determinants of trader profits in commodity futures markets

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Determinants of Trader Profits inCommodity Futures Markets

Michaël DewallyCollege of Business and Economics, Towson University

Louis H. EderingtonPrice College of Business, University of Oklahoma

Chitru S. FernandoPrice College of Business, University of Oklahoma

Using proprietary energy futures position data, we provide evidence that mean hedgerprofits are negative whereas speculator (especially hedge fund) profits are positive, thattraders (whether speculators or hedgers) who hold net positions opposite in sign to likelyhedgers in aggregate have higher profits than traders whose net positions align with likelyhedgers, and that profits on long positions vary inversely with inventories and directlywith price volatility. These findings are consistent with the risk premium, hedging pressure,and modern theory of storage hypotheses, respectively. Further, our findings suggest thatcommodity futures momentum may be due largely to hedging pressure. (JEL G12, G13,G18, Q40)

Recent price volatility in commodity markets has renewed interest amongacademics, practitioners, and policy makers in the role and impact of derivativestraders, especially speculators, in these markets. One of the persistentchallenges in this line of research has been the availability of data on actualtrader positions in sufficient detail to permit meaningful analysis. Consequently,one of the most fundamental underlying issues—the degree to which differences

We thank two anonymous RFS referees and the Editor, Geert Bekaert, for extensive suggestions that significantlyimproved the paper. We also thank seminar participants at the University of Oklahoma, the U.S. CommodityFutures Trading Commission, the FDIC-Cornell-Houston Conference on Derivatives and Risk Management, the2010 meetings of the Financial Management Association, and the 2012 meetings of the European FinanceAssociation, where earlier versions of this paper were presented. We have benefited from comments andsuggestions by Hank Bessembinder, Bahattin Büyüksahin, Jeffrey Harris, Robert Jarrow, Paul Kofman, ScottLinn, Bill Megginson, James Moser, Michel Robe, Duane Seppi, Wulin Suo, and Pradeep Yadav. We have alsobenefited from useful discussions with U.S. Energy Information Administration (EIA) officials. We thank theU.S. Department of Energy (DOE) for providing the proprietary disaggregated energy futures position data usedin this study. The views expressed in this paper reflect the opinions of the authors only, and not of the U.S. DOEor EIA. We are solely responsible for all remaining errors and omissions. A part of this research was completedwhile Louis Ederington was Visiting Professor at the University of Melbourne, which he thanks for valuablesupport. Supplementary data can be found on The Review of Financial Studies Web site. Send correspondenceto Louis H. Ederington, Price College of Business, University of Oklahoma, 307 West Brooks St., Norman, OK73019-4005, USA; telephone: (405) 325-5697. E-mail: [email protected].

© The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies.All rights reserved. For Permissions, please e-mail: [email protected]:10.1093/rfs/hht048

RFS Advance Access published August 9, 2013 at U

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The Review of Financial Studies / v 0 n 0 2013

in trading profits/losses among individual traders represent differences in risktaking, trading strategy, information or skill, or are attributable purely to luck—has not been examined. This, in turn, gives rise to the question of how thepredictions of various commodity futures pricing models are supported byactual trader performance data. This paper seeks to provide evidence on theseissues using unique proprietary data on individual trader positions in threeenergy futures markets: crude oil, gasoline, and heating oil.

Commodity futures pricing models derived from the risk premium andhedging pressure theories, the modern theory of storage, and the effect ofthe convenience yield and momentum on futures pricing have been generallytested by exploring the profitability of hypothetical commodity futures tradingstrategies, for example, by asking whether a hypothetical trader could haveprofited by trading on the basis of aggregate hedger positions or marketmomentum. In contrast, we use the actual performance of various tradersto analyze profit differentials and test these futures pricing models based ondaily open interest position data of large traders that the U.S. CommodityFutures Trading Commission (CFTC) requires brokers to report as part of itsmarket surveillance. Whereas normally available to researchers only in highlyaggregated form, the U.S. Department of Energy (DOE) provided us with theopen interest positions of individual reporting traders in the NYMEX crude oil,heating oil, and gasoline futures markets. We utilize these data on individualreporting traders to explore their trading strategies and calculate their tradingprofits. Because the likelihood that a trader is primarily hedging or speculating,their trading strategy, and their access to information may differ by line ofbusiness, each trader is classified into one of eleven trader categories: refiners,independent producers, pipelines and marketers, large energy consumers,commercial banks, energy traders, hedge funds, households, investment banksand dealers, market makers, and an unclassified group. This classification helpsmitigate the limitation of not knowing traders’ physical and swap positions.

First, we examine our data for evidence regarding the traditional riskpremium or hedging pressure theories of Keynes (1930) and Hicks (1939),which predict that if most hedgers have long positions in the underlying asset(as Keynes and Hicks assumed) and thus hedge by holding short futuresmarket positions, futures prices will be pushed below expected future spotprices (backwardation) and speculators can make positive profits on averageby holding long futures positions. If most hedgers are long in the futures market,futures prices will be biased upward (contango), and speculators can expect toprofit by shorting futures. Assuming that commercial traders are more likelythan noncommercial traders to be hedgers,1 this hedging pressure hypothesis

1 This presumption that commercial traders are primarily hedgers has been questioned by Ederington and Lee(2002). In addition, the distinction between hedgers and speculators is not sharp because many corporate hedgingprograms apparently include some speculation (see, e.g., Dolde 1993; Bodnar, Hayt, and Marston 1998; Adamand Fernando 2006).

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Trader Profits in Commodity Futures Markets

has been tested previously by exploring whether hypothetical trading profitson long (short) positions tend to be positive when commercial traders in theaggregate are net short (long), according to the CFTC’s weekly Commitmentsof Traders (COT) report.2 In this study, we exploit the availability of individual(as opposed to aggregated) net hedger positions in our data and also employa more refined distinction between likely hedgers and speculators to examinewhether some speculator types are more likely than others to take positionsopposite to those of likely hedgers and, if so, whether this trading strategy isprofitable in practice. Our findings are consistent with the predictions of the riskpremium and hedging pressure hypotheses. Likely hedgers tend to have futurestrading losses, whereas likely speculators (hedge funds, in particular) turnconsistent profits. Additionally, individual trader profits are a strong positivefunction of the extent to which they take positions opposite to the positionsof likely hedgers, that is, long (short) positions when likely hedgers are netshort (long). In particular, hedge fund trading profits, which are the highestamong our eleven trader types, appear primarily due to their exploitation ofthis hedging pressure, that is, holding positions opposite to those of likelyhedgers. Profits are a much stronger function of our refined hedging pressurevariables than they are of aggregate hedging pressure as reported in the COTreports.

Second, we study the extent to which our data are consistent with the moderntheory of storage. Extending the theory of storage,3 Gorton, Hayashi, andRouwenhorst (2013) (hereafter GHR) argue that the risk premium should varyinversely with inventory levels because low inventory levels increase spot pricevolatility, thereby elevating the required risk premium associated with holdinglong positions in futures contracts and increasing the convenience yield toholders of physical inventories. Testing these hypotheses using data for thirty-three commodity markets, GHR find that, as predicted, (1) the cash-futuresbasis is an inverse function of inventory levels, and (2) returns to a strategyof holding long futures positions are positive and inversely correlated withinventory levels. They also find that after controlling for inventory levels, thereis no evidence of futures returns varying with hedging pressure or momentum.We revisit these questions using our actual trader data. Although less importantthan hedging pressure in explaining futures trading profits differences amongdifferent trader types, we find support for the prediction of the modern theoryof storage that profits on long (short) positions are inversely (directly) relatedto inventory levels. There is also some support for the prediction that profits on

2 Based on hypothetical trades, Bessembinder (1992), Bessembinder and Chan (1992), de Roon, Nijman, and Veld(2000), and Wang (2001) find support for the hedging pressure hypothesis, but Kolb (1992) does not. Acharya,Lochstoer, and Ramadorai (2013) extend this literature to a setting with capital-constrained speculators andshow that a reduction in speculator risk capacity increases commodity producer hedging costs, reduces producerinventories, and increases the futures risk premium.

3 See, for example, Brennan (1958), Deaton and Laroque (1992), Ng and Pirrong (1994), Routledge, Seppi, andSpatt (2000), and Dinceler, Khokher, and Simin (2005).

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long (short) positions are directly (inversely) related to volatility. These resultsare robust to the inclusion of broader market systematic risk factors, such asconditional betas, term and risk premiums, and the VIX.

Third, we test whether momentum strategies are profitable in energy futuresafter controlling for hedging pressure. Erb and Harvey (2006) and Miffre andRallis (2007) find evidence of momentum in futures prices, and in two of the fewstudies of actual trades, Fung and Hsieh (1997, 2001) find that hedge funds andcommodity trading advisors tend to profitably employ momentum strategies.However, while Moskowitz, Ooi, and Pedersen (2012) show that speculatorsin general tend to follow momentum strategies, they also uncover evidence tosuggest that the momentum effect may be due to hedging pressure since theyfind that momentum is correlated with futures market roll returns. Althoughour short sample precludes us from closely replicating the analysis in theseprior studies, our findings are consistent with the latter finding of Moskowitz,Ooi, and Pedersen (2012) and provide support for the notion that momentumin commodity futures markets may be due largely to hedging pressure. Aftercontrolling for hedging pressure and theory of storage variables, we find noevidence that traders profit by holding long positions in futures contracts withpositive short-run momentum or short positions in futures with negative short-run momentum.

Finally, we seek to determine if trader profits vary because of differences ininformation and/or trading skill. It is conceivable that large energy companiesmay have superior information on supply and inventories or hedge fundsmay be better able to forecast energy demand or have superior trading skills.However, whereas Ackermann, McEnally, and Ravenscraft (1999), Ibbotsonand Chen (2006), Kosowski, Naik, and Teo (2007), Fung et al. (2008), andJagannathan, Malakhov, and Novikov (2010) find evidence that at least somehedge funds can deliver alpha, Fung and Hsieh (2001, 2004) find that much oftheir returns are a reward for risk taking, and Ramadorai (2013) argues that anyinformation advantage is short lived. Our finding of persistent profit differencesamong individual traders and between trader types refutes the possibility thatdifferential profits are just due to luck. However, we find no evidence thatsome trader types, such as hedge funds or pipelines, have better information ortrading skills than other types because mean profit differences between tradertypes disappear after controlling for the extent to which they exploit pricedifferentials created by hedging pressure and the convenience yield. Thus, ourevidence indicates that the trading profits enjoyed by speculators in general,and hedge funds in particular, are due to the risk absorption and/or liquiditythey provide to hedgers. Nonetheless, whereas we find that the hedging pressureand convenience yield variables completely account for mean profit differencesbetween trader types, we find that trading profits/losses of individual traderswithin trader groups tend to persist even after controlling for these variables,possibly indicating that some individual traders have an informational or skilladvantage.

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The remainder of this paper is organized as follows. Our unique data set andprofit measures are described in the next section. In Section 2, we documenthow futures position profits differ by trader type. Our trading strategy measuresare described in Section 3. In Section 4, we present evidence on how profitsamong individual traders relate to their trading strategies, and in Section 5 weexplore whether these trading strategy differences explain profit differencesbetween our eleven trader types. In Section 6, we further refine our analysis byadding trade and trader characteristics to the model. Section 7 concludes.

1. Data and Methodology

1.1 Trader classificationOur trading data are from the CFTC’s Large Trader Reporting System (LTRS)described by Haigh, Hranaiova, and Overdahl (2007). The CFTC requires dailyreporting of the holdings of all traders whose open interest positions exceedthresholds set such that reported positions normally account for 70% to 90% oftotal open interest. Current reporting thresholds are 350 contracts for crude oil,250 for heating oil, and 150 for gasoline. From these data, the CFTC compilesthe weekly COT report on the aggregate open interest positions of commercialand noncommercial traders used in tests of the risk premium hypothesis byBessembinder (1992), Bessembinder and Chan (1992), de Roon, Nijman, andVeld (2000), Wang (2001), and others.4

Whereas the LTRS data are normally available to researchers only in thehighly aggregated form of the weekly COT report, the U.S. Department ofEnergy (DOE) made an exception and provided us with the disaggregatedCFTC data on the open interest positions of anonymous individual reportingtraders in the NYMEX’s crude oil, heating oil, and gasoline futures marketsfrom June 1993 through March 1997. On average over our data period, thetraders in our data set accounted for 77.7% of open interest in the crudeoil futures market, 81.3% of gasoline open interest, and 68.4% of heatingoil open interest. To our knowledge, more recent CFTC data at the level ofdisaggregation and detail necessary to conduct the research in our paper arenot available to researchers through either DOE or CFTC because of traderidentity protection concerns. Whereas studies based on more recent aggregatedata document a significant increase in energy futures open interest and trading(especially following the introduction of the electronic Globex trading systemin 2006), and some changes in relative trader composition (see, e.g., Haigh,Hranaiova, and Overdahl 2007), the economic questions focused on in ourstudy are not specific to a particular time period. Additionally, because ourstudy relies on only end-of-day trader position data, and not intraday trading

4 Recently, the CFTC divided the commercial category into swap dealers and other commercials and thenoncommercial category into managed money and others. However, published work to date is based on thecommercial/noncommercial classification.

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data, our findings are unlikely to be significantly affected by changes in themarket microstructure. Therefore, from the standpoint of providing evidenceon alternate theories of the determinants of trader profits in futures markets,we expect the results based on our 1993–1997 individual trader data to be fullyrelevant in the present context.

The CFTC’s reporting form asks: “Is the reporting trader engaged in businessactivities hedged by the use of futures and options markets?” If the answer isyes, the trader is classified as “commercial” and, if no, as “noncommercial.”Because traders self-select (albeit subject to overrule by the CFTC), there maybe a bias toward the commercial category, especially because the question refersto “markets,” and not to “this market?” Indeed, Ederington and Lee (2002) arguethat in the energy futures markets the “commercial” category includes manylikely speculators.

The CFTC also assigns subclassifications, described in Haigh, Hranaiova,and Overdahl (2007) and Büyüksahin et al. (2008), to most traders. We utilizethe CFTC’s noncommercial subclassifications, which include commodity pooloperators, commodity trading advisors, managed money, futures commissionmerchants, floor traders, and floor brokers. Following Haigh, Hranaiova, andOverdahl (2007), we combine the first three subclassifications into a singlehedge fund/managed money category because they are similar and composedof primarily hedge funds. Because the trades attributed to floor brokers andfutures commission merchants by the CFTC represent only trades for theirown account, we combine them with floor traders.5 Noncommercial traderswith no subclassification code are classified as households (individuals). Insummary, we assign the CFTC’s noncommercial traders to one of three tradertypes: (1) hedge funds, (2) market makers/floor traders, or (3) households.

The CFTC’s subclassifications of commercial traders, for example,manufacturer and dealer/merchant, etc., proved less useful because theycombine traders with different objectives. For instance, the CFTC manufacturercategory includes both refiners, whom one would expect to hedge by shorting oilfutures, and airlines, whom one would expect to hedge their fuel needs by goinglong in oil futures. Consequently, in collaboration with the Office of Policy atthe U.S. DOE, we decided on the following subclassifications: (1) refiners, (2)independent producers, (3) marketers/distributors/pipelines (MDPs), (4) largeconsumers, (5) commercial banks, (6) investment banks and dealers, and (7)energy traders. To preserve trader anonymity, the final assignment of individualtraders to each subclassification was made by the Office of Policy. Hedge fundsand commodity pool operators, which were designated as commercial traderson the LTRS, instead of the usual noncommercial designation, were placed inthe “energy trader” subclassification. We separate energy producers into those

5 As described by Silber (1984) and Bryant and Haigh (2004), floor traders were the major market makers infutures markets during the 1993–1997 period. However, with the transition to electronic trading they have beensince replaced by other market makers.

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with refining capacity (refiners) and those without (independent producers)because the latter are clearly long in the physical crude oil market, whereas theformer could be short. A residual group of smaller commercial traders remainsunclassified, resulting in a total of eight commercial trader classifications.

In addition to classifying traders into the eleven types described above, weseparate the traders into (1) likely hedgers, (2) likely speculators, (3) marketmakers, or (4) others. A limitation of our study is that we know only thetraders’ energy futures market positions, not their cash market, forward, orswap positions or their positions in other futures markets.6 Additionally, ourdata do not permit us to observe intraday trades. Hence, our judgment of thelikelihood that a trader is hedging or speculating is based partially on their line ofbusiness. We regard the first five commercial classifications listed above as thetraders most likely to be hedging. The first four—(1) refiners, (2) independentproducers, (3) MDPs, and (4) large consumers—all have sizable cash and/orforward energy market positions, and banking regulations restrict the fifth type,commercial banks, to hedging activities. Conversely, we view hedge funds,individuals from the CFTC’s noncommercial category, and energy traders fromthe commercial category as likely speculators because they have no knownphysical or forward market positions. Investment banks/dealers are the majorunknown on the hedging/speculating spectrum because they could be eitherhedging their OTC swap positions or speculating. Hence, we consider themseparately. Market makers are also treated separately.7

Our data set contains 939 traders that generate 1,059,616 trader/day/contractobservations and 486,334 daily position changes. For convenience, wehenceforth refer to daily position changes as “trades,” while noting that intradaytrades are not included. Many of the 939 traders were only active on a few daysduring our data period or made small trades. To ensure a continuous series, andto make the trader classification task described above more manageable, werequire that traders included in our final sample hold a futures position for atleast 100 days during our period and make at least 50 trades. This screen resultsin a sample of 382 traders who account for 96.3% of our trader/day/contractobservations and 97.9% of total reported open interest. Variable definitions areprovided in Table A1, and descriptive statistics concerning futures trading onour three markets are reported in Table 1. Note that the crude oil market is thelargest—accounting for 64.9% of open interest in terms of contracts.

Descriptive statistics for our eleven trader types are in Table 2. In termsof both open interest and trades, the energy futures markets are dominated

6 We do have some data on option positions but in an aggregated form, which precludes trading profit calculations.However, option positions are negligible relative to futures positions for almost all traders.

7 As noted above, the LTRS data that were available to us in disaggregated form are normally available toresearchers only in highly aggregated form. Hartzmark (1987, 1991) and Leuthold, Garcia, and Lu (1994)also use disaggregated LTRS data to calculate profits of individual traders, but they use only the commercialand noncommercial classifications. Haigh, Hranaiova, and Overdahl (2007), Büyüksahin et al. (2008), andBüyüksahin and Harris (2009) utilize the CFTC subclassifications but do not calculate individual trader profits.

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Table 1Descriptive statistics: Positions and trades of large energy traders

Crude oil Gasoline Heating oil All three markets

Panel A: Full data set, 939 traders

Avg. daily gross open interest (contracts) 607,085 125,160 192,619 924,864Avg. daily gross open interest ($ billion) $10,704 $2,868 $4,234 $17,806Trader/contract/day observations 538,097 211,215 310,304 1,059,616

Panel B: Working data set, 382 traders

Avg. daily open interest (contracts) 593,723 123,165 189,522 906,410Avg. daily open interest ($ billion) $10,456 $2,821 $4,162 $17,439Trader/contract/day observations 516,172 203,864 300,438 1,020,474Trades 228,532 118,705 139,097 486,334Avg. daily open int. per trader (contracts) 4,404 1,243 1,748 4,579Avg. trade size (contracts) 264 158 151 206Mean time-to-contract-expiration (months) 14.6 1.6 2.8 5.0

Descriptive statistics are presented for futures positions and trades of large and midsized traders in the crude oil,gasoline, and heating oil futures markets for the June 1993–March 1997 period as reported in the CFTC’s LTRSfiles. Statistics for the full data set of 939 traders are presented in Panel A, and statistics for our working data setof 382 active traders are in Panel B. Open interest figures are gross, that is, longs + shorts.

by refiners, investment banks, and MDPs, which together account for 68.7%of open interest and 57.7% of trades. Energy consumers, such as airlines,are much less active. Combined, the similar hedge fund and energy traderclassifications represent roughly 11% of both open interest and trades.8 Marketmakers account for 15% of trades, which undoubtedly understates market makertrading, because we only capture positions held overnight. The descriptivestatistics in Table 2 point to some speculation or selective hedging by energyfirms. To hedge future sales, refiners should short gasoline and heating oilcontracts. Yet, long positions account for roughly a third of their open interestin these contracts. Whereas independent producers would typically hedge theirphysical positions by shorting crude oil futures, about 30% of their positionsare long. The consumer category consists primarily of airlines, which wouldbe expected to hedge their fuel needs by holding long fuel oil contracts. Thatis the case for the vast majority (85% of open interest), but not all. Householdsare generally long. Energy prices both rose and fell over our data period, andintervals of both backwardation and contango are observed.

1.2 Profit measuresBecause the LTRS data report end-of-day open interest positions of all largetraders, if a trader’s open interest in a particular contract changes betweenthe close of day t −1 and close of day t , clearly a trade occurred on day t .However, we do not observe any intraday trade reversals. Our profit measureis the daily mark-to-market holding period profit. If there is no trade on dayt , the mark-to-market profit on trader i’s holding in a given futures contract is

8 According to a U.S. Senate Staff Report (2006), Haigh, Hranaiova, and Overdahl (2007), and Büyüksahin et al.(2008), hedge fund trading in these markets increased substantially subsequent to our data period, perhapsattracted by the hedge fund trading profits that we document here.

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OIi,c,m,t (Pc,m,t −Pc,m,t−1), where OIi,c,m,t is trader i’s open interest positionin contract type c (crude oil, gasoline, or heating oil) maturing in month m

(e.g., June 1997 or March 1998) at the close on day t , and Pc,m,t is the closingprice of contract c-m on day t . Henceforth, we will use the abbreviation “c-m”to designate a contract of type c expiring in month m. If trader i’s position incontract c-m is short on day t ,OIi,c,m,t <0.

Suppose trader i goes long in contract c-m on day t so that OIi,c,m,t >

OIi,c,m,t−1. Because we do not observe the actual trade price on day t , weapproximate using an average of the closing prices on days t and t-1. Therefore,the holding period profit on day t is calculated as OIi,c,m,t−1 (Pc,m,t −Pc,m,t−1)+(OIi,c,m,t −OIi,c,m,t−1)

[Pc,m,t −0.5(Pc,m,t +Pc,m,t−1)

]. If i shorts contract c-m

on day t , the profit is OIi,c,m,t−1 (Pc,m,t −Pc,m,t−1) + (OIi,c,m,t−1 −OIi,c,m,t )[0.5(Pc,m,t +Pc,m,t−1)−Pc,m,t−1

]. Therefore, whether trader i goes long, short,

or does not trade on day t , her profit on contract c-m on day t is calculated as

PLi,c,m,t =0.5(OIi,c,m,t +OIi,c,m,t−1)(Pc,m,t −Pc,m,t−1). (1)

Because most of our traders hold spread positions consisting of numerouscontracts, we are more interested in the profits on their overall position, ratherthan individual contracts. Consequently, each day we sum trader i’s dailyprofits/losses over all contracts, obtaining

PLi,t =3∑

c=1

M∑m=1

PLi,c,m,t . (2)

Note that, aside from approximating trade prices with closing prices, thisprofit/loss measure is identical to the mark-to-market calculation used to creditprofits and debit losses to a trader’s account and to determine margin calls.

Because dollar profits and losses vary with the size of a trader’s position, wecalculate percentage profits. Although this return measure is useful, it shouldbe noted that since futures involve no upfront investment, returns do not havethe same meaning as in most other markets. Moreover, traders may hold long,short, or spread positions. We calculate trader i’s percentage profit on day t as

%PLi,t =PLi,t

3∑c=1

M∑m=1

0.5[∣∣OIi,c,m,t

∣∣Pc,m,t +∣∣OIi,c,m,t−1

∣∣Pc,m,t−1] . (3)

Finally, trader i’s average daily percentage profit/loss over the entireperiod,%PLi , is calculated as a weighted average of %PLi,t weighting eachday t’s %PLi,t by trader i’s total open interest that day.

There are some questionable observations in our data, such as instances inwhich a position (with an identical number of contracts throughout) that hasbeen recorded as short for several days, suddenly switches to long for a day ortwo and then reverts to short. To minimize the effect of possible data errors onour results, we winsorize the upper and lower 1% tails of %PLi,t .

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2. Individual Trader Profit Differences

2.1 Trader profit/loss persistenceWe begin by examining Hartzmark’s (1991) contention that futures tradingprofits are determined solely by luck. For this, we test whether the numberof traders who tend to make consistent profits or losses is more than onewould expect due to chance, by estimating an analysis of variance adjusted forheteroscedasticity, following Welch (1951) and based on the daily percentageprofit observations, %PLi,t . The null that mean profit differences among the382 traders are due to chance is rejected at the 0.0001 level. To test whethertrader profit/loss patterns are persistent, we calculate %PLi over both theJune 1993–May 1995 and June 1995–March 1997 subperiods, restricting thesample to the 224 traders with at least 25 trades and 50 observations in eachsubperiod. The correlation coefficient of +0.140 for trader returns across periodsis significantly different from zero at the 0.05 level. Clearly, some traders makepersistent profits and others persistent losses.

2.2 Trading profits and trader typeNext, we test for mean profit differences between our likely hedger (firstfive categories in Table 2) and likely speculator categories (categories 6–8in Table 2). According to the risk premium hypothesis, expected trading profitsmust be positive for speculators in order to entice them to enter the market,which (because trading is a zero sum game prior to transaction costs) impliesnegative expected trading profits for hedgers. It is possible also that informationand/or skill levels differ between the two groups.

We also test whether market makers tend to make persistent profits or losses.A basic tenet of market microstructure theory is that market makers tend to loseon trades with more informed investors and are compensated for these adverseselection losses through the bid-ask spread (see, e.g., Bagehot 1971; Glosten andMilgrom 1985). Because our measure of trading profits does not include theirbid/ask spread profits, this implies that %PLi,t should be negative on averagefor market makers. On the other hand, floor traders (the main market makersduring our data period) may derive an information advantage due to their abilityto observe order flow, implying positive average market maker profits (see, e.g.,Manaster and Mann 1996; Brown and Zhang 1997; Ready 1999). Because weonly observe positions held overnight and not market makers’ intraday trades,this latter argument may not apply in our case. Nonetheless, our data providea unique opportunity to explore market maker profits in commodity futures,albeit in the limited context of positions they hold overnight.

Statistics on hedger and speculator daily trading profits, %PLi,t , are reportedin Panel A of Table 3. Because profit variances differ substantially by trader, webase the p-values in Column 4 on standardized profits %PLi,t /σi , where σi isthe time-series standard deviation of %PLi,t . For likely hedgers, the averagedaily trading profit is −0.0172% (−4.24% annualized). For likely speculators,

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Table 3Trader futures trading profit statistics

Obs. Mean Mean p-value Standard Skewness Kurtosisdaily annualized deviationreturn return (daily ret.)

Panel A: Likely hedgers and speculators

Likely hedger 74,422 −0.0172% −4.243% 0.0001 0.974% −0.047 1.682Likely speculator 48,737 0.0453% 12.098% 0.0001 1.228% −0.007 0.779

Panel B: Trader type categories

Refiners 41,674 −0.0153% −3.774% 0.0016 0.985% −0.024 1.544Independent producers 6,898 −0.0216% −5.308% 0.1003 1.094% −0.068 1.168MDPs 14,270 −0.0085% −2.111% 0.2351 0.852% −0.094 2.318Large consumers 2931 −0.0126% −3.125% 0.6156 1.358% −0.043 0.297Commercial banks 8,649 −0.0390% −9.362% 0.0001 0.842% −0.108 1.867Energy traders 8,337 0.0073% 1.862% 0.4673 0.920% 0.048 2.283Hedge funds 30,680 0.0652% 17.844% 0.0001 1.319% −0.006 0.379Households/individuals 9,720 0.0153% 3.926% 0.1927 1.156% −0.113 1.186Investment banks 10,761 0.0036% 0.905% 0.6295 0.769% −0.074 3.972Market makers 23,826 −0.0373% −8.982% 0.0001 0.936% −0.284 3.896Unclassified 20,643 −0.0191% −4.689% 0.0382 1.321% −0.084 0.480

Statistics are presented for traders’ daily futures trading profits before transaction costs as a percentage of thetrader’s total open interest. Statistics for likely hedgers and likely speculators are presented in Panel A, andstatistics for the eleven trader type categories are presented in Panel B. p-values are for tests of the null that themean percentage profits are zero. The first five trader types in Panel B constitute the likely hedger category, andthe next three types in Panel B constitute the likely speculator category.

it is +0.0453% (+12.10% annualized).9 Both are significantly different fromzero and each other at the 0.0001 level.10 These results are consistent with thepredictions of the risk premium hypothesis. For comparison, we also calculatedmean returns for the CFTC’s commercial and noncommercial trader categories.Whereas the commercial and noncommercial means are significantly differentfrom each other at the 0.01 level, the difference is much smaller than thatbetween likely hedgers and likely speculators. For commercials, the averagereturn is −0.0092% (−2.29% annualized); for noncommercials, the averagereturn is +0.0173% (+4.46% annualized).

In panel B, the same statistics are presented for the eleven trader typecategories. Consistent with the risk premium hypothesis, mean profits arenegative for all five likely hedger subclassifications and positive for all threelikely speculator subclassifications. However, within the likely hedger category,mean profits are significantly less than zero at the 1% level only for refiners and

9 The statistics in Table 3 are unweighted, that is, large and small trader positions are weighted equally. We alsomeasured profits on hedger and speculator portfolios, which effectively weight trader positions by size. Theresults basically match those in Table 3. Indeed the difference in returns between hedgers and speculators is alittle larger.

10 The p-values shown in Column 4 of Table 3 are based on standard means tests that assume independentobservations. As discussed in Section 4.1, there is some cross-correlation in trader/day profits. Hence, we alsocalculated p-values using a bootstrap procedure similar to that described in Section 4.1 and the Appendix. Thebootstrap p-values are very close to the standard p-values reported in Table 3; when they differ, the bootstrapp-values are usually slightly lower.

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commercial banks.11 Among likely speculators, mean profits are significantlypositive only for hedge funds/money managers, for which they are a sizable17.84% on an annualized basis. Because it is unclear whether investmentbankers and dealers are primarily hedging or speculating, it is interesting thattheir mean profits are small and insignificant, suggesting a mix. The profitfigures in Table 3 are prior to transaction costs. Deducting estimated round-triptransaction costs of $15 per contract reduces mean daily profit figures by 0.3 to0.5 basis points, but mean profits remain significantly positive for both hedgefunds and speculators in general.

For market makers, mean daily profits are −0.0373%, implying anannualized loss of −8.98%, not counting their bid-ask spread earnings. Thisfinding is consistent with the aforementioned notion of market maker adverseselection losses and specifically the findings of Naik and Yadav (2003) forovernight holdings. It is also consistent with prior studies, for example, Silber(1984), Hasbrouck and Sofianos (1993), and Yao (1997), showing that anyinformation advantages possessed by market makers are short lived at best.Whereas our data do not allow us to rule out the possibility that market makerspossess information advantages in intraday trading acquired by observing orderflow, it seems clear that they do not have valuable private information onpositions held overnight.

In summary, we find significant differences in futures trading profits acrossour different trader categories. Consistent with the predictions of the riskpremium hypothesis, likely speculators make economically and statisticallysignificant profits, on average, whereas likely hedgers incur significant losses.Across the individual trader categories, hedge funds are particularly profitableand market makers make substantial losses, on average, on positions they holdovernight.

3. Measures of Trader Exposure to Hedging Pressure, Momentum,and the Convenience Yield

Having established the existence of systematic differences in trader profits,in this section we develop measures of trader exposure to hedging pressure,momentum, and hypothesized determinants of the convenience yield, which wethen use to test whether they are able to explain trader profits. It is importantto note at the outset that the impact of these variables on a trader’s profitsdepends on whether the trader is long or short, and by how many contracts.For instance, if futures markets are characterized by momentum and futuresprices have been rising, this would portend profits on long positions but losseson short. Moreover, if the trader holds x long contracts in contract A, y shortin contract B, and z long in contract C, the expected impact of momentum

11 Whereas this statement is for a one-tailed test, the p-values reported in Table 3 are two-tailed because differentsigns are expected for different categories, and no sign was hypothesized for three categories.

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on the trader’s day t profits depends on x, y, and z, as well as the individualmomentums of contracts A, B, and C. Thus, paralleling our daily profit measure%PLi,t in Equation (3), we calculate a signed and weighted net daily measureXi,t for each variable based on trader i’s holdings of long or short positions invarious contracts on day t :

Xi,t =

3∑c=1

M∑m=1

Di,c,m,tXc,m,t

∣∣OIi,c,m,t

∣∣3∑

c=1

M∑m=1

∣∣OIi,c,m,t

∣∣ , (4)

where Di,c,m,t =1 if i holds a long position in contract c-m on day t , Di,c,m,t =−1if i holds a short position, and Xc,m,t is some characteristic of contract c-m,such as its momentum, on day t . Thus Xi,t is a weighted average of variableXc,m,t , signed by whether trader i is long or short and weighted by the size of i’sposition in contract c-m that day. We further define Xi as the weighted averageof Xi,t over all days t , where Xi,t is weighted by i’s open interest positions onday t .

3.1 Hedging pressureAccording to the hedging pressure hypothesis, if a majority of hedgers arelong (short), futures prices will be pushed above (below) expected future spotprices, creating profit opportunities for speculators. Note that according to thishypothesis, a trader’s expected profits should not depend on whether she is ahedger or speculator but on her long/short position relative to the majority ofhedgers. Consider, for instance, an airline that hedges its future fuel purchasesby holding long positions in heating oil futures which, as documented in Table 2,is what they normally do. In the heating oil market, energy consumers aregrossly outnumbered by refiners and MDPs, who generally hold short heatingoil futures positions. If their sales of futures contracts push futures prices belowexpected future spot prices, the airlines’ expected trading profits should bepositive—like those of speculators. Hence, if the hedging pressure hypothesisis valid, trader i’s expected profits should be negative when her long/shortposition matches that of hedgers in general and positive when it differs.

To test this prediction, we define a hedging pressure (HP) measure asthe extent to which the signs of trader i’s open interest positions match thesigns of the aggregate net positions of likely hedgers. For this, in Equation(4) we set Xc,m,t =1 when likely hedgers in the aggregate are net long incontract c-m on day t , and Xc,m,t =−1 when they are net short. Of the 46,253contract/maturity/day observations in our sample, likely hedgers are net shortin 58.7% and net long in 41.3%. As described above, Di,c,m,t =1 if i holdsa long position in contract c-m on day t , and Di,c,m,t =−1 if i holds a shortposition. Hence, if the signs of all of trader i’s positions on day t match thoseof hedgers in general, then HPi,t =1; if trader i’s positions are all opposite in

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Table 4Descriptive statistics for hypothesized determinants of futures trading profits

Mean Standard deviation

Panel A: Means and standard deviations

Daily profit percent (%PL) −0.0018 1.0785Hedging pressure (HP) −0.0556 0.7520Momentum (MOM) 0.0022 0.0524Long-short (LS) −0.0664 0.7757Inventory (INV) −0.0205 0.4560Forecast volatility (VOL) 0.0026 0.1301

Panel B: Correlations

%PL HP MOM LS INV VOL

%PL 1.000HP −0.077 1.000MOM 0.025 −0.496 1.000LS 0.036 −0.367 0.169 1.000INV −0.037 −0.030 −0.216 0.066 1.000VOL 0.020 −0.005 0.108 0.054 −0.303 1.000

Descriptive statistics are reported for daily trader profits (%PL), and hypothesized determinants of trader profitsspecifically measures of the extent to which traders’open interest positions are exposed to hedging pressure (HP),hypothesized determinants of the convenience yield (LS, INV, and VOL), and momentum (MOM). Excepting%PL, all variables are signed + (−) for long (short) positions and weighted by position size using Equation (4).Statistics are based on 178,389 trader-day observations for 382 energy futures traders. Means and standarddeviations are reported in Panel A, and correlations are reported in Panel B.

sign to likely hedgers in the aggregate, then HPi,t =−1. We further define HPi

as the weighted average of HPi,t over all days t , where HPi,t is weighted byi’s open interest position on day t . The hedging pressure hypothesis impliesthat trader i’s profits on day t , %PLi,t , will be negatively correlated with HPi,t

and that %PLi is negatively correlated with HPi .Statistics for HPi,t are reported in Table 4 and means of HPi for the different

trader groups are provided in Table 5. Two statistics are of note at the outset.First, in Table 5, although the means for all five likely hedger types are positive,none exceeds 0.2, so there are numerous cases in which individual traders inthe likely hedger category hold positions of opposite sign to the aggregatelikely hedger position. In these cases, the traders are either speculating ortheir hedging needs run counter to the majority of likely hedgers so we expectthem to actually benefit from any hedging pressure. Second, the mean HPi

for hedge funds is −0.5588, which is much larger in absolute value than anyother trader group mean and indicates that hedge fund positions tend to beopposite to likely hedgers most of the time. Indeed, hedge fund positions areopposite in sign to aggregate positions of likely hedgers 83.4% of the time. Thenegative correlation between %PLi,t and HPi,t reported in Table 4 providesunivariate evidence in support of the hedging pressure hypothesis. We examinethis relation more closely in our regression analysis reported in Section 4.

3.2 MomentumTo test whether some traders follow a strategy intended to benefit frommomentum in futures markets and, if so, who they are and whether the

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Table 5Trader type characteristics

Hedging Momentum Long or Inventories Forecastpressure (MOM) short (INV) volatility

(HP) × 100 (LS) (VOL)

Refiners 0.1737 0.0003 −0.2902 −0.0230 0.0025Independent producers 0.1951 −0.0070 −0.2753 −0.0708 −0.0023MDPs 0.1520 0.0043 −0.2638 −0.0933 0.0131Large consumers 0.0429 −0.0027 0.5355 0.1451 0.0377Commercial banks 0.1343 −0.0013 −0.0722 0.0707 −0.0284Energy traders 0.0140 0.0038 −0.2483 −0.0365 0.0084Hedge funds −0.5588 0.0054 0.2461 −0.0569 0.0028Households/individuals −0.2573 −0.0003 0.3633 −0.0195 0.0041Investment banks 0.0282 0.0029 0.1609 0.0252 −0.0066Market makers −0.2277 −0.0008 −0.1151 0.0700 −0.0138Unclassified 0.1565 −0.0024 −0.1965 0.0242 0.0012Test of no difference null, p-value 0.0001 0.8425 0.0001 0.0008 0.1458

Means of HP, LS, INV, VOL and MOM are reported for different trader types. All variables are signed +(−) forlong (short) positions and weighted by position size using Equation (4). Statistics are based on 178,389 trader-dayobservations for 382 energy futures traders. The p-values for tests of null hypotheses that variable means do notdiffer by trader type are reported in the last row.

strategy is profitable, we calculate the momentum measures MOMi,t andMOMi by setting Xc,m,t =RETc,m,t in Equation (4), where RETc,m,t isthe return on contract c-m over the last month (21 trading days). Thus,MOMi >0(<0) indicates a tendency to long (short) futures contracts that haverisen (fallen) over the past month. According to the momentum hypothesis,%PLi,tshould be positively correlated with MOMi,t . As reported in Table 4,the simple correlation is positive but small. While not examining energy futuresspecifically, Fung and Hsieh (1997, 2001) find that hedge funds and commoditytrading advisors tend to follow momentum strategies, and Moskowitz, Ooi,and Pedersen (2012) find a similar pattern for speculators in general. Possiblybecause both the period over which momentum is calculated and the periodover which returns are observed are considerably shorter in our study than inthe studies by Fung and Hsieh (1997, 2001) and Moskowitz, Ooi, and Pedersen(2012),12 we find no evidence of momentum strategies in our data. None ofthe trader group MOM means are significantly different from zero, and thenull that MOM does not differ by trader type cannot be rejected at the 5%level. Nonetheless, the negative correlation between MOMi,t and HP reportedin Table 4 is consistent with the findings of Moskowitz, Ooi, and Pedersen(2012). We examine the implications of this relation more closely in Section 4.

3.3 Convenience yields, inventories, and volatilityAccording to the storage and convenience yield literatures, long futurespositions will tend to be profitable and short positions unprofitable, especially

12 Fung and Hsieh (2001) calculate momentum over several months, for example, three months in their mainanalysis, whereas Moskowitz, Ooi, and Pedersen (2012) calculate returns over twelve months. Both of thesestudies look at the ability of these past returns to forecast returns over the next month. Our relatively short dataperiod forces us to measure momentum over the past month and test its relation to returns over the next day.

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when inventories are low and/or price volatility is high. In other words, the riskpremium is negatively correlated with inventories and positively correlatedwith price volatility. Confirming this prediction, GHR find that both the cash-futures basis and profits on long futures positions are negatively correlated withinventory levels.

To test the hypothesis that profits tend to be positive on long positionsand negative on short positions, we define a measure, LS, of whether traderi tends to hold long or short positions by setting Xc,m,t =1 in Equation (4).Hence, if trader i holds only long (short) positions on day t, LSi,t =1(−1). Asexpected, LSi tends to be negative for energy companies and positive for energyconsumers. Hedge funds, households, and investment banks also tend to holdlong positions. The hypothesis that long positions are more profitable, whichimplies that %PLi,t should be positively correlated with LSi,t , is confirmedby the correlations reported in Table 4. Our regression analysis reported inSection 4 builds on this univariate finding.

To test the hypothesis that long (short) positions are more (less) profitablewhen inventories are low, we calculate INVi,t using Equation (4) with Xc,m,t =(1−Ratioc,t ), where Ratioc,t is the level of inventories in commodity c on dayt divided by estimated normal inventories after controlling for seasonality andtrend. In its Weekly Petroleum Status report, the Energy Information Agencyreports U.S. inventory levels for crude oil, gasoline, and distillates (whichwe, like GHR, use as our heating oil inventory measure). Because energyinventories display both trend and seasonal patterns, we follow GHR in usinga Hodrick-Prescott (1997) procedure to estimate detrended and deseasonalizedinventory levels adjusting their smoothing parameter for the fact that our dataare weekly, while theirs are monthly. Because the report is issued on Wednesday,we use the reported figures for Wednesday through the following Tuesday.Positive (negative) values of INVi indicate a stronger tendency to hold longfutures positions when inventories are low (high). The hypothesis that profitson long (short) positions vary inversely (directly) with the level of inventoriesimplies that %PLi,t should be negatively correlated with INVi,t , which is whatwe observe in Table 4 and build on in Section 4.

According to the modern theory of storage, spot prices tend to exceedfutures prices due to the convenience yield associated with holding physicalinventories, reflecting the timing option physical inventories provide to sell orconsume inventory when spot prices are high. The value of this option willtend to be greater when anticipated spot price volatility is high. Consistent withthis notion, Ng and Pirrong (1994) find that the spot-futures spread is higherwhen volatility is high. This implies that the profits to long (short) futurespositions should be positively (negatively) related to expected volatility. Totest this prediction, we define V OLi,t by setting Xc,m,t in Equation (4) equal tothe ratio of the conditional standard deviation of returns to commodity c on dayt as forecast by a GARCH (1,1) model divided by the average or unconditionalstandard deviation over the June 1993–March 1997 period. The GARCH (1,1)

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model is estimated using daily returns on the nearby contract from January1, 1993 through December 31, 1997. V OLi,t >0(<0) indicates a strongertendency to hold long futures positions when forecast volatility is high (low)and/or short positions when forecast volatility is low (high). The hypothesis thatthe risk premium varies directly with volatility implies that PLi,t should varydirectly with V OLi,t , which is confirmed by the results in Table 4. However,as reported in Table 5, the null that V OLi does not differ by trader type cannotbe rejected.

4. Regression Analyses

4.1 Basic bootstrap regression resultsIn Column 2 of Table 6, we report results of regressions of %PLi,t onthe possible trading profit determinants discussed in Section 3. Severalcharacteristics of the data raise doubts about OLS standard errors. For onething, there is considerable heteroscedasticity in that daily profit variancesare much higher for traders who hold all long or all short positions than forthose holding spread positions. In addition, there are likely cross-correlationsin the trader/day data. If the price of a futures contract rises, all tradersholding long positions in that contract will make profits and all holding shortpositions losses. Thus, the daily profits of traders holding similar (opposite)positions will tend to be positively (negatively) correlated. To obtain standarderrors consistent with these cross-correlations, we employ a clustered bootstrapprocedure similar to that in Kosowski, Naik, and Teo (2007) and Fung et al.(2008), in which we choose days at random and with replacement from the962 days in our data set, including all trader observations that day in thebootstrap sample. This process is repeated for 10,000 bootstrap samples. Detailsare provided in the Appendix. Standard deviations of the 10,000 coefficientestimates provide unbiased estimates of coefficient standard errors controllingfor both heteroscedasticity and cross-correlations. The resulting p-values areshown in parentheses. Attesting to the importance of the cross-correlations, thebootstrap standard errors are substantially higher than both the OLS and White(1980) standard errors.

Whereas the bootstrap p-values reported in parentheses in Table 6 assume anormal distribution, nonparametric confidence intervals can be calculated basedon the percentile distribution of the 10,000 bootstrap coefficient estimates. In thetables *, **, and *** designate coefficient estimates significantly different fromzero at the 5%, 1%, and 0.1% confidence levels, respectively, based on thesebootstrap quantiles. In Table 6, the p-values and percentile confidence levelsare consistent in that any coefficient that is significant at a given significancelevel (5%, 1%, or 0.1%), based on the bootstrap p-value, is also significant atthat level based on the percentile distribution. This is not always the case inlater tables.

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Table 6Trading profit regressions

Trader/day profits (%PLi,t ) Mean trader profits (%PLi )

Intercept −0.0085∗∗∗ −0.0108∗(0.0002) (0.0154)

Hedging pressure (HP) −0.1312∗∗∗ −0.0806∗∗∗(0.0000) (0.0000)

Long-short (LS) 0.0157 0.0207∗(0.7085) (0.0343)

Inventories (INV) −0.1071∗∗ −0.1303∗∗∗(0.0066) (0.0000)

Forecast volatility (VOL) 0.0722 0.2330∗∗(0.6402) (0.0058)

Momentum (MOM) −0.6692 −0.0043(0.3076) (0.9801)

Adjusted R2 0.0085 0.2651

Trader/day mark-to-market profits as a percent of open interest, %PLi,t , and mean trader profits over the 1993–1997 period, %PLi , are regressed on measures of the extent to which the trader’s open interest positions arerelated to hedging pressure (HP), hypothesized determinants of the convenience yield (LS, INV, and VOL), andmomentum (MOM). Independent variables are signed + (-) for long (short) positions and weighted by positionsize using Equation (4). There are 178,389 trader/day observations in the %PLi,t regressions and 382 traderobservations in the %PLi regressions. Bootstrap p-values are reported in parentheses. *, **, and *** denotecoefficients significantly different from zero at the 0.05, 0.01, and 0.001 levels based on bootstrap quantiles.

Because the trader sample is purely cross-sectional, the cross-correlationissue does not arise. For this sample, we form 10,000 bootstrap samplesof 382 traders each sampling with replacement from the original sample of382 traders. Results are reported in the third column of Table 6, again withbootstrapp-values in parentheses and significance levels based on the percentiledistribution designated with asterisks. As expected, because cross-correlationsare minor for these data, White (1980) and bootstrap standard errors areapproximately the same. Because daily profit variations tend to average outover time, the adjusted R2 is much higher for this data set.

Consistent with the hedging pressure hypothesis, the coefficient of HP isnegative and highly significant in both Table 6 regressions. The −0.0806coefficient in the %PLi regression implies that annualized percentage tradingprofits are a surprising 4,062 basis points higher for a trader who alwayslongs (shorts) those contracts in which the majority of likely hedgers areshort (long) versus a trader whose long and short positions are always alignedwith the aggregate positions of likely hedgers. Of course, there are no traderswhose positions are always in agreement (or in disagreement) with aggregatehedger positions. Nonetheless, the coefficient implies that a hedger withHPi =−0.5588, the hedge fund mean, tends to have annualized trading profits1,135 basis points higher than a trader whose positions are the same sign aslikely hedgers 50% of the time.

As predicted by the modern theory of storage, the coefficients of LS and VOLare positive, and the coefficients of INV are negative in both regressions.All aresignificant in the %PLi regression (LS at the 5% level,VOL and INV at the 0.1%level) but only INV is significant in the %PLi,t regression. The implication isthat long positions tend to be profitable and short positions unprofitable, with

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this tendency being more pronounced when inventories are low. Whereas GHRfind that hypothetical profits to a trading strategy based on hedging pressureas measured by the COT reports disappear after controlling for inventories, wefind that actual trader profits vary with both hedging pressure and inventories.

As noted previously, our short sample does not permit us to exactly replicatethe analysis of Fung and Hsieh (1997, 2001) and Moskowitz, Ooi, andPedersen (2012), who, while not examining energy futures specifically, findthat momentum strategies are generally profitable in commodity markets. Themomentum hypothesis predicts a positive coefficient for MOM, and the simplecorrelation between %PL and MOM is positive in Table 4, but its coefficientis negative and insignificant in both Table 6 regressions. Thus, our results donot provide support for the momentum hypothesis. We revisit this issue in thenext subsection.

4.2 Alternative specifications and robustness checksIn Table 7, we estimate variations of the Table 6 equation for the trader/daydata set. First, to provide evidence on the relative importance of the hedgingpressure variable and how it impacts the relationship between trading profitsand the other variables, we estimate the model without the HP variable.Comparing the regressions in the second columns of Tables 6 and 7, twomajor differences are noted. First, the adjusted R2 falls from 0.0085 to 0.0030,indicating that most of the model’s ability to account for differences in tradingprofits is due to the hedging pressure variable. Second, the coefficient of themomentum variable switches from negative to positive (though insignificant),which is more consistent with the findings of Fung and Hsieh (1997, 2001) andMoskowitz, Ooi, and Pedersen (2012). Other findings of Moskowitz, Ooi, andPedersen (2012) provide a possible explanation in that they find that momentumis correlated with futures market roll returns, which they argue suggests thatmomentum is partially due to hedging pressure. Our finding of a switch in thesign of the momentum variable when a measure of hedging pressure is includedin the regression is consistent with their argument. These findings suggest thatmomentum in commodity futures markets may be due to hedging pressure andthat the speculator profits that Fung and Hsieh (1997, 2001) and Moskowitz,Ooi, and Pedersen (2012) document may, in fact, be more attributable to hedgingpressure than to momentum.

As discussed above, much of the previous literature has tested for hedgingpressure by relating profits on hypothetical trades to the long/short positionsof commercial and/or noncommercial traders from the CFTC’s weeklyCommitments of Traders report (COT), treating commercial traders as likelyhedgers and noncommercials as likely speculators. In the second regressionin Table 7, we replace the HP variable with an analogous measure basedon the COT report. Specifically, COTi,t is calculated using Equation (4),where Xc,m,t =(CLc,t −CSc,t )/(CLc,t +CSc,t ) with CLc,t (CSc,t ) representing

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Table 7Alternative specifications

Without HP With COT With HP2 Nonlinearvariable inventories

Intercept −0.0007 −0.0024 −0.0102∗∗∗ −0.0066∗(0.8008) (0.2690) (0.0000) (0.0088)

Hedging pressure (HP) −0.1313∗∗∗(0.0000)

Weekly COT variable −0.4835(0.1572)

Alternative hedging −0.6673∗∗∗pressure variable (HP2) (0.0000)Long-short (LS) 0.0511 0.0239 0.0064 0.0157

(0.2140) (0.6029) (0.8804) (0.7076)Inventories (INV) −0.0848 −0.0982∗ −0.1057∗∗ −0.1267

(0.0310) (0.0179) (0.0071) (0.0514)INV2− (INV<0) −2.5171

(0.5500)INV2+ (INV>0) 0.629

(0.8794)Forecast volatility (VOL) 0.0494 0.0740 0.1068 0.0745

(0.7474) (0.6335) (0.4763) (0.6300)Momentum (MOM) 0.2167 −0.1467 −0.6866 −0.6739

(0.7344) (0.8297) (0.3132) (0.3050)Adjusted R2 0.0030 0.0037 0.0088 0.0085

Variations on the trader/day regression in Table 6 are presented. In the second column, the regression is estimatedwithout the hedging pressure variable, HP, whereas in the third column HP is replaced with a variable basedthe long versus short positions of commercial traders as reported in the CFTC’s weekly commitments of traders(COT) report. In the fourth column HP is replaced by an alternative measure of hedging pressure, HP2, basedon (likely hedger long positions-likely hedger short positions)/(likely hedger long positions+likely hedger shortpositions) In the final column, we test for nonlinearity in the profits—inventory relationship by adding squaredvalues of INV—one for observations when inventory levels are below the mean and another when they exceed themean. Bootstrap p-values are reported in parentheses. *, **, and *** denote coefficients significantly differentfrom zero at the 0.05, 0.01, and 0.001 levels based on bootstrap quantiles. The regressions are estimated overthe 178,389 trader/day observations.

commercial long (short) positions in the most recent COT report.13 Thismeasure differs from HP in three respects: (1) CLc,t and CSc,t are aggregatedover all contract maturities, m, whereas HP is maturity specific, (2) because theCFTC report is weekly, COTi,t is unchanged from Wednesday to Wednesday,whereas HPi,t differs from day to day, and (3) the commercial classificationincludes some (energy traders) whom we classify as likely speculators andothers (investment banks and brokers) for which it is unclear whether they areprimarily hedging or speculating. As shown in the third column in Table 7,when HP is replaced by COT, COT ’s coefficient is negative as predicted by thehedging pressure hypothesis but insignificant, and the adjusted R2 falls from0.0085 to 0.0037.

Next, we re-estimate the regression with a slightly different measure ofhedging pressure, HP2. For HP2 we define Xc,m,t =(LHLc,m,t −LHSc,m,t )/(LHLc,m,t +LHSc,m,t ), where LHLc,m,t represents aggregate hedger longpositions in contract c-m on day t and LHSc,m,t represents aggregate hedger

13 This ratio is the hedging pressure measure in de Roon, Nijman, and Veld (2000), and similar ratios are used inother studies.

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short positions. Signed and weighted measures are calculated for trader i onday t as usual using Equation (4). As shown in the fourth column of Table 6,results are virtually the same as those for HP; like HP, HP2 is significant at the0.1% level.

GHR argue and present evidence that the relationship between inventoriesand the risk premium is nonlinear—specifically that the risk premium increasessharply when inventories are much lower than normal because the risk of astock-out is high. To test their argument, we include INV 2− and INV 2+ definedas INV 2− =INV 2 when INV < 0 and = 0 when INV ≥0; and INV 2+ =INV 2

when INV > 0 and = 0 otherwise. The nonlinearity hypothesis of GHR impliesa negative coefficient for INV 2−. As reported in the final column in Table 7,the coefficient of INV 2− has the predicted sign but is insignificant.

In addition to the above factors pertaining specifically to commodity futuresmarkets, it is possible that returns on energy futures are related to broadermarket risk factors with which our hedging pressure and convenience yieldvariables could be correlated. To determine if these broader risk measures areresponsible for our results, we add four systematic risk factors to the Table 6regressions: conditional betas, the term premium, the default premium, andthe VIX. Results are available in the Online Appendix. All four new variablesare insignificant in the trader/day profit regression, whereas HP and INVremain highly significant. The conditional beta and term premium variablesare significantly different from zero in the mean trader profits regression buthave a negative sign. Thus, we find no evidence that futures trading profits arepositively related to the systematic risk factors that appear important in othermarkets.

5. Determinants of Inter- and Intra-trader Group Profit Differences

5.1 Trading profit differences between trader typesHaving established that futures position profits of individual traders vary withmeasures of the extent to which traders’ open interest positions are relatedto hedging pressure (HP), and hypothesized determinants of the convenienceyield, we next explore whether these variables explain the differences in futurestrading profitability between likely hedgers and speculators, and betweenthe different trader types documented in Table 3. In other words, are thehedger/speculator profit differences documented in Table 3 due to the profitdeterminants examined in the previous section or to some other factor? Apossible alternative explanation for the speculator-hedger profit difference isthat speculators have superior information or skills. Furthermore, if one regardshedge funds as particularly likely to have an information or skill advantage, thefinding that their profits are particularly high may be viewed as consistent withthis information/skill hypothesis. To examine this possibility, in this section weexplore whether trader type profit patterns are explained by hedging pressure,convenience yield, and other variables or if profit differences persist after

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Table 8Trader profit regressions with hedger, speculator, and market maker variables

Trader type All Trader type Withoutdummies and hedging hedging

only pressure pressure

Intercept −0.0174 −0.0008 −0.0048 −0.0098(0.1262) (0.9316) (0.6491) (0.3500)

Likely hedgers 0.0015 0.0024 0.0070 −0.0001(0.9056) (0.8200) (0.5531) (0.9935)

Likely speculators 0.0540∗∗ −0.0107 0.0004 0.0317∗(0.0012) (0.4624) (0.9777) (0.0349)

Market makers −0.0292 −0.0505∗∗∗ −0.0602∗∗∗ −0.0209(0.0880) (0.0004) (0.0001) (0.1642)


Long-short (LS) 0.0141 0.0410∗∗∗(0.1447) (0.0001)

Inventories (INV) −0.1176∗∗∗ −0.1087∗∗∗(0.0000) (0.0000)

Forecast volatility (VOL) 0.2183∗∗ 0.2612∗∗(0.0066) (0.0040)

Momentum (MOM) −0.0273 0.0196(0.8723) (0.9073)

p-value for test of hedger-speculator 0.0001 0.3325 0.6327 0.0103equality null

Adjusted R2 0.088 0.293 0.204 0.210

Mean trader profits are regressed on zero-one dummy variables for hedgers, speculators, and market makersand on measures of hedging pressure (HP), hypothesized determinants of the convenience yield (LS, INV, andVOL), and momentum (MOM). The regressions are estimated cross-sectionally over 382 traders. Unclassifiedtraders are the omitted category. p-values based on bootstrap estimations are reported in parentheses. p-valuesfor tests of the null that profits do not differ between hedgers and speculators based on White (1980) variancesand covariances are reported in the penultimate row. Bootstrap p-values are reported in parentheses. *, **, and*** denote coefficients significantly different from zero at the 0.05, 0.01, and 0.001 levels based on bootstrapquantiles.

controlling for these factors. We do so by regressing mean profits of the 382traders on zero-one dummies for trader type and then adding the hedgingpressure, convenience yield, and momentum variables to the regression.

As reported inTable 8, when trader profits are regressed on hedger, speculator,and market maker dummies, the estimated profit difference between likelyspeculators and likely hedgers is 0.0525 percentage points per day or 13.23%annualized and is significant at the 1% level.14 This compares with the0.0625 percentage point daily profit difference in Table 3. However, whenthe hypothesized profit determinants from Table 6 are added in the secondregression, this difference is no longer significant. Indeed, after controllingfor hypothesized profit determinants, implied profits are slightly higher forlikely hedgers than for likely speculators. As shown in the last two columnsof Table 8, the hedger-speculator profit difference in the first regression isprimarily explained by the hedging pressure variable because when only HP

14 The p-values reported in the penultimate row of Tables 8 and 9 are for Wald tests based on White (1980) variancesand covariances. As noted above, the White (1980) and bootstrap variances are virtually the same in the traderregressions.

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Table 9Trader profit regressions with trader type variables

Trader type All Trader type Withoutdummies and hedging hedging

only pressure pressure

Intercept −0.0233 −0.0014 −0.0066 −0.0129(0.1110) (0.9086) (0.6220) (0.3324)

Refiners 0.0125 0.0127 0.0173 0.0111(0.4303) (0.3397) (0.2483) (0.4300)

Independent producers −0.0031 −0.0101 0.0001 −0.0091(0.9331) (0.7104) (0.9971) (0.7596)

MDPs 0.0128 −0.0035 0.0120 0.0001(0.4287) (0.8115) (0.4330) (0.9957)

Large consumers 0.0381 0.0207 0.0241 0.0112(0.1409) (0.4744) (0.3415) (0.6288)

Commercial banks −0.0207 −0.0125 −0.0219 −0.0134(0.3111) (0.4837) (0.2685) (0.5022)

Energy traders 0.0391 0.0161 0.0233 0.03300.0455) (0.3564) (0.2131) (0.0779)

Hedge funds 0.0766∗∗∗ −0.0127 0.0017 0.0492∗(0.0001) (0.5955) (0.9468) (0.0154)

Households/individuals 0.0275 −0.0224 −0.0087 −0.0012(0.2219) (0.2310) (0.6651) (0.9565)

Investment banks 0.0262 0.0054 0.0088 0.0135(0.2016) (0.7252) (0.6089) (0.4944)

Market makers −0.0233 −0.0499∗∗ −0.0586∗∗ −0.0180(0.2127) (0.0027) (0.0017) (0.2852)


Long-short (LS) 0.0165 0.0415∗∗∗(0.1188) (0.0003)

Inventories (INV) −0.1243∗∗∗ −0.1067∗∗∗(0.0000) (0.0001)

Forecast volatility (VOL) 0.1888∗ 0.2553∗∗(0.0260) (0.0068)

Momentum (MOM) −0.0443 −0.0139(0.7944) (0.9341)

p-value: test of trader homogeneity null 0.0002 0.2869 0.3529 0.0450(market makers excluded)

Adjusted R2 0.096 0.288 0.199 0.213

Mean trader profits are regressed on zero-one dummy variables for trader type and measures of hedging pressure(HP), hypothesized determinants of the convenience yield (LS, INV, and VOL), and momentum (MOM).Unclassified traders are the omitted category. The regressions are estimated cross-sectionally over 382 traders.Bootstrap p-values are reported in parentheses. *, **, and *** denote coefficients significantly different fromzero at the 0.05, 0.01, and 0.001 levels based on bootstrap nonparametric quantiles.

is added to the regression in the penultimate column the speculator-hedgerdifference is eliminated. When the other variables from Table 6 are includedwithout HP, as shown in the final column, the speculator-hedger differenceis reduced slightly but remains positive and significant at the 0.05 level.This indicates that the speculator/hedger return difference is mostly due tospeculators taking advantage of hedging pressure by going long (short) whenhedgers in the aggregate are net short (long).

Except for market makers, Table 9 tells the same story for individualtrader categories. In the first regression, the null that there is no differencein profitability among the nine hedger-speculator categories is rejected at the

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0.001 level, and we observe that hedge funds are especially profitable.15 Whenthe hypothesized profit determinants from Table 6 are added in the secondregression, these differences disappear, and indeed the hedge fund coefficientis negative. As shown in the final columns of Table 9, it is again the HP variablethat primarily accounts for the differences. We saw in Table 5 that hedge fundsdisplay a strong tendency to take long (short) positions when likely hedgersare net short (long). This tendency seems to account almost entirely for theirhigh profitability. This complements the findings of Fung and Hsieh (2001,2004) and others that much of hedge funds returns in equity markets is due totaking on risk and Ramadorai (2013) finding that any hedge fund informationadvantage is short lived.

In summary, there is no evidence that any one particular trader group hasmore information or skill than another because (excepting market makers)intercategory profit differences are accounted for by the extent to which theyexploit price differences caused by hedging pressure. These results reaffirmour previous findings that hedge fund profits are primarily due to the riskabsorption or liquidity provision services they render hedgers rather than tosuperior information or skill.

As shown also in Tables 8 and 9, whereas the hedging pressure variable canaccount for the profit differences between the hedger and speculator categories,it does not explain the tendency for market makers to make losses on theirovernight holdings because the coefficient of the market maker dummy variableis negative and significant.16 This result reaffirms our prior findings reported inTable 3; that is, (abstracting from their bid-ask spread profits) market makerslose money, on average, on their overnight inventory positions.

5.2 Trading profit differences within trader typesNext, we explore whether our hedging pressure and convenience yield variablesexplain profit differences within, as well as between, the likely hedger andspeculator categories. In Table 10, we estimate separate regressions forlikely speculators and hedgers. It is notable that the HP variable is negativeand significant in all regressions. As observed previously, like speculators,minority hedgers should benefit from hedging pressure. For instance, ifthe energy markets are dominated by energy firms holding short futurespositions, an airline seeking to hedge its future fuel cost by holding longfutures positions would benefit from the hedging pressure exerted by theenergy firms. The hedger regression results indicate that this is, indeed,

15 Because “unclassified” is the omitted category, the coefficients represent the estimated difference between theparticular category and the unclassified category, whereas the sum of the coefficient and intercept provides ameasure of group mean profitability that is consistent with the means reported in Table 3.

16 It is not significant in the first regressions in Tables 9 and 10 because its coefficients in these regressions measurethe profit difference relative to the omitted unclassified category whose profits also tend to be negative as shownby the intercept. The sum of the market marker coefficient and the intercept is negative and significant.

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Table 10Separate speculator and hedger regressions

Trader/day profits Mean trader profits

Likely Likely Likely Likelyhedgers speculators hedgers speculators

Intercept −0.0002 −0.0094 0.0009 0.0061(0.9388) (0.2896) (0.9000) (0.6655)

Hedging pressure (HP) −0.1291∗∗∗ −0.1584∗∗∗ −0.1092∗∗ −0.0663∗(0.0000) (0.0000) (0.0093) (0.0022)

Long-short (LS) 0.0141 0.0137 −0.0003 0.0105(0.7288) (0.7580) (0.9826) (0.5308)

Inventories(INV) −0.1016∗ −0.1046∗ −0.1074∗ −0.0451(0.0090) (0.0269) (0.0134) (0.4710)

Forecast volatility (VOL) 0.0527 0.0491 0.1235 0.2646(0.7118) (0.7995) (0.3750) (0.2330)

Momentum (MOM) −0.8971 −0.8283 0.1676 −0.2804(0.1599) (0.2339) (0.4644) (0.3818)

Observations 74,422 48,737 115 114Adjusted R2 0.0075 0.0079 0.2515 0.0666

Separately for likely hedgers and likely speculators, trader/day and mean trader profits are regressed onmeasures of hedging pressure (HP), hypothesized determinants of the convenience yield (LS, INV, and VOL),and momentum (MOM). Bootstrap p-values are reported in parentheses. *, **, and *** denote coefficientssignificantly different from zero at the 0.05, 0.01, and 0.001 levels based on bootstrap quantiles.

the case. Together, the hedging pressure and convenience yield variablesexplain not only the profit difference between the likely hedger and likelyspeculator categories but also much of the profit variation within thesecategories.17

Finally, we test whether individual trader profits persist after controllingfor hedging pressure and our other variables. If so, it could be evidencethat some trading profit differences between individual traders are due todifferences in information or skill, though we find no evidence that profitsdiffer between trader types due to information or skill differences. For this, wereturn to our sample of 224 traders in Section 2.1 with at least 25 trades and50 position observations in both the June 1993–May 1995 and June 1995–March 1997 subperiods and test whether the mean trading profit residualsfrom the regressions in Table 6 over the second subperiod are correlated withmean residuals over the first subperiod. The correlation coefficient of 0.208 issignificant at the 0.01 level, indicating that trading profits/losses of individualtraders persist after controlling for hedging pressure and other variables.

In summary, we find that trading profits between trader types are completelyexplained by the extent to which they exploit profit opportunities created byhedging pressure and the convenience yield. These variables also explain muchof the profit variation within the likely hedger and likely speculator categories.However, even after controlling for these variables, there is evidence that some

17 As an additional check we compared profits across individual contracts when likely hedgers in the aggregatewere short and when they were long. On contracts in which likely hedgers were short on balance, individualtraders made losses if they were short and profits if they were long. On contracts in which likely hedgers werelong in the aggregate, individual traders made losses if they were long and profits if they were short.

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traders tend to make persistent profits and others persistent losses possiblyindicating profit differences due to differences in information or skill at theindividual level.

6. The Relation between Trader Profits and Characteristics of Tradesand Traders

6.1 Directional versus spread trading strategiesNext, we test how trader profits are related to other trade and tradercharacteristics to sharpen the insights we obtained previously on thedeterminants of futures trading profits. One issue is whether spread tradesare more or less profitable than directional trades. Whereas some traders mayspeculate that the price will rise or fall in the future by taking large longor short positions, respectively (which we term “directional trades”), othersmay speculate on future price relationships by going long in some contractsand simultaneously shorting others (“spread trades”). Examples of the latterare calendar spreads and crack spreads. To the extent that directional tradesare riskier than spread trades, average profits might have to be higher onthe former to attract speculators. On the other hand, it may be the case thatmarket mispricings lead to arbitrage possibilities, which astute spread traderscan exploit. For example, Büyüksahin et al. (2008) present evidence that prior to2002 “near- and long-dated futures prices [for crude oil] were priced as thoughtraded in separate markets[,]” implying that during our data period there wereprice differences that calendar spread traders could profitably exploit.

Letting Li,c,m,t represent trader i’s open interest position in contract c-m onday t if long, and Si,c,m,t if her position is short, we measure the extent to whichtrader i’s positions on day t are spread as

SPi,t =2∗Min

[3∑

c=1

M∑m=1

Li,c,m,t ,

3∑c=1

M∑m=1

Si,c,m,t

]/3∑

c=1

M∑m=1

(Li,c,m,t +Si,c,m,t ).

(5)If trader i holds equal numbers of long and short contracts on day t , SPi,t =1.If i holds only long or only short positions, SPi,t =0. We calculate a summarymeasure for each trader i, SPi , by averaging (weighted by open interest) SPi,t

over all days t . Note that SPi will tend to be low for both directional speculatorsand hedgers.

6.2 Trader sizeHaving explored how trader profits relate to measures of hedging pressure andthe convenience yield, we now explore if individual trader profits also dependon trader characteristics, such as size and turnover rate. There are a couple ofreasons to expect larger traders to be more informed than smaller traders andconsequently to have higher trading profits. The first is economies of scale ininformation generation. For example, spending $100,000 to obtain informationis harder to justify for a 100 contract position than for a 10,000 contract position.

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Therefore, larger traders are likely to spend more on generating information.On the other hand, Fung et al. (2008) and Ramadorai (2013) find that growthin hedge fund size is negatively correlated with future returns suggestingdiminishing returns to size. Second, larger energy firms could have superiorinformation because they observe more of the physical energy market.Althoughwe found no evidence above that some trader groups have more informationor skill than others, it is still possible that some individual traders do. Becausetrader identities are unknown to us, we can only measure size in terms of theiropen market positions, not total assets or sales. Hence, SIZEi is measuredas the log of trader i’s open interest positions averaged over all days when i

is in the market. As shown in Table 2, by this measure the largest traders areinvestment banks followed by commercial banks. The hypothesis that largertraders tend to make higher profits implies a positive coefficient for SIZEi .

In addition, it is possible that due to the lack of experience, traders whoare rarely in the market make few trades and acquire less skill than those whoare constantly in the market, thereby earning lower trading profits. To test thisprediction, we relate trading profits to the percentage of the 962 days whenthe trader maintained a nonzero open interest position, DAYSi , expecting apositive coefficient. Note that this variable is subject to a possible survivorshipbias because traders with losses may tend to drop out. DAYS is highest for MDPs(69.5%), energy traders (64.8%), and investment banks (60.9%) and lowest forunclassified (37.6%) and households (39.6%).

6.3 Trader activityAlong the same lines, we hypothesize that trading profits will be positivelycorrelated with turnover. This need not always be the case, but we expecthedgers to tend to hold their positions longer than speculators. If so, the riskpremium hypothesis would imply that traders who turn their positions overfrequently should have higher pre-trading-cost profits, on average. Becauseour trading profit calculations are before deducting trading costs, confirmationof this hypothesis does not necessarily imply higher profits after transactioncosts. On the other hand, Odean (1999) and Barber and Odean (2000) find thatmore active household traders tend to lose money on equity trades even beforededucting transaction costs.

To test the relation between trading activity and profits, we measure eachtrader i’s position turnover rate, T URNi , by dividing i’s average daily tradingvolume by i’s average open interest position, where the two averages arecalculated only over days with nonzero open interest at either the beginningor end of the day. Mean turnover rates are highest for market makers/floortraders (35.3%), followed by hedge funds (22.4%) and individuals (21.9%),and are lowest for commercial banks (7.9%), investment banks (8.3%), andindependent producers (9.0%). To test whether the profits/turnover relationdiffers for household traders, we include an interaction variable IND_T URNi ,equal to T URNi when i is a household and zero otherwise.

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Table 11Trader profit regressions with trade and trader characteristics

Full Speculator Hedgersample subsample subsample

Intercept −0.0291 −0.1039 0.0359(0.3941) (0.1919) (0.4183)

Spread trading (SP) 0.0381∗ −0.0042 0.0409(0.0167) (0.8935) (0.0896)

Log of mean open interest (SIZE) −0.0056 0.0215 −0.0193(0.5891) (0.4576) (0.1522)

Turnover (TURN) 0.1586∗ 0.3041∗∗ 0.0708(0.0033) (0.0000) (0.4591)

Percent of days in market (DAYS) 0.0158 −0.0139 0.0054(0.2956) (0.7242) (0.8320)

Household turnover −0.1477 −0.2316∗(0.0445) (0.0230)

Hedging pressure (HP) −0.0869∗∗∗ −0.0572∗ −0.0986∗∗(0.0000) (0.0037) (0.0145)

Long-short (LS) 0.0159 0.0278 −0.0027(0.1107) (0.1750) (0.8717)

Inventories (INV) −0.1065∗∗∗ −0.0652 −0.0852(0.0001) (0.3192) (0.0627)

Forecast volatility (VOL) 0.1950∗ 0.2954 0.1403(0.0148) (0.1992) (0.2648)

Momentum (MOM) −0.0216 −0.4260 0.1076(0.9020) (0.1276) (0.6434)

Market maker dummy −0.0847∗∗∗(0.0000)

Adjusted R2 0.390 0.435 0.236

Mean trader profits are regressed on a measure of spread trading (SP), trader size (SIZE) measured as the logof mean open interest, turnover (TURN), and percent of days in the market (DAYS), as well as the measures ofhedging pressure (HP), the convenience yield (LS, INV, and VOL), and momentum (MOM) and a zero-dummyfor market makers. The regression is estimated first for the full sample of 382 traders then separately for likelyspeculators and likely hedgers. Bootstrap p-values are reported in parentheses. *, **, and *** denote coefficientssignificantly different from zero at the 0.05, 0.01, and 0.001 levels based on bootstrap quantiles.

6.4 ResultsRegressions with the variables discussed in the subsections 6.1–6.3 and themarket maker dummy added to the Table 6 regressions are reported in Table 11.Because SIZEi , DAYSi , and T URNi only vary cross-sectionally, we estimatethe regression with mean trader profits, %PLi , over the entire period as thedependent variable. The regression is estimated first over all 382 traders andthen separately for likely speculators and likely hedgers.

Whereas we hypothesized that larger traders would tend to be more informedand therefore have higher profits, ceteris paribus, the coefficient of SIZEi isstatistically insignificant and has a negative sign in the full sample regression.18

This negative coefficient is consistent with the finding of Ramadorai (2013) thathedge fund asset growth is negatively correlated with future fund performance.Profits are also an insignificant function of the percentage of the 962 days that

18 We also estimated a quadratic form with both SIZEi and SIZE2i

. Consistent with diminishing marginal returns

to trader size, the SIZEi and SIZE2i

coefficients were positive and negative, respectively, but statisticallyinsignificant at the 10% level, both individually and as a pair.

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the trader held a nonzero open interest position, DAYSi . Thus, there is noevidence that larger or more active traders have an informational advantage.

As expected, mean trader profits vary positively and significantly withturnover, T URNi , indicating that traders who turn their positions over moreoften tend to have higher profits—at least before transaction costs. Althoughinsignificant, the negative sign of the IND_T URNi coefficient is in linewith the findings of Odean (1999) and Barber and Odean (2000) that in thestock market more active household traders tend to make losses. The sumof the two coefficients is an insignificant 0.0109 indicating little relationbetween turnover and profits for households. The T URNi variable is significantin the likely speculator regression, but not in the likely hedger regression.In summary, we find that nonhousehold speculators who turn their futuresportfolio over frequently tend to have higher profits than those who changetheir positions infrequently, but this relation does not hold for hedgers andhouseholds.

Consistent with the findings of Büyüksahin et al. (2008), the results for theSP variable in the regression for the full sample indicate that traders who holdspread positions tend to make higher profits than those who hold mostly longor mostly short positions. Specifically, a trader whose positions are always halflong and half short tends to make 0.0381% (10.03% annualized) more than atrader who always holds either all long or all short positions. Note, however,that this variable is insignificant in the likely speculator subsample and onlysignificant at the 10% level in the likely hedger subsample.

7. Conclusions

This study provides new evidence on alternative theories of commodity futurespricing. In contrast with prior studies that test these theories by exploring theprofitability of hypothetical commodity futures trading strategies, we provideevidence on the validity of alternative theories by using proprietary data onindividual trader positions in three energy futures markets: crude oil, gasoline,and heating oil.

We find that approximately 39% of the variation in mean profits amongdifferent large and midsize traders in energy futures markets is explained bydifferences in their trading objectives, strategies, or characteristics. We findconsiderable support for the risk premium hypothesis in that mean futuresposition profits of likely speculators (hedge funds, in particular) are significantlyhigher than mean hedger profits. Our evidence indicates that most of this riskpremium is due to hedging pressure since individual traders (whether hedgers orspeculators) who hold short positions when likely hedgers in the aggregate arenet long and long positions when likely hedgers in the aggregate are net shortmake considerably higher profits on average than traders whose open interestpositions generally match likely hedgers in sign. In addition, a part of traderprofits appears to be due to a convenience yield, which varies with inventories

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and volatility. As predicted by the modern theory of storage, those traders whotake long positions when inventories are low and/or price volatility is high makehigher profits than those who do not. After controlling for hedging pressure,we find no evidence that traders make higher profits by going long in futureswith positive short-term momentum or shorting those with negative short-termmomentum. Indeed, our results suggest that the momentum in commodityfutures markets documented in previous studies may be due in large part tohedging pressure.

Classifying futures traders into eleven line-of-business types, we findsignificant mean profit differences, with hedge funds being the most profitable.However, excepting market makers, mean profit differences among the other tentrader types are explained by the extent to which they exploit price differentialscreated by hedging pressure. Hedge funds in particular tend to exploit pricedifferentials created by hedging pressure and to earn higher than normal profitsas a result. There is no evidence that larger traders profit at the expense of smallertraders but, excepting households, speculators with higher turnover rates tendto have higher profits. Not counting the bid-ask spread, market makers incurlarge and significant trading losses, on average, on the positions that they holdovernight, indicating that any informational advantage they may have fromobserving the order flow is short lived.

While we find evidence that some individual traders may profit from superiorinformation or skills, we find no evidence of systematic informational or skilldifferences between different trader types. In particular, there is no evidenceof systematic differences in trading profits between hedgers and speculatorsor among the eleven trader types after controlling for hedging pressure andthe convenience yield, especially the former. Instead, our evidence indicatesthat the profits of speculators in general, and hedge funds in particular, are dueto the risk absorption services they provide hedgers by being willing to long(short) when hedgers in the aggregate are net short (long) and especially whenvolatility is high or inventories are low.

Appendix

Variable definitions

Variable Definition

%PL – Daily ProfitPercentage

%PLi,t is trader i’s daily percentage profit/loss on day t summed over all maturitiesm and commodities c. %PLi is trader i’s average daily percentage profit/loss overthe entire period (June 1993 through March 1997).

DAYS DAYSi is the percentage of the 362 days in our sample during which trader i

maintained a nonzero open interest position.HP – Hedging Pressure HPi,t measures the extent to which trader i’s positions on day t match in sign the

aggregate net positions of likely hedgers, averaged over all maturities m andcommodities c. HPi measures the same for trader i over the entire period. HPi,tand HPi , by construction, range from −1 to 1. A trader with a HP of −1 (1) holdspositions completely opposite to (in line with) the sign of likely hedgers’ positions.

(continued)

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Variable Definition

IND_TURN IND_TURN i is equal to TURN i when trader i is a household and is zero otherwise.INV – Inventories INV i,t measures the tendency of trader i on day t to hold long (short) positions in a

commodity when that commodity’s inventories are low (high). Inventory levels aremeasured as the ratio of the actual inventory level of commodity c on day t to theHodrick-Prescott estimate of the normal inventory level. INV i measures the sametendency for trader i over the entire period.

Likely hedger Dummy = 1 if the trader is a refiner, an independent producer, amarketer/distributor/pipeline operator, a large consumer or a commercial bank.

Likely speculator Dummy = 1 if the trader is an energy trader, a hedge fund/money manager, or ahousehold/individual.

LS – Long-Short LSi,t measures whether trader i′s positions on day t are long (+) or short (−)averaged across maturities m and commodities c. LSi measures the average fortrader i over the entire period.

Market maker Dummy = 1 if the trader is a market maker or a floor trader.MOM – Momentum MOMi,t is a signed and weighted average of the momentum over the last twenty-one

trading days in futures held by trader i on day t . MOMi measures the sametendency for trader i over the entire period.

SIZE SIZEi is the log of trader i’s average open interest position over all days trader i is inthe market.

SP – Spread Trading SPi,t measures the tendency of trader i on day t to hold spread positions acrossmaturities m and commodities c, defined as the tendency of holding futurespositions that are hedges of each other. SPi measures the same tendency for trader i

over the entire period.TURN– Turnover TURN i is the ratio of trader i’s average daily trading volume to trader i’s average

daily open interest position where the two daily averages are computed only overdays with nonzero open interest.

VOL – ForecastVolatility

VOLi,t is a signed and weighted average of the relative volatility of trader i’spositions on day t . Volatility is forecast using a GARCH(1,1) model for the nearbycontract in commodity c. VOLi,t is measured as the ratio of the conditionalstandard deviation of returns forecast on commodity c on day t to the unconditionalstandard deviation of commodity c’s returns over the entire period. VOLi measuresthe same tendency for trader i over the entire period.

1. Bootstrap Procedure

1.1 Trader-day regressionsIn the trader-day regressions, trader i’s profits as a percent of open interest on day t , %PLi,t , areregressed on characteristics of the market and the trader’s positions. Ordinary least squares (OLS)standard errors and t-tests assume that the residuals from these regressions are homoscedastic,serially uncorrelated, and cross-sectionally independent. White (1980) standard errors adjust forheteroscedasticity, but not cross-sectional correlation. The assumption that the residuals are notcross-sectionally correlated is clearly violated for our data. If the futures price of contract c-m rises,all traders holding long positions in that contract will make losses and all traders holding shortpositions losses. Moreover, because prices of futures contracts with different times to expiration arehighly correlated and prices for crude oil, gasoline, and heating oil are also highly correlated, profitson long (short) positions in any contract will tend to be positively correlated with the profits on long(short) positions in any other contract. Consequently, the profits of traders with similar positionswill tend to be positively correlated while the profits of traders with opposite positions will tend tobe negatively correlated. To obtain unbiased estimates of the coefficients and their standard errors,we employ the cross-sectional clustered bootstrap procedure described below using the STATAsoftware package:

Step 1: The coefficients reported in the tables are obtained by estimating the regression %PLi,t =

β0 +J∑

j=1βj Xj,i,t +ei,t using OLS and the 178,389 trader-day observations, where Xj,i,t

represents variable j as defined in Equation (4). Let b∗j designate the OLS estimate of βj .

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Step 2: We randomly select 962 days with replacement from the 962 trading days with databetween June 1, 1993 and March 31, 1997. For each sampled day, observations of%PLi,t and all variables Xj,i,t are added to the bootstrapped data set for all traders i

with day t positions.Step 3: The regression is re-estimated using the bootstrapped data set created in step 2.Step 4: Steps 2 and 3 are repeated 10,000 times yielding 10,000 regression estimates. Let bj,k

represent the OLS estimates of βj obtained in iteration k, where k = 1, …. 10,000.Step 5: For each variable j , bootstrap standard errors are estimated as the standard deviation of the

10,000 bj,k , σ (b)j . These standard errors, which are corrected for both heteroscedasticityand cross-sectional correlation, are considerably higher than the OLS and White (1980)standard errors reflecting the high positive (negative) correlations in profits of tradersholding similar (opposite) positions. z-values are calculated as zj =b∗

j /σ (b)j . The twosided p-values reported in the tables are calculated from the zj assuming a normaldistribution.

Step 6: The p-values described in step 5 assume the coefficient estimates are normally distributed.Nonparametric empirical (or percentile) confidence intervals are obtained by orderingthe 10,000 bj,k estimates and calculating quantiles using the STATA software. The x

percentile confidence interval for βj is calculated as bj,lower 0.5x <βj <bj,upper 0.5xwherebj,lower 0.5x is the lower x/2 quantile estimate of βj and bj,upper 0.5x is the (1-x)/2 quantileestimate of βj . For instance, for x =1% for our 10,000 ranked bj,k estimates, bj,lower 0.5x =bj,50 (i.e., the 50th lowest bj estimate) and bj,upper 0.5x =bj,9950 (i.e., the 50th highest bj

estimate). For x = 0.1%, bj,lower 0.5x =bj,5 and bj,upper 0.5x =bj,9995. Note that bj,lower 0.5x

and bj,upper 0.5x need not be symmetric around βj . Based on these percentile confidenceintervals, in the tables, we designate variables that are significantly different from zero atthe 5%, 1%, and 0.1% levels as *, **, ***. The p-values and nonparametric confidenceintervals give approximately the same results, except in the tails, which are fatter than thenormal distribution indicates.

Because there is little serial correlation in futures price changes, we do not anticipate muchserial correlation in trader-day trading profits. Nonetheless, as a robustness check, we ran thestationary bootstrap procedure of Politis and Romano (1994), which corrects the bias in standarderrors produced by serial correlation. This was run setting the probability of choosing the nextday first at 0.2 and then at 0.5. The resulting standard errors, p-values, and confidence intervalswere virtually identical to those for the clustered bootstrap described above and presented in thetables.

1.2 Trader regressions

Because daily profit fluctuations due to futures price changes tend to average out over time, we donot expect any cross-correlation to be as serious in the trader regressions. Nonetheless, we wouldexpect any two traders following similar hedging strategies over our period to have similar tradingprofits and residuals. Consequently, we conduct the following nonclustered bootstrap:

Step 1: The coefficients reported in the tables are obtained by estimating the regression %PLi =

β0 +J∑

j=1βj Xj,i +ei using OLS and the 382 trader observations.

Step 2: 382 traders are chosen at random and with replacement from trader sample.Step 3: The regression is re-estimated using the bootstrapped data set created in step 2.Step 4: Steps 2 and 3 are repeated 10,000 times yielding 10,000 estimates of each regression

coefficient.Step 5: This step is identical to that described for the trader-day regressions.Step 6: This step is identical to that described for the trader-day regressions.

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