Do Stock Exchanges Corral Investors into Herding?

Transcription

1 Do Sock Exchanges Corral Invesors no Herdng? Adya Kaul Unversy of Albera Edmonon, Canada T6G 2R6 Vkas Mehrora Unversy of Albera Edmonon, Canada T6G 2R6 Carmen Sefanescu Unversy of Albera Edmonon, Canada T6G 2R6 Verson: Sep 19, 2007 J.E.L. Classfcaon Codes: G10: General Fnancal Markes G12: Asse Prcng G14: Informaon and Marke Effcency

2 Do Sock Exchanges Corral Invesors no Herdng? Absrac We sudy wheher sock exchanges nduce herdng by examnng a sample of frms ha swch from DAQ o he E. Whle rades on an exchange may dsplay herd-lke behavor due o comovemen wh aggregae varables, we argue ha a change n lsng venue s largely free of conemporaneous changes n such comovemen, and allows us o solae he nfluence of he lsng locale on radng behavor. Our ess reveal ha, followng he swch, rades for he swchng frms commove more srongly wh E rades and less srongly wh DAQ rades, suggesng ha nvesors n frms lsed on eher he E or DAQ ac as herds. A smlar paern s found for sock reurns, wh approxmaely one-half of he change n reurn comovemen followng he swch o he E explaned by he change n radng comovemen. The resuls are no drven by changes n asse characerscs or fundamenal cash flows, nor can hey be explaned away by dfferenal speeds of nformaon ncorporaon on he wo exchanges. Overall, hese paerns are no easly reconclable wh raonal models of herdng; nsead, hey appear more conssen wh a haba vew of comovemen proposed by Barbers, Shlefer and Wurgler (2005).

3 1 Inroducon Despe he cenral poson of sock exchanges n he radng process, her nfluence on radng and prce formaon has no been fully explored. In hs paper, we conduc a seres of ess o sudy he exen o whch a sock exchange nduces herdng behavor among s lsed frms. We address hs queson by examnng changes n he comovemen of rades and reurns for a sample of frms ha swch from DAQ o he E. Herdng behavor has been examned exensvely n he areas of economcs 1, physcs 2, and socology 3. As one would expec wh nqures ha span several dscplnes, here s no clear consensus on he defnon of herdng n he leraure; however, ceran common hemes emerge. Frs, herdng s usually defned n erms of crowd behavor ha s, a group s defned as a herd f members of ha group end o move more srongly wh each oher han wh he collecve movemen of oher groups. Second, herdng can be based on fundamenals or herdng can be faddsh. In he former case, mperfecly raonal agens deduce nformaon from he behavor of oher agens n he herd perhaps because of he addonal cos of obanng or verfyng nformaon from ousde he herd. Herdng can be based on fads f agens behave rraonally and lms o arbrage preven prces from rapdly convergng o fundamenal values. Even raonal nformed agens may decde o rde he fad when fundamenal nformaon and/or arbrage are cosly. In hs paper, we ake a somewha agnosc vew of he naure of herdng on a sock exchange, 1 See Hrshlefer and Teoh (2001) for an excellen synhess of he leraure on herdng based on nformaon economcs. For a behavoral economcs slan, see Shller (1989). 2 See, among ohers, Sornee (2003a, 2003b), Sornee and Andersen (2002), and Lux and Sornee (2002), for models of herdng based on wha s somemes referred o as econophyscs. These models mmc he behavor of cluserng n non-lvng objecs. 3 See Parker and Precher (2001) for a soconomc perspecve on herdng.

4 Page 2 and leave s resoluon o fuure work. The more modes objecves n hs paper are o look for he presence of herdng on a sock exchange, and o sudy s mpac on reurns. Raonal models of asse prcng canno easly explan why a swch n lsng venue should brng abou a change n radng comovemen. In fac, an advanage of examnng swches n exchange lsngs s ha we can crcumven he queson of he exen of herdng pror o or even followng he even, and focus nsead on he change n herdng nduced by he swch o he new exchange. For example, whle s dffcul o say wheher rades n our sample frms dsplay raonal or excessve comovemen wh DAQ or E rades before he swch o he E, we can measure he change n comovemen wh DAQ and E rades afer he swch. Moreover, asse characerscs, cash flow comovemen, and he rae of nformaon ncorporaon for he swchng frms reman unchanged, mplyng ha he change of lsng venue does no concde wh real changes n frm characerscs. We also do no fnd maeral changes n aggregae nsuonal ownershp for he swchng socks. Over a perod of welve years beween 1988 and 2000, we examne a oal of 536 frms ha swch from DAQ o he E. Usng order mbalance o measure ne radng acvy, we fnd ha afer movng o he E, frms sar o rade more n sync wh E-lsed frms and less so wh he frms hey leave behnd on DAQ. Trades for a conrol sample of frms ha qualfy o ls on he E bu choose o reman on DAQ do no dsplay a change n comovemen wh eher DAQ or E rades. The conrasng paerns n he comovemen of rades for he swchng and conrol samples ndcaes ha he resuls for he swchng frms are unlkely o be drven by rends or by jumps n comovemen concden wh he swch. The fac ha he wo samples share smlar characerscs allays concerns ha he swchng frms are smlar o E socks and hs smlary accouns for common nfluences on rades.

5 Page 3 I s possble ha he change n radng comovemen, alhough sascally sgnfcan, s of lle economc consequence. To look no hs ssue, we examne changes n reurn comovemen for he swchng frms, and fnd smlar paerns. Tha s, reurns for he sample frms co-move more wh reurns for oher E frms, and less wh reurns for DAQ frms. Approxmaely half of he change n reurn comovemen for he swchng frms s assocaed wh he change n radng comovemen. The conrol sample of frms ha do no swch exchanges shows no changes n reurn comovemen. Overall, hese resuls suppor he noon ha sock exchanges provde a naural herdng envronmen, and are bes undersood n he conex of sudes documenng excess reurn comovemen n a varey of sengs. 4 Closed-end counry fund reurns commove more srongly wh her lsng marke cohors raher han wh her home counry socks. Socks n he S&P 500 ndex commove more wh oher ndex socks han wh socks ousde he ndex. As Pndyck and Roemberg (1993) pon ou, s dffcul o reconcle such excess comovemen wh sandard asse prcng models relyng on comovemen of fundamenals wh aggregae macroeconomc varables. Behavoral models of excess comovemen, n parcular work by Barbers, Shlefer and Wurgler (2005, hereafer BSW), do explan such herdng behavor. BSW ls hree behavoral explanaons for hs phenomenon. Frs, nvesors sor asses no broad caegores, and allocae funds a he level of hese caegores (and no drecly a he ndvdual secury level). Second, hey place her rades n known habas, perhaps because of ransacon coss, radng resrcons or lack of nformaon. Las, dfferenal speeds of nformaon ncorporaon see some socks 4 A paral ls of recen papers n hs area ncludes Pndyck and Roemberg (1993), Rashes (2001), Feng and Seasholes (2004), Barbers, Shlefer and Wurgler (2005), Kumar and Lee (2006), and Greenwood (2006).

6 Page 4 parsng nformaon more quckly no her prces han ohers, e.g. due o marke frcons. In each case, coordnaed demand drven by correlaed senmen or nformaon arrval nduces reurn comovemen for groups of asses, generang classc herdng behavor. The res of he paper s organzed as follows. In Secon 2 we descrbe he daa and our sample. In Secon 3, we examne changes n he comovemen of rades followng he swch o he E. Secon 4 documens he change n reurn comovemen around he swch. Secon 5 sudes changes n fundamenal (cash-flow) comovemen. In Secon 6, we consder alernave explanaons for our fndngs. Secon 7 concludes. 2 Sample and daa The ess n he paper focus on a sample of 536 DAQ common socks ha move o he E beween January 1988 and December Ths perod corresponds o he avalably of nraday daa from he ISSM and TAQ daabases. Usng he Cener of Research n Secury Prces (CRSP) fles, we selec all frms ha swch her lsng venue from DAQ o he E over hs 13-year perod. We examne ordnary common shares (share code 10 and share code 11) and exclude non-u.s. frms, real esae nvesmen russ, and closed-end funds. Frms are requred o have CRSP prce daa and nraday rade and quoe daa for one year before and one year afer he swchng dae, as well as daa from COMPUSTAT. Usng daly reurn daa, we also sudy a broader sample of 1030 DAQ frms ha swch o he E beween January 1973 and December Our purpose s o solae he excess comovemen n radng and reurns surroundng he

7 Page 5 exchange swchng even. However, s possble ha nvesors are drawn o frms of a ceran sze or ndusry for reasons unrelaed o he swch. For nsance, s possble for he reurns of all DAQ socks o dsplay greaer comovemen wh overall E reurns f he markes have become more negraed over me or E reurns capure economc prospecs more closely han do DAQ reurns. To solae he effec of he exchange swch on comovemen, we form a sze, prce and ndusry-mached conrol sample of frms ha reman on DAQ. The machng procedure follows Huang and Soll (1996). In addon o conrollng for ndusry, share prce, and sze, he machng by swchng dae provdes a naural conrol for rends n radng. Fgure 1 shows he breakdown by year of he full sample of 1030 frms ha swch o he E beween 1973 and More frms swch o he E durng he 1990s han he 1980s, wh peak acvy beween 1995 and There are fewer swches n 2000, smlar o he frs par of he 1980s, bu he number of swches ncreases somewha n 2001 and The dsrbuon of swches o he E across monhs s farly sable, wh he excepon of December, whch sees a slghly hgher frequency of swches. Summary sascs for he es and conrol samples are presened n Table 1. The fnancal varables are measured as of he fscal year end pror o he move o he E, whle share urnover s measured over he welve monhs endng wo monhs before he move. The mean and medan value of marke-o-book are smlar for he es and conrol frms o he exen hs rao s a proxy for nvesmen polcy, he swchng and conrol frms do no appear o be sgnfcanly dfferen. Turnover, a measure of radng acvy, s lkewse smlar for he wo samples. Book asses and marke equy are slghly hgher for he es frms. Overall, he smlary of he values n Table 1 for he es and conrol samples ndcaes ha our machng procedure works well, and

8 Page 6 ncreases our confdence ha any dfference n comovemen s due o he swch n radng venue. 3 Herdng n rades We sar by examnng changes n he comovemen of rades for he swchng frm wh DAQ rades and E rades. Suppose ha a sock, prevously lsed on DAQ, sars radng on he E. In he absence of herdng on eher exchange, he comovemen of he sock s rades wh aggregae E and DAQ rades should be unalered by he swch. However, f exchanges nduce local herdng, he frm s rades wll commove more wh E rades and less wh DAQ rades followng he swch. A wdely-used measure of radng acvy s order mbalance. Ths s calculaed as he dfference beween buyng and sellng volume. 5 Buys and sells are denfed usng he Lee- Ready (1991) algorhm. Before applyng hs algorhm o he ransacon daa, we frs exclude rades wh negave prces, rades repored ou of sequence, rades recorded before he open or afer he close, and rades wh specal selemen condons. Quoes ha mply a negave spread are dscarded as well. The Lee-Ready algorhm uses he frs quoe a leas fve seconds before each rade o classfy he rade, wh a ransacon occurrng above (below) he prevalng quoe mdpon regarded as a purchase (sale). If a ransacon occurs exacly a he quoe mdpon, s sgned usng he las non-zero ransacon prce change, as a buy f hs prce change s posve and a sell f s negave. By convenon, a buy s assgned a posve sgn and a sell a negave sgn. 5 Two oher measures of order mbalance are based on he number of rades and he value of rades. We repea our analyss usng hese measures and arrve a dencal conclusons.

9 Page 7 For each sock, we calculae order mbalance as he dfference beween he volume of all buys and he volume of all sells n 15-mnue nervals hroughou he day,.e. 9:30-9:45 a.m., 9:45-10:00 a.m.,..3:45-4:00 p.m. Where relevan, we aggregae order mbalance o he daly or weekly level. We sandardze order mbalance for each nerval (15-mnue, daly or weekly) by oal volume over ha nerval o make comparable across socks and hrough me. Thus, we sudy he fraconal order mbalance a nraday, daly and weekly frequences. The aggregae E or DAQ mbalance s compued as he smple (equally-weghed) average of he mbalances for all ordnary shares radng n each marke. In complng hese averages we exclude he order mbalance boh of he frm whose mbalance s he dependen varable and of s ndusry. Ths reduces he lkelhood of our mechancally fndng an assocaon beween order mbalance for he swchng sock and E and DAQ mbalances. We hen esmae he followng specfcaon (1) for order mbalance (OF) for he swchng socks as well as for he conrol socks. OF, = α 0 + α D ( φ + ( φ + θ + θ + γ + γ D ) OF D ) OF ε, (1) Here, φ s he rue comovemen of sock wh he marke, and θ s he excess comovemen before he swch o he E. Afer he swch (D=1), he change n comovemen s represened by γ. To undersand how we use he model above, assume for he momen ha he rue comovemen parameers (φ and φ ) do no change followng he swch o he E. For smplcy, we se β = φ + θ, and se β = γ and esmae equaon 2. If β and β are boh zero, we canno say anyhng abou wheher or no socks on he E or DAQ dsplay herdng. However, f we fnd ha β >0 and β <0, we can conclude ha eher here s

10 Page 8 excess comovemen on E afer he swch, or here was excess comovemen on DAQ before he swch. OF, = α 0 + α D β + Δβ OF OF + β D 1 + Δβ OF OF D 1 + ε, (2) In (2), OF, s he mbalance for sock on day, OF and OF are aggregae mbalances for he E and DAQ and D 1 s a dummy ha s one afer he swch dae for sock and zero oherwse. In hs specfcaon, β and β measure he base, pre-swch level of comovemen wh E and DAQ mbalances. Smlarly, Δβ and Δ β, he coeffcens on he neracon erms, measure he change n comovemen wh E and DAQ mbalances followng he swch. Our man neres les n he laer coeffcens. In he absence of herdng, hese coeffcens wll be zero; oherwse, posve and Δ β s expeced o be DAQ Δ β negave. The model s esmaed usng order mbalance daa from day o day +300, measured relave o he dae of he swch. We exclude days (-50, +50) around he even o remove any effecs relaed o he acual swch. Thus, D 1 =1 for radng days (+51, 300), and D 1 =0 for day ( 300, 51). Table 2 presens he resuls for order mbalance measured over nraday (15-mnue), daly and weekly wndows. A each frequency, swchng socks see a sharp ncrease n he comovemen of her order mbalance wh he E mbalance and a large reducon n he comovemen wh he DAQ mbalance. For he 15-mnue mbalance, he mean slope on he E mbalance ncreases from 0.10 o 0.53 afer he swch, whle he slope on he DAQ mbalance drops from 0.82 o Lookng a he daly mbalance, he mean E slope

11 Page 9 ncreases from 0.27 o 0.84 whle he mean DAQ slope declnes from 0.64 o The mean E slope n he weekly mbalance regresson rses from 0.40 o 0.80 and he DAQ slope drops from 0.47 o These resuls show ha rades for socks swchng o he E dsplay srong conemporaneous herdng wh E rades, and an equally srong decouplng wh DAQ rades. I s possble ha he E lsng crera separae he swchng socks from hose hey leave behnd on DAQ. To address hs ssue, we re-esmae (2) for he conrol sample of socks ha qualfy o ls on he E, bu choose no o. The coeffcens measurng he changes n order mbalance comovemen wh E and DAQ order mbalance are never sgnfcan a convenonal levels, and are sgnfcanly smaller han he coeffcens for he swchng sample. 6 The magnudes of he conrol sample means are usually no more han oneenh as large as hose for he swchng socks. The posve slope on he E order mbalance suggess ha, even hough comovemen wh he DAQ mbalance s apprecably sronger for DAQ socks, he mbalance also commoves wh he aggregae E mbalance. Ths s conssen wh broad U.S. marke-based comovemen. The resuls n able 2 show ha an exchange lsng by self nduces comovemen n rades. Before a sock swches o he E, s order mbalance commoves srongly wh he DAQ order mbalance and relavely weakly wh ha on he E. Afer he swch, comovemen wh he DAQ mbalance shrnks apprecably whle ha wh he E mbalance ncreases dramacally. In he nex secon, we examne wheher a smlar paern exss for reurns, and measure he exen o whch shfs n he comovemen n mbalances nduce shfs n reurn 6 The only excepon s he change n he DAQ slope a he weekly horzon. Even here, he conrol sample mean s less han 50% as large as he swchng sample mean.

12 Page 10 comovemen. 4 Herdng n Reurns We have shown ha comovemen n mbalances changes dramacally followng he swch o he E. The economc mporance of hs change n comovemen ress on he exen o whch nduces comovemen n reurns. To he exen ha mbalances affec prces (e.g. due o downward-slopng demand curves), we should see a smlar paern n reurn comovemen. On he oher hand, reurns are nosy, and mgh be more dffcul o deec he races of order mbalances n reurns. We examne reurn comovemen va an analogous regresson o (2). R, = α 0 + α 1D1 + β R + β R + Δβ R D1 + Δβ R D1 + ε, (3), where R, s he reurn on day for sock, R and R are he day equally-weghed E and DAQ reurns (excludng R, ), and D1 s as before, a dummy varable ha s one afer he swch dae for sock and zero oherwse. As n (2), β and β measure he base, preswch levels of reurn comovemen wh E and DAQ reurns, whle Δβ and Δ β measure he change n comovemen wh E and DAQ reurns followng he swch. If he swch n exchanges nduces a change n reurn comovemen, we expec o see Δβ > 0 and Δβ < 0. We esmae (3) usng daly reurn daa from day -300 o day +300, measured relave o he dae of he swch, and, as n (2), exclude days (-50, +50). Table 3 presens he mean coeffcen esmaes from hs model esmaed for daly reurns, and -ess of he null hypohess ha he mean s sgnfcanly dfferen from zero. The perod

13 Page 11 ha we sudy n deal, , sees an average ncrease n he daly E bea of 0.14 (sasc of 3.70) and an average declne of 0.20 (-sasc of 5.3) n he daly DAQ bea for he swchng socks. Tha s, a sock movng from DAQ o he E sees s E bea rse from 0.72 o 0.86 and s DAQ bea declne from 0.51 o When we break he perod no subperods ( and ), smlar paerns emerge. The mean E bea shf s sgnfcanly above zero n boh sub-perods and slghly larger n he second sub-perod; he mean DAQ bea shf s negave and sgnfcan n boh sub-perods and slghly more so n he frs sub-perod. Over hs perod, we measure he shf n beas for he sample of conrol socks. The mean shf n he E bea (-0.03) and n he DAQ bea (-0.03) are nsgnfcanly dfferen from zero for he perod, nor are hese coeffcens sgnfcan n eher sub-perod. 7 The dfference beween he es and conrol sample means s always large n economc erms, and usually sgnfcan a beer han he 5% level. The fac ha comovemen s unchanged for he mached sample of conrol frms suggess ha frm characerscs and ndusry afflaon are unlkely o be drvng he changes n comovemen wh DAQ and E marke reurns documened for he swchng sample. We also esmae (3) for frms ha swch o he E over an exended perod, (we do no consruc a conrol sample for hs perod). Snce DAQ was formed n 1973, hs s he longes possble perod of sudy. The effecs are smlar o hose observed n he perod. Swchng socks see her E bea ncrease by 0.16, on average (-sasc of 6.2), whle he mean DAQ bea declnes by 0.25 (-sasc = -8.6). Thus, he paerns observed n he shorer sample perod are also presen n a subsanally longer perod, one ha predaes 7 Unabulaed resuls based on medans nsead of means are dencal.

14 Page 12 elecronc ransacons, sgnfcan ndvdual nvesor parcpaon as well as he echnology bubble, and ncludes more vared economc condons. Ths suggess ha he documened effecs have perssed hrough me. One concern wh hese resuls s ha he swch concdes wh oher evens ha nduce comovemen. I s parcularly mporan o rule ou ndex addons, gven BSW s evdence ha socks added o he S&P 500 see ncreased beas on he S&P ndex reurn. We exclude he 13 socks added o he S&P 500 ndex n he one year afer he swch and repea he above analyss. We fnd ha he mean E and DAQ bea shfs for he remanng socks are smlar o hose n he full sample, and are hghly sgnfcan. As a resul, he change n comovemen s no mechancally drven by he ncluson n he S&P 500 ndex of a few of he socks n our es sample. In order o assess he srengh of herdng a dfferen frequences, we examne nraday and weekly reurn comovemen. Snce herdng or fad-nduced prcng errors should be correced over me, comovemen s expeced o be sronger a hgher frequences. Ths analyss s carred ou over he perod We proceed by calculang 15-mnue md-quoe reurns (usng ISSM and TAQ daa) for he swchng frms and all ordnary common shares on he E and DAQ, and hen re-esmang model (3) usng he 15-mnue reurns. Weekly and daly reurns are smlarly compued usng quoe mdpons. The E and DAQ marke reurns n hs regresson are consruced as equally-weghed averages of he reurns o all consuen socks. We exclude all frms belongng o he ndusry of he swchng (or conrol) frm from he aggregae E and DAQ reurns. 8 If a swchng frm s ndusry has greaer represenaon on he E, ncreased reurn comovemen wh he E could be enrely 8 Ths s accomplshed by defnng wo-dg ndusres for all socks, followng Lewellen (2002).

15 Page 13 raonal (drven by common news abou ndusry cash flows or rsks). Droppng he ndusry of he swchng frm elmnaes hs source of comovemen. As before, we exclude he swchng (or conrol) frm s reurns from he E and DAQ marke reurns. Table 4 presens he resuls. Panel B shows ha he mean change n he daly E (DAQ) bea for he es sample s 0.15 (-0.20), very smlar o he values n Panel B of Table 4, ha correspond o he same perod bu wh ndusry reurns no excluded from he aggregae E and DAQ reurns. The conrol sample bea changes are also smlar o hose repored n Table 4. Panel A conans he nraday (15-mnue) beas. The changes n he nraday beas for he es sample, 0.19 for he E bea and for he DAQ bea, are more pronounced han he changes n he daly beas. The larger values of he nraday bea shfs are conssen wh he noon ha hgh frequency prces are more suscepble o herdng effecs. Panel C conans he resuls a he weekly frequency. The mean change n he weekly E bea for he es sample s 0.21, slghly larger han he change n he nraday bea, whle he mean change n he weekly DAQ bea s -0.23, half as large as he nraday bea shf and comparable o he change n he daly bea. None of he bea shfs for he conrol sample s sascally sgnfcan or large n economc erms. Noe ha he bea shfs do no become smaller (n absolue value) as he reurn measuremen horzon changes from nraday o daly o weekly. Thus, our evdence suggess ha herdngnduced prcng errors ake longer han one week o be correced. 9 Mos mporan, here s a srkng shf n reurn comovemen for frms ha swch from DAQ o he E a every frequency, wh reurns becomng more sensve o aggregae E reurns and less sensve o aggregae DAQ reurns. 9 Snce we only examne one year before and afer he swch, we do no repea he analyss usng monhly reurns.

16 Page 14 To hs pon, our resuls have no zeroed n on he change n comovemen around he exac dae of he swch. A herdng-based explanaon for he nfluence of exchanges on radng and reurns would be more compellng f he bea shf occurs mmedaely afer he swch. Snce s dffcul o precsely esmae beas usng shor me-seres, we esmae daly reurn beas for he swchng socks over non-overlappng sx-monh nervals, measured relave o he monh of he move: [-12,-7], [-6,-1], [+1,+6], [+7,+12]. A wo-monh wndow cenered on he swchng dae s excluded from hs analyss. We should see a sharp ncrease n he E bea and a correspondng declne n he DAQ bea n he hrd wndow relave o he second wndow, and he beas should be relavely sable beween he hrd and fourh and beween he frs and second wndows. The resuls (no abulaed o save space) reveal ha he mean E bea n he four wndows s 0.59, 0.62, 0.74 and 0.75, whle he mean DAQ bea s 0.55, 0.64, 0.43 and T-ess show ha he mean E and DAQ beas over [+1,+6] are sgnfcanly dfferen from he mean beas over [-6,-1], as are he means over [+7,+12] relave o he means over [-12,-7]. However, he mean beas over [+7,+12] are no dfferen from hose over [+1,+6], nor are he beas over [-6,-1] n relaon o hose over [-12,-7]. Thus, he change n he bea s confned o wndows mmedaely adjonng he dae of he swch. Ths suppors he concluson ha he changes n comovemen documened n Table 3 and Table 4 are assocaed wh he exchange swch, raher han reflecng more general rends n comovemen for hese socks. In shor, herdng wh he new locale ncreases, wh a concoman decouplng n relaon o he group of socks lef behnd. Havng denfed srong shfs n comovemen n radng, we now address he queson of how much of he change n reurn comovemen can be explaned by he change n he

17 Page 15 comovemen of order mbalances. We proceed n wo seps. Frs, we compue he resdual reurn for every swchng sock va a regresson of s reurn on s order mbalance. To mnmze he effecs of feedback from reurns o rades, he regresson s run and he resduals compued a he 15-mnue horzon. We also do hs for every ordnary common sock on he E and DAQ, and hereby form an equally-weghed aggregae resdual reurn for each marke. In he second sep, we regress he resdual reurn for he swchng sock on he aggregae resdual reurn for he E and DAQ. As n he earler ables, we esmae hs model usng 15-mnue, daly and weekly resdual reurns. The daly and weekly resdual reurns are consruced by summng he 15-mnue resdual reurns for each sock for he enre day or week. 10 We hen esmae (3) usng resdual reurns. Table 5 conans he resuls. In almos each case, he changes n he E and DAQ slope coeffcens n he resdual reurn regressons are apprecably smaller han he changes n he raw reurn regressons n Table 4. The mean change n he DAQ bea s for nraday reurns, for daly reurns and for weekly reurns. Whle he nraday and daly changes n he DAQ bea are sll sascally sgnfcan, he weekly change s no longer sgnfcan a convenonal levels; addonally, he dfference beween he change n he daly beas for he swchng and conrol samples s only margnally sgnfcan (p-value=0.09). I s of neres o noe ha he magnude of he slope shf s one-quarer o one-half as large as he correspondng slope shf usng raw reurns n Table 4. Resdual comovemen wh he E reurn provdes smlar resuls, he only excepon beng he nraday bea shf of 0.20, whch s smlar o he value n Table 4. By conras, he daly and 10 To oban he E and DAQ resdual reurn seres, we esmae he regressons by sock and year. Noe ha, whle he me-seres average resdual for each sock wll be zero n each year, neher nor he equallyweghed E or DAQ resdual reurn s consraned o be zero n any 15-mnue nerval.

18 Page 16 weekly bea changes, 0.11 and 0.09, are 33% and 60% smaller han hose compued usng raw reurns. An examnaon of he pre-swch beas based on resdual reurns n Table 5 shows ha hese are also lower n almos every case han he correspondng values based on raw reurns n Table 4. Thus, radng can explan a poron of no only he changes n comovemen bu also he levels of comovemen pror o he swch. To our knowledge, he effec of radng on reurn beas has no been documened n he leraure. There are wo mporan messages from hs analyss. Frs, once he comovemen n radng s aken no accoun, he remanng comovemen n reurns s sgnfcanly weaker. Ths suggess ha herdng n rades on he new exchange has non-rval prcng effecs, accounng for almos half of he change n reurn comovemen assocaed wh he swch. Snce behavoral models of excess comovemen are rooed n senmen-based rades, hese resuls can be nerpreed as provdng suppor for frcon or senmen-based models of comovemen. Neverheless, resdual (non-rade) reurns show ncreased comovemen wh resdual E reurns and reduced comovemen wh resdual DAQ reurns followng he swch. Thus, herdng n radng canno enrely explan he shf n reurn comovemen documened n Table 4, and we are lef o conclude ha a leas a poron of reurn herdng arses elsewhere. Below, we nvesgae oher explanaons for he change n reurn comovemen documened n hs secon. 5 Cash flow comovemen Raonal herdng on sock exchanges (for boh rades and reurns) can resul from greaer algnmen of cash flows for he frms ha ls here. In our seng where frms volunarly shf exchanges one reason ha frms mgh swch o he E s ha hey sar o resemble oher

19 Page 17 frms radng here. Perhaps hese frms see ncreased cash flow covarance wh cash flows for oher E frms and hen decde o move. Our earler analyss has aemped o conrol for such effecs by excludng he frm s ndusry from our calculaons, and by provdng resuls for a conrol sample of ndusry, prce and sze-mached frms. Addonally, here s no evdence of a gradual ncrease n reurn comovemen, expeced n an effcen marke where reurns ancpae cash flows. In hs secon, we aemp o formally rule ou changes n cash flow comovemen as he source of our resuls. For each swchng (and conrol) frm, we calculae quarerly cash flow as EBIDT/Asses, and also compue a smlarly defned equally-weghed cash flow across E and DAQ frms. We do hs for he egh quarers (wo years) before, and 12 quarers (hree years) afer, he swch and hen esmae a panel regresson of frm cash flow on E and DAQ cash flow: CF, α 0 + αd, + β CF, + β CF, +Δβ CF, D, +Δβ CF, D, + ε, = (4), wherecf, s he quarerly cash flow for sock, CF, and CF, are he quarerly equallyweghed cash flow ndces for he E and DAQ (calculaed excludng cash flows for frm and s ndusry cohors), D, s a varable equal o 1 for he quarers followng he swchng dae and 0 beforehand. The specfcaon s esmaed wh frm- and year-fxed effecs and apples he Huber-Whe cluserng correcon o he sandard errors. The resuls, presened n Table 6, show ha here s no maeral change n cash flow comovemen. For he es sample, cash flow comovemen wh E cash flow s nsgnfcan boh before and afer he swch. Comovemen wh DAQ cash flow s posve before he swch and declnes aferward, bu neher effec s sascally sgnfcan. The lack of

20 Page 18 sgnfcance of he pre-swch E coeffcen s also of relevance snce cass doub on he dea ha frms move o he E afer seeng ncreased cash flow comovemen wh oher E frms. The paerns for he conrol sample are also no sascally sgnfcan. Overall, we do no fnd any evdence of a change n cash flow comovemen wh he cash flows for socks on eher he old exchange or he new exchange. Ths s a odds wh a fundamenals-based explanaon for he change n comovemen around he swch, and suggess a behavoral explanaon for herdng on exchanges. In he remander of he paper, we look for evdence o suppor hs herdng explanaon. 6 Behavoral explanaons for herdng Herdng can arse f nvesors use heurscs o make broad asse allocaon decsons based on denfable groups, as modeled n BSW. They ls hree behavoral explanaons ha apply o herdng. Frs, nvesors sor asses no broad caegores, and allocae funds a he level of hese caegores (and no drecly a he ndvdual secury level). Second, hey place her rades n known habas, perhaps because of ransacon coss, radng resrcons or lack of nformaon. Las, dfferenal speeds of nformaon dffuson see some socks ncorporang nformaon more quckly no her prces han ohers, e.g. due o marke frcons. In each case, coordnaed demand drven by correlaed senmen or nformaon arrval nduces reurn comovemen for groups of asses. Followng BSW, we can emprcally address he nformaon dffuson hypohess. Ths s essenally a lagged bea (equvalenly, a sum of bea) explanaon. Before he move, a DAQ

21 Page 19 sock reacs slowly o E news, and herefore has posve beas on lagged E reurns, whle he conemporaneous bea s relavely low. Afer he move, he speed of adjusmen o E news ncreases. Consequenly, he lagged beas declne and he conemporaneous bea ncreases. We proceed by adjusng he beas for lags n prce adjusmen. Specfcally, we add fve leads and lags of E and DAQ reurns o specfcaon (3) when examnng daly reurns and one lead and lag when examnng weekly reurns, and nerac hese erms wh he posswch dummy. Under he nformaon dffuson hypohess, we expec he lagged E beas o be posve and he coeffcens on he lagged neracon erms o be negave. Table 7 presens he mean coeffcens on he leadng and lagged E and DAQ reurns. In nerpreng he coeffcens noe ha, for nsance, he row labeled -5 shows he mean coeffcens on he E and DAQ reurns fve days before he sock reurn s measured. The neracon coeffcens on he lagged E reurns are no sgnfcan wh he excepon of he lag 2 coeffcen, whch s posve,.e. of he oppose sgn o ha predced by he nformaon dffuson model. The neracon coeffcens on he leadng E reurns are of mxed sgns wh hose on lead 2 and lead 5 beng sgnfcan. The sum of he conemporaneous and lagged E beas s 0.57 before he swch compared o 0.88 aferwards; smlarly, he sum of he conemporaneous, lead and lagged E beas s 0.55 before he swch and 0.93 aferwards. Lookng a he DAQ beas, he coeffcen on DAQ reurns s negave a lag 2 and s posve a lead 5, bu he overall change n he DAQ bea wh lags and leads s slghly more negave han n Table 4 (-0.29 versus -0.20). Thus, he ncorporaon of leads and lags of E and DAQ reurns does no change he concluson ha he E bea ncreases whle he DAQ bea declnes. A smlar paern s revealed by he weekly reurns.

22 Page 20 There s no evdence of an mprovemen n he speed of adjusmen o he prevous week s E reurn: he change n he lagged bea s 0.04 and nsgnfcanly dfferen from zero. Incorporang leads and lags of daly and weekly marke reurns for he conrol sample, we fnd ha he change n hese beas s nsgnfcan. The resuls of hs analyss ndcae ha socks ha swch o he E do no see an mprovemen n he speed of adjusmen o E news. Ths s a odds wh he nformaon dffuson hypohess. 11 Raher, our resuls appear mos conssen wh a haba model of comovemen. In oher words, he excess comovemen ha we documen here s no based on raonal models of herdng. Raher, we suspec ha sock exchanges hemselves nduce herdng behavor among he lsed frms. Wheher such nducemen reflecs broader asse allocaon heurscs followed by nvesors (as n BSW) s somehng we leave for fuure research. 7 Concludng commens Usng a sample of 536 socks ha swch from DAQ o he E over , we examne he exen o whch exchanges nduce herdng n rades and reurns. We fnd ha once a sock changes s radng locaon, s rades and reurns sar o move more wh hose n he new marke and less wh hose n he old marke. The changes n comovemen are evden over several measuremen wndows, and are no vsble for a mached sample of conrol frms. We are able o exclude he possbly ha he paerns n comovemen are due o changes n 11 I s perhaps no surprsng ha our resuls are less supporve of he nformaon dffuson model han hose n BSW. Ther ndex addon/excluson even s accompaned by a large change n radng acvy, whle our expermen does no see changes n radng acvy on anywhere near he same scale. The effecs on he speed of adjusmen are hus expeced o be mued.

23 Page 21 he comovemen of swchng frm cash flows wh E and DAQ cash flows. Addonally, changes n he speed of response o E or DAQ nformaon do no appear o be responsble for hese changes n comovemen. Our evdence seems mos conssen wh behavoral models of comovemen ha hnge on correlaed, senmen drven radng. In parcular, a plausble nerpreaon of our resuls s ha nvesors vew sock exchanges as separae habas. Thus, when a sock swches from DAQ o he E, sees nvesor radng n concer wh oher socks on he E, and a correspondng decouplng of rades wh hose of DAQ socks. Our fndngs denfy a comovemen effec of sock exchange lsngs hhero unexamned n he leraure, and provde addonal and broader suppor for behavoral models of herdng.

24 Page 22 References Barbers, Ncholas, Andre Shlefer and Jeffrey Wurgler (2005), Comovemen, Journal of Fnancal Economcs, vol. 75, Bodurha, J., Km, D., Lee, C.M. (1995) Closed-end counry funds and U.S. marke senmen, Revew of Fnancal Sudes 8, Devenow, Andrea and Ivo Welch (1996) Raonal Herdng n Fnancal Economcs, European Economc Revew, Vol. 40, Fama, E., French, K., 1995, Sze and book-o-marke facors n earnngs and reurns, Journal of Fnance 50, Feng, Le and Mark Seasholes, Correlaed radng and locaon. Journal of Fnance. Froo, K., Dabora, E., How are sock prces affeced by he locaon of rade? Journal of Fnancal Economcs 53, Graham, John R., 1999, Herdng among Invesmen Newsleers: Theory and Evdence. The Journal of Fnance, 54, Greenwood, R., Sosner, N., 2002, Tradng paerns and excess comovemen of sock reurns. Unpublshed workng paper, Harvard Unversy. Greenwood, Robn (2006), Shor- and long-erm demand curves for socks: heory and evdence on he dynamcs of arbrage, Journal of Fnancal Economcs, Vol. 75, Harrs, L., Gurel, E., 1986, Prce and volume effecs assocaed wh changes n he S&P 500: new evdence for he exsence of prce pressure, Journal of Fnance 41, Hardouvels, G., LaPora, R., Wzman, T., Wha moves he dscoun on counry equy funds? In: Frankel, J. (Ed.), The Inernaonalzaon of Equy Markes. The Unversy of Chcago Press, Chcago. Hrshlefer, Davd and Sew Hong Teoh (2003) Herd Behavour and Cascadng n Capal Markes: A Revew and Synhess, European Fnancal Managemen, Vol. 9, pp Huang, Roger D., and Hans R Soll, 1996, Dealer versus aucon markes: a pared comparson of execuon coss on Nasdaq and E, Journal of Fnancal Economcs, 41, Kadlec, Gregory B. and John J. McConnell, 1994, The effec of marke segmenaon and llqudy on asse prces: evdence from exchange lsngs, Journal of Fnance, XLIX (2). Kalay, A., Pornaguna, E., 2001, Swmmng agans he des: he case of Aeroflex move from E o DAQ, Journal of Fnancal Markes, 4, Kaul, A., Mehrora, V., Morck, R., 2000, Demand curves for socks do slope down: new evdence from an ndex weghs adjusmen, Journal of Fnance 55,

25 Page 23 Kumar, Alok and Charles M. Lee, 2006, Real Senmen and Reurn Comovemens, Journal of Fnance, 61(5), Lee, Charles, Mark Ready, 1991, Inferrng Trade Drecon from Inraday Daa, Journal of Fnance, Vol. 46, No. 2, Lee, C., Shlefer, A., Thaler, R., 1991, Invesor senmen and he closed-end fund puzzle, Journal of Fnance 46, Lux, T. and D. Sornee (2002) On Raonal Bubbles and Fa Tals, The Journal of Money, Cred and Bankng, Par 1, vol. 34, No. 3, Lynch, Anhony and Rchard Mendenhall (1997), New Evdence on Sock Prce Effecs Assocaed wh Changes n he S & P 500 Index, The Journal of Busness, Vol. 70, Lewellen, Jonahan (2002), Momenum and Auocorrelaon n Sock Reurns, Revew of Fnancal Sudes, Vol. 15, Meron, R. (1987), A smple model of capal marke equlbrum wh ncomplee nformaon, Journal of Fnance 42, McConnell, J., Sanger, G., 1987, The puzzle n pos-lsng common sock reurns, Journal of Fnance 42, Parker, Wayne D., and Rober R. Precher Jr. (2005) Herdng: An Inerdscplnary Inegrave Revew from a Soconomc Perspecve, n Koknov, Bocho, Ed., Advances n Cognve Economcs: Proceedngs of he Inernaonal Conference on Cognve Economcs, Sofa, Augus 5-8, Sofa, Bulgara: NBU Press (New Bulgaran Unversy), Pndyck, R., Roemberg, J., The excess comovemen of commody prces. Economc Journal 100, Pndyck, R., Roemberg, J., 1993, Comovemen of sock prces, Quarerly Journal of Economcs. Rashes, Mchael S., Massvely Confused Invesors Makng Conspcuously Ignoran Choces (MCI- MCIC). The Journal of Fnance, 56, Shller, Rober (1990), Marke Volaly and Invesor Behavor, Amercan Economc Revew, Vol. 80, No. 2, Shlefer, Andre (1986), Do Demand Curves for Socks Slope Down? Journal of Fnance, Vol. 41, No. 3, Sornee, D. and J.V. Andersen (2002) A Nonlnear Super-Exponenal Raonal Model of Speculave Fnancal Bubbles, In. J. Mod. Phys. C 13 (2), Wurgler, J., Zhuravskaya, K., Does arbrage flaen demand curves for socks?, Journal of Busness 75,

26 Fgure 1: Dsrbuon of he swches from DAQ o he E Ths able shows he number of frms ha changed her radng locaon from DAQ o E. Usng he Cener of Research n Secury Prces (CRSP) daa, we selec all frms whose exchange lsng varable for common sock (share code 10 and share code 11) changes from DAQ o he E whn ou nerval of neres. We presen he number of frms for he full sample (1,030 frms ha swched exchanges beween 1973 and 2004) and for he es sample (536 frms ha swched exchanges beween 1988 and 2000 and for whch an ndusry and sze mached conrol frm was found). To ener he full sample of 1,030 frms, a sock mus have prce daa n CRSP for one year before and one year afer he swchng dae. To ener he es sample a frm s requred o have CRSP prce daa and nra-day rade daa n ISSM / TAQ for one year before and one year afer he swch, as well as book equy daa from COMPUSTAT. Non U.S. frms, real esae nvesmen russ, and closed-end funds are excluded from he sample Full sample (1030 frms) Tes sample (536 frms)

27 Table 1: Descrpve sascs for frms ha swch lsngs from DAQ o he E The able presens summary characerscs for he es sample and he conrol sample. The es sample ncludes socks ha swch lsngs from DAQ o E durng The conrol frms are he non-even frms on DAQ mached by sze and ndusry. The samples are resrced o socks ha have avalable prce daa for he esmaon wndow of (-300, +300) radng days around he even. The machng procedure s as follows. We frs fnd all frms wh common sock as share code recorded n CRSP ha reman raded on DAQ. We oban fscal year-end daa on prce and book equy for hese frms from COMPUSTAT and her ndusry afflaon from CRSP. We delee frms wh negave values of book equy or sock prce, and calculae he marke value of equy as our proxy for sze. For each even frm ha swches o he E a dae, we rean as conrol frms ha are n he same ndusry based on wo-dg SIC. We elmnae poenal pars for whch he prce levels are oo far apar. For each remanng avalable frm n he conrol sample, we calculae he Huang and Soll (1998) sze score, and selec he conrol frm wh he lowes score value. Asses, marke o book rao, marke value of equy and sock prce are measured a he end of he mos recen fscal year pror o he swchng dae. Turnover s measured as mean over one year precedng he swchng dae and no he ncludng wo monhs before he even dae. Insuonal ownershp s measured one quarer pror o he even dae. Share Prce, $ Volume Turnover Toal Asses Marke o Book Marke Value of Equy Insuonal Invesors Ownershp Tes Conrol Tes Conrol Tes Conrol Tes Conrol Tes Conrol Tes Conrol Q % 20% Medan % 37% Mean % 38% Q % 55%

28 Table 2: Order flow comovemen before and afer frms swch from DAQ o he E The able presens esmaes of he followng model: OF, = α 0 + αd, + β OF, + β OF, +Δβ OF, D, +Δβ OF, D, + ε, OF, s he 15mn/daly/weekly order flow for sock, OF,, OF, are E and DAQ 15mn/daly/weekly equally-weghed ndusry-adjused marke order flow ndexes (excludng OF, ). D, s a varable equal o 1 for he radng days afer he swchng dae and 0 before. All ransacons are classfed as buys and sells (usng he Lee-Ready (1991) algorhm and order flow s calculaed as (buy volume sell volume) / (buy volume + sell volume). Panel A s based on 15-mnue order flow, Panel B on daly order flow and Panel C on weekly order flow. For each sock (es and conrol), he specfcaon s esmaed over (-300, +300) radng days around he even, and no ncludng (-50, +50) around he lsng dae. The values repored are means along wh her -sascs. The p-values for a wo-sde es on he dfference n means are provded. The es sample ncludes socks ha move from DAQ o E beween 1988 and The conrol frms are he non-even frms on DAQ mached on sze and ndusry. Inercep α 1 β β Δ β Δ β Panel A: Inraday OF Tes sample Conrol sample Tes of means (p-values) Panel B: Daly OF Tes sample Conrol sample Tes of means (p-values) Panel C: Weekly OF Tes sample Conrol sample Tes of means (p-values)

29 Table 3: Daly reurn comovemen before and afer frms swch from DAQ o he E The able presens esmaes of he followng model: R, = α 0 + α D, + β R, + β R, + Δβ R, D, + Δβ R, D, + ε, R, s he daly reurn on sock, ndexes (excludng R, ). R,, R, are E and DAQ daly equally-weghed marke reurn D, s an ndcaor varable equal o 1 for he radng days afer he swchng dae and 0 before. For each sock, he specfcaon s esmaed over (-300, +300) radng days around he even, and no ncludng (-50, +50) around he lsng dae, o avod any reurn effec of he acual even. The values repored are means along wh her -sascs. The esmaons are based on daly close-close reurns obaned from CRSP daabase. Panel A presens he esmaes from he above specfcaon for he full sample for he exended me nerval Panels B, C and D presen he esmaes from he above specfcaon for he es sample and he conrol sample. The p-values for a wo-sde es on he dfference n means are provded. The es sample ncludes socks ha move from DAQ o E beween (Panel A) and beween (Panel B, C and D). The conrol frms are he non-even frms on DAQ mached on sze and ndusry. Inercep α 1 β β Δ β Δ β Panel A: Tes sample Panel B: Tes sample Conrol sample Tes of means (p-values) Panel C: Tes sample Conrol sample Tes of means (p-values) Panel D: Tes sample Conrol sample Tes of means (p-values)

30 Table 4: Reurn comovemen before and afer frms swch from DAQ o he E (wh ndusry correcon for marke ndexes) The able presens esmaes of he followng model: R, = α 0 + α D, + β R, + β R, + Δβ R, D, + Δβ R, D, + ε, R, s he 15mn/daly/weekly reurn for sock, R,, R, equally-weghed ndusry-adjused marke reurn ndexes (excludng are E and DAQ 15mn/daly/weekly R, ). D, s an ndcaor varable equal o 1 for he radng days afer he swchng dae and 0 before. For each sock, he specfcaon s esmaed over (-300, +300) radng days around he even, and no ncludng (-50, +50) around he lsng dae, o avod any reurn effec of he acual even. The values repored are means along wh her -sascs. The esmaons are based on TAQ and ISSM mdquoes. Panel A s based on 15-mnue reurns; Panel B s based on daly close-close reurns; Panel C s based on close-close weekly reurns. The able presens he esmaes from he above specfcaon for he es sample and he conrol sample and he p-values for a wo-sde es on he dfference n means are provded. The es sample ncludes socks ha move from DAQ o E beween 1988 and The conrol frms are he noneven frms on DAQ mached on sze and ndusry. Inercep α 1 β β Δ β Δ β Panel A: Inraday reurns Tes sample Conrol sample Tes of means (p-values) Panel B: Daly reurns Tes sample Conrol sample Tes of means (p-values) Panel C: Weekly reurns Tes sample Conrol sample Tes of means (p-values)

31 Table 5: Resdual reurn comovemen before and afer frms swch from DAQ o he E The able presens esmaes of he followng model: RES, = α 0 + α D, + β RES, + β RES, + Δβ RES, D, + Δβ RES, D, + ε, RES, s he 15mn/daly/weekly resdual on sock, RES,, RES, 15mn/daly/weekly equally-weghed ndusry adjused marke ndexes of resduals (excludng are E and DAQ RES, ). D, s an ndcaor varables equal o 1 for he radng days afer he swchng dae and 0 before. The analyss s developed n wo sages. Frs, we compue resdual reurns for every swchng sock from a regresson of reurns on own order mbalance over 15-mnue nervals. We do hs for every ordnary common sock on he E and DAQ, and hereby form equally weghed non-radng resdual reurn ndex. In he second sage, we regress resdual reurns for he swchng sock on aggregae non-radng reurns for he E and DAQ. The nraday regresson uses 15-mnue resdual reurns (Panel A). The daly and weekly regressons (Panel B and C) use resduals bul up from he nraday resdual reurns by summng he 15 mnue reurns for he enre day or week for each sock. For each sock, he above specfcaon s esmaed over (-300, +300) radng days around he even, no ncludng (-50, +50) radng days around he lsng dae. The values repored are means along wh her -sascs. The p-values for a wo-sde es on he dfference n means are provded. The es sample ncludes socks ha move from DAQ o E beween 1988 and The conrol frms are he non-even frms on DAQ mached on sze and ndusry. Inercep α 1 β β Δ β Δ β Panel A: Inraday Tes sample Conrol sample Tes of means (p-values) Panel B: Daly Tes sample Conrol sample Tes of means (p-values) Panel C: Weekly Tes sample Conrol sample Tes of means (p-values)

32 Table 6: Tess of he nformaon dffuson model Reurns for swchng and conrol frms are regressed on leads (shown as +k) and lags (shown as -k) of E and DAQ reurns. β and β are he pre-swch E and DAQ beas whle Δ β and Δ β are he changes n he beas followng he swch o he E. Values ha are sgnfcan a 5% level from -sascs of he null hypohess ha he coeffcens are zero are presened n bold fon. β β Δ β Δ β β β Δ β Δ β Swchng Frms Conrol Frms Panel A: Daly Reurns Panel B: Weekly Reurn