Smart Money? The Forecasting Ability of CFTC Large Traders. by Dwight R. Sanders, Scott H. Irwin, and Robert Merrin

Transcription

1 Smar Moey? The Forecasg Ably of CFTC Large Traders by Dwgh R. Saders, Sco H. Irw, ad Rober Merr Suggesed cao forma: Saders, D. R., S. H. Irw, ad R. Merr Smar Moey? The Forecasg Ably of CFTC Large Traders. Proceedgs of he NCCC-34 Coferece o Appled Commody Prce Aalyss, Forecasg, ad Marke Rsk Maageme. Chcago, IL. [hp://

2 Smar Moey? The Forecasg Ably of CFTC Large Traders Dwgh R. Saders Sco H. Irw Rober Merr* Paper preseed a he NCR-34 Coferece o Appled Commody Prce Aalyss, Forecasg, ad Marke Rsk Maageme Chcago, Illos, Aprl 6-7, 2007 Copyrgh 2007 by Dwgh R. Saders, Sco H. Irw, ad Rober Merr. All rghs reserved. Readers may make verbam copes of hs docume for o-commercal purposes by ay meas, provded ha hs copyrgh oce appears o all such copes. * Dwgh R. Saders s a Assocae Professor of Agrbusess Ecoomcs a Souher Illos Uversy, Carbodale, Illos. Sco H. Irw s a Professor of Agrculural ad Cosumer Ecoomcs a he Uversy of Illos, Urbaa, Illos. Rober Merr s a graduae sude Agrculural ad Cosumer Ecoomcs a he Uversy of Illos, Urbaa, Illos.

3 Smar Moey? The Forecasg Ably of CFTC Large Traders Pracoer s Absrac The forecasg ably of he Commody Fuures Tradg Commsso s Commme s of Traders daa se s vesgaed. Bvarae Grager causaly ess show very lle evdece ha raders posos are useful forecasg (leadg) marke reurs. However, here s subsaal evdece ha raders respod o prce chages. I parcular, o-commercal raders dsplay a edecy for red-followg. The oher rader classfcaos dsplay mxed syles, perhaps dcag ha hose rader caegores capure a varey of raders. The resuls geerally do o suppor he use of he Commme s of Traders daa predcg marke movemes. Key Words: Commme s of Traders, fuures markes, forecasg Iroduco The Commody Fuures Tradg Commsso s (CFTC) Commme of Traders (COT) repor hghlghs he aggregae fuures posos held by reporg (large) raders, boh commercal (hedgers) ad o-commercal (speculaors). Commody fuures raders ofe vew hese daa as ak o sder formao abou he posos of smar moey raders ad ou s usefuless predcg prce movemes, where dcaors derved from he parcpa acvy ca provde sgh o he fuure dreco of prce (Upperma, p. ). Ths corass wh he CFTC s use of he repors as a compoe of he marke moorg ad Large Trader Reporg Sysem (CFTC). Several academc sudes have vesgaed he ably of large raders o predc or forecas reurs fuures markes. For example, a early look a hs opc, Kah uses he COT repor o mmc he posos of reporg o-commercal raders. He fds ha followg her posos (upo release of he COT repors) does o geerae sascally sgfca profs. More recely, Wag foud ha over ervals from oe o welve weeks, ha o-commercal raders posos forecas prce couaos ad commercal raders forecas prce reversals. I he eergy markes, Buchaa, Hodges, ad Thes, fd ha o-commercal speculave posos provde formao o he magude ad dreco of weekly prce chages he aural gas fuures marke. I coras, Saders, Bors, ad Mafredo fal o fd ay evdece ha reporg raders posos, eher commercal or o-commercal, are useful predcg weekly eergy fuures reurs. Mos recely, Brya, Bessler, ad Hagh use causal ferece algorhm s o es for relaoshps bewee raders posos from he COT repors ad fuures prces. The auhor s resuls call o queso he usefuless of he COT daa formulag successful speculave sraeges (p. 054). Whle hese sudes are mpora, rece shfs marke parcpao warra furher vesgao. I rece years, academc sudes have show ha commody fuures porfolos ca geerae reurs comparable o eques (Goro ad Rouwehors). As a resul, he facal dusry has developed producs ha allow suos o ves commodes hrough log-oly dex fuds. The rapd growh hese o-radoal speculaors has led raders o clam ha he excessve speculao s creag prce dsoros radoal agrculural commody markes (Morrso, 2006). Ideed, Domask ad Heah argue ha he facalsao of commody 2

4 markes warras addoal sudy o he sraeges, movao, ad he poeal marke mpac of o-radoal raders. Alog hese les, he CFTC recely revewed he COT repors ad have added a dex rader caegory o he COT classfcaos for agrculural commodes (CFTC). Clearly, he markes are chagg, ad s mpora ha researchers, regulaors, ad marke parcpas udersad he relaoshp (f ay) bewee rader posos ad prce behavor The goal of hs research s o horoughly address he predcve value of rader posos agrculural fuures markes. Specfcally, Grager causaly wll be used o drecly es for he mpac of raders posos o fuures reurs usg weekly daa from 992 hrough A umber of dffere poso measures wll be used o explore he sesvy of he resuls o hese dcaors. Tradg syles are also revealed by examg he causaly from reurs o posos, revealg f CFTC raders are red followers or adhere o value or corara ype sraeges. The research resuls are of eres o academcs ad raders alke. Academc researchers ga a more horough udersadg of hs mpora daa. Also, he research wll help o aswer some crucal quesos abou rader behavor ad he poeal mpac of speculao o agrculural fuures prces. Fally, raders ad oher marke parcpas wll beef from a rgorous aalyss of he COT daa. Aalyss may fd ha he COT daa provdes lle sgh as o fuure prce dreco. Or, aleravely, hey may wa o sar followg he smar moey. COT Daa The COT repor provdes a breakdow of each Tuesday s ope eres for markes whch 20 or more raders hold posos equal o or above he reporg levels esablshed by he CFTC. The weekly repors for Fuures-Oly Commmes of Traders ad for Fuures-ad-Opos-Combed Commmes of Traders are released every Frday a 3:30 p.m. Easer Sadard Tme. Repors are avalable boh a shor ad log forma. The shor repor shows ope eres separaely by reporable ad o-reporable posos. For reporable posos, addoal daa are provded for commercal ad o-commercal holdgs, spreadg, chages from he prevous repor, perceage of ope eres by caegory, ad umber of raders. The log repor, addo o he formao he shor repor, also groups he daa by crop year, where approprae, ad shows he cocerao of posos held by he larges four ad egh raders. I early 2007, as a respose o complas by radoal raders abou dex raders, he CFTC released supplemeal repors whch also break ou he posos of dex raders for welve agrculural markes. The release of hs repor offers hsorcal daa sarg 2006, ad largely cofrms he presupposo ha dex raders are geerally log-oly raders. As a example, he cor fuures marke, dex raders are 96% log ad hold roughly 2% of he ope eres. I Chcago Board of Trade whea fuures, dex raders are repored o rema 95% log ad maa 2% of he marke s ope eres. The raders he dex caegory were foud o have come from boh he commercal ad o-commercal caegores. As expeced, dex raders seldom aler posos oher ha o roll corac mohs, resulg vrually o varao her drecoal poso. The dex rader daa s sghful regards o marke composo, ad udoubedly wll warra alerave pahs of qury. However, for he focus of hs research, he eglgble varao hese log-oly raders posos makes hghly ulkely ha hey coa ay predcve power. Lkewse, he sraeges ad movaos for hs group of raders are clear. 3

5 Therefore, here, we focus o he radoal CFTC classfcaos: commercals, o-commercals, ad o-reporg raders. Usg he formao he shor repor, o-commercal ope eres s dvded o log, shor, ad spreadg; whereas, commercal ad o-reporg ope eres s smply dvded o log or shor. The followg relao explas how he marke s oal ope eres (TOI) s dsaggregaed: () [ NCL NCS 2( NCSP)] [ CL CS] [ NRL NRS 4 ] = 2( TOI) Reporg NoReporg where, NCL, NCS, ad NCSP are o-commercal log, shor, ad spreadg posos, respecvely. CL (CS) represes commercal log (shor) posos, ad NRL (NRS) are log (shor) posos held by o-reporg raders. Reporg ad o-reporg posos mus sum o he marke s oal ope eres (TOI), ad he umber of logs mus equal he umber of shor posos. Daa o rader posos are colleced for each Tuesday from 995 hrough 2006, resulg 66 observaos. The COT daa reflecs raders posos as of Tuesday s close; alhough, for much of he sample s o released ul Frday. A machg se of fuures reurs, R = l(p /p - ), are calculaed for earby fuures usg Tuesday-o-Tuesday closg prces. We make o assumpos abou how or why raders posos mgh chage over he course of a week, ad he daa are orgazed such ha he colleced prces are cocdeal wh he repored posos. Poso Idcaors Pror research resuls have vared, poeally due o alerave measures of poso sze. For example, Saders, Bors ad Mafredo ulze he a measure of speculave pressure proposed by De Roo, Nma, ad Veld ad fal o fd ay marke mpac caused by rader groups. I coras, Wag ulzes a seme dex ha ormalzes posos by here hree year rage. Usg hs dex, Wag fds a marke mpac of some agrculural fuures. The dspary of resuls suggess ha mulple poso dcaors should be ulzed o udersad he robusess of he resuls. The frs poso dcaor ulzed s he perce e log (PNL), whch measures he e poso of he average rader a CFTC classfcao (De Roo, Nma, ad Veld). The PNL s calculaed as he log mus he shor posos dvded by her sum. For sace, he perce e log for he reporg o-commercals s defed as follows: (2) NCL NCS No - Commercal PNL =. NCL NCS The PNL for each CFTC classfcao represes he e poso held by he group ormalzed by her oal sze. The secod poso measure s Wag s seme dex, SI,. Wag defes he e log poso for each rader caegory, S,, as he oal log posos mus he oal shor posos for ha caegory a me. The, Wag defes hs seme dex by ormalzg he e log poso by s rage over he pror hree years, 4

6 (3) SI, S, M( S, ) =. Max( S ) M( S ),, Where, he mmum ad maxmum fucos are appled o he pror hree years. Wag s seme dex s esseally a oscllaor boud he rage (0,). A value of 0 dcaes ha he e log poso s a a hree year low, whle a value of occurs whe he e log poso or rader seme s a a hree year hgh. The use of mulple poso measures s mpora o make he emprcal resuls comparable o he leraure ad more robus. I he followg seco, we demosrae how hese poso measures are used o ucover sascal lead-lag relaoshps wh he daa usg Grager causaly. Mehod Hamlo suggess he drec or bvarae Grager es for examg he lead-lag or causal relaoshp bewee wo seres. Grager causaly s a echque for deermg wheher oe me seres s useful forecasg aoher. I cosss of rug a vecor auoregresso (VAR) ad he esg he resulg coeffces. A VAR wh wo me seres varable, x ad y, cosss of wo equaos: oe, he depede varable s y ; he oher he depede varable s x. The regressors boh equaos are lagged values of boh varables. More geerally a VAR wh k me seres varables cosss of k equaos, oe for each of he varables, where he regressors all equaos are lagged values of all he varables. The coeffces of he VAR are esmaed by esmag each of he equaos usg OLS. Uder he VAR assumpos he OLS esmaors are cosse ad have a o ormal dsrbuo large samples, ad sascal ferece proceeds he usual maer. I our case he wo me seres varables we use are fuures reurs ad rader posos (PNL or SI). The followg models are esmaed, m β PNL = = m θ R = = (4) R = α γ R (5) PNL = φ λpnl ε, ad ω Each model s esmaed for lag leghs of o 2, ad he lag srucure of he mos effce model s seleced by mmzg he AIC crera. The models are esmaed wh OLS. If he resduals demosrae seral correlao (Breusch-Godfrey Lagrage mulpler es), he addoal lags of he depede varable are added ul he ull of o seral correlao cao be reeced. We es for heerskedascy usg Whe s es (980), ad Whe s robus errors where used o correc he sadard errors. I equao (4), he ull hypohess of eres s ha raders posos (PNL) cao be used o predc (do o lead) marke reurs:, H 0 : β = 0 for all. A reeco of hs ull hypohess would provde evdece rader posos are deed useful for forecasg marke reurs. I order o gauge 5

7 he aggregae mpac of rader posos, we also es he ull hypohess ha β = 0, whch wll reveal he cumulave mpac of raders posos o reurs (f ay). Fally, he ull hypohess f full raoaly (effcecy) fuures reurs ( γ = β = 0, ) ad auocorrelao reurs ( γ = 0 ). I equao (5) here are wo ull hypohess of eres. Frs, do reurs lead raders posos, θ = 0 for all? Secod, wha s he cumulave mpac of pas reurs o raders posos ( Θ = 0). If we reec ha θ = 0 ad fd ha Θ > 0, he he rader group may be = classfed as red followers or posve feedback raders because hey crease her log poso afer prces crease ad vce versa. Coversely, raders who buck he red may be called egave feedback raders or coraras. I equao (5), coraras are characerzed by fdg ha θ 0 ad Θ < 0. These raders ed buy afer prce decles ad sell afer prce ralles, = esseally a couer-red sraegy. The ess ouled equaos (4) ad (5) wll provde mpora sgh regards o he usefuless of he COT daa predcg prce movemes ad he radg syle of each rader group. Resuls Treds Posos The daa are frs examed vsually o reveal smple reds ad basc characerscs. I hs seco, we cocerae o a maor feed gra, cor, ad a maor lvesock marke, lve cale. The reds hese fuures markes are geerally represeave of he agrculural markes cluded he sudy. Fgure shows he oal ope eres (fuures plus dela-adused opos) for cor (pael A) ad lve cale (pael B). Toal ope eres for cor was relavely seady bewee 500 ad 700 housad corac hrough md The, ope eres creased seadly o over 2 mllo coracs lae Over he same perod, lve cale ope eres expereced a doublg from 300 housad o 600 housad coracs. May marke parcpas arbue hs crease o greaer overall speculave acvy, cludg log-oly dex fuds (O Hara). However, as show Fgure 2, he COT rader classfcaos are uable o cofrm (or dey) hs coecure. Ideed, over he same me perod, he commercal cor posos (pael A) were relavely fla a 45%-50% of oal ope eres. Bu, here was marked crease o-commercal acvy from roughly 30% of ope eres o more ha 35% of ope posos. However, hs crease came mosly a he expese of o-reporg speculaors, whose ope eres decled from early 25% o below 5%. Smlar reds are show for lve cale (pael B), where commercal posos are relavely sable ad he o-commercal poso sze creases a he expese of he o-reporg group. Sce o-reporg raders are o classfed as commercal or o-commercals, we do o kow f here was a relave loss of commercal or speculave raders. Sll, s clear from Fgures ad 2 ha he markes seem o have udergoe some srucural chages sce 2003, whch gves rse o hs vesgao. I hs research, wo poso measures perce e log (PNL) ad he seme dex (SI) are ulzed o summarze he e poso held by each rader caegory. To llusrae hese measures = = 6

8 hrough me, he PNL ad SI for o-commercal cor posos are ploed Fgure 3. I Fgure 3, s clear ha he PNL ad SI are hghly correlaed boh cor (pael A) ad lve cale (pael B) wh a smple correlao coeffces excess of Because of he hgh correlao across measures, s ulkely ha he emprcal mehods wll geerae dramacally dffere resuls across alerave poso measures. Traders ofe ce he COT posos as a cause of prce moves or a reaso o expec a sell-off or reboud prces. Ideed, may marke aalyss make weekly assessmes of rader posos as par of her marke commeary (Upperma). I Fgure 4, a smple vsual aalyss does deed reveal a appare relaoshp bewee cor prce levels ad he PNL for o-commercal cor raders. For sace, pael A, a hgh cor prce (325 ces per bushel) early 2004 cocded wh o-commercal raders beg over 40% e log. However, he vsual evdece ca be msleadg. A hgh coemporaeous correlao (0.62) bewee o-commercal PNL ad cor prce levels clearly does o mply causaly from posos o prces. I he followg seco, he daa are subeced o a more rgorous causal es for predcve ably. Do Posos Lead Reurs? Equao (4) s esmaed o es f rader posos are deed useful forecasg reurs. I parcular, a reeco of β = 0 would provde some evdece ha he mporace placed o he COT daa by he radg dusry s well-fouded. The p-values for hs ad he oher ull hypohess of eres are preseed Tables, 2, ad 3 for he o-commercal, commercal, ad o-reporg rader groups, respecvely. I Table, he ull hypohess ha o-commercal posos (PNL) lead or forecas marke reurs s reeced a he 5% level oly soybeas (p-value =0.046). I fve of he model specfcaos, lagged values of he posos eer he equao a us a sgle lag. I hose where here are more ha a sgle lag, he ull hypohess ( β = 0 ) s sll o reeced, ad he cumulave poso mpac ( β ) s also o sascally dffere from zero. Collecvely, here s lle evdece ha = o-commercal posos are sysemacally useful predcg reurs. Ieresgly, however, full raoaly fuures reurs ( γ = β = 0, ) s reeced a he 5% level wo markes mosly o a low-order auocorrelao reurs ( γ = 0 ), whch s reeced a he 5% level hree markes. The ull hypohess ha commercal rader posos do o lead fuures reurs s reeced CBOT whea, KCBT whea, ad lea hogs. So, hree of he e markes, here s some evdece ha commercal raders posos are useful forecasg reurs. I boh of he whea markes, we reec β = 0 ad fd ha he cumulave drecoal mpac s posve, suggesg ha = commercals crease log posos pror o prce creases. I lea hogs, he aggregae mpac s o sascally dffere from zero, suggesg a uusual drecoal mpac wh some lagged β coeffces posve ad ohers egave. Whle hs evdece s o overwhelmg, here s ceraly more evdece of forecasg ably amog commercal raders ha amog ocommercals. Aga, s worh og ha full raoaly fuures reurs ( γ = β = 0 ) s, 7

9 reeced a he 5% level sx markes prmarly due o a low-order auocorrelao reurs ( γ = 0 ), whch s reeced a he 5% level four markes. I Table 3, we see ha he ull hypohess ha o-reporg raders posos do o lead reurs s o reeced a he 5% level for ay marke. There s o subsave evdece ha o-reporg raders posos are useful for forecasg reurs. Ths fdg may o be surprsg gve he lkely mxed moves of o-reporg raders, who may be speculaors or hedgers. The resuls Table 3 aga show evdece of low order auocorrelao reurs. Collecvely, he resuls Tables, 2, ad 3 sugges ha here s lle sysemac causaly from raders posos o reurs wh he agrculural fuures markes examed. The possble excepo beg from commercal raders posos he CBOT ad KCBT whea markes. However, hese cases, he dreco of he causaly s posve, whch s couer o he resuls documeed by Wag. The emprcal resuls do po o oe poeally mpora fdg: weekly fuures reurs ed o show some low-order posve auocorrelao. Ths resul s cosse wh oher research (Bors, Saders, ad Mafredo; Saders, Irw, ad Leuhold), ad suggess ha a falure o model lagged reurs whe vesgag he COT daa may resul msspecfcao errors. I s mpora o verfy ha he resuls Tables, 2, ad 3 are robus o alerave poso measures. So, equao (4) s also esmaed usg he seme dex (SI) of Wag as well as us he chage he e rader poso for each caegory. The p-values for us he ull hypohess of o causaly from posos o reurs ( β = 0 ) are preseed Tables 4, 5, ad 6. Table 4 shows he p-values for he causaly ess usg o-commercal raders. The resuls usg he PNL are show frs (same as show Table ) followed by he chage e poso ad he seme dex (equao 2). There s a sgle reeco of he ull hypohess (soybeas) usg he PNL, zero reecos usg he chage e poso, ad oe reeco (lea hogs) usg he SI. Whle he markes ha show reecos seem o be somewha sesve o he poso dcaor, he lack of sysemac causaly s evde across he measures. Resuls for commercal raders usg alerave poso measures are show Table 5. Usg he PNL, he ull hypohess s reeced CBOT whea, KCBT whea, ad lea hogs (5% level). I coras, usg he chage e poso as he dcaor, he ull s reeced he KCBT whea ad cor (5% level). The seme dex (SI) provdes sascally sgfca forecass of reurs us he CBOT whea. So, o marke shows predcably across all measures; bu, he CBOT whea ad KCBT whea reec he ull hypohess wh wo of he hree measures. I each of hese cases he sgs o he lagged poso coeffces are posve suggesg ha commercal raders crease (decrease) log posos pror o prce creases (decreases). For o-reporg raders (Table 6), he resuls are cosse across measures. The ull hypohess of o causaly s oly reeced oe marke (feeder cale) wh a sgle poso measure (SI). There s o evdece ha he o-reporg raders posos are useful for forecasg reurs, regardless of he poso measure employed. The evdece ha raders posos lead fuures reurs s lmed. Ideed, here s o sysemac evdece ha o-reporg raders posos are useful for predcg marke reurs. Lkewse, 8

10 for o-commercals, he ull hypohess of o causaly s reeced oly sporadcally across markes ad poso measures. There s some evdece ha commercal posos CBOT ad KCBT whea may provde some forecasg ably for reurs. I hese wo markes, he ull hypohess s reeced for wo of he hree poso measures. Sll, eve wh hese specfc reecos, here s o pervasve evdece ha exeds across all markes. Do Reurs Lead Posos? I s mpora o udersad he dyamcs of raders posos. For sace, behavor face heores sugges ha posve feedback raders may be marke de-sablzg (De Log, e al). To reveal poeal radg syles amog rader groups, equao (5) s esmaed wh he focus o he ull ha reurs do o lead posos ( θ = 0 ) ad wheher or o he cumulave drecoal mpac s posve ( Θ > 0 ) or egave ( Θ < 0 ). A posve drecoal mpac s dcave = = of red followers or posve feedback raders because hey crease her log poso afer prces crease ad vce versa. A egave drecoal mpac suggess egave feedback raders or corara sraeges. The resuls from esmag equao (5) are preseed Table 7 for he o-commercal classfcao. The ull hypohess ha reurs do o cause posos s reeced a he 5% across all markes. There s a sysemac ad pervasve edecy for reurs o lead posos. Moreover, he aggregae drecoal mpac s sascally dffere from zero e ou of e markes a he 5% level wh sx of hose e markes clearly dsplayg posve feedback radg o he par of ocommercal raders. These resuls are cosse wh he fdgs of oher researchers (e.g., Saders, Irw, ad Leuhold), ad hey sugges ha he o-commercal raders may be ulzg red-followg sysems. The resuls for commercal raders (Table 8) show ha he ull hypohess ( θ = 0 ) s reeced all e markes. The cumulave drecoal mpac s posve ( Θ = ) s sascally dffere from zero a he 5% level sx of e markes, of whch half dsplay a egave cumulave mpac or value sraeges. I oal, seve of he e drecoal dcaors are egave, dcag ha shor hedgers are scale-up sellers ad log hedgers are scale-dow buyers. However, he drecoal evdece s o overwhelmg oward eher syle. Ths may sem from a heerogeeous group of raders capured he commercal caegory. Perhaps o surprsgly, he resuls for o-reporg raders are also mxed. The ull hypohess ha reurs do o lead posos s aga reeced all markes. However, he cumulave mpac s dffere from zero sx ou of e markes wh four of hose showg a egave feedback syle. I oal, he above resuls buld a srog case ha reurs lead posos. Whle he geeral radg syle of o-commercals ca be classfed as oe of posve feedback sraeges, he resuls of for he commercal ad o-reporg caegores are more mxed wh o clearly doma syle wh each group. These resuls may reflec ha boh he commercal ad o-reporg caegores are capurg a dverse group of raders, where he composo may chage across markes (Edergo ad Lee). 9

11 Coclusos The goal of hs research s o explcly exame he usefuless of COT daa predcg fuures marke reurs. I lgh of he evolvg aure of he speculave parcpas fuures markes, ad he dusry s fascao wh he posos held by fuds (o-commercals), s mpora o drecly address he usefuless of hs daa a forecasg framework. Here, we use a sadard bvarae Grager causaly approach o vesgae he lead-lag dyamcs bewee raders posos ad marke reurs. The emprcal resuls sugges wo prmary fdgs. Frs, raders posos do o show a sysemac ad pervasve edecy o lead reurs. I parcular, here s praccally o ably o forecas marke reurs usg eher o-commercal (fuds) or o-reporg (small speculaors) posos. There s some weak evdece ha commercal (hedgers) posos lead reurs a few specfc markes (.e., CBOT ad KCBT whea); however, hs s o a pervasve heme across markes. The resuls are relavely cosse across alerave poso measures. Secod, he resuls clearly demosrae ha posos follow reurs. I parcular, o-commercal raders crease log posos afer prces crease: hey are red followers. The drecoal fdgs for commercal raders ad o-reporg raders are more mxed, wh some markes showg red-followg syles ad oher showg coraras or value sraeges. The mxed drecoal evdece may reflec a hodgepodge of speculaors ad hedgers capured hese caegores. The resuls of hs work have some very praccal ramfcaos for marke parcpas ad academc researchers. Frs, academc researchers should make oe of he srog case for radg syles documeed hs work, parcular he red-followg dsplayed by o-commercal raders. Ths suggess ha here may be groups of raders who sysemacally employ smplfed radg or hedgg rules. Based o a umber of behavor face heores, he exsece of hese ose raders ca have mplcaos for marke behavor eve hough was o capured by he mehods specfc o hs sudy (De Log, e al). For pracoers, he usefuless of he COT daa forecasg reurs s suspec. I parcular, o-commercal or fud posos provde vrually o forecasg formao for marke reurs agrculural fuures markes. Ideed, o-commercal posos are bascally a lear exrapolao of pas prce chages, reflecg he red-followg sraeges of hs group. If he COT daa provde ay forecasg formao, s lkely foud he commercal caegory ad solaed markes. Overall, he evdece for predcve power s raher weak. The preseed resuls are cosse wh hose of Dale ad Zyre who sae ha ocommercal raders follow prce reds: hey do se hem (p. 23). Sll, acve raders ad marke aalyss frequely rely o he COT daa as s wdely used dscussg marke acvy. So, here s a seemg paradox bewee he predcve power of he COT daa as preseed hs research ad s perceved (or real) usefuless o hose he dusry. Perhaps he COT daa smply provde marke commeaors wh a covee alkg po or usfcao for oherwse dffcul-o-expla marke movemes. Aleravely, he COT daa may deed provde a glmpse of he smar moey a fasho o easly capured by sadard emprcs. 0

12 Refereces Brya, H., D.A. Bessler, ad M.S. Hagh. Causaly Fuures Markes. Joural of Fuures Markes. 26(2006): Buchaa, W.K, Hodges, P., ad J. Thes. Whch way he Naural Gas Prce: A Aemp o Predc he Dreco of Naural Gas Spo Prce Movemes Usg Trader Posos. Eergy Ecoomcs. 23(200): Charah, A., Y. Lag, ad F. Sog. Commme of Traders, Bass Behavor, ad he Issue of Rsk Prema Fuures Markes. Joural of Fuures Markes. 7(997): Commody Fuures Tradg Commsso. Comprehesve Revew of he Commmes of Traders Reporg Program. Federal Regser. 7(2006): umber 9. Dale, C. ad J. Zyre. Nocommercal Tradg he Eergy Fuures Marke. Peroleum Markeg Mohly, Eergy Iformao Admsrao, U.S. Deparme of Eergy, May, 996. De Log, J.B., A. Shlefer, L.H. Summers, ad R.J. Waldma. "Posve Feedback Ivesme Sraeges ad Desablzg Raoal Speculao." Joural of Face. 45 (990): Domask, D., ad A. Heah. Facal Ivesors ad Commody Markes. Bak for Ieraoal Selemes Quarerly Revew, March (2007): Edergo, L. ad J.H. Lee. Who Trades Fuures ad How: Evdece From he Heag Ol Marke. Joural of Busess. 75(2002): Goro, G., ad K.G. Rouwehors. Facs ad Faases Abou Commody Fuures. Facal Aalyss Joural. 62(2006): Harzmark, M. Luck ad Forecas Ably: Deermas of Trader Performace Fuures Markes. Joural of Busess. 64(99): Hagh, M.S., J. Hraaova, ad J.A. Overdahl. Prce Dyamcs, Prce Dscovery ad Large Fuures Trader Ieracos he Eergy Complex. U.S. Commody Fuures Tradg Commsso, Workg Paper, Aprl, 2005 (hp:// Heroymus. T.A. Ecoomcs of Fuures Tradg for Commercal ad Persoal Prof. New York: Commody Research Bureau, 97. Leuhold, R.M., P. Garca, ad R. Lu. The Reurs ad Forecasg Ably of Large Traders he Froze Pork Belles Fuures Marke. Joural of Busess. 67(994): Morrso, K. Commodes Lure Fuds Ivesors. Facal Tmes. December 28, 2004, Accessed o Ocober 9, O Hara, N. Muual Fuds Tap o Commodes. The Magaze of he Fuures Idusry. May/Jue (2006): Saders, D.R., K. Bors, ad M. Mafredo. Hedgers, Fuds, ad Small Speculaors he Eergy Fuures Markes: A Aalyss of he CFTC s Commmes of Traders Repors. Eergy Ecoomcs. 26(2004): Saders, D.R., S.H. Irw, ad R.M. Leuhold. The Theory of Corary Opo: A Tes Usg Seme Idces Fuures Markes. Joural of Agrbusess. 2(2003): Upperma, F. Commmes of Traders: Sraeges for Trackg he Marke ad Tradg Profably. Joh Wley & Sos, New Jersey, Wag, C., The Behavor ad Performace of Maor Types of Fuures Traders,. Joural of Fuures Markes, 23(2003):-3. Workg, H. Speculao o Hedgg Markes. Food Research Isue Sudes. (960):

13 Fgure. Combed Fuures ad Opos Ope Ieres, Pael A: Cor Mllos of Coracs Dae Pael B: Lve Cale 350 Thousads of Coracs Dae 2

14 Fgure 2. Perce of Ope Ieres by Trader Caegory, Pael A: Cor Perce Dae No-Commercal Commercal No-Reporg Pael B: Lve Cale Perce Dae No-Commercal Commercal No-Reporg 3

15 Fgure 3. No-Commercal Poso Measures, Pael A: Cor Perce Dae Perce Ne Log Seme Idex Pael B: Lve Cale Perce Dae Perce Ne Log Seme Idex 4

16 Fgure 4. No-Commercal Perce Ne Log ad Fuures Prces, Pael A: Cor Perce Dae Perce Ne Log Fuures Prce Ces per Bushel Pael B: Lve Cale Perce Dae Perce Ne Log Fuures Prce Dollars per Hudred Pouds 5

17 m β PNL = = Table. Grager Causaly, No-Commercals, R = γ R α ε. Hypohess Tess ad P-values Dreco Marke,m β =0, β =0 γ =0, γ =β =0,, β Whea CBOT, Cor, Feeder Cale, Whea KCBOT 6, Lea Hogs, Lve Cale, Whea MGE, Soybea Meal, Soybea Ol, Soybeas, m β PNL = = Table 2. Grager Causaly, Commercals, R = γ R α ε. Hypohess Tess ad P-values Dreco Marke m, β =0, β =0 γ =0, γ =β =0,, β Whea CBOT, Cor, Feeder Cale, Whea KCBOT 6, Lea Hogs 7, Lve Cale, Whea MGEX, Soybea Meal, Soybea Ol, Soybeas,

18 m β PNL = = Table 3. Grager Causaly, No-Reporg, R = γ R α ε. Hypohess Tess ad P-values Dreco Marke m, β =0, β =0 γ =0, γ =β =0,, β Whea CBOT, Cor, Feeder Cale, Whea KCBOT, Lea Hogs, Lve Cale, Whea MGEX, Soybea Meal, Soybea Ol, Soybeas, Table 4. Grager Causaly, No-Commercals, R = γ R P-values for β =0, Marke PNL Ne Poso SI Whea CBOT Cor Feeder Cale Whea KCBOT Lea Hogs Lve Cale Whea MGEX Soybea Meal Soybea Ol Soybeas m β Poso = = α ε. 7

19 Table 5. Grager Causaly, Commercals, R = γ R m β Poso = = α ε. P-values for β =0, Marke PNL Ne Poso SI Whea CBOT Cor Feeder Cale Whea KCBOT Lea Hogs Lve Cale Whea MGEX Soybea Meal Soybea Ol Soybeas Table 6. Grager Causaly, No-Reporg, R = γ R P-values for β =0, Marke PNL Ne Poso SI Whea CBOT Cor Feeder Cale Whea KCBOT Lea Hogs Lve Cale Whea MGEX Soybea Meal Soybea Ol Soybeas m β Poso = = α ε. 8

20 m θ R = = Table 7. Grager Causaly, No-Commercals, PNL = φ λpnl Hypohess Tess ad P-values ω Dreco Marke,m θ =0, θ =0 θ Whea CBOT, Cor 5, Feeder Cale 8, Whea KCBOT 2, Lea Hogs 2, Lve Cale 2, Whea MGEX, Soybea Meal 2, Soybea Ol 0, Soybeas, m θ R = = Table 8. Grager Causaly, Commercals, PNL = φ λpnl Hypohess Tess ad P-values Dreco Marke,m θ =0, θ =0 θ Whea CBOT 7, Cor 2, Feeder Cale 5, Whea KCBOT 2, Lea Hogs 3, Lve Cale 8, Whea MGEX 7, Soybea Meal, Soybea Ol 0, Soybeas, ω 9

21 m θ R = = Table 9. Grager Causaly, No-Reporg, PNL = φ λpnl Hypohess Tess ad P-values Dreco Marke,m θ =0, θ =0 θ Whea CBOT 5, Cor, Feeder Cale 2, Whea KCBOT 3, Lea Hogs, Lve Cale 3, Whea MGEX 3, Soybea Meal 3, Soybea Ol 5, Soybeas, ω 20