Does Stock Price Synchronicity Represent Firm-Specific Information? The International Evidence

Does Sock Price Synchroniciy Represen Firm-Specific Informaion? The Inernaional Evidence Hollis Ashbaugh-Skaife Universiy of Wisconsin Madison 975 Universiy Avenue Madison, WI 53706 608-63-7979 hashbaugh@bus.wisc.edu Joachim Gassen Humbold Universiy Berlin 10099 Berlin - Germany 49-30-093 5764 gassen@wiwi.hu-berlin.de Ryan LaFond Barclays Global Invesors 400 Howard Sree San Francisco, CA 94105 415-817-6167 Ryan.LaFond@barclaysglobal.com We hank Gavin Cassar, S.P. Kohari, and Bill Rees, Rober Verrecchia, seminar paricipans a he 005 European Accouning Associaion Annual Congress, he 005 Columbia Inernaional Symposium, Universiei van Amserdam Business School, Lancaser Universiy, and Wharon for helpful commens and suggesions.

Does Sock Price Synchroniciy Represen Firm-Specific Informaion? The Inernaional Evidence ABSTRACT Much of prior inernaional accouning research implicily assumes ha sock prices capure similar amouns of firm-specific informaion across counries. Recen research assers ha sock price synchroniciy, defined as he R from asse pricing regressions, is a useful measure of he amoun of firm-specific informaion impounded in sock prices in inernaional markes. However, he resuls of our empirical ess provide lile suppor for using sock price synchroniciy as a measure of firm-specific informaion inernaionally. We develop an alernaive measure of firm-specific informaion impounded in sock price based on he percenage of zero-reurn days, i.e., he zero-reurn meric, and repea he analyses. Overall, our resuls sugges ha he zero-reurn meric is a beer measure of firm-specific informaion impounded ino share prices han he synchroniciy measure inernaionally.

Does Sock Price Synchroniciy Represen Firm-Specific Informaion? The Inernaional Evidence 1. Inroducion Prior research documens differences in he value relevance, imeliness, and conservaism of accouning informaion across counries (see e.g., Alford, Jones, Lefwich, and Zmijewski, 1993; Ball, Kohari, and Robin, 000; Ball, Robin, and Wu, 003). An underlying assumpion of his line of research is ha sock prices reflec similar amouns of informaion abou firm fundamenals--firm-specific informaion--across counries. There are, however, significan differences in mandaed and volunary informaion flows across counries ha affec he relaive amoun of firm-specific informaion presen in inernaional markes. Morck, Yeung, and Yu (000) propose ha sock price synchroniciy, defined as he R from asse pricing regressions, can be used as a measure of he relaive amoun of firm-specific informaion refleced in reurns inernaionally. Morck e al. (000) inerpre higher R values, (greaer sock price synchroniciy) as reurns ha reflec more marke-wide informaion and lower R values as reurns ha reflec more firm-specific informaion. A low R is poenially due o firms reurns capuring unique firm-specific informaion or reflecing greaer idiosyncraic noise (Roll, 1988). The purpose of his sudy is o evaluae he informaion-based explanaion for he synchroniciy measure by conducing five analyses designed o assess he presence of firm-specific informaion in six of he larges equiy markes: Ausralia, France, Germany, Japan, he U.K., and he U.S. 1 Our firs analysis builds on he work of Durnev, Morck, Yeung, and Zarowin (003), who assess wheher he R measure is associaed wih he informaiveness of U.S. firms sock prices. If he R measure consisenly reflecs he amoun of firm-specific informaion in reurns inernaionally, we expec lower R values o be associaed wih prices ha are more informaive regarding fuure earnings. Overall, he resuls of our price informaiveness ess are no consisen wih his expecaion. We find ha higher R values are associaed wih more informaive prices 1 The Appendix displays he summary findings in 15 addiional counries ha have sufficien daa o conduc our five analyses.

in Germany and he U.S., and find no saisically significan associaion beween R values and price informaiveness in Ausralia, France, Japan or he U.K. Conrary o Durnev e al. (003), who sugges ha greaer firm-specific reurn variaion is associaed wih more informaive sock prices in he U.S., our resuls sugges ha here is no consisen relaion beween he R measure and he pricing of fuure earnings informaion in inernaional markes. Our second analysis invesigaes wheher sock price synchroniciy is associaed wih analys forecas errors. Prior research documens a negaive relaion beween firm disclosures and analyss forecas errors (Lang and Lundholm, 1996; Hope, 003). The work of Ashbaugh and Pincus (001) and Lang, Lins, and Miller (003) indicaes ha analyss forecas errors decline when a non-u.s. firm s public informaion se expands as a resul of adoping Inernaional Financial Reporing Sandards or U.S. Generally Acceped Accouning Principles, respecively. Therefore, if low R values reflec he capializaion of greaer amouns of firm fundamenals ino share prices following he release of firm-specific informaion, we expec a posiive relaion beween analyss forecas errors and R measures. The resuls of our analyss forecas errors analysis are consisen wih his expecaion in Japan, where we documen a posiive associaion beween firms R measures and analyss forecas errors. In Ausralia, France, Germany, he U.K., and he U.S., however, we find ha firms wih larger R values have smaller analyss forecas errors. These opposing findings challenge he noion ha he synchroniciy measure consisenly capures he relaive amoun of firm-specific informaion impounded in share price in inernaional markes. Durnev e al. (003) use a mached pair design o assess wheher U.S. firms ha have lower R values have more fuure earnings informaion refleced in heir reurns afer conrolling for oher variables ha proxy for risk. The use of he mached paired design poenial limis he abiliy o generalize he resuls o he marke as a whole. Since we are ineresed in assessing he associaion beween he R measure and he pricing of fuure earnings wihin he respecive marke, we use a cross-secional design. Furhermore, we do no add conrol variables o our price informaiveness ess because Morck e al. (000) view he R as a summary measure of he amoun of informaion refleced in reurns. Under his inerpreaion he inclusion of conrol variables, such as size, which influence boh he R measure and he pricing of fuure earnings informaion, is no appropriae. Thus our U.S. resuls speak o he sensiiviy of he Durnev a al. (003) findings o alernaive design choices.

In our hird analysis, we invesigae wheher here is a change in sock price synchroniciy surrounding firms cross lisings in he U.S. Cross lising in he U.S. represens a major informaion even because U.S. foreign regisrans are required o provide more disclosures han hose required in home markes (Ashbaugh, 001; Lang e al., 003). Cross lising also makes hese firms more visible o new invesors, which increases invesors informaion search (Karolyi, 004). If lower R measures represen relaively more firm-specific informaion in reurns, we expec a decline in R values following firms cross lisings in he U.S. However, we find no evidence ha Ausralian, French, German, Japanese, or U.K. firms R values decline following heir cross lising in he U.S. Raher, we find ha he R values of French firms and U.K. firms increase afer a U.S. lising. The resuls of his analysis increase doubs abou wheher he synchroniciy measure can be used o capure differences in firm-specific informaion impounded in inernaional sock prices. Our fourh analysis ess he associaion beween he synchroniciy measure and variables used in prior research o proxy for firm fundamenals. Specifically, we use he reporing of a loss, he disclosure of research and developmen coss, he sandard deviaion of sales, and he sandard deviaion of reurn-on-asses o proxy for firm fundamenals. We use he percenage of closely held shares and analys following o proxy for he quaniy and qualiy of firms informaion flows. If R values capure he relaive amoun of firm-specific informaion refleced in prices inernaionally, we expec consisen relaions beween R values and he variables ha proxy for firm fundamenals and public informaion flows wihin our sample counries. Afer conrolling for firm size, rading volume, and indusry regulaion, we find significan relaions beween he R measure and he informaion proxies (firm fundamenals) wihin each of our sample counries. However, he relaions are inconsisen in ha we find posiive relaions beween he R measure and informaion proxies in some counries and negaive relaions in oher counries. These findings provide addiional evidence ha he informaion-based inerpreaion of he R measure is no valid on a consisen basis in inernaional markes. 3

The work of Andrade e al. (005), Barberis e al. (005), Kumar and Lee (005), and Greenwood (005) suggess ha non-fundamenal facors affec firms sock price synchroniciy. Building on Barberis e al. (005), we conduc one more analysis o deermine wheher sock price synchroniciy capures firm-specific informaion flows impounded in share price. Our final sock price synchroniciy analysis examines he associaion beween German firms membership in a German-marke index and heir R values. The German marke provides a unique seing o assess he usefulness of he synchroniciy measure as a measure of informaion because membership in cerain German indices during our analysis period required firms o provide addiional disclosures ha were inended o increase firm-specific informaion flows. 3 If he synchroniciy measure reliably reflecs he relaive amoun of firm-specific informaion in a marke, we expec he R values of German firms ha are members of an index o be lower han hose of oher German firms. Conversely, we find ha membership in a German index is associaed wih significanly higher R values. This finding suggess ha non-fundamenal facors significanly influence sock price synchroniciy in he German marke. Collecively, he resuls of our analyses indicae ha he cross-secional variaion in R values is no consisenly relaed o he price informaiveness of fuure earnings, analyss forecas errors, non-u.s. firms cross lising in he U.S., or variables ha proxy for firm fundamenals or public informaion flows. Thus, our findings sugges ha firms synchroniciy measures do no consisenly capure differences in firm-specific informaion in inernaional markes. Having provided evidence ha he informaion-based inerpreaion of he synchroniciy measure is no reliable in inernaional markes, we develop and invesigae wheher an alernaive marke measure beer capures he relaive amoun of firm-specific informaion in reurns inernaionally. Building on he work of Bekaer, Harvey, and Lundblad (003) and Lesmond, Ogden, and Trzcinka (1999), we use he percenage of zero-reurn days as a simplified measure of 3 We limi his analysis o he German marke because of he difficuly in idenifying non-u.s. firms index memberships over ime. 4

firm-specific-informaion. Lesmond e al. (1999) noe ha he marginal invesor will no rade unless he value of an informaion signal is sufficien o exceed his rading coss. If he marginal invesor does no rade, hen here is no change in price, and a zero-reurn resuls. Zero reurns can also occur when rading akes place bu price does no change because here is no new valuaion-relevan informaion. We apply hese noions o he inernaional seing, assuming ha when sufficien valuaion-relevan informaion arrives in he marke, invesors rade, and a reurn is generaed. Therefore, we conjecure ha he proporion of zero-reurn days (hereafer referred o as he zero-reurn meric) represens he frequency of a firm s informaion flows, where a lower zero-reurn meric (i.e., smaller proporion of zero-reurn days) reflecs more informaionally efficien share prices. We repea our five analyses using he zero-reurn meric in place of he synchroniciy measure and find he following. In assessing wheher he zero-reurn meric is associaed wih more informaive prices wih respec o fuure earnings, we find, as expeced, ha he zero-reurn meric is negaively associaed wih he amoun of earnings-relaed informaion refleced in reurns in Germany, Japan, he U.K., and he U.S. Consisen wih expecaions, he resuls of he analyss forecas errors analysis indicae ha he zero-reurn meric is posiively associaed wih analyss forecas errors in Ausralia, France, he U.K., and he U.S. In addiion we find a significan decline in he zero-reurn meric following he cross lising of French and U.K. firms in he U.S. When we regress he zero-reurn meric on variables used o proxy for firm fundamenals and public informaion flows, we find he relaions beween he zero-reurn meric and he proxy variables o be consisen wih expecaions. Based on he resuls of hese analyses, we conclude ha he zero-reurn meric is a beer measure of he relaive amoun of firm-specific informaion impounded in inernaional share prices han he R measure. Our sudy makes several conribuions o he lieraure. Firs, he resuls sugges ha lower sock price synchroniciy does no capure he amoun of firm-specific informaion refleced in sock prices in inernaional markes. Morck e al. (000) documen ha sock prices 5

move ogeher more in poor counries relaive o rich counries, and sae ha cross-counry differences in propery righs explain he cross-counry variaion in sock price synchroniciy. Morck e al. (000) conclude ha srong propery righs promoe informed arbirage, which capializes deailed firm-specific informaion ino prices, leading o lower sock price synchroniciy. The analysis and inferences drawn from Morck e al. (000) are based on aggregae counry-level measures, i.e. counry averages. As discussed by Freeman (004), Freedman, Pisani, and Purves (1998) and Greenland and Robbins (1994), researchers using aggregae daa as opposed o firm-specific daa can draw differen and poenially incorrec inferences. While he resuls of Morck e al. (000) speak o aggregae differences in R values across counries, heir analysis does no allow hem o disenangle wheher he synchroniciy measure reflecs firm-specific informaion wihin a counry or, consequenly, wheher he synchroniciy measure capures common informaion across counries. Our sudy measures and assesses R values a he firm level wihin a counry. By examining he properies of he synchroniciy measure across firms wihin a counry, we hold consan marke micro-srucure and insiuional feaures, which poenially affec securiy pricing, hereby allowing us o es wheher differences in he synchroniciy measure wihin a counry reflec more informaion-laden sock prices. The resuls of our firm-level analyses sugges ha differences in synchroniciy across firms are no driven by differences in informaion. Thus, we conclude ha he informaion-based explanaion for he synchroniciy measure is no valid inernaionally, and quesion wheher across-marke comparisons can be made using he synchroniciy measure. 4 Second, our research conribues o prior and concurren research ha invesigaes measures inended o capure he degree o which sock prices are informaionally efficien (see 4 Ohers have begun o assess he robusness of prior inernaional research ha draws inferences based on counry-wide measures. For example, Holderness (005) demonsraes he conclusions regarding he influence of weak legal insiuions on ownership srucure appear o be due o he use of aggregae (i.e., counry-level versus firm-level) measures of ownership srucure. 6

e.g., Gelb and Zarowin, 00; Kelly, 005). We develop he zero-reurn meric as an alernaive measure of he relaive amoun of firm-specific informaion impounded in share price. Unlike oher measures of informaion efficiency (e.g., he breadh of insiuional ownership), he zeroreurn meric can be consruced for every firm lised in a public equiy marke. We provide evidence ha he zero-reurn meric is a more valid measure of informaion-laden sock prices han he synchroniciy measure, and link he zero-reurn meric o facors ha capure firm fundamenals and firms public informaion flows. Having a simple measure ha capures differences in he degree of firms price informaiveness is imporan o researchers and regulaors ineresed in he inegraion of capial markes, as well as o invesors whose opimal resource allocaion depends upon informaionally efficien prices. The paper proceeds as follows. Secion provides an overview of he prior lieraure on he R measure. Secion 3 exends he findings of Morck e al. (000) o our sample period. Secion 4 repors he resuls of he analysis examining he R measure a he firm level. Secion 5 describes he zero-reurn meric ha we develop o capure he relaive amoun of informaion refleced in sock prices and repors he resuls of he analysis using he zero-reurn meric. Secion 6 repors he resuls of sensiiviy ess, and Secion 7 concludes he sudy.. Overview of he R Measure and Relaed Lieraure Asse pricing models ypically regress a firm s reurns on a common facor or se of common facors. For example, he capial asse pricing model (CAPM) links a firm s reurn o he reurn of he marke: (1) RETi = β 0, i + β1, i RETMKT + ε i where RET i is he firms i reurn for period and RETMKT is he reurn on he marke for period. For his model o yield a high explanaory power, he firm mus rade wih he marke, meaning is share price mus align wih he share prices of oher firms in he marke, i.e., i mus exhibi synchronous sock price movemens. 7

The convenional inerpreaion of he residual from equaion (1) is ha afer removing he reurn effecs due o sysemaic facors, he remaining reurn volailiy is due o idiosyncraic, firm-specific evens. A low R from equaion (1) is poenially due o firms reurns capuring unique firm-specific informaion or reflecing greaer idiosyncraic noise in reurns. Blume (1968), King (1966), and Officer (1971) repor a decline in he explanaory power of he CAPM overime. Roll (1988) noes ha ypical asse pricing regressions yield relaively low explanaory power and proposes ha one poenial explanaion for he decline in explanaory power is he incorporaion of privae, firm-specific informaion ino prices. Roll (1988) noes ha he incorporaion of firm-specific informaion ino prices generally increases he volailiy of an individual firm s sock price, which resuls in lower explanaory power from asse pricing regressions such as equaion (1). He finds ha he low R from asse pricing models is primarily due o high firm-specific reurns volailiy and ha his volailiy is no associaed wih public news announcemens. Based on his finding Roll conends ha privae informaion or else occasional frenzy unrelaed o concree informaion (p. 566) is driving high firm-specific reurn volailiy. 5 Morck e al. (000) is he firs in a series of papers o use sock price synchroniciy as a measure of he relaive amoun of firm-specific informaion refleced in sock prices. Using counry-level R values, Morck e al. (000) find ha sock prices in poorer counries wih less developed equiy markes, weaker proecion of invesor righs, and weaker legal regimes end o move ogeher more. They conclude ha sronger proecion of invesor righs promoes informed rading, resuling in more informaive sock prices as evidenced by less synchronous rading in hese counries. 5 One poenial way in which firm-specific informaion is impounded ino prices is hrough he acions of raders wih privae informaion abou firm fundamenals. Acions underaken by informed marke paricipans such as analyss or insiders will resul in firm-specific sock price movemens, hus providing a poenial explanaion for high firm-specific reurns volailiy. 8

Jin and Myers (005) confirm he findings of Morck e al. (000) and documen a decline in counry-level R values over ime across a sample of 40 counries. In addiion, Jin and Myers (005) find ha counries wih higher average R values experience more frequen marke crashes, which ypically resul from more opaque informaion environmens. Li, Morck, Yang, and Yeung (004) invesigae he behavior of counry-level averages of R values in emerging markes, finding ha counry-level R values are generally declining over ime, and lower counry-level R values are associaed wih greaer capial marke openness, more efficien legal sysems, and less corrup economies. While he counry-level resuls of Morck e al. (000), Jin and Meyers (005), and Li e al. (004) are consisen wih an informaion-based inerpreaion of he R measure, he resuls do no address wheher he R measure reflecs informaionally efficien share prices. 6 Research examining wheher firm-specific synchroniciy measures reflec informaionladen prices has focused on he U.S. marke. One line of research builds on he fac ha firmspecific informaion is impounded ino prices hrough he public disclosure of informaion or hrough he acions of informed marke paricipans. Durnev e al. (003) examine wheher firms have low synchroniciy because more fuure earnings informaion is refleced in heir reurns. They find ha U.S. firms wih lower R values have more fuure earnings informaion refleced in sock prices, consisen wih differences in synchroniciy across firms being due o differences in he amoun of informaion refleced in prices. Pioroski and Roulsone (004) es he associaion beween synchroniciy and acions of informed marke paricipans (i.e., analyss, insiuional invesors, and insiders). 7 They find ha acions underaken by informed marke paricipans are 6 As noed by Pioroski and Roulsone (004, p. 116) radiional differences a he counry level are no likely he cause of observed differences in synchroniciy. Insead, differences in R s are a resul of he economics underlying each firm and he relaive flow of informaion ino prices. 7 Chan and Hameed (005) invesigae he associaion beween he R measure and analys following in emerging markes, finding ha higher analys following is associaed wih higher R values. They inerpre heir findings as being consisen wih analys impounding marke wide (no firm-specific) informaion ino reurns. 9

associaed wih firms sock price synchroniciy. Finally Durnev e al. (004) invesigae wheher U.S. firms wih lower R values make beer capial allocaion decisions. They find ha firms wih lower R values end o make more efficien invesmens (less over or under invesmen). Their finding is consisen wih he synchroniciy measure represening firm-specific informaion, in ha firms wih lower R values suffer from fewer problems wih asymmeric informaion, improving he coordinaion beween capial suppliers and he firm, and resuling in more efficien invesmens. Anoher line of U.S. research explores wheher low R values are a resul of excess noisein-reurns resuling from facors unrelaed o firm fundamenals. Shiller (1981) and Wes (1988) find ha he level of sock price volailiy is oo high o be explained by he volailiy in he underlying fundamenals, e.g. dividends. Wes (1988) provides a heoreical model where increased firm-specific reurn volailiy is associaed wih less firm-specific informaion and more noise-in-reurns. In Wes s model, relaively more informaion resuls in prices being closer o fundamenal values, and he release of new informaion resuls in smaller price movemens and lower firm-specific reurn volailiy. Wes empirically ess his model and repors resuls indicaing ha firm-specific reurn volailiy is posiively associaed wih bubbles, fad, and oher non-fundamenal facors. Oher sudies also sugges ha behavioral facors, bubbles, herding, and oher nonfundamenal facors affec sock reurn volailiy (see Shleifer, 000 for a review), and ulimaely he usefulness of he synchroniciy measure as a gauge of firm-specific informaion. Barberis e al. (005) find significan changes in firms R values surrounding addiions and deleions o he S&P 500 Index in he U.S., consisen wih marke fricions influencing synchroniciy. 8 In addiion, Greenwood and Sosner (00), and Greenwood (005) find similar resuls in Japan 8 Barberis e al. (005) develop a model o explain he changes in R values based on marke fricions and senimen. Boh heir empirical and heoreical work provides evidence inconsisen wih he informaionbased explanaion of he R measure. 10

using addiions and deleions from he Nikkei 5 Index. Since addiions and deleions o indices do no signal new informaion o he marke regarding firms fundamenals, he changes in firm s R values surrounding changes in he composiion of indices is inconsisen wih an informaionbased explanaion of he R measure. Consisen wih he noise-in-reurns inerpreaion of he R measure, Kumar and Lee (005) find ha noise raders (uninformed reail invesors) have a significan influence on sock price synchroniciy. Andrade e al. (005) develop a model in which rading imbalances, combined wih he limied risk-bearing capaciy of arbirageurs, resuls in correlaed price movemens across socks. An imporan feaure of heir model is ha synchronous price movemens resul from cross-sock price pressure, no informaion. Andrade e al. (005) es heir model in Taiwan, finding ha arbirageurs limied risk-bearing capaciy can explain a significan porion (more han 50%) of observed sock price synchroniciy, which is inconsisen wih he informaion-based inerpreaion of synchroniciy. Thus, he findings of Andrade e al. (005), Barberis e al. (005), Greenwood and Sosner (00), Greenwood (005), and Kumar and Lee (005) indicae ha marke fricions, i.e., facors unrelaed o informaion, have a significan influence on sock price synchroniciy. Campbell, Marin, Malkiel, and Xu (001) documen he rise in firm-specific reurn volailiy in he U.S. over ime and he resuling decrease in R values. They inerpre heir findings in he spiri of Wes s (1988) model conending ha he decrease in R values is no likely a resul of increased firm-specific informaion. Brand e al. (005) provide furher suppor for Wes s model, finding ha he recen rend in idiosyncraic volailiy in he U.S. is mos likely due o a speculaive bubble similar o ha observed in he lae 190s. Wei and Zhang (004) invesigae he poenial causes for increased firm-specific volailiy over ime in he U.S., and find ha he variance of firm fundamenals (reurn on equiy) has increased over ime, hereby providing a parial explanaion for he findings of Campbell e al. (001). However, Wei and Zhang (004) furher documen ha he increase in he volailiy of firm fundamenals and he 11

associaion beween fundamenal volailiy and reurn volailiy is driven, for he mos par, by newly lised firms. This finding cass doub on an informaion-based explanaion for declining R values. Rajgopal and Venkaachalam (005) documen a posiive associaion beween informaion risk, as measured by accrual qualiy and analys forecas dispersion, and firm-specific reurns volailiy. Their findings are consisen wih he heoreical work of Pasor and Veronesi (003), who demonsrae ha uncerainy abou firms fundamenals (informaion risk) influences reurns volailiy. These sudies provide furher evidence agains he informaion-based inerpreaion of he synchroniciy measure. If greaer firm-specific reurn volailiy is associaed wih poorer qualiy informaion (greaer uncerainy) hen how can higher firm-specific reurn volailiy also be associaed wih more firm-specific informaion being refleced in reurns? Overall, heoreical and empirical sudies provide lile suppor for he informaion-based inerpreaion of he synchroniciy measure. In addiion, argumens relaed o he limis and risk of arbirage indicae ha firm-specific reurn volailiy may hinder informed rading raher han be a consequence of informed rading as claimed by Morck e al. (000). Subsequen inernaional research ends o assume ha Morck e al. s (000) counry-wide measure of sock price synchroniciy is a measure of he relaive amoun of firm-specific informaion refleced in firms sock prices. 9 To dae, however, we know of no evidence ha validaes he informaion-based explanaion for he synchroniciy measure inernaionally. 3. Replicaion We measure a firm s sock price synchroniciy following Morck e al. (000), who define synchroniciy as he percen of he variaion in a firm s sock reurns explained by variaions in 9 For example, Wurgler (000) examines he associaion beween counry-level measures of sock price synchroniciy and counry-level measures of he efficiency of capial allocaions, and DeFond and Hung (004) invesigae he associaion beween counry-level synchroniciy measures and CEO urnover inernaionally. 1

he firm s domesic marke reurn and he U.S. marke reurn. Specifically, he synchroniciy measure is he R from esimaing he following firm-specific regression: () RETi = β 0, i + β1, i RETMKTc + β, i RETMKTUS + ε i where RET i is he reurn for firm i for he wo week period, RETMKT c is he reurn on he marke for counry c for period, and RETMKTUS is he reurn on he U.S. marke over period. 10 Like Morck e al. (000), we use bi-weekly reurns o deal wih infrequen rading in inernaional markes. We use value-weighed marke reurns, where all reurns, including he reurn on he U.S. marke, are calculaed in he local currency and colleced from Daasream. We require firms o have a minimum of 30 weeks of non-zero reurns o esimae equaion (). We esimae equaion () by firm over he 5-week period encompassing he firm s fiscal year, which resuls in 15 o 6 observaions per firm each year. To be consisen wih Morck e al. (000), we exclude all reurn observaions wih absolue values greaer han 0.5. 11 The counrylevel synchroniciy measures are defined as: (3) R c, R i i, c, SSTi = SST i i, c,, c, where SST i,c, is he oal sum of squared variaions from he firm-specific esimaes of equaion () wihin each counry. Panel A of Table 1 repors he descripive saisics for he counry-wide R measures of 1 developed equiy markes, where all firm-year observaions relae o firms ha have sufficien daa o esimae equaion (). We selec hese 1 counries because hey are a subse of he counries sudied by Morck e al. (000) ha have firms wih sufficien accouning and marke 10 For he U.S. sample we include only he reurn on he U.S. marke in equaion (). 11 Including hese reurn observaions does no change any inferences drawn from he resuls. 13

daa o be included in our empirical ess. 1 For breviy, however, we able and discuss only he resuls of our empirical ess for Ausralia, France, Germany, Japan, he U.K., and he U.S. The Appendix summarizes our findings in he 15 oher counries. Panel A of Table 1 repors he mean (median) values of he R measure presened in order of average counry rank. To calculae he average counry rank, each year we rank he 1 sample counries by heir R value and repor he average counry rank across he 13 sample years. The U.S. repors he lowes mean and median R values (mean value of 0.113 and median value of 0.097) as well as he lowes mean counry rank of 1.615, followed by Canada, Ausralia and France. The highes mean counry ranks are found in Spain (16.69), Ialy (17.077), and Singapore (19.31). Morck e al. (000) repor ha wealhier counries (as measured by gross domesic produc), wih common law legal regimes, and wih greaer proecion of invesor righs have lower sock price synchroniciy. To replicae Morck e al. (000), we esimae he following OLS regression: 13 (4) R c, = β1legalc, + β RIGHTSc, + β 3GDPc, + α fyeyear + ε i, 00 fye= 1990 where LEGAL is equal o one if he counry is classified as having a code law legal origin (La Pora e al., 1998); RIGHTS is equal o he invesor righs index developed by La Pora e al. (1998), where higher values reflec greaer invesor righs; GDP is equal o he log of he per capia gross domesic produc for he counry year; and YEAR is equal o a series of fiscal year fixed effecs. The firs hree columns in Panel B of Table 1 display he resuls of esimaing parial forms of equaion (4) where only one insiuional variable and YEAR are included in he model due o he 1 To be included in he analysis presened in Table 1 we only require firm-year observaions o have sufficien weekly reurns daa o calculae he R measure and o be on Worldscope. The requiremen ha firm-year observaions are on Worldscope reduces our sample sizes compared o Morck e al. (000). However his requiremen ensures ha firm-year observaions included in Panel A of Table 1 have he necessary financial informaion available o conduc our oher empirical ess. 13 All regressions are esimaed including fixed-year effecs and Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. We do no able he fiscal year inerceps, which in general are significan a convenional levels. 14

high correlaion beween he insiuional variables. Considered in isolaion, we find ha counries having code law legal regimes and lower levels of invesor righs have higher synchroniciy values. However, when we esimae equaion (4) wih all hree insiuional feaures (he resuls of which are repored in column 4 of Panel B), we find a significan posiive coefficien only on LEGAL. In general, he resuls presened in Panel B of Table 1 confirm he findings of Morck e al. (000). Alhough he counry-level resuls are consisen wih he resuls presened in Morck e al. (000), a counry-level analysis does no differeniae beween differences in R values across firms being due o firms sock prices reflecing relaively more informaion abou firm fundamenals or differences in R values across firms being due o nonfundamenal facors resuling in greaer noise-in-reurns. In he nex secion, we explore he inerpreaion of he Morck e al. resuls by examining he exen o which he synchroniciy measure is associaed wih facors ha represen firm informaion flows and fundamenals in inernaional equiy markes. 4. Wihin-counry Analysis of he Synchroniciy Measure 4.1 SAMPLE AND DESCRIPTIVE STATISTICS Panel A of Table presens he descripive saisics on he R measure esimaed using all firm-year observaions from 1990-00 for Ausralia, France, Germany, Japan, he U.K., and he U.S. ha have he necessary daa o conduc our empirical ess. Our empirical ess require reurns, colleced from Daasream, and accouning daa, colleced from Worldscope. Firm-year observaions meeing he daa requiremens resul in sample sizes of,895, 5,368, 3,515, 3,58, 14,48, and 56,95 for Ausralia, France, Germany, Japan, he U.K., and he U.S., respecively. The sample sizes repored in Table (e.g., Ausralia n=,895) are smaller han hose repored in Table 1 (e.g., Ausralia n=8,35) due o he addiional daa required for our ess. Japanese firms have he highes R values (mean=0.319, median=0.98), and he U.S. has he lowes R values (mean=0.118, median=0.067). 4. EMPIRICAL TESTS 15

We conduc four main analyses o assess he informaion-based explanaion for firms R values. The analyses are moivaed by prior research ha links hem o firm-specific informaion flows. Our firs analysis examines he associaion beween he synchroniciy measure and accouning measures of sock price informaiveness. Earnings are one of he primary sources of firm-specific informaion, and differences in he amoun of earnings informaion refleced in sock prices is one poenial reason for differences in R values across firms. Collins, Kohari, Shanken, and Sloan (1994); Gelb and Zarowin (00); and Lundholm and Myers (00) use he amoun of informaion abou curren and fuure changes in earnings refleced in reurns as a measure of price informaiveness. Durnev e al. (003) use his definiion of price informaiveness o draw inferences on wheher firm-specific sock price movemens in he U.S. marke reflec firm-specific informaion or increased noise-in-reurns. They find ha lower R measures are associaed wih more price informaiveness. We es he associaion beween he R measure and sock price informaiveness by esimaing he following OLS model: (5) ABRET = β 1 E + E * RR + 3 E + 1 + 4 E+ 1 * + β RR 6 β + 001 α fye fye= 1990 YEAR β + ε β RR + β ABRET 5 + 1 + where ABRET is he firm s marke adjused buy and hold reurn over fiscal year ; RR is equal o he firm s decile rank of is R value, deermined by ranking observaions each year based on he R value wihin each of he five counries; E is equal o he change in ne income before exraordinary iems scaled by beginning of period marke value of equiy over fiscal year ; and YEAR is equal o a series of fiscal year fixed effecs. The ABRET +1 erm is included in he model o correc for he errors in variables problem idenified by Collins e al. (1994). 14 Given he resuls of Durnev e al. (003) in he U.S., we 14 Collins e al. (1994) noe ha he correc specificaion of equaion (5) would include he expeced change in fuure periods earnings. Since expecaions are unobservable he acual changes in fuure periods earnings is used, inroducing an errors in variables problem which hey demonsrae can be correced by including nex period s reurn in he model. 16

expec β and β 4 o be negaive if lower R values are associaed wih more informaion abou curren and fuure changes in earnings being refleced in reurns. Table 3 displays he resuls of esimaing equaion (5). In all counries, we find a posiive and significan coefficien on he curren change in earnings (a p-values of 0.11 or less). The resuls are mixed wih respec o he change in fuure earnings. In Japan and he U.K., he coefficien on he change in fuure earnings is posiive and saisically significan as expeced, whereas in France and Germany he coefficien on he change in fuure earnings is negaive and significan. Turning o he variables of ineres, only in France and he U.S. is he R measure significanly associaed wih he curren change in earnings and reurns. However, he relaion is inconsisen wih expecaions, as higher R values in France and he U.S. are associaed wih more informaion abou he curren change in earnings being refleced in sock prices. When examining he coefficien on he ineracion of fuure earnings changes and he R measure, we find he coefficien o be posiive and significan in Germany and in he U.S., conrary o expecaions. This indicaes ha higher R values are associaed wih more informaion abou fuure earnings changes being priced. 15 Overall, he resuls presened in Table 3 indicae ha lower R values are no associaed wih more earnings informaion being refleced in reurns. 16 Our second analysis examines he associaion beween he synchroniciy measure and analys forecas errors. Our inquiry is moivaed by prior inernaional and U.S. research examining he properies of analys forecas errors. In general, his lieraure finds ha beer informaion in he form of addiional firm disclosures is associaed wih lower forecas errors (Lang and Lundholm, 1996; Ashbaugh and Pincus, 001; Hope, 003; Lang e al., 003). 15 We repea he analysis presened in Table 3 using he unranked R values in each of he five counries, he resuls of his analysis are similar o hose presened in Table 3. 16 Our findings are consisen, in par, wih Wes s (1988) model. Wes (1988) claims ha lower firmspecific reurn volailiy, higher R s, is associaed wih more informaion abou firm fundamenals being refleced in sock prices. 17

Following his line of lieraure, we expec a posiive relaion beween analyss forecas errors and he synchroniciy measure if he synchroniciy measure reflecs informaion. equaion: We es he associaion beween firms R values and forecas errors using he following 00 (6) F _ ERROR = β1rr + α fyeyear + ε fye= 1990 where RR is equal o he decile rank of he firm s R value for fiscal year ; F_ERROR is equal o he decile rank of he firm s forecas error for fiscal year where forecas error is defined as EPS ac EPS forecas / EPS forecas and EPS ac is he firm s acual earnings per share and EPS forecas is he mean consensus earnings per share forecas; and YEAR is equal o a series of fiscal-year fixed effecs. 17 Prior research models F_ERROR as a funcion of variables ha proxy for a firm s public and privae informaion flows. We esimae equaion (6) wihou hese variables due o he fac ha Morck e al. (000) posi ha a firm s R value is a summary measure of firm informaion. By omiing hese variables from our analysis, we assess he validiy of his claim. Table 4 presens he resuls from esimaing equaion (6). In Japan, he coefficien on he RR erm is posiive and significan a he 0.00 level. This indicaes ha in Japan, consisen wih he informaion-based inerpreaion of he synchroniciy measure, lower R values are associaed wih lower analys forecas errors. In Ausralia, France, Germany, he U.K., and he U.S., however, we find he coefficien on he RR erm o be negaive and significan a he 0.01 level or beer. Thus, in he majoriy of our sample counries, we find ha higher R values are associaed wih lower analys forecas errors, which is opposie of wha is expeced if lower synchroniciy measures reflec relaively more firm-specific informaion. Our hird analysis invesigaes wheher here is a change in firms synchroniciy measures afer cross lising in he U.S. Cross lising in he U.S. represens a significan informaion even as a U.S. lising subjecs firms o increased regulaion and disclosure requiremens ha resul in 17 The sample sizes in he analys forecas errors es are furher reduced due o he requiremen ha firms be followed by an analys. 18

more informaion abou he firm being made available o invesors (Ashbaugh 001, Lang e al. 003). Furhermore, cross lising in he U.S. enhances firm visibiliy, increasing he invesor base and subsequen informaion search by invesors (see Karolyi (004) for an overview). If synchroniciy is a funcion of firm-specific informaion, i follows ha non-u.s. firms R values are expeced o decrease afer hey cross lis in he U.S. Table 5 presens he resuls of he cross lising analysis, where we define he change in R values as he R value in he 1 monhs following he cross lising monh minus he R value in he 1 monhs preceding he cross lising monh. 18 Panel A of Table 5 presens he mean and median change in R measure ( R ) for all cross lisings over he 1990-00 ime period. In none of our sample counries do we find he R o be significanly negaive. In fac, conrary o expecaions, we find he mean and median R o be posiive and significanly differen from zero (a he 0.0 level or beer) in France and he U.K. As a robusness check, we examine R for only Level and Level 3 ADRs, since hese ypes of U.S. cross lisings are associaed wih he greaes informaion disclosures. The resuls presened in Panel B of Table 5 are similar o he resuls for U.S. cross lisings as a whole. We find ha in France and he U.K. he mean and median R are posiive and saisically differen from zero a he 0.0 level or beer. None of he oher R measures is significanly differen from zero. Overall, he resuls presened in Table 5 sugges ha cross lising in he U.S. is no associaed wih a decline in he R values as one would expec under an informaion-based inerpreaion of he synchroniciy measure. 19 18 Cross lising daes and he ype of cross lising (e.g. Level 1,, 3 or Rule 144A) are provided by J.P. Morgan Chase & Co. 19 We conduc wo addiional sensiiviy ess. Firs, we repea he cross lising analysis esimaing equaion () wihou he U.S. reurn due o he poenial mechanical effec ha cross lising may have on he coefficien on U.S. reurn. Second, we esimae an OLS fixed effecs model for each counry using a dummy variable o capure he pos period. We draw similar inferences from he resuls of hese wo sensiiviy ess. 19

Our las analysis uses he framework of Pioroski and Roulsone (004) o es he exen o which firm fundamenals are relaed o he R measure in inernaional markes. Pioroski and Roulsone (004) use U.S. firms R values as a benchmark of firm-specific informaion incorporaed ino prices, and es he associaion beween R values and variables proxying for firms informaion environmen. Based on heir work, we esimae he following fixed effecs model: SYNCH (7) = β LOSS β STDROA 6 1 00 α fye fye= 1990 + β R & D + β REG 7 YEAR + ε i + β ANALYST 3 + β RELSIZE 8 + β % CLHLD 4 + β % MVE 9 + β STDSALES + + β % TURNOVER + 10 5 where SYNCH is equal o log(r /(1-R )) for fiscal year ; LOSS is equal o one if ne income before exraordinary iems is negaive, and zero oherwise; R&D is equal o one if he firm repors a value for research and developmen expense, and zero oherwise; ANALYST is equal o he log of one plus he number of analyss making a forecas for fiscal year s earnings; %CLHLD is he proporion of shares ha are closely held as of he end of he fiscal year ; STDSALES is he sandard deviaion of sales scaled by oal asses over calculaed requiring a minimum of hree and maximum of five fiscal years; STDROA is he sandard deviaion of ROA calculaed requiring a minimum of hree and maximum of five fiscal years where ROA is equal ne income before exraordinary iems divided by fiscal year end oal asses; REG is equal o one if he firm is a financial insiuion or uiliy; RELSIZE is he firm s sales divided by oal sales of is primary indusry (-digi SIC code); MVE is defined as he naural log of fiscal year end marke value of equiy; TURNOVER is he average weekly urnover (number of shares raded divided by number of shares ousanding) over he fiscal year; and YEAR is equal o a series of fiscal year fixed effecs. The dependen variable in equaion (7), SYNCH, is he R measure ransformed o creae a coninuous variable ha is more normally disribued han he disribuion of R values ha are bounded by zero and one (Morck e al., 000; Pioroski and Roulsone, 004). We use six variables o proxy for firm fundamenals revealed via firms public and privae informaion flows. LOSS is included in he model, as he reporing of losses is a news even expeced o be refleced in reurns (Joos and Plesko, 005; Hayn, 1995). Likewise, he reporing of research and developmen expendiures is also considered o be a news even refleced in reurns (Aboody and Lev, 000). R&D is an indicaor variable idenifying wheher he firm discloses research and 0

developmen expendiures. Reporing research and developmen coss can signals firms invesmen sraegies, and he disclosure of research and developmen coss in many counries is volunary over our analysis period. The number of analyss following he firm, ANALYST, is included in he model as a proxy for he firms informaion environmen because higher analys following is associaed wih richer informaion environmens (Lang and Lundholm, 1996; Bushman, Pioroski, and Smih, 005). If firms R values serve as a measure of firm-specific informaion incorporaed ino prices, we expec negaive coefficiens on LOSS, R&D, and ANALYST. The sandard deviaion of sales (STDSALES) and he sandard deviaion of reurn-onasses (STDROA) are included in he model o capure he volailiy of firm fundamenals. One poenial reason for high firm-specific reurn volailiy is he volailiy of underlying fundamenals. Wei and Zhang (004) find ha wihin he U.S., greaer volailiy in firms reurn on equiy is associaed wih increased reurn volailiy. We include boh he volailiy of reurnon-asses and sales due o differences in income smoohing inernaionally and he poenial influence of income smoohing on reurn-on-asses (Leuz, Nanda, and Wysocki, 003). If firms R values reflec firm fundamenals being incorporaed ino prices, we expec negaive coefficiens on STDSALES and STDROA. We use he percen of shares ha are closely held, %CLHLD, o proxy for insider ownership (Himmelberg, Hubbard, and Love, 00; Lins and Warnock, 004). Greaer insider ownership will resul in lower R values when insiders are able o gaher and rade on privae informaion abou firm fundamenals (Roll, 1988). Alernaively, greaer insider ownership may resul in higher R values if insiders reduce financial informaion ransparency for he purpose of hiding heir wealh exracion. Higher R values may also resul if insiders own a group of firms and coordinae wihin he group, such as financing oher firms in he group, resuling in a common componen o firm s fundamenals. Given he compeing explanaions, we make no predicion on he relaion beween %CLHLD and SYNCH. 1

The remaining variables in equaion (7) (REG, RELSIZE, MVE, and TURNOVER) serve as conrol variables. REG is used o conrol for he fac ha all firms operaing in a regulaed indusry face similar consrains due o regulaion, and hus, heir prices are expeced o have high sock price synchroniciy (Pioroski and Roulsone, 004). RELSIZE is used o conrol for a firm s indusry presence. Because i is more likely he firm s sock price drives indusry reurns when i has a larger marke share, we expec a posiive relaion beween RELSIZE and SYNCH. We include MVE o conrol for firm size. Larger firms are generally associaed wih richer informaion environmens, indicaing a negaive associaion beween firm size and R values. However, larger firms also poenially have more diversified operaions, resuling in hese firms rading more in line wih he marke, and, consequenly, in a posiive associaion beween firm size and he R measure (Pioroski and Roulsone, 004). We include TURNOVER in he model o capure he level of rading in a firm s shares. Under he informaion-based inerpreaion of he R measure, he associaion beween he R measure and TURNOVER would be negaive as more rading represens increased informaion being impounded ino firms share prices. However if one assumes he R measure proxies for noise rading, rading unrelaed o fundamenals, hen he associaion beween he R values and TURNOVER is expeced o be posiive. Given he uncerainy, we make no predicion of he sign of he coefficiens on MVE and TURNOVER. Panel A of Table 6 displays he Pearson correlaions beween synchroniciy and he independen variables of equaion (7). In general, here is quie a bi of variaion across counries in he sign and significance of he correlaions beween R values and he variables proxying for firm fundamenals. In conras, he correlaions beween R values and he conrol variables drawn from prior research are more consisen across counries. Panel B of Table 6 presens he resul of esimaing equaion (7) by counry. In general, he explanaory power of he model is relaively low for each counry, ranging from 14% in he U.S. o 5% in Germany. For simpliciy, raher han discussing each esimaed coefficien in

isolaion, we focus on he proporion of esimaed coefficiens ha are significan wih he expeced sign for each counry-specific regression. Overall, he signs and significance of he esimaed coefficiens are relaively mixed. We find ha 50% of he firm fundamenals are relaed o he R measure in he U.K., whereas only 0% of he firm fundamenals are significanly relaed o Japanese firms R values. The relaively low proporion of significan coefficiens wih he prediced signs, regardless of counry, suggess ha he R measure does no reflec firm-specific informaion in inernaional markes. Our las analysis builds on he work of Barberis e al. (005) and Greenwood and Sosner (00), who find ha a firm s membership in an index increases is sock price synchroniciy. Index membership may increase he R value due o marke fricions and oher non-fundamenal facors (Barberis e al., 005). To furher invesigae he link beween sock price synchroniciy and firm-specific informaion, we examine he associaion beween German firms index membership and SYNCH using he following model. (8) SYNCH = β LOSS + β STDSALES + β MVE + β R & D β NEWMARKET 1 1 5 9 + β STDROA + β TURNOVER 10 6 i + β ANALYST + β NEMAX 50 13 3 + β REG + β DAX 30 11 7 + + β % CLHLD 4 00 + β RELSIZE + α 8 fye fye= 1990 YEAR + ε where DAX30 is equal o one if he firm is par of he DAX30 index in fiscal year, and zero oherwise; NEMAX50 is equal o one if he firm is par of he NEMAX50 index in fiscal year, and zero oherwise; NEWMARKET is equal o one if he firm s shares rade on he New Marke in fiscal year, and zero oherwise. All oher variables are as previously defined. We focus on German marke indexes for wo reasons. Firs, focusing on firms membership in a German index provides a high powered seing o examine he effec of index membership on sock price synchroniciy, as firms membership in some indexes in Germany requires increased informaion flows. Specifically, he New Marke (NEWMARKET) is a segmen of he Frankfur Exchange ha is of paricular relevance o our sudy, since lising in his 3

segmen requires firms o provide addiional informaion disclosures and follow sricer corporae governance policies. Specifically, New Marke firms are expeced o adop eiher Inernaional Financial Reporing Sandards or U.S. Generally Acceped Accouning Principles, publish quarerly financial saemens (only half-year repors are mandaory for oher publicly raded German firms), hold regular analys meeings, and accep he German code of corporae governance (a self-regulaory se of rules aimed a srenghening he posiion of shareholders). If he synchroniciy measure reflecs he amoun of firm-specific informaion capured in reurns, we expec he coefficien on NEWMARKET o have a negaive sign. On he oher hand, he New Marke is heavily covered by index-oriened raders. Thus, if he R meric is influenced by nonfundamenal noise effecs, we expec NEWMARKET o have a posiive sign. The oher reason we limi his analysis o he German marke is because i is difficul o idenify non-u.s. index membership over ime. Limiing our analysis o one counry ensures more reliable idenificaion of index membership. We idenify index membership by referring o he original hisoric index membership liss of he Deusche Börse AG. The NEMAX50 comprises he 50 larges firms (measured by dispersed marke capializaion) of he New Marke segmen of he Frankfur Sock Exchange. The DAX30 comprises he 30 German companies ha have he highes dispersed marke capializaion. Table 7 presens he resuls from esimaing equaion (8). Wih he excepion of he coefficien on R&D, which is no longer significan, he resuls on he oher independen variables are similar o hose repored in Panel B of Table 6 and are no discussed furher. The coefficiens on he hree indicaor variables idenifying index membership are all posiive and highly significan, indicaing ha index membership is associaed wih higher R values. These resuls are consisen wih he findings of Barberis e al. (005) in he U.S. and Greenwood and Sosner (00) and Greenwood (005) in Japan. This finding is paricularly imporan, as i idenifies oher facors no relaed o firm fundamenals ha significanly conribue o differences in sock price synchroniciy across firms. Finding ha German firms membership in an index is 4

posiively relaed o SYNCH is consisen wih marke fricions and/or marke sedimen, no informaion, being a significan deerminan of sock price synchroniciy. In summary, he resuls presened in Tables 3-7 do no suppor he informaion-based inerpreaion of sock price synchroniciy in inernaional markes. Collecively, he resuls of our empirical ess sugges ha using he R measure as a meric of firm-specific informaion inernaionally is no valid. 5. An Alernaive Measure of Firm-specific Informaion in Reurns The arrival of new informaion abou a firm in he marke can generae new uncerainies and expecaions regarding he firm s fuure cash flows. If he value of an informaion signal is insufficien o exceed he coss of rading, hen he marginal invesor will no rade (Lesmond e al., 1999). If he marginal invesor does no rade, hen a zero reurn is generaed. 0 Building on his concep, we use he percen of zero reurn days (hereafer referred o as he zero-reurn meric) as an alernaive measure of he relaive amoun of informaion refleced in sock prices. 1 The zero-reurn meric is defined as he number of zero-reurn rading days over he fiscal year divided by he oal rading days of he firm s fiscal year, where zero-reurn days are hose in which he price of he sock does no change compared o he price of he previous day. 0 Bekaer e al. (003) and Lesmond (005) use he percen of zero reurns days as measure of liquidiy inernaionally, and documen ha his measure is posiively correlaed wih oher more daa inensive measures of liquidiy. However Bekaer e al. (003) noe ha one poenial reason for a zero reurn unrelaed o liquidiy is a lack of news. 1 The work of Easley, Kiefer, O Hara, and Paperman (1996) in he U.S. suppors our use of he percen of zero reurns weeks as a measure of he frequency of informaion arrival. Specifically, hey find ha firms which rade more frequenly (high volume firms) have a higher probabiliy of informaion evens relaive o low volume firms, indicaing ha as expeced he increased frequency of informaion arrival resuls in increased rading. By defining our zero reurn meric in his way, we misclassify daily observaions wih rade during he day bu wih idenical beginning-of-day and end-of-day prices as zero reurn days, which may add addiional noise o he measure. We believe ha he probabiliy of such an even is low because here are no ick size limiaions in our sample counries. However, we es wheher our resuls are sensiive o his design choice by defining our non-rading variable using he urnover daa provided by DaaSream. Our inferences remain unchanged when we use his alernaive measure. The advanage of using price daa raher han volume daa is ha in some counries, DaaSream codes zero rading volume as zero while in oher counries i presumably codes zero volume as missing values. 5

Panel A of Table 8 presens he descripive saisics on he zero-reurn meric for he six sample counries. Noe ha we use he same firm-year observaions as in he empirical ess of he synchroniciy measure o faciliae comparisons of he wo measures. Boh he mean and median values of he zero-reurn meric are larges in he U.K. (mean=0.505, median=0.554). The mean zero-reurn meric is 0.33 in Germany, followed by 0.331 in Ausralia, 0.300 in France, 0.64 in Japan, and 0.195 in he U.S. Panel B of Table 8 presens he Pearson and Spearman correlaions beween he zeroreurn meric and he synchroniciy measure. In all six counries he correlaions are significanly negaive, and he magniude of he correlaions is relaively large, in absolue erms, ranging from -0.6 in he U.S. o -0.473 in France. The consisenly negaive correlaions indicae ha lower R values are, on average, associaed wih a larger proporion of zero-reurn weeks. There are wo poenial explanaions for he negaive relaion beween he zero-reurn meric and sock price synchroniciy. Firs, recall we require a firm o have a minimum of 30 weeks of non-zero reurns o calculae is R measures for each year, resuling in 15 o 6 observaions per firm each year. When a firm has more zero bi-weekly reurns, hen he number of observaions used in esimaing equaion () is lower, which can reduce he explanaory power of he model and resul in a lower R value. A he same ime, he zero-reurn meric will be moving oward one as he proporion of weeks where he firm s sock does no rade increases. The second poenial reason for he negaive correlaions relies on infrequen, small, noninformaion-based rading. If some firms in a sample counry rade relaively infrequenly and in small amouns, here is he poenial for he bi-weekly reurns o be driven by small, somewha immaerial rades. This will resul in regressing relaively small bi-weekly reurns on he marke reurn, producing a low R ha is unrelaed o firm-specific informaion. Panel C of Table 8 displays he resuls of he price informaiveness ess using he zeroreurn meric. Specifically, we es he associaion beween he zero-reurn meric and he amoun of earnings informaion refleced in sock prices using he following equaion: 6

(9) ABRET = β E 1 + β ABRET 6 + β E * R% ZR + 1 + β R% ZR 7 + β E + 4 001 + 1 α fye fye= 1990 + β E 5 YEAR + 1 + ε * R% ZR where R%ZR is equal o he decile rank of he zero-reurn meric for fiscal year ; and all oher variable are as previously defined. If he zero-reurn meric capures he degree of firm-specific informaion refleced in reurns, we expec a negaive associaion beween he zero-reurn meric and he amoun of earnings informaion refleced in reurns. In all counries, we find ha larger zero-reurn merics are associaed wih less informaion abou he curren change in earnings being refleced in reurns. In Germany, Japan, he U.K., and he U.S., we find ha less informaion abou he change in nex-period earnings is refleced in reurns when firms rade less frequenly. The resuls presened in Panel C of Table 8 sugges ha our simple zero-reurn meric is associaed wih he amoun of earnings-relaed informaion refleced in reurns. Panel D displays he resuls of esimaing equaion (10) below o es wheher here is an associaion beween he zero-reurn meric and analyss forecas errors: 00 (10) F _ ERROR = β1 R% ZR + α fyeyear + ε fye= 1990 where all variables are as previously defined. If he zero-reurn meric is a funcion of firmspecific informaion flows, we expec a posiive associaion beween he zero-reurn meric and analyss forecas errors. We esimae equaion (10) wihou conrol variables o be comparable o he R analysis repored in Table 4. The resuls of he OLS regressions indicae ha in Ausralia, France, he U.K., and he U.S., larger zero-reurn merics are associaed wih larger analys forecas errors. We find no significan associaion beween he zero-reurn meric and analys forecas errors in Germany and Japan. These findings are in conras o hose repored in Table 4, where we find inconsisen associaions beween R values and analys forecas errors across he sample counries. 7

Panels E and F of Table 8 presen he resuls of he cross lising analysis, where we define he change in zero-reurn meric values as he zero-reurn meric value in he 1 monhs following he cross lising monh minus he zero-reurn meric value in he 1 monhs preceding he cross lising monh. 3 Panel E presens he mean and median changes in all cross lisings over he 1990-00 ime period. The only counry for which he change in he zero-reurn measure is significan is France, where boh he mean and median values indicae a decline in he zero-reurn measure following cross lising. Panel F examines only he Level and 3 ADRs since hese ypes of cross lising are associaed wih he greaes informaion disclosures. The resuls presened in Panel F provide some evidence suggesing ha French and U.K. firms rade more frequenly following heir cross lising in he U.S., as he mean and median R are negaive and saisically differen from zero a he 0.01 level or beer. Our final analysis ess he exen o which firm fundamenals are relaed o he zeroreurn meric in our sample counries. Panel G presens he resuls from esimaing he following model: %ZERORET (11) = β LOSS 1 + β R & D + β STDROA + β REG 6 + β % TURNOVER + 10 7 i + β ANALYST 3 + β RELSALES 00 8 α fye fye= 1990 YEAR + ε + β % CLHLD 4 + β % MVE 9 + β STDSALES 5 where %ZERORET is equal o log(%zr/(1-%zr)) for fiscal year, and %ZR is he percen of zero-reurn days. 4 All oher variables are as previously defined. The explanaory power of he zero-reurn meric model ranges from a low of 53% in Japan o a high of 78% in he U.S. In all counries he explanaory power of he zero-reurn meric model is higher han he explanaory power of he SYNCH model repored in Table 6. The resuls 3 There is limied variance in he dependen variable in his analysis as cross-lised firms are, on average, large and well-raded. Thus, he level of zero-reurn days is low ex ane as well as ex pos he cross lising, biasing agains finding a resul. 4 In order o implemen he log ransformaion, we modify %ZR o be he amoun of non-raded days plus one divided by he oal number of days in he fiscal year plus wo. 8

displayed in Panel G of Table 8 also sugges ha he zero-reurn meric is a beer indicaor of firm-specific informaion refleced in reurns, as he signs and significance of he conrol variables are also more consisen across counries. In fac, he las row of Panel G of Table 8 indicaes ha he signs (and significance) of every coefficien are consisen wih expecaions when esimaing he deerminan model using U.K. or U.S. firms. These findings provide addiional evidence suggesing ha he zero-reurn meric more appropriaely capures he relaive amoun of informaion impounded ino share prices han he synchroniciy measure. 5 Table 9 summarizes our empirical ess. In Panel A of Table 9, we recapiulae he resuls of our empirical analysis where we es wheher he synchroniciy measure is a measure of firmspecific informaion impounded in share prices. Panel B of Table 9 summarizes our findings relaed o he zero-reurn meric. Based on he resuls of our empirical analysis, we conclude ha he easy o calculae zero-reurns meric beer capures differences in he relaive amoun of firmspecific informaion refleced in reurns han he synchroniciy measure. 6 6. Addiional Analysis To examine he robusness of our inferences, we make he following modificaions o equaion () in calculaing he synchroniciy measure: use equal weighed reurns; drop he U.S. marke reurn from equaion (); include indusry wide reurns in equaion (), where indusries are defined based on -digi SIC codes; esimae equaion () using weekly insead of bi-weekly reurns; and include lagged reurns as addiional explanaory variables. Overall, repeaing he analysis wih he alernaive synchroniciy measures resuls in similar inferences. We also examine he influence of zero reurns on he synchroniciy measure as one poenial explanaion for why he synchroniciy measure does no capure informaion. To 5 As a robusness es we use TURNOVER insead of he zero reurn measure and repea he es in Tables 3-6. While performing beer han he original R² measure, TURNOVER does no perform nearly as well as he zero-reurn meric. 6 We draw he same inferences when comparing he synchroniciy resuls o he zero-reurn resuls across he 15 oher counries ha we examine. Appendix A summarizes he inferences drawn from he empirical analyses using firm-year observaions from he oher fifeen counries idenified in Table 1. 9

invesigae he effec of infrequen rading on he usefulness of he synchroniciy measure, we conduc a sensiiviy analysis where we modify he R² measure, specifically, we calculae for each firm-year observaion: (1) R R = 1 %ZR (1 R ) TRADE where all variables are as previously defined. As %ZR approaches zero, RTRADE becomes R, and as %ZR approaches one, RTRADE becomes one. We repea he analyses presened in Tables 3-6 using his modified R measure. The resuls are similar o hose previously repored. Thus, our addiional analyses provide furher evidence ha he R measure does no appear o reflec differences in he informaional efficiency of firms share prices inernaionally. Much of our analysis relies on proxies for firms public and privae informaion flows. In he mid-1990s he qualiy of firms acual informaion flows was assessed via AIMR scores. 7 As an addiional robusness es, we examine he associaion beween AIMR scores and he R measure and he zero-reurn meric. We limi his analysis o he U.S. because AIMR scores were available primarily for U.S. firms. Inconsisen wih he informaion-based inerpreaion of he R measure, we find a posiive associaion beween AIMR scores and he R measure. The inerpreaion of his finding is ha firms wih beer disclosures as judged by AIMR had more sock synchroniciy han firms wih fewer disclosures. In conras, we find a negaive associaion beween he zero-reurn meric and AIMR scores, indicaing ha firms ha analyss rae as having beer disclosures more ofen generae reurns. To furher our inerpreaion of he zero-reurn meric as a measure ha capures he relaive amoun of firm-specific informaion refleced in reurns, we conduc one las analysis. We examine he associaion beween he average magniude of reurns and he zero-reurn meric 7 AIMR scores represen he annual reviews of corporae reporing and disclosure pracices prepared by he corporae informaion commiee of he Associaion for Invesmen Managemen Research. 30

across counries. If he zero-reurn measure capures he exen o which informaion abou he firm is accumulaing ouside of he price formaion process, we should observe a posiive associaion beween he zero-reurn measure and he magniude of reurns. In Ausralia, France, Germany, Japan, he U.K. and U.S., we find ha he magniude of he reurns (when reurns occur) is posiively associaed wih he zero-reurn measure. This finding is consisen wih he zero-reurn meric capuring he relaive amoun of informaion refleced in reurns. 7. Conclusions Morck e al. (000) documen differences in sock price synchroniciy across counries, claiming ha hese differences are due o he variaion in propery righs and he influence ha propery righs have on informed invesors rading incenives. Prior inernaional research assumes ha he counry-level R values reflec he amoun of informaion impounded in sock prices and uses his measure o explain cross-counry differences in evens of ineres o finance and accouning researchers. This paper invesigaes he validiy of he informaion-based inerpreaion of sock price synchroniciy in six markes. Collecively, he resuls of our analysis sugges ha he variaion in sock price synchroniciy across firms in inernaional markes is no due o differences in firm-specific informaion. We offer he zero-reurn meric, defined as he percen of zero-reurn days, as an alernaive measure of he relaive amoun of firm-specific informaion refleced in sock prices inernaionally. Based on he resuls of a muliude of ess, we conclude ha he zero-reurn meric is more useful in capuring he differences in informaion environmens across firms han he synchroniciy measure. Conemporaneous research uses he percen of zero-reurn days as a measure of liquidiy, where smaller values represen more liquid socks (Bekaer e al., 005). Bekaer e al. (005) documen ha in emerging markes he percen of zero-reurn days is a priced risk facor. Easley and O Hara (005) provide a heoreical foundaion for informaion being a priced risk facor. Thus, i is no clear wheher he zero-reurn meric is impounded ino 31

share prices because i proxies for liquidiy or i proxies for informaion or boh. Fuure research can furher probe he usefulness of he zero-reurn meric in marke analysis. 3

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Appendix A Summary of Resuls Addiional Counries Panel A: The R Measure Counry Fuure Earnings Analys Forecas Errors Cross Lising in he U.S. Deerminan Model Belgium No No No No Canada Yes No N/A No Denmark Yes No No No Finland No No No No Hong Kong No No No No Ireland No No No No Ialy No No No No The Neherlands No No No No Norway No No No No Singapore No No No No Souh Africa No No Yes No Souh Korea No No No No Spain No No No No Sweden No No No No Swizerland No No No No 38

Panel B: The Zero-Reurn Meric Appendix A Coninued Counry Fuure Earnings Analys Forecas Errors Cross Lising in he U.S. Deerminan Model Belgium Yes Yes No Yes Canada Yes Yes N/A Yes Denmark Yes No No Yes Finland Yes No Yes Yes Hong Kong No Yes No Yes Ireland No Yes No Yes Ialy No No No Yes The Neherlands Yes Yes No Yes Norway Yes Yes No Yes Singapore Yes Yes No Yes Souh Africa Yes No No Yes Souh Korea No No No No Spain Yes Yes No Yes Sweden No No No Yes Swizerland No No No Yes This able summarizes he resuls of he empirical analysis. Yes indicaes ha he es resuls are consisen wih he informaion-based inerpreaion, and No indicaes ha here is no resul or he resuls are no consisen wih he informaion-based inerpreaion. 39

TABLE 1 Counry-Wide R Measures Panel A: Descripive Saisics on Counry-wide R Measures (1990-00) Counry Mean Median Mean Rank n USA 0.113 0.097 1.615 75,06 Canada 0.146 0.148 3.385 10,753 Ausralia 0.148 0.149 4.077 8,35 France 0.173 0.165 6.000 8,545 Ireland 0.184 0.184 7.31 905 Germany 0.187 0.0 7.385 8,373 Souh Africa 0.183 0.174 7.93 4,14 Denmark 0.189 0.189 8.077,106 UK 0.198 0.13 8.154 18,913 Swizerland 0.14 0.18 10.308,69 Neherlands 0.1 0.17 11.846,581 Norway 0.49 0.8 1.769 1,485 Belgium 0.47 0.33 13.31 1,5 Sweden 0.53 0.50 13.93,71 Finland 0.61 0.47 15.000 1,195 Hong Kong 0.71 0.51 15.154 5,997 Souh Korea 0.84 0.71 15.93 8,343 Japan 0.94 0.84 16.000 36,553 Spain 0.85 0.70 16.69 1,489 Ialy 0.313 0.78 17.077,844 Singapore 0.359 0.34 19.31 3,43 Panel B: Insiuional Explanaions for R Measure 00 R c, = β1legalc, + β RIGHTSc, + β 3GDPc, + α fyeyear + ε c, fye= 1990 1 3 4 LEGAL 0.043*** 0.05*** RIGHTS -0.010*** 0.010 GDP 0.003-0.013 AdjR 0.30 0.7 0.4 0.31 n 73 73 73 73 40

Variable definiions: As in Morck e al (000) and RET R c R i i, c, SSTi = SST i i, c,, c,, where SST i,c, is he oal sum of squared variaions R is equal o he R from he following regression: i, c, i = β 0, i + β1, i RETMKTc + β, i RETMKTUS + ε i for all counries bu he U.S. For he U.S. we esimae he regression: RET + RETMKT β β + ε i = 0, i 1, i c i where RET i is he reurn for firm i for he wo week period, RETMKT c is he reurn on he marke for counry c for period, and RETMKTUS is he reurn on he US marke over period. All reurns are expressed in he local currency. LEGAL is equal o one if he counry is classified as having a code law legal origin (La Pora e al. 1998). RIGHTS is equal o he invesor righs index developed by La Pora e al. (1998), where counries receive one poin for each of he following, allowing voing by mail, he requiremen of invesors o deposi heir shares prior o shareholder meeings, if cumulaive voing or proporional represenaion of minoriy shareholder on he board is allowed, if here are mechanisms in place o for oppressed minoriy shareholders, he minimum ownership required o call an exraordinary shareholder meeing, and if shareholders have preempive righs. GDP is equal o he log of he per capia gross domesic produc for he counry year. YEAR is equal o a series of fiscal year fixed effecs. Mean Rank is equal o he mean yearly rank for he counry, where he 0 sample counries are ranked each year from 1990 o 00. ***, **, * indicaes significance a he 0.01, 0.05 and 0.10 levels wo-ailed, respecively. 41

TABLE Descripive Saisics on Firm-Specific R Measures Counry 5 h Mean Median 75 h Sd. Dev n Ausralia 0.07 0.19 0.158 0.77 0.15,895 France 0.056 0.183 0.137 0.68 0.16 5,368 Germany 0.066 0.03 0.156 0.301 0.17 3,515 Japan 0.155 0.319 0.98 0.463 0.199 3,58 U.K. 0.07 0.17 0.168 0.315 0.183 14,48 U.S. 0.016 0.118 0.067 0.174 0.136 56,95 Variable definiions: R is equal o he R from he following regression: RETi = β 0, i + β1, i RETMKTc + β, i RETMKTUS + ε i for all counries bu he U.S. For he U.S. we esimae he regression: RET + RETMKT β β + ε i = 0, i 1, i c i where RET i is he reurn for firm i for he wo week period, RETMKT c is he reurn on he marke for counry c for period, and RETMKTUS is he reurn on he US marke over period. All reurns are measured in he local currency. 4

TABLE 3 The R Measure and he Price Informaiveness of Earnings ABRET = β 001 1 E + β E * RR + β 3 E+ 1 + β 4 E+ 1 * RR + β 5 ABRET + 1 + β 6RR + α fyeyear + fye= 1990 ε E E * RR E +1 E +1 * RR ABRET +1 Expeced sign if RR is informaionbased + +?? RR AdjR Ausralia 0.149 0.001 0.071-0.00 0.043 0.000 0.04 0.11 0.97 0.34 0.17 0.1 0.94 France 0.163 0.043-0.098 0.011 0.054 0.004 0.08 0.0 0.01 0.01 0. 0.00 0.03 Germany 0.194 0.019-0.183 0.046 0.085 0.007 0.06 0.10 0.44 0.06 0.01 0.00 0.00 Japan 0.40 0.011 0.086 0.013-0.105-0.005 0.17 0.00 0.35 0.06 0.17 0.00 0.00 U.K. 0.39 0.007 0.10-0.001 0.034 0.00 0.10 0.00 0.59 0.00 0.9 0.00 0.0 U.S. 0.593 0.058 0.01 0.038-0.064 0.006 0.1 0.00 0.00 0.56 0.00 0.00 0.00 Variable definiions: ABRET is he marke adjused buy and hold reurn over fiscal year. RR is equal o he decile rank of he R value for fiscal year. E is equal o he change in ne income before exraordinary iems scaled by beginning of period marke value of equiy for fiscal year. YEAR is equal o a series of fiscal year fixed effecs. P-values are based on Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. 43

TABLE 4 R Measure and Analyss Forecas Errors F _ ERROR = β RR 1 + 00 fye= 1990 α fye YEAR + ε Expeced sign if RR is informaionbased Ausralia France Germany Japan U.K. U.S. RR + -0.086-0.076-0.060 0.04-0.085-0.148 0.00 0.01 0.00 0.00 0.00 0.00 Adj R 0.01 0.01 0.00 0.00 0.01 0.0 n,085 3,33,574 1,401 9,408 38,57 Variable definiions: RR is equal o he decile rank of he R value for fiscal year. F_ERROR is equal o he decile rank of EPS ac EPS forecas / EPS forecas for fiscal year, where EPS ac is he firm s acual earnings per share and EPS forecas is he mean consensus earnings per share forecas. Analys earnings forecass are provided by IBES. YEAR is equal o a series of fiscal year fixed effecs. P-values are based on Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. 44

TABLE 5 U.S. Cross Lising Analysis Panel A: Change in R afer Cross Lising in he U.S. (All ADRs) Mean R p-value Median R p-value n Expeced sign if R is informaion-based Ausralia -0.00 0.49-0.010 0.66 55 France 0.080 0.0 0.045 0.0 31 Germany 0.041 0.41 0.007 0.5 Japan -0.003 0.95 0.01 0.94 43 U.K. 0.055 0.0 0.06 0.05 89 Panel B: Change in R afer Cross Lising in he U.S. (Level and 3 ADRs) Mean R p-value Median R p-value n Expeced sign if R is informaion-based Ausralia -0.037 0.54 0.00 0.71 14 France 0.110 0.01 0.078 0.0 17 Germany 0.1 0.1 0.43 0.0 9 Japan 0.004 0.95-0.004 0.89 17 U.K. 0.086 0.01 0.048 0.0 43 Variable definiions: R is equal o he R in he year following cross lising in he U.S. minus he R in he year before cross lising in he U.S. 45

TABLE 6 Sock Price Synchroniciy and Firms Informaion Flows and Fundamenals Panel A: Pearson Correlaions of Firms Informaion Flows and Fundamenals wih R Values Ausralia France Germany Japan U.K. U.S. Variables proxying for firm fundamenals LOSS -0.113*** -0.075*** -0.047*** 0.00-0.065*** -0.076*** R&D 0.111*** 0.118*** 0.150*** 0.057*** -0.08*** 0.05*** ANALYST 0.94*** 0.45*** 0.351*** 0.077*** 0.010 0.54*** %CLHLD -0.17*** -0.69*** -0.88*** -0.19*** -0.09*** STDSALES -0.036* -0.001 0.014-0.08*** -0.14*** -0.06*** STDROA -0.110*** -0.16*** -0.06-0.017*** -0.13*** -0.056*** Conrol variables REG 0.053*** -0.048*** -0.009 0.018*** 0.74*** 0.011*** RELSIZE 0.154*** 0.61*** 0.135*** 0.076*** 0.063*** 0.078*** MVE 0.3*** 0.394*** 0.74*** 0.63*** 0.47*** 0.77*** TURNOVER 0.113*** 0.65*** 0.19*** 0.05*** 0.088*** 0.079*** 46

TABLE 6 Coninued Panel B: Sock Price Synchroniciy and Firm Fundamenals (OLS Regression) SYNCH = β LOSS + β R & D + β ANALYST + β 1 i 3 4 % + β REG 7 + β RELSIZE 8 + β MVE 9 + β TURNOVER 10 CLHLD + β STDSALES + 5 00 α fye fye = 1990 YEAR + β STDROA 6 + ε Prediced sign Ausralia France Germany Japan U.K. U.S. LOSS - 0.053-0.034-0.039 0.140*** 0.053-0.04* R&D - 0.147** 0.016 0.154** 0.18*** -0.003 0.19*** ANALYST - 0.131*** 0.61*** 0.184*** 0.069*** -0.94*** 0.07*** %CLHLD +/- -0.403*** -0.710*** -0.964*** -1.045*** -0.715*** STDSALES - 0.4* 0.473*** 0.378** -0.06-0.103 0.137*** STDROA - 0.168 0.53 1.144** 3.083*** -0.093 0.006 REG + 0.173** 0.08 0.157** -0.060** 0.635*** 0.185*** RELSIZE + 0.0* 0.095-0.10-0.15-0.113-0.179 MVE +/- 0.153** 0.134*** 0.16*** 0.138*** 0.61*** 0.06*** TURNOVER +/- 18.841 9.77** 0.436*** 9.674 16.191***.186*** AdjR 0.16 0.4 0.5 0.1 0. 0.14 % of coefficiens wih he correc sign 40% 30% 40% 0% 50% 45% Variable definiions: SYNCH is equal o log(r/(1-r)) for fiscal year ; LOSS is equal o one if ne income before exraordinary iems is negaive, and zero oherwise; R&D is equal o one if he firm repors a value for research and developmen expense, and zero oherwise; ANALYST is equal o he log of one plus he number of analyss making a forecas for fiscal year s earnings; %CLHLD is he proporion of shares ha are closely held as of he end of he fiscal year ; STDSALES is he sandard deviaion of sales scaled by oal asses calculaed requiring a minimum of hree and maximum of five fiscal years; STDROA is he sandard deviaion of ROA calculaed requiring a minimum of hree and maximum of five fiscal years where ROA is equal ne income before exraordinary iems divided by fiscal year end oal asses; REG is equal o one if he firm is a financial insiuion or uiliy; RELSALES is he firm s sales divided oal sales of is primary indusry ( digi SIC); MVE is defined as he naural log of fiscal year end marke value of equiy; TURNOVER is he average weekly urnover (number of shares raded divided by number of shares ousanding) over he fiscal year; and YEAR is equal o a series of fiscal year fixed effecs.***, **, * indicaes significance a he 0.01, 0.05 and 0.10 levels, respecively. P-values are based on Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. 47

TABLE 7 Index Analysis using German Marke SYNCH = β LOSS β REG 7 + β R & D + β RELSIZE β NEWMARKET 1 1 8 i + β ANALYST + β MVE + β NEMAX 50 13 3 9 + β % CLHLD + β TURNOVER + 10 00 4 fye= 1990 α fye + β STDSALES 5 + β DAX 30 11 YEAR + ε + + β STDROA 6 + Expeced sign Coefficien LOSS - -0.098 R&D - 0.083 ANALYST - 0.165*** %CLHLD +/- -0.775*** STDSALES - 0.35* STDROA - 0.79* REG + 0.151** RELSIZE + -0.140 MVE +/- 0.10*** TURNOVER +/- 0.6** DAX30-0.708*** NEWMARKET - 0.394*** NEMAX50-0.549*** AdjR 0.6 Variable definiions: DAX30 is equal o if he firm is par of he DAX30 index in fiscal year, zero oherwise. NEMAX50 is equal o one if he firm is par of he NEMAX50 index in fiscal year, zero oherwise. NEWMARKET is equal o one if he firm is par of he Ner Marke of Frankfur Sock Exchange in fiscal year, zero oherwise. SYNCH is equal o log(r/(1-r)) for fiscal year ; LOSS is equal o one if ne income before exraordinary iems is negaive, and zero oherwise; R&D is equal o one if he firm repors a value for research and developmen expense, and zero oherwise; ANALYST is equal o he log of one plus he number of analyss making a forecas for fiscal year s earnings; %CLHLD is he proporion of shares ha are closely held as of he end of he fiscal year ; STDSALES is he sandard deviaion of sales scaled by oal asses calculaed requiring a minimum of hree and maximum of five fiscal years; STDROA is he sandard deviaion of ROA calculaed requiring a minimum of hree and maximum of five fiscal years where ROA is equal ne income before exraordinary iems divided by fiscal year end oal asses; REG is equal o one if he firm is a financial insiuion or uiliy; RELSALES is he firm s sales divided oal sales of is primary indusry ( digi SIC); MVE is defined as he naural log of fiscal year end marke value of equiy; TURNOVER is he average weekly urnover (number of shares raded divided by number of shares ousanding) over he fiscal year; and YEAR is equal o a series of fiscal year fixed effecs. ***, **, * indicaes significance a he 0.01, 0.05 and 0.10 levels. P-values are based on Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. 48

TABLE 8 Zero-reurn Meric Analysis Panel A: Descripive Saisics on he Zero-reurn Meric Counry 5 h Mean Median 75 h Sd. Dev n Ausralia 0.165 0.331 0.81 0.450 0.08,895 France 0.119 0.300 0.5 0.47 0.9 5,368 Germany 0.13 0.330 0.4 0.496 0.53 3,515 Japan 0.135 0.64 0.196 0.37 0.189 3,58 U.K. 0.81 0.505 0.554 0.73 0.54 14,48 U.S. 0.087 0.195 0.179 0.68 0.140 56,95 Panel B: Correlaions beween he Zero-reurn Meric and R Values Ausralia France Germany Japan U.K. U.S. Pearson -0.310-0.391-0.390-0.343-0.36-0.6 0.00 0.00 0.00 0.00 0.00 0.00 Spearman -0.345-0.473-0.459-0.81-0.36-0.343 0.00 0.00 0.00 0.00 0.00 0.00 49

Panel C: Zero-reurn Meric and he Price Informaiveness of Earnings ABRET = β 001 1 E + β E * R% ZR + β 4 E+ 1 + β 5 E+ 1 * R% ZR + β 6 ABRET + 1 + β 7 R% ZR + α fyeyear + fye= 1990 ε E E *R%ZR E +1 E +1 * ABRET +1 R%ZR AdjR R%ZR Expeced sign if R%ZR is informaionbased + +?? Ausralia 0.486-0.05-0.133 0.0 0.046-0.009 0.05 0.03 0.08 0.7 0.18 0.09 0.00 France 0.576-0.046 0.003-0.01 0.057-0.007 0.08 0.00 0.00 0.96 0.1 0.00 0.00 Germany 0.557-0.059 0.47-0.05 0.081-0.005 0.07 0.00 0.0 0.0 0.00 0.00 0.0 Japan 0.875-0.079 0.37-0.043-0.106-0.011 0.18 0.00 0.00 0.00 0.00 0.00 0.00 U.K. 0.859-0.077 0.194-0.016 0.039-0.015 0.1 0.00 0.00 0.00 0.00 0.00 0.00 U.S. 1.79-0.15 0.556-0.06-0.061-0.017 0.13 0.00 0.00 0.00 0.00 0.00 0.00 50

TABLE 8 Coninued Panel D: Zero-reurn Meric and Analyss Forecas Errors F _ ERROR = β R 1 % ZR + 00 fye= 1990 α fye YEAR + ε Expeced sign if R%ZR is informaionbased Ausralia France Germany Japan U.K. U.S. R%ZR + 0.148 0.109 0.04 0.01 0.180 0.343 0.00 0.00 0.49 0.31 0.00 0.00 Adj R 0.0 0.01 0.00 0.00 0.03 0.09 n,085 3,33,574 1,401 9,408 38,57 Panel E: Change in Zero-reurn Meric afer Cross Lising in he U.S. (All ADRs) Mean ZR p-value Median ZR p-value n Expeced sign if Zero- Reurn Meric is informaion-based Ausralia 0.015 0.39-0.006 0.61 48 France -0.011 0.01-0.01 0.00 9 Germany 0.04 0. 0.004 0.8 3 Japan -0.00 0.78 0.000 0.93 41 U.K. 0.015 0.36 0.000 0.74 84 Panel F: Change in Zero-reurn Meric afer Cross Lising in he U.S. (Level and 3 ADRs) Mean ZR p-value Median ZR p-value n Expeced sign if Zero- Reurn Meric is informaion-based Ausralia -0.00 0.86-0.004 0.84 7 France -0.014 0.01-0.008 0.01 17 Germany 0.001 0.73 0.000 0.80 9 Japan -0.006 0.37-0.004 0.73 17 U.K. -0.043 0.01-0.019 0.00 35 51

TABLE 8 Coninued Panel G: Zero-reurn Meric and Firm Fundamenals (OLS Regression) % ZERORET = β LOSS 1 + β REG 7 + β R & D + β RELSIZE 8 i + β ANALYST 3 + β % MVE 9 + β % CLHLD 4 + β % TURNOVER 10 + β STDSALES 5 + 00 α fye fye = 1990 + β STDROA 6 YEAR + ε Prediced sign Ausralia France Germany Japan U.K. U.S. LOSS - -0.3*** -0.07-0.101*** -0.138*** -0.30*** -0.00*** R&D - -0.061 0.048-0.194*** -0.19*** -0.114*** -0.150*** ANALYST - -0.86*** -0.498*** -0.594*** -0.034*** -0.061*** -0.157*** %CLHLD +/- 0.791*** 1.055*** 1.308*** 0.84*** 0.706*** STDSALES - -0.70*** -0.303** -0.774*** -0.61** -0.051-0.151*** STDROA - -0.635*** -1.30** -1.644*** -3.06*** -0.410*** -0.069*** REG + 0.043 0.155** 0.075 0.07*** -0.164*** 0.037*** RELSIZE + 0.035 0.10* 0.38** 0.511*** -0.117 0.451*** MVE +/- -0.315*** -0.178*** -0.140*** -0.80*** -0.479*** -0.46*** TURNOVER +/- -71.097*** -8.401** -0.4** -163.039*** -87.993*** -5.747*** AdjR 0.70 0.65 0.67 0.53 0.75 0.78 % of coefficiens wih he correc sign 70% 80% 90% 100% 70% 100% 5

TABLE 8 Coninued Variable definiions: %ZR is equal o he percen of days in over he fiscal year for which he sock price does no change; ABRET is he marke adjused buy and hold reurn over fiscal year ; R%NT is equal o he decile rank of he %ZR value for fiscal year ; E is equal o he change in ne income before exraordinary iems scaled by beginning of period marke value of equiy for fiscal year ; F_ERROR is equal o he decile rank of EPS ac EPS forecas / EPS forecas for fiscal year, where EPS ac is he firm s acual earnings per share and EPS forecas is he mean consensus earnings per share forecas; R is equal o he R in he year following cross lising in he U.S. minus he R in he year before cross lising in he U.S. %ZERORET is equal o log(%zr/(1-%zr)) for fiscal year ; LOSS is equal o one if ne income before exraordinary iems is negaive, and zero oherwise; R&D is equal o one if he firm repors a value for research and developmen expense, and zero oherwise; ANALYST is equal o he log of one plus he number of analyss making a forecas for fiscal year s earnings; %CLHLD is he proporion of shares ha are closely held as of he end of he fiscal year ; STDSALES is he sandard deviaion of sales scaled by oal asses calculaed requiring a minimum of hree and maximum of five fiscal years; STDROA is he sandard deviaion of ROA calculaed requiring a minimum of hree and maximum of five fiscal years where ROA is equal ne income before exraordinary iems divided by fiscal year end oal asses; REG is equal o one if he firm is a financial insiuion or uiliy; RELSIZE is he firm s sales divided oal sales of is primary indusry ( digi SIC); MVE is defined as he naural log of fiscal year end marke value of equiy; TURNOVER is he average weekly urnover (number of shares raded divided by number of shares ousanding) over he fiscal year; and YEAR is equal o a series of fiscal year fixed effecs. ***, **, * indicaes significance a he 0.01, 0.05 and 0.10 levels, respecively. Regression model p-values are based on Rodgers (cluser) sandard errors which accouns for possible clusering a he firm level. 53

TABLE 9 Summary of Resuls Panel A: The Synchroniciy Measure as a Measure of Firm-specific Informaion Impounded in Share Prices Analys Forecas Errors Fuure Earnings Cross Lising in he U.S. Ausralia No No No No France No No No No Germany No No No No Japan No Yes No No U.K. No No No Yes U.S. No No n/a No Deerminan Model Panel B: The Zero-Reurn Meric as a Measure of Firm-specific Informaion Impounded in Share Prices Analys Forecas Errors Fuure Earnings Cross Lising in he U.S. Ausralia No Yes No Yes France No Yes Yes Yes Germany Yes No No Yes Japan Yes No No Yes U.K. Yes Yes Yes Yes U.S. Yes Yes n/a Yes Deerminan Model This able summarizes he resuls of he empirical analysis. Yes indicaes ha he es resuls are consisen wih he informaion-based inerpreaion, and No indicaes ha here is no resul or he resuls are no consisen wih he informaion-based inerpreaion. 54