Why Do Traders Choose to Trade Anonymously? *

Transcription

1 Why Do Traders Choose to Trade Anonymously? * Forthcomng, Journal of Fnancal and Quanttatve Analyss Carole Comerton-Forde College of Busness and Economcs, Australan Natonal Unversty Phone: Emal: carole.comerton-forde@anu.edu.au Tāls J. Putnņš Stockholm School of Economcs n Rga Phone: Emal: tals.putnns@sserga.edu.lv and Kar Me Tang Faculty of Economcs and Busness, Unversty of Sydney Phone: Emal: karmet@econ.usyd.edu.au * The authors thank Hank Bessembnder (the edtor), an anonymous referee, Heather Anderson, Doug Harrs, Joel Hasbrouck, Ronald Masuls, Avandhar Subrahmanyam, Terry Walter and partcpants at the 2005 Market Regulaton Servces DeGroote School of Busness Annual Conference on Market Structure and Market Integrty for ther helpful feedback and comments. The authors also thank the Investment Industry Regulatory Organzaton of Canada for provdng access to the data used n the paper, and the Australan Research Councl (ARC Lnkage Project LP ) for fundng. 1

2 Why Do Traders Choose to Trade Anonymously? Abstract Ths paper examnes the use, determnants and mpact of anonymous orders n a market where dsclosure of broker dentty n the tradng screen s voluntary. We fnd that most tradng occurs non-anonymously, contrary to pror lterature that suggests lqudty gravtates to anonymous markets. By strategcally usng anonymty when t s benefcal, traders reduce ther executon costs. Traders select anonymty based on varous factors ncludng order source, order sze and aggressveness, tme of day, lqudty and expected executon costs. Fnally, we report how anonymous orders affect market qualty and dscuss mplcatons for market desgn. 2

3 I. Introducton Anonymty plays a key role n market partcpants tradng strateges as part of ther efforts to obtan best executon. It s also an mportant element of market desgn for exchanges, as t affects ther compettveness vs-à-vs other markets. However, the use of anonymty by strategc traders, and ts mpact on executon costs, s nether well understood nor wdely documented. Ths paper examnes the characterstcs of anonymous orders on the Toronto Stock Exchange (TSX), where dsclosure of the broker s dentty s voluntary. We nvestgate the determnants of anonymous orders. We also examne f the strategc use of anonymty allows traders to reduce executon costs and assess how anonymous orders affect market qualty. In recent years, many exchanges ncludng the TSX, Nasdaq and the London Stock Exchange (through ts SETSmm system for small and medum-szed companes) have begun to offer the choce of tradng anonymously when accessng the central order book. Ths has occurred largely n response to market demand for tradng anonymty and ncreased competton from electronc communcatons networks (ECNs) that offer anonymous tradng. Several US exchanges, ncludng the New York Stock Exchange and NYSE AMEX Equtes, have ntroduced hybrd tradng systems that offer users the choce of (anonymous) automated order executon and (non-anonymous) aucton order executon systems. Despte the ncreasng nterest n sde-by-sde anonymous and non-anonymous tradng, there s relatvely lttle emprcal evdence on how anonymous orders are used n such systems, or how ther executon costs compare to those of non-anonymous orders. Ths ssue s relevant gven the regulatory requrements for ntermedares to be publcly accountable on ther order executon practces. It s also mportant gven the concern among market partcpants about the mpact of anonymous orders on prce dscovery and market qualty. Ths study makes three man contrbutons to our understandng of the use of anonymty n securtes tradng. Frst, we provde an overvew of the use of anonymous orders on a market 3

4 where the dsclosure of broker dentty n the tradng screen s voluntary. Our data allow us to crcumvent problems nherent n prevous studes whch ether: () compare tradng on separate anonymous and non-anonymous platforms (e.g., Barclay et al. (2003), Grammg et al. (2001), Ress and Werner (2005)); or whch () compare tradng before and after a one-off regulatory change n dentty dsclosure requrements (e.g., Foucault et al. (2007), Comerton-Forde and Tang (2009)). The detaled TSX data also allow us to nvestgate the use of anonymty for specfc order sources: clent, propretary, non-clent, specalst and optons market maker accounts. We fnd that, despte havng the opton of anonymty, most traders choose to trade nonanonymously. Ths rases the queston of whether fully anonymous markets are optmal n meetng the needs of ther users. The majorty of anonymous orders are n the form of passve lmt orders placed by clent and propretary accounts. However, specalsts account for a large proporton of aggressve anonymous market orders. It s possble that specalsts prefer to trade anonymously when they possess superor knowledge of pendng large trades. One source of such nformaton s from orders beng shopped n the upstars market (Grffths et al. (2000), Daves (2003)). In such nstances, anonymty s useful for concealng nformaton, thus reducng the mmedate prce mpact of ther trades. Our second contrbuton s n modelng the determnants of the decson to trade anonymously, and examnng f these decsons result n lower executon costs. Ths s the frst study to examne these ssues on a common tradng system wthn a common tme perod. We fnd that by strategcally selectng anonymty when t s benefcal, traders reduce ther executon costs. Ths s lkely to be because nformed traders use anonymty to reduce opportuntes for tradng-ahead and pggybackng. 1 At the same tme, patent unnformed traders can use anonymty to make t more dffcult for other traders to dentfy ther ndvdual tradng patterns 1 Whle t may be argued that traders who submt market orders that execute mmedately have no cause to be concerned about tradng-ahead, ths concern remans f such orders are part of an order splttng strategy. 4

5 and pck off ther lmt orders. Of all the order sources, specalsts and optons market makers beneft the most from the strategc use of anonymty. Our thrd contrbuton s n assessng the effects of anonymous orders on short term market qualty and hghlghtng mplcatons for market desgn. If anonymty s more advantageous to nformed traders, as suggested by our results, ceters parbus, anonymous markets could be expected to attract nformed traders, leadng to hgher adverse selecton costs. Further, our fndng that the strategc use of anonymty s able to reduce prce mpact suggests that provdng traders wth the opton to use anonymty may encourage traders to engage n more fundamental research or to trade more aggressvely on ther nformaton. II. Revew of Lterature A. Determnants of Anonymous Tradng Many theoretcal studes predct that nformed traders prefer less transparent tradng venues (e.g., Roëll (1990), Admat and Pflederer (1991), Forster and George (1992), Fshman and Longstaff (1992), Rnd (2008)). Ths s because market partcpants are better able to nfer the probablty of nformed tradng by observng the denttes of traders (Lnnanmaa (2007)) and subsequently engage n tradng-ahead and pggybackng behavor, whch ncreases nformed traders executon costs (Harrs (1996)). However, the emprcal evdence on nformed traders preference for anonymty yelds mxed results (e.g., Barclay et al. (2003), Grammg et al. (2001), Perott and Rnd (2006), Ress and Werner (2005)). The mxed results may stem n part from the dffculty n fully controllng for fundamental dfferences n market structures, costs and, n partcular, accessblty across dfferent tradng systems. These factors often play a strong role n the trader s choce of tradng system, rather than the attractveness of anonymty on that tradng system per se. 5

6 Large lqudty traders may also prefer anonymty f t allows them to reduce ther executon costs. Economdes and Schwartz (1995) report that large buy-sde nsttutonal nvestors value anonymty as t allows them to conceal ther tradng needs and better manage ther order exposure rsk. Order source s another potentally mportant determnant of anonymty. Anecdotal evdence ndcates that a substantal amount of algorthmc tradng by drect market access partcpants s conducted anonymously on the TSX. Ths may be because the lack of randomzaton n algorthmc tradng makes such strateges more susceptble to frontrunnng (Domowtz and Yegerman (2005)). In addton, potental conflcts of nterest arse where brokers can dentfy ther clents algorthmc tradng patterns and poston themselves to take advantage of these antcpated trades (Patel (2006)). Specalsts and propretary traders may use anonymty n order to: () prevent other traders from observng nformed trades; () prevent other traders from trackng ther nventory poston whch can be an ndcator of sentment or order mbalance; () prevent ssuers or clents from trackng propretary tradng that may be at odds wth the ssuer s or clent s nterests, e.g., executng sell orders n a stock for whch the broker ssued a buy recommendaton to a clent; and (v) avod complants and retalaton from other traders for enterng annoyng orders such as pennyng (postng an order one tck better than the best bd or ask n order to get executon prorty over the orders at the prevalng best quotes). Specalsts and propretary traders may possess an nformatonal advantage from ther famlarty wth order flow, knowledge of pendng clent trades or from n-house research (Daves (2003), Kurov and Lasser (2004), Nak et al. (1999), Ress and Werner (2005)). In handlng a publc lmt order, a TSX member frm s allowed 15 mnutes to shop the order n the upstars market before sendng t to the downstars market (Grffths et al. (2000)). Some partcpants therefore become aware of the presence of a large order. Daves (2003) suggests that TSX specalsts may be able to obtan nformaton from upstars traders on pendng orders, and explot such knowledge accordngly. 6

7 The level of nformaton asymmetry n a stock may also nfluence traders preferences for anonymty. Thessen (2002) reports that traders generally prefer the non-anonymous German floor tradng system for less lqud stocks and the anonymous electronc tradng system for blue chp stocks. Ths s consstent wth the predcton of Foucault et al. (2007) that anonymty tends to be less favorable for stocks wth hgh nformaton asymmetry (as t compounds adverse selecton costs and llqudty), but promotes lqudty n stocks wth low nformaton asymmetry. Lttle evdence exsts on the nteracton, f any, between trade sze and anonymty. Patent nformed traders wth slowly-decayng nformaton often use stealth tradng strateges to conceal ther presence (Barclay and Warner (1993), Patel (2006), Daves (2003), Kurov and Lasser (2004)). If such traders make use of anonymty, anonymous orders would tend to be small or medum szed. B. Anonymty and Market Qualty The lterature s not conclusve on how anonymty affects market qualty. Thessen s (2003) study on the Frankfurt Stock Exchange suggests that anonymous markets are assocated wth hgher adverse selecton rsk. Ths may deter unnformed lqudty. However, Foucault et al. (2007) report that the removal of broker IDs on Euronext Pars resulted n reduced quoted spreads and enhanced depth. Ther theoretcal model suggests the reducton n spreads s the result of nformed traders postng better prces due to reduced rsk of pggybackng by other traders. Smlarly, Comerton-Forde and Tang (2009) fnd that lqudty ncreased on the Australan Stock Exchange followng the removal of broker IDs. Smaan et al. (2003) argue that the ntroducton of an anonymous order type on the Nasdaq could mprove prce competton and narrow spreads. A market consultaton paper publshed by the Australan Stock Exchange n 7

8 2003 argues that dsclosng broker IDs encourages predatory tradng and ncreases tradng costs. 2 Consequently, dsclosure of broker IDs may deter effcent prce dscovery as traders shft lqudty off-market. Ths s consstent wth Barclay et al. (2003) who fnd that anonymous markets attract nformed traders and consequently lead prce dscovery. Rnd (2008) reconcles the seemngly mxed emprcal fndngs wth the followng theoretcal model: when nformaton acquston s exogenous (e.g., when nsder tradng s prevalent), anonymty ncreases nformaton asymmetry and leads to reduced lqudty, as unnformed traders are less wllng to supply lqudty. However, when nformaton acquston s endogenous (e.g., traders become nformed through research), anonymty ncreases the ncentve to acqure such nformaton, consequently ncreasng the number of nformed traders. The ncrease n nformed traders n Rnd s model ncreases overall market lqudty because nformed traders are effectve lqudty supplers. Perott and Rnd (2006) support these theoretcal nsghts, reportng that n an expermental market where nformaton acquston s endogenous, anonymty encourages nformaton acquston and ncreases market lqudty. III. The Toronto Stock Exchange The TSX s Canada s man stock exchange and s a fully electronc contnuous aucton market whch trades from 9:30 a.m. to 4:00 p.m. Snce 22 March 2002, TSX brokers have the opton of dsplayng ther broker IDs on ther orders (an attrbuted order, the default settng) or not dsplayng ther IDs (an unattrbuted order). Ths feature s smlar to that offered by Nasdaq s tradng platform and the LSE s SETSqx tradng system for less lqud stocks. On the TSX, anonymous orders carry a generc 001 numerc tag n place of the broker ID, whch remans wth the order after executon. The market regulator has the capacty to dentfy and 2 See 8

9 track all anonymous orders. At the end of the tradng day the exchange relays the broker IDs of anonymous orders to the central depostory for settlement purposes. The decson to trade anonymously can be made by ether the clent or the trader. In general, clent orders submtted to the tradng desk are traded anonymously at the trader s dscreton. The clent may request that the order be submtted anonymously, although anecdotal evdence ndcates that ths s relatvely uncommon. The ablty for clents to use anonymty may also be restrcted by the conflctng nterests of brokers, for whom dsplayng the broker ID has valuable advertsng effects. Some traders may choose to conceal ther tradng actvty by usng ceberg orders. However, we do not specfcally consder ceberg orders n our analyss, as they prmarly represent another dmenson of pre-trade transparency (the concealment of volume rather than the concealment of dentty). 3, 4 The TSX s a hybrd market combnng an electronc order-drven market wth market maker ntermedaton, smlar to the NYSE, Nasdaq, NYSE AMEX Equtes and LSE. On the TSX, lsted companes are assgned a specalst (also known as a Regstered Trader) who performs a market makng functon. The specalst s requred to trade all orders up to the mnmum guaranteed fll (MGF) at the nsde bd or ask when there are nsuffcent commtted orders to fll the ncomng order at that prce. The MGF s determned by the specalst, wth a 3 Anand and Weaver (2004) fnd that hdden lmt orders represent less than 1% of all lmt orders on the TSX, and less than 7% of total order volume. 4 Traders can also conceal ther denttes through the use of jtney orders, where brokers route orders to other brokers (.e., jtney brokers) for subsequent executon. Such orders are a long-standng feature of the Canadan stock market and used for a varety of reasons, such as when a broker s volume of trade s so small that t s more economcal to channel ts orders through other brokers who charge them a dscounted brokerage fee. The jtney orders only consttute 2.42% of all orders and are not expected to play a major role n the analyss of the effects of anonymty. 9

10 requred mnmum sze of two board lots less one share. Specalsts can also trade n stocks other than ther desgnated ones. IV. Data The TSX s order and tradng book data are provded by Market Regulaton Servces Inc. ( RS, now known as the Investment Industry Regulatory Organzaton of Canada), whch s responsble for nvestor protecton and the regulaton of Canada s securtes markets. Of the 1,421 companes lsted on the TSX at end-2004, we nclude n our sample those that have a sngle stock code and are contnuously lsted wth daly turnover exceedng $50,000 throughout the sample perod. 5 The fnal sample conssts of 141 securtes and covers the 59 tradng days over the perod 1 May to 31 July The data nclude the prce, volume, date and tmestamp of every order and trade, the numerc dentfer of the broker submttng the order and a marker ndcatng whether or not the order s anonymous. The data also dentfy the type of party ntatng the order (.e., clent, propretary, specalst, non-clent and optons market maker), whch s not seen by market partcpants, and the drecton of the order (.e., buy or sell). Clent orders nclude drect market access orders from partcpatng nsttutons. Propretary orders are made on behalf of the broker s prncpal account and can be motvated by ether nventory or speculatve reasons. Specalst orders are those submtted by desgnated specalsts n both ther desgnated stocks and non-desgnated stocks. Non-clent orders nclude orders for the employees, drectors and offcers of the brokng frm and ts afflates. There are a total of 88 brokers n the sample. A key feature of ths dataset s that we 5 Some companes have multple stock codes denotng dfferent types of securtes, e.g., Allance Atlants Communcatons Inc whch s lsted as AAC.A and AAC.B. These securtes are excluded from our analyss. 10

11 are able to observe the IDs of the brokers submttng anonymous orders (whch are not seen by other market partcpants) as well as those of non-anonymous orders. We lmt our study to orders submtted for executon through the central lmt order book and therefore exclude block trades executed n the upstars market and n-house crossngs. 6 Ths s done for several reasons. Frst, pre-trade anonymty between the potental counterpartes s not possble n upstars tradng and n-house crossngs, as the brokers negotatng such trades know each other s denttes. It s possble to examne post-trade effects where upstars and n-house crossng traders report ther trades anonymously, but the use of anonymty n such cases s neglgble (0.6% of all upstars trades and n-house crosses are dsclosed anonymously). 7 Second, we are unable to accurately observe the order ntaton or negotaton tmes and therefore cannot determne the prevalng market condtons at the tme of order ntaton or at the tme the trade s negotated. We exclude odd lot orders, trades ntated by the exchange s daly openng trade allocaton mechansm and orders submtted when the bd and ask quotes are temporarly overlappng. 8 The fnal sample contans 21.4 mllon orders, made up of 1.6 mllon market orders and 19.8 mllon lmt orders. We defne market orders as those orders that execute mmedately, ntatng trades (ths ncludes marketable lmt orders) and therefore the number of trades n our sample s equal to the number of market orders (1.6 mllon). 6 Ths results n the excluson of 25,139 crossngs and upstars trades consttutng 0.9% of the total number of trades and 38% of total traded value n the raw sample. 7 The types of traders that care about post-trade market effects are manly the nformed and those traders usng order splttng strateges. Upstars trades and n-house crosses typcally fall nto nether of these categores. 8 Temporary quote overlaps may occur when some stocks experence openng delays beyond the offcal openng tme of 9:30am. 11

12 V. Characterstcs of Anonymty Use A. Anonymty Use and Order Sze Contrary to the predcton of Bloomfeld and O'Hara (2000) that transparent markets wll eventually lose order flow to less transparent ones, we fnd that traders normally dsclose ther denttes despte havng the opton of anonymty. Table 1 reports anonymous and nonanonymous volumes by order drecton. Anonymous orders account for only 6% of lmt order volume and 8% of market order (trade) volume (all volumes are denoted n dollar terms). A slghtly hgher proporton (57%) of anonymous lmt order volume s n the form of sell orders compared to non-anonymous orders (51%), but the opposte s true for market orders. < TABLE 1 HERE > Anonymous lmt orders also tend to be much smaller than non-anonymous lmt orders: the average anonymous lmt order of $21,003 s less than half the average non-anonymous lmt order at $49,579. Evdence from other markets demonstrates that traders sometmes attempt to smooth ther market mpact and reduce ther executon costs by submttng small orders that are elgble for automatc fll by the specalst (Huang and Masuls (2003), Ress and Werner (2005)). It s possble that some TSX traders not only use such a strategy to reduce ther executon costs, but supplement t by usng anonymty to further reduce ther order exposure rsk. B. Order Source The breakdown of anonymous order volumes by ther sources reveals that propretary and clent accounts are responsble for the largest share of anonymous lmt order volumes (Fgure 1). Ths s expected gven that these order sources account for the bulk of all order volume. In fact, 12

13 over 90% of propretary, clent, specalst and optons market maker lmt order volume remans non-anonymous. Non-clent accounts, on the other hand, record over half of ther lmt order volume as anonymous, but only 7% of ther market order volume. < FIGURE 1 HERE > Specalsts and optons market makers contrbute dsproportonately large shares of total anonymous market order volume. They account for only 12% and 0.26% of total non-anonymous market order volume but 45% and 0.62% of total anonymous market order volume. Ths suggests that specalsts and optons market makers are more aggressve users of anonymous orders than other account types, despte ther low market share of overall tradng. Specalsts also tend to submt larger anonymous orders (averagng $53,393) than non-anonymous orders (averagng $39,763). Fgure 1 hghlghts that as much as 27% of specalsts market order volume and 18% of optons market makers s anonymous. C. Order Aggressveness Table 2 reports the composton of anonymous and non-anonymous order volume n the followng categores. Behnd-the-quote lmt orders refer to buy (sell) orders prced below (above) the best bd (ask). At-the-quote lmt orders refer to orders prced at the best quotes. Insde-the-quote lmt orders refer to orders prced between the best quotes. At-the-quote market orders refer to buy (sell) orders prced at the best ask (bd). Walks-up-the-book market orders refer to buy (sell) orders prced above (below) the best ask (bd). < TABLE 2 HERE > 13

14 Over half of total anonymous order volume s n the form of lmt orders behnd the prevalng best quotes. However, ths rato s even hgher for non-anonymous orders (slghtly under two-thrds). Clent and propretary sources are the man users of behnd-the-quote anonymous lmt orders: ther orders n ths category alone account for 20.6% and 27.4% respectvely of total anonymous volume. Ths s consstent wth the noton that lqudty traders, whch we expect propretary traders and drect market access clents to be, prefer to conceal ther denttes to reduce the rsk of ther lmt orders beng pcked off by nformed traders. At nearly all levels of order aggressveness, specalsts and non-clents account for a larger share of total anonymous orders n the market, compared to ther share of total nonanonymous orders n the market. There are no compellng reasons for usng anonymty n the normal lqudty provson or prce smoothng actvtes conducted by specalsts. Furthermore, specalsts relatvely hgh use of aggressve anonymous market orders ndcates that lqudty provson s not the reason for ther anonymous actvty (specalsts account for nearly half of total anonymous market orders that walk up the book ). The hgh usage of anonymty s more lkely to reflect attempts to mnmze nformaton leakage for nformaton-motvated trades. A relatvely large proporton of anonymous volume (36.9%) s placed as lmt orders at the best quotes. Pror lterature documents that at-the-quote lmt orders typcally get flled wth lower executon costs than behnd-the-quote lmt orders or market orders (Harrs and Hasbrouck (1996)). Therefore, f traders are competng for fast executon, some of these orders represent attempts to jump-the-queue for executon. Anonymty s useful n such cases to avod complants or retalaton by other traders for engagng n pennyng. Some orders are placed a consderable dstance from the best quotes such that they have a very small probablty of ever executng. Such orders contan lttle nformaton regardng the use and effects of anonymty. From ths pont onwards we report results ncludng market orders and lmt orders placed wthn one spread s dstance ether sde of the best quotes. Ths subset ncludes both lmt orders that are executed and ones whch are not. In robustness tests we 14

15 confrm that we obtan smlar results usng all lmt orders, although the results are generally smaller n magntude. D. Executon Costs and Market Qualty A key objectve of both the dentty dsclosure decson and order placement strategy s the mnmzaton of executon costs. Common measures of executon costs nclude prce mpact, the effectve half spread and the realzed spread (see Huang and Stoll (1996), Bessembnder and Kaufman (1997a, 1997b)). Prce mpact s the most approprate of these three measures to examne executon costs n our settng and s defned as follows: (1) Pr ce Impactt 100Dt ( M t n M t ) / M t D s a dummy varable equal to +1 (-1) for buyer-ntated (seller-ntated) trades, s the t prce at whch the order executes and M and are the bd-ask mdponts mmedately t Mt n before the order and fve mnutes after the order, respectvely. Prce mpact measures the reacton of the lmt order book quotes n a short tme perod followng an order and s vewed as an undesrable cost by most traders. For example: lqudtymotvated traders perceve prce mpact as the cost of nsuffcent lqudty to accommodate an order at the prevalng prce; for nformed traders t represents the extent to whch ther nformaton s beng mpounded nto prces, possbly through the effects of pggybackng; and for traders usng order splttng strateges t represents unfavorable prces for subsequent parcels of a trade package. Other common measures of executon cost nclude the effectve half spread and realzed spread. Effectve half spread n a lmt order market such as the TSX s equvalent to the proportonal quoted spread (defned as the dfference between the best bd and ask quotes dvded by the mdquote) and measures the cost of mmedately executng a trade by crossng the spread P t 15

16 to ht a lmt order. The decson to go anonymous cannot affect ths measure of executon cost at the tme of order submsson, because the quoted spread s set before the market sees the ncomng order. However, future values of the quoted spread can be nfluenced by the submsson of an anonymous order. We therefore examne the effects of anonymous orders on future quoted spreads usng the varable ChangeInSpread, defned as the dfference n proportonal bd-ask spread from mmedately pror to the order submsson to fve mnutes after. From a trader s perspectve, causng spreads to wden s generally undesrable because ths ncreases the executon costs of a trade package. From the perspectve of overall market qualty an ncrease n spreads ndcates a reducton n lqudty and often an ncrease n nformaton asymmetry. Realzed spread s smply the effectve half spread mnus the prce mpact and consequently provdes no addtonal nformaton n our settng where the effectve half spread at the tme of order submsson can not be affected by the anonymty decson. We also examne the effect of anonymous orders on another aspect of market qualty: short term volatlty. We calculate ChangeInVolatlty as the dfference n volatlty from the fve mnute nterval mmedately pror to the order submsson to the fve mnute nterval mmedately after. Volatlty s calculated as the standard devaton of the mdpont returns at every order wthn the nterval. Table 3 reports the averages of PrceImpact, ChangeInSpread and ChangeInVolatlty across all anonymous and non-anonymous orders, as well as pared t-test statstcs of the dfference between anonymous and non-anonymous orders. As the results are smlar for both buy and sell orders, we only report results for the full sample of orders. < TABLE 3 HERE > Anonymous orders, both market and lmt, tend to be assocated wth greater prce mpact than non-anonymous ones. The magntude s n the order of zero to fve bass ponts for market 16

17 orders dependng on the order source and zero to four bass ponts for lmt orders at or better than the best quote (wth the excepton of specalsts). Ths s consstent wth the bulk of the theoretcal lterature, whch suggests anonymous orders are on average more nformed (Roëll (1990), Admat and Pflederer (1991), Forster and George (1992), Fshman and Longstaff (1992)), as well as a number of emprcal studes (Barclay et al. (2003), Grammg et al. (2001)). Anonymous orders have less dsperson n ther effects on prce mpact and spreads across the varous order sources. For example, the average prce mpact of anonymous market orders only vares between 10 and 12 bass ponts dependng on the order source, but for nonanonymous orders t vares from fve to 11 bass ponts. When the broker dentty s concealed, the market s less able to nfer the order source and therefore the effects of the order converge to the mean effects of all order sources. Even though market partcpants cannot see the order s source, they are better able to nfer the source of an order when the broker ID s dsplayed. The prevalng ntuton of the theoretc lterature suggests that market orders are more lkely to be nformed than lmt orders. Therefore, the prce mpact of non-anonymous market orders gves the best ndcaton of whch order ntators are perceved to be most nformed. On ths bass, the results suggest that specalst and propretary market orders are perceved to be the most nformed. Non-anonymous market orders from these sources ncur prce mpact of approxmately 11 bass ponts, compared to around fve bass ponts for clent and non-clent non-anonymous market orders. The specalst s nformatonal advantage s lkely to be assocated wth better access to order flow nformaton (e.g., orders beng shopped n the upstars market), whereas for propretary trades t could be order flow or fundamental nformaton. Consstent wth ths result, the non-anonymous market orders of specalsts and propretary accounts lead to a greater ncrease n spreads (seven to ten bass ponts) than clent and non-clent orders (three to four bass ponts). 17

18 VI. Determnants and Executon Costs of Anonymous Orders Models of the determnants and uncondtonal executon costs of anonymous orders must recognze that a trader s selecton of anonymty depends on hs expected executon cost of dong so. We use the two-stage estmator ntroduced by Heckman (1979) 9. The frst stage s a probt model of the determnants of traders decsons to trade anonymously. The second stage s an endogenous swtchng regresson that uses the frst stage estmates to overcome selecton bas n estmatng the executon costs of anonymous and non-anonymous orders. The two stages are represented n the followng system: * (2) A, where Z A 1 0 f A * 0 otherwse (3) n n n n E[ y A 0] X (4) a a a a E[ y A 1] X * A s a latent varable representng the trader s preference to submt an anonymous order n a ( 1) or a non-anonymous order ( 0 ), and are the prce mpacts of orders A A y y submtted non-anonymously and anonymously, respectvely. Z W, X s a vector of varables that nfluence the anonymty decson, comprsed of the order characterstcs that affect executon cost, X, and state varables that capture the prevalng market condtons, W (see Madhavan and Cheng (1997) for more detal). The term represents unobservable (to the econometrcan) characterstcs of an order that affect both the decson to use anonymty and the subsequent prce mpact, e.g., the amount of nformaton possessed by the order ntator or the 9 See Maddala (1983) and Greene (2003) for detaled general dscussons and Bessembnder and Venkataraman (2004), Madhavan and Cheng (1997) and Conrad et al. (2003) for examples of the model s applcaton. 18

19 nature of ther tradng strategy. Ths s the term that leads to bases n models that do not address the endogenety of the anonymty decson. The second stage, eqs. (3) and (4), models the prce mpact of orders condtonal on the n a choce of anonymty. The terms and on the rght hand sde of eqs. (3) and (4) are nonlnear combnatons of the frst stage estmates. 10 Ther purpose s to correct for the endogenous selecton of anonymty. Consequently, the frst term on the rght hand sde of eqs. (3) and (4) estmates the uncondtonal prce mpact of a random order submtted nonanonymously and anonymously, respectvely. Ths model allows the explanatory varables to affect the dependent varable (prce mpact) n dfferent ways for anonymous and non-anonymous orders. It also allows for dfferent means for the prce mpact of anonymous and non-anonymous orders. The market reacton to an order s mportant n determnng the order s executon cost. Whle our data allow us to dentfy the source of an order (clent, nventory, specalst, non-clent and optons market maker), the market only observes the broker ID assocated wth an order (f submtted non-anonymously). From ther knowledge of the type of broker, market partcpants can nfer the probable source of the order. Therefore, n the analyss that follows we group orders by the type of broker ntatng the order. We classfy brokers nto three groups usng nformaton avalable to market partcpants. 11 The frst group, Agency, conssts of brokers who trade 10 n a The selectvty correcton terms are defned as Z ˆ )/(1 ( ˆ Z )) and Z ˆ ) / ( ˆ Z ), ( ( Z where ˆ are the predcted values from the frst stage probt, s the standard normal densty functon and s the cumulatve normal dstrbuton functon. 11 We use the Investment Dealers Assocaton of Canada s (now part of the Investment Industry Regulatory Organzaton of Canada) lst of member frms by peer group to dentfy agency and dual brokers, and TSX monthly reports of specalsts and ther stocks of responsblty. At the start of each day the TSX broadcasts a lst of stocks and ther assgned specalst frms. 19

20 prmarly on behalf of clents, both nsttutonal and retal. The second group, Dual, conssts of ntegrated brokers that trade for ther propretary accounts as well as servng largely nsttutonal clents. The thrd group, Market Makers, conssts of desgnated specalsts and optons market makers. Although agency brokers trade predomnantly for clents, a proporton of ther trades are from other sources such as propretary or non-clent accounts. Smlarly, desgnated specalst frms and optons market makers may engage n tradng other than market makng and submt clent, propretary and non-clent orders under the same broker ID that s assocated wth ther market makng role. Therefore, from a market partcpant s perspectve the broker classfcatons are nosy sgnals of the order source. To capture dfferences n the use of anonymty and executon costs between dfferent order sources wthn a broker type, we nclude a dummy varable, D noncore, that takes the value 1 for orders that are from sources other than the broker type s core busness. For agency brokers non-core orders are those from propretary and non-clent accounts, for dual capacty brokers non-core orders are clent and non-clent orders, and for market makers non-core orders are those not assocated wth ther stock or opton market makng roles (clent, propretary and non-clent orders). Ths desgn allows us to examne dfferences between order sources wthn a broker type, and dfferences n the market s reacton to orders based on the nformaton market partcpants could reasonably nfer from the broker ID. A. Frst Stage Probt Model of the Anonymty Decson The dependent varable n the frst stage, D anon, s equal to one f the order s submtted anonymously and zero otherwse. The order characterstcs, X, contan the followng varables: Value, the dollar volume of the order dvded by the average order dollar volume that stock-day; 20

21 Aggr (aggressveness), a contnuous varable that measures the order placement relatve to the prevalng best quotes (scaled to gve the value zero at the mdpont, postve one and negatve one at the best ask and best bd respectvely for a buy order (opposte for a sell order)); D bd, a dummy varable for bds (buy orders); and D frsthalf and D lasthalf, dummy varables for orders submtted n the frst and last half-hours of the tradng day, respectvely. The varables that capture prevalng market condtons, W, nclude: Spread, the proportonal bd-ask spread at the tme of the order placement; Volatl (volatlty), the standard devaton of the mdpont returns over the prevous 50 orders; and Momen (momentum), the average mdpont-to-mdpont return over the prevous 50 orders (sgned to the trade drecton,.e., multpled by negatve one for sell orders). We nclude fxed effects for stocks and brokers n both stages to control for unobservable cross-sectonal characterstcs. Therefore, we do not nclude varables for whch almost all of ther varaton s cross-sectonal, such as stock sze. Table 4 reports the results of the frst stage probt model estmated separately for each of the broker types. For easer comparson of magntudes across varables, we standardze all varables to have a mean of zero and standard devaton of one. < TABLE 4 HERE > Holdng all other varables constant, anonymous orders tend to be larger than nonanonymous orders, ndcated by the postve coeffcents of Value. Hasbrouck (1991) fnds that large trades lead to wder spreads and attrbutes ths effect to specalsts who nfer from the large trade that an nformaton event has occurred. Thus, a trader wth short-lved prvate nformaton that does not have the tme to execute an order-splttng strategy may opt for anonymty n an effort to reduce market mpact and prevent other traders from dentfyng the extent of hs poston n the market. Ths s consstent wth Harrs (1996) who reports that mpatent nformed traders are generally beleved to prefer large anonymous orders. 21

22 Order sze has the largest effect on market makers decson to use anonymty (Value coeffcent of 0.03 for market makers and 0.01 for the other broker types). To llustrate the magntude of these coeffcents, unreported margnal effects estmates suggest that for market makers, a one standard devaton ncrease n the relatve sze of an order (from the mean) ncreases the probablty that the order s submtted anonymously by 11%. For the other order sources, a one standard devaton ncrease n relatve sze ncreases the probablty of anonymty by 3%. Sellers, on average, prefer anonymty more than buyers. Ths s ndcated by the negatve coeffcents on D bd and s strongest for agency brokers. Averagng across the broker types, buy orders are 31% less lkely to be submtted anonymously than sell orders. One explanaton for ths result s that lqudty-motvated traders seekng to offload ther long postons may prefer anonymty n order to prevent predatory tradng by other traders (see Brunnermeer and Pedersen (2005) for a dscusson of predatory tradng strateges). It s also possble that the sell sde of the order book s perceved to be more nformatve durng perods of postve market performance (Ranaldo (2004)), as was the case for the TSX durng the sample perod. Hence, nformed sellers may prefer anonymty to avert tradng-ahead and pggybackng by other traders. Aggressvely prced orders from agency and dual capacty brokers are less lkely to be submtted anonymously (a one standard devaton ncrease n aggressveness decreases the probablty of anonymty by 13%), but aggressvely prced market maker orders are more lkely to be submtted anonymously (a one standard devaton ncrease n aggressveness ncreases the probablty of anonymty by 9%). Aggressve tradng s more lkely to be assocated wth short lved nformaton and urgent lqudty needs than long lved nformaton and non-urgent lqudty needs. The results suggest that anonymty s generally more valuable to agency and dual capacty traders wth long lved nformaton because t allows them to retan ther nformatonal advantage for longer, and to non-urgent lqudty traders because t mnmzes the rsk of ther orders beng pcked off. For market makers, on the other hand, non-aggressve tradng (lqudty provson) s 22

23 ther assgned role and n undertakng ths role they are wllng to advertse ther dentty. However, market makers may trade aggressvely when they have nformaton about future prce movements based on ther knowledge of order flow, or when they have to adjust ther nventory quckly. In such cases, market makers are more lkely to use anonymty to avod revealng ther nformaton about order flow or sgnalng ther need to adjust ther nventory. The coeffcents for D frsthalf and D lasthalf vary across the dfferent broker types. For example, agency brokers tend to use proportonally more anonymous orders n the early and late parts of the tradng day (coeffcents of 0.19 and 0.10), whereas dual capacty brokers and market makers tend to use less (coeffcents of and for dual capacty brokers and and for specalsts). The Volatl and Spread coeffcents suggest that agency brokers and market makers prefer to use anonymty when spreads are wde (coeffcents of 0.30 and 0.21, respectvely) and volatlty s low. The effects are partcularly strong for spreads. A one standard devaton ncrease n spreads from the mean ncreases the probablty of anonymty by 32% and 17% for agency broker and market makers, respectvely. A possble explanaton s that envronments characterzed by hgh nformaton asymmetry amplfy nformed traders nformatonal advantage, and consequently concealng ther dentty s more mportant to avod tradng-ahead and pggybackng. Momentum does not have a large effect on the choce of anonymty. Wthn broker types, order source has a large effect on the probablty that an order s submtted anonymously (D noncore coeffcents of 0.76, and -0.45). Agency brokers prmarly trade on behalf of clents. Margnal effects estmates suggest that ther non-core orders (propretary and non-clent orders) are 11 tmes more lkely to be submtted anonymously. Smlarly, dual capacty brokers are 25% more lkely to submt a propretary order anonymously than other order sources such as clent orders. Market makers are 81% more lkely to use anonymty when submttng a specalst or optons market maker order than an order from another order source such as a clent or propretary account. Fnally, the magntude of the coeffcents 23

24 suggest that the decson to submt an anonymous order s most senstve to the broker type, order source, the aggressveness of the order placement and, for agency brokers and market makers, the sze of the spread. B. Second Stage Model of Prce Impact In the second stage we estmate the coeffcents n eqs. (3) and (4) wth PrceImpact as the dependent varable. Table 5 reports the estmates wth all varables standardzed to a mean of zero and standard devaton of one. For the ndependent varables, we nclude the same vector of order characterstcs, X, as n the frst stage as well as broker and stock fxed effects. Due to the ncluson of the selectvty correcton varables,, the n (rows Nonanon n Table 5) are uncondtonal estmates of the effect of the ndependent varable on the prce mpact of a random a n order submtted non-anonymously. Smlarly, the - (rows Anon-Nonanon n Table 5) are uncondtonal estmates of the dfference n the effects of the ndependent varable on the prce mpact of a random order submtted anonymously relatve to a random order submtted nonanonymously. < TABLE 5 HERE > We fnd that prce mpact tends to ncrease wth order sze and order aggressveness (postve coeffcents of Value and Aggr). Large and aggressve orders are perceved as relatvely nformed, thus causng prces to follow n the same drecton. Addtonally, large market orders are more lkely to create lqudty mbalances that affect prces. Buyer-ntated orders are assocated wth greater prce mpact than seller-ntated ones (except for anonymous market maker orders). Ths s consstent wth the explanaton that 24

25 lqudty-motvated sales are more lkely than lqudty-motvated purchases, and therefore buy orders are more nformed on average and have a larger effect on prces (Allen and Gorton (1992)). Orders n the frst hour of tradng are assocated wth greater prce mpacts, partcularly anonymous market maker orders (postve coeffcents for D frsthalf ). An explanaton s that proportonally more nformed tradng, relatve to total tradng actvty, occurs early n the tradng day n response to overnght news and events. Usng the selectvty corrected parameter estmates and the average order characterstcs, X, we calculate the uncondtonal expected prce mpact for a random order submtted nonanonymously, as well as the dfference n prce mpact for a random order submtted anonymously and non-anonymously: n n (5) E[ y ] X E[ y a n y ] a n X (6) Smlarly, we calculate the condtonal expected prce mpact for a non-anonymous order and the dfference n prce mpact for an anonymous and non-anonymous order gven the choce of anonymty, E[ y n a n A 0] and E[ y A 1] E[ y A 0], respectvely. The dfference of the condtonal and uncondtonal prce mpact dfferences, a n a n E[ y A 1 E[ A 0] E[ y y ] Select ] y, measures the extent to whch traders nfluence the prce mpact of ther orders by strategcally selectng anonymty when t s benefcal to them. Ths estmate s mportant because t measures the effect of strategc behavor. Table 5 reports these estmates (Uncond, Cond and Select) separately for buy and sell orders. Unlke the regresson coeffcents that correspond to standardzed varables, here we report estmates n bass ponts for easer nterpretaton of the magntudes. The uncondtonal estmates for non-anonymous orders (random orders submtted nonanonymously) ndcate that dual capacty brokers (prmarly tradng on behalf of ther propretary 25

26 accounts and nsttutonal clents) typcally have the greatest prce mpact, followed by market maker buy orders (0.6 to 1.1 bass ponts). Ths suggests that dual capacty brokers and market makers are perceved to be the most nformed broker types. Ths s consstent wth the regresson coeffcents of D noncore, whch suggest prce mpact, and therefore nformatveness, s greater for propretary and market maker orders than for clent and non-clent orders. The estmates of Select ndcate that the prce mpact of anonymous orders relatve to nonanonymous ones s lower for the estmates that are condtonal on the choce of anonymty than the uncondtonal (random order) estmates. The magntude of the dfference s around three to four bass ponts for market makers, around bass ponts for dual capacty brokers and around bass ponts for agency brokers. Ths demonstrates the key result that by strategcally selectng anonymty when t s benefcal to them, traders reduce the prce mpact of ther orders. Ths does not suggest that submttng a random order anonymously s expected to lower ts prce mpact. In fact, the uncondtonal prce mpact estmates suggest that a random order submtted anonymously by an agency broker or market maker s expected to have greater prce mpact (by one to 11 bass ponts) than an order submtted non-anonymously by the same broker type. The key pont s that anonymty s used strategcally rather than randomly, based on order characterstcs, market condtons and, mportantly, the unobservable characterstcs,, whch affect not only the anonymty decson but also the subsequent prce mpact. The ntuton s that the average order s relatvely unnformed and because anonymty s more lkely to be used on nformed trades, submttng a random order anonymously sgnals that t s more nformed than n fact t s and therefore results n greater prce mpact than submttng the order non-anonymously. On the other hand, n certan crcumstances, by not revealng the broker s dentty, an nformed or strategc trader can conceal hs nformaton or tradng strategy and avod some of the prce mpact that would occur by tradng non-anonymously. 26

27 The magntudes of the effects suggest that market makers beneft the most from strategc use of anonymty when submttng market orders, followed by dual capacty brokers. The effect of ther strategc use of anonymty on prce mpact s n the order of three to four bass ponts per order. Two possble explanatons are: () market makers have the most control over whether ther orders are submtted anonymously; or () the nature of ther tradng strateges or nformaton makes hdng the dentty of the submttng broker more mportant. The proporton of orders submtted anonymously by clent, propretary and non-clent sources may be smaller than would be optmal for mnmzng executon costs. From the broker s perspectve, anonymty has the undesrable effect of reducng apparent market share and the advertsng effects of dsplayng the broker ID n the order book. In fact, for dual capacty brokers, the uncondtonal estmates suggest that a random market order would be expected to have lower prce mpact f t were submtted anonymously. Ths supports the argument that factors other than smply mnmzng prce mpact nfluence the use of anonymty for clent, propretary and non-clent orders. VII. Effects of Anonymous Orders on Market Qualty So far we have found that the strategc use of anonymty benefts traders that have certan types of nformaton or tradng strateges. These benefts can explan why some groups of market partcpants have pushed for anonymty n markets. From the perspectve of an exchange that determnes the degree and form of anonymty, however, there are other consderatons such as the effects on overall market qualty. 27

28 A. Effects on Lqudty and Short Term Volatlty In ths secton, we examne how anonymous orders affect two aspects of market qualty: lqudty (proxed by future spreads), and short term volatlty. Smlar to prce mpact, we expect unobservable characterstcs, such as the degree of nformaton or type of tradng strategy, to affect both the decson to use anonymty and the post-trade effects on spreads and volatlty. Therefore, we use the selectvty correcton model as n the prevous secton, utlzng the same frst stage. The dependent varables, ChangeInSpread and ChangeInVolatlty are as defned n secton V. Table 6 reports estmates correspondng to Cond, Uncond and Select (defned n the prevous secton). < TABLE 6 HERE > Smlar to the prce mpact results, uncondtonal estmates for non-anonymous orders suggest that orders submtted by dual capacty brokers and market makers typcally lead to the largest ncrease n spreads (n the order of two to seven bass ponts). Ths s consstent wth our prevous fndng that dual capacty brokers (partcularly ther propretary orders), and market makers are perceved to be the most nformed broker types and hence adverse selecton costs and spreads are hgher n ther presence. The uncondtonal estmates of the effect of anonymty on spreads suggest that a random order submtted anonymously leads, on average, to narrower future spreads than f t s submtted non-anonymously (by 10 to 81 bass ponts). The same effect holds condtonal on the choce of anonymty. Unreported results (analyzng market and lmt orders separately) suggest that ths effect s drven predomnantly by lmt orders. Unlke market orders that execute just as quckly whether submtted anonymously or non-anonymously, lmt orders provde the market wth an opton to trade. By revealng less nformaton about the order source, anonymous lmt orders make market partcpants more reluctant to take up the opton to trade and are less frequently 28