MultiMarket Trading and Liquidity: Theory and Evidence


 Chester Sullivan
 2 years ago
 Views:
Transcription
1 MultMrket Trdng nd Lqudty: Theory nd Evdence Shmuel Bruch, G. Andrew Kroly, b* Mchel L. Lemmon Eccles School of Busness, Unversty of Uth, Slt Lke Cty, UT 84, USA b Fsher College of Busness, Oho Stte Unversty, Columbus, OH 430, USA Abstrct We develop nd test new model of multmrket trdng to expln the dfferences n the foregn shre of trdng volume of nterntonlly crosslsted stocks. The model derves n equlbrum whch predcts tht, under frly generl condtons, the dstrbuton of trdng volume cross exchnges competng for order flow s relted to the correlton of the crosslsted sset returns tht rse n the respectve mrkets. Tht s, volume s proportonlly hgher on the exchnge n whch the crosslsted sset returns hve greter correlton wth returns of other ssets trded on tht mrket. We test ths predcton wth weekly stock returns nd volume dt on 5 nonu.s. stocks crosslsted on mjor U.S. exchnges. We fnd strong emprcl support for the predcton, even fter controllng for potentl endogenety effects s well s host of other frmspecfc, ssuespecfc nd countrylevel fctors. JEL clssfcton: F30, G4, G5. Keywords: Interntonl fnnce; crosslstngs; trdng; lqudty Current Verson: Februry, 006 We thnk n nonymous referee nd the edtor s well s semnr prtcpnts t the Unverstà d Bologn, Unversty of Uth, Georg Stte Unversty, Lousn Stte Unversty, New York Stock Exchnge, Unversty of North Croln t Chpel Hll, Texs A&M Unversty nd t the NYSE Conference on the Future of Globl Equty Trdng for helpful comments. We re especlly grteful to comments nd dscussons wth Pul Bennett, Hnk Bessembnder, Ekkehrt Boehmer, Robert Hrdy (Fleet Speclsts, Chrstn Leuz, Als Roell, Gdeon Sr, René Stulz, Glenn Surnmer (VDM Speclsts, nd Ingrd Werner Roger Loh provded exceptonl reserch ssstnce for whch we re lso grteful. * Correspondng uthor contct nformton: Tel.: ; fx: Eml ddress: (G. A. Kroly
2 I. Introducton Wth the enhnced globlzton of fnncl mrkets, the number of nonu.s. frms choosng to crosslst shres on U.S. exchnge hs ncresed substntlly. In 990, there were only 35 nonu.s. stocks lsted on the New York Stock Exchnge (NYSE nd Nsdq, but, by the end of 00, the number hs more thn doubled to over 850. If one ncludes stocks trdng over the counter (OTC nd s prvte plcement ssues, the number of nonu.s. compnes wth shres trdng n the U.S. now exceeds,300. Ths drmtc ncrese reflects not only U.S. nvestors' need for nterntonl dversfcton, but lso the desre of foregn compnes to ccess globl cptl, broden ther shreholder bse, nd enhnce compny vsblty. Corportons generlly vew U.S. crosslstngs s vlueenhncng decsons, but there s dsgreement bout the sources of the beneft. One of the benefts mostfrequently cted n surveys of compny mngers nd nvestors lke s the ncresed lqudty n shre trdng ssocted wth the U.S. lstng (Mttoo, 99; Fnto nd Krmel, 997. Indeed, emprcl evdence ndctes tht U.S. lstng s ccompned by lrge 40 to 50 percent ncrese n the number nd vlue of shres trded n the combned U.S. nd home mrket compred to tht n the homemrket before the lstng (Smth nd Sofnos, 997; Foerster nd Kroly, 998. However, not ll compnes experence the benefts of ncresed lqudty. In fct, remrkble feture of trdng ctvty n crosslsted stocks on U.S. exchnges s the gret vrblty n the U.S. frcton of globl trdng. For some stocks, U.S. trdng typclly represents less thn 5 percent of globl trdng n ny gven month, whle, n other stocks, U.S. trdng comprses well over 90 percent of globl trdng. Moreover, t s not smply countrylevel fctors, lke regultory restrctons or the extent of overlppng trdng hours, tht dctte these outcomes, s there s sgnfcnt crosssectonl dversty n the U.S. frcton of trdng even mong stocks from the sme country. Consder, s n exmple, the dfferent experences for two frms from the sme home mrket over the sme perod (Fgure. Monthly trdng volume (n number of shres trded on the NYSE for Tomkns, U.K. engneerng compny, hs rrely rsen over percent of ts combned globl volume (NYSE nd the London Stock Exchnge snce t lsted on the NYSE n 988, whle GlxoSmthKlne, U.K. phrmceutcl compny, hs mntned U.S. frcton of trdng over 30 percent, t lest for ll but the lst yers of the decde. Understndng why such dfferent multmrket trdng envronments rse s of prmount mportnce for mngers of ll crosslsted compnes, but especlly those wth lttle U.S.bsed trdng ctvty, s t potentlly reflects on the longterm vblty of the lstng nd ts potentl s vehcle for rsng cptl, for brodenng the shreholder bse nd for enhncng the compny s vsblty nd profle. Globl nvestors, prtculrly rbtrgeurs tht ctvely trde n both overses ordnres nd ther equvlent crosslsted shres n the U.S., lso cre bout how the trdng ctvty s pportoned cross the two mrkets s t ffects the fesblty of ther strteges. Moreover, understndng the fctors tht ffect the dstrbuton of globl trdng volume s lso mportnt to stock exchnges, whch compete wth ech other for new lstngs nd for order flow mong exstng lstngs. In ths pper, we develop new model of multmrket trdng to expln the vrton n the U.S. shre of globl trdng volume cross the smple of nonu.s. stocks crosslsted on U.S. exchnges. The model derves n equlbrum whch predcts tht, under frly generl condtons,
3 the dstrbuton of trdng volume cross exchnges competng for order flow s relted to the correlton of the crosslsted sset returns wth the returns of other ssets trded n the respectve mrkets. The model s bsed on stndrd Kyle (985 frmework wth two stock exchnges nd three ssets: one sset trded exclusvely n the frst mrket, second sset trded exclusvely n the second mrket nd the crosslsted sset, whch s trded on both exchnges. The two exchnges re segmented n tht the rskneutrl mrket mkers observe the order flow of ssets only on ther own exchnge. The nformed trders, who observe prvte nformton bout the dfferent ssets, nd dscretonry lqudty trders re ble to trde on ether mrket nd even cross mrkets. Becuse the sset returns on ech exchnge re correlted, compettve mrket mkers, when prcng n sset, cn nfer nformton not only from the sset's own order flow, but lso from the order flow of other ssets trded on the exchnge. Indeed, n equlbrum, the more correlted the returns of the two ssets, the more relevnt the order flows to one sset re for the prcng of the other sset, nd the less senstve the prce of ny one sset to ts own order flow. Ths outcome gudes the lqudty trders n choosng where to trde the crosslsted sset: the hgher the correlton n returns of the crosslsted sset wth the domestc sset, the more nformtve the domestc sset's order flow, whch leds both lqudty nd nformed trders to submt lrger proporton of ther orders n the crosslsted sset to tht exchnge. Tht s, proportonlly more volume tkes plce on the mrket n whch the crosslsted sset hs greter correlton wth the other ssets trded on tht mrket. Our new model of multmrket trdng represents n mportnt deprture from the bestknown models to dte, ncludng Pgno (989, Chowdhry nd Nnd (CN, 99 nd Domowtz, Glen nd Mdhvn (DGM, 998 n tht nether exchnge desgn nor ssumptons of dfferentl trdng costs ply centrl role n determnng the dstrbuton of trdng volume cross exchnges. 3 For exmple, Pgno s twoperod model wth rskverse nvestors bstrcts from symmetrc nformton consdertons nd focuses on the role of trders' expecttons of other trders' ctons. Hs key result s knfeedge Nshtype of equlbrum n whch both mrkets cn survve, but only f number of specl ssumptons bout exchnge desgn, such s equl trnsctons costs nd equl numbers of trders n ech mrket, hold. CN does llow for symmetrc nformton, lke we do, by extendng the frmework of Kyle (985 nd Admt nd Pflederer (988 to llow smultneous trdng n multple mrkets. Ther nformed nvestors trde strtegclly to mxmze profts from ther prvte nformton by loctng trdes cross mrkets ccordng to whch mrkets re thck wth lqudty trders. Lqudty nturlly clusters n prtculr mrket, whch CN defne s ther wnner tkes most equlbrum. We dffer from CN becuse we model the trdng decsons of both nformed nd dscretonry lqudty trders nd becuse the exchnges n our model provde dfferent lqudty n the crosslsted sset by mens of the jont dstrbuton of the sset returns trded on ech exchnge nd not by wy of ssumptons bout the number of smll lqudty trders tht re confned to ech mrket. Our model s lso dfferent from tht of DGM who extend the model of Glosten nd Mlgrom (985 to llow nvestors to trde n the homemrket or the new crosslsted mrket t dfferentl costs of executon due to the bdsk spred nd the dfferent costs of nformton cquston n the two mrkets. Ther model reles on ncrementl nformton cquston costs to ensure tht locl nvestors wll fnd t cheper to trde loclly, unless there s perfect trnsprency n quotes between the two mrkets, n whch cse crosslstng wll lower voltlty due to lower spreds from greter volume overll nd more ntense competton for order flow from both exchnges cn rse. They conclude tht CN's wnner tkes most equlbrum s complcted by
4 the degree of trnsprency between the two mrkets. Our model does not rely on ssumptons bout nformton cquston costs to yeld the mportnt dfferences n trdng costs. Rther, the choce of where to trde stems from the lqudty tht rses endogenously from the extent of the correlton of the crosslsted sset's returns wth the returns of other ssets trded on prtculr exchnge. We proceed to test the key predcton of our model nd fnd strong emprcl support. We exmne weekly trdng stock prce nd volume dt from the home mrket nd the NYSE/Nsdq for 5 compnes from 4 emergng nd developed countres round the world. We compute the verge U.S. frcton of globl trdng for ech stock bsed on vlble dt from Jnury 995 to December 004. Usng U.S. dollrdenomnted homemrket returns on the stock, we construct U.S. nformton fctor, whch s our proxy of the correlton of the crosslsted stock s returns wth nformton sgnls bout the vlues of other ssets trded n the home nd U.S. mrket. The rto s computed from multndex mrket model regressons of the ndvdul stock's returns on U.S.dollrdenomnted returns of the home mrket ndex nd U.S. mrket ndex (S&P 500. Specfclly, we mesure the fctor s the ncrementl explntory power of the U.S. mrket n terms of the dfference n RSqured of twondex model ncludng the U.S. ndex reltve to the RSqured of snglendex model wth just the home mrket ndex, djusted for degrees of freedom. Even fter controllng for potentl endogenety effects s well s host of other frmspecfc (e.g. mrket cptlzton, U.S nsttutonl ownershp, foregn ownershp restrctons, nd home mrket nlyst coverge, ssuespecfc (e.g. NYSE versus Nsdq s well s regonl nd country fctors (e.g. tmezone effects, we fnd tht the U.S. frcton of globl trdng s strongly, postvely relted to the U.S. nformton fctor, whch s consstent wth our theory. Our pper mkes n mportnt contrbuton to the exstng emprcl work on multmrket trdng. To dte, the most comprehensve emprcl study of the dstrbuton of globl trdng n nterntonlly crosslsted stocks s Pultkonk nd Sofnos ( Ther study exmnes 996 trdng volume dt for 54 NYSElsted nonu.s. stocks. They estmte n econometrc model explnng the vrton n the U.S. shre of globl trdng volume drwng from countryspecfc, compnyspecfc nd ssuespecfc fctors. The most mportnt vrble n ther model s the tmezone fctor; 40 percent of the vrton of the U.S. mrket shre cn be explned by the hours of overlp n trdng between the NYSE nd the home mrket for the stock. Surprsngly, other vrbles, lke dfferentl trdng costs, n ndustry dummy, mrket cptlzton, whether or not the ssue ws ssocted wth cptl rsng nd wht knd of crosslstng s employed (drect ordnry lstng, New York Regstered Shre, Amercn Depostry Recept (ADR, Globl Regstered Shre, expln reltvely lttle of the crosssectonl vrton. Our theoretcl model predcts new nformton fctor vrble for consderton n the model nd we fnd strong emprcl support for ths vrble, even fter controllng for tmezone effects nd other vrbles. 5 The remnder of ths pper s orgnzed s follows. Secton develops our multmrket model of trdng. We outlne the predctons of the model, s well s seres of testble lterntve hypotheses, for our emprcl nlyss whch follows n Secton 3. We descrbe the smple of frms, dt sources, vrble constructon, econometrc model nd mn results. Secton 4 concludes the pper. 3
5 II. Anlytcl Frmework nd Testble Hypotheses A. The Model We consder world wth two stock exchnges (denoted by subscrpts nd. Two ssets re lsted on ech exchnge. Exchnges re orgnzed s stndrd Kyle (985 mrket, where compettve rskneutrl mrket mkers mke the mrket for the ssets lsted on ther exchnge. To hve menngful seprton between exchnges, we ntroduce the followng segmentton. We ssume tht before settng the prces, mrket mkers observe the net order flow of ll the ssets trded on ther exchnge. They do not, however, observe the orders submtted to the other exchnge. After ech round of trdng, publc nnouncement s mde fter whch the rskneutrl mrket mkers gree tht the prces of the ssets trded on the frst nd second exchnges hve chnged by the nnovtons, υ, υ R, respectvely. Specfclly, where, υ υ = F s ε = F s ε 0 0 F = nd F =. ( b b s, ε nd ε re zero men normlly dstrbuted rndom vrbles tht tke vlues n R. Furthermore, s s ndependent from ε nd ε, nd the covrnce mtrx of s, denoted by P, s dgonl, 0 P =. ( 0 We do not mpose restrcton on the covrnce mtrx of the εs, nor ny on the correlton between ε nd ε. In prtculr, we cn ssume the second element of ε nd ε re dentcl, so the nnovtons n the second sset trded on ech exchnge re dentcl. We herefter refer to the second sset on ech exchnge s the crosslsted sset. Smlrly, we refer to the frst sset on ech exchnge s the locl sset. The nnovton n the prce of the locl sset trded on the frst exchnge depends on s, but not on s ; whle the nnovton n the prce of the locl sset trded on the second exchnge depends on s but not s. The prmeters,, b, mesure the exposure of the crosslsted sset to s nd s, respectvely. Wthout loss of generlty, we ssume the vlue of the ssets pror to the nnovton s zero nd tht nterest rtes re zero. There re two strtegc rskneutrl nformed trders. One of them observes s, whle the other observes s. The nformed trders my submt orders to both exchnges nd for ll ssets. Let x, x R ( =, be the mrket order the th nformed trder submts to the frst nd second exchnge, respectvely. Let x = x x nd x = x x be the ggregte nformed orders submtted to the frst nd second exchnge, respectvely. 4
6 Next, we consder the unnformed lqudty trders. We tke the lqudty demnd for ech sset s gven, nd ssume the lqudty demnd for both the locl sset nd the crosslsted sset re uncorrelted stndrd norml vrbles. However, to dscuss volume ptterns of the crosslsted sset, we hve to llow the lqudty trders who trde the crosslsted sset to choose to whch exchnge they prefer to submt ther order, or even to llow them to splt ther order between both exchnges. As n Admt nd Pflederer (988, we ssume tht there re n rskneutrl dscretonry lqudty trders, ech of whom hs to trde certn mount of the crosslsted stock. Let w be the mount the th dscretonry trder hs to trde. The mrket vews w s zero men norml rndom vrble. Furthermore, cov(w, w j equls zero for j. Thus, n n = vr w = vr( w (3 = = We let α(w be the frcton of the demnd the th dscretonry trder submts to the frst exchnge. When α = (0, then the th trder submts hs entre order to the frst (second exchnge. Let R nd R be the covrnce mtrx of lqudty demnd on the frst nd second exchnge, respectvely. We hve, R = 0 n = 0 vr( α ( w w nd R = 0 n = 0. vr(( α ( w w We denote by y R nd y R, the ggregte order flow (.e. the totl orders submtted by both nformed trders nd lqudty trders submtted to the frst nd second exchnge, respectvely. Anonymty of trde nd segmentton of mrkets mply tht mrket mkers n the frst exchnge cn only observe y whle mrket mkers n the second exchnge cn only observe y. Mrket mkers n ech exchnge set prces p R nd p R to cler the mrkets. The expected proft of the frst nd second nformed trders, condtonl on ther nformton, re (superscrpt t denotes the trnspose operton E E t t [( p x s ] ( υ p x s ] t t [( p x s ] E[ ( υ p x s ] υ (4 υ, (5 respectvely. The only nformton dscretonry lqudty trder possesses s the knowledge of hs own lqudty shock. The expected proft of the th dscretonry trder who trdes the crosslsted sset s, E ( υ p w E ( υ p w. (6 α w ( α w An equlbrum s defned s prce rules p, p : R R, strteges for the nformed trders x, x R, =,, nd n order splttng decson for the dscretonry lqudty trders { α w } n such = tht, 5
7 The prce rules stsfy the condton p ( y y = E[ υ ] nd p y = E[ υ ]. (7 ( y x nd x mxmze (4 nd (5, respectvely, for ech relzton of s. α (w mxmzes (6 for ech relzton of w. A lner equlbrum s n equlbrum n whch ( there exst two mtrces, Λ nd Λ, such tht, p ( y = Λ y nd p ( y Λ y =, ( the nformed trders' strteges re lner n the sgnls;.e. there exst two mtrces, β nd β, such tht, x β x = s nd x = s x β, nd ( the order splttng decsons of ll dscretonry lqudty trders re dentcl. In prtculr, they re ndependent of ther lqudty shocks;.e. α = α for some constnt α. In the followng, we focus on the propertes of lner equlbrum. Let the sclr λ be the second row, second column entry of the mtrx Λ. Then, λ s the prce mpct of mrket order for the crosslsted sset on the frst exchnge. Indeed, chnge n the demnd for the crosslsted sset on the frst exchnge by the mount results n prce chnge of the crosslsted sset on the frst exchnge by λ. Smlrly, λ, the second row second column entry of the mtrx Λ, s the prce mpct of mrket order for the crosslsted sset on the second exchnge. Theorem. A lner equlbrum exsts. Let the mtrces F, F nd P be gven by ( nd (. Let the mtrces R, R, Λ, Λ, β, β, nd the sclr, α, be soluton to the system of equtons, 0 R = 0 α ( t β = Λ Λ F t t = F Pβ ( R βpβ Λ = 0 R (8 0 ( α ( t β = Λ Λ F Λ α t t = F Pβ ( R β Pβ λ λ λ =, 6
8 7 subject to the second order condtons tht (Λ t Λ nd (Λ t Λ re negtve semdefnte mtrces. Then, the mtrces R, R, Λ, Λ, β, β, nd the constnt, α, form lner equlbrum. The proof of Theorem s gven n the Appendx. Notce tht the second order condtons mply tht α [0, ]. To solve the system, we use the elmnton method. For n rbtrry postve α, we solve the frst three mtrx equtons n (8 subject to the second order condton. To emphsze tht the soluton we get depends on the rbtrry choce of α, we wrte the soluton s functon of α. In prtculr, we get ( ( ( α α α α α λ b b b = (9 nd, ( ( ( ( ( ( ( α α α α α λ b =. (0 To fnd the equlbrum α, we solve the lst equton n (8 for α usng (9 nd (0. We get four possble solutons for α: (, 0,, ( b b b b b b. ( The frst soluton does not stsfy the condton α [0, ]. 7 The solutons α = 0 nd α = re the trvl equlbr. If dscretonry lqudty trder conjectures ll other lqudty trders trde t the frst (second exchnge, he too, regrdless of wht the vlues of nd b re, submts hs orders to the frst (second exchnge. The fourth possble soluton n ( s the nterestng one, becuse ths soluton depends on the vlues of nd b. The fourth soluton, however, s vld n certn regon. Tht sd, there s fmly of equlbr wth propertes tht chnge contnuously wth the prmeters nd b. We use tht fmly of equlbr to present our comprtve sttcs relted to trdng volume on the two exchnges. We fnd t smpler to fx b nd let vry. Becuse of the symmetry of the model, nlogous results hold when we fx nd let b vry. The followng corollry formlzes ths result. Corollry. Let b, nd be gven. Then, for ech, there s lner equlbrum, Λ (, Λ (, β (, β (, α(, such tht the element of the mtrxes, Λ (, Λ (, β (, β (, nd α(, re contnuous n. Moreover, α(, the porton of trde submtted to the frst exchnge, ncreses wth. Furthermore, f b < ( < nd f, b b b b, (
9 then ll trde of the crosslsted sset tkes plce on the frst (second exchnge. The proof of the corollry s strghtforwrd. If b < nd <, then there s lner equlbrum wth, 3 3 ( b b α =. (3 3 3 b b b Dfferentton wth respect to shows α s ncresng n. However, f b nd, then (3 my be smller thn zero or greter thn one. In the former cse, we use the trvl equlbr n whch α = 0 nd, n the ltter, we use the trvl equlbr n whch α =, so tht the fmly of equlbr hs propertes tht vry contnuously n. It s worth contrstng the equlbrum soluton from our model wth two other well known models of multmrket trdng: Pgno (989 nd Chowdry nd Nnd (99. Pgno shows tht n equlbrum exsts n hs model n whch both mrkets cn survve, but only f number of restrctve ssumptons re stsfed, such s equl trnsctons costs nd n equl number of trders n ech mrket. If these restrctons re not stsfed then only sngle mrket remns open n equlbrum. In Chowdhry nd Nnd s equlbrum, nformed trders wll llocte more of ther trdes to the mrket tht s thck wth lqudty trders. However, the extent to whch lqudty trde clusters n gven mrket s drven by exogenous ssumptons bout the number of smll lqudty trders n ech mrket. In contrst, we mke no ssumpton regrdng the dstrbuton of smll lqudty trders nd endogenze the choce of where to trde for both the nformed nd the dscretonry lqudty trders. Two of the possble equlbr n our model hve only sngle mrket survve. However, n contrst to Pgno, these sngle mrket equlbr re not stble n the followng sense: f we were to ntroduce n our model trders who re confned to trde n one mrket (smll lqudty trders, then ths mrket would ttrct lso nformed nd dscretonry trdng;.e., the trvl equlbr wth concentrton would dspper. Thus, the nturl equlbrum outcome n our model s to hve trde occurrng n both mrkets. Moreover, the fct tht the propertes of the fmly of equlbr tht we derve chnge contnuously s functon of the prmeters nd b llow us to derve crosssectonl predctons bout dfferences n trdng volume cross stocks whle holdng other prmeters constnt. Fgure llustrtes the results presented n the corollry. The fgure shows how the equlbrum porton of trdng volume n the frst exchnge vres wth the senstvty,, of the crosslsted sset's vlue to s. The grph dsplys three representtve fmles of equlbr for three dfferent rtos of the vrnce of s reltve to the vrnce of s, whch s normlzed to one. The fgure demonstrtes tht for smll enough, ll trde tkes plce on the second exchnge (.e. α = 0 whle for lrge enough ll trde occurs on the frst exchnge. The ntuton for ths result, whch forms the bss for our emprcl nlyss, s s follows. When prcng n sset, mrket mkers cn nfer nformton not only from the sset's own order flow, but lso from the order flow of other ssets trded on the sme exchnge. In equlbrum, the more correlted the returns of the two ssets, the more relevnt the order flows to one sset re for the prcng of the other sset, nd the less senstve the prce of ny one sset to ts own order flow. Ths outcome ncreses the lqudty of the crosslsted sset, whch gudes the lqudty trders n choosng where to trde. Tht s, proportonlly more volume tkes plce on the mrket n whch the crosslsted sset hs greter 8
10 correlton wth the other ssets trded on tht mrket. In ll but extreme cses both mrkets remn open, however, becuse the dscretonry lqudty trders splt ther orders cross the two exchnges n order to mnmze ther verge trdng costs. 8 B. Model Extenson One mportnt ssumpton n our model s tht the compettve rskneutrl mrketmkers, before settng prces, observe the net order flow of the ssets trded on ther own exchnge, but do not observe the order flow n the other exchnge. Ths mtters becuse locl mrket mkers re thus conferred comprtve dvntge n understndng locl nnovtons by vrtue of ther exclusve vew of the locl sset s order flow. How resonble ths ssumpton bout unobservblty of order flow cross mrkets s n relty, however, s uncler. 9 There my, fter ll, be compettve multmrkettrdng scenros n whch the mrkets re so close (sy, geogrphclly or n the sme tme zone tht ths complete unobservblty restrcton s flse. In order to nvestgte the robustness of our model s predctons, we developed n extenson to the model tht llows for complete observblty of the order flow n the other mrket for the mrket mkers whle mntnng some nformton symmetry between the two groups of mrket mkers. To keep the model trctble, we mke two smplfyng ssumptons: ( there exsts only the crosslsted sset, nd ( lqudty trders cn choose how much to trde, but re confned to do so only n ther own mrket (e.g., no ordersplttng. Now, mrket mkers n the two competng exchnges cn observe demnd for the crosslsted sset n both exchnges, but the specl role prevously plyed by the purelydomestc ssets n understndng the locl nnovton s replced wth n exogenous sgnl. The resultng equlbrum defnes prce rules tht re lner n the exogenous sgnl tht the mrket mkers observe nd n the order flows to both exchnges. More mportntly, so long s the exogenous sgnls observed by the mrket mkers re not dentcl, the key comprtve sttcs obtn. Tht s, the dfferentl prce mpct of mrket orders for the crosslsted sset on the two exchnges s drectly relted to the reltve mgntude of the exposures of the sset s prce nnovtons to the sgnls n the two mrkets. The hgher s the exposure to the sgnl on gven exchnge, the greter the lqudty tht exchnge provdes nd the more ggressvely dscretonry lqudty trders trde n tht exchnge. The model extenson mkes cler tht t s not the unobservblty of order flow per se tht drves our predctons bout volume. Rther, the necessry condton for our result s tht the locl mrket mker n ech mrket hs some comprtve dvntge reltve to hs counterprt n the other mrket n understndng the locl nnovton to whch the crosslsted stock s prtlly exposed. Of course, there my be more complex generlztons whch we hve not consdered for whch these nferences my be less cler. The detls of the extended model re furnshed n n ppendx (whch s vlble from the uthors upon request. C. Testble Alterntve Hypotheses There my be severl possble resons why overses nvestors my wsh to trde shres of crosslsted stocks n prtculr mrket tht re not relted to nformtonl motves t ll. Addtonlly, there my be nformtonl motves for trdng tht stem from nformtonl dvntges fvorng one nvestor over nother tht re unrelted to our centrl hypothess bout 9
11 mrketmkers nferrng news from the order flows of other stocks trdng on gven mrket. Wthn the frmework of our model, these ddtonl fctors cn be thought of s mrket frctons tht keep trders cptve n specfc trdng venue. In ths subsecton, we propose number of lterntve testble hypotheses whch we explot n the emprcl nlyss to render ddtonl power to the tests. Among nonnformtonrelted fctors tht mght nfluence the extent of U.S. shre of trdng volume of crosslsted stocks, dfferences n trdng costs (commssons, trnsctons txes or regultory restrctons (foregn ownershp lmts, currency convertblty constrnts re mong the most mportnt. Emprcl evdence ndctes tht U.S. mrkets hold n bsolute dvntge over most other mrkets n terms of trnsctons costs, 0 nd emergng mrkets clerly re subject to much hgher trnsctons costs nd regultory brrers thn developed mrkets, so t s mportnt to control for these countrylevel dfferences n our emprcl nlyss. Becuse most of these fctors mnfest themselves t the country level, they cnnot expln crosssectonl dfferences n the proporton of U.S. trdng mong stocks from the sme country, whch s our focus. However, there re lso frmspecfc brrers n some emergng mrkets tht relte to the mxmum number of homemrket shres tht re llowed to become elgble to trde overses n crosslsted form. Ths s the cse, for exmple, n severl Koren, Twnese nd Indn compnes trdng on mjor US exchnges wth explct celngs on the number of shres tht cn become ADRs. Cost consdertons ssocted wth the bredth nd composton of the ownershp bse of the frm mght lso nfluence the extent of trdng n dfferent mrkets. It mght be, for exmple, tht dfferences n trnsctons costs mtter less for retl thn nsttutonl nvestors, who re more strtegc n ther trdeexecuton decsons. Insttutonl trders re more lkely to splt ther orders cross mrkets to mnmze the net prce mpct of ther trdes, even f there s perceved bsolute dvntge n terms of lower overll trnsctons costs n the U.S. If ths s true, lrger U.S. ownershp bse wll be ssocted wth hgher frcton of overll trdng n the U.S., but ths effect wll be even greter f the U.S. ownershp bse s domnted by retl nvestors thn more strtegcllymotvted nsttutons tht ctvely seek to mnmze ther overll trdng costs. These nferences wll reverse, however, f nsttutons re n fct more lkely to be constrned by prudentl polces by ther pln sponsors or oversght bords tht lmt them from drectly holdng stock brod nd tht requre them to hold only ADRs or other forms of crosslsted shres nsted. Another nonnformtonl fctor s relted to the dversfcton opportuntes tht crosslsted stocks present for overses nvestors. Crosslsted stocks wth low return correltons wth the U.S. mrket should be good cnddtes for dversfcton purposes. Ths predcton s prtculrly useful to us s t represents specfc testble lterntve to the centrl hypothess of our model; tht s, crosslsted stocks whose returns re wekly correlted wth other U.S. stocks furnsh mrket mkers lower qulty nformton bout order flow dynmcs nd re less lkely to ttrct U.S. trdng volume. Informtonl motves tht re not necessrly relted to the correlton structure of returns re lso lkely n mportnt fctor n determnng the globl dstrbuton of trdng volume for crosslsted stocks. For exmple, t hs been rgued tht locl nvestors hve n nformtonl dvntge reltve to foregn nvestors. 3 The nformton herrchy tht fvors locl nvestors my rse from geogrphc proxmty to the compny s mn busness opertons (Covl nd Moskowtz, 999, 0
12 lnguge brrers for foregn nvestors (Grnbltt nd Kelohrju, 999; Hu, 00 or greter fmlrty wth frm s opertons, ccountng prctces or ts cptl mrket envronment. Whle such dvntges need not ffect the locton of trdng volume, n mny cses t wll be cheper nd qucker to trde n the home mrket, especlly f the nformton s shortlved nd speed of executon s crtcl. Bsed on ths rgument, we predct tht frms wth certn ttrbutes wll mtgte ths nformtonl dsdvntge for foregners. For exmple, the fct tht lrger compnes re more vsble thn smller frms should led to postve correlton between mrket cptlzton nd foregn trdng volume (Merton, 987; Kng nd Stulz, 997. If fmlrty bs exsts mong nvestors (Grnbltt nd Kelohrju, 999; Hu, 00, then crosslsted compnes wth lrger frcton of foregn sles should be more lkely to develop n ctve foregn mrket for the trdng of ther shres. Smlrly, greter fmlrty my rse from lrger contngent of ndustry peers trdng n one venue reltve to nother, fctor tht some hve suggested s rtonle for crosslstngs n the frst plce (Pgno, Roell nd Zechner, 003; Srkssn nd Schll, 004. If the totl mrket cptlzton of peer frms from the sme globl ndustry sector s the crosslsted frm s hgher n the foregn mrket thn n the frm s home mrket, we would predct hgher foregn trdng volume would rse. 4 Fnlly, more reserch publshed by nlysts cn ncrese the publc nformton vlble to ll nvestors (Lng, Lns nd Mller, 003; Bley, Kroly nd Slv, 005, so we predct tht the number of nlysts followng the crosslsted stocks s postvely relted to foregn trdng volume. In the next secton, we descrbe the emprcl tests of our theory. The smple of U.S. crosslsted stocks s frst dentfed; we then outlne how our nformton fctor vrble s constructed. In ddton, we dscuss vrous proxy vrbles we propose to evlute the testble lterntve hypotheses defned bove. A. Dt nd Smple III. Emprcl Anlyss In ths secton we provde some emprcl evdence regrdng the relevnce of the model developed n the erler prt of the pper. The prmry predcton of the model, s expressed n Corollry, s tht when the vlue of the crosslsted stock s more senstve to nformton n the U.S. mrket reltve to nformton n the home mrket then the U.S. wll hve hgher shre of the overll trdng volume n the stock. To exmne ths predcton we collect n ntl smple of ll stocks crosslsted on U.S. exchnges wth lstng dtes pror to Jnury, 999 tht lso hve stock prce dt vlble for both the U.S. nd the home mrket from Dtstrem Interntonl. Ths ntl smple conssts of 384 crosslsted stocks wth dly dt on stock prce, trdng volume, nd the mrket vlue of equty over the perod begnnng n Jnury 99 nd endng n December 004. Crosslsted securtes, whch trde n the U.S. n the form of ADRs, re often bundled n rto dfferent from onetoone. For exmple, the ADR for Telefonos de Mexco, Mexcn compny, represents 0 underlyng Mexcn shres, whle the ADR for Norsk Hydro, Norwegn compny, represents sngle underlyng Norwegn shre. To compute the U.S. shre of trdng volume n ech stock we requre n ccurte mesure of the bundlng rto between the home mrket nd crosslsted securtes. We obtn the most recent bundlng rto for ech stock from Ctbnk's Unversl Issunce Gude. Unfortuntely, mny crosslsted securtes lter there bundlng rtos over tme. To ensure tht we obtn n ccurte representton of the bundlng rtos for the stocks
13 n the smple we compre the bundlng rtos from the Ctbnk Unversl Issunce Gude to the yerly verge of the rto the month end prce n the U.S. mrket to the month end prce n the home mrket (converted to U.S. Dollrs. If the prce rto dffers from the reported bundlng rto by more thn 0% we ttempt to reconcle the dscrepncy by exmnng other sources (such s the rtos reported long wth the frm nmes n Dtstrem. After elmntng stocks for whch we re unble to obtn n ccurte bundlng rto we re left wth fnl smple of 5 crosslsted securtes representng 4 dfferent countres. By wy of comprson, Pultkonk nd Sofnos (999 employ smple of 54 stocks crosslsted on the NYSE. Pultkonk nd Sofnos lso provde detled descrpton of the dfferent types of crosslsted securtes. B. Vrble Constructon nd Econometrc Model To test the predcton of the model we requre mesure of the senstvty of the stock's vlue to nformton n the U.S. reltve to nformton n the home mrket. Eun nd Sberwhl (003 compute mesure of the U.S. nformton shre usng n error correcton model nd ntrdly dt for smple of 5 Cndn stocks. Whle ther mesure s ntutvely ppelng t cnnot be employed n our settng both becuse we lck ntrdly dt nd becuse mny of the stocks n our smple re not trded durng the sme hours n both the U.S. nd home mrkets due to dfferences n tme zones cross countres. Insted we tke more mcro vew of mesurng the senstvty of the stock's vlue to nformton n the U.S. mrket. Our underlyng sserton s tht stock prces respond prmrly to new nformton, nd we focus on the reltve nformtveness of mrket movements n the U.S. nd the home country. Specfclly, for ech stock, we perform vrnce decomposton of returns converted to U.S. dollrs to estmte the contrbuton of nformton contned n U.S. mrket ndex returns reltve to the nformton content of ndex returns n the home mrket. To perform the vrnce decomposton we frst estmte the followng two tmeseres regressons for ech stock: R R t t = α = α k = k = β β, H, t k, H, t k R R Home, t k Home, t k ε t k = β, US, t k R US, t k ε t, (4 where R t s the return (mesured n U.S. dollrs for stock n perod t, R Home,tk s the return denomnted n U.S. dollrs on the mrket ndex n the stock's home country n perod tk, nd R US,tk s the dollr denomnted return on the U.S. mrket ndex n perod tk. The led nd lg terms n the regressons re used to ccount for nonsynchronous trdng cross mrkets n dfferent tme zones. In ddton, to del wth the possblty of nfrequent trdng t the stock level we bse our nlyss on weekly returns. The frst regresson n equton (4 s consdered the restrcted regresson nd the second regresson n equton (4 s consdered the unrestrcted regresson. Assumng n observtons for the stock, sx regressors n the unrestrcted model, nd three restrctons, we compute n Fsttstc for ech stock tht mesures the explntory power of the unrestrcted model reltve to the explntory power of the restrcted model s follows: ( RUR ( R RR / 3, (5 /( n 6 UR
14 By constructon, ths mesure cptures the ncrementl contrbuton of U.S. mrket movements n explnng vrton n the frm's stock prce over nd bove the nformton bout the frm's stock prce contned n movements n the frm's home mrket ndex. Our theory, therefore, predcts tht hgher vlues of ths U.S. nformton fctor wll correspond to hgher U.S. shres of overll trdng volume. The U.S. nformton fctor s computed for ech stock nd ech smple yer usng mnmum of 36 months nd mxmum of 48 months of pst dt. Ths estmton perod ensures tht we obtn resonbly precse estmtes of the U.S. nformton senstvty of the stocks. 5 To the extent tht the nformton fctor contns mesurement error, we expect n ttenuton bs towrd zero for the coeffcent estmte on ths vrble n our crosssectonl regresson nlyss, whch bses gnst fndng support for our hypothess. For ech yer n the smple, the U.S. shre of overll trdng volume for ech stock s computed s the weekly verge cross the yer of the rto of the number of shres trded n the U.S. mrket, djusted usng the bundlng rto of U.S. shres to home mrket shres, dvded by the totl trdng volume n both the U.S. nd the home mrket. In computng the U.S. shre of trdng volume, we nclude only trdng volume n the U.S. nd the home mrket. Some of the securtes re lso crosslsted n London or on other globl exchnges, such s Frnkfurt. We do not nclude the trdng volume from the other exchnges n our mesure of the U.S. shre of trdng volume. Our nformton fctor s computed usng returns nd t s lkely tht returns nd volume re contemporneously correlted for resons outsde of those predcted by our model. Becuse of ths t s mportnt tht our nformton fctor s computed usng dt from dfferent perod from the perod n whch we mesure trdng volume. To ccomplsh ths, we employ pnel pproch nd lg the U.S. nformton senstvty mesure one yer. Specfclly, the mesure of the stock's U.S. nformton senstvty computed usng returns from yers s used to predct the stock's U.S. volume shre n 995 nd so on through the end of the smple perod. Ths expermentl desgn nsures tht the nformton fctor s predetermned t the tme tht t s ncluded s n ndependent vrble n the regresson. Stocks re llowed to enter nd ext the pnel over tme. Our pnel contns,46 observtons for 5 crosslsted stocks coverng the yers 995 through 004. In ddton to the U.S. nformton fctor we lso control for severl other fctors tht could ffect the U.S. shre of overll trdng volume. These other fctors re relted to the testble lterntve hypotheses dscussed n the prevous secton. Frst, there re number of country level fctors tht mght ffect the U.S. shre of trdng volume t the country level. For exmple, there re systemtc dfferences n mrket sze nd development, trdng commssons, nd the legl envronment cross countres. In ddton, Pultkonk nd Sofnos (999 fnd tht prmry fctor ffectng dfferences n the U.S. shre of trdng volume cross stocks re tme zone effects: stocks from countres wth tme zone closer to tht n the U.S. exhbt hgher shre of U.S. trdng volume. In some of our specfctons, we control for tme zone effects usng set of ndctor vrbles for dfferent tme zones clssfed n threehour ncrements reltve to New York. For exmple, Jpn nd Kore (whch re 0 hours behnd New York nd Austrl (whch s 9 hours behnd New York re clssfed n tme zone 3, whle Hong Kong nd the Phlppnes, whch re both 3 hours hed of New York re clssfed n tme zone 4. In the specfctons usng tme zone ndctors we lso nclude n ndctor for emergng mrkets versus developed mrkets where we use the emergng mrket defnton suppled by the Interntonl Fnnce Corporton (IFC. We 3
15 predct tht emergng mrkets re more lkely to hve bndng regultory constrnts nd hgher overll trdng costs compred to developed mrkets, thus ledng to proportonlly more U.S. trdng volume for these stocks. In other specfctons, we control drectly for country level dfferences usng country level fxed effects. The llencompssng use of country fxed effects llows us to focus on dfferences cross stocks wthn countres whle removng the effect of ll fctors tht re common to ll frms wthn country. Our regresson specfctons lso nclude vrety of frm specfc mesures tht could potentlly ffect the stock's U.S. shre of trdng volume. All of the stocks n our smple re lsted on the NYSE or Nsdq/Amex, nd we nclude n ndctor equl to one for frms lsted on Nsdq/Amex n order to ccount for systemtc dfferences n ether ctul or reported trdng volume cross lstng exchnges. We lso control for frm sze usng the nturl logrthm of the stock's verge mrket vlue of equty converted to U.S. dollrs. Frm sze mght proxy for the fmlrty of the frm to U.S. nvestors. If ths s the cse, then we expect frm sze to be postvely relted to the U.S. shre of trdng volume. Another reson to control for frm sze s tht lrge frms re lkely to mke up substntl proporton of the mrket cptlzton of the mrket ndex n the home country. Ths could nduce spurous postve correlton between the frm's return nd the return on the home mrket ndex nd bs downwrd the U.S. nformton fctor for the stock. As ddtonl proxes for the vsblty of the frm to U.S. nvestors, we use the percentge of foregn sles reported by the frm, nd the dfference n the percentge of the globl mrket cptlzton of the frm s ndustry locted n the U.S. nd the percentge of globl ndustry mrket cptlzton for the frm s ndustry n the home country. The foregn sles dt s drwn nnully from the Worldscope dtbse (Item WC0873 bsed on fsclyerend of December 3. The reltve ndustry cptlzton vrble s from Dtstrem Interntonl nd s bsed on Level 3 ndustry groupng codes for the home mrkets (whch nclude ten globl sectors. We expect both foregn sles nd the U.S. shre of the frm s ndustry to be postvely ssocted wth the U.S. shre of trdng volume. To cpture frmspecfc constrnts on the U.S. shre of trdng volume due to regultory restrctons on foregn ownershp, we collect the nnul nvestble weghts from Stndrd nd Poor s Emergng Mrket Dtbse whch mesure, s frcton of the mrket cptlzton of the frm, tht whch s free from explct foregn nvestment restrctons. Hgher nlyst coverge n the home mrket potentlly reduces the costs of nformton cquston for foregn nvestors trdng n the locl mrket. We mesure nlyst coverge usng the nturl logrthm of one plus the number of nlysts tht report ernngs estmtes for the stock n the home country. These dt re from the Interntonl Summry dtbse from Thomson Fnncl s Insttutonl Brokers Estmte System (I/B/E/S. We follow Lng, Lns nd Mller (003 n defnng nlyst coverge for crosslsted shre s the number of estmtes tht comprse the consensus oneyerhed ernngs estmte for tht fscl yer. The dt re obtned monthly s of the thrd Thursdy of ech month, nd we use the December vlues to mesure nlyst coverge for the followng yer. We expect hgher nlyst coverge n the home mrket to be negtvely relted to the U.S. shre of trdng volume; however, ths reltonshp could be complcted f the nformton generted by the homemrket nlysts works to overcome, rther thn excerbte, the nformtonl dvntge of locls over foregners. Fnlly, we control for the frcton of the frm's shres owned by U.S. nsttutons. We would delly lke brekdown of the full ownershp bse of these crosslsted frms, but the detled dt 4
16 re not redly vlble. Thomson Fnncl s 3F dtbse provdes qurterly dt on the percentge of shres held by U.S. nsttutons bsed on flngs to the Securtes nd Exchnge Commsson. We collpse the qurterly dt nto nnul horzons to lgn wth our pnel regresson nlyss. To the extent tht nsttutonl ownershp s correlted wth the overll sze of the U.S. nvestor bse nd, f the U.S. mrket offers bsolute dvntges n trdng costs reltve to the home mrket, we expect tht the shre of U.S. trdng volume wll be postvely correlted wth U.S. nsttutonl ownershp. Ths predcton s less cler, however, f the U.S. nsttutonl ownershp bse domntes the U.S. retl bse. If the more strtegcllymotvted nsttutons would be more nclned to splt ther orders cross mrkets to mnmze the overll net prce mpct of ther trdes, the U.S. shre of trdng volume wll be less postvely correlted wth U.S. nsttutonl ownershp. If these U.S. nsttutons, however, re bound by prudentl polces tht preclude them from drectly holdng stock brod nd tht requre them to hold the crosslsted shres nsted, then the U.S. shre of trdng volume wll be more postvely correlted wth U.S. nsttutonl ownershp. C. Summry sttstcs Tble I reports summry sttstcs for the stocks n our smple for ech country wthn subsmples of developed nd emergng mrkets. There re 0 stocks from the 4 countres n the smple clssfed s developed mrkets nd 50 stocks from the 0 countres n the smple clssfed s emergng mrkets. Cross lsted stocks from Cnd (78 nd the Unted Kngdom (40 mke up substntl porton of the smple, however, severl other countres, ncludng Austrl (3, Frnce (4, Jpn (9, the Netherlnds (4, Chle ( nd Mexco (4 re lso well represented. The U.S. shre of trdng volume for crosslsted frms from emergng mrkets verges 33%, whch s nerly twce the verge U.S. shre of trdng volume of 7% for crosslsted frms from developed mrkets. There s lso substntl vrton n the verge U.S. shre of trdng volume cross countres, vryng from low of bout % for Itly nd Swtzerlnd to hgh of round 60% for Chle nd Mexco. The verge vlue of the U.S. nformton fctor n emergng mrkets s.0, slghtly hgher thn the verge of.83 for crosslsted frms n developed mrkets. Ths suggests tht, on verge, movements n the U.S. mrket provde more ncrementl prcerelevnt nformton for crosslsted stocks from emergng mrkets reltve to those from developed mrkets. The verge mrket vlue of equty for stocks from developed mrkets s $8.43 bllon compred to $7.79 bllon for the verge emergng mrket stock, lthough these vlues re hghly skewed nd vry wdely cross countres. In terms of the U.S. venue of lstng, 33% of cross lsted frms n developed mrkets re lsted on Nsdq compred to only % of crosslsted frms n emergng mrkets. On verge, pproxmtely 0 nlysts follow frms n the home country n developed mrkets compred to n verge of bout 4 nlysts n emergng mrkets. The number of home country nlysts vres gretly cross countres, however. The percentge of shres held by U.S. nsttutons verges 8.88% n developed mrkets nd 6.74% n emergng mrkets. Chn, Hong Kong, Twn nd Itly exhbt the lowest levels of U.S. nsttutonl ownershp, whle Cnd, Fnlnd, nd Isrel exhbt hgh levels of ownershp by U.S. nsttutons. The nvestble weght s 00 percent for ll developed mrket frms, but verges only 79 percent for emergng mrket frms (nterestngly, Kore s the country wth the lowest frcton of ccessblty t 33 percent over ths perod. The dfference n the percentge of the globl mrket cptlzton of the frm s ndustry locted n the U.S. nd tht n the home mrket (U.S. Industry Reltve s bout 40% on verge cross both developed nd emergng mrkets. The verge percentge of foregn sles reported by frms s nerly 59% n developed mrkets compred to only % for frms n 5
17 emergng mrkets. Fnlly, we lso note tht severl of our vrbles re not unformly vlble for ll of the observtons n our pnel. Investble weghts (,3 observtons nd U.S. nsttutonl ownershp (,8 observtons hve the brodest coverge, nd the percentge of foregn sles the lest (8 observtons. We report ll of our subsequent results usng the lrgest number of observtons possble. Tble II reports the correlton mtrx for the vrbles. Becuse our nterest s prmrly on whether the model cn expln dfferences n the shre of U.S. trdng volume cross frms wthn countres the correltons re reported fter subtrctng the country men from ech vrble. Consstent wth the predcton of our model, the U.S. nformton fctor nd the U.S shre of trdng volume re postvely correlted. The sgns of the correltons on the other vrbles re lso generlly consstent wth ntuton. Lrger frms hve lower shre of U.S. trdng volume. The U.S. shre of trdng volume s lower when more nlysts follow the frm n the home country nd s hgher when U.S. nsttutons hold lrger frcton of the frms shres nd when the frms hve hgher nvestble weghts (fewer frmlevel foregn ownershp constrnts. The U.S. shre of trdng volume s lso hgher for frms wth more foregn sles, when the frm's ndustry s more overrepresented (by mrket cptlzton n the U.S, nd when the nvestble weght s hgher. D. Multvrte Regresson Anlyss To further nvestgte the fctors tht ffect the U.S. shre of trdng volume, Tble III reports results of multvrte regresson nlyss. The dependent vrble n the regressons s the logstc trnsformton of the U.S. shre of trdng volume. We employ the logstc trnsformton to ccount for the fct tht the U.S. volume shre s bounded between zero nd one. All of the specfctons nclude the U.S. nformton fctor from equton (5, frm sze, nd the ndctor vrble for crosslsted frms lsted on Nsdq/Amex. In some specfctons we lso nclude vrous combntons of the other control vrbles. To control for country level dfferences ll of the regressons nclude ether the ndctors for tme zone nd emergng mrkets or country fxed effects. The tble reports Whte (980 heteroscedstctyconsstent tsttstcs djusted for clusterng t the frm level. All of the ndependent vrbles re lgged one yer. The results reported n Tble III provde strong support for the predcton of the model. In model (, whch employs the brodest possble smple nd controls for country effects usng the tme zone nd emergng mrket ndctors, the coeffcent on the U.S. nformton fctor s 0.44 (tsttstc = 4.5, ndctng tht frms wth hgher vlue of the U.S. nformton fctor hve hgher U.S. shres of trdng volume. The coeffcent estmtes on the control vrbles lso pper resonble. Lrger frms exhbt lower shre of trdng volume n the U.S., nd frms from emergng mrkets hve hgher U.S. shre of trdng volume, ll else equl. The Nsdq ndctor s not sttstclly dfferent from zero. Model ( dds ddtonl control vrbles for home nlyst coverge, U.S. nsttutonl ownershp nd the reltve shre of the frm's ndustry locted n the U.S. Model (3 dds the nvestble weght nd model (4 dds foregn sles to the specfcton s well. Becuse of mssng vlues, the number of observtons drops from,46 n model ( to 958 n model (, 888 n model (3 nd 70 n model (4. In ll four specfctons, the coeffcent on the U.S. nformton fctor remns postve nd sttstclly sgnfcnt t the 0 percent level or better. Models (5 through (8 repet the regressons n Models ( through (4 but drop the tme zone nd emergng mrket ndctors n fvor of country fxed effects. The results re very smlr. The coeffcent estmte on the U.S. nformton fctor n model (5 s 0. (tsttstc =
18 ndctng slghtly smller effect of U.S. nformton on the U.S. shre of trdng volume compred to model ( fter controllng for country level dfferences. Compred to model ( the negtve effect of frm sze on the U.S. volume shre s slghtly smller. Models (6 to (8 yeld results smlr to those n models ( to (4, wth one notble excepton: the explntory power of the nvestble weght vrble s much lrger nd postve wth country dummes nd wthout the emergng mrkets ndctor. Ths result suggests tht there s strong clusterng n the nvestble weght vrble mong emergng mrkets. Overll, the multvrte nlyss reported n Tble III provdes support for the de presented n our model tht crosslsted frms for whch prce relevnt nformton contned n U.S. mrket movements s lrger wll lso exhbt lrger shre of ther trdng volume n the U.S. mrket. In ddton, frms wth more U.S. nsttutonl ownershp re ssocted wth more U.S. trdng volume nd frms wth more home country nlyst followng exhbt less trdng volume n the U.S. Also, the hgher s the nvestble weght for crosslsted frm, the hgher the U.S. shre of trdng volume. The estmtes on the U.S. shre of the frm's ndustry nd foregn sles re not sgnfcnt n ny of the specfctons. To ssess the mgntude of these effects we compute the percentge chnge n trdng volume mpled by movement from the 5th to the 75th percentle n ech of the vrbles of nterest bsed on the regresson coeffcents n Model (5. 6 The effect of the U.S. nformton fctor on the U.S. shre of trdng volume s economclly menngful, but s somewht smller n mgntude thn the effects of nlyst coverge nd nsttutonl ownershp. Movng from the 5th to the 75th percentle of the U.S. nformton fctor mples n ncrese n the U.S. shre of trdng volume of over 5%. Ths compres to 34% declne n the U.S. shre of trdng volume for chnge of smlr mgntude n homemrket nlyst coverge nd 6% ncrese ssocted wth chnge of smlr mgntude n nsttutonl ownershp. One ddtonl robustness test we nvestgted reltes to the concern bout the restrctve ssumpton bout the unobservblty of order flow n the competng mrket for mrket mkers. We developed n extended model n Secton II.B whch demonstrtes tht the key predcton of our model holds when observblty n order flow cross mrkets s llowed so long s the locl mrket mker mntns some nformtonl dvntge reltve to the foregn mrket mker n understndng the locl porton of the nnovton n the vlue of the crosslsted sset. Alterntvely, f the observblty of order flow elmntes the comprtve dvntge of the locl mrket mker, then the explntory power of our nformton fctor for the U.S. frcton of trdng volume should be gretly dmnshed. We performed supplementry regresson nlyss to test ths possblty. As proxy for the degree of observblty mong mrket mkers n competng exchnges we creted n ndctor vrble for countres tht hve substntl overlp n trdng hours wth the U.S. The overlp vrble s equl to one for crosslsted stocks from Cnd, Mexco nd Ltn Amerc, nd s equl to zero for crosslsted stocks from As nd Europe. Usng specfctons smlr to models ( nd (5 n Tble III, but ncludng the overlp vrble (n leu of tme zone ndctors nd when country ndctors re not ncluded nd n ntercton term between the overlp vrble nd the U.S. nformton fctor, we found tht the overlp vrble ws postvely nd sgnfcntly relted to the U.S. trdng volume, ndctng tht stocks from countres n smlr tme zone to the U.S. exhbt greter shre of U.S. trdng volume. Nevertheless, the nformton fctor remned sgnfcnt, postvely relted to the frcton of trdng volume. The ntercton vrble ws postve but not sgnfcnt, ndctng tht, f nythng, the effect of the nformton fctor on the shre of U.S. trdng volume s hgher n mrkets wth more overlp n trdng hours wth the U.S. 7
19 IV. Conclusons In consderng whether to crosslst ther stock on foregn exchnge, such s the NYSE or Nsdq n the U.S., n ssue of concern for mngers s the trdng ctvty tht the crosslstng wll ttrct. The dstrbuton of trdng volume potentlly reflects on the longterm vblty of the lstng nd ts potentl s vehcle for rsng cptl, for brodenng the shreholder bse nd for enhncng ts vsblty nd profle. In ddton, understndng the fctors tht ffect the dstrbuton of trdng volume n crosslsted securtes re lso mportnt for stock exchnges s they ttempt to ttrct new lstngs nd compete wth one nother for order flow. The ssue s prtculrly relevnt tody s polcy debte s mny nonu.s. frms re thretenng to deregster nd delst from U.S. mrkets followng the pssge of the SrbnesOxley Act of 00 nd ts ncresed costs of complnce, udtng ndependence nd penltes for flse reportng. 7 Indeed, n prt to llevte these concerns, the Securtes nd Exchnge Commsson proposed n December 005 new Rule h6 tht would ese the burden on nonu.s. frms to rrnge termnton of regstrton n the U.S., one mportnt condton of whch s bsed on the frcton of ther overll trdng volume tht tkes plce n the U.S. 8 In ths pper, we develop nd test theoretcl model of multmrket trdng to expln the dfferences n the foregn shre of trdng volume of nterntonlly crosslsted stocks. The model derves n equlbrum whch predcts tht, under frly generl condtons, the dstrbuton of trdng volume cross exchnges competng for order flow s relted to the correlton of the crosslsted sset returns to returns of other stocks n the respectve mrkets. Tht s, volume s proportonlly hgher on the exchnge n whch the crosslsted sset returns hve greter correlton wth returns of other ssets trded on tht mrket. We test ths predcton wth monthly stock returns nd volume dt on 5 nonu.s. stocks crosslsted on mjor U.S. exchnges. We fnd strong emprcl support for the predcton, even fter controllng for possble endogenety effects nd for other frmspecfc, ssuespecfc nd countrylevel fctors. Our work contrbutes both to the theoretcl nd emprcl lterture on multmrket trdng nd provdes n explnton for the gret vrblty tht s observed n the dstrbuton of trdng ctvty cross exchnges n crosslsted securtes. Nevertheless, there re mny questons tht we leve unnswered. An open queston remns bout the fundmentls forces underlyng the nformton fctor tht we ntroduce s n mportnt explntory vrble for the globl dstrbuton of trdng n crosslsted securtes. One possblty s tht t rses from the nture of the busness ctvtes of the frm relted to geogrphy (mportnt U.S.bsed opertons nd ssets or ndustrl membershp (globlly compettve ndustry tht we hve not cptured fully n our foregn sles nd ndustrypeer proxy vrbles. A more detled emprcl nlyss here would be worthwhle, especlly gven evdence n Pgno, Roell nd Zechner (00 nd Srkssn nd Schll (004 tht trde, colonl tes, common lnguge nd culture nd smlr ndustrl structure ply mportnt roles n the selecton of overses trdng venues for nterntonl frms. Indeed, there s sgnfcnt body of new reserch tht s reexmnng the nterntonl crosslstng decson of frms n the context of these nd other corporte governncerelted explntons (Kroly, 006. It s stll uncler how ptterns n the dstrbuton of trdng ctvty cross globl exchnges my be lnked to these explntons. For exmple, s t possble for frm to rtonlze decson to crosslst on n overses exchnge n spte of low nformton fctor nd poor prospects for ny knd of U.S. mrket shre of trdng? 8
20 Another open queston s whether the delers nd speclsts tht mke mrkets n nonu.s. stocks on U.S. exchnges perform ther functon n wy tht s lkely to nfluence the proporton of trdng ctvty n the U.S. Indeed, Bcdore nd Sofnos (00 document wth propretry dt tht the nventory mngement behvor (postons closer to zero nd prtcpton nd stblzton rtes (hgher of NYSE speclsts n nonu.s. stocks re very dfferent thn for U.S. stocks. More recent ppers by Bcdore, Bttlo, Glpn nd Jennngs (005 nd Moulton nd We (005 show tht ther mrketmkng ctvtes re sgnfcntly dfferent even wth gven trdng dy when the homemrket for the crosslsted shres re open nd when they re closed. Our effort to now hs lso mde only modest progress n understndng the jont dynmcs of nformton fctors nd the globl dstrbuton of trdng volume over tme, n generl, nd round mportnt events lke the crosslstngs themselves. Indeed, Hllng, Pgno, Rndl nd Zechner (004 show tht the foregn volume shres declne sgnfcntly n the frst three yers followng lstng. Fnlly, the problem of explnng mrket shres of trdng volume exsts n domestc mrket settngs lso, nd t remns yet to be seen whether the ntuton underlyng our model hs pplcblty there. There re mny competng explntons for why exchnges do or do not compete successfully gnst thrdmrket delers or electronc communctons networks (ECNs or why the lsted Chcgo Bord Optons Exchnge (CBOE s losng mrket shre to the newer electroncllytrded Interntonl Securtes Exchnge (ISE. To ths end, t s useful to recognze Bruch nd Sr (004 who hve extended trdng model smlr to ours to expln why some frms lst ther shres on one mrket over nother (Nsdq versus NYSE nd why some frms swtch lstngs. 9
Optimal Pricing Scheme for Information Services
Optml rcng Scheme for Informton Servces Shny Wu Opertons nd Informton Mngement The Whrton School Unversty of ennsylvn Eml: shnwu@whrton.upenn.edu eyu (Shron) Chen Grdute School of Industrl Admnstrton
More informationChapter NewtonRaphson Method of Solving a Nonlinear Equation
Chpter.4 NewtonRphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the NewtonRphson method formul,. develop the lgorthm of the NewtonRphson method,. use the NewtonRphson
More informationFaculdade de Economia da Universidade de Coimbra
Fculdde de Econom d Unversdde de Combr Grupo de Estudos Monetáros e Fnnceros (GEMF) Av. Ds d Slv, 65 300452 COIMBRA, PORTUGAL gemf@fe.uc.pt http://gemf.fe.uc.pt JOSÉ A. SOARES DA FONSECA The performnce
More informationJoint Opaque booking systems for online travel agencies
Jont Opque bookng systems for onlne trvel gences Mlgorzt OGOOWSKA nd Domnque TORRE Mrch 2010 Abstrct Ths pper nlyzes the propertes of the dvnced Opque bookng systems used by the onlne trvel gences n conjuncton
More informationCardiff Economics Working Papers
Crdff Economcs Workng Ppers Workng Pper No. E204/4 Reforms, Incentves nd Bnkng Sector Productvty: A Cse of Nepl Kul B Luntel, Shekh Selm nd Pushkr Bjrchry August 204 Crdff Busness School Aberconwy Buldng
More informationIrregular Repeat Accumulate Codes 1
Irregulr epet Accumulte Codes 1 Hu Jn, Amod Khndekr, nd obert McElece Deprtment of Electrcl Engneerng, Clforn Insttute of Technology Psden, CA 9115 USA Eml: {hu, mod, rjm}@systems.cltech.edu Abstrct:
More informationYOU FINALLY FINISHED YOUR FILM. NOW WHAT? Distributor...? Sales agent...? GOT IT: SELF DISTRIBUTION
YOU FINALLY FINISHED YOUR FILM. NOW WHAT? Dstrbutor...? Sles gent...? GOT IT: SELF DISTRIBUTION THE ADVANTAGES OF SELF DISTRIBUTION: A gurnteed openng n NY / LA prme theter nd you keep 100% of the boxoffce.
More informationIncorporating Negative Values in AHP Using Rule Based Scoring Methodology for Ranking of Sustainable Chemical Process Design Options
20 th Europen ymposum on Computer Aded Process Engneerng ECAPE20. Perucc nd G. Buzz Ferrrs (Edtors) 2010 Elsever B.V. All rghts reserved. Incorportng Negtve Vlues n AHP Usng Rule Bsed corng Methodology
More informationORIGIN DESTINATION DISAGGREGATION USING FRATAR BIPROPORTIONAL LEAST SQUARES ESTIMATION FOR TRUCK FORECASTING
ORIGIN DESTINATION DISAGGREGATION USING FRATAR BIPROPORTIONAL LEAST SQUARES ESTIMATION FOR TRUCK FORECASTING Unversty of Wsconsn Mlwukee Pper No. 091 Ntonl Center for Freght & Infrstructure Reserch &
More informationNaïve Gauss Elimination
Nïve Guss Elmnton Ch.9 Nïve Guss Elmnton Lner Alger Revew Elementry Mtr Opertons Needed for Elmnton Methods: Multply n equton n the system y nonzero rel numer. Interchnge the postons of two equton n the
More informationWiMAX DBA Algorithm Using a 2Tier MaxMin Fair Sharing Policy
WMAX DBA Algorthm Usng 2Ter MxMn Fr Shrng Polcy PeChen Tseng 1, JYn Ts 2, nd WenShyng Hwng 2,* 1 Deprtment of Informton Engneerng nd Informtcs, Tzu Ch College of Technology, Hulen, Twn pechen@tccn.edu.tw
More informationDriver Attitudes and Choices: Speed Limits, Seat Belt Use, and DrinkingandDriving
Drver Atttudes nd Choces: Speed Lmts, Set Belt Use, nd DrnkngndDrvng YoungJun Kweon Assocte Reserch Scentst Vrgn Trnsportton Reserch Councl Young Jun.Kweon@VDOT.Vrgn.gov Vrgn Trnsportton Reserch Councl
More informationThe CAT model: Predicting air temperature in city streets on the basis of measured reference data
The CAT model: Predctng r temperture n cty streets on the bss of mesured reference dt Evytr Erell 1 nd Terry Wllmson 2 1 The J. Blusten Insttute For Desert Reserch, BenGuron Unversty of the Negev, Sde
More informationALABAMA ASSOCIATION of EMERGENCY MANAGERS
LBM SSOCTON of EMERGENCY MNGERS ON O PCE C BELLO MER E T R O CD NCY M N G L R PROFESSONL CERTFCTON PROGRM .. E. M. CERTFCTON PROGRM 2014 RULES ND REGULTONS 1. THERE WLL BE FOUR LEVELS OF CERTFCTON. BSC,
More informationLoyalty Program and Customer Retention of Bank Credit Cards an Logistic Regression Analysis based on Questionnaires
oylty Progrm nd Customer Retenton of Bnk Credt Crds n ogstc Regresson nlyss sed on Questonnres ZHU Qn IN Runyo College of Economcs Zhejng Gongshng Unversty P.R.Chn 310014 strct To Chnese credt crd ssuers
More informationNewtonRaphson Method of Solving a Nonlinear Equation Autar Kaw
NewtonRphson Method o Solvng Nonlner Equton Autr Kw Ater redng ths chpter, you should be ble to:. derve the NewtonRphson method ormul,. develop the lgorthm o the NewtonRphson method,. use the NewtonRphson
More informationBoolean Algebra. ECE 152A Winter 2012
Boolen Algebr ECE 52A Wnter 22 Redng Assgnent Brown nd Vrnesc 2 Introducton to Logc Crcuts 2.5 Boolen Algebr 2.5. The Venn Dgr 2.5.2 Notton nd Ternology 2.5.3 Precedence of Opertons 2.6 Synthess Usng AND,
More informationResistive Network Analysis. The Node Voltage Method  1
esste Network Anlyss he nlyss of n electrcl network conssts of determnng ech of the unknown rnch currents nd node oltges. A numer of methods for network nlyss he een deeloped, sed on Ohm s Lw nd Krchoff
More informationPolynomial Functions. Polynomial functions in one variable can be written in expanded form as ( )
Polynomil Functions Polynomil functions in one vrible cn be written in expnded form s n n 1 n 2 2 f x = x + x + x + + x + x+ n n 1 n 2 2 1 0 Exmples of polynomils in expnded form re nd 3 8 7 4 = 5 4 +
More informationHealth insurance marketplace What to expect in 2014
Helth insurnce mrketplce Wht to expect in 2014 33096VAEENBVA 06/13 The bsics of the mrketplce As prt of the Affordble Cre Act (ACA or helth cre reform lw), strting in 2014 ALL Americns must hve minimum
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationInternational Automotive Production Networks: How the web comes together. serie. Leticia Blázquez and Belén GonzálezDíaz WPEC 201305
sere ec WPEC 201305 Interntonl Automotve Producton Networks: How the web comes together Letc Blázquez nd Belén GonzálezDíz Los documentos de trbjo del Ive ofrecen un vnce de los resultdos de ls nvestgcones
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationHealth insurance exchanges What to expect in 2014
Helth insurnce exchnges Wht to expect in 2014 33096CAEENABC 02/13 The bsics of exchnges As prt of the Affordble Cre Act (ACA or helth cre reform lw), strting in 2014 ALL Americns must hve minimum mount
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationVector Geometry for Computer Graphics
Vector Geometry for Computer Grphcs Bo Getz Jnury, 7 Contents Prt I: Bsc Defntons Coordnte Systems... Ponts nd Vectors Mtrces nd Determnnts.. 4 Prt II: Opertons Vector ddton nd sclr multplcton... 5 The
More informationBasics of Counting. A note on combinations. Recap. 22C:19, Chapter 6.5, 6.7 Hantao Zhang
Bscs of Countng 22C:9, Chpter 6.5, 6.7 Hnto Zhng A note on comntons An lterntve (nd more common) wy to denote n rcomnton: n n C ( n, r) r I ll use C(n,r) whenever possle, s t s eser to wrte n PowerPont
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationA Hadoop Job Scheduling Model Based on Uncategorized Slot
Journl of Communctons Vol. 10, No. 10, October 2015 A Hdoop Job Schedulng Model Bsed on Unctegored Slot To Xue nd Tngtng L Deprtment of Computer Scence, X n Polytechnc Unversty, X n 710048, Chn Eml: xt73@163.com;
More informationEcon 4721 Money and Banking Problem Set 2 Answer Key
Econ 472 Money nd Bnking Problem Set 2 Answer Key Problem (35 points) Consider n overlpping genertions model in which consumers live for two periods. The number of people born in ech genertion grows in
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 MultpleChoce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multplechoce questons. For each queston, only one of the answers s correct.
More informationCapital asset pricing model, arbitrage pricing theory and portfolio management
Captal asset prcng model, arbtrage prcng theory and portfolo management Vnod Kothar The captal asset prcng model (CAPM) s great n terms of ts understandng of rsk decomposton of rsk nto securtyspecfc rsk
More informationOn the Meaning of Regression Coefficients for Categorical and Continuous Variables: Model I and Model II; Effect Coding and Dummy Coding
Dt_nlysisclm On the Mening of Regression for tegoricl nd ontinuous Vribles: I nd II; Effect oding nd Dummy oding R Grdner Deprtment of Psychology This describes the simple cse where there is one ctegoricl
More informationTests for One Poisson Mean
Chpter 412 Tests for One Poisson Men Introduction The Poisson probbility lw gives the probbility distribution of the number of events occurring in specified intervl of time or spce. The Poisson distribution
More informationResearch on performance evaluation in logistics service supply chain based unascertained measure
Suo Junun, L Yncng, Dong Humn Reserch on performnce evluton n logstcs servce suppl chn bsed unscertned mesure Abstrct Junun Suo *, Yncng L, Humn Dong Hebe Unverst of Engneerng, Hndn056038, Chn Receved
More informationHelicopter Theme and Variations
Helicopter Theme nd Vritions Or, Some Experimentl Designs Employing Pper Helicopters Some possible explntory vribles re: Who drops the helicopter The length of the rotor bldes The height from which the
More informationFuzzy Clustering for TV Program Classification
Fuzzy Clusterng for TV rogrm Clssfcton Yu Zhwen Northwestern olytechncl Unversty X n,.r.chn, 7007 yuzhwen77@yhoo.com.cn Gu Jnhu Northwestern olytechncl Unversty X n,.r.chn, 7007 guh@nwpu.edu.cn Zhou Xngshe
More informationMore equal but less mobile? Education financing and intergenerational mobility in Italy and in the US
Journl of Publc Economcs 74 (1999) 351 393 www.elsever.nl/ locte/ econbse More equl but less moble? Educton fnncng nd ntergenertonl moblty n Itly nd n the US *, Aldo Rustchn b, c Dnele Checch, Andre Ichno
More informationSmall Businesses Decisions to Offer Health Insurance to Employees
Smll Businesses Decisions to Offer Helth Insurnce to Employees Ctherine McLughlin nd Adm Swinurn, June 2014 Employersponsored helth insurnce (ESI) is the dominnt source of coverge for nonelderly dults
More informationHealth insurance exchanges What to expect in 2014
Helth insurnce exchnges Wht to expect in 2014 33096CAEENABC 11/12 The bsics of exchnges As prt of the Affordble Cre Act (ACA or helth cre reform lw), strting in 2014 ALL Americns must hve minimum mount
More informationReasoning to Solve Equations and Inequalities
Lesson4 Resoning to Solve Equtions nd Inequlities In erlier work in this unit, you modeled situtions with severl vriles nd equtions. For exmple, suppose you were given usiness plns for concert showing
More informationAPPLICATION OF QUEUING THEORY TO DYNAMIC VEHICLE ROUTING PROBLEM WeiNing Chen, Kainan University Taiwan
GLOBAL JOURNAL OF BUSINESS RESEARCH VOLUME 3 NUMBER 009 APPLICATION OF QUEUING THEORY TO DYNAMIC VEHICLE ROUTING PROBLEM WeNng Chen Knn Unersty Twn ABSTRACT In ths pper we eelope n nlyze ynmc moel of
More informationDlNBVRGH + Sickness Absence Monitoring Report. Executive of the Council. Purpose of report
DlNBVRGH + + THE CITY OF EDINBURGH COUNCIL Sickness Absence Monitoring Report Executive of the Council 8fh My 4 I.I...3 Purpose of report This report quntifies the mount of working time lost s result of
More informationModels and Software for Urban and Regional Transportation Planning : The Contributions of the Center for Research on Transportation
Models nd Softwre for Urbn nd Regonl Plnnng : The Contrbutons of the Center for Reserch on Mchel Florn Aprl 2008 CIRRELT200811 Models nd Softwre for Urbn Regonl Plnnng: The Contrbutons of the Center
More informationTreatment Spring Late Summer Fall 0.10 5.56 3.85 0.61 6.97 3.01 1.91 3.01 2.13 2.99 5.33 2.50 1.06 3.53 6.10 Mean = 1.33 Mean = 4.88 Mean = 3.
The nlysis of vrince (ANOVA) Although the ttest is one of the most commonly used sttisticl hypothesis tests, it hs limittions. The mjor limittion is tht the ttest cn be used to compre the mens of only
More informationUse Geometry Expressions to create a more complex locus of points. Find evidence for equivalence using Geometry Expressions.
Lerning Objectives Loci nd Conics Lesson 3: The Ellipse Level: Preclculus Time required: 120 minutes In this lesson, students will generlize their knowledge of the circle to the ellipse. The prmetric nd
More informationON THE EFFECTIVENESS OF SINGLE SALES FACTORS FOR STATE TAXATION
ON THE EFFECTIVENESS OF SINGE SAES FACTORS FOR STATE TAXATION Chrles Senson Professor nd eventhl Reserch Fello Mrshll School of Busness Unversty of Southern Clforn June, 0 ABSTRACT Ths study odels nd eprclly
More informationLinear Open Loop Systems
Colordo School of Mne CHEN43 Lner Open Loop Sytem Lner Open Loop Sytem... Trnfer Functon for Smple Proce... Exmple Trnfer Functon Mercury Thermometer...2 Derblty of Devton Vrble...3 Trnfer Functon for
More informationNasdaq Iceland Bond Indices 01 April 2015
Nasdaq Iceland Bond Indces 01 Aprl 2015 Fxed duraton Indces Introducton Nasdaq Iceland (the Exchange) began calculatng ts current bond ndces n the begnnng of 2005. They were a response to recent changes
More informationMultiple discount and forward curves
Multple dscount and forward curves TopQuants presentaton 21 ovember 2012 Ton Broekhuzen, Head Market Rsk and Basel coordnator, IBC Ths presentaton reflects personal vews and not necessarly the vews of
More informationVehicle Navigation System Integration with GPS/INS/GSM
Chun Hu Journl of Scence nd Enneern, ol.,no., pp.3(3) ehcle Nvton System Interton wth GPS/INS/GSM JumMn Ln ChenWen Hun, nd FonLon Ts Insttute of Aeronutcs nd Astronutcs ChunHw Unversty HsnChu 3,
More informationMULTICRITERIA DECISION AIDING IN PROJECT MANAGEMENT OUTRANKING APPROACH AND VERBAL DECISION ANALYSIS
Dorot Górec Deprtment of Econometrcs nd Sttstcs Ncolus Coperncus Unversty n Toruń MULTICRITERIA DECISION AIDING IN PROJECT MANAGEMENT OUTRANKING APPROACH AND VERBAL DECISION ANALYSIS Introducton A proect
More informationON THE IMPACT OF A SINGLE SALES FACTOR ON CALIFORNIA JOBS AND ECONOMIC GROWTH
Doc 004 69 pgs ON THE IMPACT OF A SINGE SAES FACTOR ON CAIFORNIA JOBS AND ECONOMIC GROWTH Chrles W. Senson, Ph.D., CPA Professor nd eventhl Reserch Fello Mrshll School of Busness Unversty of Southern
More informationLesson 28 Psychrometric Processes
1 Lesson 28 Psychrometrc Processes Verson 1 ME, IIT Khrgpur 1 2 The specfc objectves of ths lecture re to: 1. Introducton to psychrometrc processes nd ther representton (Secton 28.1) 2. Importnt psychrometrc
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationInequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationThe Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading
The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn Arzona State Unversty & Ln Wen Unversty of Redlands MARKET PARTICIPANTS: Customers Endusers Multnatonal frms Central
More informationBuyside Analysts, Sellside Analysts and Private Information Production Activities
Buysde Analysts, Sellsde Analysts and Prvate Informaton Producton Actvtes Glad Lvne London Busness School Regent s Park London NW1 4SA Unted Kngdom Telephone: +44 (0)0 76 5050 Fax: +44 (0)0 774 7875
More informationStrategic Labor Supply
Prelmnry drft My 1999 Do not quote wthout permsson of uthor Comments re welcome Strtegc Lor Supply A dynmc rgnng model nd ts econometrc mplementton Mrm Belo Free nversty of Berln Astrct In ths pper dynmc
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationWeek 6 Market Failure due to Externalities
Week 6 Market Falure due to Externaltes 1. Externaltes n externalty exsts when the acton of one agent unavodably affects the welfare of another agent. The affected agent may be a consumer, gvng rse to
More informationThe Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading
The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn & Ln Wen Arzona State Unversty Introducton Electronc Brokerage n Foregn Exchange Start from a base of zero n 1992
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationThe Probability of Informed Trading and the Performance of Stock in an OrderDriven Market
AsaPacfc Journal of Fnancal Studes (2007) v36 n6 pp871896 The Probablty of Informed Tradng and the Performance of Stock n an OrderDrven Market Ta Ma * Natonal Sun YatSen Unversty, Tawan Mnghua Hseh
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More informationSmall Business Networking
Why Network is n Essentil Productivity Tool for Any Smll Business TechAdvisory.org SME Reports sponsored by Effective technology is essentil for smll businesses looking to increse their productivity. Computer
More informationThe New York Noncustodial Parent EITC: Its Impact on Child Support Payments and Employment
The New York Noncustodl Prent EITC: Its Impct on Chld Support Pyments nd Employment Austn Nchols, Elne Sorensen, nd ye Lppold The Urbn Insttute June 27, 2012 Support for ths reserch ws provded by the Federl
More informationN Mean SD Mean SD Shelf # Shelf # Shelf #
NOV xercises smple of 0 different types of cerels ws tken from ech of three grocery store shelves (1,, nd, counting from the floor). summry of the sugr content (grms per serving) nd dietry fiber (grms
More informationLabor Productivity and Comparative Advantage: The Ricardian Model of International Trade
Lbor Productivity nd omrtive Advntge: The Ricrdin Model of Interntionl Trde Model of trde with simle (unrelistic) ssumtions. Among them: erfect cometition; one reresenttive consumer; no trnsction costs,
More informationThe relation between Eigenfactor, audience factor, and influence weight
The relton between Egenfctor, udence fctor, nd nfluence weght Ludo Wltmn nd Nees n vn Ec Centre for Scence nd Technology Studes, Leden Unversty, The Netherlnds {wltmnlr, ecnpvn}@cwts.ledenunv.nl We present
More informationReporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide
Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB
More informationLecture 3 Gaussian Probability Distribution
Lecture 3 Gussin Probbility Distribution Introduction l Gussin probbility distribution is perhps the most used distribution in ll of science. u lso clled bell shped curve or norml distribution l Unlike
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationFactoring Polynomials
Fctoring Polynomils Some definitions (not necessrily ll for secondry school mthemtics): A polynomil is the sum of one or more terms, in which ech term consists of product of constnt nd one or more vribles
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationAddendum to: Importing SkillBiased Technology
Addendum to: Importng SkllBased Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our
More informationHomework #4: Answers. 1. Draw the array of world outputs that free trade allows by making use of each country s transformation schedule.
Text questions, Chpter 5, problems 15: Homework #4: Answers 1. Drw the rry of world outputs tht free trde llows by mking use of ech country s trnsformtion schedule.. Drw it. This digrm is constructed
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More informationDO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?
DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? Fernando Comran, Unversty of San Francsco, School of Management, 2130 Fulton Street, CA 94117, Unted States, fcomran@usfca.edu Tatana Fedyk,
More informationPricing Strategy of Platform: An Investigation to the Internet Service Provider (ISP) Industry
Prng trtegy of Pltform: n Investgton to the Internet erve Provder (IP Industry by WDEH KUMR MT, HUI P Correspondng ddress: Dept. of Computng nd Eletron ystems, Unversty of Essex, Wvenhoe Prk, Colhester,
More informationRolf Baur, Raimund Herz & Ingo Kropp
COMPUTER AIDED REHABILITATION OF SEWER AND STORM WATER NETWORKS RESEARCH AND TECHNOLOGICAL DEVELOPMENT PROJECT OF EUROPEAN COMMUNITY Lehrstuhl Stdtbuwesen Technsche Unverstät Dresden (TUD) Nürnberger Str.
More informationWHAT HAPPENS WHEN YOU MIX COMPLEX NUMBERS WITH PRIME NUMBERS?
WHAT HAPPES WHE YOU MIX COMPLEX UMBERS WITH PRIME UMBERS? There s n ol syng, you n t pples n ornges. Mthemtns hte n t; they love to throw pples n ornges nto foo proessor n see wht hppens. Sometmes they
More informationNordea G10 Alpha Carry Index
Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and
More informationWhy is the NSW prison population falling?
NSW Bureu of Crime Sttistics nd Reserch Bureu Brief Issue pper no. 80 September 2012 Why is the NSW prison popultion flling? Jcqueline Fitzgerld & Simon Corben 1 Aim: After stedily incresing for more thn
More informationRevenue Management Games: Horizontal and Vertical Competition
Revenue Mngement Gmes: Horzontl nd Vertl Competton Sergue Netessne he Whrton Shool Unversty of Pennsylvn Phldelph, PA 191036340 netessne@whrton.upenn.edu Robert A. Shumsky Smon Shool of usness Admnstrton
More informationPrice Impact Asymmetry of Block Trades: An Institutional Trading Explanation
Prce Impact Asymmetry of Block Trades: An Insttutonal Tradng Explanaton Gdeon Saar 1 Frst Draft: Aprl 1997 Current verson: October 1999 1 Stern School of Busness, New York Unversty, 44 West Fourth Street,
More informationBasic Analysis of Autarky and Free Trade Models
Bsic Anlysis of Autrky nd Free Trde Models AUTARKY Autrky condition in prticulr commodity mrket refers to sitution in which country does not engge in ny trde in tht commodity with other countries. Consequently
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationLecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annutymmedate, and ts present value Study annutydue, and
More informationFinancial Mathemetics
Fnancal Mathemetcs 15 Mathematcs Grade 12 Teacher Gude Fnancal Maths Seres Overvew In ths seres we am to show how Mathematcs can be used to support personal fnancal decsons. In ths seres we jon Tebogo,
More informationSolution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.
Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces
More informationDiscussion Papers in Economics
scusson Ppers n Economcs No. 10/0 Alloctve Effcency nd n Incentve Scheme for Reserch By Anndy Bhttchry, Unversty of York; Herbert Newhouse, Unversty of Clforn eprtment of Economcs nd Relted Studes Unversty
More informationEconomics Letters 65 (1999) 9 15. macroeconomists. a b, Ruth A. Judson, Ann L. Owen. Received 11 December 1998; accepted 12 May 1999
Economics Letters 65 (1999) 9 15 Estimting dynmic pnel dt models: guide for q mcroeconomists b, * Ruth A. Judson, Ann L. Owen Federl Reserve Bord of Governors, 0th & C Sts., N.W. Wshington, D.C. 0551,
More informationOptimal Execution of OpenMarket Stock Repurchase Programs
Optiml Eecution of OpenMrket Stock Repurchse Progrms Jcob Oded This Drft: December 15, 005 Abstrct We provide theoreticl investigtion of the eecution of openmrket stock repurchse progrms. Our model suggests
More informationCOMPARISON OF SOME METHODS TO FIT A MULTIPLICATIVE TARIFF STRUCTURE TO OBSERVED RISK DATA BY B. AJNE. Skandza, Stockholm ABSTRACT
COMPARISON OF SOME METHODS TO FIT A MULTIPLICATIVE TARIFF STRUCTURE TO OBSERVED RISK DATA BY B. AJNE Skndz, Stockholm ABSTRACT Three methods for fitting multiplictive models to observed, crossclssified
More information