Adverse selecon, ransacon fees, and mul-marke radng Peer Hoffmann November 2010 Absrac We sudy he neracon of adverse selecon and ransacon fees n a fragmened fnancal marke. Absen a rade-hrough prohbon, lqudy provders on alernave radng plaforms may be exposed o an ncreased adverse selecon rsk due o frcons n raders marke access. As a consequence, he man marke domnaes (offers beer quoes) frequenly albe chargng hgher ransacon fees. The emprcal analyss of a recen daase of radng n French and German socks suggess ha rades on Ch-X, a recenly launched low-cos radng plaform, carry sgnfcanly more prvae nformaon han hose execued n he Prmary Markes. Conssen wh our heory, we fnd a negave relaonshp beween he compeveness of Ch-X s quoes and hs excess adverse selecon rsk faced by lqudy provders n he crosssecon. Our resuls sugges ha rade-hroughs are a serous obsacle o ner-marke compeon. eywords: MFID, Iner-marke compeon, Adverse selecon, Transacon fees I am ndebed o Francsco Peñaranda for excellen supervson and many fruful dscussons. I would also lke o hank Bruno Bas, Jean-Edouard Collard, Hans Degryse, Therry Foucaul, Başak Güneş, Alber Menkveld, Alsa Roëll, arl Schlag, Jos van Bommel, and semnar parcpans a Toulouse Unversy, UPF Fnance Lunch, and aendans of he EFA Docoral Tuoral and he CFS Conference on he Indusral Organzaon of Secures Markes for useful commens and suggesons. Par of hs research projec was underaken whle I was vsng he Sad Busness School a Oxford Unversy, whose hospaly s ruly apprecaed. Fnally, I would lke o express my graude o Ch- X Europe Ld. and parcularly Enzo Sngone for provdng some of he daa used n hs sudy. Fnancal suppor by Unversa Pompeu Fabra and Generala de Caalunya s graefully acknowledged. All errors are my own. Ph.D. canddae, Unversa Pompeu Fabra, conac: peer.hoffmann@upf.edu 1
1. Inroducon The nroducon of he Markes n Fnancal Insrumens Drecve (MFID) n lae 2007 has spawned compeon among sock exchanges across Europe. Under he new legslaon, alernave radng plaforms (so-called Mullaeral Tradng Facles, henceforh MTFs) may drecly compee wh he naonal sock exchanges (Prmary Markes) for cusomer order flow. Ulmaely, MFID ams a creang a level playng feld ha promoes compeon beween marke ceners and fosers nnovaon. One ssue ha has receved a grea deal of aenon n he conex of ner-marke compeon s he desgn of bes execuon polces. Under MFID, nermedares such as banks and brokers bear he enre responsbly for obanng he bes possble resul for her clens orders. Imporanly, bes execuon s no only based on prces bu raher perms he consderaon of a wde array of addonal execuon characerscs such as lqudy, order sze, and he lkelhood of execuon, among ohers (see e.g. Perella [2009] and Gomber and Gsell [2010] for deals). Consequenly, MFID does no formally enforce ner-marke prce prory and orders are permed execue a a prce ha s nferor o he bes avalable prce across venues ( rade-hroughs ). Ths dffers consderably from he rules ha are n place n he Uned Saes under Reg NMS, whch mandaes exchanges o re-roue orders o oher marke ceners f hose are offerng a beer prce ( rade-hrough rule ). In hs arcle, we argue ha allowng for rade-hroughs may benef he Prmary Markes and herefore lm ner-marke compeon. To hs end, we sudy how adverse selecon and ransacon fees nerac n a fragmened fnancal marke where rade-hroughs are no prohbed. Inspred by he curren marke seng n Europe, we develop an exenson of he Glosen and Mlgrom [1985] sequenal rade model where lqudy provders pos quoes n wo separae radng plaforms, he Prmary Marke and a low-cos MTF. A key ngreden n our model s he exsence of marke access frcons. Followng Foucaul and Menkveld [2008], we assume ha he Prmary Marke s accessble by all agens n he economy, whle radng on he MTF requres a so-called smar order roung sysem ha s only avalable o a subse of he rader populaon. Due o he absence of a rade-hrough rule, hs access frcon gves rse o ner-marke dfferences n he adverse selecon rsk faced by lqudy provders. If nformed raders are more lkely han unnformed raders o be smar 1
rouers, suaons can arse where he Prmary Marke offers beer quoes frequenly despe chargng hgher ransacon fees. The analyss of a recen sample of ransacons and quoe daa for German and French socks confrms he exsence of mperfecons n raders roung ables, as only abou every second rade orgnaes from agens wh perfec acess o Ch-X, a recenly launched MTF. Moreover, we fnd ha rades execued on hs new radng plaform carry sgnfcanly more prvae nformaon han her counerpars on he Prmary Markes, whle rade-hroughs are parcularly unnformave. Ths mples ha lqudy provders on he MTF ncur a hgher adverse selecon rsk precsely because an mporan fracon of he unnformed order flow s held capve n he Prmary Markes. Cross-seconal regressons provde emprcal suppor for our heory, as we fnd ha hs excess adverse selecon rsk s negavely relaed o Ch- X s presence a he nsde quoe. These resuls have mporan mplcaons for he desgn of bes execuons polces. Allowng for rade-hroughs benefs he Prmary Markes because capve raders consue a sable cusomer bass ha s no subjec o compeon from oher exchanges. Addonally, lqudy provders on alernave radng venues are exposed o a hgher adverse selecon rsk because smar rouers are more lkely o be nformed han he average rader. Ths excess rsk frequenly resuls n poor quoes and herefore dvers addonal order flow from smar rouers o he Prmary Markes. Therefore, rade-hroughs consue an mporan obsacle for ner-marke compeon as he cheaper marke (n erms of ransacon fees) may end up wh very lle order flow, even from agens ha have access o. In hs sense, our model suppors he dea ha he enforcemen of ner-marke prce prory may foser compeon beween exchanges. Our fndngs are n lne wh exsng concerns abou MFID s bes execuon polcy. In he absence of ner-marke lnkages, marke fragmenaon ncreases he coss for monorng markes n real-me, as requres nermedares o adop a smar order roung sysem. For smaller marke parcpans, he subsanal coss assocaed wh such an nfrasrucure may well exceed he expeced benefs. Conssen wh hs vew, a recen arcle n he Fnancal Tmes [2010] repors ha much real order flow s rounely roued owards he Prmary Markes as small brokers shy away from nvesmens n echnology. Addonally, Ende and Lua [2010] documen a szeable 2
fracon of rade-hroughs n European socks, whch confrms he exsence of marke access mperfecons pos-mfid. Moreover, recen anecdoal evdence appears conssen wh our vew ha a hgher adverse selecon rsk negavely affecs he compeveness of MTFs. On Sepember 8h, 2008, he marke openng on he London Sock Exchange was delayed unl 4 pm n he afernoon due o a echncal problem. Whle Ch-X and Turquose were sll avalable for radng n U socks, he marke acvy ceased almos compleely durng he LSE s sysem ouage. A smlar even occurred on Euronex on Aprl 20h, 2009, when radng commenced wh a delay of one hour. Agan, radng dd no mgrae o he MTFs. Presumably, excessve adverse selecon rsk lead o a marke breakdown. Ye anoher ouage on he LSE durng he afernoon of November 9h, 2009 saw some radng mgrae o alernave venues. The fac ha he ouage occurred lae n he radng day may have helped he MTFs, as much of he day s prce dscovery had already occurred pror o he LSE s breakdown. Ths paper conrbues o he exsng leraure on ner-marke compeon. Whle early heorecal papers (e.g. Pagano [1989] and Chowdry and Nanda [1991]) argue ha markes dsplay a naural endency o consoldae as a consequence of lqudy exernales, here s a large emprcal leraure ha emprcally documens he exsence of fragmened fnancal markes (e.g. Bessembnder [2003], Boehmer and Boehmer [2004], Goldsen e al. [2007], Bas e al. [2010]). Mos closely relaed o our paper, Foucaul and Menkveld [2008] develop and es a heory of compeon beween wo markes n an envronmen ha allows for radehroughs. In her model, whch absracs from uncerany abou he asse s fundamenal value, rsk-neural compeve agens rade off he expeced revenue from lqudy provson agans order submsson fees. They fnd ha he share of lqudy provded on he alernave radng plaform (weakly) ncreases n he proporon of smar rouers. Whle our work shares her assumpon of heerogeney n raders roung ables, we consder a model wh a rsky asse and asymmerc nformaon. We herefore conrbue o he leraure by sudyng he nerplay of adverse selecon rsk and ransacon fees n he conex of ner-marke compeon under he absence of prce prory across radng venues. Naurally, our work s also closely relaed o a number of papers ha sudy dfferences n nformed radng across markes. One srand of hs leraure analyzes he effecs of cream-skmmng and paymen for order flow (e.g. Easley e al. [1996a], 3
Bessembnder and aufman [1997], Baalo e al. [1997], Parlour and Rajan [2003]). In our conex, he compeveness of alernave radng plaforms s hampered by he concenraon of unnformed order flow on he Prmary Markes due o rade-hroughs generaed by capve raders. Ths conrass srongly wh he sandard paradgm whn hs leraure, where unnformed order flow s dreced away from he man marke cener due o so-called preferencng agreemens 1. Oher papers (e.g. Grammg e al. [2001], Barclay e al. [2003], Goldsen e al. [2007]) documen dfferences n nformed radng beween dealer markes and anonymous elecronc radng sysems. Generally, hese sudes fnd order flow n elecronc markes o be more nformave, presumably because nformed raders value he hgher speed of execuon offered by hese venues and ry o preven nformaon leakage due o neracng wh nermedares such as marke makers. In conras, we show ha dfferences n nformed radng across exchanges may also arse hrough he absence of ner-marke prce prory pared wh frcons n raders marke access. Fnally, our model also accommodaes he resuls of Hengelbrock and Thessen [2009], who sudy he marke enry of he Turquose MTF n lae 2008 and fnd ha he radng acvy n larger and less volale socks ends o fragmen more. Ths paper s organzed as follows. Secon 2 nroduces our heorecal model, whle Secon 3 descrbes he nsuonal envronmen and presens he daa. Secon 4 presens esmaes for dfferences n nformed radng beween he Prmary Markes and Ch-X, and Secon 5 presens evdence on he model s emprcal mplcaon. Secon 6 concludes, whle proofs and ables are relegaed o he appendx. 2. The Model There s a sngle rsky asse wh lqudaon value V { V, V }, where we se Pr(V = V ) δ 0 = 1/2 for smplcy. The asse can be raded on wo separae radng plaforms, whch we denoe by C(h-X) and P(rmary Marke). These markes are populaed by N P 2 and N C 2 dencal, rsk neural marke makers, respecvely, who pos bd and ask quoes for a sngle un of he rsky asse. Marke P charges a 1 Preferencng agreemens usually esablsh a relaon beween a broker and a radng plaform, where brokers receve a paymen for drecng he enre order flow o a parcular venue. Ths pracce was poneered by Bernhard Madoff n he 1980 s. 4
cos c > 0 per rade o marke makers, whle he cos charged by marke C s normalzed o zero. We assume ha c s very small n comparson o he asse s fundamenal uncerany,.e. c << (V V)/2. There s a connuum of raders, who arrve sequenally a me pons =1,, T. Unlke Denner [1993], we assume ha each rader may buy or sell a mos one un of he asse. Moreover, agens may only rade once, drecly upon enerng he marke. A proporon µ of he rader populaon s perfecly nformed abou he lqudaon value V, whle he remanng raders are unnformed. Whereas all agens can rade n marke P, radng n marke C requres a smar order roung sysem ha s no avalable o all agens n he economy. Denoe he proporon of nformed and unnformed raders wh smar order roung echnology by θ I and θ U, respecvely. We call hose raders smar rouers, whle agens ha can only rade n marke P are named capve raders. Fgure 1 n he appendx graphcally depcs he srucure of he rader populaon for he case θ I > θ U. The overall proporon of smar rouers s gven by θ = µθ I + (1 µ)θ U. Le µ SR and µ CT denoe he proporon of nformed raders among smar rouers and capve raders, respecvely, whch are gven by µ SR θ I µ θ µ CT (1 θ I )µ 1 θ I s easy o see ha θ I > θ U (θ I < θ U ) mples µ SR > µ > µ CT ( µ SR < µ < µ CT ). [Inser Fgure 1 abou here] Unnformed raders buy or sell wh equal probably. Informed raders buy f V = V and he bes ask a whch hey can rade s less or equal o V a he me of her arrval, and sell f V = V and he bes avalable bd s hgher or equal o V. Oherwse, hey do no rade. Traders always choose o rade n he marke ha offers he beer prce gven her radng neres and marke access. One ssue arses n suaons where boh venues dsplay dencal quoes, such ha smar rouers are ndfferen beween markes. Gven ha marke makers are no requred o place her quoes on a dscree grd (.e. he ck sze s zero), such es 5
may arse even f boh radng plaforms charge dfferen fees for marke orders, because ulmaely all fees are borne by he marke order raders (lqudy provders smply pass hem on). Clearly, a posve ck sze can break raders ndfference, as es wll only occur before ransacon coss. Then, smar rouers wll raonally rade n he marke ha demands lower fees for marke orders. As he nroducon of a ck sze comes a he expense of addonal noaon whou provdng furher nsghs, we op for a reduced-form approach and assume ha smar rouers always rade n marke C n he case of an ner-marke e. Assumpon (Te-breakng rule): In he case of an ner-marke e, smar rouers always rade n marke C. On he oher hand, f several marke makers pos he same prce n he same marke, we assume ha one of hem s randomly seleced (wh equal probably) as a radng parner for he ncomng rader. Afer each radng round, marke makers updae her belefs abou he probably of he hgh oucome of he asse s lqudaon value usng Bayes rule and revse her quoes accordngly. We assume ha hey observe each oher s rades 2, whch mples ha hey hold dencal belefs abou he lqudaon value a all mes. Le δ 1 denoe hs common belef pror o he arrval of he -h rader. For smplcy, we resrc our analyss o he bd sde. Resuls for he ask sde can be derved followng exacly he same logc. Le b,p and b j,c he quoes of marke makers and j n markes P and C, respecvely, where =1,, N P and j=1,, N C. Moreover, defne he bes bd n marke k as b k = max b 1,k,...,b N k,k { } for k { P,C}. Gven ha capve raders can only rade n he Prmary Marke, he probably of a sell occurrng n marke P s always srcly posve. On he oher hand, rade may only occur n marke C f he bd quoe a leas maches he bd prevalng n marke P. Ths leads us o he followng defnon of marke co-exsence. 2 Ths can be nerpreed as markes beng subjec o pos-rade ransparency. See Madhavan [1995] for a model whou pos-rade ransparency. 6
Defnon (Marke co-exsence): Markes co-exs ff b C b P. We are now ready o sae our man resul, whch provdes a condon for marke coexsence o oban n equlbrum. We resrc our aenon o symmerc equlbra n pure sraeges. Proposon 1: Le N P. Then, n equlbrum, markes co-exs f and only f c (V V )(µ SR µ)ψ B (δ 1 ) (1) where 2δ Ψ B (δ 1 ) = 1 (1 δ 1 ) [ 1+ µ SR (1 2δ 1 )]1+ µ(1 2δ 1 ) [ ] Proof: See Appendx A. To undersand he nuon behnd hs resul, frs consder he case where θ I θ U. Ths mples ha µ SR µ,.e. he proporon of nformed raders among smar rouers s no greaer han he proporon of nformed raders n he overall rader populaon, such ha marke makers on plaform C face a (weakly) lower adverse rsk. Addonally, marke C does no charge any ransacon fees, so ha he bes bd on Ch-X s always srcly hgher han he bes bd n he Prmary Marke. In hs case, condon (1) s necessarly sasfed as he rgh-hand sde s always negave, such ha markes co-exs. Now consder he converse suaon, where θ I > θ U, or equvalenly µ SR > µ. In hs case, lqudy provders n marke C face an excess adverse selecon rsk due o a hgher proporon of nformed raders among smar rouers, whch s capured by he rgh-hand sde n (1). On he oher hand, hey do no ncur he ransacon fee c ha s payable for ransacons n marke P. Clearly, he bes bd on Ch-X can only mach or mprove upon he Prmary Marke f he fee savngs compensae for he excess adverse selecon rsk. The funcon Ψ B (δ 1 ) capures he behavour of he adverse selecon dfferenal over me: As he order flow s nformave abou he asse s 7
lqudaon value, marke makers belefs δ 1 converge o eher zero or one as he number of radng rounds becomes large, such ha Ψ B (δ 1 ) approaches zero. As he adverse selecon rsk dmnshes, dfferences n quoes across markes are enrely deermned by he dfference n ransacon fees, and he marke co-exsence condon s necessarly sasfed. Proposon 1 has an emprcal mplcaon for Ch-X s quoe compeveness n he cross-secon. In order o see hs, defne AS = max δ 1 AS(δ 1 ), where AS(δ 1 ) = (V V )(µ SR µ)ψ B (δ 1 ). Now consder wo asses, A and B, and suppose ha AS A (δ 1 ) > AS B (δ 1 ) for all marke maker belefs δ 1,.e. he cross-marke adverse selecon dfferenal (Ch-X mnus Prmary Marke) s always srcly greaer for asse A. If c AS A, he marke co-exsence condon (1) s sasfed for all possble belefs and he bes bd on Ch-X wll mach or mprove upon he bes bd n he Prmary Marke hroughou he enre radng day for boh asses. On he oher hand, condon (1) s no always sasfed f c < AS A. Moreover, as AS A (δ 1 ) > AS B (δ 1 ) for all δ 1, here exs belefs for whch Ch-X maches or mproves on he Prmary Marke for asse B, whle he Prmary Marke dsplays a srcly beer quoe for asse A. The converse never holds. Ths leads us o he followng emprcal predcon. Corollary 1: In he cross-secon, Ch-X s presence a he nsde quoe (weakly) decreases n he adverse selecon rsk dfferenal (Ch-X mnus Prmary Marke). 3. Insuonal deals and daa In he remander of he paper, we emprcally analyze a sample of ransacon daa from Ch-X and wo Prmary Markes, Euronex (Pars) and Xera (Frankfur), n order o valdae he emprcal predcon of our model (Corollary 1). Before we urn o he descrpon and a prelmnary analyss of our daase, we provde a bref overvew of he nsuonal deals ha peran o our sample perod (May Aprl 2008). 8
3.1 Insuonal deals Ch-X was launched on March 30h, 2007, when sared o offer radng n German and Duch blue chps. Laer he same year, radng was exended o he larges Brsh (June 29h), French (Sepember 28h), and Swss (November 23rd) eques. Several oher European markes were added subsequenly, and sarng n lae 2008, he specrum of avalable socks was exended o md-caps. As of November 2010, almos 1,400 socks from 15 European counres could be raded on Ch-X. Accordng o Fdessa 3, Ch-X s marke share durng he frs sx monhs of 2010 exceeded 20% for Belgan, Duch, French, German, and Brsh blue chps. Lke vrually all European sock markes, Ch-X s organzed as a connuous, fully elecronc lm order marke (LOM). Durng radng hours, parcpans can connuously subm, revse and cancel lm and marke orders. Non-execued lm orders are sored n he lm order book, and ncomng marke orders execue agans hose. Tradng s fully anonymous, boh pre- and pos-rade. Ch-X offers a very smple fee srucure, whch s asymmerc (a so-called make/ake fee scheme): Passve execuons (lm orders) receve a rebae of 0.2 bps, whle aggressve execuons (marke orders) are charged 0.3 bps. Therefore, he plaforms overall revenue per rade amouns o 0.1 bps. In he US marke, hese make/ake fee schemes have proven key o success for he ECNs. As opposed o Ch-X, he Prmary Markes under consderaon n hs paper (Euronex Pars and Deusche Boerse s Xera) do no dsngush beween acve and passve execuons,.e. her fee srucures are symmerc. Euronex charges 1.20 plus 0.055 bps per execued order, whch amouns o 0.455 bps for an average rade sze of ~ 30,000 (see Table 2 n Secon 3.2). Xera charges 0.552 bps per rade (subjec o a mnmum charge of 0.69 wh a cap a 20.70), whch s somewha more expensve for he average rade sze bu cheaper for smaller rades. Boh exchanges offer dfferen rebae schemes for parcularly acve members subjec o mnmum acvy charges. Overall, he ransacon fees n boh Prmary Markes are 3 See hp://fragmenaon.fdessa.com 9
relavely smlar and sgnfcanly exceed hose on Ch-X, parcularly for orders provdng lqudy 4. Besdes chargng consderably lower fees, Ch-X dsngushes self from he Prmary Markes n several oher aspecs. Mos promnenly, he MTF specfcally arges hgh frequency raders va an ulra-low sysem laency, whch accordng o he plaform 5 s up o en mes faser han he fases European prmary exchange. Moreover, Ch-X offers a wder range of admssble order ypes such as hdden and pegged orders. Whle he frs order ype s compleely nvsble unl execued 6, he laer ype s a lm order where he lm prce s pegged o a reference prce, e.g. he bes bd n he Prmary Marke, and s updaed connuously. Fnally, a he me of our sample (Aprl-May 2008), Ch-X faclaed he undercung of Prmary Markes quoes by offerng a lower ck sze for mos secures. The enry of addonal MTFs rggered a race for lower ck szes n early 2009, whch was ended wh an agreemen brokered by he Federaon of European Secures Exchanges (FESE), afer whch he MTFs adoped he ck szes used by he respecve Prmary Marke. Whle Ch-X only offers radng n a connuous LOM, boh Xera and Euronex addonally hold call aucons o se he openng and closng prces. Xera also has an nraday call aucon a 13:00 CET, whch neverheless generaes only neglgble radng volume excep on days where dervave conracs expre (see Hoffmann and Van Bommel (2010)). Moreover, unlke Ch-X, he Prmary Markes have a fxed se of rules ha rggers an auomac call aucon n mes of exreme prce movemens (so-called volaly nerrupons). 3.2 Daa and prelmnary analyss Ch-X Ld. generously provded us wh a very dealed daase for he monhs of Aprl and May 2008, comprsng a oal of 43 radng days. The daa conans nformaon on he enre order raffc generaed durng hs perod, lsng lm order addons, cancellaons/modfcaons as well as rades separaely. Tmesamps are 4 Boh Xera and Euronex have desgnaed marke makers ha are commed o manan a mnmum spread and a ceran deph for ndvdual socks. Those marke parcpans are usually exemp from ransacon fees. 5 Ch-X celebraes s frs annversary, Ch-X press release, 07.04.2008 6 Hdden orders usually mus mee mnmum sze requremens under MFID. 10
rounded o he neares mllsecond. From hs daa, we reconsruc he enre lm order book as well as he bes bd and offer (BBO) a each pon n me. Daa for rades and quoes of French and German socks on her respecve Prmary Markes (Euronex and Xera) durng he correspondng me perod was obaned from Reuers. Agan, mesamps are rounded o he neares mllsecond. Whle he Ch-X daa always conans a qualfer ha ells us wheher a marke order was a buy or a sell, we sgn rades on he Prmary Markes usng he Lee and Ready [1991] algorhm. As opposed o rades n a dealer marke such as he NYSE, he rsk of order msclassfcaon s very small n a pure lm order book. Mergng he BBO daa from Ch-X and he Prmary Markes, we oban he European Bes Bd and Offer (EBBO). We resrc our analyss o he connuous radng phase, whch spans he me beween 9:00 and 17:30 CET. A he me of our sample, Ch-X was he only exsng MTF and only offered radng n blue chps, such ha our analyss s lmed o he consuens of he CAC40 and DAX30 ndces. We drop hree French socks (Arcelor, EADS and Dexa) from our sample as hey are smulaneously raded on oher European markes (Amserdam, Frankfur, and Brussels, respecvely), such ha our fnal sample comprses of 67 socks. [Inser Tables 1 and 2 abou here] Table 1 n he appendx lss he socks conaned n our sample, whle Table 2 conans summary sascs on he radng acvy durng our sample perod. Overall, radng on Ch-X accouns on average for 5.95% of oal radng volume, or 12.31% n erms of rades. Consequenly, he average rade sze on he Prmary Markes ( 28,990) s more han wce as large as he average ransacon value on Ch-X ( 12,620). Ths s n lne wh Ch-X beng largely domnaed by algorhmc raders, who have been shown o employ much smaller rade szes han human raders (see e.g. Hendersho and Rordan [2009]). Moreover, s conssen wh small orders beng parcularly cheap o execue on he MTF due o he Prmary Markes mnmum fxed fees per order (see Secon 3.1). We also repor he resuls for ercles based on socks average radng volume. Ch-X has a consderably larger marke share (boh n erms of rades and raded value) for 11
he mos acve socks, whch s conssen wh he evdence presened n Hengelbrock and Thessen [2009] for he Turquose MTF. Panel A of Table 3 conans sascs abou he qualy of Ch-X s quoes. The MTF s frequenly presen a he EBBO (around 49% for eher bd or ask), and ofen even mproves on he Prmary Markes quoes (ca. 26% for bd or ask). Neverheless, he frequency wh whch he MTF s smulaneously presen a boh sdes of he nsde quoe (alone) s consderably lower wh approxmaely 24% (7%), ndcang ha he acvy on Ch-X s ofen resrced o one sde of he marke. Whle he Prmary Markes are naurally presen a he nsde quoe more ofen, hey frequenly face compeon for a leas one sde of he marke as hey only spend roughly 26% of he me alone a he EBBO. Invesgang he ndvdual ercles, one can see ha he MTF s quoe compeveness s somewha hgher for more acve socks, whch s n lne wh he hgher marke shares n hose socks. [Inser Table 3 abou here] Panel B of Table 3 repors he average avalable marke deph for each radng venue condonal on beng presen a he nsde quoe. Overall, he avalable deph n he Prmary Markes s roughly hree mes he deph on Ch-X, whch may n par explan he observed cross-marke dfferences wh respec o he average rade sze. Neverheless, he MTFs dsplays consderable deph for s quoes. Based on s presence a he bes quoes, Ch-X s marke share (n erms of rades or radng volume) seems srkngly low. Ths s conssen wh he marke access frcon n our model, whch forces capve raders o rade n he Prmary Marke rrespecvely of he quoes prevalng elsewhere. In order o quanfy hs frcon, we follow Foucaul and Menkveld [2008], who sugges esmang he proporon of smar rouers (θ) by he percenage of rades beng execued on Ch-X condonal on he Prmary Marke offerng a srcly worse quoe. We addonally requre ha he deph on he MTF s suffcen o ge he order flled enrely because s naural o assume ha raders ake quanes no accoun when decdng where o roue her orders. Gven ha Ch-X offers a consderably lower marke deph on average, some agens may avod splng up her orders and herefore prefer he Prmary Marke. 12
Ths wll parcularly be he case f he margnally beer prce on he MTF s only avalable for a small fracon of he oal order sze. The resuls n Table 4 (frs column) srongly confrm he mporance of mperfec order roung. Condonal on Ch-X offerng a beer quoe wh suffcen deph, every second order s sll execued n he Prmary Marke, such ha he proporon of smar rouers s roughly 50%. Ineresngly, he roung frcon vares lle across he dfferen acvy ercles, whch s n conras o he resuls n Foucaul and Menkveld [2008], who repor a marked drop n smar order roung once movng beyond he mos acve socks. [Inser Table 4 abou here] An addonal em of grea neres s he e-breakng rule assumed n he heorecal model of Secon 2. Gven he daa a hand, we can acually compue an esmae of he e-breakng rule, agan followng Foucaul and Menkveld [2008]. In parcular, he probably of a rade occurrng on he Prmary Marke condonal on equal quoes across venues and suffcen deph on Ch-X o fll he order compleely s equal o π = (1 θ) + θτ where θ s he proporon of smar rouers and τ denoes he parameer of he ebreakng rule. Ths equaon smply saes ha all capve raders plus a fracon τ of he smar rouers wll rade on he Prmary Marke n he case of a e, whle he remanng agens rade on Ch-X. The las wo columns of Table 4 conan he esmaes for he proporon of rades execung n he Prmary Marke under an nermarke e (π) and he e-breakng rule (τ) for he enre sample as well as he ndvdual ercles. We fnd ha our assumpon regardng he e-breakng rule n Secon 2 s clearly confrmed, as we canno rejec he null hypohess ha τ s equal o zero, ndcang ha smar rouers always choose o rade on Ch-X f a leas maches he quoes n he Prmary Marke. Gven ha Ch-X charges lower fees for marke orders (excep for very large orders), hs resul s no very surprsng. 13
4. Esmang dfferences n nformed radng The mplcaons of our model from Secon 2 regardng marke co-exsence crucally depend on wheher or no nformed raders are more lkely han nose raders o have access o he alernave radng plaform (.e. Ch-X). I s mporan o noce, ha raders roung ables drecly ranslae no he adverse selecon rsk faced by marke makers and he prce mpac of rades. We can herefore fler ou he relevan case for our seng by esng for dfferences n nformed radng beween Ch-X and he Prmary Markes. 4.1 Effecve spread decomposon One of he mos wdely used measures for he assessmen of radng coss s he percenage effecve half-spread, whch s defned as ES = q p m m (2) where p denoes he ransacon prce a me, m s he conemporaneously prevalng EBBO md-quoe, and q s a rade drecon ndcaor ha akes he value of 1 for buys and -1 for sells. Compared o he quoed spread, hs measure has he advanage ha measures radng coss only a he acual me of a rade, akng no accoun ha lqudy demanders wll aemp o me he marke and rade when he bd-ask spread s relavely narrow. Besdes s smplcy, hs measure has he addonal advanage ha can be decomposed no an adverse selecon (prce mpac) componen AS = q m + m m (3) and an order processng componen, usually ermed realzed half-spread 14
RS = q p m + m (4) where s he md-quoe mnues afer he ransacon, he me a whch he m + marke maker s assumed o cover her poson. Whle hese measures consue exreme smplfcaons of realy (e.g. rades beween and + are gnored), hey have become a benchmark for assessng radng coss. Moreover, hs spread decomposon also allows us o compare marke maker revenues before ransacon fees across markes hrough he realzed spread. For boh markes, we calculae all hree measures for each sock and radng day and hen calculae averages across sock-days for he separae acvy ercles. Gven ha even he leas acve socks n our sample have a consderable radng volume, hs procedure delvers relavely conservave sandard errors. Moreover, rade-weghed sascs would bas he resuls n favour of Ch-X, as has a larger marke share n he mos acve socks, whch generally exhb lower effecve spreads. In all calculaons, we exclude rades ha occur when he marke s locked or crossed,.e. when he EBBO spread s non-posve (see Shklko e al. [2008]). Neverheless, ncludng hese observaons does no aler he resuls qualavely. The resuls are lsed n Table 5. Overall, radng on Ch-X s no cheaper before fees: Across all socks and days, he effecve spread on Ch-X averages 2.67 bps, compared o 2.64 bps n he Prmary Marke (Panel A). The dfference of 0.03 bps s very small n economc erms (roughly 1%) and sascally nsgnfcan. Gven ha Ch-X charges lower fees for marke orders (excep for very large rade szes, see Secon 3.1), he dfference n effecve spreads can be expeced o be slghly negave ne of fees. Exac calculaons are no possble because parcpans n he Prmary Markes may be graned rebaes dependng on her radng acvy. Overall, he resuls sugges ha radng on Ch-X s a mos margnally cheaper han on he Prmary Markes ne of fees. [Inser Table 5 abou here] 15
Neverheless, a look a he spread decomposon 7 (Panels B and C) reveals mporan dfferences beween boh markes. Ch-X dsplays a sgnfcanly larger adverse selecon componen (2.68 bps compared o 2.27 bps), bu markedly lower realzed spreads (-0.01 bps vs. 0.37 bps). Imporanly, he dfferences are boh sascally and economcally very sgnfcan. Lqudy provders on Ch-X are exposed o a much greaer adverse selecon rsk, whle her gross revenues are essenally equal o zero. Neverheless, as Ch-X grans a 0.2 bps rebae per execued lm orders, hey sll ne a small prof afer fees. Gven ha lqudy provders on he Prmary Markes face a posve ransacon fee 8, marke makers revenues appear o be very smlar across markes. Addonally, he fac ha revenues from lqudy provson are very close o zero ndcaes a very compeve marke. Overall, he effecve spread decomposon clearly suggess ha lqudy provders on Ch-X face a hgher adverse selecon rsk. Ths resul appears very robus as holds across all acvy ercles, and we observe a hgher prce mpac on Ch-X for all bu 3 socks 9 (hese negave dfferences are no sascally dfferen from zero). Moreover, he realzed spreads ncely llusrae ha he lqudy rebae on he MTF helps marke makers o susan hs excess rsk. In our model, cross-marke dfferences n adverse selecon rsk arse because he proporon of nformed raders dffers beween smar rouers and capve raders. Whle a hgher prce mpac for orders execued on Ch-X ndcaes ha smar rouers are more lkely han capve raders o be nformed (θ I > θ U or equvalenly µ SR > µ), also mples ha we should observe a lower prce mpac for rade-hroughs, as hose sem exclusvely from less nformed capve raders (µ CT < µ ). In order o verfy hs, we separae he Prmary Marke rades no rade-hroughs and non-radehroughs and calculae he effecve spread decomposon for boh ypes of ransacons. [Inser Table 6 abou here] 7 We se = 5 mnues. Seng he nerval o 15 or 30 mnues delvers qualavely smlar resuls. 8 Excep for desgnaed marke makers. 9 For he sake of parsmony, we do no repor he resuls for ndvdual socks (avalable upon reques). 16
The resuls n Table 6 srongly confrm ha rades volang ner-marke prce prory are more lkely o sem from unnformed raders han ransacons ha occur whle he Prmary Marke s a he nsde quoe. The esmaed average prce mpac of a radehrough s 0.91 bps, whch s less han half of he 2.39 bps prce mpac of non-radehroughs. Naurally, rade-hroughs dsplay a sgnfcanly larger effecve spread (4.53 bps), as hey leave money on he able by radng hrough a beer avalable quoe. Pared wh he lower prce mpac, hs booss he realzed spread (3.62 bps), whch s pockeed by he marke maker. Overall, hese resuls ndcae ha he observed excess adverse selecon rsk on Ch-X s drven by he absence of manly unnformed capve raders. 4.2 Hasbrouck s srucural VAR In a semnal conrbuon, Hasbrouck [1991] suggess a srucural VAR model o esmae he permanen prce mpac of a rade. Snce hen, hs measure has emerged as one of he mos frequenly employed procedures n he emprcal marke mcrosrucure leraure. The basc dea behnd Hasbrouck s model s ha here exss a dynamc lnear relaonshp beween prce (quoe) changes and rades, where curren rades have an mpac on curren and fuure prce changes, whle curren prce changes can only rgger fuure rades. In our conex, he model can be wren as P C r r = a r + b x + c x + ε =1 = 0 = 0 x P P C P = d r + e x + f x + ε =1 =1 =1 x C P C C = g r + h x + x + ε =1 =1 =1 (5) (6) (7) where r denoes log changes n he EBBO md-quoe and he x k, k { P,C} are dscree varables ha ake he value of 1 for a buy, -1 for a sell, and 0 oherwse. As dealed by Hasbrouck [1991], he dscree naure of he x k does no consue any obsacle for he srucural VAR. We esmae he model n ck me, such ha rades 17
across markes are necessarly uncorrelaed 10, and runcae he VAR afer 10 lags 11. A he begnnng of each radng day, all lags are se o zero. To judge he long-erm (permanen) prce mpac of a rade, he VAR s nvered o oban he VMA represenaon r P x x C A(L) B(L) C(L) = D(L) E(L) F(L) G(L) H(L) I(L) ε r ε P ε C (8) where A(L) I(L) are lag polynomals. Ths s he mpulse response funcon, and he long-erm prce responses o an unexpeced rade n eher marke are gven by he coeffcen sums PI P = B and PI C = C. = 0 = 0 [Inser Table 7 abou here] Table 7 conans he permanen prce mpacs for boh markes (mpulse responses are runcaed afer 20 perods), where we agan repor sock-day averages for he enre sample and he acvy ercles. The resuls are n lne wh hose from he effecve spread decomposon. On average, he permanen prce mpac of a rade on Ch-X amouns o 1.86 bps, compared o 1.61 bps for Prmary Marke rades. The dfference s sascally sgnfcan a he 1% level. As n he prevous secon, we observe a hgher prce mpac on Ch-X for each acvy ercle, whch underlnes he robusness of our fndngs. For ndvdual socks, we fnd a hgher prce mpac for Prmary marke rades n only 6 cases, and none of hese dfferences are sascally sgnfcan 12. Overall, hese resuls provde furher evdence for lqudy provders facng a hgher adverse selecon rsk on Ch-X. In order o check wheher he dfference n adverse selecon across markes s ndeed due o capve raders beng manly unnformed, we modfy he VAR from equaons 10 Esmang he model wh daa aggregaed o 5-second nervals delvers qualavely smlar resuls bu requres placng upper and lower bounds on a venue s prce mpac as he order flows are no longer uncorrelaed. 11 The ncluson of addonal lags does no aler our conclusons. 12 The resuls for ndvdual socks, whch we do no repor for brevy, are avalable upon reques. 18
(5) (7) and spl he Prmary Marke order flow no rade-hroughs and non-radehroughs. Ths resuls n he followng VAR sysem (12) (11) (10) (9), 1 1, 1, 1, 1 1, 1, 1,, 1 1, 1, 1,, 0 0, 0, 1 C C P TT NTT P C TT C P TT NTT P P TT NTT C P TT NTT P NTT P r C P TT NTT P x p x o x n m r x x l x k x j r x x h x g x f r e x x d x c x b r a r ε ε ε ε + + + + = + + + + = + + + + = + + + + = = = = = = = = = = = = = = = = = where x P,TT and x P,NTT refer o Prmary Marke order flow due o rade-hroughs and non-rade-hroughs, respecvely. Table 8 repors he permanen prce mpacs obaned from he correspondng VMA represenaon. The resuls are qualavely smlar o hose obaned from he effecve spread decomposon. The permanen prce mpac of a rade-hrough s 0.44 bps, whch s sgnfcanly lower han 1.72 bps mpac of a non-rade-hrough, wh a -sasc of around 15. Ths suppors he vew ha marke makers on Ch-X face a hgher adverse selecon rsk precsely because her quoes are no exposed o he relavely unnformed capve raders. [Inser Table 8 abou here] 4.3 PIN Gven our heorecal framework, he PIN model by Easley e al. [1996b] s a naural choce for assessng dfferences n nformed radng beween Ch-X and he Prmary Markes. Neverheless, a number of recen papers (e.g. Duare and Young [2009] and Akas e al. [2007]) have cas doub on he model s ably o capure he presence of nformed raders. Moreover, s well-known ha he PIN model s subjec o numercal problems, parcularly for socks wh hgh radng acvy (see e.g. Yan and Zhang [2009] and Easley e al. [2010]). In our case, hese problems are addonally amplfed by he relavely shor sample (43 radng days) and he need o esmae addonal parameers for a wo-marke PIN model as n Easley e al. [1996a] 19
or Grammg e al. [2001]. Consequenly, we fnd ha he numercal maxmzaon of he lkelhood funcon s no successful for mos socks n our sample. In Appendx C, we provde an alernave mehod for esmang dfferences n nformed radng beween wo markes va he PIN model and presen he assocaed resuls. 5. Dfferences n adverse selecon and Ch-X s quoe compeveness The resuls of he prevous secon sugges ha rades on Ch-X carry more prvae nformaon han hose execung place n he Prmary Markes. From he perspecve of our heorecal model, hs corresponds o he case where nformed raders have a hgher lkelhood of beng smar rouers han capve raders (.e. θ I > θ U or equvalenly µ SR > µ). Recall from he dscusson of Proposon 1 n Secon 2 ha n hs case, lqudy provders on Ch-X are only able o mach he prmary marke s quoes f her cos advanage from ransacon fees (c) exceeds he excess adverse selecon rsk hey face. In order o valdae our model emprcally, we adop a cross-seconal perspecve. Accordng o Corollary 1, Ch-X s presence a he nsde quoe s expeced o decrease (weakly) n he adverse selecon rsk dfferenal. As he emprcal evdence suggess ha he adverse selecon rsk dfferenal s posve for almos all socks, we acually expec o observe a srcly negave relaonshp. We begn by calculang, for each sock, he fracon of me durng whch Ch-X s presen a he EBBO, akng he average of boh sdes of he marke 13. We hen regress hs measure of Ch-X s quoe compeveness on measures ha capure he dfference n adverse selecon across radng venues and addonal conrol varables,.e. we esmae he cross-seconal regresson Ch_ a _bes = α 0 + α 1 1 [ Euronex] + γ( AS ) + φ X + ε (13) 13 Consderng only one sde of he marke (eher bd or ask) delvers qualavely smlar resuls. 20
where 1 s an ndcaor varable ha akes he value of 1 f he sock s lsed on [ Euronex] Euronex and 0 oherwse, AS denoes he excess adverse selecon rsk on Ch-X for sock, and X s a vecor of conrol varables. We employ hree dfferen varables n order o quanfy he excess adverse selecon rsk on Ch-X. The frs wo are he sock-specfc cross-marke dfferences of he prce mpac measures from Secons 4.1 and 4.2, denoed AS SD and, AS HB respecvely. For he hrd varable, we gnore any cross-seconal varaon n he proporon of nformed raders and smply proxy (µ SR µ)(v V ) by σ, whch denoes annualzed reurn volaly based on closng prces for he calendar year pror o our sample perod. Table 9 n he appendx conans he cross-seconal correlaon marx of our hree explanaory varables. Unsurprsngly, we fnd a srong crossseconal correlaon of 0.63 beween AS SD and AS HB. More neresngly, boh measures are hghly correlaed wh sock prce volaly (beween 0.42 and 0.45), whch ndcaes ha all hree varables are pckng up smlar effecs. [Inser Table 9 abou here] We nclude a number of conrol varables ha we expec o nfluence Ch-X s presence a he bes quoe. Whle we have no ncorporaed he effec of ck szes n our model for racably reasons, s known ha a dscree prcng grd leads o roundng errors and herefore arfcally nflaes he bd-ask spread (see e.g. Harrs [1994]). As a consequence, we expec Ch-X s quoe compeveness o ncrease n he ck sze dfferenal 14, whch we defne as he average 15 dfference n ck szes (Prmary marke mnus Ch-X) for sock scaled by he sock s average ransacon prce. We furhermore nclude he proporon of smar rouers as a conrol varable n order o dsenangle our sory from 14 For some socks, he dfference n ck szes s consderable. For example, durng mos of he sample, he ck sze for Infneon s 0.01 on Xera, compared o 0.001 on Ch-X. Gven he sock s low prce level (below 10), he bd-ask spread on he Prmary Marke s frequenly equal o he ck sze. Consequenly, Ch-X s parcularly aracve for radng n hs sock as allows he placemen of orders whn he prmary quoes. A few days before he end of he sample perod, Deusche Börse reduced he ck sze o 0.005. 15 A oal of 9 socks experence a change n ck szes durng our sample perod, all of hem correspondng o a reducon n he Prmary Marke ck sze. One sock (STMcroelecroncs) experences wo changes. 21
ha of Foucaul and Menkveld (2008). Addonally, we also conrol for he log of radng volume and a sock s reurn synchroncy (based on he R-Square of a marke model regresson 16 ). Whle radng volume s smply a varable ousde our model, he resuls n Thessen and Hengelbrock (2009) sugges ha radng n more acve socks has a hgher endency o fragmen. Reurn synchroncy may capure effecs of algorhmc raders, whch ofen engage n ndex arbrage rades and have been shown o be parcularly quck n reacng o hard nformaon (Jovanovc and Menkveld [2010]). [Inser Table 10 abou here] The coeffcen esmaes are lsed n Table 10 n he Appendx. As predced by our model, he resuls ndcae ha an ncrease n he adverse selecon rsk dfferenal s assocaed wh Ch-X beng less frequenly a he nsde quoe. The coeffcens on AS SD HB, AS and σ are all negave and srongly sgnfcan (-sascs rangng from 3.3 o 8.2). Imporanly, he observed effecs are also economcally mporan. For example, a one sandard devaon ncrease n AS SD (~0.37 bps) s assocaed wh a decrease of around 4.35% n Ch-X s presence a he nsde quoe. The oher varables have margnal effecs of smlar magnude (7.07% and 3.95% for and σ, respecvely) AS HB All conrol varables carry he expeced sgn. Parcularly he dfference n ck szes across venues plays an mporan role for Ch-X s quoe compeveness. Increasng he ck sze dfferenal by one sandard devaon (~2.2 bps) leads Ch-X s presence a he bes quoe o ncrease by 7-11%, dependng on he specfcaon. Dfferen from Menkveld and Foucaul [2008], we fnd ha he proporon of smar rouers s no sgnfcanly relaed o Ch-X s quoe compeveness. Ths s lkely due o he fac ha exchanges do no charge any fees for order submsson, whch s an mporan feaure of her model and daa. For he oher conrol varables, we fnd ha boh hgher radng volume and hgher reurn synchroncy are assocaed wh Ch-X beng a he nsde quoe more frequenly. Fnally, here s some weak evdence for 16 We use he ransformaon SYNCH = ln(r 2 /(1 R 2 )), see e.g. Teoh e al. [2008]. Marke model regressons are esmaed usng 1 year of daly daa pror o our sample perod. 22
Ch-X offerng worse quoes n socks lsed on Euronex. Ths may be due o he saggered enry of he MTF across counres. Whle Ch-X enered he German marke roughly one year before he sar of our sample perod, dd no offer radng n French socks unl half a year laer. Whle our heorecal model s srcly speakng abou quoes, has very smlar mplcaons regardng Ch-X s acual marke share. Gven a fxed number of radng rounds, a hgher excess adverse selecon rsk leads o less rade on Ch-X afer conrollng for he proporon of smar rouers. We herefore re-esmae equaon (12), bu replace Ch-X s presence a he bes quoe wh he MTF s marke share n erms of rades. The resuls (Table 11) are very smlar han he resuls for quoes. All varables capurng he adverse selecon rsk dfferenal are negave and sascally sgnfcan. Agan, he economc effecs are subsanal. For example, a one sandard devaon ncrease n s assocaed wh an ncrease of 1.10% n Ch-X s AS SD marke share. Unsurprsngly, he explanaory effec of he proporon of smar rouers s srongly sgnfcan. The coeffcens on he remanng conrol varables are, by and large, smlar o he resuls usng Ch-X s presence a he nsde quoe. [Inser Table 11 abou here] Overall, he cross-seconal evdence suggess ha Ch-X s compeveness s sgnfcanly hampered by excess adverse selecon rsk. These fndngs srongly suppor our heorecal model. 6. Concluson Movaed by he curren regulaory framework n Europe se forh under MFID, we analyze how adverse selecon rsk and ransacon fees nerac n a fragmened fnancal marke where rade-hroughs are no prohbed. We argue ha lqudy provders on alernave radng plaforms wll be subjec o an ncreased adverse selecon rsk f nformed raders are more lkely o have access o hs marke va a smar order roung sysem. Consequenly, he Prmary Marke wll domnae (dsplay beer quoes) mos of he radng day despe chargng hgher ransacon fees. We 23
formalze hs argumen wh an exenson of he Glosen and Mlgrom [1985] sequenal rade model. The analyss of a recen sample of ransacons and quoe daa for German and French socks reveals ha lqudy provders on Ch-X (a recenly launched radng plaform) face a sgnfcanly greaer adverse selecon rsk. Moreover, rade-hroughs ha execue by defaul n he Prmary Markes are parcularly unnformed. In lne wh our heorecal model, we fnd a negave relaonshp beween he excess adverse selecon rsk and Ch-X s presence a he nsde quoe. Moreover, our vew s addonally suppored by anecdoal evdence from Prmary Marke ouages. Our fndngs have some mplcaons for he desgn of bes execuon polces. Allowng for rade-hroughs favors he Prmary Markes by ensurng ha he leas nformave order flow does no reach he MTFs, hereby hamperng lqudy provson on hese plaforms due o an ncreased adverse selecon rsk. Our fndngs sugges ha proecng orders from rade-hroughs n he spr of RegNMS may foser compeon beween radng venues as helps o level he playng feld. There are some neresng avenues for fuure research. In our heorecal analyss, we have aken exchanges ransacon fees and nvesors roung echnologes as gven. Ths choce follows from nose raders wllngness o rade a any prce and he assumpon ha agens do no have he chance o rade mulple mes. Clearly, a more realsc model would am o deermne hese varables endogenously. 24
References Akas, N., DeBond, E., Declerck, F., and Van Oppens, H. (2007) The PIN anomaly around M&A announcemens, Journal of Fnancal Markes, Vol. 10, pp. 169 191 Barclay, M. J., Hendersho, T., and McCormck, D. T. (2003) Compeon among radng venues: Informaon and Tradng on Elecronc Communcaon Neworks, Journal of Fnance, Vol. 58, pp. 2637-2666 Baalo, R., Greene, J., Hach, B., and Jennngs, R. (1997) Does he lm order roung decson maer?, Revew of Fnancal Sudes, Vol. 15, pp.159-194 Bessembnder, H. (2003) Quoe-based compeon and rade execuon coss n NYSE-lsed socks, Journal of Fnancal Economcs, Vol. 70, pp. 385-422 Bessembnder, H., and aufman, H. (1997), A cross-exchange comparson of execuon coss and nformaon flow for NYSE-lsed socks, Journal of Fnancal Economcs, Vol. 46, pp. 293-319. Bas, B., and Bsère, C. (2010) Imperfec Compeon n Fnancal Markes: An Emprcal Sudy of Island and Nasdaq, Managemen Scence, forhcomng Boehmer, B. and Boehmer, E. (2004), Tradng your Neghbor s ETFs: Compeon and Fragmenaon, Journal of Bankng and Fnance, Vol. 27, pp. 1667-1703. Denner, J. (1993) Prce compeon beween marke makers, Revew of Economc Sudes, Vol. 60, pp. 735-751 Duare, J., and Young, L. (2009) Why s PIN prced?, Journal of Fnancal Economcs, Vol. 91, pp. 119-138 Easley, D., efer, N. M., and O Hara, M. (1996a) Cream-skmmng or profsharng? The curous role of purchased order flow Journal of Fnance, Vol. 51, pp 811-836 Easley, D., efer, N. M., O Hara, M., and Paperman, J. B. (1996b) Lqudy, Informaon, and nfrequenly raded socks, Journal of Fnance, Vol. 51, pp. 1405 1436 25
Easley, D., Hvdkjaer, S., and O Hara, M. (2010) Facorng nformaon no reurns, Journal of Fnancal and Quanave Analyss, Vol. 45,pp. 293 309 Ende, B., and Lua, B. (2010) Trade-hroughs n European Cross-raded Eques Afer Transacon Coss Emprcal Evdence for he EURO STOXX 50, Workng Paper Fnancal Tmes, Prvae nvesors fal o see MFID benefs, Aprl 7 h 2010 Foucaul, T., and Menkveld, A. (2008) Compeon for order flow and smar order roung sysems, Journal of Fnance, Vol. 63, pp 119 158 Goldsen, M. A., Shklko, A. V., Van Ness, B. F., and Van Ness, R. A. (2008) Compeon n he marke for Nasdaq secures, Journal of Fnancal Markes, Vol. 113-143 Gomber, P., and Gsell, M. (2010) Cachng up wh echnology The mpac of regulaory changes on ECNs/MTFs and he radng venue landscape n Europe, Compeon and Regulaon n Nework Indusres Glosen, L., and Mlgrom, P. (1985) Bd, ask and ransacon prces n a specals marke wh heerogeneously nformed raders, Journal of Fnancal Economcs, Vol. 14, pp 71-100 Grammg, J., Schereck, D., and Thessen, E. (2001) nowng me, knowng you: Trader anonymy and nformed radng n parallel markes, Journal of Fnancal Markes, Vol. 4, pp 385-412 Harrs, L. E. (1994) Mnmum Prce Varaons, Dscree Bd-Ask Spreads, and Quoaon Szes, Revew of Fnancal Sudes, Vol. 7, pp. 149-178 Hasbrouck, J. (1991) Measurng he nformaon conen of sock rades, Journal of Fnance, Vol. 46, pp. 179 207 Hendersho, T., and Rordan, R. (2009) Algorhmc Tradng and Informaon, Workng Paper Hengelbrock, J., and Thessen, E. (2009) Foureen a one blow: The marke enry of Turquose, Workng Paper 26
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Appendx A: Proof of Proposon 1 Before we sae he proof of Proposon 1, s useful o nroduce some addonal noaon. As rader populaons dffer across markes, so does he funcon ha maps marke makers (henceforh MMs) pror no her poseror belefs. Le δ ( ) denoe sell,δ 1 he poseror belef abou he probably of a hgh realzaon of he lqudaon value afer a sell by he -h rader, gven he pror δ 1. Then, usng Bayes rule, (1 µ)δ δ (sell,δ 1 ) = 1 1+ µ(1 2δ 1 ) (1 µ CT )δ δ (sell,δ 1 ) = 1 1+ µ CT (1 2δ 1 ) (1 µ SR )δ δ (sell,δ 1 ) = 1 1+ µ SR (1 2δ 1 ) f sell n P and b P > b C f sell n P and b P b C f sell n C (A.1) (A.2) (A.3) From hs follows ha he expeced lqudaon value of he asse condonal on he arrval of a sell order s gven by B(µ,δ 1 ) E(V sell,δ 1 ) = (1 µ)δ 1V + (1+ µ)(1 δ 1 )V (1 + µ(1 2δ 1 )) B(µ CT,δ 1 ) E(V sell,δ 1 ) = (1 µct )δ 1 V + (1+ µ CT )(1 δ 1 )V (1 + µ CT (1 2δ 1 )) B(µ SR,δ 1 ) E(V sell,δ 1 ) = (1 µsr )δ 1 V + (1 + µ SR )(1 δ 1 )V (1+ µ SR (1 2δ 1 )) f sell n P and b P > b C f sell n P and b P b C f sell n C (A.4) (A.5) (A.6) Clearly, µ SR > µ > µ CT (µ SR < µ < µ CT ) mples B(µ CT,δ 1 ) B(µ,δ 1 ) B(µ SR,δ 1 ) ( B(µ CT,δ 1 ) B(µ,δ 1 ) B(µ SR,δ 1 )), where equaly apples o he lmng cases of δ 1 = 0 and δ 1 =1. We are now ready o sae he proof of Proposon 1. For noaonal smplcy, we om he me subscrps on MMs belefs. 28
Proof of Proposon 1: Par A: θ I > θ U Case A1: B(µ,δ) c > B(µ SR,δ) In hs case, b,p P * = B(µ,δ) c for =1,, N and, C b j * < b P * for j=1,, N consues a Nash equlbrum. All rade occurs n marke P, and MMs oban zero expeced profs n boh markes. Unlaerally decreasng he bd n marke P does no mprove on zero expeced profs, as such a quoe does no arac any marke order, gven he oher MMs quoe. On he oher hand, B(µ,δ) c s he maxmum bd ha does no lead o expeced losses, hereby rulng ou any unlaeral ncreases n he quoe. Concernng marke C, any bd b j, C B( µ, δ ) c wll yeld an expeced loss for marke maker j as he expeced lqudaon value condonal on he sell of a smar rouer s B(µ SR,δ) < B(µ,δ) c. Lowerng he bd unlaerally does no mprove on zero expeced profs. To see ha we mus have b P * > b C * n equlbrum, frs consder he case where b P * < b C *. Clearly, equlbrum requres b C * B(µ CT,δ). Gven b P * < b C *, marke makers n marke P make expeced profs. Thus for every b P * < b C *, here exss some b,p = b P * +ε < b C * ha allows marke maker o overbd her rval n he same marke and hereby ncrease her profs. Hence, hs canno be an equlbrum. Now consder he suaon where b P * = b C *. Gven ha MMs n marke P (C) face proporons µ CT (µ SR ) of nformed raders, we mus have b P * B(µ CT,δ) c and b C * B(µ SR,δ) and herefore b P * B(µ SR,δ). By symmery, we have b,p * = b P * and b j,c * = b C P C * for =1,, N and j=1,, N. If b P * = b C * < B(µ SR,δ), hen marke maker j n marke C can capure he marke by posng b j,c = b C * +ε, such ha hs s no an equlbrum. On he oher hand, f b P * = b C * = B(µ SR,δ), marke maker n marke P makes an expeced prof equal o CT SR P Π = ( 1 θ )( B( µ, δ ) B( µ, δ ) c) / N > 0. Increasng her bd margnally o b,p = B(µ SR,δ) + ε < B(µ,δ) c, her prof s equal o B(µ,δ) B(µ SR,δ) c ε, whch s always greaer han Π for N P. C 29
Case A2: B(µ CT,δ) c B(µ SR,δ) B(µ,δ) c j, C For hs case, *, P * ( µ SR P C b = b = B, δ ) for =1,, N and j=1,, N s a Nash equlbrum. Gven our assumpon regardng ner-marke es, smar rouers rade n marke C and capve raders n marke P. MMs n marke C earn zero expeced profs and MMs n marke P make expeced profs as B(µ CT,δ) c B(µ SR,δ) 0. Unlaerally decreasng any bd leads o zero profs, as aracs no marke order, gven he oher quoes. On he oher hand, a unlaeral ncrease n he bd quoe n marke P (C) generaes expeced losses as such a quoe hen faces a proporon µ ( µ SR ) of nformed raders. To see ha any equlbrum mus sasfy b C * = b P *, frs assume ha b C * > b P *. Clearly, we mus have ha b C * = B(µ SR,δ). Gven he quoes n marke C, marke makers n marke P face a proporon µ CT of nformed raders. As long as b C * > b P *, here always exss some b,p = b P * +ε < b C * ha allows marke maker o overbd her rvals n he same marke and hereby ncrease her profs. Hence, hs canno be an equlbrum. Now consder he converse suaon,.e. b P * > b C *. Gven ha marke P dsplays he bes quoes across markes, he adverse selecon rsk s defned by a proporon µ of nformed raders, such ha we mus have b P * = B(µ,δ) c. Bu hen, marke maker j n marke C faces a proporon µ SR of nformed raders, such ha she can oban an expeced prof by ncreasng her bd o b j,c = B(µ,δ) c. Hence, hs canno an equlbrum eher. Case A3: B(µ SR,δ) > B(µ CT,δ) c Under hs consellaon, b,p * = B(µ CT,,δ) c and b j C * = B( µ SR, δ ) for =1,, N C and j=1,, N consues a Nash equlbrum. Smar rouers rade n marke C, capve raders n marke P, and all MMs oban zero expeced profs. Unlaerally lowerng a bd quoe n eher does no arac any marke orders, gven he oher quoes. Increasng he quoe n marke P leads o expeced losses because such a quoe faces proporons µ CT (f b,p (B(µ CT,δ) c,b(µ SR,δ)]) or µ (f b,p > B(µ SR,δ)) of nformed raders. Smlarly, hgher bd quoes n marke C lead o expeced losses as P 30
he expeced lqudaon value condonal on a sell order n marke C s equal o B(µ SR,δ). To see ha we mus have b C * > b P * n equlbrum, frs consder he case where b C * < b P *. Under hs consellaon, markes makers n marke P face a proporon µ of nformed raders and equlbrum requres ha b P * = B(µ,δ) c. As n he prevous case, marke maker j n marke C can make an expeced prof by posng b j,c * = B(µ,δ) c, such ha hs s no an equlbrum. Now suppose ha b C * = b P *. Clearly, we mus have ha b P * B(µ CT,δ) c, because any hgher bd n marke P wll lead o losses gven a proporon µ CT of nformed raders. Bu hen, any marke maker j n marke C has an ncenve o ncrease her bd margnally o some b j,c = b C * +ε, hereby capurng he marke and ncreasng her profs. Combnng cases A1-A3, we fnd ha b C * b E * f and only f B(µ SR,δ) B(µ,δ) c. Usng equaons (A.4) and (A.6), he marke co-exsence condon (1) follows. Par B: θ U θ I In hs case, he nequaly B(µ SR,δ) > B(µ CT,δ) c s always sasfed because µ SR < µ CT. I follows from case A3 ha we mus have b j,c * = B(µ SR,δ) for j=1,, N and b,p * = B(µ CT P,δ) c for =1,, N n equlbrum, such ha markes co-exs. Condon (1) s sasfed because µ SR µ < 0. C Q.E.D. 31
Appendx B: Tables and Fgures Table 1: Sample Socks Ths Table conans a ls of he 67 French and German socks conaned n our sample, separaed no ercles based on her average radng volume (n ). Hgh Volume Socks Medum Volume Socks Low Volume Socks (N=22) (N=23) (N=22) Toal Carrefour Posbank Deusche Bank BMW Lnde Allanz Deusche Pos Mcheln Semens Vvend Bouygues Damler Thyssenrupp Pernod Rcard E.ON Cred Agrcole Alcael Lucen Socee Generale EDF PPR BNP Parbas Lafarge Accor Deusche Telekom Renaul Addas France Telecom Schneder Cap Gemn RWE Vallourec Gaz de France Volkswagen L'Oreal STMcroelecroncs AXA Veola Merck SAP Lufhansa Mero Bayer Danone Unbal Rodamco BASF LVMH Hypo Real Esae Deusche Börse MAN Ar France - LM Suez Alsom Henkel Munch Re San Goban TUI Sanof Synhelabo Vnc Fresenus Medcal Care Connenal Peugeo Esslor Commerzbank Ar Lqude Lagardere Infneon 32
Table 2: Sample Sascs Ths Table conans summary sascs of he radng acvy on Ch-X and he Prmary Markes for our sample of 67 French and German socks, aggregaed no ercles based on radng acvy. MS Ch-X denoes he marke share of Ch-X for rades and radng volume as a percenage of he consoldaed marke (Ch-X plus prmary marke). Rao C/E denoes he average rade sze on Ch-X as percenage of he average rade sze n he Prmary Marke. Panel A: Avg. daly # of rades (1,000 rades) Ch-X Prmary MS Ch-X (%) Hgh Volume 1.06 5.85 14.98 Medum Volume 0.52 3.99 11.47 Low Volume 0.30 2.59 10.52 All 0.63 4.14 12.31 Panel B: Avg. daly radng volume (Mo. ) Ch-X Prmary MS Ch-X (%) Hgh Volume 18.33 244.31 6.99 Medum Volume 5.83 96.85 5.53 Low Volume 2.96 51.54 5.36 All 8.99 130.39 5.95 Panel C: Average rade sze ( 1,000) Ch-X Prmary Rao C/P (%) Hgh Volume 17.38 42.26 41.82 Medum Volume 10.99 24.86 43.63 Low Volume 9.55 20.04 47.94 All 12.62 28.99 44.45 33
Table 3: Quoe compeveness and marke deph Ths able conans sascs on he quoe compeveness and he avalable marke deph for our sample of 67 French and German socks, aggregaed no ercles based on radng acvy and repored separaely for Ch-X and he Prmary Markes. Panel A repors he average frequency wh whch a gven marke s presen (alone) a he nsde quoe, whle Panel B repors he average marke deph (n 10,000) for each marke condonal on beng presen (alone) a he nsde quoe. A bes bd A bes ask A boh A bes bd alone A bes ask alone A boh alone Panel A: Presence (%) a he nsde quoe Ch-X Hgh Volume 52.65 53.56 27.45 27.75 28.18 7.11 Medum Volume 49.77 48.59 23.84 27.28 25.66 6.54 Low Volume 45.13 45.63 20.98 24.99 23.00 6.21 All 49.30 49.25 24.08 26.68 25.62 6.62 Prmary marke Hgh Volume 72.25 71.82 51.18 47.35 46.44 21.24 Medum Volume 72.72 74.34 53.59 50.23 51.41 25.48 Low Volume 75.01 77.00 58.22 54.87 54.37 30.22 All 73.32 74.38 54.32 50.81 50.75 25.64 Panel B: Deph (n 10,000) condonal on presence a he nsde quoe Ch-X Hgh Volume 31.02 31.02 30.64 27.08 27.27 26.94 Medum Volume 17.69 17.53 17.45 16.43 15.51 15.94 Low Volume 14.49 15.29 14.77 14.06 13.29 13.86 All 21.02 21.22 20.90 19.15 18.64 18.88 Prmary marke Hgh Volume 91.74 102.41 103.04 74.62 87.42 95.70 Medum Volume 50.42 53.35 55.78 43.20 46.94 50.78 Low Volume 39.25 42.46 42.91 35.16 38.28 39.20 All 60.32 65.88 67.07 50.88 57.39 61.73 34
Table 4: The proporon of smar rouers and he e-breakng rule Ths able conans esmaes for he proporon of smar rouers (θ), he proporon of execuons n he Prmary Marke under ner-marke es (π), as well as for he e-breakng rule parameer (τ) for our sample of 67 French and German socks, aggregaed no ercles based on radng acvy. All varables are defned n Secon 3. Sandard errors are robus o seral and cross-seconal correlaon. The sandard errors for he e-breakng rule parameer are based on he dela mehod. Proporon of smar rouers Proporon of Prmary Marke Trades under ner-marke es Te-breakng rule parameer Hgh Volume 0.514 0.510 0.046 (0.018) (0.022) (0.035) Medum Volume 0.495 0.513 0.015 (0.018) (0.018) (0.033) Low Volume 0.494 0.525 0.039 (0.020) (0.027) (0.041) All 0.501 0.516 0.033 (0.013) (0.015) (0.025) 35
Table 5: Effecve spread decomposon (Ch-X vs. Prmary Marke Trades) Ths able conans he average effecve spreads as well as he decomposon no he adverse selecon (prce mpac) and realzed spread componens for rades on Ch-X and he Prmary Markes, respecvely, followng equaons (2) o (4) n Secon 4.1. Averages are based on sock-days and aggregaed no ercles based on radng acvy. For dfferences, sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Sandard errors are robus o seral and cross-seconal correlaon. Panel A: Effecve Spread Ch-X Prmary Dfference Hgh Volume 1.999 1.996 0.003 (0.126) (0.138) (0.020) Medum Volume 2.672 2.705-0.033 (0.151) (0.245) (0.035) Low Volume 3.341 3.217 0.124** (0.217) (0.219) (0.052) All 2.671 2.640 0.031 (0.128) (0.140) (0.074) Panel B: Prce Impac Ch-X Prmary Dfference Hgh Volume 2.088 1.628 0.460*** (0.149) (0.119) (0.073) Medum Volume 2.693 2.306 0.387*** (0.190) (0.161) (0.091) Low Volume 3.270 2.877 0.393*** (0.210) (0.163) (0.122) All 2.684 2.271 0.413*** (0.139) (0.122) (0.076) Panel C: Realzed Spread Ch-X Prmary Dfference Hgh Volume -0.090 0.367-0.457*** (0.102) (0.098) (0.074) Medum Volume -0.021 0.399-0.419** (0.104) (0.152) (0.191) Low Volume 0.071 0.340-0.269 (0.114) (0.158) (0.168) All -0.013 0.369-0.382*** (0.078) (0.094) (0.104) 36
Table 6: Effecve spread decomposon (Trade-Throughs vs. Non-Trade-Throughs) Ths able conans he average effecve spreads as well as he decomposon no he adverse selecon (prce mpac) and realzed spread componens for boh rade-hroughs and non-radehroughs on he Prmary Markes, followng equaons (2) o (4) n Secon 4.1. Averages are based on sock-days and aggregaed no ercles based on radng acvy. For dfferences, sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Sandard errors are robus o seral and cross-seconal correlaon. Panel A: Effecve Spread Tradehroughs Dfference Hgh Volume 1.883 3.173-1.290*** (0.135) (0.196) (0.140) Medum Volume 2.527 4.659-2.132*** (0.191) (0.256) (0.152) Low Volume 3.052 5.762-2.710*** (0.196) (0.436) (0.294) All 2.488 4.533-2.046*** (0.122) (0.232) (0.145) Panel B: Prce Impac Non-radehroughs Non-radehroughs Non-radehroughs Tradehroughs Dfference Hgh Volume 1.713 0.804 0.909*** (0.120) (0.154) (0.116) Medum Volume 2.453 0.690 1.763*** (0.200) (0.247) (0.171) Low Volume 2.995 1.253 1.742*** (0.170) (0.201) (0.200) All 2.388 0.912 1.476*** (0.130) (0.151) (0.124) Panel C: Realzed Spread Tradehroughs Dfference Hgh Volume 0.170 2.369-2.199*** (0.085) (0.248) (0.242) Medum Volume 0.074 3.969-3.896*** (0.079) (0.286) (0.286) Low Volume 0.057 4.509-4.452*** (0.115) (0.484) (0.465) All 0.100 3.621-3.521*** (0.068) (0.262) (0.247) 37
Table 7: Permanen Prce Impacs (Ch-X vs. Prmary Marke Trades) Ths able conans he average permanen prce mpac measures obaned from he VAR model n Secon 4.2, equaons (5) (7). Averages are based on sock-days and aggregaed no ercles based on radng acvy. For dfferences, sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Sandard errors are robus o seral and cross-seconal correlaon. Ch-X Prmary Dfference Hgh Volume 1.345 1.128 0.218*** (0.078) (0.068) (0.039) Medum Volume 1.883 1.610 0.273*** (0.094) (0.071) (0.056) Low Volume 2.337 2.106 0.232*** (0.138) (0.110) (0.081) All 1.856 1.614 0.241*** (0.087) (0.078) (0.043) 38
Table 8: Permanen Prce Impacs (Trade-Throughs vs. Non-Trade-Throughs) Ths able conans he average permanen prce mpac measures obaned from he VAR model n Secon 4.2, equaons (9) (12). Averages are based on sock-days and aggregaed no ercles based on radng acvy. For dfferences, sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Sandard errors are robus o seral and cross-seconal correlaon. Non-radehroughs Tradehroughs Dfference Hgh Volume 1.208 0.274 0.934*** (0.071) (0.064) (0.071) Medum Volume 1.745 0.326 1.419*** (0.113) (0.091) (0.114) Low Volume 2.196 0.725 1.471*** (0.109) (0.129) (0.109) All 1.717 0.440 1.277*** (0.083) (0.087) (0.085) 39
Table 9: Correlaon marx of explanaory varables Ths able conans he correlaon marx for he explanaory varables capurng he excess adverse selecon rsk on Ch-X. All varables are descrbed n Secon 5. Sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. AS(SD) AS(HB) σ AS(SD) 1.000 0.635*** 0.424*** AS(HB) 1.000 0.453*** σ 1.000 40
Table 10: Cross-seconal regressons Ths able conans esmaes for he lnear cross-seconal regresson followng equaon (13). All varables are descrbed n Secon 5. Robus sandard errors are gven n parenheses. Sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Dependen Varable: Avg. Presence of Ch-X a he bes quoe AS(SD) - 11.748*** (2.692) (1) (2) (3) AS(HB) - 29.460*** (3.606) σ - 0.564*** (0.169) % Smar Rouers 0.092-0.095 0.040 (0.175) (0.152) (0.185) ln(volume) 7.176*** 8.253*** 5.267*** (1.304) (1.162) (1.339) Synch 3.406** 2.288** 4.847*** (1.409) (1.007) (1.616) ck (bps) 4.044*** 4.705*** 3.449*** (0.790) (0.460) (0.750) Euronex dummy -2.280 2.863-2.624 (1.915) (1.726) (1.967) Consan - 86.932*** - 99.333*** -34.504 (27.592) (20.508) (30.037) N 67 67 67 Adj. R² 0.606 0.733 0.580 41
Table 11: Cross-seconal regressons Ths able conans esmaes for he lnear cross-seconal regresson followng equaon (13), where we replace he ndependen varable by Ch-X s marke share n erms of rades. All varables are descrbed n Secon 5. Robus sandard errors are gven n parenheses. Sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. Dependen Varable: Avg. Marke Share Ch-X (# of Trades) AS(SD) - 2.989*** (1.091) (1) (2) (4) AS(HB) - 6.997*** (1.862) σ - 0.137** (0.059) % Smar Rouers 0.318*** 0.273*** 0.305*** (0.059) (0.056) (0.061) ln(volume) 2.491*** 2.735*** 2.020*** (0.419) (0.403) (0.419) Synch 1.390** 1.106** 1.726*** (0.549) (0.500) (0.575) ck (bps) 1.094*** 1.235*** 0.939*** (0.271) (0.217) (0.244) Euronex dummy - 3.472*** - 2.232*** - 3.542*** (0.748) (0.827) (0.757) Consan - 47.826*** - 50.598*** - 34.990*** (8.668) (7.702) (9.344) N 67 67 67 Adj. R² 0.588 0.643 0.572 42
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Appendx C: A wo-marke PIN model usng regresson As maxmum lkelhood esmaon of he PIN model has become ncreasngly dffcul, Easley e al. [2009] pon ou ha ha PIN can be approxmaed by PIN Approx = ( ) E I ( ) EB+ S (C.1) where B and S denoe he number of buys and sells, and I=B-S s he order mbalance. The nuon behnd hs approxmaon s sraghforward. Toal order flow s he sum of nformed and unnformed rades, whle he order mbalance s enrely arbuable o nformed rades. As a resul, PIN can be approxmaed by he average mbalance dvded by he average number of rades. In our case, unforunaely, s no suffcen o smply calculae hs approxmaon for each marke n solaon, as hs would no mpose equal even probables across markes. A wo-marke PIN model mples ha he order mbalance n each marke has he same sgn (n expeced erms), and he relave order mbalance s hgher n he marke wh a larger proporon of nformed raders. Le I k mbalances n marke marke s gven by k { C,P} denoe he order on day, such ha he mbalance n he consoldaed I T = I C + I P. Moreover defne venue k s marke share as S k = (B k + S k )/(B T + S T ), where he superscrp T refers o he consoldaed (oal) marke. Then, he produc I T S k s he expeced order mbalance n marke k n he case where he probably of nformed radng n hs marke s equal o he probably of nformed radng n he consoldaed marke. Then, we can es for dfferences n nformed radng across venues va he smple lnear regresson I k = λ k (I T S k ) + ε (C.2) If λ k > 1(λ k < 1), he probably of nformed radng n marke k s hgher (lower) han n he consoldaed marke, as observed mbalances are of greaer (lower) magnude han expeced from s acual marke share. 44
Table C.1 deal he esmaon resuls for boh Ch-X (Panel A) and he Prmary Markes (Panel B). The frs wo columns repor he mean and medan coeffcens from sock-specfc OLS regressons for he enre sample and he dfferen acvy ercles, whle he laer wo columns conan he p-values from -ess and Wlcoxon rank sum ess. Overall, he evdence ndcaes excess nformed radng on Ch-X and a shorfall of nformed raders on he Prmary Markes. Excep for he ercle wh he mos acve socks, he mean (medan) regresson coeffcen for he MTF exceeds one, whle he one for he Prmary Markes s less han one. We hen use he produc ( ˆ λ C 1)σ as anoher measure of AS n regresson (12), where ˆ C λ s he regresson coeffcen from equaon (C.2) usng he Ch-X mbalance as ndependen varable, and σ denoes sock s annualzed reurn volaly. The resuls can be found n Table C.2, where we also show he coeffcens for he alernave specfcaon usng Ch-X s marke share n erms of rades. The coeffcens are negave and sascally sgnfcan, whch s n lne wh he resuls obaned usng he oher measures of he adverse selecon rsk dfferenal. Neverheless, he effec s magnude n erms of economc sgnfcance s consderably smaller han for he oher varables employed n secon 6. For example, a one sandard devaon ncrease n ( ˆ λ 1)σ (~15.44) s assocaed wh a decrease of 1.96% n Ch-X s presence a he bes quoe, whch s less han half of he effec for AS SD. 45
Table C.1: Imbalance Regressons Ths able conans summary sascs on he order mbalance regresson n equaon (C.2) for esmang dfferences n he probably of nformed radng beween Ch-X, he Prmary Markes, and he consoldaed marke. Resuls are based on our sample of 67 French and German socks, aggregaed no ercles based on radng acvy. The frs wo columns presen he mean and medan regresson coeffcens mnus one, whle he las wo columns conan p-values from -ess and non-paramerc Wlcoxon sgned-rank ess. Panel A: Imbalance Regressons for Ch-X λ-1 p-value (Ho: λ=1) Mean Medan T-es Wlcoxon Hgh Volume 0.095-0.003 0.419 0.518 Medum Volume 0.322 0.301 <0.001 <0.001 Low Volume 0.275 0.279 0.034 0.012 All 0.232 0.219 <0.001 <0.001 Panel B: Imbalance Regressons for Prmary Markes λ-1 p-value (Ho: λ=1) Mean Medan T-es Wlcoxon Hgh Volume 0.001 0.003 0.952 0.863 Medum Volume -0.034-0.032 0.002 0.002 Low Volume -0.031-0.030 0.026 0.018 All -0.021-0.027 0.012 0.008 46
Table C.2: Cross-seconal regressons Ths able conans esmaes for he lnear cross-seconal regresson followng equaon (13), where column headngs denoe he dependen varable. All varables are descrbed n Secon 5. Robus sandard errors are gven n parenheses. Sascal sgnfcance a he 10%, 5%, and 1% level s denoed by *, **, and ***, respecvely. % Ch-X a nsde quoe Ch-X Marke Share (λ-1)σ - 0.127* - 0.061** (0.072) (0.029) % Smar Rouers 0.095 0.330*** (0.196) (0.055) ln(volume) 5.780*** 1.971*** (1.804) (0.588) Synch 2.009 0.965* (1.569) (0.530) ck (bps) 3.066*** 0.838*** (0.706) (0.210) Euronex dummy -1.984-3.589*** (2.538) (0.833) Consan - 64.334* - 39.253*** (37.762) (12.077) N 67 67 Adj. R² 0.505 0.575 47