Centralised Order Books versus Hybrid Order Books: A Paired Comparison of Trading Costs on NSC (Euronext Paris) and SETS (London Stock Exchange)

Transcription

1 Cenralised Order Books versus Hybrid Order Books: A Paired Comparison of Trading Coss on NSC (Euronex Paris) and SETS (London Sock Exchange) Jean-François Gajewski Universiy of Paris 12 Val de Marne, IRG gajewski@univ-paris12.fr Carole Gresse Paris-10-Nanerre Universiy, CEROS & Paris-Dauphine Universiy, CEREG carole.gresse@u-paris10.fr hp:// JEL classificaion: Keywords: G19 ransacion coss, spread componens, order books, hybrid markes, cenralised markes, fragmenaion This draf: May 2004 We are graeful o Erik Theissen, Laurie Praher and paricipans a he 2003 EFMA meeings and he 2003 MFS conference for helpful commens. The daa from he London Sock Exchange was provided by he CEREG (Paris-Dauphine Universiy) and he daa from Euronex Paris was provided by he IRG (Universiy of Paris 12 Val de Marne). Corresponding auhor: Carole Gresse Universié Paris X Nanerre UFR SEGMI 200, avenue de la République Nanerre cedex FRANCE. a carole.gresse@u-paris10.fr.

2 Cenralised Order Books versus Hybrid Order Books: A Paired Comparison of Trading Coss on NSC (Euronex Paris) and SETS (London Sock Exchange) Absrac This aricle aims a comparing he cos of rading on SETS, he hybrid order-driven segmen of he London Sock Exchange for he rading of large capialisaions, and NSC, he cenralised elecronic order book of Euronex. Based on wo sock samples paired according o economic secor, free floa capialisaion and rading volumes, our research shows ha ransacion coss are lower on NSC han SETS. By allowing larger ransacions, he presence of dealers on SETS admiedly improves immediacy and conribues o he developmen of he block marke, bu a he expense of higher execuion coss for all oher rades and heavier adverse selecion and invenory coss inside he order book.

3 Cenralised Order Books versus Hybrid Order Books: A Paired Comparison of Trading Coss on NSC (Euronex Paris) and SETS (London Sock Exchange) Trading coss on a financial marke depend on he characerisics of he raded securiy, bu also on he srucure of he marke, and he order placemen sraegies of marke paricipans. The rules of rading affec he probabiliy of various rading sraegies (Harris (1997)) and hence affec price formaion and implici ransacion coss, and he way he marke design impacs he cos of rade execuion is a fundamenal issue. Firsly, i may subsanially aler porfolio performances. Recenly, basing heir research on he order and ransacion daabase of Elkins/McSherry, 1 Domowiz e al. (2001) sudy he range of, and he facors deermining, rading coss, and analyse he ineracions beween coss, liquidiy and volailiy in 42 counries, beween Sepember 1996 and December They offer evidence of a high degree of variabiliy of rading coss across counries, which migh limi he gains from inernaional diversificaion. Secondly, in a conex of compeiion beween sock exchanges and of revision of he European ISD, he comparison of rading coss beween exchanges wih differen rading mechanisms is exremely relevan for regulaors. A large number of aricles have already compared he liquidiy of he wo main marke models: quoe-driven markes as NASDAQ or SEAQ 2 or mos fixed income markes, where marke makers commi hemselves o pos coninuously bid and ask prices for minimal quaniies of asses, and order-driven markes, as mos equiy markes, where buy and sell orders originaing from final cliens are direcly mached agains one anoher. In realiy, mos sock exchanges have now converged, a leas for he rading of Blue Chips, o a common marke model: he elecronic order book. Even he London Sock Exchange (LSE), where he dealership srucure had always been he radiionally prevailing model, finally changed is rading mechanism for Blue Chips. In Ocober 1997, he previous quoe-driven sysem has progressively been replaced by an elecronic order-driven sysem. However, in spie of heir common feaures, some differences in marke organisaion sill remain beween aucion markes: differen ick sizes, differen roles and duies assigned o inermediaries ec. 1 Some consulans sell measuremens of heir rading coss o insiuional invesors, be hey asse managers or brokers. For example, he Elkins/McSherry consulancy, now a branch of he Sae Sree bank, measures explici and implici rading coss for insiuional invesors. Implici coss are measured by he spread beween he invesor s rading prices and a reference price calculaed as an average of four indicaors: he day high, low, opening and closing quoes. 1

4 In paricular, he rules regulaing he off-order-book ransacions and inernalising pracices may differ subsanially, so ha, on some exchanges, he order flow mainly concenrae in he order book while, on ohers, he proporion of order flow execued ouside he order book is significan. Our sudy focuses on his las issue and addresses he quesion of wheher rading in a pure order-driven sysem is less cosly han rading in a hybrid order-driven sysem where dealers sill execue a subsanial par of he order flow. To his goal, on he basis of inra-day marke daa (boh orders and rades), we analyse ransacion coss on SETS, 3 he elecronic order book open for he rading of Blue Chips a he LSE, and NSC, he elecronic order-driven sysem of Euronex Paris. A he LSE, SETS securiies are raded in he order book as well as wih dealers off he order book, whereas on Euronex Paris, all socks are raded on NSC, 4 which is an almos pure cenralised elecronic order book, for which a concenraion rule 5 applies. Our mehodology consiss in consrucing wo samples of securiies, paired according o heir economic secors, free floa marke capialisaions and rading volumes. This mehodology compares o ha of Huang and Soll (1996a) and Venkaaraman (2001). I ensures ha empirical resuls can be assigned o differences in marke srucures and no o corporae differences in compared socks. In a firs sage, we compue various measures of spreads and heir componens across he paired samples; in a second sage, we check he qualiy of he maching procedure and aemp o idenify explanaory variables for he observed differences. Secion 1 provides an overview of previous research and ses he esable hypoheses. Secion 2 compares he organisaion of NSC for Euronex Paris and SETS for he LSE. Secion 3 describes he daa and he mehodology. Secion 4 compares boh markes ransacion coss and heir componens. Secion 5 invesigaes o which exen some economic facors may explain he differences and Secion 6 concludes. 2 SEAQ is he Sock Exchange Auomaed Quoaions sysem. This screen-based rading sysem was inroduced on Ocober 27, 1986, as par of he Ciy's Big Bang, o carry marke makers' bid and offer quoes and rade repors for UK securiies. 3 The Sock Exchange Elecronic Trading Service (SETS) replaced he quoe-driven marke sysem for Blue Chips in Ocober NSC sands for Nouveau Sysème de Coaion. 5 The concenraion rule implies ha any rade has o be execued inside he order book. If a rade is negoiaed off he elecronic order book, according o Euronex regulaion, i should sill be execued in he order book (he buyer (he seller) submiing a buy and a sell order simulaneously) a a price inside he bid-ask spread, oherwise all sanding limi orders a a beer price are o be absorbed before execuing he rade. 2

5 1. Previous research and esable hypoheses 1.1. Previous lieraure A hos of heoreical and empirical papers have compared diverse marke srucures wih differen rading rules, differen ransparency duies, differen pricing consrains ec. (Tinic and Wes (1974), Hamilon (1976), Ho and Macris (1985), Hasbrouck and Schwarz (1986), Marsh and Rock (1986), Reinganum (1990), Madhavan (1992), Biais (1993), Pagano and Roëll (1992, 1996), Affleck-Graves e al. (1994), Biais, Foucaul and Salanié (1998)), Keim and Madhavan (1994, 1998)). Mos of hese works are in favour of elecronic order-driven markes. According o Domowiz e al. (2001), Euronex Paris ranks among he cheapes (30 bp), whereas implici coss on he LSE before he inroducion of he SETS order book locae his exchange among he mos expensive. Jain (2001), analyses, in a high number of exchanges, he impac of insiuional facors on marke performance, measured by quoed, effecive and realised spreads, volailiy and urnover raios. The facors idenified are: marke organisaion, rading mechanism, rading sysem, marke ransparency, degree of fragmenaion, share ownership srucure and heir variabiliy over ime. Based upon he 15 bigges Blue Chips of 51 sock exchanges, Jain (2001) s sudy evidences ha order-driven marke srucures record lower coss and volailiy han quoe-driven markes, and ha in Europe, Euronex Paris and he Swiss Sock Exchange show he lowes spreads. The main limi of hese works lays in ha hey may measure corporae differences beween sample socks insead of marke design effecs. To eliminae his poenial bias, some sudies have compared he cos of rading socks lised on an order-driven exchange and he cos of rading of he same socks in a compeing quoe-driven marke (Blume and Goldsein (1992), Roëll (1992), De Jong, Nijman and Roëll (1993), Lee(1993), Peersen and Fialkowski (1994), Huang and Soll (1996b)) generally measure larger spreads on dealer markes. Sudying LSE rading in French socks, Gresse (2001) concludes o a greaer deph of he dealer marke associaed o larger spreads. However, hese resuls may be subjec o a domesic bias. Recen research papers have hen made wo-marke liquidiy comparisons by maching samples in order o esimae he impac of he marke srucure and organisaion. Huang and Soll (1996a) calculae liquidiy indicaors for he NASDAQ and he NYSE. Their sample is made of pairs of securiies from boh markes, formed by considering he company secor, long-erm deb, share price crieria, as well as he number of admied shares and he book value. Venkaaraman (2001) uses he same mehodology o compare he Paris Sock 3

6 Exchange and he NYSE, and maches firms on indusry, marke price and marke size. Our mehodology follows his approach. Finally, while nowadays on almos every sock exchange, large capialisaions are raded in elecronic order-driven sysems, very few sudies focus on he impac of he design of order books on rading coss. Venkaaraman (2001) provides a paired comparison of an auomaed order-driven marke and a floor-based order-driven marke; closer o our analysis, Kasch- Harouounian and Theissen (2003), wih mached samples, show ha spreads are slighly lower on Xera (he elecronic order book of Deusche Börse) han on NSC (Euronex Paris), bu find ha he observed differences are mainly relaed o corporae facors; ye, o our knowledge, no empirical work pu ino balance a hybrid order-driven sysem wih muliple dealers and a cenralised order-driven sysem Tesable hypoheses Given he relaive advanages of dealer and aucion sysems, many exchanges have chosen mixed srucures allowing dealers o negoiae rades ouside he order book. Very few sudies have compared he liquidiy of hese mixed sysems wih he one of pure cenralised order books. On he one hand, dealers may provide addiional deph and immediacy o he order book and hus complemen he rading services offered by he exchange. Some empirical sudies as hose of Tinic and Wes (1974), Jain (2001), Gresse (2001), Swan and Weserholm (2004), conclude o he superioriy of such mixed srucures. On he oher hand, he fragmenaion of he order flow may lower he probabiliy of execuion in he order book and increase adverse selecion coss in he marke, and hus be derimenal o liquidiy (see Mendelson (1987), Chowdry and Nanda (1991)). Moreover, dealer may skim off profiable uninformed orders from he order book, which would increase spreads in he book (Easley, Kiefer and O Hara (1996)). Therefore, by using SETS and NSC bes quoe and rade daa, we propose o es he following hypoheses. Volailiy H1. As shown in Biais (1993), prices are less volaile on fragmened markes han on cenralised markes, so ha volailiy is lower on a hybrid order-driven marke involving muliple dealers in compeiion beside he book (HOM) like SETS, han on a pure cenralised order-driven marke (COM) like NSC. 4

7 Spreads and rade size H2. The inernalisaion of a subsanial par of he order flow by dealers in a HOM fragmens he marke and enlarges quoed and effecive spreads in comparison wih a COM. H3. Dealers provide addiional immediacy in a HOM in comparison wih a COM, so ha rade sizes are superior in he HOM (H3a); he marginal cos rading one uni of asse is less in he HOM (H3b); large ransacions are more expensive in he COM (H3c). Spread componens H4. The invenory cos componen of spreads is greaer in he HOM han in he COM, because of he dealers liquidiy-providing aciviy beside he order book. H5. A COM being more ransparen by naure, he adverse selecion componen of is spreads exceeds he one of HOM spreads. H6. In a HOM, dealers ry o arac he leas informed par of he order flow, so ha he adverse selecion risk borne inside he order book of a HOM is more han he one incurred in a COM. 2. A comparison of he organisaions of SETS and NSC 2.1. Common feaures The London and Paris markes have similar fundamenal characerisics: inermediaries have muliple capaciies, which means ha hey may ac eiher on heir own accoun, possibly in he conex of marke making, or on accoun of heir cliens. The 1986 reform of he LSE enabled o remove he disincion beween brokers and marke makers (all financial insiuions could from hen on, play he role of a marke maker), and liberalised pricing regulaions. A presen, he LSE admis only a single ype of inermediaries, he so-called broker-dealers, which have a dual capaciy. This reform enabled o inroduce SEAQ, a single display sysem for he quoes of all London lised shares. The second imporan reform happened in 1997 and led o he creaion of SETS, an elecronic and coninuous order-driven rading sysem for Blue Chips. Euronex Paris, for is par, implemened wo significan marke reforms in 1986 and 1988: 5

8 he agens de change were replaced by he sociéés de bourse, eniled, like Briish inermediaries, o ac boh as brokers or on heir own accoun; he old cenralised elecronic order-driven CAC sysem was replaced by a new one (now called NSC 6 ) wih improved funcionaliies. On order-driven marke sysems like NSC or SETS, buy and sell orders coming from final cliens are cenralised in he order book, where hey are auomaically mached. Unlike on quoe-driven markes, invesors hemselves provide liquidiy by placing limi orders. Securiies may be raded eiher hrough a call aucion, or coninuously. On boh SETS and NSC, he rading day opens wih a call aucion, hen swiches o a coninuous aucion, and ends wih a closing fixing procedure Main organisaional differences beween SETS and NSC Though boh markes have idenical cenral funcionaliies, some differences remain. Reail marke A Euronex Paris, orders from reail cliens are roued owards he order book and paricipae o he general maching of buy and sell orders. In London, orders from reail cliens are mosly processed ouside he SETS sysem: Reail Service Providers (RSP) ac as a counerpar o he orders from privae brokers. Those orders are in general marke orders and are processed a a price a leas as favourable as he SETS order book bes limi. Large rades On boh markes, i is possible o rade blocks wihou using he auomaic order maching sysem. Moreover, Euronex adds a frequenly used funcionaliy, called cross rades. A cross rade (applicaions) is an already mached rade enered by an exchange member who found a buyer and a seller (he can ac on his own accoun as a counerpar of a clien, bu can also execue clien orders agains one anoher). Cross rades have o be processed a a price in he range beween he order book buy and sell bes limis a he ime of he rade. Once enered ino he rading sysem cross rades become immediaely visible by all marke members. 6 This sysem was laer adoped by all of Euronex equiy markes. 6

9 Ordinary rades A he LSE, any rade can be freely execued eiher elecronically in he order book, or off he order book wih a dealer wihou any consrain on execuion prices. The only obligaion for he dealer who execues he rade is o repor he ransacion wihin 3 mns o he exchange. Conversely, on Euronex, any rade ha is no a block rade eligible o he specific block rade procedure has o be execued auomaically inside he order book a a price ha is compaible wih sanding limi orders according o he so-called concenraion rule. Table 1 synhesises he differences beween SETS and NSC. Table 1 abou here 3. Daa and mehodology 3.1. Sock selecion and sample maching The presen research considers all spreads and rades during he firs semeser of Trading daa and quoes were exraced from he BDM 7 marke daabase as far as Euronex Paris is concerned, and from he inra-day daabase of he LSE (Transacion Daa Service). Our sample conain all securiies ha belong o he index SBF250 for he French marke or o he index FTSE250 for he Briish marke a he dae of January he 1 s of 2001, excluding hose for which quoes are available for less han 10 days in any monh of he observaion period and Euronex socks ha are raded a daily call aucions only. These crieria produce a sample of 211 securiies for he French marke and 492 for he Briish marke from which 157 are lised on SETS. To pair he samples from Euronex Paris and he LSE, a pairing algorihm almos similar o ha of Huang and Soll (1996a) and Venkaaraman (2001) was used, which relaes securiies according o: heir Dow Jones Economic Secor, 8 heir free floa on January 2001, according o Dow Jones Indexes free floa definiion, 9 heir oal rading volumes in euros during he firs semeser of Bases de données de marché. 8 Dow Jones indexes classificaion considers en economic secors: Basic Maerials, Consumer Cyclical, Consumer Non-cyclical, Energy, Financial, Healhcare, Indusrial, Technology, Telecommunicaions, Uiliies. 9 The free floa of a securiy is equal o is marke capialisaion minus cross-paricipaions of 5% or more held by public organisaions or individuals. 7

10 The Dow Jones secor was available for only 96 Euronex firms; for each of hese 96 Euronex-lised companies, all possible pairs wih LSE-lised firms of he same secor were considered. Among all LSE socks belonging o he same secor as he Euronex sock i, he London securiy j* ha minimised he difference beween he wo pairing characerisics oher han he indusrial secor, was reained: where 2 2 Euronex LSE X ip X jp Min j Euronex LSE p= 1 ( X ip X jp ) 2 (1), + Euronex X ip is he value of he p h mach crierion for sock i on Euronex Paris; LSE X jp is he value of he p h mach crierion for sock j on he LSE. The join mach resuled in 55 pairs of firms. The comparison of he mached samples is developed in Secion Measuremen of implici rading coss A financial marke paricipan bears wo ypes of rading coss: explici and implici rading coss, he laer resuling from he difference beween bid and ask prices. The sudy only focuses on implici coss by looking a diverse measures of spreads Quoed spreads On agency markes, where quoes appear in he order book, quoed spreads refer o he bes buy and sell limis. They measure he cos ha would be borne by an invesor ha would buy and immediaely sell a securiy whose fundamenal value remains unchanged, for a quaniy lower or equal o he quaniies available a he order book bes limis. ( Bes selling limi Bes buying limi) Quoed spread = (2). Mid For each sock, he ime-weighed average quoed spread over he whole observaion period is compued, hen an equally-weighed mean of individual averages is calculaed across each sample. 8

11 Effecive spreads Effecive spreads measure he implici cos borne in a ransacion by he one ou boh counerpars ha is demanding liquidiy. 10 The effecive spread is expressed as a difference beween he price of a rade and he midprice of he bes bid and ask prices, jus before he rade happens. Trading price Mid Effecive spread = 2 Mid (3). For each sock, wo averages of effecive spreads are calculaed for he oal observaion period: an equally-weighed mean and a quaniy-weighed mean of effecive spreads. Then, markes are compared on he basis of he samples equally-weighed mean Effecive marginal coss To compare execuion coss on acual rades, we also use an empirical esimaion of he Kyle λ coefficien, which represens he variaion in price necessary o rade a marginal uni of asse immediaely (Kyle (1985)). Kyle (1985) models he cos of liquidiy as a linear funcion of he raded quaniy: a equilibrium, P, he raded price equals µ + λq, where µ is he risky asse s fundamenal value, Q is he signed rade size and λ is an illiquidiy parameer. Kyle inerpres 1 / λ as he deph measure as i equals he marginal quaniy immediaely radable wihou no price impac. Assuming ha he mid price quoed a he ime of a rade is he bes esimae of he sock s fundamenal value, he λ coefficien may be esimaed from he rade characerisics by Trading price Mid Q. Unforunaely, his measure is sensiive o corporae acions and price level, and does no allow unbiased cross-secional comparisons. Reporing he relaive effecive spread o he moneary rade size provides a measure of he marginal cos of immediacy ha is insensiive o price level: Effecive m arg inal cos Trading price Mid = Mid Trade size in euros (4). 10 Cross rades on Euronex can only be made a prices comprised beween he order book bes buy an sell prices a he ime of he rade. Thus, for his caegory of rades, he effecive spread is necessarily lower han he quoed spread. Boh in London and in Paris, for rades processed in he order book, on he reverse, a new order is a bes, mached only a he bes limi on he oher marke side, and possibly by oher less favourable limis if he quaniy offered for he bes bid price is no sufficien: for hese ransacions, he effecive spread is hus necessarily bigger han he quoed spread. 9

12 The effecive marginal cos as calculaed in equaion (4) may be viewed as he marginal increase in spread (in %) suppored o buy or sell 1 of capial immediaely. For each sock of our samples, his raio is compued wih rade sizes in housand and averaged on boh an equally-weighed and a quaniy-weighed basis. Sample means are equally-weighed Measuremen of spread componens Spreads are saic measures of execuion coss. Anoher way of comparing ransacion coss consiss in considering he dynamics of quoes and rade prices and emporary price changes, o esimae he weigh of he differen componens of he spreads on each marke, which are order-processing, adverse selecion and invenory coss. As dealers execue a subsanial par of he order flow of he SETS hybrid sysem, we could fairly expec invenory coss o be superior on SETS han on NSC (hypohesis H4). The mehods of spread decomposiion exising in he lieraure divide ino wo main caegories: a firs group of mehodologies based on he covariance of ransacion price variaions (Soll(1989), George, Kaul and Nimalendram (1991)), a second group of works based on rade direcion (Glosen and Harris (1988), Lin, Sanger and Booh (1995), Madhavan, Richardson and Roomans (1997) and Huang and Soll (1997)). As rade direcion can be idenified from our inra-day daa, we choose he second approach. Moreover, given ha we aim a measuring he impac of dealer aciviy around he order book on he invenory cos componen, we need a hree-way decomposiion mehodology, which leads us o selec he Huang and Soll s model. Huang and Soll (1997) firs propose a wo-way spread decomposiion in which λ is he proporion of he effecive spread aribuable o adverse selecion and invenory coss and 1 λ corresponds o he par due o order processing coss. They model he effecive spread as a consan S expressed in moneary unis. S and λ are GMM esimaed on he basis of he following equaion: where S S P = ( Q Q 1 ) + λ Q 1 + e (5) 2 2 P is he price of he ransacion a ime ; Q is he buy-sell rade indicaor of he ransacion a ime ; e is an error erm. 10

13 In a second sage, Huang and Soll (1997) decompose he λ parameer ino an adverse selecion componen α and an invenory cos componen β (wih λ = α + β ), using he assumpion ha, under invenory models, changes in quoes affec he subsequen arrival rae of rades and hus induce a serial correlaion in rade flows. From here, hey derive wo esimaion procedures. The firs one requires o esimae four parameers, S, α, β plus π, he probabiliy of a reversal in rade sign, from he following sysem: Q 1 P = = ( 1 2π ) Q 2 + η S S Q + ( α + β 1) Q 1 α ( 1 2π ) Q 2 + e S (6). The second procedure hey develop uses he mid-quoe variaion insead of he ransacion price variaion, and he observed posed spread ha prevails a he ime of a rade insead of he consan spread S, which reduces o hree he number of parameers o esimae. These hree parameers, α, β and π are esimaed in order o verify: Q 1 M = = ( 1 2π ) Q S ( α + β ) Q 1 α( 1 2π ) Q 2 + e 2 + η S 2 (7) where M and S are respecively he mid-quoe and he quoed spread prevailing jus before he rade ime. We uilise he second mehodology as i produces a higher frequency of convergence of he GMM ieraion algorihm. Moreover, as shown in Huang and Soll (1997), spliing order pracices in order books possibly biases he esimaion of he α parameer by generaing undervalued π esimaes. To deal wih his bias, consecuive rades a he same price, on he same side of he marke, wihin a 2 mn (3 mn) window on NSC (SETS), and wih no change in he bid and ask quoes, are bunched ino one single rade This procedure probably overcorrecs he problem. Consequenly, empirical resuls provide upper bounds on α and π. 11

14 4. Comparaive analysis of rading coss on SETS and NSC 4.1. Volailiy, spreads and rading characerisics Table 2 repors descripive saisics on volailiy, rading coss, quoe and rade frequency, volumes and rade sizes, for he paired samples. The figures show ha he srucure of he order flow grealy differs from one sock exchange o he oher. More han a half of rading volumes in euros and in number of rades are execued by dealers ouside he order book a he LSE. Trades are less frequen bu larger on SETS han on NSC: he average size of an order-book (off-order-book) rades in a SETS sock reaches 1.4 (2.9) imes he one of a NSC rade, while he average number of rades per day on SETS (all rades combined) is wice he one on NSC, which confirms H3a. Table 2 abou here The average daily reurn volailiy of SETS socks significanly exceeds he one of NSC socks, alhough he difference is less han 0.5%, so ha we rejec hypohesis H1. Quoed spreads, effecive spreads and marginal coss are significanly higher on SETS han on NSC, which validaes H2 bu invalidaes H3b. The igher quoed spreads on NSC are relaed o a superior frequency of quoe revisions. Besides, he difference in effecive spreads is less han he one in quoed spreads. In fac, in he SETS environmen, invesors may ofen improve he price of a rade compared o he bes limis observed in he order book, while on NSC, prices displayed a he bes limis mos frequenly relae o acual rading prices. NSC bes limis hus appear as a beer indicaor of he value a which a share is poenially radable, which makes NSC more ransparen in erms of pre-rade ransparency. A breakdown of average effecive spreads by 9 classes of ransacion size is presened in Table 3. I shows ha SETS average effecive spreads on order book rades (iob) are larger han NSC spreads for every class of rades, he single excepion being class 9 (rade size > 2,500 housand ) for which he difference does no significanly exceeds 0. SETS off-orderbook rades are more expensive han SETS order book rades whaever he class ha is considered, and he spread difference increases wih rade size. However, he weigh of large rades in he disribuion of volumes is greaer in London han in Paris: as insance, classes 7 o 9 accoun for 52% of oal rading volumes and 3.4% of he oal number of rades while hese percenages are respecively 33% and 1.3% on NSC. Moreover, wih regard o classes 8 and 9, he main par of SETS sock rades are execued ou of he order book: as a maer of 12

15 fac, i is much easier o negoiae such rades wih dealers on he phone, like in an upsairs marke, han o find he required deph in he order book. Two oher classes have more rades execued ou of he book a he LSE: class 2 and above all class 1, which conforms o he fac ha reail orders are no roued o he order book bu o specialised dealers, he so-called RSPs. Given he class 1 and class 2 spread saisics, hese reail orders could find more favourable execuion inside he order book. In conclusion, wih regard o immediacy, our resuls allow us o validae H3a and o rejec H3b whereas H3c only holds for very large rades. Table 3 abou here All hings combined, effecive spreads are higher on SETS, where ransacions are also larger and less frequen. The SETS marke is globally more expensive bu offers superior immediacy in counerpar. This is paricularly acual for class 9, which can be viewed as a block rade segmen. Firs, as we already menioned, i is he only class for which SETS order book rading coss are equivalen o hose observed on NSC. Second, alhough he average effecive spread on SETS off-order-book class-9 rades is apparenly superior o he one measured on NSC class-9 rades, i applies o bigger and more numerous rades of whom many would no even be radable on Euronex Paris Spread componens Using GMM, we esimae he λ and he S parameers of equaion (5) in a firs sage, and he α, β and π parameers of sysem (7) in a second sage, for boh samples of socks. Concerning SETS socks, wo cases are considering: he oal universe of rades combining order book and off-order-book 13 rades and he universe of book rades only (excluding rades execued by dealers) The wo-way decomposiion: he order processing componens versus oher componens Table 4 displays descripive saisics of he λ esimaes. Table 4 abou here 12 In fac, his caegory of ransacions are radable hrough a special block rade procedure available on Euronex Paris for he larges capialisaions. Block rade daa are no par of he public daa we use here, bu Gajewski and Gresse (2004) have analysed hem over April 2002 and found ha hey represened lile volume and were much more cosly han LSE block rades. 13 VWAP rades are always excluded of he analysis. 13

16 The esimaed values indicae ha he order processing componen of spreads is maximum for SETS socks when all rades are aken ino accoun, bu is minimum for SETS order book rades, he mean 1 λ esimaed value for Euronex socks being inermediary. However, he difference in λ beween he SETS order book (off-order-book rades excluded) and Euronex is no as significan as he difference observed beween SETS (all rades included) and Euronex. In oher words, he componen of spreads due o adverse selecion and invenory coss is lower on he hybrid marke han on he cenralised aucion marke, bu his is aribuable o he rades execued by dealers ouside he order book. When considering all rades on SETS (i.e. including hose made ouside he order book), he presence of dealers may logically increase he order processing cos componen. Conversely, in a cenralised aucion marke like Euronex, limi order raders do no bear all he direc coss incurred by dealers. Moreover, he wo segmens of SETS probably do no arac he same ype of invesors. The informed will have a endency o capure limi orders in he order book while he dealers will ry o arac he less informed order flow (reail invesors, insiuional invesors) The hree-way decomposiion: adverse selecion versus invenory holding In order o disinguish he adverse selecion cos componen from he invenory cos componen, we now use he Huang and Soll hree-way decomposiion mehodology corresponding o sysem (7). The GMM esimaion procedure converged o consisen esimaes (0<α <1 and 0< β <1) for only 18 socks lised on SETS and 25 lised on Euronex. The model mos performed for SETS securiies when off-order book are excluded from he daa. In ha case, he GMM algorihm converged o correc esimaes for 44 socks. Table 5 displays descripive saisics on he esimaed values. Table 5 abou here The π probabiliies, corresponding o he probabiliy of a reversal in rade direcion, are inferior o 0.5 for all SETS socks and for 11 NSC socks upon 25, which, unexpecedly, means ha order persisence is higher on SETS. This is probably relaed o he smaller frequency of rades in he SETS marke. Conversely o he inuiion, β coefficiens, ha is he percenage of spread assignable o invenory coss, are greaer for order books (NSC and SETS wih in-order-book rades only) han for he hybrid case (SETS all rades combined), so ha H4 is rejeced. Furhermore, he mean β coefficien obained on SETS order book rades 14

17 (25.57%) is significanly superior o he one measured on NSC (17.43%). This counerinuiive resul could be due o dealers marke pracices as hose idenified in Jacquilla and Gresse (1998): when marke makers are compeing wih an order book, hey end o rade preferably in he order book when hey need o rebalance invenories. We suspec SETS socks o be subjec o such sraegies, so ha invenory coss are no direcly refleced by he off-order-book order flow bu affec he order book. As for α coefficiens, he highes cross-secional mean value is observed once again for SETS in-order-book rades (28.60%). This value decreases dramaically when off-order-book rades are included (5.82%) in he analysis. NSC α coefficiens are slighly bu significanly superior a he 10% hreshold, wih a cross-secional mean equal o 9.24%. These findings confirm wha we suspeced wih regard o he wo-componen decomposiion: mos probably, he informed and he uninformed cluser beween he order book and he off-order-book marke, wih he major par of he uninformed orders being execued by dealers. In so, we fail o rejec hypoheses H5 and H6. As hese observaions could be due o sample disorions, we focus on he pairs of securiies for which he model was correcly esimaed for boh socks of he pair. 7 pairs are idenified and he esimaed π, α and β coefficiens for each member of hese pairs are repored in Table 6. They confirm our conclusions. Table 6 abou here 5. Which economic facors or insiuional specifics bes explain he difference in execuion coss? Given he significan difference in rading coss of mached Euronex and SETS socks, a naural quesion is wheher he socks are acually well mached. Anoher possibiliy is ha some economic variables no included in he maching procedure explain he observed differences. These variables comprise sock characerisics such as reurn volailiy or price, rading characerisics like he number of rades per day or he imbalance beween purchases and sales, or finally insiuional feaures including ick size or fragmenaion degree Sock characerisics Theoreical models of he bid-ask spread sugges ha execuion coss differ sysemaically by firm-specific characerisics such as volailiy, marke size, and sock price. Marke size is par of our maching crieria. We measured i as he floaing marke value of he firm a he 15

18 beginning of he observaion period. The raionale for he use of his variable is ha firm size, and more specifically floaing capialisaion, assers he probabiliy of locaing a counerpary for a rader (Soll(2000)), and empirically, spreads are negaively relaed o marke value (Huang and Soll (1996a), Soll(2000), Venkaaraman (2001) ). Anoher fundamenal variable which posiively affecs spreads, is daily reurn variance, 14 as i measures he risk of adverse price changes of a sock pu ino invenory. For each sock, we calculae he monhly variance of daily reurns calculaed in logarihm on closing mid-quoes. The hird corporae specific we consider is price level measured as he monhly average closing mid-quoe in. The cross-secional relaion beween spreads and price is usually negaive, reflecing he fixed order-processing componen of spreads (Venkaaraman (2001)), and also because price is an addiional proxy for risk in ha low price socks end o be riskier and because of he fixed order-processing componen of spreads (Soll (2000)). On ha poin, here is a srucural difference in sock price beween LSE and Euronex securiies, price levels being, in pracice, subsanially inferior on he LSE Trading characerisics On op of he variables previously menioned, he cross-secion of spreads is generally relaed o wo firms rading characerisics: rading volume and number of rades, he main one being rading volume which serves as maching facor. Trading coss vary inversely o rading volume and rade frequency, as hey reflec economies of scale and lower invenory and adverse selecion coss. For he same reasons, Soll (2000) shows ha spreads are posiively o order imbalance. For each sock and each monh of he observaion period, we hen calculae he following variables: he daily average rading volume in housand, he average number of rades per day, he average imbalance per day measured by ( buy volumes sell volumes) buy volumes sell volumes Insiuional feaures We consider wo main insiuional differences ha could explain execuion coss: ick size and rading volumes ouside he order book. For a given sock and a given monh, we calculae he ick variable in percenage by dividing he ick size in by he average closing mid price in. The percenage of volume raded ou of he order book is se o zero for all Euronex socks and equals he raio off-order-book volume over oal inra-day volume for 14 Soll (1978) shows heoreically ha oal risk, no sysemaic risk, is he relevan measure because he unwaned invenory 16

19 each SETS sock on a given day. Monhly sock-by-sock means of his variable are hen calculaed Regression design and resuls We run cross-secional regressions using sock-by-sock monhly average values of spreads and heir explaining variables. The general esed model sands as follows: where s = a0 + b1 σ i, j i, j + b2 lnv + b3 ln FMV + b4 ln P j + b5 ln N + b6 I + b7 ick + b8 FR + s is he difference in average spread beween he Euronex sock i and is ε i (8), mached SETS sock j over he monh ; hree measures of average spreads are considered, he ime-weighed average quoed spread ( s aqs ), he equallyweighed average effecive spread ( s aes ) and he quaniy-weighed average effecive spread ( s wes ); i, σ j equals sock i s daily reurn volailiy minus sock j s daily reurn volailiy over monh ; i j lnv = ln( V ) ln( V ), wih V i being he average daily rading volume of sock i in housand over monh ; i, j i j ln FMV = ln( FMV ) ln( FMV ), wih in housand of sock i a he sar of January 2001; i j ln P = ln( P ) ln( P ) i FMV being he floaing marke value, wih P i denoing he average closing mid-quoe of sock i for monh ; i j ln N = ln( N ) ln( N ) is he difference in he average number of rades per day aken in logarihm, of sock i wih is paired sock j, over monh ; i, I j is he difference in he average imbalance per day of sock i wih is paired sock j, over monh ; i, j i j ick = ick ick and mid-price for sock k and monh ; k ick is he ick in percenage of he average closing of a supplier of immediacy is no diversified. 17

20 j FR is he order flow fragmenaion rae during monh for he SETS sock j mached o he Euronex sock i measured by off-order-book volume over oal inra-day rading volume; ε i is he error erm. Compuing variance inflaion facors for he variables included in regression (8) deecs a collineariy relaion beween hree variables: he consan variable, he difference in price P, and he fragmenaion rae FR. For ha reason, he model displayed in equaion (8) is no esimaed direcly bu hree alernaive regressions are run separaely. In he firs one, he P and FR variables are dropped: s i, j = α 0 + α σ 1 + α lnv 2 + α ln FMV α I 5 i, j + α ln N + α ick 6 + ε i (9). The second one has no inercep bu includes P : s i, j = β σ 1 i, j + β lnv 2 + β ln FMV 3 + β ln N 4 + β ick 6 + β I 5 i, j + β ln P 7 + ε i (10). Finally, he hird linear model has no consan eiher bu he fragmenaion rae variable is inroduced insead of he price variable: s i, j = γ 1 σ + γ lnv 2 + γ ln FMV 3 + γ ln N 4 + γ ick 6 + γ i, j 5 I + γ FR 7 j + ε i (11). The comparison of he hree differen models will permi of idenifying which facor among he marke place, he price level, or he fragmenaion rae bes explain he cross-secional variance of spreads. Regressions are OLS-esimaed bu saisics and R² values are correced for heeroskedasiciy following Whie (1980). Resuls are displayed in Table 5. Table 5 abou here The difference in floaing marke value does no significanly impac any of he differences in spread (excep in one regression a he 10% hreshold), proving ha samples are well mached on his crierion. The lnv coefficiens are nearly always significanly negaive, which indicaes ha he maching is no perfec for his variable and ha he differences in volumes parially explain he differenial spreads. Unsurprisingly, volailiy appears o be he major explaining facor: coefficiens for his independen variable are all posiive, as expeced, and 18

21 associaed wih he highes -values. Differences in spread are posiively relaed o imbalance differences (excep in he case of wes ), while he number of rades, wih significanly negaive coefficiens in only 3 regressions, has a weak explanaory power. Finally, he difference in price has a subsanial impac and affec spreads in he expeced way. Le us focus now on insiuional feaures. Consisenly wih Ronen and Weaver (2001), all measures of differenial spreads, aqs, aes and wes, significanly increase wih he difference in ick size. This posiive link beween icks and spreads canno be assigned o he fac ha ick could play he role of a proxy for price level, as, when P is inroduced in he model (regression design (10)), he ick coefficiens remain significan a he 1% level. Regressions designed as equaion (9) have a significan posiive inercep for he hree measures of differenial spread, meaning ha even when conrolling for sock characerisics and ick size, spreads are sill superior a he LSE han on Euronex. Their adjused R² exceeds he ones we obain in he regressions of ype (10), which evidences ha he fac ha a securiy is raded on SETS raher han on NSC (represened by he consan variable) has more explanaory power for he difference in execuions coss han he difference in price. When he consan is replaced by he SETS sock s fragmenaion rae, adjused R² increase for every measure of spreads. The fragmenaion rae coefficiens are all posiive and heir - values are superior o hose associaed o he inerceps in regressions of ype (9). This finding leads us o he conclusion ha he main feaure explaining he excess in spread on SETS is he marke aciviy of dealers ouside he order book, confirming hypohesis H2. 6. Conclusion Boh SETS and NSC are elecronic order-driven sysems wih similar funcionaliies. The main difference lies in ha a grea par of he order flow in SETS socks is execued by dealers ou of he order book while almos all rades in Euronex socks are execued inside he NSC order book. More han a half of rading volumes in euros and in number of rades are acually execued by dealers ouside he SETS order book. In order o measure he impac of his fragmenaion of he order flow on execuion coss, we compare wo samples of socks mached on economic secor, free floa capialisaion and rading volumes, and find subsanial differences in he rading characerisics of boh markes. Firs, alhough he risk level of boh samples socks are of similar range, he variance of closing reurns is significanly higher for SETS socks. Second, all measures of rading coss (quoed spreads, effecive spreads and marginal coss) are in favour of NSC. Concerning effecive spreads, a 19

22 breakdown per class of rade size shows ha SETS in-order-book average effecive spreads are larger han NSC spreads for every class excep for rades over 2,500 housand. The disribuion of rade sizes evidences ha ransacions are larger and less frequen in he hybrid order-driven marke. This marke is globally more expensive bu offers superior immediacy in counerpar. To inerpre he spread differences, we proceeded o spread decomposiions in a firs sage, and o cross-secional regressions involving economic and insiuional deerminans of spreads in a second sage. The spread decomposiions sae ha he order processing cos componen logically accouns for a much bigger par of he spread on he hybrid marke han on he cenralised order book. Moreover, he weigh of he invenory holding componen is, conversely o he inuiion, higher on NSC han on SETS, bu is maximum on SETS when considering order book rades only. This migh be he reflecion ha, a he LSE, dealers probably rade in he SETS order book when hey need o rebalance invenories. The adverse selecion componen rises o relaively comparable levels on SETS and NSC; ye, when excluding off-order-book rades from he SETS daase, i increases dramaically. We inerpre his resul as a possible clusering of rades beween SETS marke segmens according o heir informaional conen, he dealers skimming off he leas informed. The cross-secional analyses pu forward ha wo insiuional feaures explain he differenial spreads beween SETS and NSC: he ick size and he share of volume execued by dealers in he SETS marke, he laer having he highes significance. We conclude ha he dealers marke aciviy beside he order book brings addiional immediacy and conribues o he developmen of an efficien block marke, bu, in counerpar, is unfavourable o small and medium raders who pay higher execuion coss han on a cenralised order book. 20

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