Stock Market Declines and Liquidity

Soc Mare eclnes and Lqudy ALLAUEEN HAMEE, WENJIN KANG, and S. VISWANATHAN* ABSTACT Conssen wh recen heorecal models where bndng capal consrans lead o sudden lqudy dry-ups, we fnd ha negave mare reurns decrease soc lqudy, especally durng mes of ghness n he fundng mare. The asymmerc effec of changes n aggregae asse values on lqudy and commonaly n lqudy canno be fully explaned by changes n demand for lqudy or volaly effecs. We documen ner-ndusry spll-over effecs n lqudy, whch are lely o arse from capal consrans n he mare mang secor. We also fnd economcally sgnfcan reurns o supplyng lqudy followng perods of large drop n mare valuaons. *Allaudeen Hameed and Wenjn Kang are from he eparmen of Fnance, NUS Busness School, Naonal Unversy of Sngapore. S. Vswanahan s from he Fuqua School of Busness, ue Unversy. We han Vral Acharya, Yaov Amhud, Mchael Brand, Marus Brunnermeer, Andrew Ellul, Bob Engle, oug Foser, Joel Hasbrouc, avd Hseh, Fran de Jong, Pee Kyle, av Jagannahan, Chrsne Parlour, avd obnson, Ioand osu, Avandhar Subrahmanyam, Sherdan Tman, wo anonymous referees, and parcpans a he NBE 2005 mcrosrucure conference, 2007 Amercan Fnance Assocaon meeng, 2007 European Fnance Assocaon Meeng, 2008 Frs Erasmus Lqudy Conference, Ausralan Naonal Unversy, Case Wesern eserve Unversy, Erasmus Unversy, Hong Kong Unversy, Hong Kong Unversy of Scence and Technology, Nanyang Technologcal Unversy, Naonal Unversy of Sngapore, New Yor Unversy, Peng Unversy, Unversy of Albera, Unversy of Evry (France), Unversy of Melbourne, and Unversy of Texas (Ausn) for her commens. Hameed and Kang acnowledge fnancal suppor from he NUS Academc esearch Grans and Vswanahan hans IIMA Bangalore for her hospaly durng year 2005 when hs paper was sared. 1

In recen heorecal research, he dea ha mare declnes cause asse llqudy has receved much aenon. Lqudy dry-ups are argued o occur because mare parcpans engage n panc sellng (a demand effec), or fnancal nermedares whdraw from provdng lqudy (a supply effec), or boh. In hs paper, we explore emprcally wha happens o mare lqudy afer large mare declnes and wheher supply effecs exs n equy mares. I s dffcul o esablsh he acual deny of fnancal nermedares n equy mares as hey could be specalss, floor raders, lm order provders, or oher raders le hedge funds. Furhermore, he acual posons and balance shees of hese nermedares are unnown. We herefore ae an encompassng approach by nvesgang he mpac of mare declnes on varous dmensons of lqudy, ncludng: (a) me-seres as well as cross-seconal varaon n lqudy; (b) commonaly n lqudy; and (c) cos of lqudy provson. Theorecal models oban llqudy afer mare declnes n a varey of ways. In collaeral-based models, mare maers mae mares by absorbng emporary lqudy shocs. However, hey also face fundng consrans and oban fnancng by posng margns and pledgng he secures hey hold as collaeral. Thus, when soc prces declne consderably, he nermedares h her margn consrans and are forced o lqudae. In Brunnermeer and Pedersen (2009), for nsance, a large mare shoc rggers he swch o a low lqudy, hgh margn equlbrum, where mares are llqud, resulng n larger margn requremens. Ths llqudy spral furher resrcs dealers from provdng mare lqudy. Anshuman and Vswanahan (2005) presen a slghly dfferen model where leveraged nvesors are ased o provde collaeral when asse values fall and decde o endogenously defaul, leadng o asse lqudaon. A he same 2

me, mare maers face fundng consrans as hey are able o fnance less n he repo mare for he asses hey own. Garleanu and Pedersen (2007) show ha gher rs managemen by nsuons n response o hgher volaly n mare downurns reduces her rs bearng capacy and lowers mare lqudy. Garleanu and Pedersen also sress a feedbac effec, where he decrease n mare lqudy furher ghens rs managemen. Gromb and Vayanos (2002) emphasze ha he reducon n supply of lqudy due o capal consrans has mporan welfare and regulaory mplcaons. Parly movaed by he Long Term Capal Managemen (LTCM) crss, he balance shees of nermedares maer n hese collaeral-based models as he nermedares face fnancal consrans ha are ofen bndng precsely when s mos ncumben for hem o provde lqudy. 1 In lms-o-arbrage based models such as Kyle and Xong (2001) and Xong (2001), shocs o nose raders mae prces move away from fundamenals and arbrageurs provde lqudy and ae advanage of he arbrage opporunes. These lqudy provders have decreasng absolue rs averson preferences, or face capal consrans wh mar-o-mare losses, and her demand for rsy asses declnes followng mare downurns -- hey become lqudy demanders as hey lqudae her posons n rsy asses. Mchell, Pedersen, and Pulvno (2007) show ha converble hedge funds, whch provde lqudy n normal mes, were forced o lqudae her converble bond posons due o bndng capal consrans followng large capal redempons from nvesors n 2005 and he large drop n secury values durng he LTCM crss. 3

In he coordnaon falure models of Bernardo and Welch (2003) and Morrs and Shn (200), raders face dfferng radng lms ha cause hem o sell. Snce one rader hng hs lm may push down he prce and mae oher raders lms be h, early lqudaon gves a beer prce han lae lqudaon. Here, raders rush o lqudae followng negave shocs, and when prces fall enough, lqudy blac holes emerge, analogous o a model of ban runs. Vayanos (200) presens an asse prcng model where nvesors have o lqudae when asse prces fall below a lower bound, leadng o lqudaon rs beng prced. Vayanos lns he rs of needng o lqudae o volaly, especally for socs wh large exposure o mare volaly. Whle he exac deals of he heorecal models above dffer, hey all predc ha large mare declnes ncrease he demand for lqudy as agens lqudae her posons across many asses and reduce he supply of lqudy as lqudy provders h her wealh or fundng consrans. Usng he proporonal bd-as spread (as a proporon of he soc s prce) as one of our ey measures of lqudy, we fnd ha changes n spreads are negavely relaed o mare reurns. In parcular, large negave mare reurns have a sronger mpac on weely changes n a frm s bd-as spread han posve reurns, and he average spread ncreases by 2.8 (6.2) bass pons afer a (large) mare declne. These changes n lqudy las for abou wo wees and hen reverse n he subsequen wees. Moreover, we fnd ha he mpac of negave mare reurns on lqudy s sronger when fnancal nermedares are more lely o face fundng consrans. For example, negave mare reurns reduce lqudy more when here are also large declnes n he aggregae balance shees of fnancal nermedares or n he mare value of he nvesmen banng

secor. 2 Ths asymmerc relaon beween mare reurns and lqudy s robus o he ncluson of frm-level conrol varables such as lagged own soc reurns, urnover, and buy-sell order mbalance, as well as changes n volales as suggesed n Vayanos (200). Brunnermeer and Pedersen (2009) sugges ha a deeroraon of dealer capal leads o greaer cross-seconal dfferences n lqudy of hgh and low volaly socs. Conssen wh hs flgh o lqudy predcon, we fnd ha he mpac of mare declnes on lqudy s sronges for hgh volaly frms. Our fndngs lend suppor o he hypohess ha he relaon beween lqudy and mare declnes s relaed o changes n he supply of lqudy. Brunnermeer and Pedersen (2009) also sugges ha a huge mare wde declne n prces reduces he aggregae collaeral of he mare mang secor, whch feeds bac as hgher comovemen n mare lqudy. Whle here s some research on comovemens n mare lqudy n soc and bond mares (Chorda, oll, Subrahmanyam (2000), Hasbrouc and Sepp (2001), Huberman and Hala (2001), and ohers) and evdence ha mare mang collapsed afer he soc mare crss n 1987 (see he Brady commsson repor on he 1987 crss), here s lle emprcal evdence on he effec of soc mare movemens on commonaly n lqudy. Na and Yadav (2003) and Coughenour and Saad (200) consder he effec of capal consrans on lqudy commonaly. Kamara, Lou, and Sada (2008) sugges ha he me varaon n sysemac lqudy s relaed o concenraon of nsuonal ownershp and ndex radng. 3 However, he exan emprcal leraure does no consder wheher lqudy comovemen ncreases dramacally afer large mare declnes n a manner smlar o he fndng ha soc reurn comovemen goes up afer large mare drops (see Ang, Chen, and Xng (2006) on 5

downsde rs and especally Ang and Chen (2002) for wor on asymmerc correlaons beween porfolos). We documen ha he commonaly n lqudy (spreads) ncreases durng perods of mare declnes. Specfcally, we fnd ha he lqudy bea ncreases by 0.31 (0.39) durng perods when he mare has experenced a (large) drop n valuaons. We also documen ha lqudy commonaly s posvely relaed o mare volaly bu unrelaed o dosyncrac volaly, ndcang ha nvenory effecs are no lely o be he man source. In a follow-up o our paper, Comeron-Forde e al. (2008) provde supporve evdence ha capal consrans, proxed by hgher nvenory holdngs by NYSE specalss, lower mare lqudy and are bndng afer negave mare reurns. We furher fnd ha whle large negave reurn shocs o ndusry and mare ndces ncrease commonaly n lqudy, he mare effec s larger n magnude. These fndngs sugges ha spllover effecs across all secures afer negave mare shocs are mporan and provde srong suppor o he dea of a conagon n llqudy due o supply effecs. Nex, usng shor-erm prce reversals as our measure of he reurn o supplyng lqudy, we examne f he cos of supplyng lqudy depends on he sae of mare reurns. In Campbell, Grossman, and Wang (1993), rs-averse mare maers requre paymen for accommodang heavy sellng by lqudy raders. Ths cos of provdng lqudy s refleced n he emporary decrease n prce accompanyng heavy sell volume and he subsequen ncrease as prces rever o fundamenal values. We use wo reurn reversal based radng sraeges o emprcally gauge he cos of supplyng lqudy n dfferen mare saes: a zero-cos conraran nvesmen sraegy (Avramov, Chorda, 6

Goyal (2006)) and a lm order radng sraegy (Handa and Schwarz (1996)). 5 The zero-cos conraran nvesmen sraegy ha capures prce reversals on heavy radng yelds an economcally sgnfcan reurn of 1.18% per wee when condoned on large negave mare reurns, and s much hgher han he uncondonal reurn of 0.58%. The sronger prce reversals n large down mares lass up o wo wees, s hgher n perods of hgh lqudy commonaly, and canno be explaned by sandard Fama-French (1993) rs facors. We oban smlar resuls usng he lm order radng sraegy. Overall, our cumulave fndngs are conssen wh he collaeral-based vew of lqudy pu forward n recen heorecal papers. The remander of he paper s organzed as follows. Secon I provdes a descrpon of he daa and ey varables. The mehodology and resuls on he relaon beween pas reurns and lqudy are presened n Secon II. Secon III presens he emprcal resuls on he effec of mare reurns on commonaly n lqudy. The fndngs from he nvesmen sraegy based on shor-erm prce reversals are produced n Secon IV. Secon V concludes he paper. I. aa The ransacon-level daa are colleced from he New Yor Soc Exchange Trades and Auomaed Quoaons (TAQ) and he Insue for he Sudy of Secures Mares (ISSM). The daly and monhly reurn daa are rereved from he Cener for esearch n Secury Prces (CSP). The sample socs are resrced o NYSE ordnary socs from January 1988 o ecember 2003. We exclude NASAQ socs because her radng proocols are dfferen. As, uns, shares of benefcal neres, companes ncorporaed 7

ousde he U.S., Amercus Trus componens, close-ended funds, preferred socs, and EITs are also excluded. In addon, o be ncluded n our sample, he soc s prce mus be whn $3 and $999 each year. Ths fler s appled o avod he nfluence of exreme prce levels. The soc should also have a leas 60 monhs of vald observaons durng he sample perod. Afer applyng all he above flers, he fnal daabase ncludes more han 800 mllon rades across abou 1800 socs over 16 years. The large sample enables us o conduc a comprehensve analyss on he relaons among lqudy level, lqudy commonaly, and mare reurns. For he ransacon daa, f he rades are ou of sequence, recorded before he mare open or afer he mare close, or wh specal selemen condons, hey are no used. Quoes posed before he mare open or afer he mare close are also dscarded. The sgn of he rade s decded by he Lee and eady (1991) algorhm, whch maches a radng record o he mos recen quoe precedng he rade by a leas fve seconds. If a prce s closer o he as quoe s classfed as a buyer-naed rade, and f s closer o he bd quoe s classfed as a seller-naed rade. If he rade s a he mdpon of he quoe, we use a c-es and classfy as buyer- (seller-) naed rade f he prce s hgher (lower) han he prce of he prevous rade. Anomalous ransacon records are deleed accordng o he followng flerng rules: () negave bd-as spread; () quoed spread > $5; () proporonal quoed spread > 20%; (v) effecve spread / quoed spread >.0. In hs paper, we use bd-as spread as our measure of lqudy. We compue he proporonal quoed spread (QSP) by dvdng he dfference beween as and bd quoes by he mdquoe. We repea our emprcal ess wh he proporonal effecve 8

spread, whch s wo mes he dfference beween he rade execuon prce and he mdquoe scaled by he mdquoe, and fnd smlar resuls (avalable n an Inerne Appendx 6 ). The ndvdual soc daly spread s consruced by averagng he spreads for all ransacons each day. urng he las decade, spreads have narrowed wh he decrease n c sze and growh n radng volume. Thus, o asceran he exen o whch he change n spread s caused by pas reurns, we adjus spreads for deermnsc me-seres varaon such as changes n c-sze, me rend, and calendar effecs. Followng Chorda, Sarar, and Subrahmanyam (2005), we regress soc s QSP on day s on a se of varables nown o capure seasonal varaon n lqudy: QSP, s 2, 11 = d AY e,, s,, s 1, s = 1 = 1 (1) f TICK1 f3, TICK 2 f, YEA1 f5, YEA2 ASP, s MONTH s f s HOLIAY In equaon (1), he followng varables are employed: () day of he wee dummes (AY,s ) for Monday hrough Thursday; () monh dummes (MONTH,s ) for January hrough November; () a dummy for radng days around holdays (HOLIAY s ); (v) c change dummes TICK1 s and TICK2 s o capure he c change from 1/8 o 1/16 on 06/2/1997 and he change from 1/16 o he decmal sysem on 01/29/2001, respecvely; and (v) me rend varables YEA1 s (YEA2 s ), equal o he dfference beween he curren calendar year and 1988 (1997) or he frs year when soc sared radng on NYSE, whchever s laer. The regresson resduals, ncludng he nercep, gve he adjused proporonal quoed spread (ASP). The me-seres regresson equaon s esmaed for each soc n our sample. s s, 9

In resuls repored n he Inerne Appendx, he cross-seconal average of he esmaed parameers show seasonal paerns n he quoed spread: he bd-as spreads are ypcally hgher on Frdays and around holdays, and spreads are lower from May o Sepember relave o oher monhs. The c-sze change dummes also pc up a sgnfcan decrease n spreads afer he change n c rule on NYSE. These resuls compor well wh he seasonaly n lqudy documened n Chorda, Sarar, and Subrahmanyam (2005). Afer adjusng for he seasonaly n spreads, we do no observe any sgnfcan me rend. In Table I, he unadjused spread (QSP) exhbs a clear me rend wh he annual average spread decreasng from 1.26% n 1988 o 0.26% n 2003, bu he rend s removed n he me seres of he seasonally adjused spread (ASP) annual averages. When plong he wo seres, QSP and ASP, n Fgure 1, we fnd ha our adjusmen process does a reasonable job n conrollng for he deermnsc me-seres rend n soc spreads. [Inser Table I and Fgure 1 abou here] II. Lqudy and Pas eurns A. Tme-seres Analyss We sar our analyses by frs aggregang he daly adjused spreads for each soc o oban average weely spreads. enong frm s adjused proporonal spread n wee as ASP,, we perform our analyss on changes n weely spreads, (ASP, mnus ASP,-1 ) or ΔASP,. 7 We regress ΔASP, on he lagged mare reurn ( m,-1 ), proxed by he CSP value-weghed ndex. Snce he exac horzon over whch declnes n aggregae asse values affec lqudy s an emprcal queson, we examne he effec of up o four lags of weely reurns. 8 We es he ey predcon of he underlyng 10

heorecal models ha lqudy s affeced by lagged mare reurns, parcularly, negave reurns. A he same me, s possble ha changes n lqudy are affeced by lagged frm-specfc reurns, snce large changes n frm value may have smlar wealh effecs. Frm s dosyncrac reurns (, ) are defned as he dfference beween wee reurns on soc and he mare ndex. 9 We nroduce a se of frm-specfc varables o conrol for oher sources of neremporal varaon n lqudy. Mare mcrosrucure models n emsez (1968), Soll (1978), and Ho and Soll (1980) sugges ha hgh volumes reduce nvenory rs per rade and hus should lead o smaller spreads. We add weely changes n urnover (ΔTUN ), measured by oal radng volume dvded by shares ousandng for frm, o conrol for he spread changes arsng from he mare maer s nvenory concerns. Chorda, oll, and Subrahmanyam (2002) repor ha order mbalances are correlaed wh spreads and conjecure ha hs could arse because of he mare maer s dffculy n adjusng quoes durng perods of large order mbalances. To conrol for hs effec, we add changes n he relave order mbalance (ΔOIB ), measured by he change n absolue value of he weely dfference n he dollar amoun of buyer- and seller-naed orders sandardzed by he dollar amoun of radng volume over he same perod. I s well nown ha bd-as spreads are posvely affeced by reurn volaly due o hgher adverse selecon and nvenory rs (see, for example, Soll (1978)). In he volaly-reurn leraure, a drop n soc prces ncreases fnancal leverage, whch maes he soc rser and ncreases s subsequen volaly (see Blac (1976) and Chrse (1982)). Ths mples ha negave reurns may ncrease spreads hrough her mpac on subsequen volaly. In Vayanos (200), varaon n demand for lqudy s 11

drven by changes n mare volaly and durng volale perods ncreased rs averson leads o a flgh o qualy (here ransacons coss are fxed over me). Vayanos (200) suggess ha f ransacon coss are hgher durng volale mes, he mpac of volaly on lqudy (prema) would be even sronger, emphaszng an mporan connecon beween changes n mare volaly and lqudy. We accoun for hese volaly effecs by ncludng conemporaneous and lagged changes n weely volaly of mare reurns (ΔST m, ) and volaly of soc reurns (ΔST, ). Weely volaly esmaes are obaned from daly reurns over he prevous four wees usng he mehod descrbed n French, Schwer, and Sambaugh (1987). Fnally, we add lagged changes n spreads o accoun for any seral correlaons. Weely changes n adjused spreads for each frm are regressed on weely varables as defned above: ΔASP, = α c ΔST β = 1, m,, 1 5 c ΔTUN γ = 1,,, 1 c 6, c ΔST 1 ΔOIB, 1 m, c 2 ΔST, φ ΔASP 3 = 1,, c ΔST ε, m, 1, (2) We run he me-seres regresson n equaon (2) for each soc and repor he mean and medan of he esmaed regresson coeffcens across all frms n our sample, ang no accoun he cross-equaon correlaons n he esmaed parameers n compung he sandard errors. 10 Table II presens he equally weghed average coeffcens. Conssen wh prevous leraure, we fnd ha a decrease n urnover or an ncrease n mare and dosyncrac volaly predc hgher spreads. Furher, spreads ncrease when here s an ncrease n conemporaneous or lagged volaly. The coeffcen assocaed wh changes n order mbalance (ΔOIB ), on he oher hand, has a posve value as expeced, bu s sascally nsgnfcan. 12

More mporanly, we fnd ha he lagged mare reurns n each of he pas four wees affec curren changes n spreads, wh he effecs declnng rapdly as we move o longer lags. Addonally, lagged dosyncrac reurns have a monooncally decreasng and sgnfcan relaon wh curren changes n adjused spreads. Thus, conssen wh he heorecal predcons n Kyle and Xong (2001), Brunnermeer and Pedersen (2009), Garleanu and Pedersen (2007), and ohers, he wealh effec of a mare wde drop n asse prces s assocaed wh a fall n lqudy. 11 The models ha ln changes n mare prces and lqudy acually pose a sronger predcon: he relaon should be sronger for pror losses han gans. Accordngly, we modfy equaon (2) o allow spreads o reac dfferenally o posve and negave lagged reurns: ΔASP c, γ = 1 OWN,,, OWN,, ΔST = α, 1 c 5 = 1, m, β ΔTUN, 1 c c 6, 1 β = 1 OWN,, m, OWN, m, ΔST ΔOIB m,, 1 c 2 ΔST φ, c 3 ΔASP ΔST = 1,, = 1,, m, 1 ε, γ, (3) where OWN,m, ( OWN,, ) s a dummy varable ha s equal o one f and only f m, (, ) s less han zero. The conrol varables are dencal o hose defned n equaon (2). In Panel B of Table II, we fnd a sgnfcanly greaer effec of negave mare reurns on lqudy: he regresson coeffcen on lagged mare reurns n wee -1 rses sgnfcanly from -0.13 o -1.223 when he mare reurn s negave. Spreads are also asymmercally relaed o lagged dosyncrac reurns, alhough he magnude of he asymmery s less dramac, wh he regresson coeffcen changng from -0.73 o -0.631. Ineresngly, he sharp ncrease n spreads n wee -1, due o negave mare 13

reurns, reverses o s mean n wees -3 and -, ndcang ha he lqudy effecs las up o wo wees. [Inser Table II abou here] To examne wheher he magnude of lagged reurns has any maeral mpac on lqudy, he regresson specfcaon s ΔASP c c 1 = 1 UP LAGE,, m, UP LAGE, m,, β γ = 1 OWN LAGE,,, OWN LAGE,, ΔST ΔST = α m,, 1 c c 5 2 = 1, m, β ΔST ΔTUN, c, 1 3 c ΔST 6, β = 1 OWN LAGE,, m, OWN LAGE, m, m, 1 ΔOIB, 1 γ = 1,, γ = 1 UP LAGE,,, UP LAGE,, φ ΔASP = 1,, ε,, () where OWN LAGE,m, ( UP LAGE,m, ) s a dummy varable ha s equal o one f and only f m, s more han 1.5 sandard devaons below (above) s uncondonal mean reurn. Smlarly, OWN LAGE,, ( UP LAGE,, ) s a dummy varable ha s equal o one f and only f, s more han 1.5 sandard devaons below (above) s mean reurn. 12 In Table II, Panel C, large negave mare shocs sgnfcanly wden he bd-as spreads whle large posve mare reurns have an nsgnfcan margnal effec, renforcng he srng asymmerc effec of mare reurns on lqudy. Our fndngs add o hose n Chorda, oll, and Subrahmanyam (2001, 2002), who show ha a he aggregae level, daly spreads ncrease dramacally followng days wh negave mare reurns bu decrease only margnally on posve mare daly reurns. Conssen wh he resuls n Panel B, he ncrease n spreads followng large negave mare reurns n wee -1 reverses a longer lags. Addonal analyses avalable n he Inerne Appendx provde more nsghs. Frs, we fnd a sgnfcan ncrease n adjused spreads of 2.8 (6.2) bass pons followng a 1

(large) negave mare reurn n wee -1, afer conrollng for oher deermnans of spreads. Second, eusar (2007) presens a model where an ncrease n nvesors perceved asse rs reduces curren prces and maes he mare more llqud. In her model, hgher forecass of volaly affec nvesor senmen and hence realzed volaly and lqudy. Specfcally, her model predcs lowers lqudy when msperceved volaly, measured by he dfference beween mpled volaly of S&P 100 ndex opons (VIX) and realzed ndex volaly, s hgher. Conssen wh uesar (2007), we fnd ha changes n weely adjused spread are sgnfcanly and posvely relaed o conemporaneous and lagged msperceved weely volaly. However, he msperceved volaly effecs do no dsplace he srong negave nfluence of lagged reurns. 13 Moreover, addng more lags of volaly does no affec our resuls, ndcang ha he neremporal nfluence of volaly s dfferen from he reurn effecs. Hence, our evdence on lower lqudy followng a decrease n aggregae mare value of secures s robus. B. Evdence on he Effecs of Fundng Consrans We nerpre he relaon beween mare declnes and lqudy dry-ups as ndcave of capal consrans n he mareplace. A drec es of hs supply-sde explanaon requres ha we denfy ndependen changes n fundng lqudy a weely frequences. Alhough we do no have access o drec measures of aggregae supply shocs, we use ndrec measures from he fnancal secor o nvesgae f he conracon n lqudy s conssen wh lqudy provders becomng more capal consraned. Wh equaon (3) as a sarng pon, we examne f he sensvy of changes n spreads o negave mare 15

reurns dffers durng perods when he supplers of lqudy are lely o face capal ghness. The followng regresson model s esmaed: ΔASP β c, OWN, CAP,, γ = 1 OWN,,, OWN,, ΔST = α, 1 c m, 1 5 = 1, m, ΔTUN OWN, m, 1 β, 1 c CAP, 1 c 6, 1 β = 1 OWN,, m, OWN, m, ΔST ΔOIB = 1,, m,, 1 γ c 2 ΔST φ, c 3 ΔASP ΔST = 1,, m, 1 ε,, (5) where CAP, s a dummy varable ha aes a value of one only f wee s assocaed wh perods of hgher capal consrans. We use hree proxes o capure ghness of capal n he mare. The frs proxy s based on he (value-weghed) reurn on he porfolo of nvesmen bans and secures broers and dealers lsed on NYSE, defned by SIC code 6211. 1 We compue he excess reurns on he porfolo of socs n he nvesmen banng secor by he resduals from a one-facor mare model regresson. A large fall n he mare value of he frms operang n nvesmen banng and secures broerage servces s lely o reflec a wea aggregae balance shee of he fundng secor. Hence, when he excess reurns on hs porfolo of fnancal nermedares are negave n wee, CAP, s se o one. 15 Adran and Shn (2008) show ha he fnancal nermedares adjus her leverage n a procyclcal manner, wh expanson and conracon of her balance shees effeced hrough repos. For example, when fnancal nermedares have wea balance shees, her leverage s oo hgh. The ensung capal shorage forces he nermedares o conrac her balance shees. 16 Adran and Shn show ha hese changes n aggregae nermedary balance shees are lned o fundng lqudy hrough shfs n mare wde rs appee. We herefore use he weely changes n aggregae repos as our second 16

measure of consrans n he fundng mare and se CAP, o one when here s a declne n aggregae repos n wee. 17 Our hrd measure of fundng lqudy reles on he weely spread n commercal paper (CP), measured as he dfference n he weely reurns on he hree-monh commercal paper rae and hree-monh Treasury bll rae. 18 I s well nown ha he CP mare s very llqud. As Krshnamurhy (2002) shows, he dfference n he reurn on CP and T-blls (or he CP spread) reflecs a lqudy premum demanded by he large nvesors n CP such as money mare muual funds and oher fnancal corporaons. Gaev and Srahan (2006) use he CP spread o measure lqudy supply and show ha he spread wdens durng lqudy evens. Snce changes n CP spreads are relaed o he wllngness of hese nermedares o provde lqudy, we argue ha an ncrease n he weely CP spread s lely o be assocaed wh a perod when he fundng mare s capal consraned. Hence, CAP, s equal o one when here s an ncrease n he CP spread n wee. Panel A of Table III shows ha a declne n aggregae mare valuaons leads o a sgnfcanly greaer ncrease n bd-as spreads when here s also an underperformance n he nvesmen banng and broerage secor. 19 In Panel B, a conracon n he balance shee of he fnancal nermedares, measured by a decrease n repos, has a smlar effec. To be precse, a negave reurn on he mare ndex n wee -1 lowers he regresson coeffcen for mare reurns from -0.3 o -0.95. A smulaneous decrease n aggregae repos n he capal mares magnfes he mpac of negave mare reurns o -1.63. Fnally, our fndngs are renforced by a smlar amplfcaon of he effec of negave mare reurns n Panel C, followng an ncrease n CP spreads. Togeher, he 17

evdence n Table III s srongly supporve of our nerpreaon ha lqudy dry-ups followng mare declnes are relaed o ghness n fundng lqudy. [Inser Table III abou here] C. Cross-seconal Evdence The heorecal models n Brunnermeer and Pedersen (2009) and Garleanu and Pedersen (2007) sugges ha hgh volaly socs requre greaer use of rs capal and are more lely o suffer hgher harcus (margn requremens) when fundng consrans bnd. Consequenly, a drop n fundng lqudy (large negave mare reurn shoc) ncreases he dfferenal lqudy beween hgh and low volaly secures. In hs subsecon, we examne he cross-seconal dfferences n he relaon beween lagged reurns and spreads among socs ha dffer n volaly, conrollng for frm sze. Frms are sored no nne sze-volaly porfolos based on wo-way dependen sors on each frm s begnnng-of-year mare capalzaon and s reurn volaly n he prevous year, and he porfolo composon s rebalanced each year. In each wee, we average he adjused spreads on each frm o produce nne porfolo-level spreads, ASP p,. Smlar o he frm-specfc varables defned n equaon (3), for each wee, we average he relave order mbalance across all frms n each porfolo, denoed as OIB p,, and calculae porfolo urnover, TUN p,, porfolo-specfc reurns ( p, ) and volaly, ST p,. We regress he change n spreads a he porfolo level on changes n he conrol varables as well as porfolo and mare reurns, analogous o equaon (3), bu for porfolo p, where p=1,2,,9: 18

ΔASP c p p, γ = 1 OWN, p, p, OWN, p, ΔST = α p, 1 c 5 p = 1 p, m, β ΔTUN p, 1 c c 6, p 1 p β = 1 OWN, p, m, OWN, m, ΔST ΔOIB m, p, 1 c 2 p ΔST φ p, c 3 p ΔASP = 1 p, p, ΔST γ = 1 p, p, m, 1 ε p,, (6) The sysem of equaons n (6) s esmaed usng he seemngly unrelaed regresson (SU) mehod, allowng for cross-equaon correlaons. Conssen wh he resuls n Table II, Table IV shows ha changes n spreads are negavely relaed o mare reurns, conrollng for porfolo-specfc facors. The sensvy of spreads o mare reurns s larger for hgh volaly porfolos, parcularly durng mare downurns. These sharp ncreases n spreads reverse n subsequen wees, revealng he shor-run naure of he phenomenon. Our resuls are no a manfesaon of sze-relaed effecs snce we fnd analogous resuls whn each of he sze hrles. The reacon of spreads o ownporfolo negave reurns are less dramac, however. Hence, less lqudy s avalable for hgh volaly socs when he lqudaon of hese asses (collaeral) becomes more cosly, conssen wh a flgh o lqudy. [Inser Table IV abou here] I s neresng o noe ha he mpac of negave mare reurns on lqudy s n he same drecon for each of he nne sze-volaly porfolos, suggesng a hgh commonaly n lqudy, an ssue ha we nvesgae furher n he nex secon. III. Commonaly n Lqudy A. Commonaly n Lqudy and Mare eurns When mare maers and oher nermedares are consraned by her capal base, a large negave reurn reduces he pool of capal ha s ed o mareable secures and 19

hence reduces he supply of lqudy. The heorecal model n Brunnermeer and Pedersen (2009), for example, predcs ha he fundng lqudy consrans ncrease he commonaly n lqudy across secures. In a recen paper, Kamara, Lou, and Sada (2008) fnd ha lqudy beas change over me and ha hese changes are affeced by mare volaly as well as mare reurns. We sar wh an nvesgaon of he mpac of mare reurns on a frm s lqudy bea, usng he regresson framewor n (3). We do hs by nroducng a measure of weely mare adjused spreads, ASP m,, whch s obaned by averagng he frm-level adjused spreads, ( N ASP = ) / 1, N. The weely change n mare spreads (ASP m, -ASP m,-1 ) s denoed as ΔASP m, and he sensvy of frm s spread o ΔASP m, s s lqudy bea, b LIQ,. ΔASP c, = 1, m, β γ = 1 OWN,,, OWN,, ΔST = α b, 1 c 5 LIQ, ΔTUN ΔASP = 1 OWN,, m, OWN, m, β, 1 m, c b c 6, LIQ, OWN, 1 ΔST ΔOIB m,, 1 ΔASP c 2 m, ΔST φ OWN, m, = 1,,, γ c 3 ΔASP ΔST = 1,, m, 1 ε, (7) ΔASP b c LIQ, OWN LAGE = 1, m,, β γ = 1 OWN,,, OWN,, ΔST = α b, 1 c, 5 LIQ, ΔASP ΔTUN ΔASP m, = 1 OWN,, m, OWN, m, β, 1 m, OWN c b c 6, LIQ, OWN LAGE, m, 1 ΔST ΔOIB m,, 1 SMALL, c 2 ΔASP ΔST = 1,,, m, γ c OWN 3 φ ΔASP ΔST = 1,, SMALL, m, m, 1 ε,. (8) I should be noed ha we exclude frm n he compuaon of mare spreads. Alhough changes n lqudy levels are dfferen from lqudy commonaly, s possble ha hey are correlaed. For example, f low mare reurns predc low lqudy 20

for all socs, hen lqudy covarance wh aggregae lqudy may ncrease followng low mare reurns. Hence, we es for boh lqudy level and commonaly effecs n equaon (7). Specfcally, we examne wheher b LIQ, changes durng perods of negave mare reurns, as capured by b LIQ,OWN,. In equaon (8) we also nvesgae wheher b LIQ, changes when mare reurns are negave and small (b LIQ,OWN,SMALL, ) or negave and large (b LIQ,OWN,LAGE, ), where small (large) s defned as negave mare reurns ha are less (more) han 1.5 sandard devaons below he uncondonal mean mare reurns. Conssen wh he fndngs n Kamara, Lou, and Sada (2008), Panel A of Table V shows ha b LIQ, ncreases sgnfcanly from 0.56 o 0.87 n down mare saes. In Panel B, he larges ncrease n lqudy commonaly happens durng large mare downurns, when b LIQ, ncreases o 0.95. Moreover, he asymmerc effec of mare reurns on spreads documened n Secon II persss afer accounng for changes n lqudy commonaly. Hence, he resuls n Table V emphasze wo separae effecs: an ncrease n llqudy levels as well as commonaly n response o mare downurns. [Inser Table V abou here] We also nvesgae he effec of mare reurns on commonaly n lqudy usng an alernae merc ha capures comovemen. The 2 sasc from he mare model regresson has been exensvely used o measure comovemen n soc prces (e.g., oll (1988), Morc, Yueng, and Yu (2000)). We follow Chorda, oll, and Subrahmanyam (2000) and use a sngle-facor mare model o compue he commonaly n lqudy. Changes n daly adjused proporonal spreads for frm on day s (ΔASP,s ) are regressed on changes n daly mare average adjused spreads (ΔASP m,s ): 21

Δ ASP = a b ΔASP ε. (9), s LIQ, m, s, s For each soc wh a leas 15 vald daly observaons n monh, he mare model regresson yelds a regresson 2 denoed as 2,. A hgh 2, ndcaes ha a large poron of he varaon n lqudy for soc n monh s due o common, mare wde lqudy movemens. For each monh, he srengh of lqudy commonaly s measured by ang an equally weghed average of 2,, denoed as 2. Fgure 2 shows sgnfcan me-seres varaon n lqudy commonaly, 2, over he sample perod 1988 o 2003. We observe spes n lqudy commonaly assocaed wh perods of lqudy crss. For example, he hghes levels of commonaly n lqudy n Fgure 2 concde wh lqudy dry-ups durng he Asan fnancal crss (1997), LTCM crss (1998), and Sepember 11, 2001 errors aacs. These perods are also accompaned by large negave mare reurns, hghlghng he epsodc naure of llqudy. The average lqudy 2 ncreases o 10.1% n large negave mare reurns saes, compared o 7.% when mare reurns are posve. In resuls avalable n he Inerne Appendx, he dynamc condonal correlaons (CC) mehodology nroduced by Engle (2002) yelds supporve fndngs: he condonal correlaons n llqudy (spreads) among sze-sored porfolos are sgnfcanly hgher followng large mare declnes. We also fnd a smlar ncrease n he condonal correlaon beween wo porfolos of socs consruced based on wheher he soc s an S&P 500 ndex soc or no. The laer resul suggess ha he common varaon n lqudy canno be fully explaned by demand for lqudy by ndex-lned funds or ndex arbrageurs (see 22

Harford and Kaul (2005)). Our resuls underscore he man dea ha llqudy becomes more correlaed across all asses followng mare declnes. [Inser Fgure 2 abou here] The neremporal varaon n lqudy commonaly may also be affeced by facors relaed o changes n demand for lqudy. In Vayanos (200), nvesors become more rs averse and her preference for lqudy ncreases n volale mes. Consequenly, a jump n mare volaly, he man sae varable n hs model, s assocaed wh hgher demand for lqudy and concevably ncreases lqudy commonaly. Exreme aggregae mbalances n he buyer- and seller-naed orders for secures may ncrease he demand for lqudy as shown by Chorda, oll, and Subrahmnayam (2002). If hgh levels of aggregae order mbalance mpose smlar pressure across secures, hey are also lely o ncrease commonaly n spreads. In addon, correlaed shfs n demand by buyer- or seller-naed rades would lead o commonaly n order mbalances. Hence, we explore he mpac of boh he level and commonaly n order mbalances on lqudy commonaly. The level of order mbalances (OIB) s defned above n Secon II. To measure commonaly n order mbalances (OIBCOM), we esmae he 2 from a regresson of ndvdual frm order mbalances on mare (equally weghed average) order mbalances, smlar n spr o he lqudy commonaly measure usng proporonal spreads n equaon (9). We nroduce hese addonal varables ha may affec lqudy commonaly whn a regresson framewor. Snce he 2 values are consraned o be beween zero and one by consrucon, we defne lqudy comovemen as he log ransformaon of 2, LIQCOM = ln[ 2 /(1 2 )]. We regress our comovemen measure on he above varables 23

as well as mare reurns ( m ), ang no accoun he sgn and magnude of mare reurns: LIQCOM β UP LAGE = a β m, m, UP LAGE, β OWN LAGE c 1 ST m, c m, 2 OWN LAGE, OIB c OIBCOM 3 ε, (10) where he reurn and dummy varables are defned n equaon (). As shown n Table VI, lqudy comovemen s sronges when here s a large drop n aggregae mare prces. Shfs n he order mbalance comovemen, whch we nerpre as a measure of correlaon n demand for lqudy, are posvely assocaed wh lqudy commonaly. The level of aggregae order mbalance (OIB) also posvely affecs lqudy commonaly. Conssen wh he predcon n Vayonas (200), uncerany n he mare (ST m, ) ncreases nvesor demand for lqudy and subsequenly ncreases lqudy commonaly. Neverheless, addng hese measures of demand effecs does no elmnae he sgnfcan asymmerc effec of mare reurns on lqudy commonaly. To he exen ha comovemen n order mbalances across secures pcs up correlaon n demand for lqudy, would be neresng o documen he sources ha drve he common varaons n order flow. We consder one more explanaory varable: monhly ne flow of funds no U.S. equy muual funds for our sample perod from 1988 o 2003. We dvde he ne fund flow daa from Invesmen Company Insue by he oal asses under managemen by U.S. equy funds o generae our monhly me seres of ne muual fund flow. When here s a large whdrawal of money by muual fund owners n aggregae, fund managers are less wllng and able o hold (parcularly llqud) asses, creang correlaed demand for lqudy across socs. 2

As repored n column () of Table VI, order mbalance comovemen ncreases wh mare volaly and s negavely relaed o ne muual fund flows, correspondng o changes n demand for lqudy. Unle he evdence on lqudy commonaly, order mbalances across socs decrease afer a large declne n mare valuaons. The laer resul s no surprsng snce mare reurns and consrans on aggregae capal are no expeced o affec lqudy demand n he same way. [Inser Table VI abou here] The posve correlaon beween he wo comovemen measures suggess ha hese varables may affec each oher smulaneously. To address hs endogeney problem, we re-esmae he coeffcens based on wo-sage leas squares (2SLS) esmaon, usng ne muual fund flow and lagged order mbalance comovemen o denfy he demand (commonaly n order mbalance) equaon. As shown n he las wo columns of Table VI, our fndng ha lqudy commonaly ncreases n large down mare saes s robus. Overall, he resuls show ha whle lqudy commonaly s drven by changes n supply as well as demand for lqudy, he demand facors canno explan he asymmerc effec of mare reurns on lqudy. On he oher hand, he ncrease n lqudy commonaly n down mare saes s conssen wh he adverse effecs of a decrease n he supply of lqudy. B. Indusry Spllover Effecs Vrually all he heorecal models, ncludng Kyle and Xong (2001), Gromb and Vayanos (2002), Brunnermeer and Pedersen (2009), and Garnealu and Pedersen (2007), 25

sugges a conagon n llqudy. We broaden our nvesgaon by addressng wheher ndusry-wde comovemen n lqudy s affeced by a decrease n he valuaon of socs from oher ndusres, over and above he effec of own-ndusry porfolo reurns. If commonaly n lqudy s drven by capal consrans faced by he mare mang secor n supplyng lqudy, we should observe correlaed llqudy whn an ndusry ncrease wh a declne n he mare values of secures n oher ndusres. We begn by esmang n each monh he followng ndusry facor model for daly changes n spreads for secury (ΔASP,s ): Δ ASP = a b ΔASP ε, (11), s LIQ, INj, s, s where he ndusry lqudy facor (ΔASP INj,s ) s he daly change n he equally weghed average of adjused spreads across all socs n ndusry j on day s. Smlar o our approach n equaon (9), we average he regresson 2 from equaon (11) for each monh, across all frms n ndusry j. To oban an ndusry-wde measure of commonaly n lqudy for each monh, we perform a log ransformaon of he ndusry average 2, denoed as LIQCOM INj,. We form 17 ndusry-wde comovemen measures usng he SIC classfcaon derved by Fama and French, whch s provded by Kenneh French s onlne daa lbrary. 20 LIQCOM INj,, s regressed on he monhly reurns on he ndusry porfolo j ( INj, ) and he reurns on he mare porfolo, excludng porfolo j ( MKTj, ), ang no accoun he sgn and magnude of hese reurns: LIQCOM β MKTj, INj, β = a δ OWN MKTj, INj, δ OWN OWN, MKTj, INj, ε OWN, INj, (12) 26

LIQCOM β MKTj, INj, β = a δ INj, δ OWNLAGE MKTj, OWNLAGE INj, OWNLAGEMKTj,, OWNLAGEINj,, β UP LAGE MKTj, δ UP LAGE INj, UP LAGEMKTj,, ε UP LAGEINj,,, (13) where he dummy varables are defned n he same way as n equaons (3) and (). The regresson coeffcen assocaed wh he ndependen varable MKTj, measures lqudy spllover effecs. As presened n Table VII, we fnd ha he reurns on he mare porfolo (.e., he porfolo of secures n oher ndusres, excludng own ndusry) exer a srong nfluence on comovemen n lqudy whn he ndusry, especally when he mare reurns are negave. In fac, he mare porfolo reurns domnae he ndusry reurns n erms of he effec on ndusry-wde lqudy movemens. The regresson coeffcen esmae on negave mare reurns s a sgnfcan -1.995 whle he coeffcen on negave ndusry reurns s smaller a -0.986. When we separae he reurns accordng o her magnude, large negave mare reurns urn ou o have he greaes mpac on ndusry-level lqudy movemens. In Table VII, we also oban smlar spllover effecs of mare wde reurns when we replace LIQCOM INj, wh he ndusry average lqudy beas, b LIQ, (defned n equaon (11)). These resuls srongly suppor he dea ha when large negave mare reurns occur, spllovers due o capal consrans exend across ndusres, ncreasng he commonaly n lqudy. [Inser Table VII abou here] IV. Lqudy and Shor-erm Prce eversals In Campbell, Grossman, and Wang (1993), rs-averse mare maers requre compensaon for supplyng lqudy o mee flucuaons n aggregae demand for 27

lqudy. Ths cos of provdng lqudy s refleced n he emporary decrease n prces accompanyng heavy sell volume and he subsequen ncrease as prces rever o fundamenal values. 21 Conrad, Hameed, and Nden (199), Avramov, Chorda, and Goyal (2006), and Kanel, Saar, and Tman (2008) provde emprcal suppor for he relaon beween shor-erm prce reversals and llqudy and show ha hgh volume socs exhb sgnfcan weely reurn reversals. Accordng o he collaeral-based models dscussed earler, he reurn reversals should be sronger followng mare declnes. We examne he exen of prce reversals n dfferen mare saes usng wo emprcal radng sraeges, namely, conraran and lm order radng sraeges. The frs sraegy reles on he formulaon n Avramov, Chorda, and Goyal (2006). We consruc Wednesday o Tuesday weely reurns for all NYSE socs n our sample for he perod 1988 o 2003. Sppng one day beween wo consecuve wees avods he poenal negave seral correlaon caused by he bd-as bounce and oher mcrosrucure nfluences. Nex, we sor he socs n wee no posve and negave reurn porfolos. For each wee, reurns on soc ( ) ha are hgher (lower) han he medan reurn n he posve (negave) reurn porfolo are classfed as wnner (loser) secures. We use soc s urnover n wee (Turn ) o measure he amoun of radng. The conraran porfolo wegh of soc n wee 1 whn he wnner and loser porfolos s gven by w, p, 1 =, Turn, / = Turn 1,,, where Np denoes he number of secures n he loser or wnner porfolos n wee. The conraran nvesmen sraegy s long on he loser secures and shor on he wnner secures. The Np 28

conraran profs for he loser and wnner porfolos for wee are: π Np p, = = w p 1,, 1,, whch can be nerpreed as he reurn o a $1 nvesmen n each porfolo. The zero-nvesmen profs are obaned by ang he dfference n profs from he loser and wnner porfolos. We nvesgae he effec of lagged mare reurns by condonng he conraran profs on cumulave mare reurns over he prevous four wees. Specfcally, we examne conraran profs n four mare saes: large up (down) mares defned as mare reurn beng more han 1.5 sandard devaons above (below) he mean reurn; and small up (down) mares, defned as mare reurns beween zero and 1.5 (-1.5) sandard devaons around he mean reurn. In he second radng sraegy, we follow Handa and Schwarz (1996) and devse a smple lm order radng rule o measure he profs o supplyng lqudy. 22 When a lm buy order s submed below he prevalng bd prce, he lm order rader provdes lqudy o he mare. If prce varaons are due o shor-erm sellng pressure, he lm buy order wll be execued and we should observe subsequen prce reversals, reflecng compensaon for lqudy provson. A he same me, he lm order rader expecs o lose from he rade upon arrval of nformed raders, n whch case he prce drop would be permanen (.e., a lm buy order mbeds a free pu opon). The lm order sraegy s mplemened as follows. A he begnnng of each wee, a lm buy order s placed a x% below he openng prce (P o ). We consder hree values of x: 3%, 5%, and 7%. If he ransacon prce falls o P o (1- x%) or below whn wee, he lm order s execued and he nvesmen s held for a perod of wees ( = 1 and 29

2). If he lm order s no execued n wee, we assume ha he order s whdrawn. A smlar sraegy s employed o execue lm sell orders f prces reach or exceed P o (1 x%). For he wee 1, we consruc he cross-seconal average weely reurns (for buy and sell orders), weghng each soc by s urnover n wee w Np, 1 = Turn, / = Turn 1,. Agan, we nvesgae wheher he payoff o he lm order radng sraegy vares across mare saes. Table VIII, Panel A repors a sgnfcan conraran prof of 0.58% n wee 1 (-sasc=5.38) for he full sample perod. The conraran prof declnes rapdly and becomes nsgnfcan as we move o longer lags. Snce he conraran profs and prce reversals appear o las for a mos wo wees, we lm our subsequen analyses o he frs wo wees afer porfolo formaon. Panel B of Table VIII shows ha he larges conraran prof s regsered n he perod followng a large declne n mare prces. Wee 1 profs n he large down mare ncrease noceably o 1.18% compared o profs of beween 0.52% and 0.6% n oher mare saes. We fnd a smlar prof paern n wee 2, alhough he magnude falls qucly. I s worh nong ha he loser porfolo shows he larges prof (above 1.0% per wee) followng large negave mare reurns. To asceran wheher he dfference n loser and wnner porfolo reurns can be explaned by loadngs on rs facors, we esmae he alphas from a Fama-French hree-facor model. We regress he conraran profs on mare (reurn on he value-weghed mare ndex), sze (dfference n reurns on small and large mare capalzaon porfolos), and boo-o-mare (dfference n reurns on value and growh porfolos) facors. 23 The rs-adjused profs n large down mares reman 30

economcally large a 1.16% per wee, ndcang ha hese rs facors canno explan he prce reversals. In resuls avalable n he Inerne Appendx, we fnd ha he conraran profs jump o 1.73% followng perods of hgh lqudy commonaly (as defned n Secon III) and large mare declnes. [Inser Table VIII abou here] Table IX, Panel A shows ha our lm order radng sraegy generaes sgnfcan profs for all hree dscoun values, ha s, 3%, 5% and 7%, wh weely buy-mnus-sell porfolo reurns rangng from 0.37% o 0.97% n he frs wee. These reurns become economcally small n magnude beyond one wee. In Panel B, he buy-mnus-sell porfolo reurns are smlar n all he mare saes, excep for large down saes. For example, he 5% lm order radng rule generaes buy-mnus-sell reurns of beween 0.63% and 0.68% per wee n mos mare saes. The srng excepon s n large down mares, where he porfolo reurns more han double o 1.56%. [Inser Table IX abou here] Hence, he evdence from boh sraeges shows ha he compensaon for supplyng lqudy ncreases n large down mares, ndcave of supply effecs n equy mares arsng from ghness n capal. V. Concluson Ths paper documens ha lqudy responds asymmercally o changes n asse mare values. Conssen wh heorecal models emphaszng changes n he supply of lqudy, negave mare reurns decrease lqudy much more han posve reurns ncrease lqudy, wh he effec beng sronges for hgh volaly frms and durng mes when he mare mang secor s lely o face capal ghness. We show a drasc 31

ncrease n commonaly n lqudy afer large negave mare reurns, and peas n he commonaly measure concde wh perods ofen assocaed wh lqudy crss. Hence, mare declnes affec boh lqudy level and lqudy commonaly. We also documen ha lqudy commonaly whn an ndusry ncreases sgnfcanly when he reurns on oher ndusres (excludng he specfc ndusry) are large and negave, suggesng conagon n llqudy: llqudy n one ndusry splls over o oher ndusres. The conagon n llqudy and ncrease n lqudy commonaly as aggregae asse values declne provde ndrec evdence of a drop n he supply of lqudy affecng all secures. We argue ha demand effecs, such as buy-sell order mbalances, canno fully explan our resuls. Fnally, we use he dea ha shor-erm soc prce reversals followng heavy radng reflec compensaon for supplyng lqudy and examne wheher he cos of lqudy provson vares wh large changes n aggregae asse values. We fnd ha, ndeed, he cos of provdng lqudy s hghes n perods wh large mare declnes and hgh commonaly n lqudy. For example, conraran or lm order radng sraeges based on reurn reversals produce economcally sgnfcan reurns (beween 1.18% and 1.56% per wee) afer a large declne n aggregae mare prces. Taen ogeher, our resuls suppor a supply effec on lqudy as advocaed by Kyle and Xong (2001), Gromb and Vayanos (2002), Anshuman and Vswanahan (2005), Brunnermeer and Pedersen (2009), Garleanu and Pedersen (2007), and Mchell, Pedersen, and Pulvno (2007). Furher, our emprcal resuls ndcae ha he llqudy effec n he equy mare lass beween one o wo wees, on average. We nerpre our resuls as suggesng he presence of supply effecs even n lqud mares le U.S. eques wh capal flowng no he mare farly qucly. 32

Overall, our paper presens evdence supporve of he collaeral vew of mare lqudy: mare lqudy drops afer large negave mare reurns because aggregae collaeral of fnancal nermedares falls and many asse holders are forced o lqudae, mang dffcul o provde lqudy precsely when he mare needs. However, our evdence s ndrec. A fruful avenue for fuure research would be o nvesgae he effec of fundng consrans usng hgh frequency daa on he balance shee posons held by nermedares. 33

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Table I escrpve Sascs: aw and Adjused Spreads Ths able presens he summary sascs of he annual average of he daly proporonal quoed spread (QSP) and adjused spread (ASP) for he sample perod January 1988 o ecember 2003. For each frm on day s, QSP,s s he average spread of all ransacons whn a day. The daly quoed spreads are adjused for seasonaly o oban he adjused spreads, ASP,s : QSP 11, s = d, AY, s = 1 = 1 f 2, e, MONTH, s TICK1 s f3, TICK 2 s f, YEA1 s f5, YEA2 s ASP, f 1, HOLIAY where we employ () day of he wee dummes (AY,s ) for Monday hrough Thursday ; () monh dummes (MONTH,s ) for January hrough November; () a dummy for he radng days around holdays (HOLIAY s ); (v) c change dummes (TICK1 s, TICK2 s ) o capure he c change from 1/8 o 1/16 on 06/2/1997 and he change from 1/16 o he decmal sysem on 01/29/2001, respecvely; (v) and me rend varables YEA1 s (YEA2 s ), equal o he dfference beween he curren calendar year and he year 1988 (1997) or he frs year when he soc s raded on NYSE, whchever s laer. s s, Year Number of Secures QSP (Unadjused Proporonal Quoed Spread) Mean Medan Coeffcen of Varaon ASP (Adjused Proporonal Quoed Spread) Mean Medan Coeffcen of Varaon 1988 1027 1.26% 1.0% 0.618 1.33% 1.08% 0.61 1989 1083 1.13% 0.91% 0.671 1.2% 0.98% 0.708 1990 116 1.1% 1.09% 0.720 1.56% 1.23% 0.78 1991 122 1.32% 1.02% 0.712 1.50% 1.16% 0.723 1992 1320 1.25% 0.98% 0.71 1.7% 1.17% 0.703 1993 130 1.18% 0.92% 0.736 1.5% 1.17% 0.692 199 197 1.1% 0.90% 0.717 1.7% 1.20% 0.657 1995 1562 1.06% 0.82% 0.71 1.3% 1.17% 0.657 1996 161 0.97% 0.7% 0.769 1.38% 1.15% 0.69 1997 1709 0.77% 0.59% 0.812 1.31% 1.07% 0.670 1998 1709 0.78% 0.57% 0.83 1.32% 1.07% 0.692 1999 1607 0.85% 0.62% 0.822 1.3% 1.11% 0.679 2000 182 0.93% 0.62% 0.930 1.38% 1.15% 0.666 2001 1328 0.55% 0.32% 1.213 1.38% 1.15% 0.68 2002 123 0.39% 0.21% 1.266 1.26% 1.06% 0.657 2003 1200 0.26% 0.13% 1.251 1.13% 0.95% 0.692 38

Table II Spreads and eurns Weely changes n adjused spreads for each secury (ΔASP, ) are regressed on lagged mare and dosyncrac soc reurns. Panel A uses he regresson specfcaon: ΔASP, = α β, m γ, conrol varables ε, 1,, = = 1, where ASP, s soc s seasonally adjused proporonal spread n wee ; m, s he wee reurn on he CSP value-weghed ndex; and, s he dosyncrac reurn on soc n wee. The frm-specfc weely conrol varables are: urnover (TUN, ); relave order mbalance (OIB,,); and dosyncrac volaly (ST, ). We also nclude he volaly of he mare reurn n wee (ST m, ). The Δ operaor represens he frs-order dfference operaor. In Panel B, we add an neracon dummy varable OWN,m, ( OWN,, ), whch ae he value of one f and only f m, (, ) s less han zero, ha s, ΔASP, = 1, = 1, = 1, = 1, = α β, m β OWN,, m, OWN, m γ, γ OWN,,, OWN, conrol varables ε,, Panel C uses he specfcaon: ΔASP, = α β 1, = m, β 1 OWN LAGE,, m, = OWN LAGE, m, β 1 LAGE,, m, = UP UP LAGE, m, γ 1, =, γ conrol varables,, 1 OWN LAGE,,, OWN LAGE,, γ 1 UP LAGE,,, UP LAGE,, ε = = where OWN LAGE,m, ( UP LAGE,m, ) s a dummy varable ha s equal o one f and only f m, s more han 1.5 sandard devaons below (above) s uncondonal mean. Cross-seconal means (-sascs), medans, and percenage of sgnfcan coeffcen esmaes a he 5% level (one-aled) are repored below. Panel A: Spreads and Lagged eurns Esmaed Coeffcens m,-1 m,-2 m,-3 m,-,-1,-2,-3,- Mean -0.830-0.397-0.216-0.052-0.59-0.282-0.177-0.089 (-sascs) (-17.19) (-8.15) (-.8) (-1.09) (-27.26) (-13.92) (-8.73) (-.3) Medan -0.528-0.23-0.101-0.003-0.23-0.200-0.117-0.051 % posve (negave) (98.%) (86.8%) (71.6%) (50.5%) (98.9%) (9.1%) (86.2%) (72.2%) % posve (negave) sgnfcan (78.2%) (35.%) (13.9%) (6.0%) (92.7%) (63.5%) (38.0%) (15.9%) Esmaed Coeffcens ΔST m,-1 ΔST,-1 ΔTurn,-1 ΔOIB,-1 ΔST m, ΔST, Mean 0.221 0.233-0.019 0.008 0.311 0.213 (-sascs) (1.71) (5.90) (-.01) (0.68) (.35) (8.78) Medan 0.17 0.162-0.010 0.006 0.159 0.169 % posve (negave) 63.2% 78.5% (76.7%) 5.7% 73.3% 85.0% % posve (negave) sgnfcan 11.% 27.6% (20.7%) 9.% 20.% 5.8% 39

Panel B: Spreads and Sgned Lagged eurns Esmaed Coeffcens m,-1 m,-2 m,-3 m,-,-1,-2,-3,- Mean -0.13-0.321-0.307-0.163-0.73-0.298-0.20-0.126 (-sascs) (-.10) (-3.55) (-3.7) (-1.89) (-12.61) (-9.20) (-6.3) (-3.93) Medan -0.221-0.195-0.175-0.051-0.33-0.209-0.13-0.073 % posve (negave) (73.9%) (73.3%) (71.%) (58.0%) (91.3%) (89.%) (80.%) (69.9%) % posve (negave) sgnfcan (15.%) (1.5%) (12.1%) (6.7%) (56.5%) (2.2%) (2.8%) (1.9%) Esmaed Coeffcens m,-1 own,m,-1 m,-2 own,m,-2 m,-3 own,m,-3 m,- own,m,-,-1 own,,-1,-2 own,,-2,-3 own,,-3,- own,,- Mean -0.810-0.038 0.257 0.208-0.158 0.08 0.073 0.09 (-sascs) (-.79) (-0.25) (1.83) (1.2) (-2.33) (0.83) (1.28) (1.65) Medan -0.3 0.028 0.155 0.086-0.117 0.036 0.00 0.057 % posve (negave) (76.7%) (8.0%) 62.3% 57.9% (63.1%) 56.3% 56.5% 59.1% % posve (negave) sgnfcan (17.2%) (.6%) 8.9% 6.0% (15.1%) 7.9% 7.9% 9.1% Panel C: Spreads and he Magnude of Lagged Mare eurns Esmaed Coeffcens m,-1 m,-2 m,-3 m,- m,-1 ownlarge,m,-1 m,-2 ownlarge,m,-2 m,-3 ownlarge,m,-3 m,- ownlarge,m,- Mean -0.715-0.308-0.203-0.153-0.30-0.063 0.121 0.23 (-sascs) (-10.00) (-.30) (-2.8) (-2.15) (-3.56) (-0.55) (1.03) (2.01) Medan -0.59-0.192-0.097-0.02-0.196 0.02 0.088 0.09 % posve (negave) (92.2%) (7.%) (6.6%) (57.1%) (68.1%) (7.0%) 58.6% 60.1% % posve (negave) sgnfcan (6.6%) (19.9%) (10.3%) (8.2%) (13.%) (5.1%) 7.0% 7.9% Esmaed Coeffcens m,-1 UpLarge,m,-1 m,-2 UpLarge,m,-2 m,-3 UpLarge,m,-3 m,- UpLarge,m,- Mean 0.161-0.222-0.209 0.065 (-sascs) (1.30) (-1.55) (-1.51) (0.5) Medan 0.113-0.066-0.09 0.001 % posve (negave) 60.8% (57.6%) (59.3%) 50.2% % posve (negave) sgnfcan 6.6% (7.1%) (7.6%) 5.% 0

Table III Spreads, Mare eurns, and Impac of he Fundng Mare Proxes Weely changes n he adjused spreads for each secury (ΔASP, ) are regressed on sgned lagged mare reurns wh an neracon dummy varable CAP, ha s equal o one when he fundng mare s lely o face capal consrans n wee : ΔASP =, = β m OWN m OWN m OWN, CAP,,1m, 1OWN, m, 1 1,, β 1,,,,, β CAP, 1 conrol varables = α ε. All oher varables are defned n Table II. In Panel A, CAP, s equal o one when he excess reurn on a porfolo of nvesmen bans n wee s negave. CAP, n Panel B s equal o one when here s a decrease n he aggregae repos n wee. Fnally, when here s an ncrease n he commercal paper spread, we assgn a value of one o CAP, n Panel C. Panel A: Invesmen Ban & Broer Secor eurns Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~-, m,-1 own,m,-1 CAP,-1 Mean -0.13-0.333-0.230-0.673-0.026 0.216-0.297 (-sascs) (-.13) (-3.72) (-3.69) (-3.82) (-0.18) (1.97) (-2.20) Medan -0.210-0.196-0.118-0.353 0.035 0.136-0.155 % posve (negave) (7.0%) (73.9%) (71.1%) (72.7%) (6.9%) 65.8% (61.8%) % posve (negave) sgnfcan (1.%) (15.6%) (11.7%) (1.7%) (.%) 9.0% (10.6%) Panel B: Change n epos Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 Mean -0.26-0.33-0.210-0.528-0.0 0.207-0.672 (-sascs) (-.37) (-3.83) (-3.5) (-3.06) (-0.30) (1.93) (-.98) Medan -0.230-0.197-0.110-0.26 0.030 0.139-0.372 % posve (negave) (75.%) (7.0%) (69.2%) (68.3%) (7.9%) 65.8% (75.%) % posve (negave) sgnfcan (15.6%) (15.%) (10.6%) (10.7%) (.7%) 9.0% (20.3%) Panel C: Commercal Paper Spread Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 Mean -0.33-0.320-0.218-0.92-0.02 0.202-0.53 (-sascs) (-.36) (-3.59) (-3.53) (-2.57) (-0.28) (1.85) (-3.3) Medan -0.230-0.187-0.11-0.28 0.015 0.132-0.267 %posve(negave) (75.0%) (72.8%) (70.0%) (65.3%) (8.8%) 65.% (71.9%) %posve(negave) sgnfcan (15.6%) (1.5%) (11.1%) (8.6%) (.6%) 8.9% (1.1%) 1

Table IV Spreads and eurns: Cross-seconal Esmaes Socs are sored no nne sze-volaly porfolos usng wo-way dependen sors on mare capalzaon and reurn volaly. Weely changes n porfolo average adjused spreads (ΔASP p, ) are regressed on lagged mare reurns ( m, ) and porfolo-specfc reurns ( p, ) usng he SU mehod: = ΔASP p, = α β conrol varables,, 1 p, m, β 1 OWN, p, m, OWN, m, γ 1 p, p, γ 1 OWN, p, p, OWN, p, ε = = = p where OWN,m, ( OWN,p, ) s a dummy varable ha s one f and only f m, ( p, ) s less han zero. The conrol varables are defned n Table II. -sascs are repored n parenheses. The Hgh-Low column, repors he es of he null hypohess ha he coeffcens correspondng o he Hgh and Low Volaly porfolos are equal, and sgnfcan dfferences a he 99%, 95%, and 90% confdence levels are ndcaed by ***, **, and *, respecvely. Hgh Volaly Medum Volaly Small-Sze Medum-Sze Large-Sze Low Volaly Hgh - Low Hgh Volaly Medum Volaly Low Volaly Hgh - Low Hgh Volaly Medum Volaly Low Volaly m,-1-1.23-0.7-0.38-0.85*** -0.27-0.21-0.2-0.0-0.13-0.10-0.08-0.05 (-.3) (-2.69) (-3.0) (-2.76) (-2.50) (-3.59) (-2.33) (-2.26) (-2.25) m,-2-0.57-0.61-0.25-0.31-0.2-0.21-0.18-0.06-0.13-0.10-0.10-0.03 (-2.1) (-3.81) (-2.22) (-2.68) (-2.78) (-2.95) (-2.58) (-2.27) (-2.88) m,-3~- -0.39-0.37-0.33-0.06-0.16-0.15-0.08-0.08-0.05-0.02-0.02-0.03 (-2.13) (-3.30) (-.06) (-2.5) (-2.81) (-1.88) (-1.25) (-0.80) (-0.69) m,-1 own,m,-1-1.7-1.26-0.71-1.03*** -0.67-0.57-0.0-0.28*** -0.3-0.29-0.21-0.13** (-3.85) (-.0) (-3.) (-.11) (-.10) (-3.57) (-3.65) (-3.75) (-3.3) m,-2 own,m,-2 0.2 0.32 0.03 0.21 0.10 0.09 0.07 0.03 0.12 0.10 0.15-0.03 (0.58) (1.2) (0.1) (0.67) (0.75) (0.66) (1.5) (1.3) (2.62) m,-3~- own,m,-3~- 0.63 0.51 0.50 0.13 0.28 0.27 0.1 0.1 0.1 0.09 0.08 0.06 (2.17) (2.71) (3.67) (2.53) (2.89) (1.86) (2.20) (1.77) (1.8) p,-1-1.92-0.98-0.61-1.30*** -0.55-0.3-0.39-0.16* -0.25-0.19-0.16-0.10* (-8.17) (-6.19) (-.8) (-5.2) (-.63) (-5.01) (-.53) (-.0) (-3.33) p,-2-0.28-0.13-0.22-0.06-0.11-0.13-0.19 0.08-0.15-0.13-0.02-0.12 (-1.23) (-0.87) (-1.82) (-1.07) (-1.5) (-2.5) (-2.76) (-3.19) (-0.53) p,-3~- -0.2-0.18-0.05-0.19-0.03-0.03-0.06 0.03-0.01-0.03-0.05 0.0 (-1.63) (-1.79) (-0.5) (-0.6) (-0.5) (-1.06) (-0.31) (-1.00) (-1.65) p,-1 own,p,-1 0.78 0.37-0.03 0.80** 0.07 0.03 0.02 0.05-0.08-0.10-0.08 0.00 (1.87) (1.0) (-0.12) (0.0) (0.21) (0.15) (-0.82) (-1.31) (-0.90) p,-2 own,p,-2-0.06-0.35-0.07 0.01-0.2-0.20-0.03-0.21 0.18 0.18 0.01 0.18 (-0.15) (-1.38) (-0.31) (-1.38) (-1.28) (-0.2) (1.96) (2.8) (0.09) p,-3~- own,p,-3~- 0.18-0.02-0.22 0.39-0.13-0.10-0.01-0.11-0.06-0.03 0.09-0.15 (0.66) (-0.08) (-1.6) (-1.01) (-0.95) (-0.15) (-0.86) (-0.62) (1.9) Hgh - Low 2

Table V Lqudy Beas and Mare eurns Weely changes n adjused spreads for each secury (ΔASP, ) are regressed on lagged mare reurns ( m, ), dosyncrac soc reurns (, ), and he change n mare average spreads (ΔASP m, ) usng he followng wo specfcaons: ΔASP ΔASP,, = α b β LIQ, ΔASP m, b LIQ, OWN, = 1 OWN,, m, OWN, m, = α b LIQ, ΔASP m, b ΔASP LIQ, OWN SMALL, γ m, = 1,, ΔASP m, OWN, m, γ β = 1, m, = 1 OWN,,, OWN,, OWN SMALL, m, b LIQ, OWN LAGE, conrol varables ε ΔASP OWN LAGE, m, β conrol varables,, 1 OWN,, m, OWN, m, γ 1,, γ 1 OWN,,, OWN,, ε = = = where OWN,m, s a dummy varable ha s equal o one f and only f m, (, ) s less han zero, OWN SMALL,m, s a dummy varable ha s equal o one f and only f m, s negave and less han 1.5 sandard devaons below s uncondonal mean reurn, OWN LAGE,m, s a dummy varable ha s equal o one f and only f m, s more han 1.5 sandard devaons below s uncondonal mean. All oher varables are defned n Table II. Esmaed Coeffcens m,-1 m,-2 m,-3~- Panel A m,-1 own,m,-1 m,-2 own,m,-2 m, m,-3~- own,m,-3~-, ΔASP m, β 1, = m, ΔASP m, own,m, Mean -0.19-0.227-0.161-0.293-0.281 0.091 0.562 0.310 (-sascs) (-21.62) (-12.50) (-12.36) (-9.83) (-9.90) (.32) (6.3) (19.76) Medan -0.211-0.125-0.07-0.125-0.136 0.061 0.381 0.208 %posve(negave) (7.7%) (65.3%) (63.0%) (58.7%) (60.9%) 56.8% 91.5% 73.7% %posve(negave) (15.5%) (10.0%) (8.5%) (7.0%) (8.2%) 5.6% 57.0% 27.8% sgnfcan Esmaed Coeffcens m,-1 m,-2 m,-3~- Panel B m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- ΔASP m, ΔASP m, ownsmall,m, ΔASP m, ownlarge,m, Mean -0.2-0.227-0.163-0.29-0.277 0.097 0.561 0.268 0.387 (-sascs) (-19.33) (-11.1) (-11.33) (-8.8) (-8.59) (.06) (3.09) (13.13) (23.07) Medan -0.212-0.125-0.077-0.12-0.10 0.067 0.381 0.173 0.252 %posve(negave) (7.5%) (65.8%) (63.6%) (59.0%) (60.5%) 57.7% 91.5% 67.0% 73.0% %posve(negave) (15.7%) (10.0%) (8.7%) (6.8%) (8.0%) 6.3% 57.0% 22.% 27.3% sgnfcan 3

Table VI Commonaly n Lqudy and Mare eurns aly changes n adjused spread for each soc are regressed on changes n mare average spreads whn each monh o generae monhly 2 values. Commonaly n lqudy n monh (LIQCOM ) s defned as he log ransformaon of he cross-secon average 2. Commonaly n order mbalance n monh (OIBCOM ) s obaned from whn-monh regressons of daly ndvdual frm relave order mbalance on he mare average, smlar o LIQCOM. We esmae he followng regresson equaons: LIQCOM a β β β conrols ε = m, OWN LAGE m, OWN LAGE, UP LAGE m, UP LAGE, OIBCOM = a β m, β OWN LAGE m, OWN LAGE, βup LAGE m, UP LAGE, conrols ε, where he dummy varable ownlarge,m, ( UpLarge,m, ) s equal o one f he mare reurn n monh ( m, ) s more han 1.5 sandard devaons below (above) s uncondonal mean. The conrol varables nclude OIB, he cross-seconal average relave order mbalance level; equy muual fund flows as a proporon of oal muual fund nvesmen; and mare volaly (ST m ). The frs four columns presen OLS esmaes whle he las wo columns presen esmaes from a wo-sage leas squares (2SLS) regresson. -sascs are repored n parenheses. OLS 2SLS ependen Lqudy OIB Lqudy OIB Varables Commonaly Commonaly Commonaly Commonaly Inercep -2.102-2.005-0.67-2.32-0.69 (-9.91) (-7.19) (-2.70) (-7.7) (-1.07) m, 0.233-0.00-1.650-0.09-1.650 (0.35) (-0.06) (-.32) (-0.62) (-.11) m, * -3.715-2.392 1.919-2.116 1.916 ownlarge,m, (-3.06) (-1.80) (2.61) (-1.87) (2.11) m, * -0.696-1.27 0.632-1.180 0.631 UpLarge,m, (-0.97) (-1.65) (0.85) (-1.08) (0.88) ST m, 0.115 0.079 0.135 0.079 (2.0) (2.71) (2.86) (2.09) OIB 1.316 1.730 (1.78) (2.61) OIB 0.182 0.082-0.119 Commonaly (2.1) (0.90) (-0.88) Lqudy 0.096 0.095 Commonaly (1.81) (0.1) OIB 0.99 0.99 Commonaly -1 (7.91) (7.50) Muual Fund -0.652-0.653 Flow (-2.88) (-2.2)

Table VII Commonaly n Lqudy, Mare eurns, and Indusry eurns aly changes n adjused spreads for each soc are regressed on changes n ndusry average spreads whn each monh o generae monhly 2 values and lqudy beas (b LIQ, ). Commonaly n lqudy (LIQCOM ) s defned as he log ransformaon of he cross-secon average 2 for all socs whn he same ndusry n each monh. We esmae he followng regressons: LIQCOM = a δ δ LIQCOM INj, INj, β INj, MKTj, = a δ β MKTj, β INj, β OWN OWN δ INj, MKTj, OWN LAGE OWN LAGE OWN, INj, OWN, MKTj, INj, MKTj, ε OWN LAGE, MKTj, OWN LAGE, INj, δ β UP LAGE UP LAGE INj, MKTj, UP LAGE, INj, UP LAGE, MKTj, ε, where INj, and MKTj, denoe he monh reurn on he value-weghed reurns on ndusry j and he mare (excludng ndusry j). The dummy varable own,inj, ( ownlarge,inj, ) s equal o one f INj, s less han zero (more han 1.5 sandard devaons below s mean). own,mktj, ( ownlarge,mktj, ) s smlarly defned based on MKTj,. In he las wo columns, we replace LIQCOM wh lqudy beas (b LIQ, ) as he dependen varable. Whe s heerosedascyconssen -sascs are repored n parenheses. ependen Varable Inercep LIQCOM Lqudy Beas -2.38-2.05 0.668 0.711 (-271.1) (-01.05) (60.1) (97.69) INj, 0.192-0.023 0.159-0.178 (1.15) (-0.15) (0.72) (-0.88) MKTJ, 0.327-0.206 1.07 0.327 (1.35) (-1.02) (3.6) (1.29) INj, * -0.986-0.999 own,inj, (-3.01) (-2.69) MKTj, * -1.995-2.122 own,mktj, (-.39) (-.06) INj, * -0.875-0.701 ownlarge,inj, (-3.10) (-2.25) INj, * 0.098 0.292 UpLarge,IN, (0.8) (1.01) MKTj, * -1.359-0.726 ownlarge,mktj, (-3.86) (-1.77) MKTj, * 0.210-0.039 UpLarge,MKTj, (0.72) (-0.10) 5

Table VIII Conraran Profs and Mare eurns Weely soc reurns are sored no wnner (loser) porfolos f he reurns are above (below) he medan of all posve (negave) reurns n wee. Conraran porfolo wegh for soc n wee s gven by: Np w p,, = (, 1Turn, 1 ) / =, 1, 1Turn, 1 where, and Turn, are soc s reurn and urnover n wee. Conraran profs for wee, for =1,2,3, and are repored n Panel A. Panel B repors conraran profs condonal on mare reurns. Large Up (Large own) refers o cumulave mare reurns from wee - o -1 beng more han 1.5 sandard devaons above (below) he mean. Small Up (Small own) mare refers o cumulave mare reurns beween zero and 1.5 (-1.5) sandard devaons. Facor-adjused reurns represen he alphas from regressng he reurns on he Fama-French hree facors: mare, sze, and boo-o-mare. Newey-Wes auocorrelaon-correced -sascs are gven n parenheses. Panel A: Uncondonal Conraran Profs Wee Porfolo 1 2 3 Loser 0.75% 0.3% 0.39% 0.37% Wnner 0.17% 0.29% 0.37% 0.1% Loser mnus Wnner 0.58% 0.1% 0.03% -0.0% (-sascs) (5.38) (1.69) (0.38) (-0.52) Panel B: Conraran Profs Condonal on Mare eurns Wee 1 Pas Mare eurn Porfolo Large Up Small Up Small own Large own Loser 0.5% 0.83% 0.8% 1.37% Wnner -0.10% 0.29% -0.0% 0.19% Loser mnus Wnner 0.6% 0.5% 0.52% 1.18% (-sascs) (0.93) (.07) (2.51) (3.01) Loser mnus Wnner (adjused for French-French facors) 0.57% 0.8% 0.50% 1.16% (-sascs) (0.83) (3.83) (2.1) (2.90) Porfolo Wee 2 Pas Mare eurn Large Up Small Up Small own Large own Loser 0.86% 0.% 0.21% 0.97% Wnner 0.3% 0.0% 0.09% 0.07% Loser mnus Wnner 0.3% 0.03% 0.12% 0.90% (-sascs) (1.21) (0.33) (0.88) (1.93) Loser mnus Wnner (adjused for Fama-French facors) 0.3% -0.01% 0.12% 0.8% (-sascs) (0.88) (-0.09) (0.87) (1.88) 6

Table IX Lm Order Tradng Profs A he begnnng of each wee, a soc s sored no sell (buy) porfolo f s prce hs x% above (below) s openng prce. If he soc prce hs he lm, he soc s added o he buy or sell porfolos, wh he soc s wegh Np proporonal o s urnover (Turn, ) n he ranng wee,.e., he wegh for frm n wee s Turn, / = Turn. 1, 1 We consder x equal o 3%, 5%, or 7%. Conraran profs n wees 1 and 2 are repored n Panel A. Panel B repors conraran profs condonal on lagged mare reurns. Large Up (Large own) refers o cumulave mare reurns from wee - o -1 beng more han 1.5 sandard devaons above (below) he mean reurn. Small Up (Small own) mare refers o cumulave mare reurns beween zero and 1.5 (-1.5) sandard devaons. Newey-Wes auocorrelaon-correced -sascs are gven n parenheses. Porfolo Panel A: The Uncondonal Profs of Lm Order Conraran Sraegy Open Prce /- 3% Open Prce /- 5% Open Prce /- 7% Wee Wee Wee 1 2 1 2 1 2 Lm Buy 0.70% 0.0% 0.93% 0.39% 1.07% 0.39% Lm Sell 0.33% 0.30% 0.21% 0.29% 0.10% 0.29% Buy-mnus-Sell 0.37% 0.10% 0.71% 0.10% 0.97% 0.09% (-sascs) (7.69) (2.59) (10.7) (1.6) (9.65) (1.02) Panel B: Profs of Lm Order Conraran Sraegy Condonal on Mare eurns n wee 1 Crera = Open Prce /- 3% Pas Mare eurn Porfolo Large Up Small Up Small own Large own Lm Buy 0.58% 0.72% 0.62% 0.90% Lm Sell 0.2% 0.1% 0.29% -0.06% Buy-mnus-Sell 0.33% 0.31% 0.3% 0.96% (-sascs) (1.51) (5.70) (.05) (.16) Crera = Open Prce /- 5% Porfolo Pas Mare eurn Large Up Small Up Small own Large own Lm Buy 0.68% 0.96% 0.79% 1.36% Lm Sell 0.0% 0.33% 0.12% -0.21% Buy-mnus-Sell 0.6% 0.63% 0.68% 1.56% (-sascs) (1.81) (7.52) (5.52) (5.17) Crera = Open Prce /- 7% Pas Mare eurn Porfolo Large Up Small Up Small own Large own Lm Buy 0.76% 1.12% 0.86% 1.73% Lm Sell -0.10% 0.2% -0.01% -0.0% Buy-mnus-Sell 0.86% 0.88% 0.87% 2.13% (-sascs) (1.75) (6.72) (5.36) (.89) 7

Fgure 1. A me-seres plo of he average raw and adjused quoed spreads. The fgure below shows he cross-seconal mean of he raw and adjused proporonal quoed spreads for a consan sample of socs ha have vald observaons hroughou he full sample perod: 1988-2003. 0.2 q y 0.18 Sepember 11 Crss 0.16 0.1 Asa Fnancal Crss LTCM Crss Worldcom Scandal 0.12 0.1 Gulf War I 0.08 0.06 0.0 0.02 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-9 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 ec-03 Tme Fgure 2. The me-seres varaon n lqudy commonaly. 8

1 Ths spral effec of a drop n collaeral value s emphaszed n a number of heorecal papers, sarng wh he foundaonal wor n Kyoa and Moore (1997), where lendng s based on he value of land as collaeral. See also Allen and Gale (2005). 2 Adran and Shn (2008) show ha he changes n he balance shees of fnancal nermedares are lned o fundng lqudy hrough shfs n he mare wde rs appee. In Esfeld (200), lqudy s endogenously deermned and procyclcal: asses are less lqud n bad mes. 3 Oher relaed wor ncludes Pasor and Sambaugh (2003), who show ha lqudy s a prced sae varable, and Amhud and Mendelson (1986), who show ha llqud asses earn hgher reurns. In Acharya and Pedersen (2005), a fall n aggregae lqudy prmarly affecs llqud asses. Sada (2006) documens ha he earnngs momenum effec s parly due o hgher lqudy rs. Karoly, Lee, and j (2008) repor a smlar asymmerc effec of mare reurns on lqudy commonaly n oher developed as well as developng equy mares. 5 A sharp shor-erm prce reversal due o lqudy shocs s predced by models such as Campbell, Grossman and Wang (1993) and Morrs and Shn (200). Pasor and Sambaugh (2003) use a smlar dea o show ha lqudy rs s prced and lqudy evens seem o occur ofen afer large prce declnes (e.g., he crash of 1987). 6 The Inerne Appendx o hs ex s avalable a hp://www.afajof.org/supplemens.asp. 7 Esmaes of he regresson equaons based on spread levels (ASP, ) nsead of changes n spreads (ΔASP, ) produce qualavely smlar resuls a boh monhly and weely horzons. However, usng changes n he varables has he advanage of reducng he economerc bas arsng from hghly auoregressve dependen and ndependen varables. Focusng our analyss a weely nervals provdes us a large number of me-seres observaons whle mnmzng measuremen problems assocaed wh daly reurns. 8 We also consder he effec of up o egh wees of lagged reurns. These addonal lags are n general nsgnfcan and do no change our fndngs. The resuls are repored n he Inerne Appendx. 9 Our resuls are unchanged when dosyncrac reurns are compued as he excess reurns from a mare model specfcaon: ( b m ). 10 The -sascs assocaed wh he mean coeffcens n Table II have been adjused for cross-equaon correlaons. We exend he correcon n sandard errors proposed n Chorda, oll, and Subrahmanyam (2000) by allowng he varance and parwse covarances beween coeffcen esmaes o vary across secures. The varance of each esmaed coeffcen β s obaned from soc s lqudy-reurn regresson n equaon (2). The emprcal correlaon beween he regresson resduals for socs and j s used o esmae he parwse correlaon beween he coeffcens β and β j. Hence, he sandard error of he mean esmaed coeffcen s provded by: N N N N 1 1 Sdev( β ) = Sdev( β ) = Var( β ) ρ Var( β ) Var( β j ). N N = 1 = 1, j = 1 j= 1, j 11 To allevae any concerns arsng from he fac ha he frm-specfc conrol varables n equaon (2) are correlaed wh spreads, we re-esmae he equaon whou hese conrols. We connue o fnd ha changes n spreads are (more) sensve o (negave) mare reurns. 9

12 We also consder oher cu-offs of 2.0 and 1.0 sandard devaons from he mean o denfy large mare reurn saes and oban smlar resuls. 13 ecen behavoral models argue ha a posve relaon beween pas reurns and frm lqudy could arse from an ncrease n he supply of overconfden ndvdual raders followng prce run-ups (Gervas and Odean (2001)), overreacon o senmen shocs ((Baer and Sen (200)), or dsposon effecs (Shefrn and Saman (1985)). We examne hs possbly usng he percenage of small rades, defned as rades below $5000, o proxy for unnformed, behavorally based rades by ndvduals (see Lee (1992), Lee and adharshna (2000), Barber, Odean and Zhu (2008)). Whle we fnd an ncrease n he percenage of small rades followng posve mare reurns, we do no fnd any evdence of decreases n small rades followng negave mare reurns. Hence, he asymmerc effec of mare reurns on lqudy canno be explaned by hese behavoral bases. ealed resuls are avalable n he Inerne Appendx. 1 For example, n 1996, he 10 larges frms ha belong o SIC code 6211 (Secury Broers, ealers and Floaaon Companes) are: Alex Brown, Bear Serns, ean Wer, A.G. Edwards, Lehmann Brohers, Merrll Lynch, Morgan Sanley, John Nuveen, Charles Schwab, and Travellers Group. The composon of frms s updaed annually. Adran and Shn (2008) use a smlar porfolo of frms o examne he effec of changes n asse values on leverage of fnancal nermedares. 15 We also consder addonal lags o CAP,, bu fnd hem o be nsgnfcan. The resuls are repored n he Inerne Appendx. 16 Adran and Shn (2008) argue ha here s also a poenal feedbac effec: weaer balance shees lead o greaer sale of asses, whch pus downward pressure on asse prces and leads o even weaer balance shees. 17 We han Tobas Adran for generously sharng he weely daa on he prmary dealer repo posons compled by he Federal eserve Ban of New Yor. 18 The weely daa are downloaded from he Federal eserve webse a www.federalreserve.gov. 19 For ease of exposon, we repor he coeffcens for he combned mare (and porfolo) reurns n wees -3 and -. 20 The ndusry classfcaons are obaned from Kenneh French s webse a hp://mba.uc.darmouh.edu/pages/faculy/en.french/daa_lbrary.hml 21 Pasor and Sambaugh (2003) use a smlar movaon o develop a lqudy rs facor for emprcal asse prcng models. 22 We han Joel Hasbrouc for suggesng hs alernave radng sraegy. 23 The weely reurns on he hree Fama-French facors are consruced usng daly porfolo reurns downloaded from Kenneh French s daa lbrary. 50

Inerne Appendx o Soc Mare eclnes and Lqudy * Appendx A: ynamc Condonal Correlaon of Spreads and Mare eurns In hs appendx, we examne he relaonshp beween mare reurns and he condonal correlaons n soc lqudy, measured by he dynamc condonal correlaon (CC) mehod proposed by Engle (2002). The CC model reles on he parsmonous unvarae GACH esmaes of lqudy for each asse and has a compuaonal advanage over he mulvarae GACH model. The esmaon sars wh frs obanng a seres of lqudy shocs from a unvarae GACH specfcaon of he lqudy varable and. Then, n he second sage, we esmae he condonal correlaon beween asse lqudy shocs. We use he CC mehodology o model he lqudy movemens beween a par of porfolos. We consder pars of sze-sored porfolos (small, medum, and large sze porfolos) and also he correlaon n lqudy beween porfolos composed of S&P and non-s&p consuen socs. We sor he socs n our sample no hree sze porfolos (or S&P and non-s&p porfolos) and ae he equally weghed average daly adjused spread as he porfolo daly spread. As spreads end o be hghly auocorrelaed, we f an A(1) model for average spreads and use he resduals as our lqudy varable. We oban weely dynamc correlaon esmaes beween a par of porfolo lqudy shocs by ang he average of all he daly CC esmaes n a wee. Fnally, we repor he weely dynamc correlaons for each mare sae based on he magnude and sgn of mare reurns, as defned n he ex n Secon III. Table IA.AI presens he condonal correlaons n lqudy beween sze porfolos for each mare sae. The average CC esmae of he correlaon n spreads beween large and small soc porfolos ncreases from 0.25 o 0.31 afer a large negave mare reurn. A large drop n mare prces has a smlar effec on condonal correlaons beween oher pars of sze porfolos. The condonal correlaon beween lqudy of he S&P and non-s&p consuen socs exhbs smlar behavor: he condonal correlaon beween hese wo porfolo spreads ncreases afer a large negave mare reurn from 0.38 o 0.. The CC mehod confrms ha he sharp ncrease n commonaly n spreads followng large mare declnes. *Caon forma: Allaudeen Hameed, Wenjn Kang, and S. Vswanahan, [year], Inerne Appendx o Soc Mare eclnes and Lqudy, Journal of Fnance [volume], [pages], hp://www.afajof.org/[year].asp 1

Table IA.AI CC Esmaes Condonal on Mare eurns The sample socs are sored no hree sze porfolos (or he S&P and non-s&p consuen porfolos). The porfolo daly spread s he equally weghed average of he soc daly adjused spread n he porfolo. We frs oban he porfolo spread resduals from a frs-order auoregresson model. The resduals for he correspondng pars of porfolo spreads are hen fed usng he CC model wh mean-reverson. The daly CC esmaes are averaged no he weely dynamc correlaon esmaes. The weely dynamc correlaon condonal on mare saes s repored below. Mare saes are defned based on he cumulave CSP value-weghed reurn from wee - o wee -1. Large Up (Large own) refers o cumulave mare reurns beng more han 1.5 sandard devaons above (below) he mean. Small Up (Small own) mare refers o cumulave mare reurns beween zero and 1.5 (-1.5) sandard devaons. The CC dfferences ha are sgnfcan a he 99%, 95%, and 90% confdence level are labelled wh ***, **, and *, respecvely. Pas Mare eurn CC Esmaes (a): Large Up (b): Small Up (c): Small own (d): Large own (e): Average excludng (d) (d) - (e) CC beween small and large sze porfolos CC beween small and medum sze porfolos CC beween medum and large sze porfolos 0.226 0.23 0.260 0.307 0.28 0.060*** 0.39 0.399 0.05 0.51 0.01 0.051*** 0.23 0.67 0.97 0.537 0.7 0.063*** CC beween S&P and non-s&p porfolos 0.362 0.372 0.393 0.2 0.378 0.063*** 2

Appendx B: Supplemenary Tables Table IA.BI Proporonal Effecve Spreads and eurns The emprcal ess n hs able are based on he proporonal effecve spread, whch s wo mes he dfference beween he rade execuon prce and he mdquoe scaled by he mdquoe. Weely changes n adjused proporonal effecve spreads for each secury are regressed on lagged mare and dosyncrac soc reurns n Panel A, usng he followng regresson specfcaon: ΔASP, β m OWN m OWN m OWN 1,, β 1,,,,, γ 1,, γ = = = = 1,,, OWN,, = α conrol varables ε, where ASP, s soc s seasonally adjused proporonal effecve spread n wee ; m, s he wee reurn on he CSP value-weghed ndex; and, s he dosyncrac reurn on soc n wee. The frm-specfc weely conrol varables are: urnover (TUN, ); relave order mbalance (OIB,,); and dosyncrac volaly (ST, ). We also nclude he volaly of mare reurn n wee (ST m, ). The Δ operaor represens he frs-order dfference operaor. We also add lagged changes n spreads o accoun for any seral correlaons. The neracon dummy varable OWN,m, ( OWN,, ) aes he value of one f and only f m, (, ) s less han zero. Ths panel corresponds o Table II n he man arcle. In Panel B, weely changes n he adjused effecve spreads for each secury are regressed on sgned lagged mare reurns wh an neracon dummy varable, CAP,, whch s equal o one when he fundng mare s lely o face capal consrans n wee. CAP,, here s se equal o one when here s a decrease n he aggregae repos on he nvesmen ban balance shee n wee. ΔASP, = α 1 β, m, 1 β OWN,, m, OWN, m, β OWN, CAP,, m, 1OWN, m, 1CAP, 1 conrol varables ε. Ths panel corresponds o Table III = =, n he man arcle. In Panel C, weely changes n he adjused effecve spreads for each secury are regressed on lagged mare reurns ( m, ), dosyncrac soc reurns (, ), and he change n mare average spreads (ΔASP m, ) usng he specfcaon: ΔASP, = α b LIQ, ΔASP m, b LIQ, OWN, ΔASP m, OWN, m, β 1, = m, β conrol varables,, 1 OWN,, m, OWN, m, γ 1,, γ 1 OWN,,, OWN,, ε = = = where OWN,m, s a dummy varable ha s equal o one f and only f m, s less han zero. Ths panel corresponds o Table V n he man arcle., 3

Panel A: Effecve Spreads and Sgned Lagged eurns Esmaed Coeffcens m,-1 m,-2 m,-3 m,-,-1,-2,-3,- Mean -0.298-0.29-0.20-0.128-0.361-0.233-0.10-0.091 (-sascs) (-.03) (-3.7) (-3.68) (-2.01) (-13.1) (-9.83) (-5.93) (-3.89) Medan -0.137-0.115-0.12-0.055-0.232-0.15-0.089-0.05 % posve (negave) (70.3%) (69.9%) (72.6%) (61.1%) (90.9%) (89.0%) (78.7%) (71.2%) % posve (negave) sgnfcan (1.8%) (13.0%) (13.2%) (6.5%) (56.6%) (2.0%) (23.1%) (15.1%) Esmaed Coeffcens m,-1 own,m,-1 m,-2 own,m,-2 m,-3 own,m,-3 m,- own,m,-,-1 own,,-1,-2 own,,-2,-3 own,,-3,- own,,- Mean -0.509-0.073 0.228 0.151-0.069 0.016 0.009 0.03 (-sascs) (-.10) (-0.66) (2.08) (1.0) (-1.91) (0.37) (0.21) (1.03) Medan -0.281-0.033 0.138 0.082-0.05 0.011 0.009 0.025 % posve (negave) (7.1%) (5.1%) 66.0% 59.7% (58.8%) 51.9% 51.8% 56.0% % posve (negave) sgnfcan (16.1%) (6.0%) 9.2% 6.6% (13.3%) 7.0% 6.3% 8.8% Esmaed Coeffcens ΔST m,-1 ΔST,-1 ΔTurn,-1 ΔOIB,-1 ΔST m, ΔST, ΔASP,-1 ΔASP,-2 Mean 0.089 0.083-0.010 0.005 0.29 0.077-0.568-0.381 (-sascs) (0.92) (2.77) (-2.81) (0.67) (5.33) (.1) (-73.72) (-.29) Medan 0.092 0.057-0.00 0.003 0.17 0.067-0.590-0.395 % posve (negave) 62.0% 65.7% (67.9%) 53.3% 79.5% 70.6% (100.0%) (100.0%) % posve (negave) sgnfcan 10.7% 16.9% (13.8%) 10.0% 26.9% 32.% (99.0%) (97.6%) Esmaed Coeffcens ΔASP,-3 ΔASP,- Mean -0.21-0.122 (-sascs) (-28.37) (-16.32) Medan -0.29-0.122 % posve (negave) (98.2%) (95.0%) % posve (negave) sgnfcan (93.0%) (75.0%)

Panel B: Effecve Spreads, Sgned Lagged eurns, and Changes n Aggregae epos Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 Mean -0.311-0.256-0.165-0.305-0.07 0.183-0.93 (-sascs) (-.3) (-3.99) (-3.68) (-2.0) (-0.69) (2.32) (-.93) Medan -0.10-0.12-0.085-0.168-0.038 0.127-0.28 % posve (negave) (71.2%) (72.6%) (71.%) (6.5%) (5.0%) 69.8% (76.7%) % posve (negave) sgnfcan (1.9%) (13.%) (12.%) (10.0%) (6.3%) 10.5% (22.8%) Panel C: Effecve Spreads, Sgned Lagged eurns, and Lqudy Beas Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- ΔASP m, ΔASP m, own,m, Mean -0.307-0.185-0.129-0.175-0.203 0.090 0.555 0.270 (-sascs) (-16.82) (-11.01) (-10.8) (-5.9) (-7.53) (.50) (0.0) (1.81) Medan -0.131-0.073-0.059-0.098-0.128 0.068 0.35 0.199 %posve(negave) (70.%) (62.2%) (66.3%) (58.7%) (63.5%) 62.0% 90.9% 71.1% %posve(negave) sgnfcan (15.1%) (9.2%) (9.1%) (7.6%) (9.2%) 6.7% 56.8% 30.3% 5

Table IA.BII Seasonally Adjused Quoed Spreads The daly quoed spreads are adjused for seasonaly o oban he adjused spreads, ASP,s : QSP 11, s = d, AY, s = 1 = 1 e, MONTH, s f 1, HOLIAY f 2, TICK1 s f3, TICK 2 s f, YEA1 s f5, YEA2 s ASP, s, where we employ () day of he wee dummes (AY,s ) for Monday hrough Thursday ; () monh dummes (MONTH,s ) for January hrough November; () a dummy for he radng days around holdays (HOLIAY s ); (v) c change dummes (TICK1 s, TICK2 s ) o capure he c change from 1/8 o 1/16 on 06/2/1997 and he change from 1/16 o he decmal sysem on 01/29/2001, respecvely; (v) and me rend varables YEA1 s (YEA2 s ), equal o he dfference beween he curren calendar year and he year 1988 (1997) or he frs year when he soc s raded on NYSE, whchever s laer. Cross-seconal means and medans of he coeffcen esmaes are repored below. The mean coeffcens ha are sgnfcan a he 99%, 95%, and 90% confdence levels are ndcaed by ***, **, and *, respecvely. s Esmaed Coeffcens Monday Tuesday Wednesday Thursday Mean 0.005* -0.007*** -0.00** -0.003* Medan 0.000-0.005-0.00-0.002 Esmaed Coeffcens January February March Aprl Mean 0.006-0.007-0.020-0.019 Medan 0.031 0.02 0.013 0.012 Esmaed Coeffcens May June July Augus Mean -0.05*** -0.062*** -0.0*** -0.030** Medan -0.011-0.017-0.012-0.008 Esmaed Coeffcens Sepember Ocober November Holday Mean -0.020* 0.028** 0.016 0.018** Medan -0.003 0.022 0.007 0.010 Esmaed Coeffcens Tc1 Tc2 Year1 Year2 Mean -0.579*** -0.297*** -0.07*** 0.035*** Medan -0.0-0.18-0.00 0.000 6

Table IA.BIII Spreads and eurns: Addonal Lagged eurns Weely changes n adjused spreads for each secury (ΔASP, ) are regressed on lagged mare and dosyncrac soc reurns, wh lagged reurns of up o egh wees: 8 8 8 8 ΔASP, = α β conrol varables,, 1, m, β 1 OWN,, m, OWN, m, γ 1,, γ 1 OWN,,, OWN,, ε = = = = where ASP, s soc s seasonally adjused proporonal spread n wee ; m, s he wee reurn on he CSP value-weghed ndex; and, s he dosyncrac reurn on soc n wee. The neracon dummy varable OWN,m, ( OWN,, ) aes he value of one f and only f m, (, ) s less han zero. For ease of exposon, we repor he coeffcens for he combned mare (and dosyncrac) reurns n wees -3 and -, and he combned mare (and dosyncrac) reurns from wee -5 o -8. The conrol varables are defned n Table II. The Δ operaor represens he frs-order dfference of he correspondng varables. Cross-seconal means (-sascs), medans, and percenage of sgnfcan coeffcen esmaes a he 5% level (one-aled) are repored below. Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-5~-8,-1,-2,-3~-,-5~-8 Mean -0.395-0.3-0.268-0.112-0.75-0.316-0.185-0.051 (-sascs) (-3.75) (-3.68) (-.09) (-2.61) (-12.27) (-9.5) (-7.58) (-2.75) Medan -0.21-0.198-0.12-0.070-0.333-0.223-0.115-0.030 % posve (negave) (73.5%) (75.%) (73.8%) (69.8%) (91.6%) (89.8%) (81.9%) (6.8%) % posve (negave) (13.8%) (15.3%) (1.8%) (12.0%) (56.1%) (3.6%) (30.3%) (11.3%) sgnfcan Esmaed Coeffcens m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-5~-8 own,m,-5~-8,-1 own,,-1,-2 own,,-2,-3~- own,,-3~-,-5~-8 own,,-5~-8 Mean -0.833-0.069 0.159 0.075-0.180 0.027 0.009-0.00 (-sascs) (-.67) (-0.) (1.39) (0.89) (-2.56) (0.6) (0.20) (-0.13) Medan -0.79 0.020 0.109 0.071-0.133 0.033 0.005 0.003 % posve (negave) (77.%) (8.8%) 62.5% 60.6% (65.1%) 5.5% 50.8% (8.7%) % posve (negave) (18.8%) (.6%) 8.3% 7.1% (16.%) 7.2% 6.% (5.7%) sgnfcan 7

Table IA.BIV Spreads and eurn ummes Weely changes n adjused spreads for each secury are regressed on lagged mare reurn dummes and dosyncrac soc reurn saus dummes. Panel A uses he followng regresson specfcaon: ΔASP, = α μ1 2 conrol varables,, 1 OWN,, OWN, m, μ 1 OWN,, OWN,, ε = = where ASP, s soc s seasonally adjused proporonal spread n wee ; OWN,m, ( OWN,, ) s a dummy varable ha s equal o one f and only f m, (, ) s less han zero. m, s he wee reurn on he CSP value-weghed ndex; and, s he dosyncrac reurn on soc n wee. For ease of exposon, we repor he coeffcens for he combned mare (and dosyncrac) reurn dummes n wees -3 and -. The conrol varables are defned n Table II. The Δ operaor represens he frs-order dfference of he correspondng varables. Panel B uses he followng regresson specfcaon: ΔASP, = α ω1 1 OWN LAGE,, m, = OWN LAGE, m, θ1 1 OWN LAGE,,, = OWN LAGE,, ω2 1 OWN SMALL,, m, = OWN SMALL, m, θ 2 1 OWN SMALL,,, = OWN SMALL,, conrol varables ε where OWN LAGE,m, ( OWN LAGE,, ) s a dummy varable ha s equal o one f and only f m, (, ) s more han 1.5 sandard devaons below s uncondonal mean, and OWN SMALL,m, ( OWN SMALL,, ) s a dummy varable ha s equal o one f and only f m, (, ) s beween zero and -1.5 sandard devaons below s uncondonal mean. Cross-seconal means (-sascs), medans, and percenage of sgnfcan coeffcen esmaes a he 5% level (one-aled) are repored below.,, Panel A: Spreads and Lagged eurn ummes Esmaed Coeffcens own,m,-1 own,m,-2 own,m,-3~- own,,-1 own,,-2 own,,-3~- Mean 0.028 0.012 0.005 0.00 0.018 0.010 (-sascs) (11.70) (.89) (2.12) (16.59) (7.9) (.33) Medan 0.017 0.007 0.002 0.026 0.011 0.006 %posve(negave) 96.2% 80.0% 62.% 97.9% 86.8% 75.7% %posve(negave) sgnfcan 62.5% 20.% 8.1% 80.5% 3.7% 17.1% 8

Panel B: Spreads and Large/Small Lagged eurn ummes Esmaed Coeffcens ownlarge,m,-1 ownlarge,m,-2 ownlarge,m,-3~- ownlarge,,-1 ownlarge,,-2 ownlarge,,-3~- Mean 0.062 0.021-0.003 0.088 0.032 0.018 (-sascs) (12.59) (.32) (-0.70) (15.11) (5.55) (3.12) Medan 0.036 0.010-0.00 0.053 0.017 0.007 %posve(negave) 95.3% 71.% (61.0%) 95.% 77.8% 6.9% %posve(negave) sgnfcan 63.1% 15.3% (8.6%) 71.5% 25.8% 1.5% Esmaed Coeffcens ownsmall,m,-1 ownsmall,m,-2 ownsmall,m,-3~- ownsmall,,-1 ownsmall,,-2 ownsmall,,-3~- Mean 0.022 0.010 0.007 0.036 0.017 0.009 (-sascs) (9.15) (.27) (2.6) (15.08) (7.03) (3.77) Medan 0.01 0.006 0.003 0.023 0.010 0.005 %posve(negave) 91.9% 77.3% 66.9% 96.9% 85.7% 73.8% %posve(negave) sgnfcan 7.5% 18.6% 8.9% 7.1% 32.6% 15.5% 9

Table IA.BV Spreads and Msperceved Volaly Weely changes n adjused spreads for each secury are regressed on lagged mare reurns and dosyncrac soc reurn and msperceved volaly (MsST) as defned n eusar (2007): Δ 1 1 1 ASP, = α β conrol varables,, 1, m, γ 1,, ΔMsST 0 m, ΔST 0 m, ΔST 0, ε = = = = = where ASP, refers o soc s seasonally adjused daly proporonal quoed spread averaged across all radng days n wee ; m, s he wee reurn on he CSP value-weghed ndex;, s he dosyncrac reurn on soc n wee, where dosyncrac soc reurns are calculaed as ndvdual soc reurns mnus mare reurns; ST m, s he volaly of he mare reurn n wee ; and ST, s he volaly of soc s dosyncrac reurns n wee. Oher conrol varables are defned n equaon (2) n he ex. The Δ operaor represens he frs-order dfference of he correspondng varables. Esmae Sascs m,-1 m,-2 m,-3 m,-,-1,-2,-3,- Mean -0.990-0.505-0.236-0.151-0.58-0.312-0.191-0.099 (-sascs) (-18.12) (-10.19) (-.83) (-3.11) (-29.79) (-15.82) (-9.68) (-5.05) Medan -0.70-0.33-0.130-0.073-0.6-0.233-0.137-0.059 % posve (negave) (96.0%) (86.6%) (67.%) (61.9%) (98.2%) (93.0%) (82.7%) (69.7%) % posve (negave) (66.%) (37.1%) (13.1%) (10.0%) (86.%) (56.5%) (32.8%) (1.3%) sgnfcan Esmae Sascs ΔST m,-1 ΔST,-1 ΔST m, ΔST, ΔMsST,-1 ΔMsST, ΔTurn,-1 ΔOIB,-1 Mean 0.311 0.27 0.280 0.21 0.90 0.55-0.02 0.007 (-sascs) (2.21) (3.55) (7.06) (10.00) (5.51) (11.00) (-.0) (0.71) Medan 0.263 0.168 0.20 0.193 0.619 0.33-0.013 0.006 % posve (negave) 6.3% 66.9% 78.7% 8.7% 75.% 87.2% (75.%) 5.0% % posve (negave) 12.9% 13.8% 25.2% 0.7% 36.9% 5.8% (19.%) 8.1% sgnfcan 10

Table IA.BVI Proporon of Small Trades and Mare eurns Weely changes n percenage of small rades for each secury (ΔSmallTrade%, ) are regressed on lagged mare and dosyncrac soc reurns: ΔSmallTrade %, = α γ 1 UP,, =, β 1 UP,, m, = UP, m, UP,, β 1 OWN,, m, = OWN, m, γ 1 OWN,,, = OWN,, conrol varables where SmallTrade%, s he number of small rades, defned as he rade whose sze s below $5000, dvded by he oal number of rades for soc n wee ; m, s he wee reurn on he CSP value-weghed ndex; and, s he dosyncrac reurn on soc n wee. The neracon dummy varable UP,m, ( UP,, ) aes he value of one f and only f m, (, ) s greaer han zero, and he neracon dummy varable OWN,m, ( OWN,, ) aes he value of one f and only f m, (, ) s less han zero. The conrol varables are defned n Table II. The Δ operaor represens he frs-order dfference of he correspondng varables. Cross-seconal means (-sascs), medans, and percenage of sgnfcan coeffcen esmaes a he 5% level (one-aled) are repored below. ε,, Esmaed Coeffcens m,-1 Up,m,-1 m,-2 Up,m,-2 m,-3 Up,m,-3 m,- Up,m,- Mean 0.073 0.0 0.02 0.01 (-sascs) (3.5) (2.32) (2.25) (2.25) Medan 0.02 0.026 0.026 0.008 % posve (negave) 56.3% 5.% 5.9% 52.2% % posve (negave) sgnfcan 9.2% 6.7% 6.% 5.2% Esmaed Coeffcens m,-1 own,m,-1 m,-2 own,m,-2 m,-3 own,m,-3 m,- own,m,- Mean 0.011 0.013 0.008 0.019 (-sascs) (0.58) (0.69) (0.3) (1.02) Medan 0.001 0.000 0.000 0.000 % posve (negave) 50.1% 9.% 7.7% 7.3% % posve (negave) sgnfcan 5.3% 5.0%.5%.2% 11

Table IA.BVII Spreads, Mare eurns, and Impac of he Fundng Mare Proxes Weely changes n he adjused spreads for each secury (ΔASP, ) are regressed on sgned lagged mare reurns wh an neracon dummy varable CAP, ha s equal o one when he fundng mare s lely o face capal consrans n wee : ΔASP, = β m m OWN OWN m OWN CAP m OWN m 1,, β 1,,,,, β 1,,,,,, CAP, conrol varables = = = α ε. All oher varables are defned n Table II. In Panel A, CAP, s equal o one when he excess reurn on a porfolo of nvesmen bans n wee s negave. CAP, n Panel B s equal o one when here s a decrease n he aggregae repos n wee. Fnally, when here s an ncrease n he commercal paper spread, we assgn a value of one o CAP, n Panel C. For ease of exposon, we repor he coeffcens for he combned mare (and dosyncrac) reurns and fundng mare consran dummes n wees -3 and -. Cross-seconal means (-sascs), medans, and percenage of sgnfcan coeffcen esmaes a he 5% level (one-aled) are repored below., Esmaed Coeffcens m,-1 m,-2 m,-3~- Panel A: Invesmen Ban & Broer Secor eurns m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 m,-2 own,m,-2 CAP,-2 m, -3~- own,m,-3~- CAP,-3~- Mean -0.22-0.326-0.226-0.658-0.052 0.15-0.302 0.009 0.087 (-sascs) (-.23) (-3.67) (-3.66) (-3.75) (-0.33) (1.26) (-2.26) (0.07) (0.83) Medan -0.215-0.193-0.118-0.35 0.030 0.119-0.153-0.002 0.015 % posve (negave) (73.8%) (73.6%) (70.5%) (72.0%) (7.9%) 62.6% (61.5%) 9.7% 51.9% % posve (negave) sgnfcan (15.0%) (15.%) (11.8%) (1.9%) (.6%) 8.6% (10.6%).6% 7.1% 12

Panel B: Change n epos Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 m,-2 own,m,-2 CAP,-2 m, -3~- own,m,-3~- CAP,-3~- Mean -0.50-0.33-0.21-0.90-0.176 0.156-0.655 0.307 0.138 (-sascs) (-.61) (-3.8) (-3.5) (-2.8) (-1.1) (1.35) (-.83) (2.26) (1.32) Medan -0.29-0.196-0.11-0.22-0.0 0.111-0.367 0.191 0.070 % posve (negave) (76.0%) (7.2%) (69.3%) (66.6%) (53.5%) 62.3% (7.9%) 66.2% 58.5% % posve (negave) sgnfcan (16.3%) (15.9%) (11.0%) (9.7%) (6.2%) 6.8% (20.2%) 11.0% 6.8% Panel C: Commercal Paper Spread Esmaed Coeffcens m,-1 m,-2 m,-3~- m,-1 own,m,-1 m,-2 own,m,-2 m,-3~- own,m,-3~- m,-1 own,m,-1 CAP,-1 m,-2 own,m,-2 CAP,-2 m, -3~- own,m,-3~- CAP,-3~- Mean -0.3-0.323-0.219-0.500-0.17 0.22-0.58 0.137-0.062 (-sascs) (-.35) (-3.61) (-3.56) (-2.62) (-0.8) (1.85) (-3.0) (1.01) (-0.57) Medan -0.239-0.187-0.113-0.25-0.032 0.137-0.263 0.073 0.00 % posve (negave) (7.7%) (73.%) (70.0%) (65.3%) (52.%) 62.2% (71.7%) 57.0% (9.2%) % posve (negave) sgnfcan (15.7%) (1.5%) (11.3%) (8.5%) (6.0%) 7.7% (1.1%) 6.6% (5.1%) 13

Table IA.BVIII Conraran Profs, Mare eurns, and Lqudy Commonaly Weely soc reurns are sored no wnner (loser) porfolos f he reurns are above (below) he medan of all posve (negave) reurns n wee. The conraran porfolo wegh for soc n wee s gven by Np wp,, = (, 1Turn, 1) / = Turn, where 1, 1, 1, and Turn, are soc s reurn and urnover n wee. We repor conraran profs condonal on mare reurns and lqudy commonaly. Large Up (Large own) refers o cumulave mare reurns from wee - o -1 beng more han 1.5 sandard devaons above (below) he mean. Small Up (Small own) mare refers o cumulave mare reurns beween zero and 1.5 (-1.5) sandard devaons. We furher spl he sample based on wheher lqudy commonaly s above (below) he medan. Porfolo Wee 1 Pas Mare eurn: Large Up Small Up Small own Large own Lqudy Commonaly: Lqudy Commonaly: Lqudy Commonaly: Lqudy Commonaly: Hgh Low Hgh Low Hgh Low Hgh Low loser 1.0% -0.33% 0.97% 0.69% 0.39% 0.56% 1.99% 0.7% wnner 0.67% -0.87% 0.52% 0.06% -0.06% -0.02% 0.27% 0.11% loser-wnner 0.73% 0.55% 0.% 0.63% 0.5% 0.58% 1.73% 0.6% (-sasc) (0.98) (0.56) (2.09) (3.71) (1.21) (3.0) (3.16) (1.0) Porfolo Wee 2 Pas Mare eurn: Large Up Small Up Small own Large own Lqudy Commonaly: Lqudy Commonaly: Lqudy Commonaly: Lqudy Commonaly: Hgh Low Hgh Low Hgh Low Hgh Low loser 1.07% 0.65% 0.55% 0.33% 0.13% 0.28% 1.76% 0.17% wnner 0.87% -0.01% 0.53% 0.28% -0.10% 0.27% 0.5% -0.32% loser-wnner 0.19% 0.66% 0.02% 0.05% 0.23% 0.01% 1.31% 0.9% (-sasc) (0.1) (1.28) (0.13) (0.35) (1.08) (0.06) (1.91) (0.70) 1

EFEENCES Engle, ober, 2002, ynamc condonal correlaon: A smple class of mulvarae generalzed auoregressve condonal heerosedascy models, Journal of Busness & Economc Sascs 20, 339-350. uesar, Prach, 2007, Exrapolave expecaons: Implcaons for volaly and lqudy, Worng paper, Unversy of Illnos a Urbana-Champagn 15