explaining country and cross-border liquidity commonality in international equity markets

The financial support from Research Grant (Project No. 70432002) from National Natural ScienceFoundation of China (Zhang), Hong Kong Special Administrative Government UGC Grant (CERG9041176), and City University of Hong Kong (Cai) is gratefully acknowledged. We thank the editor (RobertWebb) and an anonymous referee for their valuable suggestions. We also thank the seminar participants atthe Asian-Pacific Finance Association Annual Conference in Yokohama 2008, Waseda University in Japan,Singapore Management University, and Sungkyunkwan University in Korea for their helpful comments.Rachel Gordon provided the editorial assistance. All errors remain our own responsibility.

*Correspondence author, Guanghua School of Management, Peking University, Beijing 100871, People’sRepublic of China. Tel: �86-10-6276-7856, Fax: �86-10-6275-1463, e-mail: [email protected]

Received September 2008; Accepted October 2008

■ Zheng Zhang is at the Guanghua School of Management, Peking University, Beijing, People’sRepublic of China.

■ Jun Cai and Yan Leung Cheung are in the Department of Economics and City University ofHong Kong, Hong Kong, People’s Republic of China.

The Journal of Futures Markets, Vol. 29, No. 7, 630–652 (2009)© 2009 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/fut.20383

EXPLAINING COUNTRY AND

CROSS-BORDER LIQUIDITY

COMMONALITY IN

INTERNATIONAL EQUITY

MARKETS

ZHENG ZHANG*JUN CAIYAN LEUNG CHEUNG

Using a large cross section of intraday data from 25 developed countries, westudy commonality in liquidity, both within and across international equity mar-kets, over 15-minute intervals. Within-country and cross-border liquidity com-monalities are found to be significant and, after controlling for country andindustry effects, relate to such firm-specific measures as size, bid–ask spread, and the extent of analyst coverage. Additionally, within-country liquidity common-ality is lower for firms with depository receipts cross listed in New York or London.

Explaining Country and Cross-Border Liquidity 631

Journal of Futures Markets DOI: 10.1002/fut

Cross-border liquidity commonality is particularly high for firms with relativelyhigh actual ownership by foreign institutions. © 2009 Wiley Periodicals, Inc. JrlFut Mark 29:630–652, 2009

INTRODUCTION

The literature documenting patterns and sources of commonality in asset liq-uidity has grown rapidly in recent years, beginning with the study of Chordia,Roll, and Subrahmanyam (2000), Hasbrouck and Seppi (2001), and Hubermanand Halka (2001). These studies document the degree to which individual stockliquidity is correlated with market-wide measures of liquidity. More recently, anumber of articles have investigated commonality in liquidity for stocks andbonds (Chordia, Sarkar, & Subrahmanyam, 2005; Darbha & Subramanian,2008; Goyenko, 2005), for stocks handled by the same specialist firm(Coughenour & Saad, 2004), across order types (Domowitz, Hansch, & Wang,2005), and for stocks across countries (Brockman, Chung, & Perignon, 2008;Karolyi, Lee, & van Dijk, 2007; Stahel, 2003, 2005).

Understanding the co-movement of international liquidity is important forglobal investors, as systematic liquidity appears to be priced (Amihud &Mendelson, 1986; Brennan & Subrahmanyam, 1996; Pastor & Stambaugh,2003) and can affect strategies that attempt to minimize the liquidity impact oftrades (Chordia, Roll, & Subrahmanyam, 2001). The co-movement of interna-tional liquidity is also important for those investors who are relying more andmore on single stock futures (SSF), which have experienced rapid growth inrecent years.1 For institutional investors, SSF are a more cost-effective alterna-tive to long or short selling positions in equity cash markets. This is especiallytrue for international markets, where trading large portfolios might incur highcosts as a result of the relative illiquidity of the underlying stocks. Whether agroup of stocks respond significantly to their home or neighboring countries’liquidity movement (indicated by a widening or a narrowing of the bid–askspread) will significantly influence choices made by portfolio managers decid-ing between cash and derivative instruments, especially when they have a largeasset base.

The purpose of this article is to comprehensively study commonalities in liq-uidity within and across international equity markets. We will first documentwhether common variation in liquidity is prevalent in world equity markets and whether there exists any cross-border linkage in liquidity variation. Then we

1In the Asian-Pacific region, the leading SSF markets are the National Stock Exchange of India, the KoreaExchange, the Australian Stock Exchange, and the Hong Kong Exchange. In Europe, SSF contracts aremainly available on the Spanish Exchange, Eurex, and Euronext.liffe. South Africa’s Johannesburg StockExchange has the second largest volume, after India. See World Federation of Exchanges Annual Report andStatistics (2007).

632 Zhang, Cai, and Cheung


will examine what are the factors that determine the cross-sectional variation inliquidity commonality. For our “within-country” tests, liquidity measures for eachstock are individually regressed on the aggregate liquidity measure of its homecountry, as previous authors have done for U.S. stocks. Cross-sectional regres-sions relate the resulting slope coefficients (liquidity “bs”) to firm characteristics.For our “cross-border” tests, liquidity measures for each stock are individuallyregressed on the aggregate liquidity measure for a group of neighboring coun-tries. The resulting liquidity bs are then related to firm characteristics.

Our analysis offers several contributions to the literature. First, we notonly document liquidity commonality, but also explore the cross-sectionaldeterminants of commonality across the thousands of individual firms in oursample. We relate each firm’s liquidity commonality to individual firm meas-ures of size, bid–ask spread, analyst coverage, measures of foreign investoraccessibility and ownership, and other variables. Our use of these variablesyields interesting insights into the origins of commonality and allows us tospeculate about underlying investor behavior. In contrast, Brockman et al.(2008) measure the liquidity commonality of individual stocks across countriesbut do not explain its cross-sectional variation. Karolyi et al. (2007) not onlyconstruct monthly liquidity measures by country and relate them to countrycharacteristics, but also do not look at cross-sectional variation.

Second, we do not focus on a single country as most previous studies havedone. We study 4,119 stocks traded in 25 developed economies: Austria, Belgium,Luxembourg, Denmark, Finland, France, Germany, Greece, Iceland, Ireland,Italy, The Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, theUnited Kingdom, Australia, Hong Kong, Japan, New Zealand, Singapore,Korea, and Canada. By looking at the co-movement in liquidity within coun-tries beyond the United States, we can compare and contrast findings acrosscountries with the existing U.S. data. By looking at the co-movement in liquid-ity across countries, we help extend the literature on cross-border effects on stock returns and return volatilities to cross-border linkages in liquiditydynamics.2 As a result, we have a greater understanding of how trading activityspills cross borders and how trading may evolve during a crisis.3

Third, we base our study on very comprehensive tick data from an averageof 48-day period in the fall of 2004. The data consist of detailed information onquotes, time-stamped to the nearest second. Studies such as Stahel (2003,2005) and Karolyi et al. (2007) rely on liquidity measures derived from dailytrading data (Amihud, 2002), which are less precise than our measures that areconstructed from intraday quotes. Furthermore, our use of 15-minute intervals

2Gagnon and Karolyi (2006) comprehensively survey price and volatility transmission cross borders.3See, for example, evidence on contagion in returns, trading, and liquidity during Mexico’s peso crisis inBailey, Chan, and Chung (2000).



allows us to synchronize liquidity measures for very precise measurement ofcross-border liquidity commonality.4

Our major findings can be summarized as follows. First, “within-country”tests indicate a strong degree of commonality in liquidity for most of the 25countries in the sample. Therefore, the country effect in liquidity documentedby earlier studies extends beyond single-country studies of the United Statesand other countries.5 “Within-country” liquidity bs are significantly related toseveral cross-sectional firm characteristics that reflect size, the level of bid–askspreads, and whether the stocks are available for trading as depository receiptsin London or New York.

Second, “cross-border” tests also indicate strong evidence of commonalityin liquidity, although the number of countries with a large proportion of firmswith significant cross-border liquidity bs is smaller than what was found for“within-country” liquidity. As was the case in the within-country tests, cross-border liquidity bs are related to several firm characteristics. Commonality ofcross-border liquidity is heightened for those firms with high scores on actualownership by foreign institutions.

The rest of the article is organized as follows. The second section discussesthe data sources, explains the construction of the sample of stocks, and providessummary statistics. The third section presents results of the within-country tests ofcommonality in liquidity. The fourth section presents results of the cross-border tests of commonality in liquidity. Finally, the last section concludes witha summary and a discussion of implications.

DATA, SAMPLE CONSTRUCTION, AND SUMMARYSTATISTICS

Data

Our sample of stocks was drawn from the S&P/Citigroup Global Equity IndexSystem, which is of considerable interest to institutional investors. The systemmonitors and evaluates all major global stock markets. Any country with a floatadjusted market capitalization of one billion U.S. dollars or more is eligible forinclusion in the index. The system then creates Broad Market Indices (BMIs)on a country-by-country basis. On a top-down basis, within index-eligible coun-tries, all equity share classes of every company with a free float of at least 100million dollars and a minimum value traded of 25 million dollars over the pre-vious 12 months are included in their respective country BMIs. We begin with

4Inactively traded stocks may result in stale prices. We check for this by including own country lagged mar-ket liquidity measures and neighboring country market liquidity measures to synchronize pricing as well astime. See footnote 15.5See Brockman and Chung (2002) on Hong Kong and Domowitz et al. (2005) on Australia.



a total of 4,437 stocks from 25 countries in the developed world. Among them,1,898 stocks are from 18 European countries, 2,111 stocks are from Asiancountries, and 428 stocks are from Canada.6

S&P/Citigroup provides monthly data on market capitalization, shares out-standing, exchange rate against the U.S. dollar, and industrial classificationsthat correspond to our sample period. We obtain the tick data for these stocksfrom Bloomberg. The data contain the security identifier (SEDOL), tradingdate, time of bid and ask tick to the nearest second, bid price, bid size, askprice, ask size, transaction price, transaction size, exchange code, and condi-tion code of bid and ask ticks. The sample period is from September 23rd toDecember 3rd, 2004. The number of trading days ranges from 46 to 50 for the25 countries, with an average of 48 trading days. We collect the intraday trad-ing hours and tick size schedules from the webpage of each stock exchange. Wealso collect dates of stock splits and minimum trading unit changes fromBloomberg News Service.

Sample Construction

Beginning with 4,437 stocks from 25 countries covered by S&P/Citigroup, weapply the following filtering rules to the sample stocks. First, we screen outlarge-price stocks because high price affects the percentage spread measures.Therefore, we eliminate stocks with an average price exceeding the following:Canada (300 Canadian dollars), Denmark (5,000 Danish crowns), stocks quotedin Euros7 (500 Euros), Japan (30,000 yen), Korea (500,000 won), andSwitzerland (5,000 Swiss francs). Overall, 125 large-price stocks are eliminated,with the majority (112) from Japan. Second, we screen out infrequently tradedstocks by eliminating stocks with fewer than five daily trades on average. Thisexcludes additional 132 stocks. Third, we screen out stocks with very large per-centage quoted spreads, that is, more than 10% of spreads of 10% or larger,eliminating additional 29 stocks. Finally, we eliminate additional 32 stocks thatexperienced stock splits or changes in minimum trading units during the sam-ple period. Panel A of Table I summarizes the number of stocks in each countryto begin with and the number of stocks eliminated following each of the above four filters. Four thousand one hundred and nineteen stocks remain.Among them, Japan has the largest number of stocks (1,248), followed by the

6Canada includes the Toronto and Venture Exchanges. German markets include Xetra, Frankfurt, Berlin,Hanseatic, Bavarian, and Baden-Wurttemberg Exchanges. Japanese markets include Tokyo, Fukuoka,Nagoya, Osaka, Japan Securities Dealers Associations, and Nippon New Market. Korean markets include theKorea Stock Exchange and KOSDAQ. Swiss stocks trade either at home or on Virt-X in London.7That is, all European countries except Denmark, Iceland, Norway, Sweden, Switzerland, and the UnitedKingdom.



TA

BL

E I

Sam

ple

Sto

cks

All

Sto

cks

Hig

hS

tock

Spl

itin

the

Fr

eque

ncy

or M

inim

umS

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eque

ntly

of L

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ing

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lC

ount

ryIn

dex

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ceTr

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ted

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ead

Uni

t C

hang

eS

ampl

e

Pane

l A: S

ampl

e st

ocks

Aus

tria

306

24B

elgi

um48

345

Den

mar

k54

35

46F

inla

nd71

71

63F

ranc

e20

22

219

8G

erm

any

176

223

151

Gre

ece

5959

Icel

and

81

7Ir

elan

d23

320

Italy

151

214

9Lu

xem

bour

g4

13

The

Net

herla

nds

8080

Nor

way

541

53P

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gal

211

20S

pain

852

83S

wed

en12

59

116

Sw

itzer

land

136

212

122

Uni

ted

Kin

gdom

571

2118

532

Tota

l Eur

ope

1,89

810

9621

1,77

1A

ustr

alia

245

24

523

4H

ong

Kon

g14

92

114

6Ja

pan

1,39

911

214

124

1,24

8N

ew Z

eala

nd31

229

Sin

gapo

re96

195

Sou

th K

orea

191

218

9To

tal A

sia-

Pac

ific

2,11

111

419

532

1,94

1C

anad

a42

81

173

407

Tota

l4,

437

125

132

2932

4,11

9

(Con

tinu

ed)



Indu

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asic

Con

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1114

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252

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194

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2536

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ific

209

379

3920

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8431

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921

8447

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ada

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3

Not

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Alis

ts th

e 25

dev

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cono

mie

s in

the

S&

P/C

itigr

oup

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bal E

quity

Inde

x S

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wis

s st

ocks

trad

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n th

e Lo

ndon

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ed V

irt-X

are

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t sep

arat

e du

e to

tim

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ffere

nces

with

Sw

itzer

land

. We

excl

ude

larg

e-pr

ice

stoc

ks, i

nfre

quen

tly tr

aded

sto

cks,

sto

cks

with

a s

igni

fican

t por

tion

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rge

perc

ent q

uote

d sp

read

s, a

nd s

tock

s ex

perie

ncin

g sp

lits

orch

ange

s in

min

imum

trad

ing

unit

durin

g th

e sa

mpl

e pe

riod

(Sep

tem

ber-

Dec

embe

r 20

04).

Pan

el B

tabu

late

s th

e in

dust

ry d

istr

ibut

ion

of th

e st

ocks

in th

e fin

al s

ampl

e.

TA

BL

E I

(C

onti

nued

)



United Kingdom (532), Canada (407), Australia (234), France (198), Korea(189), Germany (151), Italy (149), Hong Kong (146), and Sweden (116).8

Industrial Classification

Panel B of Table I provides the distribution of industrial classification (fromS&P/Citigroup) for each region and each country. Most firms belong to theconsumer cyclical, financial, industrial goods and services, basic materials,technology, or consumer non-cyclical groups. The number of firms in conglom-erates and telecommunications is small. For Canada, the industries with themost stocks are basic materials and energy. For Australia, basic materialsaccounts for about 18% of all stocks listed. For Switzerland and Hong Kong,the finance sector accounts for about a quarter of all listed firms. For Japan, theleading industries are consumer cyclical, industrial goods and services, andfinancial. The same is true for the United Kingdom, Germany, and France.

Firm Characteristics

Table II provides summary statistics on several firm characteristics. Marketcapitalization is the product of end-of-month market value at the end-of-monthexchange rate of the local currencies. The U.S. dollar monthly market value isaveraged over the sample period for each stock. Total market capitalization is the sum of market value for all stocks within a country. Firm Size is the cross-sectional median among all stocks within each country. Daily stock price is cal-culated as the product of daily closing quote midpoints times the exchangerate. The U.S. dollar daily stock price is then averaged over the sample period.Average daily stock price is the cross-sectional median among all stocks withineach country. Similarly, a cross-sectional median of the average daily number oftrades and the daily return volatility over the sample period is summarized.Return volatility is measured by the standard deviation of daily returns calcu-lated using midpoints. The next column gives the cross-sectional median ofquoted percentage spreads from all stocks in each country. The intraday per-centage quoted spreads are first averaged for each day, then the daily percentagequoted spreads are averaged over the sample period. Throughout the article, weutilize percentage quoted spreads, as the quoted spreads in local currenciescannot be compared with each other.

8To understand the extent of missing data, we compute the percentage of 15-minute intervals with no trad-ing for each stock, and then calculate the median for all stocks within a country. Finally, we find the medianpercentage among all countries in our sample. Results (unreported but available upon request) indicate thatthe median percentage of non-trading intervals is 12.5. Non-trading intervals are excluded from subsequentregression estimates.



TA

BL

E I

I

Sum

mar

y S

tati

stic

s

Firm

Siz

e(M

arke

tAv

erag

eTo

tal M

arke

t Va

lue

inD

aily

Cap

ital

izat

ion

Mil

lion

S

tock

Aver

age

Dai

lyD

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urn

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ted

DR

Fore

ign

(Bil

lion

U.S

.$)

U.S

.$)

Pri

ceN

umbe

r of

Tra

des

Vola

tili

ty (

%)

Spr

ead

(%)

Dum

my

Ow

ners

hip

(%)

Aus

tria

3172

143

.2

631.

160.

4912

7.8

Bel

gium

122

657

53.0

761.

110.

434

9.9

Den

mar

k68

406

45.2

831.

290.

638

8.9

Fin

land

134

356

14.7

651.

380.

608

17.2

Fra

nce

833

481

51.6

185

1.21

0.37

5611

.6G

erm

any

624

569

30.3

120

1.47

0.42

4418

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e49

163

6.5

165

1.68

0.62

170.

8Ic

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d6

843

0.4

442.

411.

060

0.1

Irel

and

771,

331

13.4

271.

450.

9717

20.6

Italy

408

489

5.3

206

1.13

0.32

224.

2Lu

xem

bour

g3

678

142.

77

1.51

0.88

00.

0T

he N

ethe

rland

s39

570

525

.413

11.

290.

4131

14.2

Nor

way

5528

510

.393

2.25

0.64

132.

9P

ortu

gal

4451

65.

311

40.

910.

515

5.9

Spa

in37

295

217

.224

40.

940.

2915

9.3

Sw

eden

234

546

14.8

911.

380.

5313

10.4

Sw

itzer

land

608

469

145.

157

1.20

0.53

2514

.6U

nite

d K

ingd

om2,

368

656

5.4

166

1.54

2.49

116

20.4

Aus

tral

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955

73.

013

62.

580.

8746

3.3

Hon

g K

ong

158

290

0.5

981.

970.

8644

10.3

Japa

n1,

816

363

7.9

130

1.70

0.41

156

5.7

New

Zea

land

1631

52.

831

1.07

0.60

20.

9S

inga

pore

9327

30.

950

1.32

0.78

234.

6S

outh

Kor

ea22

525

914

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026

3.07

0.38

4611

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anad

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040

713

.714

71.

780.

5587

0.0

Not

e.T

his

tabl

e pr

ovid

es s

umm

ary

stat

istic

s fo

r th

e 4,

119

stoc

ks fr

om 2

5 de

velo

ped

econ

omie

s in

the

final

sam

ple.

Exc

ept f

or to

tal m

arke

t cap

italiz

atio

n, th

e nu

mbe

rs r

epor

ted

are

the

cros

s-se

ctio

nal m

edia

n fo

r ea

ch c

ount

ry. M

arke

t cap

italiz

atio

n is

end

-of-

mon

th m

arke

t. D

aily

sto

ck p

rices

in lo

cal c

urre

ncie

s ar

e ad

just

ed b

y th

e da

ily e

xcha

nge

rate

. Dai

ly r

etur

ns a

re c

al-

cula

ted

usin

g cl

osin

g qu

ote

mid

poin

ts. F

or e

ach

stoc

k, in

trad

ay p

erce

ntag

e qu

oted

spr

eads

are

ave

rage

d to

obt

ain

daily

obs

erva

tions

, whi

ch a

re th

en a

vera

ged

over

the

48-t

radi

ng-d

aysa

mpl

e pe

riod.

DR

Dum

my

equa

ls 1

if th

e fir

m is

cro

ss li

sted

in th

e U

nite

d S

tate

s or

in L

ondo

n. F

orei

gn O

wne

rshi

p is

the

frac

tion

of s

hare

s he

ld b

y th

e U

.S. i

nstit

utio

nal i

nves

tors

, as

indi

-ca

ted

by 1

3F fi

lings

.



Table II reports that the United Kingdom has the largest total market cap-italization of the sample countries at 2,368 billion dollars, followed by Japan,France, Canada, and Germany. On the other hand, Firm Size is largest for Irishfirms, with a median market value of 1,331 million dollars.9 With the largestnumber of sample stocks, the median market values are smaller for the U.K.and Japanese firms, at 656 and 363 million dollars, respectively. In terms ofaverage stock price, typical Swiss stocks have the highest average daily price at145.1 dollars. In contrast, typical U.K. and Japanese stocks have average pricesof 5.4 and 7.9 dollars, respectively. Korean stocks are most actively traded, withthe median of the daily average daily number of trades exceeding 1,000. Stocksfrom Iceland, Norway, Australia, and Korea are most volatile. The median dailyreturn standard deviations for stocks in these countries all exceed 2%. Percentquoted spreads are the lowest for Spanish stocks, with a cross-sectional medi-an of 0.29%. The percentage quoted spread is the highest, at 2.49%, for theU.K. stocks. As the U.K. sample is large (532 stocks), the large percentagequoted spread reflects trading costs for many relatively small capitalizationstocks.10

Table II also summarizes two measures (defined in detail later) of accessto, and participation in, a particular company’s stock by foreign investors. First,the number of cross-listed firms is typically larger for the larger capital markets,such as the United Kingdom (115), Japan (156), and Canada (87), whereasequal to or approaching zero for a handful of relatively small markets. Second,the median percentage of shares held by the U.S. institutional investors rangesfrom zero for Luxembourg firms to over 20% for firms from Ireland and theUnited Kingdom. Note that our use of medians, rather than means, mayobscure the fact that foreign ownership is often concentrated in only a fractionof a particular country’s equities. For example, the median cross-sectional for-eign ownership for Canadian firms is reported as zero in the table. In contrast,the mean (unreported but available upon request) indicates that Canadianfirms average 15.63% foreign ownership. Foreign ownership is non-zero foronly the top quartile of Canadian firms. Another good example is Norway, witha median foreign ownership of 2.86% but an average foreign ownership of13.19%.

9Our Irish sample includes relatively few stocks, but a number of large firms such as banks, insurance com-panies, and an airline. In contrast, the large number of Japanese and U.K. stocks include many smaller firms.10An electronic limit order book (SETS) is used to trade all FTSE 100 securities, leading FTSE 250 securi-ties, and FTSE 100 reserves. A similar system (SETSmm) supported by market makers handles FTSE 250securities not trading on SETS, all U.K. FTSE Eurotop 300 securities, and exchange-traded funds. All otherU.K. securities with at least two or more registered market makers trade on SEAQ, a quote-display system fortelephone trading.



COMMONALITY IN WITHIN-COUNTRY LIQUIDITY

Liquidity Measures

We estimate liquidity commonality using percentage spreads, which can beaccurately measured as they are provided in the original data set.11 For eachstock, we first average the tick-by-tick intraday percentage quoted spreads in15-minute intervals to obtain a time-series of the percentage quoted spread,Li, t, where i refers to the ith stock and t refers to the tth 15-minute trading inter-val. Then we measure the percentage change in the percentage quoted spread,DLi, t � Li, t /Li, t�1 � 1, and the point change in the percentage quoted spread, Li, t

� Li, t�1. To assure that the evidence is robust to the measurement of liquiditychange, all subsequent analyses are performed for both the percentage changeand the point change in Li, t. Results are essentially the same using either meas-ure. For brevity, the following discussions will rely on the percentage changeonly.

Within-Country Factor Model

The standard approach to estimate common variation in liquidity is the “mar-ket model” developed in Chordia et al. (2000):

DLi,t � ai � biDLCountry,t � ei,t. (1)

The model yields an estimate of within-country liquidity, b̂, for stock i.DLCountry, t � LCountry, t/LCountry, t�1 � 1 is the percent change in market liquidityfor that stock’s home market measured over 15-minute interval t. LCountry, t is thecorresponding equal- or value-weighted average of individual stock liquiditywithin each country.

In actual estimation, the country-wide liquidity factor used in the regres-sion for the ith stock excludes the liquidity of the ith stock to minimize thecross-sectional dependence in the estimated slope coefficients. Fifteen-minuteintervals and trading times that are typically between four and nine hours result in16–36 fifteen-minute intervals for each day. Our selection of a 15-minute inter-val length is a compromise: a significant number of our sample stocks may gen-erate missing values in quotes at five-minute intervals, whereas a 30-minuteinterval may obscure the intraday patterns in liquidity. Therefore, the total pos-sible number of intraday 15-minute observations ranges from 736 to 1,800,given anywhere from 46 to 50 trading days across our sample countries.

11Measurement errors may occur in effective spreads (Peterson, & Sirri, 2003). Unreported tests indicatethat in results based on Lee and Ready (1991), effective spreads are similar but weaker.



The actual number of observations differs slightly due to no trading occurringduring some intervals.12–15

Table III tabulates ample evidence of co-movement in intraday 15-minuteliquidity. As the results from equally and value-weighted country liquidity fac-tors are essentially the same, the following discussions focus on the equallyweighted factors. The average intraday liquidity b, b̂i, ranges from a low of 0.102for Iceland to a high of 0.980 for Japan. With the exception of Luxembourg,with the smallest sample size at only 4 firms, all of the 25 cross-sectional aver-ages of individual firm liquidity bs are highly significant. The highest averageadjusted R2 at 0.294 is from Korea. The last column in the equally weightedpanel reports the percentage of positive and significant coefficients within eachcountry. Except for Iceland, the majority of stocks in each country report a sta-tistically significant loading on within-country market-wide liquidity.

The Cross-Sectional Determinants of Within-CountryLiquidity Bs

Although earlier studies typically focus on documenting the liquidity bs pro-duced by time-series regressions such as (1), they usually do not examine thecross-sectional determinants of liquidity bs beyond sorting them by size. Tounderstand the forces underlying measured liquidity bs, we regress the esti-mated liquidity bs from the first pass, (1), on firm characteristics as follows:

(2)

The dependent variable, Liquidity b̂i, is the estimated slope for stock i from(1). The independent variables are defined as follows. Firm Size is the log of

� a11

k�1dikIndustry Dummyik � ei,t.

� d6Foregn Owershipi � a25

j�1cijCountry Dummyij

� d4Number of Analystsi � d5DR Dummyi

Liquidity b̂i � d0 � d1Firm Sizei � d2Spreadi � d3Return Volatilityi

12We experiment with dummy variables to identify 15-minute intervals at the beginning and end of the trading day.However, we find no significant impact on computed liquidity bs and cross-sectional regressions. Specifically, werun the following regression: DLi,t � (ai � bi � di � DummyCountry, t)DLCountry, t � ei, t, where DummyCountry, t takeson the value of 1 when it is in the first 30 minutes in the morning or the last 30 minutes in the afternoon tradingsessions. We find that the coefficient di is not significant for the majority of stocks in our sample.13We also truncate the observations in the first 30 minutes in the morning and the last 30 minutes in theafternoon and run the market model. The liquidity best estimates remain robust for all countries.14For countries like Japan, where there is a lunch break during the trading day, we construct dummy variablesthat correspond to the first and last 30 minutes of the morning and afternoon sessions, respectively. We find that most of the estimated coefficients on the dummy variables are not significant.15Like Chordia et al. (2000), we also include lagged market liquidity in the regressions but find that most ofthem are not significant.



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market capitalization in U.S. dollars calculated as the average end-of-monthmarket value over the sample period.16 Spread is the average percentage quotedspread. Number of Analysts is the log of one plus the number of analysts pro-ducing earnings forecasts for the stock.17 DR Dummy, which is detailed below,equals one if the firm is cross listed in the United States, the United Kingdom,or Japan. Foreign Ownership, which is detailed below, is a proxy for the extentof foreign institutional shareholdings. The regression also includes country andindustry dummies.

As summarized earlier in Table II, the regressions include two explanatoryvariables that are intended to measure the extent to which a particular securityis available to foreign investors. First, DR Dummy is set to one for those firmsthat are cross listed in the United States, the United Kingdom, or Japan. UsingJ. P. Morgan’s spreadsheet on American Depositary Receipts (ADRs), we findthat 236 of our sample companies are formally listed on a U.S. exchange, 360trade over-the-counter, and 27 are available as Rule 144a securities. We alsofind that 101 of our sample firms are cross listed in London, whereas 80 of ourCanadian firms are cross listed in the United States. Finally, 19 stocks haveDRs listed on the Tokyo Stock Exchange. Second, Foreign Ownership is con-structed using data on the U.S. institutional holdings from 13F filings.18 Tomatch our sample period of September 23rd to December 3rd, 2004, we con-struct the total U.S. holdings from October 1st, 2003, to September 30th,2004, using each institution’s latest report from that one-year window. For allsample stocks, the cross-sectional mean and median holdings by the U.S.mutual funds are 14.7 and 10.9%, respectively. The mean and median holdingsby the U.S. companies are much smaller at 1.0 and 0.3%, respectively.19

Panel A of Table IV provides cross correlations for the explanatory vari-ables used in the second-pass cross-sectional regressions to explain estimatedliquidity bs. There are many significant correlations that relate to Firm Size.For example, Firm Size and Number of Analysts are strongly positively correlatedand are strongly negatively correlated with Spread. Among the other variables,our two measures of foreign access and participation (DR Dummy and ForeignOwnership20) are not overwhelmingly correlated.

16We also use number of trades in the cross-sectional regressions. The results remain essentially the same.Number of trades has a positive and highly significant correlation with Firm Size (0.793).17Number of Analysts is obtained from the I/B/E/S database of Thomson Financial.18These filings exclude securities not held by any U.S. institution or not registered in any form in the United States.19For robustness, we also construct an ownership variable over a six-month window from April 1st toSeptember 30th, 2004. The mean and median mutual fund holdings are 12.1 and 8.9%, respectively. Themean and median U.S. other companies’ holdings are 0.9 and 0.3%, respectively.20Foreign Ownership is only available for those securities that are reported on 13F filings. To maximize thenumber of observations for regression analyses, we use a value of zero for other securities. If, however, theseobservations are excluded, there is no substantial impact on the regression results.



Panel B of Table IV summarizes the results of the second-pass regressionsto explain the liquidity bs.21 The columns report a variety of regression specifi-cations for the full sample. Firm Size has a significant negative impact on theliquidity b, which is consistent with evidence from Chordia et al. (2000) for the U.S. equity markets. The most important variable is Spread. Thus, stockswith a large bid–ask spread tend to have significantly lower liquidity bs. Somestocks may trade on stock-specific opinions and recommendations of analysts andother professionals, rather than as baskets or purely on market-wide movement.

21We report the equal-weighted results only, as the value-weighted results are essentially the same.

TABLE IV

The Cross-Sectional Determinants of Own Country Liquidity bs

Return Number ForeignSpread Volatility of Analysts DR Dummy Ownership

Panel A: Pearson correlation coefficients for explanatory variables

Firm Size �0.357** �0.195** 0.656** 0.535** 0.162**Spread 0.100** �0.403** �0.176** 0.103**Return Volatility �0.102** �0.035* 0.030Number of Analysts 0.400** 0.213**DR Dummy 0.106**

Panel B: Regressions to explain own country liquidity bs

Intercept 2.299 2.257 2.183 2.018 2.042(14.53)** (13.72)** (11.71)** (10.09)** (10.18)**

Firm Size �0.080 �0.078 �0.073 �0.065 �0.066(�11.18)** (�10.81)** (�7.57)** (�6.31)** (�6.39)**

Spread � 100 �0.342 �0.342 �0.343 �0.341 �0.343(�20.92)** (�20.99)** (�20.94)** (�20.84)** (�20.81)**

Return Volatility 1.012 1.029 1.145 1.110(1.11) (1.13) (1.25) (1.21)

Number of Analysts �0.018 �0.017 �0.021(�0.082) (�0.077) (�0.94)

DR Dummy �0.040 �0.040(�2.15)** (�2.13)**

Foreign Ownership 0.100(1.48)

Country Dummy Yes Yes Yes Yes YesIndustry Dummy Yes Yes Yes Yes YesAdjusted R2 0.397 0.398 0.398 0.399 0.399Observations 2,447 2,447 2,447 2,447 2,447

Note. Panel A presents Pearson correlations among the explanatory variables: the log of firm size (Firm Size), average percentagequoted spread over the sample period (Spread), return volatility (Return Volatility), a dummy variable equal to 1 if the firm is cross list-ed in the United States or in London (DR Dummy), the log of one plus the number of analysts following each stock (Number ofAnalysts), and the fraction of shares held by the U.S. institutional investors as indicated by 13F filings (Foreign Ownership). Panel Bpresents Weighted Least Squares (WLS) regressions of liquidity bs obtained from the 15-minutes intraday country factor model onthe above variables plus country and industry dummies. Liquidity bs are those computed from equal-weighted country liquidity fac-tors. Results based on value-weighted bs are very similar. Observations are weighted by Firm Size. * indicates significance at the10% level; ** indicates significance at the 5% level.



Large spread stocks may, by definition, be somewhat detached from market-wideliquidity trends.

Given our interest in explaining liquidity bs with investor behavior, theproxies for foreign investor access and participation are particularly important.The last two variable rows of the table indicate significant negative slopes forthe DR Dummy and insignificant positive slopes for Foreign Ownership. Thus,stocks that are relatively available to foreigners due to cross listing tend to havelower liquidity bs.

Although (unreported) Variance Inflation Factor tests suggest that correla-tions among the regression’s explanatory variables are not overwhelming, we areparticularly concerned with the potential for multicollinearity among ourexplanatory variables. We confirm that the negative signs on the DR Dummy arerobust to a variety of specifications that exclude certain variables. Therefore, weconclude that, as above, accessibility to foreigners in international markets inthe form of depository receipts tends to detach a stock’s liquidity from liquidityin its own market. The low liquidity bs for these stocks are most likely due to thefact that rather than direct shareholdings in their home markets, which mightincur additional costs, institutional investors prefer to invest in DRs in NewYork, London, or Tokyo.

COMMONALITY IN CROSS-BORDER LIQUIDITY

Overview

In this section, we investigate cross-border liquidity dynamics. Although theissue of international financial market linkages has long attracted the interestof academic researchers, the literature on this subject typically focuses on asso-ciations between stock returns or return volatilities rather than cross-borderliquidity effects.22 This is understandable because most commonality studiesfocus on a single country. Good liquidity data across multiple countries are noteasy to obtain.

We begin by identifying markets whose trading hours overlap and, there-fore, offer a chance to gauge cross-border commonalities in intraday liquidity.The most obvious group of overlapping markets is that of the 18 Europeancountries in our sample. Thus, for each individual firm from the four majorEuropean economies (France, Germany, Italy, and the United Kingdom), wedefine the neighboring countries as that group of four minus the firm’s ownhome country. For other European firms, the neighboring area is also thosefour major European economies. For Ireland, we use the United Kingdom as

22A well-known exception is Werner and Kleidon (1996). See also recent working papers by Stahel (2003,2005), Brockman et al. (2008), and Karolyi et al. (2007).



the neighboring country. The second group of overlapping countries is that ofthe Asian-Pacific countries. Unfortunately, the differences in time zones andtrading hours for these countries complicate the designation of neighbors withsufficient overlap in trading hours. We use Japan as the neighboring country forAustralia and Korea, Australia as the neighboring country for Japan and NewZealand, Singapore for Hong Kong, and Hong Kong for Singapore.23 Finally, aliquidity factor for the U.S. S&P 500 is used to document cross-border com-monalities for Canada and for European markets, which overlap a bit with theUnited States.

Cross-Border Factor Model

We adapt the model, (1), for measuring commonality in liquidity within a coun-try to commonality in liquidity across countries by including a second inde-pendent variable:

DLi,t � ai � b1,iDLCountry,t � b2iDLneighbor,t � ei,t. (3)

This yields the cross-country liquidity b estimate, b̂2i, for stock i. DLneighbor,t � Lneighbor,t/Lneighbor,t�1 � 1 is the percent change in 15-minute marketliquidity for the country or group of countries we designate as the neighboringarea. The neighbor liquidity factor, Lneighbor, t, is an equal- or value-weightedaverage of individual stock liquidity within the country or countries we desig-nate as the neighboring area.24

Table V reports the results of estimating the first-pass regression, (3). As inour earlier discussion of within-country bs, the following discussions focus onequally weighted neighbor liquidity factors. Furthermore, only the cross-borderliquidity b estimates are reported. The range of average cross-border liquidity bsacross countries is much larger than that of within-country liquidity bs, from alow of �1.118 for Luxembourg to a high of 0.828 for Ireland.25 Most of thecross-sectional averages of individual firms’ cross-border liquidity bs are signifi-cant for European countries and Canada, but are largely insignificant for Asian countries. The highest average adjusted R2 (0.187 for Ireland) is lowerthan what was found for within-country liquidity bs in Table III. In contrast towithin-country test results, only a fraction of the countries in our sample werefound to have a majority of their stocks significantly correlated with cross-border

23For both European and Asian countries, we also experiment with different countries as the benchmark; theoverall conclusions remain unchanged.24Specification (3) suggests that we return to specification (1) for within-country liquidity and include aninternational factor. Unreported tests, however, indicate that this has no impact on our within-countryresults.25Negative signs may reflect “flight to quality” or other trading patterns as investors move funds cross borders.



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.4

Not

e.T

his

tabl

e pr

esen

ts re

gres

sion

resu

lts fo

r the

intr

aday

15-

min

utes

acr

oss-

coun

try

liqui

dity

fact

or m

odel

. The

dep

ende

nt v

aria

ble

is th

e pe

rcen

tage

quo

ted

spre

ad o

f ind

ivid

ual s

tock

i. T

he in

depe

nden

t var

iabl

es a

re th

e m

arke

t-w

ide

own

coun

try

liqui

dity

fact

or a

s de

fined

for

Tabl

e III

and

the

neig

hbor

ing

coun

try

liqui

dity

fact

or c

ompu

ted

sim

ilarly

. The

nei

ghbo

ring

area

is d

efine

d as

follo

ws.

For

indi

vidu

al fi

rms

from

the

four

maj

or E

urop

ean

econ

omie

s (F

ranc

e, G

erm

any,

Ital

y, a

nd th

e U

nite

d K

ingd

om),

the

neig

hbor

ing

area

is th

at g

roup

of f

our

min

us th

efir

m’s

ow

n ho

me

coun

try.

For

oth

er c

ontin

enta

l Eur

opea

n fir

ms,

the

nei

ghbo

ring

area

is a

lso

thos

e fo

ur m

ajor

Eur

opea

n ec

onom

ies.

For

Ire

land

, w

e us

e th

e U

nite

d K

ingd

om a

nd f

orC

anad

a w

e us

e th

e U

nite

d S

tate

s. F

or th

e A

sian

firm

s, d

iffer

ence

s in

tim

e zo

nes

and

mar

ket h

ours

com

plic

ate

the

desi

gnat

ion

of n

eigh

bors

with

suf

ficie

nt o

verla

p in

trad

ing

hour

s. W

e us

eJa

pan

as t

he n

eigh

borin

g co

untr

y fo

r A

ustr

alia

and

Sou

th K

orea

, Aus

tral

ia a

s th

e ne

ighb

orin

g co

untr

y fo

r Ja

pan

and

New

Zea

land

, S

inga

pore

for

Hon

g K

ong,

and

Hon

g K

ong

for

Sin

gapo

re. F

or e

ach

coun

try,

the

tabl

e on

ly r

epor

ts o

n th

e ne

ighb

orin

g co

untr

y’s

liqui

dity

b, a

nd in

clud

es th

e cr

oss-

sect

iona

l t-s

tatis

tics,

the

aver

age

adju

sted

R2 , a

nd th

e pe

rcen

tage

of

posi

tive

and

sign

ifica

nt c

oeffi

cien

ts. T

he t-

stat

istic

s ar

e ad

just

ed fo

r th

e cr

oss

corr

elat

ions

in th

e re

sidu

als

of in

divi

dual

sto

ck r

egre

ssio

ns b

y a

fact

or o

f [1+

(N�

1)r]0.

5 , whe

re r

is p

roxi

edby

the

aver

age

of r

esid

ual c

orre

latio

ns fr

om a

djac

ent i

ndiv

idua

l sto

ck r

egre

ssio

ns. *

indi

cate

s si

gnifi

cant

ly p

ositi

ve a

t the

10%

leve

l; **

indi

cate

s si

gnifi

cant

ly p

ositi

ve a

t the

5%

leve

l. +

indi

-ca

tes

sign

ifica

ntly

neg

ativ

e at

the

10%

leve

l; �

�in

dica

tes

sign

ifica

ntly

neg

ativ

e at

the

5% le

vel.



market-wide liquidity. Nonetheless, the cross-border liquidity effect appearslarge for most European economies and for Canada.

The Cross-Sectional Determinants of Cross-BorderLiquidity Bs

Paralleling our investigation of within-country liquidity bs reported in Table IV,we regress the cross-border liquidity bs from (3) on the same set of firm char-acteristics used in the within-country tests. Table VI reports estimates of thissecond-pass regression. Although the R2 coefficients are high, the individualcoefficient results are sometimes weaker than those found for within-countryliquidity bs. For example, the coefficients (t-tests) on Spread from the first twospecifications in Table IV are �0.342 (�20.92) and �0.342 (�20.99). Table VIindicates much smaller and much less significant coefficients, �0.044 (�5.72)and �0.044 (�5.68). Notably, Firm Size changes sign relative to Table IV(where it is �0.080 with a t-statistic of �11.18) and is now significantly posi-tive (0.10 with a t-statistic of 2.05 in the first column). When we also includeNumber of Analysts in the last three model specifications, the correspondingcoefficients are positive and highly significant. As Firm Size and Number of Analysts are positively correlated, both variables capture the relative size effect of the underlying stocks. Spread remains significantly and reliablynegative.26

We also examine the sign and significance of the two foreign access andparticipation measures (DR Dummy and Foreign Ownership). We find the DRDummy to be significantly negative. This implies that for stocks with DRs avail-able to foreign investors, liquidity changes are less responsive to liquiditychanges in their neighboring countries. Again, this reflects foreign (most likelyinstitutional) investors’ preference for trading DRs as opposed to home markettrading. As a result, institutional investors’ shareholdings are small in homemarkets and respond less to capital flow and liquidity changes cross borders. Inaddition, we can confirm that the coefficient on Foreign Ownership is robust toa variety of specifications, with the estimated slopes (t-statistic) in the lastspecification being 0.147 (3.00). Thus, the cross-border liquidity commonalityincreases with actual foreign holdings, as represented by our ForeignOwnership variable.

26Lindley’s (1957) paradox may apply when the sample size is large. We did not find it to be a serious prob-lem in our case, because the prior of the estimated coefficients (liquidity bs) is within a relatively narrowrange. In the cross-sectional regressions, we eliminated those liquidity b estimates that were larger than 2and smaller than 1. Lindley’s paradox is most significant when the prior is diffuse.



Overall, size effects (Firm Size and Number of Analysts) are positive, justthe opposite of what was found in the within-country tests. Thus, the cross-border liquidity bs are highest for large companies with lots of analyst coverage.The opportunity to trade DRs in international markets lowers the cross-borderliquidity bs. The reliably positive slopes on Foreign Ownership suggest that thepresence of foreign institutional investors induces a common cross-border ele-ment into the liquidity of such companies.

It is worth mentioning that in the cross-sectional regressions Japanaccounts for the largest percentage of stocks in the sample, as given in Table I.Interestingly, the estimate for Japan’s country dummy in the last column ofTable VI (neighboring country liquidity b) is a reliable �0.065 with a t-statisticof 6.06. Stocks in Japan respond to neighboring country’s liquidity changes to alesser extent. This evidence is consistent with the general perception thatJapanese capital markets are less integrated with other markets in the world. Incontrast, the estimate for Japan’s country dummy in the last column of Table IV(own liquidity b) is a reliable 0.295 with a t-statistic of 9.87. In other words,stocks in Japan respond more to their own domestic market liquidity variationthan do stocks in other countries.

TABLE VI

The Cross-Sectional Determinants of Across-Country Liquidity bs

Intercept �0.133 �0.161 �0.006 �0.198 �0.167(�1.27) (�1.47) (�0.05) (�1.60) (�1.34)

Firm Size 0.10 0.11 �0.001 0.009 0.007(2.05)** (2.18)** (�0.13) (1.42) (1.20)

Spread � 100 �0.044 �0.044 �0.042 �0.041 �0.043(�5.72)** (�5.68)** (�5.45)** (�5.25)** (�5.41)**

Return Volatility 0.589 0.578 0.721 0.689(1.03) (1.02) (1.28) (1.22)

Number of Analysts 0.039 0.040 0.035(3.33)** (3.45)** (3.01)**

DR Dummy �0.044 �0.043(3.63)** (3.60)**

Foreign Ownership 0.147(3.00)**

Country Dummy Yes Yes Yes Yes YesIndustry Dummy Yes Yes Yes Yes YesAdjusted R2 0.574 0.574 0.576 0.579 0.581Observations 2,499 2,499 2,499 2,499 2,499

Note. This table summarizes Weighted Least Squares (WLS) regressions to explain across-country liquidity bs. Observations areweighted by Firm Size. The estimated intraday neighboring country liquidity b (Liquidity b) is regressed on the log of firm size (FirmSize), average percentage quoted spread over the sample period (Spread), return volatility (Return Volatility), the log of one plus thenumber of analysts (Number of Analysts) following each stock, a dummy variable equal to 1 if the firm is cross listed in the UnitedStates or in London (DR Dummy), and the fraction of shares held by the U.S. institutional investors as indicated by 13F filings(Foreign Ownership). The regressions also include country and industry dummies. Liquidity bs are those computed from equal-weighted country liquidity factors. Results based on value-weighted bs are very similar. Robust t-statistics are in parentheses belowthe estimates. * indicates significance at the 10% level; ** indicates significance at the 5% level.



CONCLUSIONS

This study addresses several questions. Does the common variation in liquiditywithin an equity market, as seen in the United States and a few other markets,extend more generally to equity markets around the world? What are the cross-sectional determinants of that commonality? Is there any evidence of cross-border transmission of liquidity paralleling what previous authors havefound for stock returns and return volatilities? What are the determinants ofsuch linkages? Our sample of intraday data and other information on severalthousand companies from virtually all developed countries allows us to havemany new insights into these questions.

For our sample of more than 4,000 stocks from 25 developed countries,we report significant intraday common variation in liquidity for virtually allcountries. Thus, the within-country liquidity commonality documented for theUnited States in earlier studies extends to other countries. Broadly, similareffects are found for cross-border liquidity bs. These effects are much strongerin Europe and North America than in the Asian-Pacific region.

The explanatory variables used in the second-pass tests help us to crafttentative explanations for our findings based on the behavior of institutionalinvestors. Although we lack comprehensive data on global institutional tradingand cannot offer concrete proof, our evidence may reflect a variety of invest-ment tactics and strategies, ranging from herding to bottom-up fundamentalanalysis to indexing.27 A cross listing in New York or London seems to reducethe dependence of a particular firm’s liquidity on the conditions of its homemarket and neighboring markets. Furthermore, large actual ownership by for-eign institutional investors appears to increase the dependence of a particularfirm’s liquidity on common cross-border liquidity conditions.

We suggest a number of directions for additional research on this subject.First, our conjectures about associations between our results and the behaviorof institutional traders can be tested, given proprietary data on institutionaltrading. Second, our cross-border results can be conditioned on more precisemeasures of international portfolio flows and on the existence and extent ofactivity in cross-listed shares, such as ADRs. Finally, there is room for moreexploration on the question of whether commonality patterns observed in equitymarkets carry over to derivative instruments, such as SSF contracts.Specifically, whether there is any linkage between the stronger commonalitypatterns observed in emerging markets (Karolyi et al., 2007) and a heaviervolume in SSF in those markets.

27See, for example, Tesar and Werner (1995), Bohn and Tesar (1996), and Froot, O’Connell, and Seasholes(2001).



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explaining country and cross-border liquidity commonality in international equity markets

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