LIQUIDITY, PRICE IMPACT AND TRADE INFORMATIVENESS EVIDENCE FROM THE LONDON STOCK EXCHANGE

Transcription

1 ECONOMIC ANNALS, Volume LVI, No. 188 / January March 011 UDC: 3.33 ISSN: Scienific Papers DOI:10.98/EKA T Naaša Teodorović* LIQUIDITY, PRICE IMPACT AND TRADE INFORMATIVENESS EVIDENCE FROM THE LONDON STOCK EXCHANGE ABSTRACT: The rapid developmen of elecronic rading has significanly changed sock exchange markes. Elecronic sysems providing rading processes have defined a new sock marke environmen. Such a new environmen requires rading process redefiniion (generally defined as algorihmic rading), as well as redefiniion of well known microsrucure hypoheses. This paper conducs sandard Hasbrouck s (1991a, 1991b) marke microsrucure ime series analysis o examine adverse selecion and informaion asymmery issues on diverse liquidiy levelled socks lised on he London Sock Exchange, which is a marke wih a significan algorihmic rading share. Based on he resuls obained from he considered sample, his paper suggess ha he conribuion of unexpeced rade in he volailiy of he efficien price is larger for inensively raded socks, arguing ha Hasbrouck s (1991a, 1991b) model recognizes algorihmic rading as an unexpeced rade, i.e. as a rade caused by superior informaion. KEY WORDS: liquidiy measures, price impac, rade informaiveness, algorihmic rading JEL CLASSIFICATION: C0, C10, C3, C60, D80, D8 * Faculy of Naural Science, Deparmen of Mahemaics and Informaics, Universiy of Novi Sad, Serbia. eodorovic.naasa@gmail.com 91

2 Economic Annals, Volume LVI, No. 188 / January March INTRODUCTION The rapid developmen of elecronic rading in recen years has significanly improved he process of ransferring securiies from one marke paricipan o anoher. Elecronic rading enables a large number of paricipans o inerac in he marke, decreases ransacion coss, improves he speed of rade execuion, and also requires paricipans fas reacion o any new informaion. Such powerful echnological improvemens have led o more sophisicaed rading - algorihmic rading. Algorihmic rading usually refers o he use of compuer algorihms o break up a large order ino a sequence of smaller orders, and he engagemen of auomaed rading sraegies for heir execuion wih respec o numerous user-defined parameers, such as ime horizon, liquidiy consrains, deph of marke, volailiy, ec. The overwhelming rend in recen years has been o creae unique rading algorihms ha need o be esed and applied in he marke as quickly as possible, due o fas marke changes. Such a new financial environmen requires he reinvesigaion of marke microsrucure hypoheses. The marke microsrucure concep has been defined in various ways, by focusing on diverse aspecs of i. I follows he definiion given by O'Hara (1995), "marke microsrucure is a sudy of he process and oucomes of exchanging asses under a specific se of rules. Microsrucure heory focuses on how specific rading mechanisms affec he price formaion process." The main objecive in his paper is he informaion aspec of marke microsrucure. Marke microsrucure hypoheses are ypically empirically esed by vecor auoregressive (VAR) models (Hasbrouck (007), Kuns (007), Lükepohl (1993)). In he empirical marke microsrucure lieraure wo approaches are predominan when sudying he impac of rades on price formaion. The firs one is he approach for invesigaing he effecs of rade informaiveness on price formaion, based on he vecor auoregression of reurn and rade equaion. The second one is he exension of Hasbrouck's (1991a) model by incorporaing he waiing ime (duraion) beween successive ransacions given by Engle e al. (000). This model was used o empirically es he role of duraion in he process of price formaion in he sample of socks raded on he 9

3 LIQUIDITY ON LONDON STOCK EXCHANGE New York Sock Exchange (NYSE). The ime beween rades is modelled by he auoregressive condiional duraion (ACD) model (Engle (1998)). To model duraion, volume, and reurns simulaneously, Manganelli (005) inroduced he model which provides a link beween Hasbrouck (1991a) and Engle e al. (000), by incorporaing he rade sign ino he specificaion of mean of reurn and adding an exra equaion for he rade sign. The model is esed on he sample of NYSE socks. The heoreical background of he approaches of Hasbrouck (1991a, 1991b), Engle e al. (000), and Manganelli (005) o he effecs of rades o price formaion is he heory of he asymmerically informed marke, which basically criicizes he efficien marke hypohesis. The efficien marke hypohesis assumes ha a marke is anonymous and all he paricipans in he marke are equally informed abou he raded insrumen. Therefore, no paricipan can make economic profi by rading such informaion, and informaion conained in rades is immediaely refleced in sock prices. However, hese assumpions would hardly hold in pracice. In realiy, all informaion is no available o all paricipans a he same ime; hence some marke paricipans have a definie advanage over he ohers. Moreover, alhough informaion is public, here is sill a difference in he speed of processing hem by various paricipans, which produces a lag effec beween he news announcemen and rade realizaion. Traders may be classified ino informed raders - raders wih superior informaion - and uninformed or liquidiy raders - raders wih public informaion only. Informed raders may possess informaion on he rue value of securiies, fundamenals, or quaniies. They end o rade he specific sock on which hey have privae informaion. Liquidiy raders rade o decrease coss or o adjus he risk reurn profiles of heir porfolios. They buy socks if hey have excess cash or become more risk oleran, and hey sell socks if hey need cash or become less risk oleran. The presence of informed and uninformed raders causes an asymmeric disribuion of informaion among marke paricipans. Bagheo (1971) was he firs o consider a marke wih heerogeneously informed raders. This problem was hen analyzed by Copeland e al. (1983) and formulaed and developed by Kyle (1985), Glosen e 93

4 Economic Annals, Volume LVI, No. 188 / January March 011 al. (1985), Easely e al. (1987), Admai e al. (1988), and Foser e al. (1990), among ohers. Hasbrouck (1991a, 1991b) analyzed he influence of an asymmeric disribuion of informaion among marke paricipans on fuure price formaion. In a marke wih asymmerically informed paricipans he marke environmen is measured by bid-ask spread and he rade is described by is direcion - posiive if he rade is buyer-iniiaed and negaive if he rade is seller-iniiaed. The concep of an asymmerically informed marke implies ha marke makers possessing only public informaion inerac wih oher marke paricipans who have superior privae informaion. The informed and uninformed raders are undisinguishable o marke makers. Hence hey compensae for he loss ha appears from rading wih informed raders by fixing a spread. The main idea of he model is ha he rade conveys informaion and ha marke makers pos bid and ask prices afer he realized ransacion and wih respec o ha informaion. The model is represened by he vecor auoregression sysem of he reurn and rade equaion based on boh price and order flow hisory. Taking ino consideraion such a sysem he effec of public and privae informaion on price formaion is analyzed, and he ransiory and permanen price impac is idenified. Hasbrouck's (1991a, 1991b) empirical findings from he sample of NYSE socks indicae ha he effec of permanen price impac is no insananeous and ha i akes several ransacions before i is fully realized. By using he impulse response echnique (Hasbrouck (007), Kuns (007), Lükepohl(1993), Hasbrouck consruced he permanen price impac as a cumulaive response of reurn o a shock in he innovaion of rade equaion, where privae informaion mus arise if such exiss. Also, by he variance decomposiion echnique (Hasbrouck (007), Kuns (007), Lükepohl(1993)) he calculaed he conribuion of privae informaion, i.e. unexpeced rade o variaion in efficien price. Informaion asymmery influences he bid-ask spread in he marke and hence he liquidiy. Before he informaion is publicly available he spread ends o be wider, producing a lo of marke volailiy. The informed raders, knowing ha he spread will narrow once he informaion becomes public, end o ake he liquidiy from he marke by execuing rades a he available price. 94

5 LIQUIDITY ON LONDON STOCK EXCHANGE The described concep of he asymmerically informed marke basically coincides wih he classical marke, or he so-called quoe-driven marke. In such a marke, marke makers have an obligaion o coninuously quoe woway prices a which hey are prepared o buy and sell a securiy. In his way hey fill gaps arising from imperfec synchronizaion beween he arrival of buyers and sellers. They are he counerpar in all ransacions a he quoed prices: he bid price, a which hey are willing o buy securiies, and he ask price, a which hey are willing o sell. They are he only providers of liquidiy in he quoedriven marke. The developmen of elecronic rading echnology in recen years has led o a rapid spread of so-called order-driven rading. In an order-driven marke here are no designaed marke makers. Any rader can choose o execue rade via a limi 1 or a marke order. They inpu buy and sell orders for a securiy ino a cenral compuer sysem where hey are auomaically execued whenever hey can be mached in erms of price and amoun. In his paper we were ineresed in he informaional aspec of price formaion across diverse liquidiy levelled socks raded on he London Sock Exchange (LSE), which is predominaely an order-driven marke. We chose he sample of 18 LSE socks wih differen liquidiy levels from he FTSE 100 index - he share index of he 100 larges publicly quoed UK companies. The rading process of FTSE 100 socks is provided by he sock elecronic order driven sysem, called he Sock Exchange Trading Sysem, or SETS. The SETS is an order-maching sysem based on he concep of prioriy rading, where orders are ranked in prioriy of price, hen in ime wihin he price. The order book is conveyed publicly in real ime. As a resul he marke benefis from pre-rade ransparency, which means ha paricipans have access o he whole order book, and pos-rade ransparency, which means ha paricipans can immediaely observe he las rades recorded by he sysem. On he oher hand, orders and rades are mosly anonymous. Regarding liquidiy and price seings, he order-driven marke is significanly differen from he classical quoe-driven 1 A limi bid or ask sock price a which he ransacion has o be execued. A buy or sell order of a cerain number of socks a he curren sanding (bid or ask) price. 95

6 Economic Annals, Volume LVI, No. 188 / January March 011 marke. Limi orders allow a rader o se a limi price a which he order can be filled, bu here is a risk he order will no be execued. Therefore liquidiy and price (bid, ask) seings in an order-driven marke rely only on limi orders. Ineracion beween marke paricipans in he elecronic sock-driven sysem environmen becomes much more complex, requiring he developmen of dynamic rading sraegies or algorihms ha consider everyhing ha can affec price formaion. A he London Sock Exchange here is a significan proporion of algorihmic rading. According o he Inernaional Banking Sysems Journal (June 007), in 006 over 40% of all orders were enered by algorihmic raders, wih 60% prediced for 007. Algorihmic rading influence on marke microsrucure has recenly been invesigaed by Hasbrouck e al. (007), Bloomfield e al. (005), and Payne (003), among ohers. In he chosen sample we considered differen liquidiy dimensions according o several liquidiy measures - volume, rade size in pounds, duraion, and flow raio. Following Hasbrouck (1991a, 1991b)} and Payne (003), he oal permanen price impac and conribuion of unexpeced rade in volailiy of he efficien price are calculaed for all 18 socks. To enable he comparison of esimaed permanen price impac across differen socks, a sligh modificaion of he reurn variable in Hasbrouck's (1991a) model is applied. Applying he Spearman rank correlaion es, he obained resuls are compared wih resuls on liquidiy across differen socks. Resuls obained from he considered sample of 18 LSE socks sugges ha he conribuion of unexpeced rade in he volailiy of he efficien price is larger for inensively raded socks, where rade inensiy is measured by duraion and flow raio. Also, we did no find any significan correlaion beween hese liquidiy measures and permanen price impac. We sugges ha such resuls can be explained by algorihmic rading. I is expeced ha he proporion of algorihmic rading is larger for inensively raded socks. Therefore in his paper we sugges ha algorihmic rading behaves as an unexpeced rade in Hasbrouck s model (1991a, 1991b). The paper is organized as follows. Secion presens Hasbrouck's model of permanen price impac and conribuion of unexpeced rade in efficien price 96

7 LIQUIDITY ON LONDON STOCK EXCHANGE volailiy. Secion 3 describes he daa we used in our analysis as well as he cleaning procedure. Secion 4 describes variables for liquidiy and Hasbrouck's analysis. The esimaion procedure is also described. The resuls are given in Secion 5. Finally, Secion 6 summarizes and concludes.. THE MODEL We followed Hasbrouck's (1991a) basic vecor auoregression model o examine differen informaion componens in price changes. For a sample of NYSE socks Hasbrouck considered he following rading mechanism. The ransacion x is realized a ime and a price p Afer he ransacion and announcemen of b a rade x he marke makers pos bids and ask quoes denoed by p and p. In his noaion he ransacion realized a ime is realized a he bid or ask price b prevailing before ha ransacion, denoed by p 1 and p a 1. Midquoe m p a b p is aken as an unbiased proxy of he efficien price. The change in he naural logarihm of he midquoe ha follows he curren rade a ime r ln( m 1) ln( m ) is considered as a reurn variable. For he rade variable Hasbrouck suggesed a rade indicaor variable which akes value 1 if he rade is buyer iniiaed, and value -1 if he rade is seller iniiaed. The reurn and rade dynamic is modelled as a non-sandard bivariae vecor auoregression VAR r a1 r 1 ar... b0x b1x , (1)... () x c1 r 1 cr d1x 1. dx.., Theoreically, his model can be of infinie order, bu for pracical purposes i is runcaed a some lag. 97

8 Economic Annals, Volume LVI, No. 188 / January March 011 Coefficien b 0 in he reurn equaion represens he immediae impac of conemporaneous rade x. Coefficiens b i, i=1,, in he same equaion capure ransiory rade and effec prices. The innovaion erm 1, represens he effec of non-rade public informaion. The innovaion in he rade equaion capures an unexpeced ransacion aciviy where he privae informaion, resides, if such exiss. The model assumes predeermined regressors, i.e. ha he innovaions 1, and, are uncorrelaed wih regressors. Also, i is assumed ha hey have zero mean, i.e. ha E ) E( ) 0. ( 1,, In addiion, i is assumed ha hey are joinly and serially uncorrelaed E ) E( ) E( ) 0, for all s ( 1, 1, s,, s 1,, s The described VAR model is no enirely sandard since i assumes ha a marke maker has informaion on all lagged reurns and lagged rades, as well as informaion on conemporaneous rades available a ime. Tha means ha reurn r conains all publicly available informaion a ime, and ha marke makers ac primarily on his informaion se. This model permis Granger's causaliy (1963) running from rade o reurn boh conemporaneously and wih lags. The model also permis Granger's causaliy running from he lagged reurns o rades, bu i does no permi conemporaneous causaliy running from reurns o rades. The presence of conemporaneous rade x, and he assumpion of predeermined regressors implies ha errors are conemporaneously orhogonal, i.e. E( 1, ) E(, ) 0, which does no hold for he sandard VAR model in general. Under assumpions of predeermined regressors and conemporaneous orhogonaliy of errors 1, and,, he leas squares esimaion of he described VAR model is consisen and efficien. Hasbrouck formally defined he informaional impac of he rade as he ulimae impac on he sock price resuling from an unexpeced componen of he rade, i.e. he persisen price impac of he rade innovaion. This impac will probably no be insananeous, bu raher occurs over a long period of ime and will be permanenly impounded in he sock prices. Hasbrouck (1991a) obained he permanen price impac by calculaing he impulse responses from 98

9 LIQUIDITY ON LONDON STOCK EXCHANGE he moving average represenaion as in Hasbrouck (007), Kuns (007), Lükepohl (1993) of he sysem (1), (). Under a weak saionariy assumpion of ime series sysem (1), (), by Wold's (1938) heorem he VAR is inverible and i has he vecor moving average represenaion of an infinie order given by r x * * * 1, a1 1, 1... b0, b1, 1... (3) * * * 1 1, 1, 1, 1, c... d d... (4) * i The impulse response coefficiens b, i=1,,... give he effec of a uni rade innovaion on he reurn a an i period horizon. The sum l b i i0 * represens he impac of an unexpeced rade on reurns afer l ransacions. The cumulaive sum of impulse responses creaes he price impac funcion. Since he model operaes wih he daa indexed in ick ime, he price impac is measured in unis of ransacions. Hasbrouck's (1991a) esimaes for a sample of NYSE socks sugges ha he price impac akes many periods before i is fully realized, and ha he price impac funcion is concave wih posiive horizonal asympoe. The asympoe of price impac funcion represens he oal price impac of he rade. To measure a proporion of he unexpeced rade in he fuure price formaion, Hasbrouck (1991b) assumed ha he midquoe may be divided ino wo unobservable componens m e s The erm e is he efficien price ha is he expeced value of he asse condiional on all currenly available public informaion modelled as a random walk process e e e1 Efficien price is he permanen componen of he midquoe. The whie noise innovaion ~ WN(0, ) reflecs updaes o he public informaion se. The second componen s is a zero mean sochasic process joinly covariance saionary wih. I is a ransiory componen of he midquoe. I represens 99

10 Economic Annals, Volume LVI, No. 188 / January March 011 he disurbance erm ha incorporaes invenory conrol, price discreeness, and oher marke imperfecions ha drive he midquoe away from he efficien price. Since he random walk decomposiion is unobservable, Hasbrouck(1991a) proved ha he variance decomposiion coefficien R - he par of variance in he efficien price aribuable o he rade innovaion - can be calculaed from he rade/reurn VMA represenaion (3), (4) R * * (1 ai ) 1 ( bi ) i1 i1, * ( bi ) i1 1, ) 1 Var ( i Var ) (5) (, The final resul needs o be undersood as follows. Public informaion evens are incorporaed ino reurn via he innovaion 1,. The permanen effec on midquoes of a uni reurn innovaion is given as he sum of one (he * conemporaneous impac) and a i. Hence, he variaion in efficien price i1 implied by public informaion is given by he firs erm in he numeraor of equaion (5). The variaion in efficien price implied by privae informaion is he second erm in he numeraor of equaion (5). The variaion in efficien price caused by boh public and privae informaion is hen a sum of variaions in he efficien price caused by public and privae informaion separaely. 3. DATA AND CLEANING Over a period of 6 days, from March 1, 006 o May 31, 006, he rading aribues of ineres for our analysis were observed rade-by-rade for a group of 18 LSE socks lised on he FTSE 100 index. The 6-day rading sample is long enough o allow reasonably precise esimaions (Easley e al. (1993, 1996), Engle e al. (000)). The rading aribues of ineres for our analysis were ime, price and volume of he execued rade, and pre-rade bid and ask prices wih heir relaed volumes. All daa ha occur ouside he normal rading hours, i.e. before 8:00 a.m. and afer 4:30 p.m., were deleed from he sample. We mached he execued rades wih heir relaed pre-rade bid and ask prices and bid and ask sizes. Afer ha we excluded all rows in he order book for which column is 100

11 a rade 3 was zero. We hen eliminaed all anomalous daa obviously caused by human and sysem errors, such as negaive spreads, zero bid prices, and spreads larger han 10% of acual sock prices. Since here migh be several ransacions repored a he same ime execued a differen price levels, we applied an aggregaion procedure which was consisen wih liquidiy analysis and Hasbrouck's VAR model. The rades ha occurred a he same ime wih he same price and in he same direcion were reaed as one rade. The volume of such rade was hen simply a sum of he volume corresponding o individual rades. Afer his aggregaion procedure he number of observaions for each sock decreased on average more han wo imes. The informaion abou he number of ransacions afer he aggregaion procedure and average midquoe for each sock are given in Table 1. Table 1. Basic sock informaion afer he aggregaion procedure. LIQUIDITY ON LONDON STOCK EXCHANGE symbol company name number of average ransacions midquoe ABF Associaed Briish Foods AZN Asra Zenaca BARC Barclays CPI Capia Group GSK Glaxosmihkline HBOS Hbos HSBA Hsbc Hldgs-Uk IAP Icap KAZ Kazakhmys LLOY Lloyds Tsb PRU Prudenial RB Recki Bencksr RIO Rio Tino SHP Shile SLOU Slough Esaes VOD Vodafone WPP Wpp Group XTA Xsaa The column is a rade in an order book akes value 1 if he rade was execued, and 0 if he rade was no execued. 101

12 Economic Annals, Volume LVI, No. 188 / January March VARIABLES AND ESTIMATION For he purpose of our analysis we considered he following four liquidiy measures a he rade execuion ime. Compared o he known definiion of hese measures in he lieraure, some of hem are slighly modified so hey can be calculaed rade-by-rade as well as compared across differen socks. 1. Volume per rade, V. Higher volume per rade indicaes higher liquidiy.. Trade size in pounds TS p V where p is he execued price. Higher rade size in pounds indicaes higher liquidiy. 3. Duraion beween wo successive rades Dur i i 1 i where i is he execuion ime of rade i, and i1 is he execuion ime of he subsequen rade. Lower duraion beween wo successive rades indicaes higher liquidiy. 4. Flow raio beween wo successive rades TS1 FR Dur where Dur is ime beween a ransacion a he ime poin and a ransacion prior o ha, and TS1 is he size in pounds of he las ransacion realized prior o ime poin. Higher flow raio indicaes higher liquidiy. The variables of ineres for Hasbrouck's VAR model are he rade x 0 and reurn r. The rade variable can be deermined by he Lee and Ready rule (1991): 1 - buyer iniiaed, 0 -undeermined, -1 seller iniiaed, i.e. 10

13 LIQUIDITY ON LONDON STOCK EXCHANGE 1, p m 1 0 x 0, p m 1 (6) 1, p m 1 To see he real effec of he price impac of he rade on price formaion i is naural o observe is pressure on he spread. By sligh modificaion of reurn variable r m ln( 1 ) ln( ) prop (7) S m where prop S is he average of proporional spread prop S a m b p p obained price impacs of he rade will be expressed as par of he average proporional spread. In such a way i is possible o compare oal price impac across socks. Following Hasbrouck (1991a, 1991b) and Engle e al. (000), we assumed ha he model given by equaions (1), () can be runcaed a five lags, i.e. 5 5 r air i bix i 1, i1 i0 (8) 5 5 x cir i dixi, i1 i1 (9) The rade variable is a limied dependen variable and i is quie unusual o have such a variable in vecor auoregression. The limied dependen variable presens no economeric difficulies when i is an explanaory variable, which is he case for he reurn equaion, bu in he case of he rade equaion he linear specificaion is poenially inappropriae. The leas squares esimaion yields o 103

14 Economic Annals, Volume LVI, No. 188 / January March 011 an inefficien esimaion of he rade coefficiens and he sandard errors are biased. Following Engle e al. (000) we avoided his problem by correcing he sandard errors afer using Whie's heeroskedasiciy consisen covariance esimaor (Whie (1980)) o correc he Wald and -saisics. For each day of he sample of 6 observaion days we calculaed he reurn vecor given by (7) and he rade vecor given by he Lee and Ready rule (6). The reurn and rade prior o he firs observaion of each day was se o zero. Adding he reurn vecor of day i+1 a he end of he reurn vecor of day i, i=1,..., 61, we obained he n 1 reurn vecor r, where n is he size of he 6 0 days sample. In he same way, he n 1 rade vecor x is obained from he rade vecors of each day. For esimaing Hasbrouck's VAR (5) given by (8), (9) 0 we made vecors r and x by cuing he firs five enries of he reurn vecor r 0 and rade vecor x. The lagged vecors r k and x 0 k, k=1,,..., 5 were obained by cuing he firs 5-k, and he las k enries of he reurn and rade vecor by order. Furher, o obain he leas squares coefficiens of he reurn equaion we regressed vecor r on he marix r, r, r 3, r 4 r 5, x, x, x, x, x, x ]. [ To obain he leas squares coefficiens of he rade equaion, we regressed 0 vecor x on he marix [ r, r, r 3, r 4 r 5, x, x, x, x, x ]. All calculaions were performed using Malab sofware. 104

15 LIQUIDITY ON LONDON STOCK EXCHANGE 5. RESULTS The average liquidiy measures are provided in Table. The esimaed leas squares coefficiens for he reurn and rade equaion, ogeher wih he corresponding -saisics for all 18 socks, is given in Table 3 and Table 4. The - saisics in he rade equaion are correced by using Whie's heeroskedasiciy consisen covariance esimaor. Table. Average liquidiy measures. Vx10 7 TS Dur S prop FRx10 10 ABF AZN BARC CPI GSK HBOS HSBA IAP KAZ LLOY PRU RB RIO SHP SLOU VOD WPP XTA Noe: V -volume per rade; TS - rade size in pounds; Duri - duraion beween wo successive prop rades; S - proporional spread in ick size FR - flow raio beween wo successive rades. 105

16 Economic Annals, Volume LVI, No. 188 / January March 011 Table 3. The coefficien esimaions and -saisics for he reurn equaion (8) a1 a a3 a4 a5 b0 b1 b b3 b4 b5 ABF * * -6.85* 68.3* 8.59* 3.13* * -0.7 AZN * -.31* 4.83* 6.55* 3.51* 166.6* 13.01* 3.93* -.30* -4.61* -3.97* BARC * * 3.78* 3.61* 139.4* 16.55* 4.0* -1.76* -3.35* -5.90* CPI * * -3.61* * 11.14*.4* -.5* GSK * * 3.53* 3.* * 0.6* 4.70* * -3.97* HBOS * 4.3* 6.3* 3.43* * 13.54*.54* * -3.37* HSBA * -6.09* * 18.09* 6.31* -3.18* -3.19* -6.3* IAP * * * 7.8*.08* * KAZ * -16.4* 7.60* -.3* * 15.95* 5.69* * -.87* LLOY * * * 17.65* 3.56* * PRU * -1.37*.09* 11.89* -7.4* 114.4* 13.6* 7.* * 0.5 RB * 5.89*.* * 13.0*.57* * -3.13* RIO * * 8.01* 5.11* 193.6* 19.60* 7.31*.67* SHP * * * * 10.17* 7.18*.77 * SLOU * * -6.98* -.69* -4.36* 53.85* 8.09* 4.48* 3.38* VOD * * -10.1* * 7.03* 10.46* 6.09* * WPP * *.99* * 9.51* 4.09* -.94* * XTA * -.14* 3.76* * 154.7* 18.8* 6.6* Noe: r is he reurn variable viewed as a par of he proporional spread. x 0 is he rade indicaor variable which akes value +1 for buy order, -1 for sell order, and 0 for indeerminae. The sample covers 18 socks lised on he London Sock Exchange from he FTSE 100 index over a period from March 1, 006 o May 31, 006. R r 106

17 LIQUIDITY ON LONDON STOCK EXCHANGE Table 4. The coefficien esimaions and -saisics for rade equaion (9) c1 c c3 c4 c5 d1 d d3 d4 d5 ABF * * * 1.15* 10.31* 6.47* 5.55* AZN * -5.1* * 18.7* 10.5* 6.91* 6.86* BARC * -4.55* -.67* -.55* * 15.71* 11.56* 8.95* 6.40* CPI * * -.39* -.4* 40.9* 1.64* 10.17* 6.95* 4.60* GSK * -4.18* * 74.03* 17.56* 14.04* 8.46* 6.65* HBOS * * 14.35* 9.97* 7.01* 6.36* HSBA * -1.49* -6.7* -.57* * 0.41* 17.71* 9.10* 10.03* IAP * -3.44* -.51* * 11.8* 8.91* 4.61* 5.97* KAZ * -.3* -.93* -.47* * 11.30* 10.43* 7.10* 5.78* LLOY * -4.40* -4.34* -.56* * 1.1* 11.53* 9.73* 5.37* PRU * -.7* * 13.30* 9.49* 5.76* 4.83* RB * * 16.56* 7.70* 6.67* 5.95* RIO * -3.13* 3.60* 3.93* 3.38* 6.49* 1.11* 11.5* 9.44* 6.60* SHP * -3.39* * -4.14* 19.* 13.70* 7.9* 6.55* 6.80* SLOU * -3.47* -.33* -3.08* * 8.76* 6.9* 4.09* 3.46* VOD * -6.85* * -9.5* -4.71* 74.40*.54* 0.9* 14.0* 15.39* WPP * -.01* * 13.65* 7.56* 5.19* 4.15* XTA * -4.78* * 19.78* 11.39* 7.54* 6.63* 0 Noe: r is he reurn variable viewed as a par of he proporional spread. x is he rade indicaor variable which akes value +1 for buy order, -1 for sell order, and 0 for indeerminae. The - saisics are compued using Whie's heeroskedasiciy consisen covariance esimaor. The sample covers 18 socks lised on he London Sock Exchange from he FTSE 100 index over a period from March 1, 006 o May 31, 006. R r The aserisks above -saisics denoe he significance a he 5% level. For each equaion he coefficien of muliple deerminaion R is given. We proceed wih he Wald es of hypohesis of esimaed coefficiens. The resuls are provided in 107

18 Economic Annals, Volume LVI, No. 188 / January March 011 Table 5. The Wald saisics of rade equaion coefficiens is compued by using Whie's heeroskedasiciy consisen covariance esimaor. The mos imporan coefficiens are coefficiens of rade variables in he reurn equaion and rade equaion. The coefficien b 0 in he reurn equaion for each of he 18 socks represens he immediae impac of conemporaneous rade x 0. I measures an average rise of reurn wih respec o average proporional spread immediaely afer he buy order. The coefficiens b i, i=1,,..., 5 in he reurn equaion for all 18 socks end o be posiive, meaning ha he buys end o increase and sells end o decrease he reurn. According o he Wald es, he null hypohesis ha he coefficiens b i, i=1,,..., 5 are joinly equal o zero is rejeced. The sum of hem is posiive, and according o he Wald es, significanly differen from zero a he 1% level, indicaing ha he order flow has a posiive influence on he reurn. Posiive auocorrelaion in rades is visible in posiive coefficiens on he lagged rade variable, indicaing ha a purchase ends o follow a purchase, and a sell ends o follow a sell. These coefficiens are significanly differen from zero, even a he 1% level. Negaive auocorrelaions in reurns are visible in negaive coefficiens on lagged reurn variables in he rade equaion, which is predominan for socks wih he symbols ABF, CPI, KAZ, SHP, SLOU, and VOD. For oher socks his behaviour is weaker. The Wald es of he hypohesis ha he coefficiens of reurn variables in he rade equaion are joinly zero is rejeced a he 1% level, indicaing Granger's causaliy running from reurns o rades. 108

19 LIQUIDITY ON LONDON STOCK EXCHANGE Table 5. The Wald es of hree hypoheses of esimaed coefficiens. ABF AZN BARC CPI GSK HBOS HSBA IAP KAZ LLOY PRU RB RIO SHP SLOU VOD WPP XTA Noe: The Wald saisics of rade equaion coefficiens is compued using Whie's heeroskedasiciy consisen covariance esimaor. We calculaed he reurn/rade moving-average represenaion (3), (4) runcaed a 40 lags. The algorihm for calculaing VMA(q) represenaion of he Hasbrouck VAR(p) in Malab is given in Appendix 1. The price impac funcion is calculaed as a cumulaive impulse response of reurn o he innovaion in he rade equaion. The graphs of price impac funcions for each sock are provided in Appendix. The oal price impac PI is calculaed as well as he number of ransacions k needed for is realizaion. Noice ha he obained impulse * response coefficiens b i, i 1,,..., 40 from (3), as well as he obained oal price impac, are expressed as a par of he average proporional spread. The variance 1 of innovaion in he reurn equaion and he variance of innovaion in he rade equaion are calculaed. A he end he variance decomposiion coefficien R given by (5) is calculaed. These resuls are provided in Table 6. For his es all measures are ranked as in Table 7, i.e. from he highes o he lowes liquidiy. 109

20 Economic Annals, Volume LVI, No. 188 / January March 011 This means ha, for example, socks are ranked from he lowes o he highes duraion and from he highes o he lowes rade volume. Therefore, socks are ranked from he lowes o he highes oal price impac. We ranked socks from he highes o he lowes variance decomposiion coefficien. From all calculaed correlaions we are mos ineresed in correlaions beween oal price impac PI and considered liquidiy measures, and beween he variance decomposiion coefficien R and considered liquidiy measures. To es he null hypohesis H 0 : There is no relaionship beween diverse liquidiy measures, oal price impac $PI$, and variance decomposiion coefficien R, agains he alernaive hypohesis H 1 : There is a relaionship beween he diverse liquidiy measures, oal price impac $PI$, and variance decomposiion coefficien R, we use he Spearman rank correlaion es, whose resuls, wih relaed P-values in brackes, are provided in Table

21 Table 6. VMA represenaion (3), (4) of VAR (8), (9) runcaed a 40 lags. b 0 PI k 1 R % ABF AZN BARC CPI GSK HBOS HSBA IAP KAZ LLOY PRU RB RIO SHP SLOU VOD WPP XTA Noe: b0 - immediae price impac wih respec o he average proporional spread; PI - oal price impac wih respec o he average proporional spread; k - number of ransacions needed for full realizaion of oal price impac; - variance of innovaion in reurn equaion; - variance of 1 innovaion in rade equaion; R - variance in decomposiion coefficien. LIQUIDITY ON LONDON STOCK EXCHANGE 111

22 Economic Annals, Volume LVI, No. 188 / January March 011 Table 7. Socks ranked from he highes o he lowes liquidiy according o diverse liquidiy measures, and from he highes o he lowes variance decomposiion coefficien R. V TS Dur FR PI ABF AZN BARC CPI GSK HBOS HSBA IAP KAZ LLOY PRU RB RIO SHP SLOU VOD WPP XTA Table 8. Spearman rank correlaion es for diverse liquidiy measures, oal price impac PI, and variance decomposiion coefficien R. V TS Dur FR PI V TS 0.46 (0.0789) Dur (0.537) (0.0001) FR (0.141) (0.0001) (0.0001) PI (0.003) (0.0330) (0.454) (0.064) R (0.5657) (0.069) (0.0100) (0.0359) (0.6860) Noe: Socks are ranked from he highes o he lowes liquidiy, from he lowes o he highes oal price impac, and from he highes o he lowes variance decomposiion coefficien R R. 11

23 LIQUIDITY ON LONDON STOCK EXCHANGE Correlaions beween oal price impac and volume (rade size in pounds) are posiive and significan a he 1% level, indicaing ha higher liquidiy measured by hese measures means lower oal price impac. The duraion is posiively bu no significanly correlaed wih oal price impac. The flow raio is posiively correlaed o oal price impac a he 10% level. On he oher hand, correlaions beween he variance decomposiion coefficien R and diverse liquidiy measures are mosly insignifican. The duraion and he flow raios are he only wo measures ha are significanly correlaed o R a he 5% level. These correlaions are posiive, implying ha inensively raded socks presen a larger conribuion of unexpeced rade in he variaion of efficien price. Also, he rade size in pounds is posiively correlaed o he variance decomposiion coefficien a he 10% level. I is ineresing ha here is no significan correlaion beween oal price impac and variance decomposiion coefficien. Figure 1 shows he variance decomposiion coefficien versus oal price impac across he 18 analyzed socks. Cerain kind of sock clusering can be noiced. The mos isolaed socks are he hree mos illiquid socks according o Table 7, IAP, KAZ and SLOU, and hey have he lowes R. The oher more liquid socks have higher R. The Vodafone sock is quie separae from his group, showing is own behaviour. Our findings presen ha he variance decomposiion coefficien R srongly depends on he reurn and rade equaion's predicabiliy. According o he coefficiens of muliple deerminaion Rr and R x, he reurn equaion presens higher predicabiliy han he rade equaion for each sock, excep for Vodafone. Such resuls are reasonable, since in he rade equaion he rade variable x 0 as a limied dependen variable akes only hree values, -1, 0, 1. I can be observed from Table 3 and Table 4 ha he socks wih higher predicabiliy of he reurn equaion compared o predicabiliy of he rade equaion have higher R. The predicabiliy of he reurn equaion srongly depends on he reurn volailiy. The proporion of posiive reurns, zero reurns, and negaive reurns for each of he 18 analyzed socks are given in Table

24 Economic Annals, Volume LVI, No. 188 / January March 011 Table 9. The proporion of posiive reurns, zero reurns, and negaive reurns for each sock. Posiive reurn (%) Zero reurn (%) Negaive reurn (%) ABF 5,34 49,99 4,67 AZN 8,95 41,84 9,1 BARC 1,31 56,87 1,8 CPI 7,51 44,73 7,77 GSK 19,44 61,08 19,48 HBOS 5,16 49,49 5,35 HSBA 16,33 67,45 16, IAP 30,7 38,0 31,6 KAZ 33,39 3,73 33,87 LLOY 0,48 58,83 0,69 PRU 5,35 48,56 6,09 RB 7,77 44,14 8,09 RIO 3,75 34,43 3,81 SHP 7,7 43,58 8,7 SLOU 4,9 49,8 5,3 VOD 6,81 86,5 6,94 WPP 3,4 53,59 3,17 XTA 3,91 33,87 33, Figure 1. Variance decomposiion R (%) coefficien vs. oal price impac PI, wih respec o average proporional spread. 114

25 / r x The Pearson correlaion coefficien beween R R and he proporion of zero reurns is wih he P-value of , indicaing significance a he 1% level. Vodafone has an exremely large proporion of zero reurns, considering all 18 socks. This can explain he low predicabiliy of is reurn equaion compared o he predicabiliy of he rade equaion, and is specific behaviour is shown in Figure 1. Hasbrouck (1991b) formally suggesed ha he variance decomposiion coefficien R indicaes he proporion of volailiy in he efficien price caused by he presence of informed raders represened in he unexpeced componen of he rade. According o Table 5, socks wih he symbols IAP, KAZ, and SLOU ake 17h, 15h, and 18h rank by order according o duraion, and heir flow raio has he lowes coefficien R. Excluding hese hree socks, Vodafone has he smalles R. According o he Spearman rank correlaion es, i seems ha for inensively raded socks (socks wih lower duraion), and socks wih abiliy o absorb large rades in a shor ime inerval (socks wih higher flow raio), he coefficien R is highly overesimaed. 6. CONCLUSIONS LIQUIDITY ON LONDON STOCK EXCHANGE We have examined he empirical es of Hasbrouck's (1991a, 1991b) VAR - VMA model on diverse liquidiy levelled socks lised on he London Sock Exchange from he FTSE 100 index. The resuls on coefficiens of reurn/rade equaions (8) and (9) are consisen wih Hasbrouck (1991a). The oal price impac in his model is calculaed as a response of he reurn scaled by he average proporional spread o he rade innovaion. Our resuls sugges ha for more liquid socks, where liquidiy is measured by volume per rade and rade size in pounds, his impac is lower. However, according o he Spearman rank correlaion es, duraion is posiively bu no significanly correlaed o oal price impac. The flow raio is posiively correlaed o oal price impac a he 10% level. Considering he conribuion of unexpeced rade in he variaion of he efficien price, our resuls sugges ha for inensively raded socks (small duraion) and socks wih abiliy o absorb large rades in a shor ime inerval 115

26 Economic Annals, Volume LVI, No. 188 / January March 011 (large flow raio), his conribuion is higher. We have found ha duraion and flow raios are posiively and significanly correlaed o he conribuion of unexpeced rade in variaion of he efficien price a he 5% level. The conribuion of unexpeced rade in variaion of he efficien price is posiively correlaed o rade size in pounds a he 10% level. Hasbrouck's model assumes a raher simplified rading mechanism beween marke paricipans. I assumes he presence of marke makers who pos bid and ask quoes afer he realized ransacion a ime and according o informaion conained in he recen order flow. Every rade his model canno predic is aken as an unexpeced rading aciviy caused by he presence of raders wih privae informaion. In an order-driven marke ineracion beween marke paricipans his is raher complicaed. Firs, here are no classical marke makers. Transacions are realized by complemening he price and he amoun of various orders placed in he cenral compuer sysem by diverse paricipans. A large number of ransacions can be realized in a shor ime inerval, or even more in he same ime period. Trading on such a marke becomes complex and mulidimensional, requiring he developmen of dynamic rading sraegies, i.e. he implemenaion of diverse algorihms. As discussed in Parlour and Seppi (008), "when choosing limi prices and quaniies for (poenially muliple) limi orders and choosing quaniies for marke orders, a rader needs o condiion on everyhing ha can affec he fuure evoluion of he rading process. This poenially includes a complee descripion of he exising limi order book - namely, all quaniies for muliple orders a muliple prices from muliple pas invesors a muliple poins in ime - as well as he hisories of all pas rades and orders. Dynamic rading sraegies also involve decisions abou how frequenly o monior changing marke condiions and when and how o modify or cancel unexecued limi orders." Following he previous discussion, rades realized by algorihms will behave as an unexpeced rade, i.e. as a rade caused by he superior informaion in Hasbrouck's VAR model. Since for inensively raded socks significan employmen of algorihmic rading is expeced, his could explain he obained resuls on he conribuion of unexpeced rade o variaion of he efficien price. 116

27 LIQUIDITY ON LONDON STOCK EXCHANGE Resuls obained in his paper sugges ha algorihmic rading behaves as informed rading, i.e. rading wih superior or privae informaion. Furhermore, here is a weaker indicaion ha algorihmic rading increases he oal money value of ransacions (rade size in pounds), which is quie consisen wih expecaions. This research was limied by he relaively small sock sample. A larger se of observaions would provide more robus resuls. However, even based on such a small sample, hese resuls sill indicae a significan relaionship beween liquidiy and informaion asymmery on he LSE. More sensiive analysis of he liquidiy / informaion asymmery relaionship would consider he proporion of rades realized by differen ypes of order marke or limi. Copeland and Galai (1983) emphasize ha limi orders, giving opions o oher raders o rade a he quoed price, can be picked off by laer raders who possess updaed public informaion or privae informaion. Parlour and Seppi (008) sae ha he "limi order book should impound forward-looking informaion abou fuure price volailiy, he inensiy of fuure adverse selecion, and fuure order flow". Therefore, "limi orders are no jus suscepible o being picked off by informed raders; hey are also poenially a vehicle for informed rading hemselves." REFERENCES Admai, R. & Pfeiderer, P. (1988). A heory of inraday paerns: Volume and price variabiliy, Review of Financial Sudies 1, Easely, D., & O'Hara, M. (1987). Price, rade size, and informaion in securiies markes, Journal of Financial Economics 19, Easley, D., Kiefer, N. M. & O'Hara, M. (1993). One day in he life of a very common sock, Working paper, Cornell Universiy. Easley, D.,Kiefer, N. M., O'Hara, M. & Paperman,J. B. (1996), Liquidiy, informaion, and infrequenly raded socks, Journal of Finance, 51,

28 Economic Annals, Volume LVI, No. 188 / January March 011 Engle, R. F. & Dufour, A. (000) Time and he Price Impac of a Trade, Journal of Finance 55, Engle, R. F. & Russell, J. (1998) Auoregressive Condiional Duraion: A new model for irregularly spaced ransacion daa, Economerica, 66(5) Foser, D. & Viswanahan, S. (1990) A Theory of he Inraday Variaions in Volume, Variance, and Trading Coss in Securiies Markes, Review of Financial Sudies 3, Glosen, L. R. & Milgrom, P. (1985) Bid, ask and ransacion prices in a specialis marke wih heerogeneously informed raders. Journal of Financial Economics, Granger, C.W.J. (1969). Invesigaing Causal Relaions by Economeric Models and Cross- Specral Mehods, Economerica 37, Hasbrouck, J. (1991a). Measuring he Informaion Conen of Sock Trades, Journal of Finance, vol. 46, No. 1, Hasbrouck, J. (1991b). The Summary Informaiveness of Sock Trades: An Economeric Analysis, The Review of Financial Sudies, Vol. 4, No. 3, Hasbrouck, J. (007). Empirical Marke Microsrucure, Oxford Universiy Press. Hasbrouck, J. & Saar, G. (007). Technology and liquidiy provision: he blurring of radiional definiions. Working Paper, Sern School of Business, New York Universiy. Inernaional Banking Sysems Journal, 007. Kuns M. R. (007). Economerics II-A lecure course for he Insiue for Advanced Sudies. Kyle, A. S.(1985). Coninuous aucions and insider rading. Economerica 53, Lee, C. & Ready, M., Inferring rade direcion from inraday daa, Journal of Finance 46, Lükepohl, H.(1993). New Inroducion o Muliple Time Series Analysis, Springer-Verlag, Manganelli, S.(005). Duraion, volume and volailiy impac of rades, Journal of Financial Markes 8, O'Hara, M.(1995). Marke Microsrucure Theory, Cambridge, MA, Blackwell Publishers. Payne, R. (003). Informed rade in spo foreign exchange markes: an empirical invesigaion. Journal of Inernaional Economics 61,

29 LIQUIDITY ON LONDON STOCK EXCHANGE Parlour, C. A. & Seppi, D. J.(008). Limi Order Markes: A Survey, Handbook of Financial Inermediaion & Banking. Wold, H. O.(1938). A Sudy in he Analysis of Saionary Time Series, Sockholm, Almqvis and Wiksell. Whie, H. (1980). A Heeroskedasiciy-Consisen Covariance Marix Esimaor and a Direc Tes for Heeroskedasiciy, Economerica, 48, Wrigh, J. H., Bollerslev, T.(1999) High frequency daa, frequency domain inference and volailiy forecasing. Inernaional Finance Discussion Papers