maturity). The price impact of trader groups also needs 

to be carefully examined. Finally, there is a need to re-examine the fundamental question of 

whether agricultural futures markets are still hedging markets, as defined by Working. 
19

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21

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23

Table 1. Percent of Open Interest Held by Trader Category, COT Reports, 1995-2008 

 1995-‘05 2006-‘08 

Market 

NonCommercial Commercial NonReporting  NonCommercial Commercial NonReporting 

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

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

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% 

Table 2. Original COT Classification for CIT Positions, 2006-2008 

 NonCommercial NonCommercial NonCommercial Commercial Commercial 

Market Long Short Spread Long Short 

Corn 11% 0% 0% 86% 3% 

Soybeans 10% 0% 0% 87% 3% 

Soybean Oil 7% 0% 0% 91% 2% 

CBOT Wheat 11% 0% 0% 84% 5% 

KCBOT Wheat 15% 0% 0% 84% 1% 

Cotton 8% 0% 0% 90% 2% 

Live Cattle 15% 0% 0% 83% 1% 

Feeder Cattle 36% 0% 0% 63% 1% 

Lean Hogs 14% 0% 0% 86% 1% 

Table 3. Percent of Open Interest Held by COT Groups, 2006-2008 

Market 

NonCommercial Commercial Index Non-Reporting 

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% 
24

Table 4. Percent Net Long by COT Trader Groups, 2006-2008 

Market 

NonCommercial Commercial Index Non-Reporting 

Corn 48% -37% 94% -19% 

Soybeans 28% -35% 94% -18% 

Soybean Oil 46% -42% 95% 22% 

CBOT Wheat -6% -54% 90% -25% 

KCBOT Wheat 66% -47% 98% -11% 

Cotton 15% -47% 96% 28% 

Live Cattle 21% -57% 97% -39% 

Feeder Cattle 33% -20% 98% -45% 

Lean Hogs -7% -65% 98% -24% 

Table 5. Percent of Long and Short Positions by COT Trader Group, 2006-2008. 

Panel A: Long Positions 

Market 

NonCommercial Commercial Index Non-Reporting 

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 B: Short Positions 

Market 

NonCommercial Commercial Index Non-Reporting 

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% 
25

Table 6. Average Number of Reporting Traders, COT Trader Category, 2006-2008 

 Non-Commercial Commercial Index Index

Market Total Long Short Spread Long Short Long Short 

Corn 758 203 133 234 275 331 24 10 

Soybeans 463 138 111 166 113 152 24 7 

Soybean Oil 197 64 38 55 49 57 16 3 

CBOT Wheat 370 102 118 142 65 101 24 9 

KCBOT Wheat 182 57 24 37 50 72 15 2 

Cotton 297 112 78 87 63 62 21 4 

Live Cattle 328 79 68 87 80 137 23 3 

Feeder Cattle 146 35 29 29 35 51 16 1 

Lean Hogs 213 57 68 80 24 43 21 2 

Table 7. Average Position Size (contracts), COT Trader Category, 2006-2008 

 Non-Commercial Commercial Index Index

 

Reportable Spec. 

Market Long Short Spread Long Short Long Short Position Limita

Corn 1,218 644 2,062 1,422 2,542 16,805 1,176 250 22,000 

Soybeans 616 398 955 1,081 1,743 6,123 691 150 10,000 

Soybean Oil 861 483 1123 1,647 3,527 4,550 688 200 6,500 

CBOT Wheat 573 553 981 1,091 2,297 8,597 1,092 150 6,500 

KCBOT Wheat 723 349 549 626 1,223 1,948 180 150 6,500 

Cotton 382 393 891 921 2,652 4,104 361 100 5,000 

Live Cattle 566 434 722 408 864 4,569 462 100 5,150 

Feeder Cattle 267 152 203 153 153 473 62 50 1,000 

Lean Hogs 401 394 565 754 1,885 3,853 212 100 4,100 

a

Limits for futures plus delta-adjusted options positions, all contract months combined. 

Table 8. Bank Participation Data, 2006-2008 

 Bank % of Futures Position Size Position Size % of CIT 

Market Count Open Interest Long Short Open Interest 

Corn 13 5% 10,057 551 31% 

Soybeans 10 4% 3,555 259 25% 

Soybean Oil 7 4% 2,634 192 27% 

CBOT Wheat 14 9% 4,970 425 35% 

KCBOT Wheat 7 3% 1,147 22 26% 

Cotton 10 6% 2,093 56 25% 

Live Cattle 7 5% 3,333 245 25% 

Feeder Cattle 5 2% 231 29 18% 

Lean Hogs 7 5% 2,795 20 22% 
26

Table 9. Working’s Speculative Index, 1996-2008 

Market 

COT 

1995-‘01 

COT 

2002-‘03 

COT 

2004-‘05 

COT 

2006-‘08 

CIT Adjusted 

2006-‘08 

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 

Table 10. Working’s Speculative Index Reported in Prior Research 

Market 

Workinga

1954-1958 

Labys & 

Grangerb

1950-1965 

Peckc

1947-1971 

Peckc

1972-1977 

Leutholdd

1969-1980 

CIT Adjustede

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 

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 1.26-1.68 1.155-1.411 1.08-4.94 1.12-1.55 

a

Working (1960), Table 3, p. 194. Non-reporting traders are allocated to hedging or speculating based on the levels of 

hedging and speculating in reported positions (see Working’s technical appendix 2, p.p. 214-216) 

b

Laby’s and Ganger (1970), Table 5-6, p. 127. Non-reporting 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, p. 1039 and Table 2, p. 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, p. 131. Leuthold estimates an upper (lower) bound by assuming all non-reporting 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 non-reporting traders are speculators (hedgers). 
27

Table 11. 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 -25,752 -0.109 

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 -32,377 -0.215 

Soybean Oil 

2006 66,636 124,134 92,515 35,599 1.187 

2008 121,196 228,515 128,546 25,844 1.074 

Change 54,560 104,381 36,032 -9,755 -0.113 

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 -14,045 -12,330 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 -2,906 -0.099 

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 -19,579 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 -4,397 -4,290 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 -0.034 

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

a

The data reflect average positions in the first calendar quarter of 2006 and 2008, respectively. 
28

Figure 1. Combined Futures and Options Open Interest for CBOT Wheat, 1995-2008 

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

Mar-95Mar-96Mar-97Mar-98Mar-99Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08

Date

Contracts (millions) 

Non-Commercial Commercial Non-Reporting

Figure 2. Combined Futures and Options Open Interest for Soybeans, 1995-2008 

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

Mar-95Mar-96Mar-97Mar-98Mar-99Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08

Date

Contracts (millions) 

Non-Commercial Commercial Non-Reporting
29

Figure 3. Percent of Open Interest for CBOT Wheat by COT Category, 1995-2008

0%

10%

20%

30%

40%

50%

60%

Mar-95Mar-96Mar-97Mar-98Mar-99Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08

Date

Percent 

Non-Commercial Commercial Non-Reporting

Figure 4. Percent of Open Interest for CBOT Wheat by COT Category, 2006-2008 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Percent 

Non-Commercial Commercial

Index Traders Non-Reporting
30

Figure 5. Percent of Open Interest in CBOT Wheat, Corn, and Soybeans 

Held by CIT Traders, 2006-2008

0%

5%

10%

15%

20%

25%

30%

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Percent 

Chicago Wheat Corn Soybeans

Figure 6. Percent of Open Interest in Live Cattle, Lean Hogs and Feeder Cattle 

Held by CIT Traders, 2006-2008

0%

5%

10%

15%

20%

25%

30%

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Percent 

Live Cattle Lean Hogs Feeder Cattle
31

Figure 7. CIT and Bank Position Size in Corn, 2005-2008 

250

300

350

400

450

500

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Index Contracts 

40

60

80

100

120

140

160

Banker Contracts 

Index Traders Bankers

Figure 8. Bank Percent of Futures Open Interest in CBOT Wheat, Corn, 

and Soybeans, 2003-2008 

0%

2%

4%

6%

8%

10%

12%

14%

Jan-03

May-03

Sep-03

Jan-04

May-04

Sep-04

Jan-05

May-05

Sep-05

Jan-06

May-06

Sep-06

Jan-07

May-07

Sep-07

Jan-08

Date

Percent

Chicago Wheat Corn Soybeans
32

Figure 9. Bank Percent of Open Interest and Position Size in Soybeans, 

2003-2008 

0%

1%

2%

3%

4%

5%

6%

Jan-03

May-03

Sep-03

Jan-04

May-04

Sep-04

Jan-05

May-05

Sep-05

Jan-06

May-06

Sep-06

Jan-07

May-07

Sep-07

Jan-08

Date

Percent 

0

5

10

15

20

25

30

Contracts (1,000's) 

Percent Position

Figure 10. CIT Percent of Open Interest and Position Size in Soybeans, 

 2006-2008. 

6%

8%

10%

12%

14%

16%

18%

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Percent 

40

50

60

70

80

90

100

110

120

Contracts (1,000's) 

Percent Position
33

Figure 11. CIT Percent of Open Interest and Position Size in Lean Hogs, 

 2006-2008.

18%

19%

20%

21%

22%

23%

24%

25%

26%

27%

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Percent 

15

20

25

30

35

40

45

50

55

60

65

Contracts (1,000's) 

Percent Position

Figure 12. Working’s Speculative Index for CBOT Wheat, Original COT Data, 

1995-2008 

1.00

1.05

1.10

1.15

1.20

1.25

1.30

Mar-95Mar-96Mar-97Mar-98Mar-99Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08

Date

Index Value 
34

Figure 13. Short Hedging, Long Speculation, and Working’s T Based on CIT 

Adjusted Positions in Soybeans, 2006-2008. 

0.000

0.100

0.200

0.300

0.400

0.500

0.600

Jan-06

Mar-06

May-06

Jul-06

Sep-06Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07Nov-07

Jan-08

Mar-08

Date

Contracts (millions) 

1.00

1.10

1.20

1.30

1.40

1.50

1.60

Index 

HS SL "T" Index

Figure 14. Percent Change (return) in Agricultural Futures Prices, 2006-2008. 

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Corn

Soybeans

Soybean

Oil

CBOT

Wheat

KCBT

Wheat

Cotton

Live

Cattle

Feeder Cattle

Lean

Hogs

Return (%)

Note: The returns are the cumulative Tuesday-to-Tuesday log-relative price changes for the nearby 

futures contracts for the weeks ending January 3, 2006 to April 15, 2008. Price changes and returns 

are adjusted for contract roll over.