Impact of ADR Listing on the Trading Volume and Volatility ... · explore the impact of number of traders and the variance (diversity) of traders’ endowment on expected utility

* The paper is based on a part of Ejara’ s dissertation at University of Connecticut. We thank an anonymous referee, Ken Nunn and seminar participants at the University of Connecticut, Storrs, University of Massachusetts, Boston, 1999 European Financial Management Association meetings at Helsinki, Finland and Financial Management Association Meeting at Orlando, FL. for their comments and suggestions.

(Multinational Finance Journal, 2004, vol. 8, no. 3 & 4, pp. 247–274)© Multinational Finance Society, a nonprofit corporation. All rights reserved. DOI: 10.17578/8-3/4-5

1

Impact of ADR Listing on the Trading Volumeand Volatility in the Domestic Market

Demissew Diro EjaraWilliam Paterson University of New Jersey, U.S.A.

Chinmoy GhoshUniversity of Connecticut, U.S.A.

This paper investigates the impact of ADR listing on the trading volume andvolatility of the domestic market. Existing theories indicate that trading shiftsto a market with lower transaction costs, and the level of volatility is directlyrelated to the level of trading activity. The analyses provide empirical evidenceshowing increase in both trading volume and price volatility in the domesticmarket after ADR listing. The increase in volatility is attributed to noiseresulting from public information as opposed to from increased trading friction.This suggests improvement in liquidity following ADR listing. Comparisonacross country groups indicates marginally higher gain for emerging marketstocks although the difference is not statistically significant. Auction typemarkets gain more in terms of increase in trading volume than dealer typemarkets (JEL: G15, N20).

Keywords: ADR, cross listing, market segmentation, market liquidity.,transparency.

I. Introduction

Capital markets around the globe are being increasingly liberalized.Liberalization usually does away with capital controls, foreignownership restrictions of domestic companies, and entry restrictions toforeign traders in the domestic capital markets. A stream of literatureexplores the impact of liberalization on the financial integration of

Multinational Finance Journal248

1. Levine and Zervos (1996) discuss the impact of liberalization on the capital marketin the emerging countries in more detail.

2. Quotation transparency refers to the degree of information linkage between the USand domestic capital markets.

capital markets.1 In financially integrated markets, financial assets withrelated risk characteristics have similar expected returns. Goldberg andDelgado (2001) use data on individual stocks to examine the financialintegration of Latin America versus South Asian companies. Theydemonstrate that Argentina, Chile, Mexico and Thailand’s stockmarkets are financially integrated. In general, the stocks in integratedcountries become more correlated with world and industry indexes, andmean returns for these stocks decrease and become more aligned withthe mean returns of their respective industry indexes. Niarchos et al.(1999) investigate the information transmission between the U.S. andGreek stock markets. Their tests reveal no spillover between these twomarkets for the conditional mean and variance, and fail to detect anycommon trend in the movement of these markets. They conclude thatU.S. and Greek markets are not integrated.

An important way to facilitate liberalization is the issuance ofAmerican Depositary Receipts (ADR). ADRs are negotiable instrumentsrepresenting foreign securities. ADRs trade on US stock exchanges, inUS dollars. In addition to facilitating foreign ownership of domesticcompanies, issuance of ADRs opens an alternate trading venue for thesecurities of the companies. The ADRs trade on US exchanges while theunderlying shares trade in the domestic markets.

An important benefit of the availability of alternative trading venueis to enable and encourage foreign traders to participate in the localmarket thereby increasing liquidity of the stock. Increase in liquidityattracts foreign information traders and analysts, which in turn increasesthe precision of price information, and reduces volatility in the domesticmarket. According to Domowitz et al. (1997), hereafter called DGM,such improvement in market quality depends on the degree of quotationtransparency between the U.S. market and the domestic market.2 DGMargue that, “If inter-market information is freely available, cross-listingresults in improvement in market quality....In the opposite case, whereinter-market information linkages are extremely poor, cross-listingreduces liquidity and increases volatility in the local market.”

This study has two objectives. First is the analysis of the impact ofADR listing on the liquidity and volatility of trading in the domestic

249ADR, Trading Volume and Volatility

3. Miller (1999) shows that announcements of ADR listing decisions are associatedwith significantly positive valuation effects in the stocks in the domestic market. Millerattributes his results to relaxation of international trading barriers. Hertzel et al. (2000)examine the impact on prices of German stocks in the home market around the filing,announcement, and listing of German ADRs. They find that filing of reports in accordancewith U.S. GAAP is associated with positive abnormal returns, announcement period returnsare mixed, and listing elicits positive abnormal returns. Recently, Cantale (1998) and Fuerts(1998) develop models to demonstrate that management signals its quality by listing onstricter exchanges and that listing on stricter exchanges increases shareholder wealth becauseit reduces private benefits from control.

shares of the foreign firm. Previous studies by DGM and Hargis (1996)provide evidence that after ADR listing, average daily trading volumeincreases in the domestic market. These studies, however, report mixedresults on the impact of ADR listing on volatility of the domestic shares.The analysis in DGM and Hargis (1996) is extended by expanding thesample to include ADRs from Africa, Asia, Europe and Latin America.The DGM study is limited to 25 ADRs issued by 16 Mexicancompanies. Hargis (1996) is based on 65 ADRs by four Latin Americancountries (Argentina, Brazil, Chile and Mexico). Including ADRs fromAfrica, Asia and Europe provides an opportunity to examine the impactof inter-country differences in information transmission on liquidity andvolatility of trading in the domestic market. Conceivably, inter marketinformation linkage between the U.S. market and other developedmarkets are relatively more transparent than that between the U.S.market and emerging markets. Finally, the impact of increase in totaltrading time resulting from ADR listing for countries in different timezones is explored. Increase in trading time increases the flow of publicand private information into the market, which in turn increasesvolatility; see Makhija and Nachtmann (1990).

The issue of the impact of ADR listing on U.S. exchanges on thestandard measures of market quality in the domestic market is especiallyimportant to the investors and corporations of emerging markets. It isequally important to the policy makers in those countries since there isconcern that ADR listing may cause shift in trading from domesticcapital markets to the more liquid U.S. capital markets. There iscompelling evidence that ADR listing reduces cost of capital.3 But theeffect on volatility, trading volume, and migration of traders from thedomestic to the ADR market remains unresolved. Does the effect varyacross countries, or across U.S. exchanges? Is the overlap of tradingtime between the domestic and U.S. market materially important?

The remainder of the paper proceeds as follows. The next section


4. For a detailed discussion of American Depositary Receipts; see Pulatkonak andSofianos (1999) and Hertzel et al. (2000).

5. Source: Bank of New York and Citibank.

provides a brief introduction to ADRs, the specifics of different ADRprograms, and their trading mechanisms. Section III presents adiscussion of theoretical and empirical literature on the subject. SectionIV describes the sample and data. Section V presents the method ofanalyses and results. Section VI concludes.

II. American Depositary Receipts (ADRs)

ADRs facilitate the trading of shares by foreign companies in the U.S.capital market. A trustee holds shares underlying the ADRs in thedomestic market, and receipts are issued in the U.S. market by adepository bank (Bank of New York, Citibank, etc.). These receipts,called ADRs, are treated as negotiable instruments representing foreigncompany shares and trade in U.S. dollars in the U.S. capital market.4

A sponsored ADR program is initiated by the issuing company toraise capital, increase liquidity of shares, and achieve broader dispersionof ownership. In these programs, a single depository handles theinvestor relations activities including clearance, settlement, transfer, andownership changes. The issuing company pays all costs associated withthese activities. A non-sponsored ADR is initiated by the depositorybank to meet U.S. investors’ demand for foreign company shares. As ofNovember 1996, 1,687 ADRs (1,315 sponsored and 372 non-sponsored)are trading in the U.S. 1,067 (775 sponsored) of which are fromdeveloped countries, while emerging markets account for the remaining620 (540 sponsored) cases.5

ADR programs fall in four main categories depending on purpose,trading locale, and listing requirements. Level I ADRs are traded in theOver the Counter Market (OTC) or on the Pink Sheet and do not requireSEC approval or compliance with U.S. Generally Accepted AccountingPrinciples (GAAP). Being the most convenient and least expensive wayto enter the U.S. market, Level I ADRs comprise the largest and mostrapidly growing group with more than 1,000 cases. Level II ADRs aretraded on major exchanges (NYSE, AMEX, and NASDAQ) and requirefull SEC disclosure and compliance with U.S. GAAP. For levels I andII ADRs, no new shares are issued; rather, shares are purchased by the


broker in the foreign market, deposited with the custodian bank, andADRs are issued in exchange in a fixed ratio. Level III ADRs are forraising capital. Traded on major exchanges (NYSE, AMEX, andNASDAQ), they must comply with full SEC disclosure and U.S. GAAPrequirements. Companies issuing Level III ADRs either go public forthe first time or are public companies in their home countries.

III. Related Literature and Hypotheses

Can two markets co-exist simultaneously for the same security or doesone market dominate the other? Pagano (1989) develops a model toexplore the impact of number of traders and the variance (diversity) oftraders’ endowment on expected utility of agents’ terminal wealth. Hedemonstrates that expected utility of terminal wealth decreases with thevariance of the price at which the stock can be traded. Higher varianceof prices provides more opportunities of buying low and selling highresulting in potentially high speculative profits. Under this scenario, alarger number of traders–which implies less variation in averageendowment, and therefore in market prices–entails lower expectedprofits. Pagano denotes this “speculative value”. Alternatively, lowervariation in endowment and a larger number of traders in the marketprovide greater liquidity value.

Pagano shows that when traders can choose between two marketswhere a stock trades simultaneously, the choice reflects differentialtransaction costs, and the offsetting effects of speculative and liquidityvalues. If trading is costless or cost the same in both markets, thenequilibrium implies that the markets can coexist if they are identical inthe number of traders and the distribution of their equity endowments.But, this equilibrium is not sustainable under the slightest disturbance,and all traders will migrate to only one market. With differential tradingcosts, two markets can coexist if (1) the distribution of traders’endowment is more diverse in one market; or (2) one market has bothgreater diversity of endowment (higher speculative value) and moretraders (higher liquidity value). In either case, higher transaction costsin the otherwise favorable market prevent all trading from concentratingthere. In essence, the greater variance of equity endowment providesgreater depth and higher speculative value, and compensates for thehigher transaction cost.

Pagano’s proposition implies that for companies listing ADRs in the


6. This result is clearly contradictory to the predictions to Pagano’s model wherein anincrease in price volatility improves the potential for trading profits. The contradiction isattributable to Pagano’s assumption that all traders are speculators. As he notes in footnote4 (p. 260), liquidity traders would lose from an increase in price volatility.

U.S. market, unless the domestic market has greater diversity ofendowment to offer higher speculative value, and/or attract moreliquidity traders following ADR listing, trading shifts to the U.S. marketwhere transaction cost is lower.

DGM derive a model where the probability of a trade is a decreasingfunction of bid-ask spread, and the information content of a trade is anincreasing function of bid-ask spread. Intuitively, probability of a tradeand hence trading intensity increases as bid-ask spread decreases. Astrading intensity increases, market liquidity improves so thatinformation content or the price impact of each trade decreases, and viceversa. In this framework, the variance of successive changes in closingprices can be broken down into two components. The first component,referred to as base level volatility, captures the variance due to bid-askbounce and the asymmetry of overnight price information. Increasedtrading frequency reduces bid-ask bounce, and increase in the numberof traders improves price information. In essence, base level volatilityis an inverse function of the number of traders in the market. Thesecond component, referred to as volatility due to trading friction,depends on expected daily trading volume through a factor, which is adecreasing function of market liquidity. Measured as the inverse of theprice impact of a given order imbalance, market liquidity is a decreasingfunction of bid-ask spread, and therefore positively related to tradingfrequency, or number of traders. Therefore, increase in number oftraders reduces price volatility through the second component as well.6

DGM posit that the effect of ADR listing on liquidity and volatilityin the domestic market depends on the degree of “quotationtransparency” between the domestic and the U.S. markets. For ADRs,“quotation transparency” reflects the extent to which price informationin the domestic and the U.S. markets is observable. With perfectquotation transparency, information on prices and quotes in bothmarkets is freely available at all times. If the cost of trading ADRs in theU.S. market is less than that in the domestic market for some investorsthen these investors who would otherwise not trade are induced to tradein the U.S. market after ADR listing. This increases total number oftraders in the two markets. The resulting improvement in tradingfrequency reduces bid-ask spread, and enhances market liquidity. The


combined effect of these factors is to reduce volatility, and increaseexpected trading volume. Under perfect quotation transparency(complete integration), both markets are impacted favorably, althoughthe distribution of volume may depend on the relative transaction costs.

If the markets are not informationally linked (zero quotationtransparency between the two markets, complete fragmentation), foreigntraders and possibly some local traders for whom trading costs are lowerin the U.S. market will migrate to the ADR market, but no new traderswill enter the domestic market. Although new traders may enter theADR market increasing overall trading volume, the decrease in tradingactivity in the domestic market increases bid-ask spread and volatility,and reduces liquidity and trading volume.

Overall, the impact on market quality, bid-ask spread, and volatilityin the domestic market following ADR listing reflects the interaction ofrelative trading costs, and the degree of integration between the markets.DGM study twenty-five Mexican stocks after they list ADRs in the U.S.market. For fifteen of these, they find an increase in average dailytrading volume in the domestic market after ADR listing. Standarddeviation of daily price changes increases in twenty-two stocks, anddecreases in two. Consistent with the hypothesis that volatility mayincrease after ADR listing both because of increase in base levelvolatility and in volatility due to trading friction, their analyses showincrease in base level volatility for twenty-one stocks, and increase involatility related to trading volume for fourteen of them.

In a related study involving sixty-five companies from four LatinAmerican countries, Hargis (1996) finds an increase in trading volumein the domestic market after ADR listing for fifty of them. Using anapproach similar to DGM’s, Hargis reports decrease in base levelvolatility for seventeen stocks in a sub-sample of twenty-six stocks, anddecrease in volatility related to trading volume for eighteen of them.These results indicate an improvement in liquidity after ADR listing,and are consistent with Merton’s (1987) model, which shows positiverelation between ownership dispersion and stock’s liquidity.

Bloomfield and O’Hara (2000) argue that low-transparency dealercan capture early order flows than high-transparency dealers.Subsequently, the low-transparency dealers can use their informationaladvantage, quote narrower spreads and earn more profit thanhigh-transparency dealers. In an experimental setting, they test theabove proposition and find results that support it. They also find thatmost dealers choose to be of low-transparency and the high-


7. The Level I ADRs trade in the Over the Counter Market (OTC) or on the Pink Sheetand do not require SEC approval or compliance with US Generally Accepted AccountingPrinciples (GAAP). ADRs trading on the NASDAQ market are Levels II and III.

8. There may be selection bias in our sample as the majority of the ADRs trade eitheron the OTC and the Portal (under Rule 144A). The reason is the majority of the ADRs thattrade on major U.S. exchanges (NYSE, Amex and NASDAQ) are either IPOs or with very

transparency dealers will exist and being a sole transparent dealer issometimes advantageous. The implication of their finding in the ADRsetting is dealers may prefer to trade in the home country market, whichin general are less transparent than U.S. markets. The low transparencyand greater spread in the home country enables dealers to make greaterprofits. This may increase trading activity in the home country.Bloomfield and O’Hara and O’Hara (1999) further state that someinformation traders prefer to send their orders to a non-transparentmarket (dealer) to hide their trades. Sometimes they send false tradingsignal to the transparent market and actually trade in the non-transparentmarket. This also tends to favor the argument that trading activityincreases in the home country after ADR listing.

IV. Sample and Data

The sample includes one hundred and fifty-three companies that issuedADRs between December 1994 and July 1997 for which sufficient dailytrading data are available before and after ADR listing. The data werecollected from the lists of ADRs from Bank of New York, Citibank, andJP Morgan, and cross-referenced with the list of foreign companiestrading on the NYSE, AMEX, NASDAQ, and Over the Counter (OTC)markets.7 These companies represent twenty-three different countries.Fifty-six companies are from nine emerging markets (China, Indonesia,Korea, Mexico, Philippines, Portugal, South Africa, Singapore andTaiwan) and the remaining ninety-seven are from fourteen developedcountries (Australia, Austria, Finland, France, Germany, Hong Kong,Japan, Netherlands, New Zealand, Norway, Spain, Sweden,Switzerland, and UK). The country with the largest number of ADRs inthe sample is Hong Kong with thirty-three stocks. Twelve ADRs eachtrade on NYSE and NASDAQ. One hundred and three ADRs are Level1, which trade in the OTC market, and twenty-six ADRs trade on thePortal (Rule 144A ADRs).8


limited trading in their home country markets before listing in the US.

9. We are grateful to Bank of New York for allowing us access to this data source.

10. Appendix A is available from the authors upon request.

Daily trading volume and closing prices in the domestic markets arecollected from the Bridge Data Services.9 Official trading hours of thehome country stock exchanges are obtained from the 1994 Handbook ofWorld Stock and Commodity Exchanges, and Directory of World StockExchanges compiled by the Economist Publications.

V. Analyses and Results

The names of companies in the sample, their countries of origin, andU.S. exchanges where the ADRs trade are presented in Appendix A.10

The appendix also reports the following statistics before and after ADRlisting: the number of trading days, the percent of trading days with zerovolume, average number of shares traded daily, and average volatilityof daily returns. The ADR listing day and two trading days before andafter the listing day are excluded from all analyses. The number oftrading days in the overall sample average 253 and 420 before and afterthe ADR listing, respectively. The percent of days with zero volumeaverage 13.459% and 8.531% before and after ADR listing,respectively. The number of trading days with zero volume decreasedafter ADR listing for 112 of the 153 companies in the sample. Thissuggests increase in trading activity after ADR listing.

A. Trading Volume Changes Around ADR Listing

If Vi,1 and Vi,2 represent average daily trading volume before and afterADR listing, respectively, for company i, then volume ratio Λi = (Vi,2

/Vi,1), is greater than 1 if trading volume increases after ADR listing. Weuse t-statistics to test if the cross-sectional average of Λi is significantlydifferent from one for samples of firms categorized by emerging anddeveloped markets, as well as by the U.S. stock exchange where theADR is listed (NYSE, NASDAQ, OTC, or 144A).

Average daily trading volume by country are presented in table 1.109 of the 153 stocks experience increase in average daily tradingvolume. The average volume ratios for the entire sample of 153 stocks


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11. New Zealand and Sweden have one stock each. For Norway and Korea, tradingvolume increases for two of the three stocks in the respective samples, although the averagevolume of the three stocks is lower after ADR listing.

is 2.48, which is significantly greater than 1 at 5% level. The highestaverage trading volume ratio of 8.53 is for Singapore whose ADRs tradeon the OTC market. This is largely attributable to SingaporeTelecomexperiencing a 44-fold increase in trading volume after ADR listing.Excluding this stock, the average volume ratio for Singapore is 2.6,which is lower than the average for several other countries. GreenfieldCoal Ltd. of Australia whose ADR also trades on the OTC marketexperiences the second largest relative gain in volume. Itspost-ADR-listing average daily trading volume is over 17 times higherthan the pre-ADR level.

Average daily trading volume decreases for New Zealand, Norway,Sweden, and Korea, however.11 The average volume ratios for the restof the countries are greater than 1, although some of their stocksexperience decreases in trading volume after ADR listing. For example,trading volume decreases for five of the 33 stocks from Hong Kong, andfor four of the 8 Australian stocks.

The mean volume ratios for developed and emerging market stocks,as well as the entire sample are significantly greater than 1. The meanvolume ratio for emerging market stocks is greater than that fordeveloped market stocks, but the difference is not statisticallysignificant. Some patterns can be discerned from the distribution ofvolume ratios for European and Asian countries among the developednations. For most of the European countries, the volume ratios are closeto 1. In contrast, the ratio is well above 1 for Australia and Hong Kong,but it is close to 1 for Japan. Among the emerging countries, Korea andMexico have the smallest volume ratios. In general, ADRs fromcountries with whom the U.S. has strong relations and where marketsare relatively more informationally efficient register smaller gains intrading volume following ADR listing. This is inconsistent with theevidence in DGM, and Hargis.

B. Analysis of Volatility Changes

If σ2i ,1 and σ2

i ,2 represent variance of daily returns for stock i before andafter ADR listing, then φi = σ2

i ,2 /σ2i ,1 is an F distribution with N2 –1 and

N1–1 degrees of freedom, where N1 and N2 are the number of tradingdays in the sample before and after ADR listing. The change in


volatility for each stock and the percentage of stocks for which volatilitychanges significantly following ADR listing are examined. Themagnitude and direction of the change in volatility across firms fromemerging and developed markets, and between exchange listed, OTC,and 144A ADRs are also explored.

The last two columns of table 1 report volatility ratios before andafter ADR listing for groups of stocks from each country categorized asdeveloped or emerging nation. Volatility ratio of each stock is reportedin appendix A. Average volatility increases in 93 (significantly in 69)of the 153 stocks, and decreases in 60 (significantly in 44) of them. Theaverage of volatility ratios for the entire sample is 1.12, implying anaverage 12% increase in volatility after ADR listing. The largestvolatility ratio is 2.85 for Yorkshire Food Group of UK, which tradesin the OTC market. The smallest volatility ratio is 0.37 for FEMSAFomento Economico Mexicano of Mexico, which trades on the Portal.Average volatility decreases for six countries (Australia, France, NewZealand, Indonesia, Mexico, and Portugal).

Table 2 presents a contingency table of changes in trading volumeand volatility for developed and emerging countries. The significancefor the difference between the actual (or observed) and expectedfrequencies is tested using the chi-square statistic. The proportion ofstocks for which trading volume increases after ADR listing issignificantly greater than expected under equally likely scenarios forboth developed and emerging markets, as well as for the combinedsample. 71% (69/97) of developed country stocks and 71% (40/56) ofemerging market stocks experience increase in trading volume. As such,there is no difference between these markets insofar as the proportionof stock for which trading volume increases following ADR listing.

For change in volatility ratio, the results indicate that for developedcountries and the total sample, the proportion of stocks with increase involatility is significantly greater than what can be expected underequally likely scenario. For the emerging market stocks, the proportionof stocks with increase or decrease in volatility is not significantlydifferent from the expected level. Overall, volatility increases for 61%of the stocks.

Analysis by Exchange

Table 3 reports averages of trading volume and volatility ratios by theU.S. exchanges where the ADRs are trading. It is instructive to recall atthis point that for ADRs, the OTC market represents trading on pinksheets for mostly Level I ADRs, while the NASDAQ is the dealer market


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Dev

elop

ed C

ount

ries

9769

2845

1523

14E

mer

ging

Mar

kets

5640

1624

921

2A

ll F

irm

s 15

310

944

6924

4416

Not

e: V

olum

e is

num

ber

of s

hare

s tr

aded

and

vol

atil

ity

is s

tand

ard

devi

atio

n of

dai

ly p

rice

ret

urns

. Inc

reas

es a

nd d

ecre

ases

in v

olat

ilit

y ar

edi

vide

d in

to s

ubgr

oups

of s

igni

fica

nt o

r not

sig

nifi

cant

incr

ease

or d

ecre

ase

base

d of

the

F-d

istr

ibut

ion

of v

aria

nce

rati

os a

t 5%

leve

l of s

igni

fica

nce.


for trading mostly Levels II and III ADRs. The mean volume ratios aresignificantly greater than 1 for all exchanges except NYSE. The highestvolume ratio of 2.85 is OTC ADRs, followed by 1.95 for NASDAQ,1.83 for Rule 144A, and 1.17 for NYSE ADRs. Pair-wise comparison ofthe mean volume ratios by exchanges indicates that only the differencebetween the mean ratios of NYSE and NASDAQ stocks is statisticallysignificant at 5% level. A potential explanation is that relatively moretrading activity shifted from domestic markets to the U.S. market forNYSE listed ADRs. This is consistent with NYSE’s superior liquidity toOTC and NASDAQ markets. Analyses of volatility ratios by groups ofstocks based on the U.S. exchanges show that the largest averagevolatility ratio is for stocks whose ADRs trade in the NASDAQ, followedby those trading on the OTC. None of these volatility ratios except thosefor NASDAQ ADRs is statistically different from one, and there is nosignificant difference between any pair of ratios.

Table 4 shows the contingency table of changes in trading volumeand volatility of daily returns by U.S. exchanges. Assuming increases anddecreases in trading volume to be equally likely, the proportion of stockswhich experience increase in trading volume is significantly greater than50% for the entire sample and for groups of NYSE, NASDAQ and OTCstocks. There is no significant difference in the frequency of relativeincrease in trading volume across U.S. exchanges, however.

In terms of frequencies of changes in volatility ratios, 8 of 12 NYSEstocks experience increase in volatility (6 significantly), while volatilityratio decreases significantly in 2 cases. For NASDAQ listed ADRsvolatility increases for eight (significantly in seven) stocks, anddecreases in four (significantly in 2). For OTC listed ADRs volatilityincreases in 62 (significantly in 44), and decreases in 41 (significantly

TABLE 3. Difference of Means Tests of Changes in Trading Volume andVolatility Following ADR Listing by U.S. Exchange

NYSE NASDAQ OTC 144A All Firms

N 12 12 103 26 153Mean volume ratio 1.1722 1.9483*, a 2.8554* 1.8263* 2.4772*Mean volatility ratio 1.1140 1.3071* 1.1162 1.0621 1.1222

Note: Volume ratio is the ratio of average daily number of shares traded after ADRlisting to that before ADR listing. Volatility ratio represents the ratio of standard deviationof daily returns after ADR listing to before ADR listing.. *Significantly greater than 1 at 5%level. a This mean volume is significantly different from that for the NYSE at the 5% level.


TA

BL

E 4

.F

requ

ency

Dis

trib

utio

n of

Cha

nges

in D

aily

Tra

ding

Vol

ume

and

Vol

atili

ty F

ollo

win

g A

DR

Lis

ting

by

U.S

. Exc

hang

e

Dai

ly tr

adin

g vo

lum

eV

olat

ilit

y of

dai

ly r

etur

ns

Incr

ease

Dec

reas

e

U.S

. exc

hang

eN

Incr

ease

Dec

reas

eS

igni

fica

ntN

ot s

igni

fica

ntS

igni

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ot s

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SE

129

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93

71

22

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374

2944

1830

1114

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179

123

101

All

fir

ms

153

109

4469

2444

16

Not

e: V

olum

e is

the

num

ber

of s

hare

s tr

aded

and

vol

atil

ity

is th

e st

anda

rd d

evia

tion

of

dail

y pr

ice

chan

ges.

Cha

nges

in v

olat

ilit

y ra

tio

are

divi

ded

into

gro

ups

of s

igni

fica

nt o

r no

t sig

nifi

cant

incr

ease

or

decr

ease

bas

ed o

f th

e F

-dis

trib

utio

n of

var

ianc

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tios

at 5

% le

vel o

f si

gnif

ican

ce.


in 30) of the 103 stocks in the sample. Out of the 26 stocks trading onthe Portal (Rule 144A), volatility increases significantly in 12 anddecreases significantly in 10 of them. The difference between theproportion of actual and expected frequency is significant for the OTCstocks and the combined sample.

In sum, the above analyses indicate that:

1. Average daily trading volume increases in the home market afterADR listing for a significant majority of stocks. Firms fromemerging countries show greater gains in trading volume than thosefrom developed countries, but the difference is not significant.Similarly OTC stocks show largest volume gains.

2. Similarly, average volatility of daily returns increases for most ofthe stocks following ADR listing on U.S. exchanges. Further,increase in volatility is larger for developed market stocks thanemerging market stocks, although the difference is not significant.And, the number of stocks with increase in volatility following ADRlisting is significantly greater than expected for OTC listed ADRsthan for those listed on NYSE, NASD or Rule 144A.

C. Cross-Sectional Analysis of Changes in Volume and Volatility

To determine the differential impact of the country of origin and U.S.exchange on the changes in trading volume and volatility followingADR listing, the following multivariate cross-sectional regressionequations are estimated:

(1)0 1 2 3 4( 1)i i i i i iHR Dev OTC Portalα α α α α εΛ − = + + + + +

(2)0 1 2 3 4 5i i i i i i iHR Dev OTC Portal eσ β β β β β β υΔ = + + + + + +

(Λi–1) is the percentage change in average daily trading volume, Δσi, isthe percentage change in standard deviation of daily returns (which isequal to square root of φi minus 1). Devi is a dummy variable with avalue of 1 for developed countries, and 0 otherwise; OTCi is a dummyvariable with a value of 1 if the ADR is trading in the OTC market and0 otherwise; and, Portali is a dummy variable with a value of 1 if theADR is trading on the Portal (Rule 144A ADR) and 0 otherwise. The


residual from equation 1, captures the association between change involatility due to change in trading volume. (Λi–1) is not used in equation2 to avoid multicollinearity because in equation 1, (Λi–1) depends onthe same set of variables as Δσi in equation 2.

Pulatkonak and Sofianos (1999) argue that distance from the NYTime zone, the time zone effect, is the most important country specificfactor. In their data, time zone explains 40 percent of the variation in theU.S. market share with proximity to NY, all else equal, increasing U.S.market share. Following them, the model includes Hri, which representsincrease in relative trading hours for the stock after ADR listing. HRi ismeasured as the increase in trading time in hours as a result of listingADRs divided by the total trading time in hours in home country. Forexample, Korean stock market is open during 9:40-11:40 a.m. and13:20-15:20 p.m. local time (4 hours of trading). Time differencebetween Korea and NY city is 14 hours, and there is no overlap in theopening trading hours of the two markets. So Korean stocks getadditional trading of 6½ hours after ADR listing. So the relativeincrease in trading hours is 6.5/4 = 1.625.

Equation (1) is estimated first, to generate the residuals gi. Bothequations are next estimated simultaneously using the SeeminglyUnrelated Regression (SUR) model. α1 and β1 are expected to havepositive signs because increase in trading hours increases both tradingvolume and volatility. Consistent with this, Makhija and Nachtmann(1990) find increase in variance for U.S. stocks after cross-listing onTokyo Stock Exchange. They attribute the increase in variance toincrease in the flow of private information due to increase in tradingtime. According to DGM, if there is perfect quotation transparencybetween the U.S. market and the foreign market, ADR listingencourages competition between the markets, improves the precision ofprice quotations, increases liquidity, and reduces volatility. If this istrue, and if there is a higher degree of inter-market linkage between theU.S. market and developed markets than between the U.S. market andemerging markets [Harvey (1995)], α2 is expected to be positive whileβ2 is expected to be negative. β5 is expected to be positive becauseincrease in trading volume increases volatility. Under the premise thatOTC and Portal markets are considered less liquid than NYSE andNASDAQ, the coefficients α3 and α4 are expected to be negative in thevolume equation 1, and β3 and β4 are expected to be positive in thevolatility equation 2.

The regression estimates of equations 1 and 2 are contained in table5. In the trading volume equation, only the coefficient for developed


country dummy is statistically significant. The negative sign of thecoefficient implies that emerging market stocks gain more in tradingvolume than developed market stocks. Consistent with our hypothesis,the coefficients representing relative increase in trading time arepositive in both equations, but they are statistically insignificant. Thecoefficient of OTC dummy is positive in the volume equation andnegative in the volatility equation, but both are insignificant. Thisimplies that volume increases and volatility decreases for OTC listedADRs relatively more than for NYSE and NASD listed ADRs. The

TABLE 5. Heteroskedasticity Corrected Cross-Sectional Regression Estimates ofTrading Volume and Volatility of Returns Equations

Regression estimates Regression estimatesVariable for the (Λi–1) equation for the Δσi equation

HRI 0.5091 0.0422(0.5872) (0.0562)

Dev –1.4813* 0.0421(0.8032) (0.0763)

OTC 0.9350 –0.1122(0.9813) (0.0932)

Portal –0.9011 –0.1412(1.3223) (0.1265)

e –0.0060(0.0083)

Constant 1.2871 0.1411(1.0923) (0.1043)

R-squared 0.0392 0.0243N 153 153

Note: The regression equations for trading volume and volatility of returns are:

(Λi–1) = α0 +α1 HRi +α2 Devi +α3 OTCi +α4 Portali +gi

Δσi = β0 +β1 HRi +β2 Devi +β3 OTCi +β4 Portali +β5 ei +ui,

where (Λi–1) and Δσi are the percentage change in average daily trading volume andpercentage change in standard deviation of daily price returns for stock i, HRi is increase intrading hours for the stock after ADR listing relative to the length of trading hours beforeADR listing, Devi is a dummy variable with a value of 1 for developed country stock, and 0otherwise, OTCi is a dummy variable with a value of 1 if the ADR is trading in the OTCmarket and 0 otherwise, Portali is a dummy variable with a value of 1 if the ADR is tradingon the Portal (Rule 144A ADR) and 0 otherwise, and ei is the residuals from the volatility ofreturns regression equation. ei captures the dependence of changes in volatility on changes involume. Standard errors are given in parentheses. *significant at the 10% level.


TA

BL

E 6

.A

naly

sis

of th

e D

iffe

renc

es in

Vol

ume

Rat

ios

and

Vol

atili

ty R

atio

s by

the

Typ

e of

Hom

e C

ount

ry M

arke

t and

Whe

ther

the

AD

R is

Tra

ding

on

the

Por

tal o

r N

ot

A: W

heth

er h

ome

mar

ket i

s au

ctio

n or

dea

ler

type

Auc

tion

Dea

ler

Dif

fere

nce

t-ra

tio

N11

241

Ave

rage

vol

ume

rati

o2.

7982

1.59

911.

1991

3.53

*A

vera

ge v

olat

ilit

y ra

tio

1.09

341.

1996

–0.1

062

–3.3

2*P

erce

nt w

ith

volu

me

rati

o >

1 ra

tio

74%

63%

11%

Per

cent

wit

h vo

lati

lity

rat

io >

1 ra

tio

58%

68%

–10%

Ave

rage

vol

ume

rati

o ex

clud

ing

outl

ier

2.42

631.

5992

0.82

714.

10*

B: W

heth

er th

e A

DR

is li

sted

as

144A

(po

rtal

) or

oth

erw

ise 14

4AO

ther

sD

iffe

renc

et-

rati

o

N26

127

Ave

rage

vol

ume

rati

o1.

8260

2.61

02–0

.785

1–2

.31*

*A

vera

ge v

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ilit

y ra

tio

1.06

211.

1342

–0.0

721

–2.2

5**

Per

cent

wit

h vo

lum

e ra

tio

>1

rati

o65

%72

%–7

%P

erce

nt w

ith

vola

tili

ty r

atio

>1

rati

o58

%61

%–4

%A

vera

ge v

olum

e ra

tio

excl

udin

g ou

tlie

r1.

8260

2.28

11–0

.455

1–2

.26*

*

Not

e: A

ssum

ing

an a

ucti

on m

arke

t is

mor

e tr

ansp

aren

t tha

n a

deal

er m

arke

t, th

e ab

ove

anal

ysis

indi

cate

s th

at th

e m

ore

tran

spar

ent m

arke

tbe

nefi

ts m

ore

foll

owin

g A

DR

list

ing.

Ass

umin

g th

e P

orta

l is

less

tran

spar

ent t

han

the

othe

r U

S m

arke

ts, c

ompa

nies

whi

ch li

st th

eir

AD

Rs

on th

ele

ss tr

ansp

aren

t mar

ket b

enef

it le

ss in

term

s of

vol

ume,

but

mor

e in

term

s of

vol

atil

ity.

*S

igni

fica

nt a

t 1%

leve

l; *

*sig

nifi

cant

at 5

% le

vel.


12. An alternative proxy for daily price volatility is the square of the residual in theregression of price on its lag. This has its root in the Rational Expectations Hypothesis ofLucas and Sargent (1981). Price deviation unexplained by its lag is assumed to result fromerror in public information and trading friction (the same interpretation as DGM’s).

coefficients of the Portal dummy are negative, but statisticallyinsignificant in both equations. The coefficient of the volume proxy in thevolatility equation is negative and insignificant.

Table 6 presents the same analyses of volume and volatility changesfrom the point of view of degree of market openness. Panel A classifiesthe home country markets as auction or dealer type. 112 of the 153 stocks(73%) trade in auction type market while the remaining 41 stocks’ (27%)home country markets are dealer type. For both market types averagetrading volume ratios have increased after ADR listing and the increasefor auction type markets is significantly (at 1%) greater than the increasefor dealer type market. Volatilities, however, increase significantly morefor the dealer type market over the auction type. The difference in averagevolatility ratios is statistically significant at 1% level. Assuming auctionmarkets are more open than dealer markets, these results are consistentwith DGM and Hargis (1996). Companies whose home market is moreopen benefit more in terms of increased trading activity and improvedliquidity following ADR listing.

Panel B of table 6 presents the same analyses based on whether theADR is listed on the Portal (Rule 144A) or other markets. 26 of 153stocks (17%) trade as portal, under rule 144A. Only qualified institutionalbuyers trade on the portal. Therefore, it is plausible to assume that theportal is less open than the other exchanges. Both average volume ratiosand volatility ratios are significantly less for the stocks whose ADRs arelisted on the Portal than the others. This implies that companies that listtheir ADRs on a more transparent U.S. market benefit more in terms oftrading volume gain but less in terms of volatility compared to companiesthat list their ADRs on less transparent U.S. market.

D. GMM Estimation of DGM Model

In this section, the generalized method of moments (GMM) is used to testthe implications of DGM’s model. In the DGM model, volatility isdecomposed into base level volatility, which captures bid-ask bounce anduncertainty about price information, and volatility due to trading friction,which is a function of market liquidity. Increase in number of tradersreduces both components of volatility. Following DGM, we use thesquare of daily price change as a proxy for volatility to estimate thefollowing model:12


TA

BL

E 7

.F

requ

ency

Cou

nts

of t

he S

igns

of α 1

and

γ 1 in

the

GM

M V

olat

ility

Reg

ress

ion

α 1γ 1

NP

osit

ive

Neg

ativ

eP

osit

ive

Neg

ativ

e

A. F

requ

ency

cou

nts

by c

ount

ry o

f or

igin

: D

evel

oped

cou

ntri

esA

ustr

alia

87

10

8A

ustr

ia7

70

07

Fin

land

22

00

2F

ranc

e2

20

02

Ger

man

y11

65

110

Hon

g K

ong

3320

139

24Ja

pan

33

01

2N

ethe

rlan

ds2

11

02

New

Zea

land

10

11

0N

orw

ay3

30

03

Spa

in1

10

01

Sw

eden

10

10

1S

wit

zerl

and

42

21

3U

K19

154

217

Tot

al f

or d

evel

oped

mar

kets

9769

2815

82

(Con

tinu

ed)


TA

BL

E 7

.(C

onti

nued

)

Em

ergi

ng m

arke

tsC

hina

32

10

3In

done

sia

11

00

1K

orea

32

11

2M

exic

o10

55

19

Phi

lipp

ines

22

00

2P

ortu

gal

11

00

1S

. Afr

ica

2316

71

22S

inga

pore

74

31

6T

aiw

an6

42

06

Tot

al f

or E

mer

ging

Mar

kets

5637

194

52G

rand

Tot

al

153

106

4719

134

Num

ber

sign

ific

ant (

at 5

% le

vel)

2912

971

B: F

requ

enci

es b

y U

.S. e

xcha

nge

NY

SE

129

30

12N

AS

D12

93

210

OT

C10

369

3415

8814

4A26

197

224

Tot

al15

310

647

1913

4N

umbe

r si

gnif

ican

t (at

5%

leve

l)29

129

71

Not

e: N

ote α 1

and

γ 1 m

easu

re c

hang

e in

bas

e le

vel v

olat

ilit

y an

d ch

ange

in v

olat

ilit

y ca

used

by

trad

ing

fric

tion

aft

er A

DR

list

ing

resp

ecti

vely

.N

egat

ive

sign

of α 1

impl

ies

decr

ease

in b

ase

leve

l vol

atil

ity.

Neg

ativ

e si

gn o

f γ 1

impl

ies

incr

ease

mar

ket l

iqui

dity

.


13. A table that lists the coefficient estimates of equation 3 for each of the 153 stocks inthe sample is excluded for the sake of brevity. It can be obtained as an appendix from theauthors on request.

2 21 ,t t t t t t tVσ α β σ γ ε−= + + +

0 1t tADRα α α= +(3)

0 1t tADRβ β β= +

0 1t tADRγ γ γ= +

σ2t and σ2

t –1 represent average daily volatility and its lag. Vt is averagedaily trading volume. ADRt is a dummy variable with a value of 1 forthe period after ADR listing and 0 for the period before. gt is error term.α0 captures the effect of base level volatility caused by error in publicinformation and bounce in bid-ask spread, and α1 represents change inthis coefficient caused by ADR listing. β0 measures the relationshipbetween current volatility and its lagged values, and β1 measures changein this relationship after ADR listing. γ0 measures volatility caused bytrading friction, and γ1 measures the change in the strength of suchassociation after ADR listing.

According to DGM, under perfect quotation transparency betweenthe U.S. market and the domestic market, ADR listing increases totalnumber of traders and reduces base level volatility. Accordingly, α1 ispredicted to have a negative sign. Defining market liquidity as inverseof the price impact of a given order imbalance, DGM demonstrate thatvolatility caused by trading friction depends on trading volume througha factor which is a decreasing function of market liquidity. So increasein γ0 or a positive γ1 represents a decrease in liquidity, and a negative γ1

indicates improvement in liquidity. If ADR listing induces an increasein trading volume, and decrease in bid-ask spread, such that liquidityimproves, then γ1 is predicted to have a negative sign. Finally, under thepremise that fundamental (or, base-level) volatility may be related to theprice movements on the previous day, β0 is expected to be positive.

The results of GMM estimation of equation 3 show that α0 ispositive for 108 of the 153 stocks (71%), and γ0 is positive for 141 ofthe 153 stocks (92%).13 This is consistent with the notion that volatilityhas two components: base level volatility caused by inaccuracy ofpublic information and bid-ask bounce, and volatility caused by tradingfriction. β0, which measures the relationship between current volatility


14. When the countries are grouped by geographical location, the frequency comparisonshows that about 93% of European stocks have negative γ1 compared to about 81% of Asian,Australian and New Zealand stocks. But in the case of α1, only 26% of European stocks havenegative sign compared to 33% for Asian, Australian and New Zealand stocks. Thedifferences between these proportions are not statistically significant, but the results indicatethat European stocks gained relatively better in terms of reduction of volatility caused bytrading friction while Asian, Australian and New Zealand stocks gained relatively better interms of reduction of base level volatility.

and its lag, is negative for 107 (70%) of the stocks, but only six of thestocks have significant coefficients. β1, which measures change in therelationship between volatility and its lag after ADR listing is positivefor 118 of the 153 stocks (77%), but most of them are statisticallyinsignificant. These results are somewhat at odds with DGM, whoreport positive coefficient in twenty-one of twenty-five cases, and noappreciable change after ADR listing.

The most critical coefficients in the model are α1 and γ1. α1 measuresif there is any improvement in the dissemination of public informationafter ADR listing. The results show that base level volatility increasefollowing ADR listing in 106 (significantly in 29) of the 153 stocks(69%) and decreases in 47 (31%, significantly in 12) of them. Thisresult is similar to DGM’s findings, who report increase in base levelvolatility for 21 of the 25 stocks in their sample. γ1 measures change involatility caused by trading friction and hence it is a proxy for changein liquidity induced by ADR listing. Liquidity improves (i.e., γ1 isnegative) for 134 (significantly for 71) of the 153 (88%) stocks in thesample, and liquidity decreases in 19 (significantly in 9) of them. Thisis a stronger result than DGM, who report improvement in liquidity for11 of the 25 stocks (44%) in their sample (but the change is notsignificant for any of them), and decrease in liquidity in 14 of them(significantly in 7 cases).

Table 7, panel A summarizes the analysis by country of origin. Baselevel volatility decreases in 28 of 97 (29%) of the developed countrystocks. In contrast, it decreases in 19 of 56 (34%) of emerging marketstocks. Liquidity improves in 82 (85%, significantly in 49) of the 97developed country stocks, and in 52 (93%, significantly in 22) of the 56emerging market stocks. Although the difference is not significant,emerging market stocks benefit more from ADR listing than developedmarket stocks in terms of improvement in base level volatility andliquidity.14

Comparison across the U.S. exchanges (table 7, panel B) shows thatα1 is negative (signifying decrease in base level volatility) for 3 of the 12


NYSE stocks (25%), 3 of the 12 NASD stocks (25%), 34 of the 103 OTCstocks (33%), and 7 of the 26 Rule 144A stocks (27%). These proportionsare not significantly different from each other. For γ1, analysis ofobservations by exchanges demonstrates NYSE ranks first in terms ofimprovement in liquidity followed by Rule 144A, OTC and NASD ADRs,respectively. But the differences are not statistically significant.

VI. Summary and Conclusion

This paper analyzes the impact of ADR listing on the trading volumeand volatility of the underlying stock in the domestic market. Averagedaily trading volume increases for a significant majority of the stocksafter ADR listing. Indeed, average daily trading volume more thandoubled for the stocks in the overall sample. Comparison of emergingmarket and developed market samples indicates that although thedifferences are not statistically significant, emerging market stocks gainrelatively more than developed market stocks in terms of bothmagnitude of increase in trading volume and proportion of stocks forwhich volume increased. Grouping the stocks based on the U.S.exchange where their ADRs trade indicates that increase in averagedaily trading volume is the highest for OTC listed stocks followed byNASD and then Rule 144A. For these three groups the average dailytrading volume after ADR listing is significantly greater than that beforeADR listing. For NYSE listed ADRs the average ratio is greater than 1,but not significantly so. There is no significant difference among thefour exchange groups in terms of the magnitude of increase in tradingvolume and the proportion of the stocks for which trading volumeincreased.

When the sample is grouped based on whether the home countrymarket is auction or dealer type, the analysis indicates that tradingvolume increases significantly more for auction type market than fordealer type market.

Similarly, volatility increased on the average for the stocks in theoverall sample as well as for the sub-groups, but the change is notsignificant in most instances. There is no significant difference betweenvolatility changes after ADR listing between developed and emergingcountry stocks, or the listing exchange. But increase in volatility issignificantly more for dealer type home market than for their auctioncounterpart.


Next, volatility is decomposed into two main components: base levelvolatility caused by bid-ask bounce and error in public information, andvolatility caused by trading friction. For both of these components, therelationship between change in volatility and trading volume aroundADR listing is examined. The analyses show that base level volatilityincreased for most of the stocks. Volatility caused by trading frictiondecreases for majority of the stocks in the sample, which indicates animprovement in market liquidity after ADR listing. Finally, theproportion of stocks for which liquidity improves is not significantlydifferent across sample groups although emerging market stocks gainrelatively more than developed market stocks. The evidence thatemerging market stocks gain relatively more than their developedcountry counterparts in terms of liquidity does not support thecontention that developed country markets and U.S. markets havegreater linkages than emerging markets and U.S. markets.

Overall, ADR listing does not retard the home country market; itrather increases its trading volume and improves its liquidity. Theseimprovements are positively related to the degree of market openness(transparency) of the home country.

References

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Impact of ADR Listing on the Trading Volume and Volatility ... · explore the impact of number of traders and the variance (diversity) of traders’ endowment on expected utility