ines whether positivefeedback trading characterized large hedge fund and CTA trading behavior.These results suggest that trading decisions by large hedge funds and CTAsare influenced only in small part by past price changes. The third test consists of estimating the profits and losses associated with the positions of largehedge funds and CTAs. This test is based on the argument that speculativetrading can be destabilizing only if speculators buy when prices are high andsell when prices are low, which, in turn, implies that destabilizing specula-
T
The authors thank Ron Hobson, and John Mielke of the Commodity Futures Trading Commission for their assistance in obtaining the hedge fund and CTA data andanswering many questions. This chapter is dedicated to the memory of Blake Imelof the CFTC, who first suggested that we analyze the hedge fund and CTA data andprovided invaluable encouragement. We appreciate the helpful comments providedby Wei Shi.
151
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tors lose money. Across all 13 markets, the profit for large hedge funds andCTAs is estimated to be just under $400 million. This fact suggests that trading decisions are likely based on valuable private information. Overall, theevidence presented in this study indicates that trading by large hedge fundsand CTAs is based on private fundamental information.
INTRODUCTIONIn recent years, hedge funds and commodity trading advisors (CTAs) havedrawn considerable attention from regulators, investors, academics, and thegeneral public.1 Much of the attention has focused on the concern thathedge funds and CTAs exert a disproportionate and destabilizing influenceon financial markets, which can lead to increased price volatility and, insome cases, financial crises (e.g., Eichengreen and Mathieson 1998). Hedgefund trading has been blamed for many financial distresses, including the1992 European Exchange Rate Mechanism crisis, the 1994 Mexican pesocrisis, the 1997 Asian financial crisis, and the 2000 bust in U.S. technologystock prices. A spectacular example of concerns about hedge funds can befound in the collapse and subsequent financial bailout of Long-TermCapital Management (e.g., Edwards 1999). The concerns about hedge fundand CTA trading extend beyond financial markets to other speculativemarkets, such as commodity futures markets. These concerns were nicelysummarized in a meeting between farmers and executives of the ChicagoBoard of Trade, where farmers expressed the view that “the funds—managed commodity investment groups with significant financial and technological resources—may exert undue collective influence on marketdirection without regard to real world supply-demand or other economicfactors” (Ross 1999, p. 3).Previous empirical studies related to the market behavior and impactof hedge funds and CTAs can be divided into three groups. The first set ofstudies focuses on the issue of “herding,” which can be defined as a groupof traders taking similar positions simultaneously or following one another(Kodres 1994). This type of trading behavior can be destabilizing if it isnot based on information about market fundamentals, but instead is basedon a common “noise factor” (De Long, Schleifer, Summers, and Waldman1990). Kodres and Pritsker (1996) and Kodres (1994) investigate herdingbehavior on a daily basis for large futures market traders, including hedgefunds and CTAs, in 11 financial futures markets. Weiner (2002) analyzes
1See Eichengreen and Mathieson (1998) for a thorough overview of the hedge fund
industry. A similar overview of the CTA industry can be found in Chance (1994).
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
153
herding behavior for commodity pool operators using daily data for theheating oil futures market. Findings are consistent across the studies.Herding behavior within the various categories of traders is positive andstatistically significant in some futures markets, but typically explains lessthan 10 percent of the variation in position changes.The second set of studies focuses on whether futures market participants rely on positive feedback trading strategies, where buying takes placeafter price increases and selling takes place after price decreases. If this trading is large enough, it can lead to excessively volatile prices. Kodres (1994)examines daily data on large accounts in the Standard & Poor’s (S&P) 500futures market and finds that a significant minority employ positive feedback strategies more frequently than can be explained by chance. Dale andZryen (1996) analyze weekly position reports and find evidence of positivefeedback trading for noncommercial futures traders in crude oil, gasoline,heating oil, and treasury bond futures markets. Irwin and Yoshimaru(1999) examine daily data on commodity pool trading and report significant evidence of positive feedback trading in over half of the 36 marketsstudied, suggesting that commodity pools use similar positive feedbacktrading systems to guide trading decisions.The third set of studies directly analyzes the relationship between pricemovements and large trader positions. Brorsen and Irwin (1987) estimatethe quarterly open interest of futures funds and do not find a significantrelationship between futures fund trading and price volatility. Brown, Goetzmann, and Park (1998) estimate monthly hedge fund positions in Asiancurrency markets during 1997 and find no evidence that hedge fund positions are related to falling currency values. Irwin and Yoshimaru (1999)analyze daily commodity pool positions and do not find a significant relationship with futures price volatility for the broad spectrum of marketsstudied. Fung and Hsieh (2000a) estimate monthly hedge fund exposuresduring a number of major market events and argue there is little evidencethat hedge fund trading during these events causes prices to deviate fromeconomic fundamentals.Overall, the available empirical evidence provides limited support forconcerns about the market impact of hedge fund and CTA trading. Thereis evidence of positive feedback trading, but this is offset by the lack of evidence with respect to herding and increased price volatility. Cautionshould be used, however, in reaching firm conclusions due to the limitednature of evidence on the direct market impact of hedge funds and CTAs.With one exception, previous studies estimate market positions using lowfrequency (quarterly or monthly) data. Fung and Hsieh (2000a, p. 3) arguethat this is due to the difficulty of obtaining data on hedge fund and CTAtrading activities:
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A major difficulty with this kind of study is the fact that hedge fund positions are virtually impossible to obtain. Except for very large positions incertain futures contracts, foreign currencies, US Treasuries and publicequities, hedge funds are not obliged to and generally do not report positions to regulators. Most funds do not regularly provide detailed exposure estimates to their own investors, except through annual reports andin a highly aggregated format. It is therefore nearly impossible to directlymeasure the impact of hedge funds in any given market.Ederington and Lee (2002) report that hedge fund and CTA positions turnover relatively quickly on a daily basis. This fact suggests that higherfrequency data are needed to accurately estimate the market impact ofhedge fund and CTA trading.A unique data set is available that allows measurement of hedge fundand CTA positions on a daily basis in a broad cross-section of U.S. futuresmarkets. Specifically, the Commodity Futures Trading Commission (CFTC)conducted a special project to gather comprehensive data on the tradingactivities of large hedge funds and CTAs in 13 futures markets betweenApril 4 and October 6, 1994. The purpose of this study is to use the CFTCdata to investigate the market impact of futures trading by large hedgefunds and CTAs. This is the first study to directly estimate the impact ofhedge fund and CTA trading in any market.The first part of the chapter analyzes the relationship between hedgefund and CTA trading and market volatility. Drawing on the specifications of Bessembinder and Seguin (1993) and Chang, Pinegar, and Schacter(1997), regression models of market volatility are expressed as a functionof: (a) trading volume and open interest for large hedge funds and CTAs,(b) trading volume and open interest for the rest of the market, and (c)day-of-the-week effects. The second part of the chapter analyzes whetherthe relationship between large hedge fund and CTA trading and marketvolatility is harmful to economic welfare. Three tests are used to distinguishbetween alternative hypotheses. The first test relies on a series of varianceratios to determine whether there are significant departures from randomness in futures returns over the sample period. The second test determineswhether positive feedback trading is a general characteristic of hedge fundand CTA trading. The third test examines the profitability of hedge fund andCTA trading during the sample period.
DATATo obtain the data used in this chapter, the CFTC applied a special collection process through which market surveillance specialists identified those
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
155
accounts known to be trading for large hedge funds and CTAs (J. Mielke,personal communications, 1998). Once identified in the CFTC’s largetrader reporting database, the accounts were tracked and positions compiled.2 Through this procedure, a data set was compiled over April 4through October 6, 1994, consisting of the reportable open interest positions for these accounts across 13 different markets. A total of 130 businessdays are included in the six-month sample period. The U.S. futures marketssurveyed are coffee, copper, corn, cotton, deutsche mark, eurodollar, gold,live hogs, natural gas, crude oil, soybeans, Standard and Poor’s (S&P 500),and treasury bonds. For simplicity, large hedge fund and CTA accountswill be referred to as managed money accounts (MMAs) in the remainderof this chapter.As received from the CFTC, data for a given futures market are aggregated across all traders for each trading day. These figures represent thetotal long and short open interest (across all contract months) of MMAs foreach day. Then the difference between open interest (for both long andshort positions) on day t and day t − 1 is computed to determine the minimum trading volume for day t. The computed trading volumes representminimum trading volumes (long, short, net, and gross) and serve only as anapproximation to actual daily trading volume, because intraday trading isnot accounted for in the computation. In summary, the CFTC data consistof the aggregate (across contract months and traders) reportable open interest positions (both long and short), as well as the implied long, short, net,and gross trading volume attributable to MMAs.Due to the aggregated nature of this data set, it is assumed that tradingby MMAs is placed in the nearby futures contract. This is consistent withEderington and Lee’s (2002) finding that nearly all commodity pool (whichincludes hedge funds) and CTA trading in the heating oil futures market isin near-term contracts, and permits the use of nearby price series in theanalysis. Five markets (corn, soybeans, cotton, copper, and gold), however,do not exactly follow the conventional nearby definition. In each of thesemarkets there is a contract month, which even in its nearby state does nothave the most trading volume and open interest. For example, the September corn and soybean contracts are only lightly traded through their existence. Liquidity in these markets shifts in late June from the July contractto the new crop contract (November for soybeans and December for corn).
2Ederington and Lee (2002) provide a detailed explanation of the line-of-business
classification procedures used internally by the CFTC as a part of the large traderposition reporting system.
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Therefore, to follow the liquidity of these markets, a price series is developed that always reflects the most liquid contract. For most markets exceptthe five listed above, it is equivalent to a nearby price series that rolls forward at the end of the calendar month previous to contract expiration.
Descriptive Analysis of Trading BehaviorThe 13 markets included in this data set range from the more liquid financialcontracts to some of the less liquid agricultural markets. Table 8.1 reportsdescriptive statistics on general market conditions between April 4 and October 6, 1994, including the average daily trading volume and open interest (for
TABLE 8.1
Average Levels of Volume, Open Interest, and Volatility for 13 FuturesMarkets, April 4, 1994–October 6, 1994 and January 4, 1988–December 31, 1997Daily Average
FuturesMarketCoffeeCopperCornCottonCrude oilDeutschemarkEurodollarGoldLive hogsNaturalgasS&P 500SoybeansTreasurybonds
April 4, 1994–October 6, 1994
January 4, 1988–December 31, 1997
Contracts
Contracts
Volume
Open VolatilityInterest%
Volume
OpenInterest
Volatility%
8,0818,01323,9845,17050,897
24,33032,585121,23026,09496,306
2.601.030.900.921.43
5,0725,93826,8494,32840,640
19,71822,515127,37821,79680,689
1.691.150.840.881.33
42,895145,50528,8102,639
92,186446,93282,34411,933
0.470.050.491.01
33,13082,70927,0943,411
71,328329,26869,87812,545
0.460.050.520.95
9,88065,70026,922
22,409190,62668,876
1.690.520.89
8,00254,19825,976
19,614150,67560,649
1.770.680.88
392,204
363,407
0.61
294,987
307,308
0.49
Note: Parkinson’s (1980) extreme-value estimator is used to estimate the dailyvolatility of futures returns.
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
157
TABLE 8.2
Composition of Large Managed Money Account TradingVolume across 13 Futures Markets, April 4, 1994–October 6, 1994Percentage of Total Managed MoneyAccount Trading VolumeFutures Market
Gross Volume%
Net Volume%
CoffeeCopperCornCottonCrude oilDeutsche markEurodollarGoldLive hogsNatural gasS&P 500SoybeansTreasury bonds
1.62.95.42.34.08.26.025.77.40.95.56.823.2
1.73.05.72.68.47.322.98.00.94.57.16.121.8
Note: Managed money accounts are defined as large hedgefunds and CTAs. Gross volume equals long plus short volume.Net volume in this case equals the absolute value of long minusshort volume. Percentages may not add to 100 due to rounding.
the modified nearby series) and the average daily volatility of futures returns.3To provide a basis for comparison, the table also reports descriptive statisticsfor the previous 10 years (January 4, 1988, to December 31, 1997). Comparison of these statistics suggests market conditions for the six-month periodbeing studied is representative of longer-term conditions.To reach conclusions regarding the effects of MMA trading, it is important first to understand which markets are traded. Any potential effects fromtheir trading may be dependent on whether trading is concentrated in themore liquid financial futures or the less liquid commodity markets. Theresults shown in Table 8.2 are computed by dividing the gross (long plusshort) or net (absolute value of long minus short) MMA trading volume foreach day in each futures market by the total MMA trading volume across all
3Daily volatility is estimated by Parkinson’s (1980) extreme-value (high-low) volatil-
ity estimator. Further details are provided here in the section entitled “Volume andPrice Volatility Relationship.”
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futures markets for each day. More specifically, averages of the daily percentages across the six-month sample period are presented. Consistent with thefindings in Irwin and Yoshimaru (1999), the results show that MMA tradingvolume is largely concentrated in the most liquid financial futures markets.The two most liquid markets (eurodollar and treasury bonds) accountfor approximately 49 percent of MMA gross trading volume and 45 percent of MMA net trading volume. Only about 14 percent of MMA grossvolume and 8 percent of MMA net volume is found in the four least liquidmarkets (live hogs, cotton, copper, and coffee, based on volume over the sixmonths). The concentration of MMA trading volume in the most liquidfutures markets suggests that hedge fund operators and CTAs are wellaware of the size of their own trading volume and seek to minimize tradeexecution costs associated with large orders in less liquid markets.Although, according to contract volume figures, MMAs concentratetrading in more active markets, it is also important to analyze their tradingvolume relative to the size of each market. The percentages shown in Table8.3 are the average of the daily MMA gross or net (absolute value) tradingvolume divided by the nearby contract volume. The results show that MMA
TABLE 8.3
Trading Volume of Large Managed Money Accounts as a Percentageof Total Trading Volume in 13 Futures Markets, April 4, 1994–October 6, 1994Gross Volume ofManaged Money Accounts
Net Volume ofManaged Money Accounts
Futures Market
Average%
Maximum%
Average%
Maximum%
CoffeeCopperCornCottonCrude oilDeutsche markEurodollarGoldLive hogsNatural gasS&P 500SoybeansTreasury bonds
6.911.17.012.95.45.37.28.611.614.03.76.72.4
26.739.823.039.419.520.128.524.747.854.414.921.610.3
5.99.36.011.14.44.85.37.39.412.23.26.01.8
26.734.623.039.416.320.123.624.747.853.612.021.67.5
Note: Managed money accounts are defined as large hedge funds and CTAs. Grossvolume equals long plus short volume. Net volume in this case equals the absolutevalue of long minus short volume.
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The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
Proportion of Trading Volume
0.60.5
0.4
0.30.2
0.1
9/26/94
9/9/94
8/24/94
8/9/94
7/25/94
7/8/94
6/22/94
6/7/94
5/20/94
5/5/94
4/19/94
4/4/94
0
FIGURE 8.1
Large Managed Money Account Net Trading Volume as aProportion of Total Nearby Trading Volume, Natural Gas Futures Market,April 4, 1994–October 6, 1994.
trading ranges from about 2 to 14 percent of total market volume, whethermeasured on a gross or a net basis. MMA gross trading volume averages 7.9percent of market volume across all 13 markets, while MMA net tradingvolume averages 6.7 percent.4 These statistics clearly show that MMAs areimportant participants in most of the 13 futures markets during the sampleperiod. Furthermore, the one-day maxima are quite large, ranging fromabout 10 to 54 percent for gross volume and 7 to 54 percent for net volume.Figure 8.1 provides a graphical representation of the “spiky” nature ofMMA trading for the natural gas market. To summarize, although MMAstend to focus trading in terms of numbers of contracts in the most liquidmarkets, their trading still may represent a large proportion of total marketvolume, especially for less liquid futures markets.
4The averages reported in Table 8.3 are roughly consistent with results found inEderington and Lee (2002) for heating oil futures. Over the June 1993–March 1997period, they report that the daily trading volume of commodity pools (which includehedge funds) and commodity trading advisors averages 11.3 percent.
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To better understand the timing of trading by MMAs relative to trading by the rest of the market, simple correlation coefficients are computedbetween the contemporaneous trading volume of MMAs and the rest of themarket. As reported in Table 8.4, estimated correlation coefficients are allpositive and range from about 0.01 to 0.70. The average correlation acrossall markets is 0.39 and 0.38 on a gross and net basis, respectively. Statistically significant correlations (at the 5 percent level) are observed in 10 markets for gross volume of MMAs and 10 markets for net volume. Theoverwhelmingly positive relationships suggest that MMAs generally tradewhen others are trading. This result is the opposite of the negative relationships that Kodres (1994) found between position changes of hedgefunds and other types of large traders. It is uncertain whether the positiverelationships indicate the potential for stabilizing or destabilizing prices. Onone hand, the positive relationships indicate MMAs tend to trade in moreliquid market conditions, all else being equal. On the other hand, the positive relationships also may indicate that other traders follow the “leadership” of MMAs, which could destabilize prices through a herd effect(Kodres, 1994).
TABLE 8.4 Correlation between Large Managed Money Account Trading and AllOther Market Trading Volume in 13 Futures Markets, April 4, 1994–October 6, 1994Correlation CoefficientFutures Market
Gross Trading Volume ofManaged Money Accounts
Net Trading Volume ofManaged Money Accounts
CoffeeCopperCornCottonCrude oilDeutsche markEurodollarGoldLive hogsNatural gasS&P 500SoybeansTreasury bonds
0.35*0.53*0.61*0.66*0.160.42*0.44*0.66*0.050.070.28*0.52*0.30*
0.33*0.50*0.58*0.64*0.21*0.44*0.340.67*0.010.060.25*0.56*0.31*
Note: Managed money accounts are defined as large hedge funds and CTAs. Grossvolume equals long plus short volume. Net volume in this case equals the absolutevalue of long minus short volume.*Statistically significant at the 5 percent level.
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
161
Overall, the picture of MMA trading behavior that emerges is mixed.MMAs tend to focus trading in terms of numbers of contracts in the mostliquid futures markets. However, MMA trading can represent a large proportion of total market volume, especially on certain days and in less liquidfutures markets. Consequently, direct tests are needed to better understandthe market impact of MMA trading. The next section investigates therelationship between the trading volume of MMAs and price volatility infutures markets.
Volume and Price Volatility RelationshipKarpoff (1987) provides an extensive and widely cited survey of the methodology and results of studies focusing on the relationship between volume andvolatility. The chief difference between model specifications, up to the dateof Karpoff’s survey and since then, is the procedure used to accommodatepersistence in volume and volatility. Due to the lack of a commonly acceptedmodel specification for the relationship between volume and volatility, threebasic specifications are used in the analysis for this study.1. Following Chang, Pinegar, and Schachter (1997), the volume andvolatility relationship is modeled without including past volatility.2. Following Irwin and Yoshimaru (1999), volatility lags are included asindependent variables to account for the time series persistence ofvolatility.3. Following Bessembinder and Seguin (1993), the persistence in volumeand volatility is modeled through specification of an iterative process.5Since estimation results for the different model specifications are quite similar, only results for a modified version of Chang, Pinegar, and Schachter’sspecification are reported here.6Chang, Pinegar, and Schachter (1997) regress futures price volatility onvolume associated with large speculators (as provided by the CFTC largetrader reports) and all other market volume. Including two additional sets
5Another approach would be to use a model with a mean equation and a volatility
equation that has both volume and GARCH (generalized autoregressive conditionalheteroskedasticity) terms. This approach is not used due to the limited time series ofobservations available for each market. Monte Carlo simulation results generatedrecently by Hwang and Pereira (2003) indicate that at least 500 observations areneeded to efficiently estimate models with GARCH effects, substantially more thanthe number of daily observations available in this study (130).6The full set of regression results can be found in Holt (1999).
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RISK AND MANAGED FUTURES INVESTING
of independent variables expands this basic specification. Daily effects onvolatility are well documented, implying that a set of daily dummy variablesshould be included. In addition, the estimated specification includes theopen interest for each market. As outlined by Bessembinder and Seguin(1993), open interest serves as a proxy for market depth, which is anticipated to have a negative relationship to volatility. This relationship impliesthat changes in volume have a smaller effect on volatility in a more liquidmarket (represented by higher open interest). Therefore, the regressionmodel specification for a given futures market isst = b1 + b2MMATVt + b3MMAOIt + b4AOTVt + b5AOOIt +(8.1)b6Mont + b7Tuet + b8Wedt + b9Thut + etwherest = daily volatility (standard deviation) of futures returnsMMATVt = absolute value of net MMA trading volumeMMAOIt = absolute value of net MMA open interestAOTVt = other market trading volumeAOOIt = other open interestMont, Tuet, Wedt, and Thut = dummy variables that representday-of-the-week effectset = a standard normal error term.Following Chang, Pinegar, and Schachter (1997) and Irwin and Yoshimaru (1999), the extreme-value estimator developed by Parkinson (1980) isused to estimate daily volatility of futures returns. For a given commodity,Parkinson’s estimator can be expressed asŝt = 0.601 ln(Ht / Lt)
(8.2)
where Ht = trading day’s high priceLt = the day’s low.Wiggins (1991) reports that extreme-value estimators are more efficientthan close-to-close estimators in many applications. Previous empiricalresults suggest that a positive relationship is expected between volume andvolatility. They also suggest a negative relationship between volatility andopen interest, as shown by Bessembinder and Seguin (1993) for example.However, open interest within any six-month period may not vary enoughto efficiently estimate its impact on volatility. For the same reason, it is possible that daily dummy variables will not exhibit the U-shape documentedin previous volatility studies.
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The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
Table 8.5 reports the estimated coefficients, corresponding t-statistics,and adjusted R2 for each market. Due to the relative insignificance of theday-of-the-week variables, only the F-statistic for testing the joint significanceof the dummy variables is reported. As shown by this F-statistic, significant dailyTABLE 8.5
Volatility Regression Results for 13 Futures Markets, April 4,1994–October 6, 1994
Intercept
MMANetVolume
Rest ofNearbyVolume
MMANetOpenInterest
Rest ofNearbyOpenInterest
Coffee
3440.1*(6.39)
−0.1200(−0.73)
0.4590*(11.19)
−0.1444*(−4.85)
−0.1831*(−6.31)
1.31
0.51
Copper
522.6*(3.98)
0.0973*(3.22)
0.1091*(9.67)
−0.0018(−0.37)
−0.0214*(−4.53)
1.12
0.61
Corn
916.5*(3.17)
0.0411*(2.30)
0.0253*(6.41)
−0.0147*(−3.53)
−0.0046*(−1.98)
1.15
0.49
Cotton
331.7(1.57)
0.0379(0.98)
0.1279*(6.77)
0.0070(0.71)
−0.0009(−0.14)
0.97
0.41
Crude oil
739.4*(2.69)
0.0539*(2.24)
0.0357*(9.05)
−0.0189*(−4.22)
−0.0094*(−