Crossng Network Tradng and the Lqudty of a Dealer Market: Cream-Skmmng or Rsk Sharng? Carole Gresse Unversté de Pars-Nanterre & CEREG e-mal: carolegresse@hotmal.com http://www.carolegresse.com JEL classfcaton: Keywords: G19 crossng network, alternatve tradng system, dealer market, lqudty, transacton costs, rsk sharng September 2002 Ths research was launched wth the collaboraton of ITG Europe, whch provded data from ts crossng network. It has benefted from the expert knowledge of John Mnderdes and Davd Karat. The nternal data were retreved thanks to the techncal assstance of George McClntock, Nurt Bacharach and Shmon Rozenzweg. I am also grateful to Annaïck Guyvarc'h, Ryan Daves, Alfonso Dufour, Güzhan Gülay, Asl Ascoglu, Maureen O Hara and partcpants at the European FMA 2002 conference, the EFMA 2002 meetngs, the MFS 2002 conference and the FMA 2002 conference, for helpful comments and dscussons. Besdes, the LSE market data were provded by the CEREG (Pars-Dauphne Unversty). Fnally, results and conclusons n the paper have been acheved n total ndependence from any publc or prvate nsttuton and wholly reflect the opnon of the author. All errors are mne. Suggestons are welcome.
Crossng Network Tradng and the Lqudty of a Dealer Market: Cream-Skmmng or Rsk Sharng? Abstract Ths artcle nvestgates the relatonshp between the tradng actvty of a crossng network (CN) and the lqudty of a tradtonal dealer market (DM) by comparng data from the SEAQ quote-drven segment of the London Stock Exchange (LSE) and nternal data from the POSIT crossng network over two 6-months' perods. Ths exploratory study, whch s the frst one to use nternal CN data, provde new nsghts nto market competton between tradtonal exchanges and alternatve tradng systems n Europe, and opens further research leads. Based on a cross-sectonal analyss n a sample of UK md and small cap stocks, the fndngs support that CN-tradng does not sgnfcantly ncrease adverse selecton and nventory rsk on the central market. It appears that the competton between market makers strengthens wth the CN actvty, and CN-tradng gves dealers a rsk sharng opportunty that leads them to mprove quotes.
Crossng Network Tradng and the Lqudty of a Dealer Market: Cream-Skmmng or Rsk Sharng? Durng the last decades, new electronc tradng systems, desgnated by the SEC as Alternatve Tradng Systems (ATS), 1 have developed all around the world, n response to the need expressed by nsttutonal nvestors as well as broker-dealers 2 for alternatve systems that would help reduce the cost of tradng. These ATSs dvde n two categores: Electronc Communcaton Networks (ECNs), whch work as anonymous electronc order books, and Crossng Networks (CNs), whch cross unprced buyng and sellng nterests. Lke ECNs, CNs generally promse anonymty and lower transacton costs, but the man dfference wth ECNs s that partcpants do not enter the prces at whch they wsh to trade, so that no prce dscovery takes place n a CN. Instead, at desgnated cross tmes, nterested buyers and sellers are matched and the prce at whch the trades execute s taken from an exchange. Ths prce can be the central market md-quote, or, n some cases, the precedng closng prce or the volume-weghted average prce over some perod. In a CN, trades execute wth no market mpact, yet executon s not guaranteed. In such, CN address the needs of a certan type of traders, ready to sacrfce mmedacy and executon guarantees so as to obtan lowcost executon. ECNs have ganed substantal market share n tradng volumes n the US, snce the openng of Instnet 3 n the 70s, and now account for approxmately 40% of the NASDAQ tradng volume. 4 As for CNs, the man ones currently operatng n North Amerca are the Reuters Instnet Crossng Network, ITG s POSIT 5 and the New York Stock Exchange s afterhours Crossng Network, the most promnent beng POSIT. POSIT developed quckly n the Unted States. 35 mllons of shares are presently traded daly on ths system, whch represents around 2,5% of volumes. The competton comng from these new tradng facltes have changed the structure of fnancal markets, and probably also the role of ntermedares on these markets. The mplcatons for lqudty are of much nterest for academcs, regulators and nvestors. Several artcles have already addressed ths ssue by usng ECN data, but no work has been done yet wth CN data. Ths paper focuses and provdes new evdence on the consequences of the tradng actvty of a CN, by testng market data from the London Stock Exchange dealer market segment (SEAQ 6 ) and prvate data from the POSIT European crossng network. 1
To ths goal, the London Stock Exchange s an nterestng case of nvestgaton because, conversely to the stuaton n the US, ECNs have not developed n Europe, where only CNs have emerged. For that reason, the mpact of CN-tradng can be tested ndependently from the potental effect of other ATSs. The very frst attempt to create crossng facltes n Europe took place n the UK, n the 80s, wth the crossng system ARIEL, whch, at that tme, faled n attractng and executng substantal order flow. Ever snce, two London-based crossng networks have emerged on European stock markets. Frst, after beng already operatng n the North-Amercan and Australan markets, POSIT was adapted to Europe and launched there on the 18 th of November 1998 by ITG Europe. 7 It s now workng for eleven European countres. 8 A second CN followed n March 2000. A team of nsttutonal nvestors, led by Barclays Global Investors and Merrll Lynch Investments Managers, created E-crossnet. Up to now, E- crossnet has not really succeeded n reachng a crtcal mass and the rate of executon n ts system has kept qute low from ts start, so that POSIT has remaned the bggest CN on the European market. At the current date, the man part of the POSIT order flow s related to UK md and small caps. As a consequence, snce 1998, nsttutonal nvestors and broker-dealers have several venues to trade wthn the UK stock market: they can ether submt an order to the central market of the London Stock Exchange (LSE) or submt t to a CN. In the former case, they ncur the bd-ask spread but get hgher executon guarantee. In the latter case, ther probablty of executon s lttle but they are provded anonymty, they ncur no adverse selecton cost as ther orders are not vsble from the rest of the market, and, f executed, they trade at the md-quote wth no market mpact. The objectve of ths paper s to analyse the effects on the lqudty of a dealershp market of CN submtted and crossed order flow, by usng both prvate data from a CN and publc market data from SEAQ, over two 6 months' perods. These data are partcularly welladapted to the am of the research, as the SEAQ tradng platform s a nearly pure quotedrven market where the POSIT crossng network attracts the major part of multlateral crossng. The paper s organsed as follows. Secton 1 sets the theoretcal framework of the study and derves a seres of testable hypotheses about the mpact of CN tradng actvty on the lqudty of a DM. Secton 2 provdes nformaton on the organsaton of the SEAQ market segment and the workngs of the POSIT crossng network. After descrbng the data and the samples n Secton 3, stylsed facts about market actvty, CN order flow and CN- 2
traded stocks are reported n Secton 4. Fnally, the testable hypotheses developed n secton 1 are tested: methodology and results are presented n secton 5. Secton 6 concludes. 1. Theoretcal fndngs on market competton wth a CN and testable hypotheses The emergence of ATSs has gven rse to a stream of research around the trade-off between the benefts of competton and the potental costs of order flow fragmentaton that ATSs may cause. The debate 9 began wth Hamlton (1979), who ponted out the two opposte effects of mult-market tradng and the devaton of a part of the order flow from the central market. Ether mult-system tradng ncreases competton among lqudty provders and thus reduces bd-ask spreads, or, conversely, the fragmentaton of the order flow between several locatons lowers economes of scale and probabltes of executon, resultng n hgher volatlty and spreads. 1.1. The potental benefts and costs of mult-system tradng A common argument n favour of mult-system tradng s that ncreased competton could reduce the market power of prce-settng agents and thus result n better executon condtons, as mentoned by Easley, Kefer and O'Hara (1996). Gvng the opportunty to trade at md-quote, CNs such as POSIT, contrbute to reduce the average cost of tradng, ncrease the competton between lqudty provders, as market makers 10 or lmt order traders, resultng n lower bd-ask spreads. A substantal number of papers provde emprcal evdence on the gans from the competton between ATSs and tradtonal markets as well as on the cost savngs nsttutonal nvestors may get from tradng on ECNs or CNs (Barclay, Hendershott and McCormck (2002), Næs et Ødegaard (2001), and Conrad, Johnson and Wahal (2002), Huang (2002), Weston (2001)). At the opposte, some authors argue that tradng at multple locatons s potentally detrmental to lqudty. Wth the fragmentaton of the order flow, each tradng system wll have fewer people wllng to trade, makng t more dffcult to fnd a counterparty. Mendelson (1987) demonstrates that tradng n a securty market possesses a network externalty, whch means that a securty market s more valuable to customers as more customers engage n tradng at that locaton. The dspersal of orders between several tradng locatons lowers the probablty of executon at each locaton and therefore reduces lqudty. Under nformaton asymmetry, Chowdry and Nanda (1991) show that nformed tradng, and thus adverse selecton costs, ncrease wth the number of markets lstng an asset. 3
Moreover, when a new market opens for a stock, t may skm the least nformed and consequently more proftable orders, and then harm the lqudty of the prmary market. Barclay, Hendershott and McCormck (2002) hghlght that such s not the case for ECNs competng wth NASDAQ: the more nformed orders spll onto the ECNs and market makers' preferencng and nternalsaton agreements allow them to retan the less nformed order flow. At the opposte, Easley, Kefer and O'Hara (1996) and Bessembnder and Kaufman (1997) show that US regonal exchanges attract mostly unnformed orders n the NYSE stocks. However, most of these theoretcal predctons and emprcal fndngs on adverse selecton costs assume that prce dscovery s actve at all tradng locatons and that trades have market mpact on every market. Hence, they cannot fully apply to the case of a CN competng wth another market. No prce dscovery s takng place n a CN, where transacton prces are derved from another market wthout producng market mpact. Because of ths specfc feature, potental competton or fragmentaton effects nduced by a CN may slghtly dffer from those caused by tradng systems wth prcng mechansms, lke ECNs. To address the partcular case of CNs, Hendershott and Mendelson (2000) model the nter-market competton between a CN and a compettve dealershp market (DM). Ther theoretcal predctons are wdely dscussed n the next paragraphs and wll serve as a bass to the testable hypotheses nvestgated n secton 5. 1.2. Theoretcal fndngs on mult-market competton nvolvng a CN Hendershott and Mendelson (2000) and Dönges and Henemann (2001) specfcally study the case of a CN operatng next to a DM. They show that, when traders assgn the same value to tradng, t s very unlkely that both markets co-exst. Conversely, when the value assgned to order executon dffers across nvestors, the CN and the DM should coexst: traders wth hgh tradng value cluster on the DM whle traders wth low tradng value choose the CN. The consequences of ths double-market stuaton on lqudty are wdely dscussed n Hendershott and Mendelson (2000). Ther model shows that the effects of CN-tradng on a DM are ambguous. On the one hand, traders who exclusvely use the CN can provde a counterbalancng effect that reduces adverse selecton and nventory costs for market makers on the central market. On the other hand, the CN may fragment the market: traders who use the DM as a "market of last resort", 4
.e. submt orders to the CN frst and then go to the DM f not executed, can nduce dealers to wden ther spread. They consder a populaton of traders compounded of: nformed traders wth short-lved prvate nformaton, nformed traders wth long-lved prvate nformaton unnformed traders wth dfferent preferences for lqudty. There are two tradng venues: a DM, where orders are charged the half-spread but executed wth certanty, and a CN, where executon s rsky but provded at md-quote. Thus, traders have four possble strateges: do not trade, trade exclusvely n the CN and do nothng f the order fals to execute, trade opportunstcally n the CN, n other words, submt an order to the CN and f t fals to execute, trade n the DM, trade only n the DM wthout frst attemptng to trade n the CN. The game results n multple equlbra, all charactersed by the followng structure. 1. By hypothess, the nformed traders' decsons are drven by the longevty of ther nformaton: short-lved nformaton forces tradng n the DM exclusvely; long-lved nformaton leads to opportunstc tradng n the CN. 2. Any lqudty trader, accordng to the value they assgn to ther trades, wll choose from the followng strateges: unnformed traders wth very low preference for lqudty do not trade at all; low-preference-lqudty traders trade exclusvely n the CN; medum-preference-lqudty traders use the CN opportunstcally; hgh-preference-lqudty traders trade mmedately n the DM. From there, the global effect of CN-tradng on the DM n equlbrum s unclear. On the one hand, addtonal lqudty tradng n the CN produces postve rsk-sharng effects. On the other hand, opportunstc CN-tradng nduces negatve cream-skmmng effects. The postve rsk-sharng effects on the dealers' spread 1. Dealers nventory costs are reduced because the expected dealer mbalance s decreased due to long-lved nformaton tradng n the CN. 5
2. The CN attracts both new low-lqudty-preference traders who would not otherwse trade and lqudty traders who would otherwse go drectly to the DM: n case of long-lved prvate nformaton, a part of the adverse selecton rsk s then born by these CN lqudty traders. The negatve cream-skmmng effects on the dealers' spread 1. Allowng lqudty traders to use the CN opportunstcally rather than go drectly to the DM tends to wden dealers spreads. The CN s then skmmng off a part of the unnformed trades, whch cannot be used anymore by dealers to compensate ther losses on nformed trades. Actually, the traders that strategcally use the DM as a "market of last resort", ether nformed or unnformed, make the DM rsker and force dealers to quote larger spreads. 2. If CN-tradng leads to hgher dealer spreads, then the lower-lqudty-preference traders, many of whom would not trade wth the DM as the only opton, are made better off because they get a lower-cost tradng opportunty. However, the hgher-lqudtypreference traders are made worse off: they stll trade n the DM, but now at hgher cost. In whole, the CN can ether mprove or harm socal welfare. 1.3. Dealer tradng n the CN Hendershott and Mendelson (2000) clearly demonstrate that the CN may create an nterestng rsk-sharng beneft for dealers. In ther model, ths beneft comes from poolng customer orders n the CN and not from nter-dealer tradng or dealer tradng n the CN, yet the CN structure can also be vewed as an deal mechansm for facltatng nter-dealer tradng or dealer prncpal tradng. As shown n Ress and Werner (1998), nter-dealer tradng, by producng rsk-sharng benefts, reduces dealers' costs and consequently allow them to mprove ther spreads. Therefore, another role of the CN, that has not been modelled n prevous lterature, mght consst of provdng dealers wth a mean to trade at low cost after executng customers' demand. If the CN actually plays the role of a rsk-sharng tool, ts actvty could then result n lower spreads on the DM. A seres of hypotheses ensung from all these theoretcal predctons about the mplcatons of CN-tradng for market lqudty, are now developed n the next subsecton. 6
1.4. Testable hypotheses Four groups of testable hypotheses are derved from the theoretcal lterature, and more specfcally from Hendershott and Mendelson's model. The frst group of hypotheses concerns the consequences of CN opportunstc tradng wth respect to the harmful mpact of CN unexecuted order flow. Then, follows two categores of hypotheses on the rsk-sharng effects: the frst one focusng on the trade-off between the so-called cream-skmmng effect and the potental rsk sharng effect due to new lqudty tradng n the CN, the second one addressng the ssue of dealer tradng n the CN. Fnally, the last set of hypotheses ams at defnng tests of the net global effect of CN-tradng on the compettveness of prces and transacton costs. 1.4.1. The cream-skmmng effect due to opportunstc tradng n the CN As ponted out by Hendershott and Mendelson (2000), when patent lqudty traders and fundamental nformed traders are present n the market, they wll use the CN opportunstcally: they wll frst submt orders to the CN, and use the DM as a "market of last resort", f CN orders get no executon. As a result, the CN frst skms off lqudty trades from the central market; secondly, the unexecuted opportunstc CN order flow makes the DM rsker when comng back to the dealers after crossng hours, and ncreases market makng costs at the end of the tradng sesson. For ths reason, hypotheses H1 and H2, related to the cream-skmmng effect, are based upon the mpact of the POSIT unexecuted order flow. Hypotheses H1 and H2 H1. Crossng actvty makes the DM rsker at the end of the tradng day because unexecuted opportunstc CN orders come back to dealers after the crosses. If H1 holds, the volatlty of closng prces per unt of traded volume would ncrease wth the CN unexecuted order flow. H2. Unexecuted order flow comng back from the CN to the central market for executon, creates temporary tenson on lqudty, ether because t ncreases adverse selecton, as demonstrated n Hendershott and Mendelson (2000), or because t suddenly generates abnormal nventory costs for market makers. Provded H2, quoted spreads would wden wth the amount of unexecuted CN order flow, and closng spreads would wden more than ntra-day spreads. 7
1.4.2. Rsk-sharng effect due to new lqudty tradng n the CN vs cream-skmmng effect The cream-skmmng effect may nevertheless be offset by a postve rsk sharng effect nduced by new lqudty tradng attracted nto the CN. On the one hand, the CN may skm off unnformed tradng from the DM, and thus ncrease adverse selecton costs on the central market; but, on the other hand, t can also attract new lqudty traders, who would not trade out of the CN because tradng n the DM would be too costly for them. These new lqudtymotvated orders help to absorb long-lved nformaton-based orders n the CN, whch reduces market-makng costs on the DM and creates a rsk sharng effect. Hypotheses H3a and H3b focus on ths alternatve. Hypotheses H3 H3a. The fragmentaton of the order flow between the central market and the CN creates addtonal adverse selecton costs on the DM, because the CN skms off opportunstc lqudty tradng from the DM, where the proporton of nformed tradng gets hgher. Under H3a, ntra-day quoted spreads would ncrease wth the share of order flow submtted to POSIT. H3b. The fragmentaton of the order flow between the central market and the CN lowers adverse selecton and nventory rsk on the DM because the CN attracts new lqudty traders whose orders help absorb the opportunstc nformed order flow. If ntra-day quoted spreads decrease n the share of order flow submtted to POSIT, H3b s accepted and H3a s rejected: the rsk-sharng effect can then be consdered domnant over the cream-skmmng effect. 1.4.3. The rsk-sharng effect due to dealer tradng n the CN One major specfc of the POSIT crossng network les n that t s open to the sell sde. Ths specfc feature allows me to test H4. Hypothess H4 H4. The CN gves an opportunty to market makers to reallocate ther postons wth no mplct tradng cost and thus lowers nventory costs. Under H4, ntra-day and closng quoted spreads should be negatvely related to the share of volume traded by market makers through the CN. 8
1.4.4. Global effect on mplct transacton costs and prce compettveness Fnally, the last hypotheses all relate to the general queston: does the competton effect domnate the fragmentaton effect when a CN operates wthn a DM? If so, the competton between prce-settng agents would ntensfy wth crossng (cf. H5), resultng n lower temporary market mpact of trades (H6) and less expensve cost of tradng for all traders (H7). Hypotheses H5, H6 and H7 H5. The competton n prce-settng between market makers ntensfes wth CN-tradng. If H5 holds, then the number of quote revsons per day would ncrease wth the share of order flow gong to the CN. H6. Due to competton effects, temporary market mpact decreases wth the crossng network actvty. The hgher the temporary market mpact of trades, the more sell prces dffer from buy prces, the dfference ncreasng n the assocated quanttes. Trade prces are then more volatle around the daly average transacton prce,.e. the so-called VWAP (volumeweghted average prce). Provded H6, that s provded crossng globally helps to reduce the market mpact of transactons, ntra-day volatlty around VWAP would be negatvely related to the share of traded volume executed n the CN. H7. CN-tradng helps reduce the cost of tradng for all traders. Under H7, quoted spreads should be negatvely related to the share of traded volume executed n the CN (postvely otherwse). To test these hypotheses, the SEAQ market segment and the POSIT crossng network form an deal feld of nvestgaton for the two followng reasons: SEAQ operates as an almost pure quote-drven market; CNs are the only ATSs to operate wthn ths market segment, POSIT beng the major one and the only one to accept dealer orders, at the tme of the study. Before presentng the data and the characterstcs of the sample, I frst descrbe market mechansms. 9
2. The DM organsaton and the CN tradng mechansms At the LSE, mddle and small captalsaton domestc equtes 11 are lsted on SEAQ, an almost pure compettve dealershp system. 2.1. SEAQ 12 SEAQ s the screen-based compettve market makng segment of the LSE for non order book domestc equty securtes. A SEAQ securty s a domestc equty market securty for whch a mnmum of two market makers regsters wth the Exchange. Each market maker s oblged to dsplay frm two-way prces on SEAQ n quanttes at least equal to the Normal Marketable Sze (NMS), 13 or reduced NMS n the case of reduced sze market makers, 14 durng the Mandatory Quote Perod (MQP), whch lasts from 8:00 am to 4:30 am. From 7:30 am to 8:00 am, quotes may be opened but prces are regarded as beng ndcatve only. From 4:30 pm to 5:15 pm, market makers may contnue to dsplay frm quotes but are not oblged to do so and the tradng system remans open for tradng reportng. Durng the tradng day, the best bd and best offer prces quoted by market makers on SEAQ are commonly referred to as the yellow strp. In the event that quotatons by two or more market makers are dentcal n terms of prce, the best quote wll be the one that was entered frst. The LSE offers crossng facltes three tmes a day for SEAQ securtes that are part of the FTSE 250 Share Index. Three crosses 15 are run durng the tradng day, takng place at 11:00 am, 3:00 pm and 4:45 pm. Up to the current date, they have faled n attractng suffcent order flow and no substantal volume has been transacted through these crosses. 2.2. The POSIT crossng network Run n Europe by the agency stockbroker ITG Europe, POSIT s an ntra-day electronc tradng system, whch matches buy and sell orders at predetermned tmes n the day and uses md-market prcng for executon. Sngle or portfolo orders can be submtted to POSIT contnuously, at any tme of the tradng day. Anonymty s protected and order detals are never dvulged externally or dsclosed to the market. Submssons are free of charge. The matchng algorthm wthn POSIT s run at desgnated tmes each day. 16 In order not to allow gamng and manpulatng strateges, at the desgnated tme of a match, a random executon tme wthn a seven mnute wndow s generated from the POSIT computer so that no one s aware of the exact match tme. Any order receved before the desgnated match 10
tme wll be ncluded n the match pool, but any order receved after the start of the match wndow wll be taken on a best endeavour bass up to the tme the match s run. Any order subsequently receved would be for the next scheduled match. The POSIT algorthm compares all submtted orders confdentally and s set to maxmse the total value of shares traded, gven the constrants 17 assocated wth submtted orders. Matchng orders are crossed at the rulng md-prce taken from the lead market quote for each stock, and reported to the relevant authorty after executon. Only executed orders are charged a 10 bass pont brokerage commsson. At the current date, the match tmetable (n UK tme) conssts of seven ntra-day matchng tmes as follows: 9:00 am, 10:00 am, 11:00 am, 12:00 am, 2:00 pm, 3:00 pm and 4:00 pm. Ths tmetable results from several changes: when ITG Europe launched POSIT for UK equtes n November 1998, only two daly matches were run at 11:00 am and at 3:00 pm, then other matches were added. The observaton perod consdered n ths artcle covers the second semester of 2000 and the frst semester of 2001. From the 1 st of July 2000 to the 15 th of January 2001, there were four matches a day, at 9:30 am, 11:00 am, 12:00 am and 3:00 pm. Then, a new 8:45 am match was added on the 16 th of January 2001. Fnally, n March 2001, wth the launch of a 2:00 am match, the match tmes were moved to the current hourly tmetable, excludng the 4 o clock match whch became offcal later. 18 SEAQ and POSIT data avalable over the selected perod are presented n the next secton. 3. Data, emprcal measures and sample descrpton The theoretcal hypotheses presented n secton 1 are tested on hgh frequency market data from the LSE and POSIT order data provded by ITG Europe, for SEAQ UK and Irsh stocks, over a frst perod of sx months from the 1 st of July 2001 to the 31 st of December 2001 (Perod 1). Then, to apprecate the stablty of the results, a second observaton perod s consdered from the 1 st of January to the 30 th of June 2001. 3.1. Market and POSIT data Tck by tck market data from the London stock market nclude trade and best prces data. Best prces correspond to the best bd and offer market makers' quotes at any tme a new quote s posted or a quote s revsed. Quanttes assocated to best prces are not avalable so that the NMS s used as a proxy. 11
POSIT data consst of two seres of fles. One seres ncludes the characterstcs of the orders submtted to the CN, such as the sedol code dentfyng the stock, the sze of the order n number of shares, the type of the ntator, that s "nsttutonal nvestor" or "brokerdealer", the date and tme of the match to whch the order s beng submtted. The second seres ncludes the characterstcs of the orders executed n the CN: the stock sedol code, the executed quantty, the type of ntator, the md-prce used for executon and the date and tme of the correspondng match. Before runnng any emprcal tests, ths raw data has been rearranged for the purpose of the research n a few ways. The submsson fles were merged wth the executon fles, n order to show whether each submsson was totally or partally executed, or not executed at all. Then, a procedure was set up to determne whether a submsson to POSIT was made for the frst tme or whether t was an order resubmtted after remanng unexecuted n the prevous match. In the end, a sngle table was bult up, contanng for each submsson to POSIT: the stock sedol code, the date and tme of the match, the type of the ntator, the submtted quantty, the executed quantty, the prce of executon f any and a flag ndcatng whether the order was newly submtted or resubmtted after total or partal non executon at the prevous match. Both categores of data are avalable for 1663 SEAQ UK domestc stocks over Perod 1 and for 1612 stocks over Perod 2, but only a subset of these stocks are selected for the study: for test feasblty, very low traded stocks are abandoned. In order to select and characterse sample stocks, ther rsk and lqudty are evaluated through a set of emprcal measures such as the volatlty of daly close returns, the average NMS n number of shares and n GBP, the average sze of a trade, the average spreads, the average number of quote revsons throughout the MQP, the average trade number per day, the average daly traded volume as a multple of the NMS and the average market mbalance between sales and purchases. 3.2. Rsk and lqudty measures Let us note: T the number of tradng days for stock wthn a gven observaton perod, NMS t the NMS, n number of shares, of stock on day t, CA t the closng ask prce, that s the last ask quote of the MQP, for stock on day t, CB t the closng bd prce, that s the last bd quote of the MQP, for stock on day t, CM t the closng quote, that s the last md-quote of the MQP, for stock on day t, 12
CM t r t = ln the return of stock on day t, computed n logarthm on closng md- CM t 1 quotes, V t the volume n number of shares, traded for stock wthn the tradng day t, 19 BVt the sum of buyng volumes n stock on day t, and SVt, the sum of sellng volumes n stock on day t, 20 A n the best ask prce for stock at tme n, B n the best bd prce for stock at tme n, M n the md prce for stock at tme n, d n the duraton of market quotes posted on stock at tme n, N the total number of dfferent market spreads quoted for stock throughout a gven observaton perod, P k the trade prce for stock on trade k, Q k the sze of transacton k on stock, n number of shares, µ k the current md quote at the tme of trade k on stock, K the total number of trades for stock over the consdered perod. Volatlty and lqudty measures are computed as follows. 3.2.1. Volatlty For each stock, the volatlty σ s measured by the unbased estmator of the close return standard devaton: T 2 1 T σ = t rt (1). = T t= 1 1 r T 1 t 1 3.2.2. Depth Let us recall that the NMS s the mnmum quantty for whch market makers are due to quote frm prces. For that reason, the average value of the NMS s used as an ndcator of depth. For each stock, the average NMS s calculated n number of shares ( NMS ) as shown n equaton (2), and n GBP ( NMS ) lke n equaton (3). 13
1 T T NMS = NMS (2). t t= 1 1 T NMS = CM t NMS (3). t T t= 1 Besdes, the average GBP sze of a trade, denoted TS, and equal to 1 K K TS = Qk Pk (4), k= 1 may also be consdered as related to the depth of the market. 3.2.3. Spreads Three measures of spreads are used to apprecate the lqudty of each stock : QS, the average quoted touch21 or market spread (.e. the dfference between the best offer and the best bd quoted on the market reported to the md-quote), calculated by weghtng each quoted spread wth ts duraton of valdty, ES, the average effectve spread, that s the mean of spreads actually appled on trades weghted by trade szes, 22 CS, the average closng spread computed as the equally-weghted mean of daly closng market spreads. Equatons (5), (6) and (7) dsplay the explct formulas of calculaton: N = dn An Bn QS (5), N n= 1 M n dn n= 1 ES K = K k= 1 Q n= 1 k Q P k k P k P k µ k (6), µ k 1 T T CA CB n n CS = (7). t= 1 CM n 3.2.4. Quote frequency Quote frequency can be measured by NQR, the average number of market quote changes wthn a MQP, for stock. NQR, that s N T, ncreases wth the ntensty of the 14
nformaton flow conveyed on a securty and the level of competton between the market makers who quote prces for the stock. 3.2.5. Tradng frequency So as to apprecate the level of tradng frequency of a gven stock, I do not only look at the average number of trades per day, denoted TN and equal to K T, but also at the number of tmes the NMS s traded, on average, nsde a tradng sesson. Ths varable s denoted TF and s estmated n the followng way: 1 T T V t TF = (8). t= 1 NMSt For the remander of the paper, tradng frequency shall be referred to as TF. 3.2.6. Market mbalance By market mbalance, I mean the dsequlbra between buyng and sellng trades. The market mbalance for stock on day t s then defned as BV SV ( BV + SV ) average market mbalance, calculated as IMB t t t t. The IMB = 1 T T BV t t= 1 BVt + SV SV t t (9). s an ndcator of llqudty. The hgher IMB, the hgher the cost of makng the market for stock. Lookng at the ndvdual values of these measures, a certan number of stocks wth mssng data or very low tradng actvty are dropped from the samples. 3.3. Deletons for mssng data and thn tradng The emprcal tests conducted n secton 5 are based on stock-by-stock aggregated measures. In order to obtan ndvdual measures wth comparable statstcal meanng, they should be estmated on a mnmum number of tradng days. Therefore, stocks for whch market makers' quotes were avalable for less than 100 tradng days of the observaton perod, were dropped from the samples. However, some very low-prced and llqud stocks are stll ncluded n the remanng set of securtes: these equtes exhbt surprsngly hgh spreads and very low tradng volumes. 15
As such extreme values could well bas the results of the tests, I have deleted from the samples, any stock wth one, at least, of the followng features: the average quoted spread QS exceeded 20%; the average volume traded nsde a sesson, TF, was less than 2 NMS; or the total number of trades throughout the consdered sx months' observaton perod, K, was less than 20. Consequently, the sample for Perod 1 (Sample 1) s reduced to 1,400 stocks, for whch a total amount of 78,850 mllon GBP were traded n the market. As for Perod 2, the fnal sample (Sample 2) ncludes 1,378 stocks for whch the total traded volume over the perod equalled 79,526 mllon GBP. 3.4. Characterstcs of selected stocks In terms of rsk, depth and transacton costs, the selected subset of SEAQ stocks exhbt the typcal features of md and small cap equtes: relatvely low prces, hgh volatlty, lmted depth and relatvely hgh spreads, wth large dscrepances across stocks. Descrptve statstcs on volatlty, depth, spreads and quote frequency are reported n Table 1. Table 1 about here The stocks of the samples are qute rsky: the average close-to-close return volatlty across Sample 1 (Sample 2) reaches 2.83% (2.66%), ths mean beng probably pulled up by most rsky stocks, as the medan volatlty only equals 2.11% (2.27%). The market for these stocks s not very deep, the medan NMS beng 2,000 shares for Sample 1 and 2,512 shares for Sample 2. Let us notce that the market for the most lqud stocks of the samples s much deeper, as the cross-sectonal means of the NMS stand far above the medan values. Consstently, average spreads are relatvely hgh compared to what would be observed on a Blue Chp market segment. The cross-sectonal mean of average quoted spreads equals 2.38% for Sample 1 and 2.28% for Sample 2, whle the average effectve spreads are substantally lower. The cross-sectonal mean of effectve spreads s no more than 1.74% across Sample 1 and 1.58% across Sample 2. Large dscrepances n spreads can be observed from one stock to another. For a great number of stocks, average spreads are superor to the mean value of the sample: as an llustraton, medan values of spreads always exceed the mean values. 16
Fnally, market makers do not revse ther quotes very frequently on ths market segment. On average across each sample, quotes are revsed 12 tmes a day for a stock, wth, agan, huge dfferences between stocks. For most stocks n the samples, the daly number of changes n prce s even less than that, the medan standng at 6 on each observaton perod. 4. Stylsed facts on CN tradng actvty By nature, the probablty of executon n crossng systems remans low, as t cannot be mproved by prce adjustments. As mentoned n the ntroducton, crossng only addresses the needs of a very specal type of traders, for whch the reducton of market mpact prevals over mmedacy. Therefore, crossng s unlkely to attract more than a few per cent of the total order flow. As an llustraton, 11 bllon pounds were crossed through POSIT last year whle 12,000 bllon pounds were traded on the UK market. Concernng SETS FTSE100 stocks, sgnfcant volumes are crossed, but they have represented an extremely low share of the total market turnover (under 1%). In fact, the man part of POSIT order flow n UK domestc stocks s related to md caps, the potental gan from crossng beng superor for these stocks, and the major share of the crossng busness concentrates on SEAQ stocks that belong to the FTSE250. Let us now examne the level of CN-tradng for the stocks selected n samples 1 and 2. 4.1. Market actvty and CN-tradng over the observaton perods When only consderng ntra-day tradng, 23 about 77 bllon GBP were traded for the stocks of the samples wthn each 6-months' perod. Ths volume represented more than 2 mllon trades on each semester, the average sze of a trade standng between 33 and 36 thousand GBP. Over Perod 1, 1.28% of the total tradng volumes (n GBP) were transacted through POSIT. Ths market share represented only 0.34% of the total number of trades, as POSIT trades are larger than others. The average sze of order executed n the CN s more than 3 tmes the average sze of trade on the market. Crossng actvty n POSIT rose substantally n Perod 2, reachng 2.34% of total traded volumes and 0.67% of the total number of trades. Ths ncrease n crossng cannot only be assgned to a rse n the relatve amount of submssons n the CN. POSIT-submtted orders n percentage of the total market volume reman around 50% (49% n Perod 1 and 51% n Perod 2). If accountng for resubmssons, ths rato goes up to 94% n Perod 1 and to 104% n Perod 2, as many orders are submtted 17
to several consecutve matches n case of non executon. The ncrease n POSIT market share s then more probably due to the lower mbalance between sellng and buyng orders (cf. lnes 3 and 4 of Table 2) n the CN, resultng n a better rate of executon n the CN: 4.13% of volumes submtted to the CN n Perod 2 were executed nstead of 2.63% n Perod 1. Table 2 about here Lookng at the breakdown of CN orders between trader types dsplayed n Table 2, t s notable that the reducton n the order mbalance s manly related to market makers strateges. As already mentoned, POSIT s open to both the buy and the sell sdes, so that two categores of traders submt orders to POSIT: nsttutonal nvestors and broker-dealers, n other words market makers. The latter tend to submt more sellng orders than buyng orders but ths mbalance n market makers' orders seem to vary over tme and lessens sgnfcantly over the 2 nd perod: market makers placed nearly twce (1.8 tmes) more sell orders than buy orders n the CN n Perod 1, whle ths rato s no more than 1.3 over Perod 2. Wth respect to the rates of executon, market makers get hgher fll rates. Insttutonal nvestors account for about 52% or 53% of the total CN-submtted order flow and submt larger orders, whereas market makers represent the major part of crossed volumes (70% n Perod 1 and 59% n Perod 2). They submt smaller orders and have a lower rate of resubmsson on unexecuted orders. They are undoubtedly more opportunstc n ther way to use the CN. Beyond these global fgures, the share of CN-tradng can hghly dffer from one stock to another. Over Perod 1, orders had been submtted nto the CN for 1,251 stocks out of 1,400. 568 stocks had been traded at least once n the CN, the CN market share exceedng 1% for 281 of these stocks and 5% for 20 of them. As for Sample 2, 1,265 out of 1,378 stocks were CNsubmtted and 703 were actually crossed n POSIT, 475 wth a POSIT market share superor to 1% and 54 wth a POSIT market share over 5%. 4.2. Characterstcs of CN-traded stocks Ths subsecton focuses on the specfctes, f any, of stocks for whch some orders are actually submtted nto or executed on the CN. To ths am, the followng sub-samples are consdered on each observaton perod: 18
sub-sample A ncludes stocks for whch at least one order was submtted to the CN, whle sub-sample B contans non submtted stocks; sub-sample A1 ncludes stocks actually crossed n POSIT, whereas sub-sample A2 corresponds to CN-submtted but never executed stocks; sub-sample α ncludes CN-traded stocks wth a POSIT market share n volumes of less than 1%; sub-sample β ncludes CN-traded stocks wth a POSIT market share between 1 and 5%; and fnally, sub-sample γ ncludes CN-traded stocks wth a CN market share over 5%. Measures of depth, volatlty, tradng frequency, market mbalance and quote frequency, as defned n sub-secton 3.2, are computed for each stock and then, equally-weghted means of these measures are computed and compared by par of sub-samples. Accordng to the mean values, stocks for whch no order was submtted to POSIT are the most llqud n the samples: they have the smallest NMS and the largest market mbalance. They are very nfrequently traded (less than 3 trades a day on average) and have very stcky quotes (less than 5 quote revsons per day on average). Ths s consstent wth the postve externalty hypothess. Accordng to Hendershott and Mendelson (2000), CNs are charactersed by two opposte externaltes: a postve externalty because an ncrease n the CN tradng volume wll rse the CN executon rate, and a negatve externalty, that s a crowdng effect, when orders accumulate on the same sde n the CN. The lqudty effect related to the postve externalty can hardly be acheved for very llqud and scarcely quoted stocks because the potental number of traders reman lmted. Moreover, there s probably lttle fundamental research on these equtes, so that traders wth long-lved nformaton on these stocks are few and CN-tradng s less proftable. Insde the group of CN-submtted stocks, about half of them have effectvely been crossed n POSIT. The dfferences n depth, rsk, tradng frequency and quote frequency between crossed stocks and other submtted stocks are all hghly sgnfcant on both observaton perods. They show that CN-traded stocks are less rsky and more actvely traded: A1 stocks have larger NMS, hgher tradng volumes and more frequent trades, and more compettve quotes, than A2 stocks. In a way, ths fndng s rather ntutve and consstent wth theory. As demonstrated n Hendershott and Mendelson (2000), a CN needs to acheve crtcal mass so as to execute order flow. As a CN does not provde a prcng 19
mechansm, t needs a relable prce dscovery process on the prmary market and a mnmum threshold of tradng volume by partcpants to buld a pool of lqudty. The ablty of reachng such a crtcal mass wll obvously be more probable when the market s actve, deep and well-balanced n terms of sellng and buyng nterest, as hgh market mbalance may generate crowdng effects on one sde of the market. However, the crtcal mass argument s not suffcent to explan the cross-sectonal dfferences n POSIT market share. When examnng the sub-sample of POSIT-crossed stocks, t s nterestng to notce that the stocks whch have the hghest CN market share by volume, are not necessarly the most lqud n Sample A1, n terms of turnover. In fact, γ stocks, for whch the CN market share exceeds 5% of the total traded volume, are less frequently traded and quote-revsed than others, although ther volatlty s nferor to the one of less CN-traded stocks. Apparently, the CN over-performs when the market lacks depth and remans surprsngly nactve n comparson wth the level of rsk. Ths observaton suggests that the CN attracts patent traders who would be reluctant to trade drectly n the market and thus gves lqudty to the market on latent orders. Potental benefts from the CN openng, such as ths, are based on the cost savngs provded by the crossng system. 4.3. Cost savngs on CN-executed orders The potental benefts from CN-tradng rest on the lower executon costs CNs offer. Frst, crossng commssons are usually less than full servce brokerage commssons. Secondly, CN partcpants obtan lower mplct costs: they bear no bd-ask spread and no prce mpact snce the trade prce s ndependent of order sze. For orders wth a sze less or equal to the NMS, the mplct cost savng from tradng n the CN strctly equals the half of the bd-ask spread. For CN-executed orders wth a sze superor to the NMS, whch s the case for most POSIT orders, the mplct cost savng exceeds the half spread as the order may move the prce out of the touch. The estmaton of the mplct costs related to market mpact would requre specfc modellng. As t s not the focus of ths work, for smplcty, I do not compute the total mplct costs saved by POSIT traders but only the part correspondng to the market bd-ask spread. Dong so, I obtan that POSIT users saved 9,195 thousand GBP over Perod 1 and 15,750 thousand GBP over Perod 2. On average, the cost savng per share correspondng to 20
spread savng was 0.0232 GBP over Perod 1 and 0.0217 GBP over Perod 2. Ths cost savng per share was hgher for nsttutonal nvestors than for market makers (see Table 3). Table 3 about here However, the potental cost savngs for CN traders are lmted because the CN does not guarantee executon. Fll rates are no more than a few per cent and ths non-executon rsk s assocated wth potental opportunty costs, as shown n Næs and Ødegaard (2001). Hence, the total beneft from CN-tradng s the result of a trade-off between cost savngs and opportunty costs. 24 After ths glmpse on POSIT tradng actvty, the next secton s dedcated to the man purpose of ths study, that s the analyss of the way market lqudty s related to CNtradng, and t s based on the testable hypotheses establshed at Secton 1. 5. Testng the relatonshp between the CN tradng actvty and the dealer market lqudty: methodology and results The methodology used to test hypotheses H1 to H7 conssts of cross-sectonal regressons of stock-by-stock market qualty measures on varables that measure the tradng actvty n the CN. The analyss s voluntarly conducted at a macro-level, on aggregated measures for the market, and dffers from a trade-by-trade analyss. For ths reason, the cross-sectonal regressons conducted hereafter do not suffer from the selectvty bas 25 that characterses trade-by-trade regressons, where the choce of the tradng mechansm for each trade s endogenous. The results are homogenous from Perod 1 to Perod 2. They globally show that the gans from competton domnate the potental costs of fragmentaton. The CN mght well skm off lqudty tradng from the DM. However, the unexecuted CN order flow does not brng addtonal rsk and lqudty costs on to the market. The rsk sharng benefts offset the cream-skmmng costs, and dealer tradng n the CN nduces a competton effect. 5.1. Opportunstc CN-tradng and unexecuted CN order flow Hypotheses H1 and H2 address the mpact of CN unexecuted order flow on the rskness and the lqudty of the DM. The level of unexecuted order flow for a stock, on a gven perod, s measured by the rate of non executon n the CN, denoted U, and equal to 21
the total volume of unexecuted POSIT orders reported to the total volume of orders newly submtted to POSIT durng ths perod. When no orders are submtted to the CN, U s set to zero. Accordng to H1, U would ncrease the rsk of the market after the matches. To test ths relaton, the rskness of the DM at the end of the tradng day wll be represented by the close-to-close return volatlty per unt of traded volume, that s the rato σ V, where σ s the return volatlty as estmated n equaton (1) and V s the average daly traded volume for stock. Pror to further nvestgaton, several varables have been dentfed as potental control varables for σ V : wth all measures of spreads defned at paragraph 3.2.3 ( QS, ES and CS ), gven the well-known relatonshp between rsk and spreads, the average tradng frequency measured n logarthm, ln ( TF ), and the average number of trades per day TN, as a less rsky stock s probably more frequently traded, the logarthm of the average NMS n number of shares, ln ( NMS ), and n GBP, ln ( NMS ), as I expect stocks wth a deeper market to be less volatle, fnally, the average mbalance IMB, as the volatlty per unt of traded volume could be ncreasng wth the dsequlbra between sellng and buyng orders. When regressng σ V on each of the varables, no sgnfcant relaton s found ether ln ( NMS ) or wth TN. As expected, σ V s postvely related to any spread measure, negatvely related to tradng frequency, negatvely related to ln NMS and postvely related to market mbalance. Interestngly, the most explanatory varable s the average closng spread, CS. No addtonal varable mproves the qualty of the regresson, whatever the consdered perod. ( ) Consequently, H1 s tested OLS-regressng σ on U across each sample and V controllng for CS. Regresson coeffcents and assocated t-values are reported n Table 4. 22
Table 4 about here U coeffcents are not sgnfcantly dfferent from zero for any perod and H1 s rejected. H2 also focuses on the mpact of CN unexecuted order flow. Accordng to H2, opportunstc CN orders, f not executed, would produce addtonal lqudty costs when comng back to the DM, so that spreads would be postvely related to U. Any test of H2 then requres to dentfy the relevant control varables for spreads. The followng range of potental control varables are examned. Frst of all, several measures of volatlty are consdered, as spreads are obvously dependng on volatlty: the standard devaton of close-to-close returns σ, 2 the varance of close-to-close returns σ, the standard devaton of open-to-close returns σ oc, 2 and the varance of open-to-close returns σ oc. Second, spreads are expected to be decreasng n tradng volumes, represented by the logarthm of the average GBP daly traded volume ln ( V ), and n tradng frequency agan measured by ln ( TF ). Also, they should be postvely related to IMB and negatvely related to NQR, as the number of quote revsons s an ndcator of competton ntensty between market makers. Fnally, three other varables are consdered: ( ) NMS ( ) ln, ln NMS and BD, the average share of daly volume declared as broker-dealer to broker-dealer trades n the data. 26 In order to choose the most relevant control varables, OLS-regressons are run followng a stepwse procedure. The varable havng the hghest explanatory power s frst selected. Then, the varable that most ncrease the explanatory power of the model s added, and so on untl the model cannot be mproved. In the end, for each spread measure and for each observaton perod, three control varables are selected: ln ( V ), coeffcents beng negatve, except the one for σ and ln ( NMS ), all ln ( NMS ). The postve relaton between the spreads and the NMS may be nterpreted n the followng way. If two stocks are dentcal n 23