College of Business and Economics, Towson University ... Ederington was Visiting Professor at the University of Melbourn
Determinants of Trader Profits in Commodity Futures Markets* Michaël Dewally College of Business and Economics, Towson University 8000 York Road, Suite 316 A, Baltimore, MD 21252-0001 Tel: (410) 704-4902;
[email protected] Louis Ederington (corresponding author) Price College of Business, University of Oklahoma 307 West Brooks St., Norman, OK 73019-4005 Tel: (405) 325-5697;
[email protected] Chitru Fernando Price College of Business, University of Oklahoma 307 West Brooks St., Norman, OK 73019-4005 Tel: (405) 325-2906;
[email protected] March 2012 Initial Draft: August 2009
JEL Classifications: G12, G13 Keywords: commodity futures, risk premium, hedging pressure, theory of storage, momentum, trader profits, speculators, hedgers, hedge funds, market makers
*
We thank two anonymous RFS referees and the Editor, Geert Bekaert, for extensive suggestions that significantly improved the paper. We also thank seminar participants at the University of Oklahoma, the US Commodity Futures Trading Commission, the FDIC-Cornell-Houston Conference on Derivatives and Risk Management, and the 2010 meetings of the Financial Management Association, where earlier versions of this paper were presented. We have benefited from comments and suggestions by Hank Bessembinder, Bahattin Büyüksahin, Jeffrey Harris, Robert Jarrow, Scott Linn, Bill Megginson, James Moser, Michel Robe, Wulin Suo, and Pradeep Yadav. We have also benefited from useful discussions with US Energy Information Administration (EIA) officials. We thank the US Department of Energy (DOE) for providing the proprietary disaggregated energy futures position data used in this study. The views expressed in this paper reflect the opinions of the authors only, and not of the US DOE or EIA. We are solely responsible for all remaining errors and omissions. A part of this research was completed while Louis Ederington was Visiting Professor at the University of Melbourne, which he thanks for valuable support.
Determinants of Trader Profits in Commodity Futures Markets
Abstract Using a unique proprietary data set of positions held by all large traders in the crude oil, gasoline, and heating oil futures markets, we use actual trader profits to test the predictions of various commodity futures pricing models. We find strong support for: (a) the risk premium hypothesis – mean hedger profits are significantly negative while speculator profits are significantly positive – and (b) the hedging pressure hypothesis – traders (whether speculators or hedgers) who hold long (short) positions when likely hedgers in aggregate are net short (long) have significantly higher profits than traders whose net positions are aligned with likely hedgers. We find also that profits on long positions vary inversely with inventories and directly with price volatility, as predicted by the modern theory of storage. While we find no evidence of profits derived from short term momentum after controlling for hedging pressure, our results provide support for the notion that momentum in commodity futures markets may be due largely to hedging pressure.
JEL Classifications: G12, G13 Keywords: commodity futures, risk premium, hedging pressure, theory of storage, momentum, trader profits, speculators, hedgers, hedge funds, market makers
Determinants of Trader Profits in Commodity Futures Markets 1. Introduction Recent price volatility in commodity markets has renewed interest among academics, practitioners and policymakers in the role and impact of derivatives traders, especially speculators, in these markets. While this topic has spawned several recent studies,1 one of the persistent challenges in this line of research has been the availability of data on actual trader positions in sufficient detail to permit meaningful analysis. Consequently, one of the most fundamental underlying issues – the degree to which differences in trading profits/losses among individual traders represent differences in risk taking, 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 the predictions of various commodity futures pricing models are supported by actual trader performance data. This paper seeks to provide evidence on these issues using proprietary data on individual trader positions in three energy futures markets: crude oil, gasoline, and heating oil. Theories of commodity futures pricing, including hedging pressure, the convenience yield, and momentum, have generally been tested by exploring the profitability of hypothetical commodity futures trading strategies, for example by asking whether a hypothetical trader could have profited by trading on the basis of aggregate hedger positions or market momentum. In contrast, we use the actual performance of various groups of traders to analyze profit differentials and test these futures pricing models based on daily open interest position data of large traders that the CFTC requires brokers to report as part of its market surveillance. While normally available to researchers only in highly aggregated form, the US Department of Energy (DOE) provided us with data on the open interest positions of individual reporting traders in the NYMEX crude oil, heating oil and gasoline futures markets. We utilize this data on individual reporting traders to explore their trading strategies and
1
See, for example, Dinceler, Khokher, and Simin (2005), NYMEX (2005), Erb and Harvey (2006), Miffre and Rallis (2007), Haigh, Hranaiova, and Overdahl (2007), Interagency Task Force on Commodity Markets (2008), Büyükşahin, Haigh, Harris, Overdahl, and Robe (2008), Khan, Khokher, and Simin (2008), Gorton, Hayashi, and Rouwenhorst (2008), Büyükşahin and Harris (2009), Acharya, Ramadorai, and Lochstoer (2010), Stoll and Whaley (2010), and de Roon and Szymanowska (2010).
1
calculate their trading profits. Since the likelihood that a trader is primarily hedging or speculating, their trading strategy, and their access to information may differ by line of business, 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 banks and dealers, market makers, and an unclassified group. First, we examine our data for evidence in support of the traditional risk premium or hedging pressure theory of Keynes (1930) and Hicks (1939), which predicts that if most hedgers have long positions in the underlying asset (as Keynes and Hicks assumed) and hedge by holding short futures market positions, futures prices will be pushed below expected future spot prices (backwardation) and speculators can make positive profits on average by holding long futures positions. If most hedgers are long in the futures market, futures prices will be biased upward (contango) and speculators can expect to profit by shorting futures. Presuming that commercial traders are more likely than non-commercial traders to be hedgers,2 this hedging pressure hypothesis has been tested previously by exploring whether hypothetical trading profits on long (short) positions tend to be positive when commercial traders in the aggregate are net short (long) according to the CFTC’s weekly COT report.3 In this study, we exploit the availability of individual (as opposed to aggregated) net hedger positions in our data and also employ a more refined distinction between likely hedgers and speculators to examine whether some speculator types are more likely than others to take positions opposite to those of likely hedgers and, if so, whether this trading strategy is profitable in practice. Our findings strongly support the risk premium and hedging pressure hypotheses. Likely hedgers tend to have futures trading losses while 2
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 since many hedging programs apparently include some speculation. Dolde (1993), Bodnar, Hayt and Marston (1998) and Glaum (2002) provide survey evidence suggesting that the practice of selective hedging, i.e., firms incorporating their views about future market movements into their hedging programs, is widespread. However, Adam and Fernando (2006) and Brown, Crabb, and Haushalter (2006) find no evidence that firms are able to outperform the market by selective hedging. 3
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 et al. (2010) extend this literature to a setting with capital-constrained speculators, and show that a reduction in speculator risk capacity increases commodity producer hedging costs, reduces producer inventories, and increases the futures risk premium. Fama and French (1987) find some evidence for a risk premium but conclude it is too weak “ to resolve the long-standing controversy about the existence of non-zero expected premiums.” (1987, p. 72).
2
likely speculators (hedge funds in particular) turn consistent profits. Additionally, individual trader profits are a strong positive function of the extent to which they take positions opposite to the aggregate positions of likely hedgers, i.e., long (short) positions when likely hedgers in the aggregate are net short (long). In particular, hedge fund trading profits, which are the highest among our eleven trader types, are primarily due to their exploitation of this hedging pressure, i.e., holding positions opposite to those of likely hedgers. Second, we study the extent to which our data supports the modern theory of storage. Extending the theories of storage and convenience yields,4 Gorton, Hayashi, and Rouwenhorst (2008) (hereafter GHR) argue that the risk premium should vary inversely with inventory levels since low inventory levels increase spot price volatility,5 thereby elevating the required risk premium associated with holding futures contracts and increasing the convenience yield to holders of physical inventories.6 Testing these theories using data for 33 commodity markets, GHR find that as predicted: 1) the cashfutures basis is an inverse function of inventory levels, and 2) returns to a strategy of holding long futures positions are positive and inversely correlated with inventory levels. They also find that after controlling for inventory levels, there is no evidence of futures returns varying with hedging pressure or momentum. We revisit these questions using our actual trader data. Although less important than hedging pressure in explaining futures trading profits differences among different trader types, we also find strong support for the predictions of the modern theory of storage – profits on long (short) positions are inversely (directly) related to inventory levels and directly (inversely) related to volatility. Third, we test whether momentum strategies are profitable in energy futures after controlling for hedging pressure. Erb and Harvey (2006) and Miffre and Rallis (2007) find evidence of momentum in futures prices, and in two of the few studies of actual trades, Fung and Hsieh (1997, 2001) find that hedge funds and commodity trading advisors tend to profitably employ momentum strategies.
4
See, for example, Kaldor (1939), Working (1949), Brennan (1958), Deaton and Laroque (1992), and Routledge, Seppi, and Spatt (2000).
5
See, for example, Fama and French (1987) and Ng and Pirrong (1994).
6
See, for example, Dinceler et al. (2005).
3
However, while Moskowitz, Ooi, and Pedersen (2010) show that speculators in general tend to follow momentum strategies, they also uncover evidence to suggest that the momentum effect may be due to hedging pressure in that they find that momentum is correlated with futures market roll returns. Our findings are consistent with this latter finding of Moskowitz et al. (2010) and provide support for the notion that momentum in commodity futures markets may be due largely to hedging pressure. After controlling for hedging pressure and the theory of storage variables, we find no evidence that traders profit by holding long positions in futures contracts with positive short-run momentum or short positions in futures with negative short-run momentum. Our aforementioned analysis explicitly allows for the possibility that in addition to the above factors that pertain specifically to commodity futures markets, returns on energy futures are also correlated with broader market systematic risk factors.7 To allow for this possibility, we include conditional betas as control variables in our analysis. All our findings above are robust to controlling for this additional factor that could potentially influence commodity futures returns. Finally, we seek to determine if trader profits vary due to differences in information and/or trading skill. It is certainly conceivable that large energy companies may have superior information on supply and inventories or that hedge funds may be better able to forecast energy demand or have superior trading skills.8 We document persistent profit differences among individual traders and between trader types, which refutes the possibility that differential profits are just due to luck.9
7
See, for example, Hirshleifer (1988), de Roon et al. (2000), and Khan et al. (2008).
8
While there has been little research on this issue for futures traders, a number of studies find that informational and/or skill levels differ between stock market traders leading to differential trading profits - at least before transaction costs. Papers by Carhart (1997), Grinblatt and Titman (1989, 1993), Wermers (2000), and Chan, Jegadeesh and Wermers (2000), among others, find that some mutual funds have superior stock picking ability while Ackerman, McEnally and Ravenscraft (1999), Ibbotson and Chen (2005), Kosowski, Naik and Teo (2007), and Jagannathan, Malakhov and Novikov (2010) find the same for hedge funds. We would expect informational differences to be at least as great in the energy futures markets as in equities - particularly since companies are not under the equal disclosure requirements in these markets that they are in stock markets. 9
Based on individual trader data for nine major futures markets, Hartzmark (1991) finds that: 1) the number of traders with consistent trading profits over his sample period is no more than one would expect due to chance, and 2) winners in the first half of his study period are no more likely than first half losers to be winners in the second half. However, Leuthold, Garcia and Lu (1994) find in the pork bellies futures market that “a subset of elite traders possesses significant forecasting ability,” leading to consistent trading profits.
4
However, we find no evidence that some trader types, such as hedge funds or pipelines, have better information or trading skills than other types. While profits tend to be higher for more active speculators (except households) we find no evidence that larger traders have an information advantage. Moreover, while our findings do not allow us to rule out the possibility that some individual traders have superior information or skill, there is no evidence that particular trader types, such as hedge funds, have an informational advantage since (excepting market makers) we find that the mean profit differences between different trader types are explained by the extent to which they exploit price differentials caused by hedging pressure. The remainder of the paper is organized as follows. Our unique data set and profit measures are described next. In section 3, we document how futures position profits differ by trader type. Our trading strategy measures are described in section 4. In section 5, we present evidence on how profits among individual traders relate to their trading strategies and in section 6 we explore whether these trading strategy differences explain profit differences between our eleven trader types. In section 7, we further refine our analysis by adding trade and trader characteristics to the model. Section 8 concludes.
2. Data and Methodology 2.1 Trader classification Our trading data comes from the Commodity Futures Trading Commission (CFTC)’s Large Trader Reporting System (LTRS) described by Haigh et al. (2007). The CFTC requires daily reporting of the holdings of all traders whose open interest positions exceed thresholds set such that reported positions normally account for 70% to 90% of total open interest. Current reporting thresholds are 350 contracts for crude oil, 250 for heating oil, and 150 for gasoline. From this data, the CFTC compiles the weekly COT report on the aggregate open interest positions of commercial and non-commercial traders. This aggregate data is used in tests of the risk premium hypothesis by Bessembinder (1992), Bessembinder and Chan (1992), de Roon et al. (2000), Wang (2001) and others.10 10
Recently the CFTC divided the commercial category into swap dealers and other commercials, and the noncommercial category into managed money and others. However, published work to date is based on the commercial/non-commercial classification.
5
While the LTRS data is normally available to researchers only in the highly aggregated form of the weekly COT report, the U.S. Department of Energy (DOE) made an exception and provided us with the disaggregated CFTC data on the open interest positions of individual reporting traders in the NYMEX’s crude oil, heating oil, and gasoline futures markets from June 1993 through March 1997. On average over our data period, the traders in our data set accounted for 77.7% of open interest in the crude oil futures market, 81.3% of gasoline open interest, and 68.4% of heating oil open interest. We have been informed that more recent CFTC data at the level of disaggregation and detail necessary to conduct the research in our paper is not available to researchers through either DOE or CFTC due to trader identity protection concerns. While studies based on more recent aggregate data document a significant increase in energy futures open interest and trading (especially following the introduction of the electronic Globex trading system in 2006), and some changes in relative trader composition (see, for example, Haigh et al. (2007)), the economic questions that are the focus of our study are not specific to a particular time period. Additionally, since our study relies only on end-of-day trader position data and not intra-day trading data, our findings are unlikely to be significantly affected by changes in market microstructure over time.11 Therefore, from the standpoint of providing evidence on alternate theories of the determinants of trader profits in futures markets, we expect that the results based on our 19931997 individual trader data to be fully relevant in the present context. The CFTC’s reporting form asks: “Is the reporting trader engaged in business activities hedged by the use of futures and options markets?” If the answer is yes, the trader is classified as “commercial” and, if no, as “non-commercial.” Since traders self-select (albeit subject to overrule by the CFTC), there may be a bias toward the commercial category, especially since the question refers to “markets,” and not to “this market?” Indeed, Ederington and Lee (2002) argue that in the energy futures markets the “commercial” category includes many likely speculators. The CFTC also assigns sub-classifications, described in Haigh et al. (2007) and Büyükşahin et al. (2008), to most traders. We utilize the CFTC’s non-commercial sub-classifications which include:
11
As shown by Pirrong (1996), a change from an open outcry to an electronic market has the potential to affect several microstructural features of the market, including liquidity, spreads and depth.
6
commodity pool operators, commodity trading advisors, managed money, futures commission merchants, floor traders, and floor brokers. Following Haigh et al. (2007), we combine the first three sub-classifications into a single hedge fund/managed money category since they are similar and composed primarily of hedge funds. Trades by floor brokers and futures commission merchants on behalf of their clients are reported by the clients. Since the trades reported under these two subclassifications represent only trades for their own account, we combine them with floor traders.12 Noncommercial traders with no sub-classification code are classified as households (individuals). In summary, we assign the CFTC’s non-commercial traders to one of three trader types: (1) hedge funds, (2) market makers/floor traders, and (3) households. The CFTC’s sub-classifications of commercial traders, e.g., manufacturer and dealer/merchant, etc., proved less useful since they combine traders with different objectives. For instance, the CFTC manufacturer category includes both refiners, whom one would expect to hedge by shorting oil futures, and airlines, whom one would expect to hedge their fuel needs by going long in oil futures. Consequently, in collaboration with the Office of Policy at the US DOE, we decided on the following sub-classifications: (1) refiners, (2) independent producers, (3) marketers/distributors/pipelines (MDPs), (4) large consumers, (5) commercial banks, (6) investment banks and dealers, and (7) energy traders. To preserve trader anonymity, the final assignment of individual traders to each subclassification was made by the Office of Policy. Hedge funds and commodity pool operators which were designated as commercial traders on the LTRS instead of the usual non-commercial designation were placed in the “energy trader” sub-classification. We separate energy producers into those with refining capacity (refiners) and those without (independent producers) since the latter are clearly long in the physical crude oil market while the former could be either long or short.13 A residual group of
12
As described by Silber (1984) and Bryant and Haigh (2004), floor traders were the major market makers in futures markets during the 1993-1997 but have since been replaced by other market makers with the transition to electronic trading. 13
Ederington and Lee (2002) regard independent producers as likely speculators in the heating oil market because they do not produce heating oil. Since they produce crude oil, we regard them as potential hedgers in that market. To avoid complicating the results presentation, we keep the classifications the same across all three markets.
7
smaller commercial traders remains unclassified, resulting in a total of eight commercial trader classifications. In addition to classifying traders into the eleven types described above (i.e., three noncommercial and eight commercial), we separate the traders into: 1) likely hedgers, 2) likely speculators, 3) market makers, and 4) others. A limitation of our study is that we know only the traders’ energy futures market positions, not their cash market, forward, or swap positions or their positions in other futures markets.14 Additionally, our data does not permit us to observe intra-day trades. Hence, our judgment of the likelihood that a trader is hedging or speculating is based partially on their line of business. We regard the first five commercial classifications listed above as the traders most likely to be hedging. The first four – (1) refiners, (2) independent producers, (3) MDPs, and (4) large consumers – all have sizable cash and/or forward energy market positions, and banking regulations restrict the fifth type, commercial banks, to hedging activities. Conversely, we view hedge funds and individuals from the CFTC’s non-commercial category and energy traders from the commercial category as likely speculators since they have no known physical or forward market positions. Investment banks/dealers are the major unknown on the hedging/speculating spectrum since they could be either hedging their OTC swap positions or speculating. Hence, we consider them separately. Market makers are also treated separately. As noted above, the LTRS data that was available to us in disaggregated form is normally available to researchers only in highly aggregated form. Hartzmark (1987, 1991) and Leuthold et al. (1994) also use disaggregated LTRS data to calculate profits of individual traders but they use only the commercial and non-commercial classifications. Haigh et al. (2007), Büyükşahin et al. (2008), and Büyükşahin and Harris (2009) utilize the CFTC sub-classifications but do not calculate individual trader profits. Our data set contains 939 traders that generate 1,059,616 trader/day/contract observations and 486,334 daily position changes. For convenience, we henceforth refer to daily position changes as
14
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.
8
“trades” while noting that intra-day trades are not included. Many of the 939 traders were active only on a few days during our data period or made small trades. To ensure a continuous series,15 and to make the trader classification task described above more manageable, we require that traders included in our final sample hold a futures position for at least 100 days during our period and make at least 50 trades. This screen results in a sample of 382 traders who account for 96.3% of our trader/day/contract observations and 97.9% of total reported open interest. Descriptive statistics concerning futures trading on our three markets are reported in Table 1.16 Note that the crude oil market is the largest – accounting for 64.9% of open interest in terms of contracts and 50.6%, of our observations. [Place Table 1 about here] Descriptive statistics for our eleven trader types are in Table 2. In terms of both open interest and trades, the energy futures markets are dominated 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 together, the similar hedge fund and energy trader classifications represent roughly 11% of both open interest and trades.17 Market makers, who make numerous small trades, account for 15% of trades but only 4.7% of open interest. These figures undoubtedly understate market maker trading since we only capture positions held overnight. While numerous, unclassified traders represent only a minor portion of trading and open interest. [Place Table 2 about here] The descriptive statistics in Table 2 point to some speculation or selective hedging by energy firms. To hedge future sales, refiners should short gasoline and heating oil contracts. Yet, long positions account for roughly a third of their open interest in these contracts. While independent producers would
15
Brokers are only required to report a trader’s open interest if it exceeds the set threshold. In practice, once a trading system is initiated for a trader it appears to typically continue even when open interest falls below the minimum. Nonetheless, reporting could stop once a trader’s open interest falls below the minimum. 16
The gross open interest reported in Table 1 is the sum of total long positions and total short positions. This differs from the usual open interest figure reported for the market as a whole – total long or short positions. 17
According to US Senate Staff Report (2006), Haigh et al. (2007) and Büyükşahin et al. (2008), hedge fund trading in these markets increased substantially subsequent to our data period, perhaps attracted by the hedge fund trading profits that we document below.
9
typically hedge their physical positions by shorting crude oil futures, about 30% of their positions are long. The consumer category consists primarily of airlines, who would be expected to hedge their fuel needs by holding long fuel oil contracts. That is the case for the vast majority (85% of open interest) but not all. Commercial banks’ open interest is concentrated in crude oil where they are generally short – the expected pattern if hedging loans to energy producers. Households are generally long. As shown on the graphs provided in the Internet Appendix, energy prices both rose and fell over our data period, and intervals of both backwardation and contango are observed.
2.2 Profit measures Since the LTRS data report end-of-day open interest positions of all large traders, if a trader’s open interest in a particular contract changes between the close of day t-1 and close of day t, clearly a trade occurred on day t. However, we do not observe any trades that are reversed the same day. Our profit measure is the daily mark-to-market holding period profit. If there is no trade on day t, the markto-market profit on trader i’s holding in a given futures contract is OIi,c,m,t (Pc,m,t-Pc,m,t-1), where OIi,c,m,t is trader i’s open interest position in 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 closing price of contract c-m on day t. Henceforth we will use the term “c-m” to designate a contract of type c expiring in month m. Note that m is the month the futures contract expires, such as December 1997, not the time to maturity, and therefore does not change over time. If i’s position in contract 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. Since we don’t observe the actual trade price on day t, we approximate 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-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(Pc,m,t-Pc,m,t-1) + (OIi,c,m,t-1-OIi,c,m,t)[.5(Pc,m,t+Pc,m,t-1)-Pc,m,t-1]. Therefore, whether i goes long, short, or does not trade on day t, her profit on contract c-m on day t is: (1)
10
Since most of our traders hold spread positions consisting of numerous contracts we are more interested in their daily profits on their total position rather than on any individual contract. Consequently, each day we sum trader i’s profits/losses over all contracts, obtaining: (2) Note that, aside from approximating trade prices with closing prices, this profit/loss measure is identical to the mark-to-market calculation used to credit profits and debit losses to a trader’s account and to determine margin calls. Since PLi,t does not incorporate bid/ask spreads or other transaction costs, the net trading profits are slightly overstated for all traders except market makers for whom they are understated. Since dollar profits and losses vary with the size of a trader’s position, we calculate percentage profits. While this return measure is useful, it should be noted that since futures involve no up-front investment, returns do not have the 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:
(3)
Finally, trader i’s average daily percentage profit/loss over the entire period, %PLi, is calculated as a weighted average of %PLi,t weighting each day’s %PLi,t by trader i’s total open interest that day. There are some questionable observations in our data, such as instances where a position (with an identical number of contracts throughout) is recorded as short for several days, suddenly switches to long for a day or two, and then reverts to short. To minimize the effect of possible data errors on our results, we winsorize the upper and lower 1% tails of %PLi,t.
11
3. Individual Trader Profit Differences 3.1 Trader profit/loss persistence We begin by examining Hartzmark’s (1991) contention that futures trading profits are determined solely by luck. For this, we test whether the number of traders who tend to make consistent profits or losses is more than one would expect due to chance, by estimating an analysis of variance (ANOVA) based on the daily percentage profit observations, %PLi,t. With a F statistic of 2.15, the null that mean profit differences among the 382 traders are due to chance is rejected at the .0001 level.18 For further evidence on this issue, for each trader i, we test the null hypothesis that the mean of %PLi,t across all days t is zero. By chance, approximately 3.82 traders should have mean profits significantly greater than zero at the 1% level and 19.1 at the 5% level. In our sample, actual figures are 12 at the 1% level and 35 at the 5% level. At the other end of the spectrum, 22 have mean profits significantly less than zero at the 1% level and 58 at the 5% level. To test whether trader profit/loss patterns are persistent, we calculate %PLi over both the June 1993 - May 1995 and June 1995 - March 1997 sub-periods, restricting the sample to the 224 traders with at least 25 trades and 50 observations in each sub-period. The correlation coefficient of +0.140 is significantly different from zero at the .05 level. Clearly some traders make persistent profits and others persistent losses.
3.2 Trading profits and trader type Next, we test for mean profit differences between our likely hedger (first five categories in Table 2) and likely speculator categories (categories 6-8 in Table 2). According to the risk premium hypothesis, expected trading profits must be positive for speculators in order to entice them to enter the market, which (since trading is a zero sum game prior to transaction costs) implies negative expected trading profits for hedgers. It is possible also that information and/or skill levels differ between the two groups. 18
One issue in ANOVA estimations is whether to assume cross-sectional variances that are the same or different across the sample. Since profits should be less variable for traders with spread positions than for those whose open interest positions are mostly long or mostly short, we expect profit variances to differ by trader. Confirming this expectation, the variance homogeneity null is rejected at the .0001 level using the Brown-Forsythe test. Given this result, we employ Welch’s ANOVA statistic, which presumes differing variances.
12
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 lose on trades with more informed investors, and are compensated for these adverse selection losses through the bid-ask spread.19 Since our measure of trading profits does not include their bid/ask spread profits, this implies that %PLi,t should be negative on average for market makers. On the other hand, floor traders (the main market makers during our data period) may derive an information advantage due to their ability to observe order flow, implying positive average market maker profits.20 Since we only observe positions held overnight and not market makers’ intra-day trades, this latter argument may not apply in our case. Nonetheless, our data provides a unique opportunity to explore market maker profits in commodity futures, albeit in the limited context of positions they hold overnight. Hedger-speculator results are reported in Panel A of Table 3. In columns 2-5, we present statistics based on individual trader/day observations, %PLi,t. Results for combined positions of all hedgers and all speculators are reported in columns 6-8. Hence the means in column 3 are unweighted, treating each %PLi,t equally, while the means in column 6 are weighted by the size of each trader’s open interest position that day. The statistics in columns 2-5 are based on 97,510 hedger/day observations and 50,561 speculator/day observations and those in columns 6-8 on 962 day observations for both categories. Observing that profit variances differ substantially by trader, we base the p-values in column 4 on standardized profits %PLi,t/σi, where σi is the time series standard deviation of %PLi,t. [Place Table 3 about here] For likely hedgers, the average unweighted daily trading profit is -0.0154% (-3.80% annualized). For likely speculators, it is +0.0394% (+10.40% annualized). Both are significantly different from zero and each other at the .0001 level. These results are consistent with the predictions of the risk premium hypothesis. For comparison, we also calculated mean returns for the CFTC’s commercial and non-commercial trader categories. While the commercial and non-commercial means
19
See, for example, Bagehot (1971) and Glosten and Milgrom (1985).
20
See, for example, Manaster and Mann (1996), Brown and Zhang (1997) and Ready (1999).
13
are significantly different from each other at the .01 level, the difference is much smaller than that between likely hedgers and likely speculators. For commercials, the average return is -.0092% (-2.29% annualized); for non-commercials, +0.0173% (+4.46% annualized). In panel B, the same statistics are presented for the eleven trader type categories. Consistent with the risk premium hypothesis, unweighted mean profits are negative for all five likely hedger subclassifications and positive for all three likely speculator sub-classifications. However, two of the combined means switch signs, and within the likely hedger category, mean profits are significantly less than zero (at the 1% level for the unweighted means and 5% level for the combined means) only for refiners and commercial banks.21 Among likely speculators, mean profits are significantly positive only for hedge funds/money managers for which they are a sizable 15.2% on an annualized basis. While it is unclear whether investment bankers and dealers are primarily hedging or speculating, it is interesting that their mean profits are small and insignificant, suggesting a mix. The profit figures in Table 3 are prior to transaction costs. Deducting round-trip transaction costs of $15 per contract22 reduces mean daily profit figures by 0.3 to 0.5 basis points but mean profits remain significantly positive for both hedge funds as well as speculators in general. For market makers, unweighted mean daily profits are -0.0370%, implying an annualized loss of -8.9% not counting their bid-ask spread earnings. This finding is consistent with the aforementioned notion of market maker adverse selection losses, and specifically the findings of Naik and Yadav (2003) for overnight holdings. It is also consistent with prior studies showing that any information advantages possessed by market makers are short-lived at best.23 While our data does not allow us to rule out the possibility that market makers possess information advantages in
21
While this statement is for a one-tailed test, the p-values reported in Table 3 are two-tailed since different signs are expected for different categories and no sign was hypothesized for three categories. 22
Estimates for transaction costs for small traders in energy futures markets revolve around this figure. Haigh and Holt (2002) use round trip costs of $15 per contract while Girma and Paulson (1998) use $100 for the round trip cost of six contracts. This figure likely overstates transaction costs for the large traders in our sample. 23
See, for example, Silber (1984), Hasbrouck and Sofianos (1993) and Yao (1997).
14
intra-day trading acquired by observing order flow, it seems clear that they do not have valuable private information on positions held overnight. In summary, we find significant differences in futures trading profits across our different trader categories. Likely speculators make economically and statistically significant profits, on average, while likely hedgers incur significant losses, with the profit difference between the two groups being statistically significant also. This finding is consistent with the predictions of the risk premium hypothesis. Across the individual trader categories, hedge funds are particularly profitable and market makers make substantial losses, on average, on positions they hold overnight.
4. 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. We use these measures in the rest of the paper to test whether they are able to explain trader profits. It is important to note at the outset that the impact of any one of these variables on a trader’s profits depends on whether the trader is long or short, and by how many contracts. For instance, if futures markets are characterized by momentum and futures prices have been rising, this would portend profits on long positions but losses on short. Moreover if the trader holds x long contracts in contract A, y short in contract B, and z long in contract C, the expected impact of momentum on the trader’s day t profits depends on x, y, and z as well as the individual momentums 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 measure for each variable based on whether a trader holds long or short positions each day in various contracts. Specifically for each variable, VAR-Xi,t , we calculate:
(4)
15
where Di,c,m.t =1 if i holds a long position in contract c-m on day t and Di,c,m.t = -1 if i holds a short position, and Xc,m,t is some characteristic of contract c-m, such as its momentum, on day t. Thus VAR-Xi,t is a weighted average of variable Xc,m,t , signed by whether trader i is long or short, of i’s holding, and weighted by the size of i’s position in that contract on that day. The average of VARXi,t for trader i over the sample period is calculated as:
(5)
4.1 Hedging pressure According to the hedging pressure hypothesis, if a majority of hedgers are long (short), futures prices will be pushed above (below) expected future spot prices, creating profit opportunities for speculators. Note that according to this hypothesis, a trader’s expected profits should depend not on whether she is a hedger or speculator but on her long/short position relative to the majority of hedgers. Consider, for instance, an airline that hedges its future fuel purchases by 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 are grossly outnumbered by refiners and MDPs who generally hold short heating oil futures positions. If their sales of futures contracts push futures prices below expected future spot prices, the airlines’ expected trading profits should be positive – like those of speculators. The airline hedger might be willing to accept expected losses on average but does not have to. Hence, if the hedging pressure hypothesis is valid, trader i’s expected profits should be negative when her long/short position matches that of hedgers in general and positive when it differs. To test this prediction, we define a hedging pressure (HP) measure of the extent to which the signs of trader i’s open interest positions match the signs 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 in contract c-m on day t and = -1 when they are net short. Of the 46,253 contract/maturity/day 16
observations in our sample, likely hedgers are net short in 58.7% and net long in 41.3%. As described above, Di,c,m.t = 1 if i holds a long position in contract c-m on day t and Di,c,m.t = -1 if i holds a short position.24 If the signs of all of trader i’s positions on day t match those of hedgers in general, then HPi,t =1; if trader i’s positions are all opposite in sign to likely hedgers in the aggregate, then HPi,t = -1. If some positions match and some do not, then -1 < HPi,t < 1. We further define HPi as the weighted average of HPi,t over all days t where HPi,t is weighted by i’s open interest position on day t. The hedging pressure hypothesis implies that 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, while the means for all five likely hedger types are positive, none exceeds 0.2 so there are numerous cases where individual traders in the likely hedger category hold positions of opposite sign to the aggregate likely hedger position. In these cases, the traders are either speculating or their hedging needs run counter to the majority of likely hedgers and we expect them 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 any other trader group mean and indicates that hedge fund positions tend to be opposite to likely hedgers most of the time. Further investigation reveals that (unweighted by position size), hedge fund positions are opposite in sign to aggregate positions of likely hedgers 83.4% of the time. Clearly hedge funds tend to go long when hedgers in the aggregate are net short and short when hedgers are net long. The negative correlation between %PLi,t and HPi,t reported in Table 4 provides univariate evidence in support of the hedging pressure hypothesis. We examine this relation more closely in our regression analysis reported and discussed in section 5. [Place Tables 4 and 5 about here]
24
The few observations in which there are no likely hedger positions in a contract are excluded from the calculation.
17
4.2 Momentum To test whether some traders follow a strategy intended to benefit from momentum in futures markets, and if so, who they are and whether the strategy is profitable, we calculate the momentum measures MOMi,t and MOMi by setting Xc,m,t = RETc,m,t in equations 4 and 5 where RETc,m,t is the return on contract c-m over the last month (21 trading days). Thus MOMi > 0 (< 0) indicates a tendency to long (short) futures contracts which have risen (fallen) over the past month. According to the momentum hypothesis25, %PLi,t should be positively correlated with MOMi,t. As reported in Table 4, the simple correlation is positive but small. While not examining energy futures specifically, Fung and Hsieh (1997, 2001) find that hedge funds and commodity trading advisors tend to follow momentum strategies, and Moskowitz et al. (2010) find a similar pattern for speculators in general. Unfortunately, our relatively short data period precludes a direct replication of these previous studies for our data.26 Possibly because both the period over which momentum is calculated and the period over which returns are observed are considerably shorter in our study than in the studies by Fung and Hsieh (1997, 2001) and Moskowitz et al. (2010), we find no evidence of momentum strategies in our data. None of the trader group MOM means are significantly different from zero and the null that MOM does not differ by trader type cannot be rejected at the 5% level. Nonetheless, the negative correlation between MOM and HP reported in Table 4 is consistent with the findings of Moskowitz et al. (2010), and we examine the implications of this relation more closely in section 5.
4.3 Convenience yields, inventories, and volatility According to the storage and convenience yield literatures, long futures positions will tend to be profitable and short positions unprofitable, especially when inventories are low and/or price 25
See, for example, Fung and Hsieh (1997, 2001), Erb and Harvey (2006), Miffre and Rallis (2007), and Moskowitz et al. (2010). 26
Fung and Hsieh (2001) calculate momentum over several months, e.g., three months in their main analysis, while Moskowitz et al (2010) calculate returns over twelve months. Both these studies look at the ability of these past returns to forecast returns over the next month. Our relatively short data period forces us to measure momentum over the past month and test its relation to returns over the next day.
18
volatility is high. In other words, the risk premium is negatively correlated with inventories and positively correlated with price volatility. Confirming this prediction, GHR find that both the cashfutures basis and profits on long futures positions are negatively correlated with inventory levels. To test the hypothesis that profits tend to be positive on long positions and negative on short positions, we define a measure, LS, of whether i tends to hold long or short positions by setting Xc,m,t = 1 in equations 4 and 5. Hence, if trader i holds only long (short) positions on day t, LSi,t = 1 (-1). As expected, LSi tends to be negative for energy companies and positive for energy consumers. Hedge funds, households, and investment banks also tend to hold long positions. The hypothesis that long positions are more profitable, which implies that %PLi,t should be positively correlated with LSi,t, is confirmed by the correlations reported in Table 4. Our regression analysis reported in section 5 builds on this univariate finding. To test the hypothesis that long (short) positions are more (less) profitable when inventories are low, we calculate INVi,t and INVi using equations 4 and 5 with Xc,m,t = (1- Ratioc,t), where Ratioc,t is the level of inventories in commodity c on day t divided by estimated normal inventories after controlling for seasonality and trend. In its Weekly Petroleum Status report, the Energy Information Agency reports U.S. inventory levels for crude oil, gasoline, and distillates (which we, like GHR, use as our heating oil inventory measure). Since energy inventories display both trend and seasonal patterns, we follow GHR in using a Hodrick-Prescott procedure to estimate normal inventory levels adjusting their smoothing parameter for the fact that our data is weekly while theirs is 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 long futures positions when inventories are low (high). As shown in Table 5, large consumers, who normally hold long futures positions, show an even greater propensity to do so when inventories are low. INVi also tends to be positive for commercial banks and market makers and negative for energy companies and hedge funds. The hypothesis that the profits on long (short) positions vary inversely (directly) with the level of inventories implies that %PLi,t should be negatively correlated with INVi,t , which is what we observe in Table 4 and build on in section 5. 19
According to the modern theory of storage, spot prices tend to exceed futures prices due to the convenience yield associated with holding physical inventories that reflects the timing option physical inventories provide to sell or consume inventory when spot prices are high. The value of this option will tend to be greater when anticipated spot price volatility is high. Consistent with this notion, Ng and Pirrong (1994) find that the spot-futures spread is higher when volatility is high. This implies that the profits to long (short) futures positions should be positively (negatively) related to expected volatility. To test this prediction, we define VOLi,t by setting Xc,m,t in equation 4 equal to the ratio of the conditional standard deviation of returns to commodity c on day t as forecast by a GARCH (1,1) model divided by the average or unconditional standard deviation over the June 1993 - March 1997 period.27 The GARCH (1,1) model is estimated using daily returns on the nearby contract from January 1, 1993 through December 31, 1997. VOLi > 0 (< 0) indicates a stronger tendency to hold long futures positions when forecast volatility is high (low) and/or short positions when forecast volatility is low (high). The hypothesis that the risk premium varies directly with volatility implies that PLi,t should vary directly with VOLi,t., which is confirmed by the results in Table 4. However, as reported in Table 5, the null that VOLi does not differ by trader type cannot be rejected.
4.4 Systematic effects on futures returns In addition to the above factors that pertain specifically to commodity futures markets, several studies argue that even in the case of commodity futures returns, these factors are complemented by systematic risk factors,28 despite limited empirical support for systematic risk factors alone as explanatory variables for commodity futures returns.29 To allow for the possibility that returns on energy futures are related to their correlation with broader market risk factors, we 27
VOLi,t is based on the forecast volatility on the nearby contract on day t and does not differ depending on contract maturity m. 28
See, for example, Hirshleifer (1988), de Roon et al. (2000), and Khan et al. (2008). Breeden (1980) and Jagannathan (1985) apply the intertemporal consumption CAPM to pricing commodity futures. 29
See, for example, Dusak (1973), Bessembinder (1992), de Roon et al. (2000), and Erb and Harvey (2006).
20
include conditional betas as control variables in our analysis. Following the procedures pioneered by Ng (1991) and others, we obtain our conditional betas by estimating bivariate GARCH models for each of the energy futures markets and excess stock market returns measured as the daily return on the value weighted (VW) stock market index minus the 1-month T-bill rate using daily data from January 4, 1993. These estimates are then used to forecast conditional variances and covariances as of day t based on data through day t-1. Conditional betas (BETA) are then calculated as the conditional covariance between the futures contract and excess VW market returns divided by the conditional variance of excess VW returns. Consistent with the findings of Bessembinder (1992) and Erb and Harvey (1996), we find near-zero mean conditional betas for our three futures contracts, i.e., -0.13 for crude oil and close to zero for gasoline and heating oil. Signed and weighted averages are calculated using equations 4 and 5. We also observe considerable variation over time in the conditional betas.
5. Results of Regression Analysis 5.1 Main specifications We report in Table 6 the results of the regressions of %PLi,t and %PLi on the variables defined in section 4 to capture various potential trading strategies. Since daily profits depend heavily on daily price movements that tend to average out over time, adjusted R2s are much higher in the %PLi regressions. There are several characteristics of the data that raise doubts about OLS standard errors and t-statistics. For one thing, daily profit variances are much higher for traders who hold all long or all short positions than for those holding spread positions. We can adjust for this heteroskedasticity using the White procedure. However, in addition, there are a number of likely correlations in the data. We have seen in section 3 that some traders make consistent profits and others consistent losses, implying serial correlation in trader/day profits for a single trader. Also, we would expect to observe positive correlations within the trader classifications. For example, one refiner’s trading profits should be correlated with those of other refiners since they tend to hold similar positions. 21
[Place Table 6 about here] To control for heteroskedasticity, serial correlation, and cross-correlations, we employ a bootstrap procedure, in which we construct 10,000 samples of 178,389 trader/day or 382 trader observations by sampling with replacement from the original samples and re-estimating the regressions.30 Standard deviations of the 10,000 coefficient estimates provide unbiased estimates of coefficient standard errors controlling for heteroskedasticity, serial correlation, and crosscorrelations. Invoking the usual normality assumption, these standard errors can be used in the standard t-test. A non-parametric alternative is to calculate p-values directly from the 10,000 ranked coefficient estimates. We follow both procedures and find that they lead to virtually the same results, which is not surprising since the normality null cannot be rejected for the bootstrap coefficients. In Table 6, we report White standard errors in parentheses, bootstrap standard errors in brackets and bootstrap p-values in braces. Consistent with the hedging pressure hypothesis, the coefficient of HP is negative and highly significant in both Table 6 regressions. The -0.0809 coefficient in the %PLi regression implies that annualized percentage trading profits are a surprising 4012 basis points higher for a trader who always longs (shorts) those contracts in which the majority of likely hedgers are short (long) versus a trader whose long and short positions are always aligned with the aggregate positions of likely hedgers. Of course, there are no traders whose positions are always in agreement or always in disagreement with net aggregate hedger positions. Nonetheless, the coefficient implies that a hedger with HPi = -0.5588, the hedge fund mean, tends to have annualized trading profits that are 1179 basis points higher than a trader whose positions are the same sign as likely hedgers 50% of the time. This exceeds the mean speculator/hedger profit difference documented in Table 3. In fact, the HP variable accounts for much of the explanatory power of the regressions. For instance, the adjusted R2 when %PL is regressed on only HP is higher than when regressed on all variables except HP.
30
For a good discussion of bootstrap techniques, see Efron and Tibshirani (1993). Also, see Stine (1995).
22
As predicted by the modern theory of storage - convenience yield hypothesis, the coefficients of LS and VOL are positive and significant, and the coefficients of INV are negative and significant in both regressions. The implication is that long positions tend to be profitable and short positions unprofitable, with this tendency being more pronounced when inventories are low and/or forecast volatility is high. While GHR find that hypothetical profits to a trading strategy based on hedging pressure as measured by the COT reports disappear after controlling for inventories, we find that actual trader profits vary with both hedging pressure and inventories. As noted previously, our sample does not permit us to exactly replicate the analysis of Fung and Hsieh (1997, 2001) and Moskowitz et al. (2010) who, while not examining energy futures specifically, find that momentum strategies are generally profitable in commodity markets. While the momentum hypothesis predicts a positive coefficient for MOM, and the simple correlation between %PL and MOM was positive in Table 4, its coefficient is insignificant in the %PLi regression and actually negative and significant in the %PLi,t regression. These results do not provide support for the momentum hypothesis in the context of our sample and analytical approach. We examine this issue more closely in the next subsection. All the results reported in this subsection are robust to controlling for systematic risk factors following the procedure described in section 4. The coefficient for conditional beta (CB) is insignificant in the %PLi regression, and negative and significant in the %PLi,t regression.
5.2 Alternative specifications and robustness checks In Table 7, we estimate several variations of the Table 6 equation for the trader/day dataset. First, to provide some evidence on the relative importance of the hedging pressure variable and how it impacts the relationship between trading profits and the other variables, we estimate the model without the HP variable. Comparing the regressions in the second columns of Tables 6 and 7, two major differences are noted. First, the adjusted R2 falls from 0.0087 to 0.0031, indicating that most of the model’s ability to account for differences in trading profits is due to the hedging pressure variable. Second, the coefficient of the momentum variable switches from negative and significant 23
to positive and significant, which is more consistent with the findings of Fung and Hsieh (1997, 2001) and Moskowitz et al. (2010). Other findings of Moskowitz et al. (2010) provide a possible explanation in that they find that momentum is correlated with futures market roll returns, which they argue suggests that momentum is partially due to hedging pressure. Our above finding of a switch in the sign of the momentum variable is consistent with their argument. In other words, what we find is that a momentum strategy is profitable because it is apparently proxying for a hedging pressure strategy. These findings suggest that momentum in commodity futures markets may be due largely to hedging pressure, and that the speculator profits that Fung and Hsieh (1997, 2001) and Moskowitz et al. (2010) document may, in fact, be attributable more to strategies based on hedging pressure than to momentum. [Place Table 7 about here] As discussed above, much of the previous literature has tested for hedging pressure by relating profits on hypothetical trades to the long/short positions of commercial and/or noncommercial traders from the CFTC’s weekly Commitments of Traders report (COT), treating commercial traders as likely hedgers and non-commercials as likely speculators. In the second regression in Table 7, we replace the HP variable with an analogous measure based on the COT report. Specifically COTi,t is calculated using equations 4 and 5, where Xc,m,t = (CLc,t - CSc,t)/(CLc,t + CSc,t) with CLc,t (CSc,t) representing commercial long (short) positions in the most recent COT report.31 This measure differs from HP in three respects: 1) CLc,t and CSc,t are aggregated over all contract maturities while HP is maturity specific, 2) since the CFTC report is weekly, COTi,t is unchanged from Wednesday to Wednesday while HPi,t differs from day to day, and 3) the commercial classification includes some (energy traders) whom we classify as likely speculators and others (investment banks and brokers) where it is unclear whether they are primarily hedging or speculating. As shown in the third column in Table 7, when HP is replaced by COT, COT’s
31
This ratio is the hedging pressure measure in de Roon et al. (2000), and similar ratios are used in other studies.
24
coefficient is negative as predicted by the hedging pressure hypothesis and significant at the .0001 level but the adjusted R2 falls from .0086 to .0039. GHR argue and present evidence that the relationship between inventories and the risk premium is non-linear – specifically that the risk premium increases sharply when inventories are much lower than normal since the risk of a stock-out is high and hence the convenience yield is high. To test this argument, we include INV2- defined as INV2 when INV < 0 and 0 when INV > 0 and INV2+ = INV2 when INV > 0 and 0 otherwise. The non-linearity hypothesis of GHR implies a negative coefficient for INV2-. As reported in the final column in Table 7, there is evidence that this is the case since the coefficient of INV2- is negative and significant at the .05 level. However the incremental explanatory power of this variable is negligible.
6. Determinants of Inter- and Intra-Trader Group Profit Differences Having established that futures position profits of individual traders vary with measures of the extent to which traders’ open interest positions are related to hedging pressure (HP), and hypothesized determinants of the convenience yield (LS, INV, and VOL), we next explore whether these variables explain the differences in futures trading profitability between likely hedgers and speculators, and between the different trader types documented in Table 3. In other words, are the hedger/speculator profit differences documented in Table 3 due to the profit determinants examined in the previous section or to some other factor, such as superior information? A possible alternative explanation for the speculator-hedger profit difference is that speculators have superior information or skills. Furthermore, if one regards hedge funds as particularly likely to have an information or skill advantage, the finding that their profits are particularly high may be viewed as consistent with this information/skill hypothesis. To examine this possibility, in this section we explore whether trader type profit patterns are explained by hedging pressure, convenience yield, and other variables, or if profit differences persist after controlling for these factors. We do so by regressing mean profits of the 382 traders on zero-one dummies for trader types and then add hedging pressure, convenience yield, and other variables to the regression. 25
As reported in Table 8, when trader profits are regressed on hedger, speculator, and market maker dummies, the estimated profit difference between likely speculators and likely hedgers is 0.0525 percentage points per day or 13.23% annualized, and is significant at the .001 level. This is approximately the same as the 0.0548 percentage point daily profit difference in Table 3. However, when the hypothesized profit determinants from Table 6 are added in the second regression, this difference is no longer significant. Indeed, after controlling for hypothesized profit determinants, implied profits are slightly higher for likely hedgers than for likely speculators. As shown in the last two columns of Table 8, the hedger-speculator profit difference in the first regression is primarily explained by the hedging pressure variable since when only HP is added to the regression in the penultimate column, the speculator-hedger difference is eliminated. When the other variables from Table 6 are included but without HP, as shown in the final column, the speculator-hedger difference is reduced slightly but remains positive and significant at the .05 level. This indicates that the speculator/hedger return difference is mostly due to speculators taking advantage of hedging pressure by going long (short) when hedgers in the aggregate are net short (long). [Place Table 8 about here] Except for market makers, Table 9 tells the same story for individual trader categories. In the first regression, the null that there is no difference in profitability among the nine hedger-speculator categories is rejected at the .001 level, and we observe that hedge funds are especially profitable.32 When the hypothesized profit determinants from Table 6 are added, these differences disappear and, as shown in the final columns of Table 9, it is again the HP variable that primarily accounts for the differences. We saw in Table 5 that hedge funds display a strong tendency to take long (short) positions when likely hedgers are net short (long). This tendency seems to account almost entirely for their high profitability. [Place Table 9 about here]
32
Since “unclassified” is the omitted category, the coefficients represent the estimated difference between the particular category and the unclassified category, while the sum of the coefficient and intercept provides a measure of group mean profitability that is consistent with the means reported in Table 43.
26
In summary, while we cannot completely rule out the possibility that some individual traders have superior information or trading skill, there is no evidence that any particular trader group has more information or skill than another since (excepting market makers) inter-category profit differences are completely accounted for by the extent to which they exploit price differences caused by hedging pressure. These results reaffirm our previous findings that hedge fund profits are primarily due to the risk absorption services they render hedgers rather than to superior information or skill. As shown also in Tables 8 and 9, while the hedging pressure variable can account for the profit differences between the hedger and speculator categories, it does not explain the tendency for market makers to make losses on their overnight holdings since the coefficient of the market maker dummy variable is negative and significant.33 This result reaffirms our prior findings reported in Table 3, that (abstracting from their bid-ask spread profits) market makers lose money, on average, on their overnight inventory positions. Finally, we explore whether our hedging pressure and convenience yield variables explain profit differences within, as well as between, the likely hedger and speculator categories. In Table 10, we estimate separate regressions for likely speculators and hedgers. It is notable that the HP variable is negative and significant in all regressions. As we observed previously, minority hedgers should also benefit from hedging pressure as speculators do. For instance, if the energy markets are dominated by energy firms who hold short futures positions, an airline seeking to hedge its future fuel cost by holding long futures positions would benefit from the hedging pressure exerted by the energy firms. The hedger regression results indicate that this is, indeed, the case. Therefore, the hedging pressure variable explains not only the profit difference between the likely hedger and likely speculator categories but also much of the profit variation within these categories. However, two of the convenience yield variables, LS and VOL, lose significance in the separate hedgerspeculator regressions and INV is not significant in one of the four likely speculator regressions. 33
It is not significant in the first regressions in Tables 9 and 10 because its coefficients in these regressions measure the profit difference relative to the omitted unclassified category whose profits also tend to be negative as shown by the intercept. The sum of the market marker coefficient and the intercept is negative and significant.
27
[Place Table 10 about here]
7. Relation Between Trader Profits and Characteristics of Trades and Traders 7.1 Directional versus spread trading strategies Next, we test how trader profits are related to other trade and trader characteristics to sharpen the insights we obtained previously on the determinants of futures trading profits. One issue we are interested in is whether spread trades are more or less profitable than directional trades. While some traders may speculate that the price will rise or fall in the future by taking large long or short positions, respectively (which we term “directional trades”), others may speculate on future price relationships by going long in some contracts and simultaneously shorting others (“spread trades”). Examples of the latter are calendar spreads and crack spreads, which according to market observers with whom we have conversed represent the major spreads involving these contracts. To the extent that directional trades are riskier than spread trades, average profits might have to be higher on the former in order to attract speculators. On the other hand, it may be the case that market mispricings lead to arbitrage possibilities which astute spread traders can exploit. For example, Büyükşahin et al. (2008) present evidence that prior to 2002 “near- and long-dated futures prices [for crude oil] were priced as though traded in separate markets” implying that during our data period there were price differences that calendar spread traders could profitably exploit. Let Li,c,m,t represent trader i’s open interest position in contract c-m on day t if long, and Si,c,m,t if her position is short. In other words, Li,c,m,t = OIi,c,m,t if Oii,c,m,t > 0 and = 0 otherwise and Si,c,m,t = OIi,c,m,t if OIi,c,m,t < 0 and = 0 otherwise. We measure the extent to which trader i’s positions on day t are spread as: (6) 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 summary measure for each trader i, SPi, by averaging (weighted by open interest) SPi,t over all days t. Note that SPi measures only the degree to which 28
trader i’s futures position is hedged with other futures so SPi will tend to be low for both directional speculators and hedgers.
7.2 Trader size Having explored how trader profits relate to measures of hedging pressure and the convenience yield, we now explore if individual trader profits also depend on trader characteristics such as size and turnover rate. There are a two reasons to expect larger traders to be more informed than smaller traders and consequently to have higher trading profits. The first is economies of scale in information generation. For example, spending $100,000 to obtain information is harder to justify for a 100 contract position than for a 10,000 contract position. Therefore, larger traders are likely to spend more on generating information including having larger and better analyst teams. Second, larger energy firms could have superior information because they observe more of the physical energy market. While we found no evidence above that some trader groups have more information or skill than others, it is still possible that some individual traders do. Since trader identities are unknown to us, we can only measure size in terms of their open market positions, not total assets or sales. Hence, SIZEi is measured as 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 are investment banks followed by commercial banks. The hypothesis that larger traders tend to make higher profits implies a positive coefficient for SIZEi. In addition, it is possible that due to the lack of experience, traders who are rarely in the market make few trades and acquire less skill than those who are constantly in the market, thereby earning lower trading profits. To test this prediction, we relate trading profits to the percentage of the 962 days in our sample when the trader maintained a non-zero open interest position, DAYSi, expecting a positive coefficient. Note that this variable is subject to a possible survivorship bias 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 for unclassified (37.6%) and households (39.6%). 29
7.3 Trader activity Along the same lines, we hypothesize that trading profits will be positively correlated with turnover. While this need not always be the case, we expect hedgers to tend to hold their positions longer than speculators. If so, the risk premium hypothesis would imply that traders who turn their positions over frequently should have higher pre-trading-cost profits, on average. Since our trading profit calculations are before deducting trading costs, confirmation of this hypothesis does not necessarily imply higher profits after transaction costs. On the other hand, Odean (1999), and Barber and Odean (2000) find that more active household traders tend to lose money on equity trades even before deducting transaction costs. To test the relation between trading activity and profits, we measure each trader i’s position turnover rate, TURNi, by dividing i’s average daily trading volume by i’s average open interest position where the two averages are calculated only over days with non-zero open interest at either the beginning or end of the day. Mean turnover rates are highest for market makers/floor traders (35.3%) followed by hedge funds (22.4%) and individuals (21.9%) and lowest for commercial banks (7.9%), investment banks (8.3%), and independent producers (9.0%). To test whether the profits/turnover relation differs for household traders, we include an interaction variable IND_TURNi, equal to TURNi when i is a household and zero otherwise.
7.4 Results Regressions with the variables discussed in the above subsections and the market maker dummy added to the Table 6 regressions are reported in Table 11. Since SIZEi, DAYSi, and TURNi only vary cross-sectionally, we estimate the regression with mean trader profits over the entire period as the dependent variable. The regression is estimated first over all 382 traders and then separately for likely speculators and likely hedgers. [Place Table 11 about here] While we hypothesized that larger traders would tend to be more informed and therefore have higher profits, ceteris paribus, the coefficient of the SIZEi is statistically insignificant and has a 30
negative sign. Profits are also an insignificant, though positive, function of the percentage of the 962 days that the trader held a non-zero open interest position, DAYS. Thus, there is no evidence that larger or more active traders have an informational advantage. As expected, mean trader profits vary positively and significantly with turnover, TURNi, indicating that traders who turn their positions over more often tend to have higher profits – at least before transaction costs. Although insignificant, the negative sign of the IND_TURNi coefficient is in line with the findings of Odean (1999), and Barber and Odean (2000) that in the stock market, more active household traders tend to make losses. The sum of the two coefficients is an insignificant 0.0141 indicating little relation between turnover and profits for households. The TURNi variable is significant in the likely speculator regression, but not in the likely hedger regression. In summary, we find that non-household speculators who turn their futures portfolio over frequently tend to have higher profits than those who change their positions infrequently, but this relation does not hold for hedgers and households. Consistent with the findings of Büyükşahin et al. (2008), the results for the SP variable in the regression for the full sample indicate that traders who hold spread positions tend to make higher profits than those who hold mostly long or mostly short positions. Specifically, a trader whose positions are always half long and half short tends to make .0384% (10.12% annualized) more than a trader who always holds either all long or all short positions. Note, however, that this variable is not significant in the likely speculator sample. It is sizable but only significant at the 10% level in the likely hedger subsample.
8. Conclusions This study provides new evidence on alternative theories of commodity futures pricing. In contrast to prior studies that have tested these theories by exploring the profitability of hypothetical commodity futures trading strategies, we provide evidence on the validity of alternative theories by using proprietary data on individual trader positions in three energy futures markets: crude oil, gasoline, and heating oil. 31
We find that approximately 39% of the variation in mean profits among different large and mid-size traders in energy futures markets is explained by differences in their trading objectives, strategies, or characteristics. We find strong support for the risk premium hypothesis in that mean futures position profits of likely speculators (hedge funds in particular) are significantly higher than mean hedger profits. Our evidence indicates that much of this risk premium is due to hedging pressure since individual traders (whether hedgers or speculators) who hold short positions when likely hedgers in the aggregate are net long and long positions when likely hedgers in the aggregate are net short make considerably higher profits on average than traders whose open interest positions generally match likely hedgers in sign. In addition, a part of trader profits appears to be due to a convenience yield, which varies with inventories and volatility as posited by the modern theory of storage. As predicted by the modern theory of storage, those traders who take long positions when inventories are low and/or price volatility is high make higher profits than those who do not. After controlling for hedging pressure, we find no evidence that traders make higher profits by going long in futures with positive short-term momentum or shorting those with negative short-term momentum. Indeed, our results suggest that the momentum in commodity futures markets documented in previous studies may be due in large part to hedging pressure. Classifying futures traders into eleven line-of-business types, we find significant mean profit differences, with hedge funds being the most profitable. However, excepting market makers, mean profit differences among the other ten trader types are explained by the extent to which they exploit price differentials created by hedging pressure. Hedge funds in particular tend to exploit price differentials created by hedging pressure and to earn higher than normal profits as a result of this strategy. There is no evidence that larger traders profit at the expense of smaller traders but, excepting households, speculators with higher turnover rates tend to have higher profits. Not counting the bidask spread, market makers incur large and significant trading losses, on average, on the positions that they hold overnight, indicating that any informational advantage they may have from observing the order flow is short-lived. 32
While it is possible that some individual traders profit from superior information or skills, we find no evidence of systematic informational or skill differences in our data. In particular, there is no evidence of systematic differences between hedgers and speculators or among the eleven trader types. Instead, our evidence indicates that the profits of speculators in general, and hedge funds in particular, are due to the risk absorption services they provide hedgers by being willing to long (short) when hedgers in the aggregate are net short (long) and especially when volatility is high or inventories are low.
33
Appendix: Variable Definitions This appendix provides the definitions of the variables used in the analysis. Variable %PL – Daily Profit Percent
Definition %PLi,t is trader i’s daily percentage profit / loss on day t over all maturities m and commodities c. %PLi is trader i’s average daily percentage profit / loss over the entire period (June 1993 through March 1997).
CB - Conditional Beta
CBi,t is the daily weighted and signed average of the conditional betas for each of the commodity markets for trader i on day t and CBi is the average of same for trader i over the entire period. The conditional betas are computed using estimated bivariate GARCH models for each of the energy futures markets and excess stock market returns.
DAYS
DAYSi is the percentage of the 362 days in our sample during which trader i maintained a non-zero 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 and commodities c. HPi measures the same for trader i over the entire period. A robustness check measure computes the same variable but bases the aggregate net positions of likely hedgers on the weekly COT data. HPi,t and HPi by construction, range from -1 to 1. A trader with a measure of -1 (1) holds positions completely opposite to (in line with) the direction of likely hedgers’ positions.
IND_TURN – Household Turnover
IND_TURNi is equal to TURNi when trader i is a household and zero otherwise.
INV – Inventory
INVi,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). This is averaged over all three commodities. Levels of inventory are measured by Ratioc,t, defined as the ratio of the actual inventory level of commodity c on day t to the Hodrick-Prescott estimate of the normal inventory level for that period. INVi measures the same tendency for trader i over the entire period.
Likely hedgers
Dummy = 1 if the trader is a refiner, an independent producer, a marketer / distributor / pipeline operator, a large consumer or a commercial bank.
Likely speculators
Dummy = 1 if the trader is an energy trader, a hedge fund / money manager, or a household / individual.
LS – Long-Short
LSi,t measures the tendency of trader i on day t to hold net long positions averged across maturities m and commodities c. LSi measures the same tendency for trader i over the entire period.
Market maker
Dummy = 1 if the trader is a market maker or a floor trader.
34
Appendix: Variable Definitions (continued) Variable MOM – Momentum
Definition MOMi,t measures the tendency of trader i on day t to hold long (short) positions in maturities m and commodities c that have risen (fallen) over the last month averaged over all maturities m and commodities c. MOMi measures the same tendency 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 in the market.
SP – Spread Trading
SPi,t measures the tendency of trader i on day t to hold spread positions across maturities m and commodities c, defined as the tendency of holding futures positions that are hedges of each other. SPi measures the same tendency for trader i over the entire period.
TURN – Turnover
TURNi 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 over days with non-zero open interest.
VOL – Forecast Volatility
VOLi,t measures the tendency of trader i on day t to hold net long positions in commodity c when expected volatility is high averaged over all commodities c. Volatility is forecast using a GARCH(1,1) model for the nearby contract in commodity c. The level of expected volatility is measured by the ratio of the conditional standard deviation of returns forecast on commodity c on day t to the unconditional standard deviation of commodity c’s returns over the entire period. VOLi measures the same tendency for trader i over the entire period.
35
References Acharya, Viral, Tarun Ramadorai, and Lars Lochstoer, 2010, Limits to Arbitrage and Hedging: Evidence from Commodity Markets, Working paper. Ackermann, Carl, Richard McEnally, and David Ravenscraft, 1999, The performance of hedge funds: Risk, return, and incentives, Journal of Finance 54, 833-874. Adam, Tim R. and Chitru S. Fernando, 2006, Hedging, speculation, and shareholder value, Journal of Financial Economics 81, 283-309. Bagehot, Walter, 1971, The only game in town, Financial Analysts Journal 27, 12-14 & 22. Barber, Brad and Terrance Odean, 2000, Trading is hazardous to your wealth: The common stock investment performance of individual investors, Journal of Finance 55(2), 773-806. Bessembinder, Hendrik, 1992, Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies 5, 637-667. Bessembinder, Hendrik, and Kalok Chan, 1992, Time varying risk premia and forecastable returns in futures markets, Journal of Financial Economics 32(2), 169-93. Bodnar, Gordon M., Gregory S. Hayt and Richard C. Marston, 1998 Wharton survey of derivatives usage by U.S. non-financial Firms, Financial Management 27(4), 70-91. Breeden, Douglas T., 1980, Consumption risk in futures markets, Journal of Finance 35, 503-520. Brennan, Michael, 1958, The supply of storage, American Economic Review 48, 50-72. Brown, David P.and Zhi Ming Zhang, 1997, Market orders and market efficiency, Journal of Finance, 52, 277-308 Brown, Gregory W., Peter R. Crabb, and David Haushalter, 2006, Are firms successful at selective hedging? Journal of Business 79, 2925-2949. Bryant, Henry L. and Michael S. Haigh, 2004, Bid-ask spreads in commodity futures markets, Applied Financial Economics 14, 923-936. Büyükşahin, Bahattin, Michael Haigh, Jeffrey Harris, James Overdahl, and Michel Robe, 2008, Fundamentals, trader activity, and derivative pricing, working paper, Commodity Futures Trading Commission. Büyükşahin, Bahattin, and Jeffrey H. Harris, 2009, The role of speculators in the crude oil futures market, working paper, Commodity Futures Trading Commission. Carhart, Mark, 1997, On persistence in mutual fund performance, Journal of Finance 52, 57-82.
36
Chan, Hsiu-Lang, Narasimhan Jegadeesh, and Russ Wermers, 2000, The value of active mutual fund management: An examination of the stockholdings and trades of fund managers, Journal of Financial and Quantitative Analysis 35, 343-368. Deaton, Angus and Guy Laroque, 1992, On the behavior of commodity prices, Review of Economic Studies 59, 1-23. de Roon, Frans, Theo Nijman, and Chris Veld, 2000, Hedging pressure effects in futures markets, Journal of Finance 55, 1437-1456. de Roon, Frans and Marta Szymanowska, 2010, The cross-section of commodity futures returns, Working paper. Dinceler, Cantekin, Zeigham Khokher, and Timothy Simin, 2005, An empirical analysis of commodity convenience yields, Working paper. Dolde, Walter, 1993, The trajectory of corporate financial risk management, Journal of Applied Corporate Finance 6, 33-41. Dusak, Katherine, 1973, Futures trading and investor returns: An investigation of commodity market risk premiums, Journal of Political Economy 81, 1387-1406. Ederington, Louis and Jae Ha Lee, 2002, Who trades futures and how: Evidence from the heating oil futures market, Journal of Business 75, 353-373. Efron, Bradley, and Robert J. Tibshirani, 1993, An introduction to the bootstrap, Monographs on statistics and applied probability #57, Chapman & Hall/CRC. Erb, Claude, and Campbell Harvey, 2006, The strategic and tactical value of commodity futures, Financial Analysts Journal 62, 69-97. Fama, Eugene, and Ken French, 1987, Commodity futures prices: Some evidence on forecast power, premiums, and the theory of storage, Journal of Business 60, 55-73. Fung, William and David Hsieh, 1997, Survivorship bias and investment style in the returns of CTAs, Journal of Portfolio Management 24, 30-41. Fung, William and David Hsieh, 2001, The risk in hedge fund strategies: Theory and evidence from trend followers, Review of Financial Studies 14, 313-341. Girma, Paul B. and Albert S. Paulson, 1998, Seasonality in petroleum futures spreads, Journal of Futures Markets 18, 581-598. Glaum, Martin, 2002, The determinants of selective exchange risk management – evidence from german non-financial corporations, Journal of Applied Corporate Finance 14, 108-121. Glosten, Lawrence R. and Paul R. Milgrom, 1985, Bid, ask, and transaction prices in a specialist market with heterogeneously informed traders, Journal of Financial Economics 14, 71-100. 37
Gorton, Gary, Fumio Hayashi, and K. Geert Rouwenhorst, 2008, The fundamentals of commodity futures returns, Yale International Center for Finance working paper No. 07-08. Grinblatt, Mark, and Sheridan Titman, 1989, Mutual fund performance: An analysis of quarterly portfolio holdings, Journal of Business 62, 393-416. Grinblatt, Mark, and Sheridan Titman, 1993, Performance measurement without benchmarks: An examination of mutual fund returns, Journal of Business 66, 47-68. Haigh, Michael S. and Matthew T. Holt, 2002, Crack spread hedging: Accounting for time-varying volatility spillovers in the energy futures markets, Journal of Applied Econometrics 17, 269-289. Haigh, Michael S., Jana Hranaiova, and James A. Overdahl, 2007, Price volatility, liquidity provision, and the role of hedge funds in energy futures markets, Journal of Alternative Investments 4, 10-38. Hartzmark, Michael, 1987, Returns to individual traders of futures: Aggregate results, Journal of Political Economy 95, 1292-1306. Hartzmark, Michael, 1991, Luck versus forecast ability: Determinants of trader performance in futures markets, Journal of Business 64, 49-74. Hasbrouck, Joel, and George Sofianos, 1993, The trades of market makers: An empirical analysis of NYSE specialists, Journal of Finance 48, 1565-1593. Hicks, John R., 1939, Value and Capital (Oxford University Press, Cambridge). Hirshleifer, David, 1988, Residual risk, trading costs, and commodity futures risk premia, Review of Financial Studies 1, 173-193. Ibbotson, Roger, and Peng Chen, 2005, Sources of hedge fund returns: Alphas, betas, and costs, Working paper 05-17, International Center for Finance, Yale University. Interagency Task Force on Commodity Markets, 2008, Interim report on crude oil, US Commodity Futures and Trading Commission, Washington, DC. Jagannathan, Ravi, 1985, An investigation of commodity futures prices using the ConsumptionBased Intertemporal Capital Asset Pricing Model, Journal of Finance 40, 175-191. Jagannathan, Ravi, Alexey Malakhov, and Dmitry Novikov, 2010, Do hot hands exist among hedge fund managers? An empirical evaluation, Journal of Finance 65, 217-255. Kaldor, Nicholas, 1939, Speculation and economic stability, Review of Economic Studies 7, 1-27. Keynes, John M., 1930, A Treatise on Money, Vol II (MacMillan, London). Khan, Saqib, Zeigham Khokher, and Timothy Simin, 2008, Expected commodity futures returns, 38
Working paper. Kolb, Robert, 1992, Is normal backwardation normal? Journal of Futures Markets 12, 75-91. Kosowski, Robert, Narayan Naik, and Melvyn Teo, 2007, Do hedge funds deliver alpha? A bayesian and bootstrap analysis, Journal of Financial Economics 84, 229-264. Leuthold, Raymond, Philip Garcia, and Richard Lu, 1994, The returns and forecasting ability of large traders in the frozen pork bellies futures market, Journal of Business 67, 459-473. Manaster, Steven and Steven C. Mann, 1996, Life in the pits: Competitive market making and inventory control, Review of Financial Studies 9, 953-975. Miffre, Joëlle and Georgios Rallis, 2007, Momentum strategies in commodity futures markets, Journal of Banking and Finance 31, 1863-1886. Moskowitz, Tobias, Yao Hua Ooi, and Lasse H. Pedersen, 2010, Time series momentum, Journal of Financial Economics, forthcoming. Naik, Narayan and Pradeep Yadav, 2003, Risk management with derivatives by dealers and market quality in government bond markets, Journal of Finance 58, 1873-1904. New York Mercantile Exchange (NYMEX), 2005, A review of recent hedge fund participation in NYMEX natural gas and crude oil futures markets, March 2005 Staff Report. Ng, Lilian, 1991, Tests of the CAPM with time-varying covariances: A multivariate GARCH approach, Journal of Finance 46, 1507-1521 Ng, Victor and Craig Pirrong, 1994, Fundamentals and volatility: Storage, spreads and the dynamics of metals prices, Journal of Business 67, 203-230. Odean, Terrance, 1999, Do investors trade too much? American Economic Review 89, 1279-98. Pirrong, Craig, 1996, Market liquidity and depth on computerized and open outcry trading systems: A comparison of DTB and LIFFE bund contracts, Journal of Futures Markets 16, 519–43. Ready, Mark J., 1999, The specialist's discretion: Stopped orders and price improvement, Review of Financial Studies 12, 1075-1112. Routledge, Bryan, Duane Seppi, and Chester Spatt, 2000, Equilibrium forward curves for commodities, Journal of Finance 55, 1297-1338. Silber, William L., 1984, Market maker behavior in an auction market: An analysis of scalpers in futures markets, Journal of Finance 34, 937-953. Stine, Robert A., 1985, Bootstrap prediction intervals for regression, Journal of the American statistical Association 80, 1026-1031.
39
Stoll, Hans R., and Robert E. Whaley, 2010, Commodity index investing and commodity futures prices, Journal of Applied Finance 20, 7-46. US Senate Staff Report, 2006, The role of speculation in rising oil and gas prices: A need to put the cop back on the beat, Staff Report of the Permanent Subcommittee on Investigations, Senate Print: 109-65. Wang, Changyun, 2001, Investor sentiment and return predictability in agricultural futures markets, Journal of Futures Markets 21, 929-52. Wermers, Russ, 2000, Mutual fund performance: An empirical decomposition into stock-picking talent, style, transaction costs, and expenses, Journal of Finance 55, 1655-94. Working, Holbrook, 1949, The theory of the price of storage, American Economic Review 39, 125462. Yao, Jian, 1997, Spread components and dealer profits in the interbank foreign exchange market, Working Paper.
40
Table 1 - Descriptive statistics – positions and trades of large energy traders Descriptive statistics are presented for futures’ positions and trades of large and mid-sized traders in the crude oil, gasoline, and heating oil futures markets for the June 1993 - March 1997 period as reported in the CFTC’s LTRS files. Statistics for the full data set of 939 traders are presented in Panel A and for our working data set of 382 active traders in Panels B and C. Open interest figures are gross, i.e., longs + shorts. 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
924,864
Avg. daily gross open interest ($ billion)
$10,704
$2,868
$4,234
$17,806
Trader/contract/day observations
538,097
211,215
310,304
1,059,616
Avg. daily open interest (contracts)
593,723
123,165
189,522
906,410
Avg. daily open interest ($ billion)
$10,456
$2,821
$4,162
$17,439
Trader/contract/day observations
516,172
203,864
300,438
1,020,474
Trades
228,532
118,705
139,097
486,334
4,404
1,243
1,748
4,579
264
158
151
206
14.6
1.6
2.8
5.0
5.8
1.6
2.3
2.7
1 month or less
14%
32%
22%
18%
2 months or less
27%
62%
45%
41%
3 months or less
35%
78%
60%
54%
6 months or less
50%
97%
88%
73%
12 months or less
62%
99%
99%
88%
24 months or less
72%
100%
100%
96%
Panel B - Working data set - 382 traders
Avg daily open int. per trader (contracts) Avg trade size (contracts)
Panel C - Time-to-expiration of open interest positions Mean time-to-expiration (months) Median time-to-expiration (months) Cumulative percent of contracts expiring in
Table 2 - Descriptive statistics for eleven categories of futures traders Descriptive statistics are presented for the eleven trader line-of-business categories. Each of the 382 energy futures traders in our sample are assigned to one of eleven trader type classifications. If a trader was designated as "non-commercial" in the CFTC's Large Trader Reporting System (LTRS), the CFTC's sub-classifications were used to categorize the trader as a hedge fund or money manager (category 7), individual (8), or market marker/floor trader (10). Since the CFTC's commercial trader sub-classifications combined traders with different objectives, the commercial sub-classifications 1-6 and 9 below were decided jointly by us and the US Department of Energy Office of Policy. To preserve trader anonymity, final assignment of individual traders to each sub-classification was done by the Office of Policy. Commercial traders who did not fit any of these sub-classifications are in the residual group (category 11). The percentages of the 1,020,474 trader/day/contract observations accounted for by each category are reported in column 3 (% obs). The percentages of total open interest accounted for by each are reported in column 5 and the same for each of the three markets in columns 8-10. In the last three columns (11-13), we report the percentage of the open interest positions in each market which are long (the remainder being short). 2
Trader categories
# traders
3
% of total obs
4
% of total trades
1
Refiners
57
2
Independent producers
14
3
MDPs
22
4
Energy consumers
6
0.7%
0.7%
5
Commercial banks
16
9.3%
6
Energy traders
14
7
Hedge funds
8
5
6
7
8
9
10
% of Mean daily % of market open int. total trader Mean open O.I. trade size Crude GasoHeat. int. (contracts) in oil line oil contracts
28.1% 28.1% 28.6%
11
12
13
% Long (of open interest positions) Crude oil
Gasoline
Heat. oil
5,842
213
26.0%
36.5%
31.5%
45.2%
31.4%
34.9%
2.2%
2,690
160
2.6%
0.5%
1.8%
30.1%
68.8%
26.8%
12.2% 12.1% 12.8%
7,665
210
10.4%
17.9%
17.2%
54.3%
42.5%
48.5%
0.7%
2,197
211
0.5%
0.2%
1.9%
76.7%
42.5%
85.5%
9.1%
9.1%
8,950
159
11.6%
3.2%
4.9%
37.4%
85.6%
70.8%
5.8%
4.4%
4.5%
4,574
151
5.1%
3.1%
3.4%
54.2%
47.8%
35.7%
72
7.7%
7.7%
6.9%
1,916
289
5.7%
7.9%
10.1%
63.4%
65.4%
54.1%
Households/Individuals
28
2.6%
3.0%
1.8%
1,612
190
2.1%
1.5%
1.0%
66.2%
87.0%
71.3%
9
Investment banks
19
17.9% 17.5% 27.3%
21,606
285
29.9%
21.3%
23.1%
55.6%
73.9%
48.2%
10
Market makers
69
9.4% 15.0%
4.7%
1,687
132
5.0%
4.3%
4.0%
42.8%
49.5%
58.8%
11
Unclassified
65
3.9%
1.4%
576
114
1.0%
3.6%
1.1%
41.4%
28.0%
42.9%
2.4%
1.4%
3.8%
Table 3 - Trader profits Statistics are presented for traders’ daily futures’ trading profits before transaction costs as a percentage of the trader’s total open interest. Statistics for likely hedgers and likely speculators are presented in Panel A and for the eleven trader type categories in Panel B. The p-values are for tests of the null that the mean percentage profits are zero. * and ** denote means significantly different from zero at the .05 and .01 levels respectively. Panel A - Likely hedgers and speculators 2
3
4
5
Individual trader/day observations
Category Obs.
Mean
p-value
6
7
8
Combined positions - 962 day obs.
Std. Dev.
Mean
p-value
Std. Dev.
Likely hedger
97510
-0.0154%**
.0001
1.0548%
-0.0173%*
.0212
0.2330%
Likely speculator
50561
0.0394%**
.0001
1.2288%
0.0312%*
.0329
0.4540%
-0.0129%**
.0087
.9808%
-0.0193%*
.0476
0.3026%
7077
-0.0204%
.1567
1.0859%
-0.0407%
.0640
0.6819%
14594
-0.0062%
.5903
0.8461%
0.0035%
.4550
0.1440%
Large consumers
2999
-0.0095%
.6466
1.3516%
-0.0230%
.5050
1.0684%
Commercial banks
8838
-0.0397%**
.0001
.8371%
-0.0252%*
.0218
0.3409%
Energy traders
8557
0.0113%
.6283
0.9165%
-0.0030%
.6573
0.2132%
0.0561%**
.0001
1.3295%
0.0519%*
.0415
0.7901%
9993
0.0102%
.2298
1.1548%
0.0119%
.5556
0.6259%
Investment banks
11037
0.0021%
.7146
.7671%
0.0066%
.3719
0.2306%
Market makers
24537
-0.0370%**
.0001
0.9346%
-0.0228%**
.0014
0.2210%
Unclassified
21242
-0.0159%
.1248
1.3176%
-0.0086%
.5697
0.4665%
Panel B - Trader type categories Refiners Independent producers MDPs
Hedge funds Households/Individuals
42760
32011
Table 4 - Descriptive statistics for hypothesized determinants of futures trading profits Descriptive statistics are reported for trader profits and hypothesized determinants of trader profits specifically 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), momentum (MOM), and the contract’s conditional beta (CB) calculated using equation 4. Statistics are based on 178,389 trader-day observations for 382 energy futures traders. Means and standard deviations are reported in Panel A and correlations in Panel B. Panel A - Means and Standard Deviations Variable
Mean
Standard Deviation
Daily profit percent (%PL)
-.0018
1.0785
Hedging Pressure (HP)
-.0556
.7520
.0022
.0524
Long-short (LS)
-.0664
.7757
Inventory (INV)
-.0205
.4560
Forecast volatility (VOL)
.0026
.1301
Conditional Beta (CB)
.0087
.2955
Momentum (MOM)
Panel B - Correlations %PL
HP
MOM
LS
INV
VOL
%PL
1.000
HP
-.077
1.000
MOM
.025
-.496
1.000
LS
.036
-.367
.169
1.000
INV
-.037
-.030
-.216
.066
1.000
VOL
.020
-.005
.108
.054
-.303
1.000
-.019
.069
-.031
-.305
-.089
-.098
CB
CB
1.000
Table 5 - Trader type characteristics Means of 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), momentum (MOM), and conditional beta (CB) calculated using equation 4 are reported for different trader types. Statistics are based on 178,389 trader-day observations for 382 energy futures traders. The p-values for tests of null hypotheses that variable means do not differ by trader type are reported in the last row. Hedging Pressure (HP)
Momentum (MOM) x100
Long or short (LS)
Inventories (INV)
Forecast Volatility (VOL)
Refiners
.1737
.0003
-.2902
-.0230
.0025
.0250
Independent producers
.1951
-.0070
-.2753
-.0708
-.0023
.0393
MDPs
.1520
.0043
-.2638
-.0933
.0131
-.0086
Large consumers
.0429
-.0027
.5355
.1451
.0377
-.0595
Commercial banks
.1343
-.0013
-.0722
.0707
-.0284
.0113
Energy traders
.0140
.0038
-.2483
-.0365
.0084
.0113
Hedge funds
-.5588
.0054
.2461
-.0569
.0028
-.0035
Households/Individuals
-.2573
-.0003
.3633
-.0195
.0041
-.0552
Investment banks
.0282
.0029
.1609
.0252
-.0066
.0042
Market makers
-.2277
-.0008
-.1151
.0700
-.0138
.0077
Unclassified
.1565
-.0024
-.1965
.0242
.0012
.0250
Test of no difference null - p-value
.0001
.8425
.0001
.0008
.1458
.0001
Conditional Beta
Table 6 - Trading strategy regressions Trader/day mark-to-market profits as a percent of open interest, %PLi,t, and mean trader profits, PLi, are regressed on measures of the extent to which the trader’s open interest positions are related to hedging pressure (HP), hypothesized determinants of the convenience yield (LS, INV, and VOL), momentum (MOM), and the conditional beta of the futures contract (CB). There are 178,389 trader/day observations in the %PLi,t regressions and 382 trader observations in the %PLi regressions. Standard errors with the White correction for heteroskedasticity are reported in parentheses, bootstrap standard errors based on 10,000 replications are reported in brackets, and bootstrap p-values in braces {}. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively based on the bootstrap p-values. Trader/Day Profits (%PLi,t)
Mean Trader Profits (%PLi)
Intercept
-.0086** (.0026),[.0052],{.0001}
-.0109* (.0124),[.0131],{.0001}
Hedging Pressure (HP)
-.1324** (.0047),[.0095],{.0001}
-.0809** (.0132),[.0267],{.0001}
Long-short (LS)
.0093* (.0045),[.0044],{.0367}
.0200* (.0133),[.0136],{.1434}
Inventories (INV)
-.1109** (.0073),[.0073],{.0001}
-.1307** (.0247),[.0255],{.0001}
Forecast volatility (VOL)
.0585* (.0232),[.0230],{.0114}
.2309** (.0833),[.0858],{.0050}
Momentum (MOM)
-.6746** (.0761),[.0761],{.0001}
-.0041 (.1774),[.1775},{.9904}
Conditional Beta (CB)
-.0536** (.0113),[.0114],{.0001}
-.0051 (.0707),[.0719},{.9448}
.0086
.2631
Adjusted R2
Table 7 - Alternative specifications Variations on the trader/day regression in Table 6 are presented. In the second column, the regression is estimated without the hedging pressure variable, HP, while in the third column , HP is replaced with a variable based the long versus short positions of commercial traders as reported in the CFTC’s weekly commitments of traders (COT) report. In the final column, we test for non-linearity in the profits - inventory relationship by adding squared values of INV – one for observations when inventory levels are below the mean and another when they exceed the mean. Bootstrap p-values are reported in parentheses. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively based on the bootstrap p-values.
Intercept
Without HP
With COT variable
Non-linear inventories
-.0007 (.7921)
-.0025 (.3310)
-.0067* (.0142)
Hedging Pressure (HP)
-.1326** (.0001)
Weekly COT variable
-.5130** (.0001)
Long-short (LS)
.0467** (.0001)
0.0164** (.0034)
.0092* (.0386)
Inventories (INV)
-.0874** (.0001)
-.1024** (.0001)
-.1344** (.0001)
INV2- (INV0)
.9671 (.4942)
Forecast volatility (VOL)
.0392 (.0892)
0.0622** (.0072)
.0606** (.0090)
Momentum (MOM)
.2188** (.0010)
-.1661* (.0424)
-.6803** (.0001)
Conditional Beta (CB)
-.0395** (.0004)
-.0516** (.0001)
-.0547** (.0001)
Adjusted R2
.0031
.0039
.0087
Table 8 - Trader profit regressions with hedger, speculator, and market maker variables Mean trader profits are regressed on zero-one dummy variables for hedgers, speculators, and market makers and on measures of hedging pressure (HP), and hypothesized determinants of the convenience yield (LS, INV, and VOL), momentum (MOM), and the conditional beta (CB). The regressions are estimated cross-sectionally over 382 traders. Unclassified traders are the omitted category. P-values based on bootstrap estimations are reported in parentheses. P-values for tests of the null that profits do not differ between hedgers and speculators based on White variances and covariances are reported in the penultimate row. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively. Trader type dummies only
All
Trader type and hedging pressure
Without hedging pressure
Intercept
-.0174 (.1262)
-.0007 (.9680)
-.0048 (.6491)
-0.0096 (.3746)
Likely hedgers
.0015 (.9056)
.0023 (.8456)
.0070 (.5531)
.0003 (.9894)
Likely speculators
.0540** (.0012)
-.0109 (.4440)
.0004 (.9777)
.0305* (.0348)
Market makers
-.0292 (.0880)
-.0506** (.0001)
-.0602** (.0001)
-.0213 (.1454)
Hedging Pressure (HP)
-.0992** (.0001)
-.1032** (.0001)
Long-short (LS)
.0121 (.3792)
.0499** (.0008)
Inventories(INV)
-.1188** (.0001)
-.1030** (.0001)
Forecast volatility (VOL)
.2124** (.0096)
.2887** (.0018)
Momentum (MOM)
-.0267 (.8832)
.0151 (.9256)
Conditional Beta (CB)
-.0142 (.8408)
.0704 (.3550)
p-value for test of hedgerspeculator equality null
.0001
.3315
.6327
.0147
Adjusted R2
.088
.292
.204
.211
Table 9 - Trader profit regressions with trader type variables 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), momentum (MOM) and conditional beta (CB). Unclassified traders are the omitted category. The regressions are estimated cross-sectionally over 382 traders. P-values based on bootstrap estimations are reported in parentheses. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively. Trader type dummies only
All
Trader type and hedging pressure
Without hedging pressure
Intercept
-.0233
(.1110)
-.0014
(.9436)
-.0066
(.6220)
-.0124 (.3565)
Refiners
.0125
(.4303)
.0127
(.3658)
.0173
(.2483)
.0111
(.4519)
-.0031
(.9331)
-.0099
(.6992)
.0001
(.9971)
-.0098
(.7143)
MDPs
.0128
(.4287)
-.0035
(.7906)
.0120
(.4330)
-.0000
(.9710)
Large consumers
.0381
(.1409)
.0200
(.4986)
.0241
(.3415)
.0137
(.5946)
-.0207
(.3111)
-.0124
(.4815)
-.0219
(.2685)
-.0137
(.4940)
Energy traders
.0391* (.0455)
.0163
(.3658)
.0233
(.2131)
.0319
(.0878)
Hedge funds
.0766** (.0001)
-.0127
(.5814)
.0017
(.9468)
.0475* (.0178)
Households/Individuals
.0275
(.2219)
-.0226
(.2170)
-.0087
(.6651)
-.0009
(.9548)
Investment banks
.0262
(.2016)
.0057
(.7447)
.0088
(.6089)
.0122
(.5465)
-.0233
(.2127)
-.0505** (.0040)
-.0586** (.0017)
-.0186
(.2650)
-.1012** (.0001)
-.1045** (.0001)
Independent producers
Commercial banks
Market makers Hedging pressure (HP) Long-short (LS)
.0143
Inventories(INV) Forecast volatility (VOL)
(.2746)
.0484** (.0010)
-.1256** (.0001)
-.1028** (.0001)
.1825* (.0301)
.2749** (.0024)
Momentum (MOM)
-.0438
(.8053)
-.0165
(.9358)
Conditional beta (CB)
-.0159
(.8532)
.0544
(.4838)
p-value - test of trader homogeneity null (market makers excluded) Adjusted R2
.0002
.2943
.3529
.0646
.096
.286
.199
.213
Table 10 - Separate speculator and hedger regressions Separately for likely hedgers and likely speculators, trader/day and mean trader profits are regressed on measures of hedging pressure (HP), hypothesized determinants of the convenience yield (LS, INV, and VOL), momentum (MOM), and the conditional beta (CB). P-values based on bootstrap estimations are reported in parentheses. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively. Trader/day profits
Mean trader profits
Likely hedgers
Likely speculators
Likely hedgers
Likely speculators
Intercept
-.0005 (.8976)
-.0090 (.1398)
.0015 (.7640)
.0060 (.5996)
Hedging Pressure (HP)
-.1304** (.0001)
-.1601** (.0001)
-.1273** (.0040)
-.0660* (.0160)
Long-short (LS)
.0085 (.2346)
.0043 (.6058)
-.0255 (.3822)
.0121 (.6340)
Inventories(INV)
-.1043** (.0001)
-.1107** (.0001)
-.1199* (.0102)
-.0445 (.5040)
Forecast volatility (VOL)
.0407 (.2258)
.0275 (.5898)
.0832 (.5184)
.2665 (.2430)
Momentum (MOM)
-.9013** (.0001)
-.8353** (.0001)
.2126 (.3264)
-.2798 (.3574)
Conditional Beta (CB)
-.0474** (-.0072)
-.0723** (.0004)
-.1521 (.3160)
.0099 (.8964)
Observations
74,422
48,737
115
114
Adjusted R2
.0077
.0082
0.2359
.0579
Table 11 - Trader profit regressions with trade and trader characteristics Mean trader profits are regressed on a measure of spread trading (SP), trader size (SIZE) measured as the log of mean open interest, turnover (TURN), and percent of days in the market (DAYS), as well as the measures of hedging pressure (HP), the convenience yield (LS, INV, and VOL), conditional betas (CB) from Table 8 and a zero-dummy for market makers. The regression is estimated first for the full sample of 382 traders then separately for likely speculators and likely hedgers. Bootstraped p-values based on 10,000 random samples are shown in parentheses. * and ** denote coefficients significantly different from zero at the .05 and .01 levels respectively. Full sample
Speculator subsample
Hedger subsample
Intercept
-.0291 (.4248)
-.1039 (.1968)
.0422 (.4204)
Spread trading (SP)
.0384** (.0100)
-.0045 (.9028)
.0431 (.0820)
Log of mean open interest (SIZE)
-.0057 (.5498)
.0216 (.4812)
-.0202 (.1988)
Turnover, (TURN)
.1588* (.0170)
.3042* (.0120)
.0517 (.5596)
Percent of days in market (DAYS)
.0158 (.3092)
-.0142 (.7708)
.0037 (.8820)
Household turnover
-.1474 (.1174)
-.2320 (.0946)
Hedging Pressure (HP)
-.0860** (.0001)
-.0575** (.0050)
-.1163** (.0070)
Long-short (LS)
.0182 (.1746)
.0264 (.3398)
-.0274 (.3468)
Inventories(INV)
-.1052** (.0001)
-.0657 (-.2968)
-.0975* (.0414)
Forecast volatility (VOL)
.2016** (.0098)
.2938 (.2074)
.1010 (.3686)
Momentum (MOM)
-.0220 (.8294)
-.4270 (.1084)
.1566 (.4576)
Conditional beta (CB)
.0166 (.8014)
-.0092 (.9756)
-.1479 (.3554)
Market maker dummy
-.0847** (.0001) .430
.254
Adjusted R2
.388
