Pacific-Basin Finance Journal 21 (2013) 1160–1174

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Pacific-Basin Finance Journal

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Is there a reversal in the price discovery process underdifferent market conditions? Evidence from Korean ADRs andtheir underlying foreign securities☆

Ming-Chieh Wang⁎Department of International Business Studies, National Chi Nan University, 545 Puli, Nanton, Taiwan

a r t i c l e i n f o

☆ This study has been supported by the National Sthank Charles Cao (Editor) and an anonymous referauthor's responsibility.⁎ Tel.: +886 49 2910960#4646; fax: +886 49 29

E-mail address: mcwang@ncnu.edu.tw.

0927-538X/$ – see front matter © 2012 Elsevier B.V.doi:10.1016/j.pacfin.2012.05.001

a b s t r a c t

Article history:Received 18 March 2011Accepted 2 May 2012Available online 10 May 2012

This paper investigates whether the price discovery ability of AmericanDepository Receipts (ADRs) increaseswhen largemovements occur in theU.S. stock market, using an examination of the information transmissiondynamics between Korean ADRs and their underlying foreign stocksunder various U.S. andKoreanmarket conditions.When theU.S.market isstable, the underlying stocks dominate the price discovery process; whenit is volatile, regardless of the state of the Korean market, the pricediscovery process reverses and the trading of ADRs leads to greater pricediscovery than that of the underlying stocks. Therefore, ADR tradingdominates as the source of relevant price informationwhen large changesoccur in the U.S. market.

© 2012 Elsevier B.V. All rights reserved.

JEL classification:G14G15

Keywords:ADRsCross-listed stocksPrice discovery

1. Introduction

Globalization and technology advances in financial markets have prompted many foreign firms to issueAmerican Depository Receipts (ADRs) on United States (U.S.) stock exchanges. Because ADRs can beexchanged for the underlying foreign stocks, and vice versa, their foreign and U.S. prices should be identicalacross markets, in accordance with the law of one price to financial assets. However, legal barriers andunsynchronized trading disguise which market actually makes the greater contribution to the discovery ofefficient prices. Most studies of cross-border listings indicate that the home market dominates the pricediscovery process, but a few findings have indicated that price discovery occurs largely in the U.S. market.This study aims to explain the dynamics of price transmission between Korean ADRs and their underlyingstocks more clearly, as well as provide market participants with more direction for opening security prices.

cience Council of Taiwan (NSC 97-2410-H-260-010). The author would like toee for their helpful comments and suggestions. Any remaining errors are the

12595.

All rights reserved.

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Prior research offers some insights. For example, Grammig et al. (2005) indicate that during the overlappingtrading hours of the New York and Frankfurt stock exchanges (NYSE and XETRA, respectively), the value ofNYSE-listed German stocks is substantially determined by XETRA, and exchange rate innovations have moreinfluence on price changes in the NYSE than in XETRA. Pascual et al. (2006) find similar results for five Spanishstocks dually listed on the NYSE. In their analysis of eight Chinese stocks cross-listed on the NYSE and the HongKong stock exchange, Su and Chong (2007) show that Hong Kong yields price information, whereas trading onthe NYSE makes a negligible contribution to the cross-listed stocks. Similarly, Agarwal et al. (2007) assert thatthe daytime returns of underlyingHongKong stocks can fully explain the overnight returns of cross-listed stockson the London exchange, and the stock returns of both Hong Kong and London exchanges are affected by HongKongmarketmovements. Lim (2008) alsofinds that the price information of KoreanADRs is determinedmainlyby the underlying Korean markets. Frijn et al. (2010) indicate that the home market is dominant in the pricediscovery process for cross-listed stocks bilaterally traded on the Australian and New Zealand stock exchanges,but the larger market (Australia) is more important for both Australian and New Zealand domiciled firms.

This existing literature, however, does not reveal whether the price discovery ability associated withADRs increases when the U.S. stock market experiences a large movement. Emerging Asian stock marketsare greatly affected by U.S. market performance,1 such that a rapid raise in the U.S. stock indices inspiresconfidence among Asian investors and increases the probability that their respective markets will openstrong on the next trading day. In contrast, market participants express negative expectations about theirsecurity prices when the U.S. market declines sharply. The information leadership role thus influencesopening prices of the Asian markets. The motivation of this paper originated from the observation that theKorean market is highly correlated with the U.S. market,2 but Korean ADRs' contribution to price discoveryunder different market conditions remains unclear in extant literature.

In light of prior findings and the remaining gaps, this study formulates several predictions. A drastic changein the U.S. stock market should improve the price discovery ability of ADRs, because investors expect the shockto spread to foreignmarkets. TheADR trading process depends on arbitrage opportunities and reflects investors'expectations about the impact of underlying foreign prices. Therefore, trading should be information-drivenwhen the U.S. market is extremely volatile. In the absence of a major change in the U.S. market though, ADRtrading reflects price information about the underlying stocks, and trading should be for liquidity provision.

Of the few studies suggesting that U.S. trading provides price information, Werner and Kleidon (1996)indicate that the intraday patterns of U.S. and U.K. trading of British cross-listed stocks closely resemble thoseof otherwise similar, non-cross-listed stocks, and therefore ADR trading reveals private information thatoriginated in the NYSE. Kim et al. (2000) examine the relative importance and adjustment speeds of thepricing factors for ADRs; though the price of underlying stocks emerges as the most important factor, theexchange rate and U.S. market movements also have some impact. Iwatsubo and Inagaki (2007) suggest thatcross-border market information moves from international financial centers to underlying foreign markets.Using the EGARCHmodel to examine the information flows of Asian ADRs, they show that price transmissionmostly occurs from the United States to Asian stock markets.3

To reconcile previous findings regarding the price discovery ability of ADRs, this study investigateswhether the process reverses when U.S. market conditions shift between stable and volatile. To the best ofour knowledge, this study is the first to examine empirically the reverse mechanism of price discovery,which is important for determining the most efficient use of information inflows in international trading,as well as giving investors the information about opening security prices. This research focuses on Koreanstocks cross-listed on the NYSE for several reasons. First, similar to other Asian countries, the Koreaneconomy is deeply affected by the United States. Second, foreign institutional investors hold over 30% ofthe Korean equity market, and arbitrage trading may enhance the information flows of ADRs. Third, theKorean equity market is gaining increasing importance; MSCI Inc. and FTSE Group both recently upgradedits status to a developed market, yet few studies have examined the price behavior of Korean cross-listedstocks. Fourth, the trading hours of the NYSE and Korean stock exchange (KRX) do not overlap, whichsupports a clearer investigation of the relationship between their opening and closing prices.

1 See Kim and Roger (1995), Ng (2000), and Miyakoshi (2003).2 See Lim (2008).3 Bodurtha et al. (1995), Chan et al. (2003), and Froot and Dabora (1999) find that stock returns are dependent on the markets

where they are traded. Suh (2003), Grossmann et al. (2007), and Chen et al. (2009) find that ADR returns are driven by U.S. marketsentiment and U.S. investors may treat ADRs as local issues.

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To explore the reversal effect of price discovery when the U.S. market experiences large movements, it isnecessary to obtain a sufficient number of observations of drastic market changes over a long time period.Because of the well-developed state of the U.S. financial market, few dramatic rises or declines have occurredin the major stock indices over a long period. However, large changes have emerged in recent years,particularly in the Dow Jones Industrial Average (DJIA), which rose extremely rapidly in July 2006 andreached a record level on October 9, 2007. The bear market of late 2008, which resulted first from very highenergy prices and then from the subprimemortgage crisis, led the DJIA to its worst annual performance sincethe early 1930s. Themarket reversed course onMarch 9, 2009,when the DJIA reboundedmore than 50% fromits low to the close of the year. This cycle thus provides an excellent setting for investigatingwhether the pricediscovery process of ADRs reverses when extremely volatile movements occur in the U.S. stock market.

The non-synchronous trading hours of the NYSE and KRX also makes recent empirical research that hasexamined the role of price discovery during the pre-opening period helpful for determining the contributionof ADRs and underlying stocks.4 Using the weighted price contribution (WPC) model developed by Barclayand Warner (1993) and Cao et al. (2000), this study categorizes the daily returns of sample stocks asovernight or daytime returns and then examines the relationship between the overnight and daytime returnson both U.S. and Koreanmarkets. If the price discovery process originates in the U.S. market, then the closingprice of the ADRs should help investors set the opening price of their underlying stocks, and the daytimereturns of ADRs should constitute a larger portion of overnight returns for the underlying stocks. This studyalso uses a regression analysis to examine the co-movement of stock returnswith the U.S. and Koreanmarketmovements to determine which market has a greater impact on the stock pricing. Therefore, this study canmore precisely explain ADR price behavior under different market conditions and provide evidence ofefficient information flows between the two markets.

Several key findings result from this paper. First, in line with previous studies, when the U.S. stockmarketis stable, the underlying Korean stocks affect their ADRs more than the ADRs affect the underlying stocks.Therefore, ADR trading reflects the foreign information arrival. Second, when the U.S. market is volatile, pricediscovery occurs in the United States, which indicates investors' anticipation that market fluctuations willaffect underlying prices. In particular, when the market dropped sharply during the 2008 global financialcrisis, ADRs offered the greatest price discovery ability.

The remainder of this paper is organized as follows. The next section describes the data andmethodology,and section 3 presents the empirical results. Finally, section 4 provides a brief summary.

2. Data and methodology

2.1. Data

This study examines five Korean companies cross-listed on the NYSE, from January 1, 2004, to December31, 2010. Table 1 provides detailed transaction data pertaining to these companies. The industry to which thefive ADRs belong is diversified, and trading volume is sufficient for the tests. Despite the large declines in stockmarkets worldwide in 2008, the average daily returns remain positive, because the Korean economy hasgrown rapidly in the period after the Asian financial crisis. Furthermore, the issuing companies are blue-chipstocks in the Korean market,5 and have high foreign shareholdings.6 To determine the impact of U.S. andKorean market movements on the ADRs and underlying stocks, this study uses measures gathered from theDJIA and Korea Composite Stock Price Index (KOSPI) to reveal general movements of the two stock markets.Although Korean ADRs are not components of the DIJA, this index is a good representative of the U.S. stockmarket, because it is popular among foreign investors. The daily data source is the Datastream database;Koreanmarket prices are converted into U.S. dollars. In addition, two equally weighted portfolios, comprisedof ADR and underlying stock returns, serve to test co-movements with the stock markets.

4 Barclay and Warner (1993) investigate cumulative price changes between the close and open of 108 NYSE tender-offer targets.Cao et al. (2000) and Barclay and Hendershott (2003, 2008) examine the effects of trading on the price discovery process during theNASDAQ pre-open.

5 These five Korean companies appeared in Fortune Magazine's Global 500 Companies list for 2007.6 The Korean government controls 51% of Korean Electric Power.

Table 1Data descriptions and summary statistics for Korean ADRs and underlying stocks.

This table presents the summary statistics for five Korean firms cross-listed on the NYSE as ADRs. Ratio is the number of ordinaryunderlying shares that each ADR represents. The mean and standard deviation (in parentheses) of ADRs are on a daily basis and fromthe NYSE prices in U.S. dollars. The market capitalizations and foreign shareholdings of the Korean underlying companies areobtained from the Korean stock exchange and measured in 2009. The sample period is from January 1, 2004, to December 31, 2010.

Company name(U.S. symbol)

Industryclassification

Ratio Average dailyvolume

Market capitalizationranking

Ratio of holdingby foreigners

Mean (std. dev.)

Kookmin Bank (KB) Banks 1 466,446 9 55.19% 0.08% (3.57%)Korea Electric Power(KEP)

Electricity 2 819,714 12 3.95% 0.05% (2.81%)

KT (KTC) Fixed line telecom 2 652,895 24 48.65% 0.03% (2.27%)POSCO (PKX) Industrial metals 4 715,844 2 48.27% 0.11% (3.18%)SK Telecom (SKM) Mobile telecom 9 1,198,623 16 48.75% 0.01% (1.91%)

500

1000

1500

2000

4000

6000

8000

10000

12000

14000DJIA

KOSPI

2004/1/1 4/21 10/29 2006/7/17 2007/3/16 10/9 2008/5/15 11/20 2009/3/9 10/15 2010/12/31

Morestable period

Stable period Rising period Falling period

Rapidrising period

Sharpdrop period

Rising period

Recoveringperiod

Fig. 1. The dynamics of Dow Jones Industrial Average (DJIA) andKorea Composite Stock Price Index (KOSPI). Thisfigure presents the timeseries of theDJIA andKOSPI from January 1, 2004, to December 31, 2010. These sub-periods are divided according to theDJIAmovements.

1163M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

2.2. Sample period selection

To select periods that contain both stability and wide fluctuations in the U.S. stock markets, we use theDJIA chart (Fig. 1) to determine trends over four periods: a stable period (January 1, 2004–July 17, 2006),the first rising period (I) (July 18, 2006–October 9, 2007), a falling period (October 10, 2007–March 9,2009), and the second rising period (II) (March 10, 2009–December 31, 2010).7 Each major period alsoconsists of sub-periods, characterized by extreme movements, including the more stable period (April 21,2004–October 29, 2004), rapid rising period (March 16, 2007–October 9, 2007), sharp drop period (May15, 2008–October 20, 2008), and recovering period (March 10, 2009–October 15, 2009).8

7 The separation of rising periods (I) and (II) reflects the stronger performance of the Korean market, compared with the U.S.market, in the second period.

8 The more stable period indicates DJIA volatilities are at a minimum. The rapid rising (sharp drop) period occurs whenaccumulated DJIA returns are at their greatest (least). To ensure adequate time for research, the intervals are nearly six months.

Table 2Descriptive statistics of DJIA and KOSPI.

This table reports the means and standard deviations (in parentheses) of the DJIA and KOSPI for different periods from January 1,2004 to December 31, 2010. The mean and standard deviation are on a daily basis.

Period Date DJIA KOSPI

Stable period 2004/1/1–2006/7/17 0.01% (0.68%) 0.12% (1.48%)More stable period 2004/4/21–2004/10/29 −0.02% (0.47%) −0.03% (1.93%)Rising period (I) 2006/7/18–2007/10/9 0.09% (0.76%) 0.18% (1.36%)Rapid rising period 2007/3/16–2007/10/9 0.12% (0.91%) 0.29% (1.65%)Falling period 2007/10/10–2009/3/9 −0.19% (2.25%) −0.27% (3.58%)Sharp drop period 2008/5/15–2008/11/20 −0.38% (2.85%) −0.71% (4.72%)Rising period (II) 2009/3/10–2010/12/31 0.14% (1.18%) 0.23% (1.82%)Recovering period 2009/3/10–2009/10/15 0.29% (1.47%) 0.50% (2.15%)

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According to Fig. 1, the price dynamics of KOSPI and DJIA are near each other beginning in July 2006,especially during the 2008financial crisis. However, KOSPI exhibits a bullishmarketwhenDJIA is in the stableperiod, and the Korean market shows a more rapid recovery than the U.S. market after the financial crisis. Atthe end of 2010, the KOSPI rose more than in October 2007, but the DJIA failed to reach its original level.Table 2 provides the summary statistics for the twomarketmovements in these periods. After themore stableperiod, the dailymarket return and volatility of DJIA reached only−0.02% and 0.47%, respectively. Because ofthe financial crisis, the return and volatility then reached −0.38% and 2.85%, respectively, in the sharp dropperiod. In the Korean market, the return (in absolute value) and volatility of KOSPI are higher than thosevalues in the U.S. market in each period. Using these market conditions, this study investigates whether theprice discovery of ADRs reverses due to drastic movements in the U.S. and Korean markets.

The U.S. market timing listed in Fig. 2 confirms that there are no overlapping trading hours between theNYSE and KRX. Therefore, it is possible to divide the daily stock returns into overnight and daytime returns, inorder to examine the relationship between the overnight returns of the underlying stocks (ADRs) and thetrading returns of ADRs (underlying stocks). Let OPi, tA and Pi, t

A (OPi, tK and Pi, tK ) denote the opening and closing

prices, respectively, of ADR (underlying stock) i at day t. The corresponding overnight and trading returns ofthe ADR (RNi, t

A and RDi, tA ) and underlying stock (RNi, t

K and RDi, tK ) can be described as follows:

Fig. 2. TKorean

RNAi; t ¼

OPAi; t−PA

i; t−1

PAi; t−1

; RDAi; t ¼

PAi; t−OPA

i; t

OPAi; t

; and

RNKi; t ¼

OPKi; t−PK

i; t−1

PKi; t−1

; RDKi; t ¼

PKi; t−OPK

i; t

OPKi; t

:

Table 3 contains a comparison of the descriptive statistics of the daytime and overnight returns of ADRsand underlying stocks for periods marked by greatly stability and sharp drops, as defined in Table 2. In themore stable period, the volatilities of Korean daytime returns are greatest, indicating that the transactions

rading hours of New York and Korean stock exchanges. Korea standard time is 13 h ahead of New York. The trading hours ofstock exchange (KRX) are 9:00 a.m. to 3:00 p.m. in local time. The trading hours of NYSE are 9:30 a.m. to 4:00 p.m. in local time.

Table 3Descriptive statistics of Korean ADRs and their underlying stocks.

This table reports the means and standard deviations (in parentheses) of the overnight returns and daytime returns on Korean ADRsand their underlying stocks. The more stable period is from 2004/4/21 to 2004/10/29; the sharp drop period is from 2008/5/15 to2008/11/20.

More stable period Sharp drop period

Name Returns Underlying stocks ADRs Underlying stocks ADRs

KB Overnight 0.03% (1.56%) −0.24% (2.47%) −0.43% (3.57%) −0.74% (4.22%)Daytime 0.08% (3.92%) 0.20% (1.19%) −0.25% (2.92%) 0.09% (3.27%)

KEP Overnight 0.02% (0.74%) 0.02% (0.89%) −0.32% (3.27%) −0.22% (2.96%)Daytime 0.09% (1.89%) 0.14% (0.92%) 0.02% (2.79%) −0.06% (3.34%)

KTC Overnight −0.04% (0.84%) 0.01% (0.82%) −0.42% (2.56%) −0.37% (2.83%)Daytime 0.05% (2.14%) 0.01% (0.88%) 0.03% (2.11%) −0.02% (2.30%)

PKX Overnight 0.03% (1.30%) −0.13% (1.55%) −0.59% (3.69%) −0.10% (3.84%)Daytime 0.09% (3.01%) 0.30% (1.37%) 0.29% (3.11%) −0.22% (3.60%)

SKM Overnight −0.07% (1.16%) −0.20% (1.27%) −0.27% (2.47%) −0.38% (2.15%)Daytime 0.11% (2.93%) 0.19% (1.04%) 0.12% (1.54%) 0.22% (2.37%)

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involving the underlying stocks reflect the arrival of new information during the trading hours of the KRX,which increases the degree of fluctuation. The lower volatilities of ADRs' daytime returns indicate thatADR trading primarily reflects home market information.

When the DJIA falls sharply, the volatilities of ADRs' daytime returns in contrast are greater than that ofKorean daytime returns; the volatilities of Korean overnight returns are also greater than that of Koreandaytime returns. The results suggest that in this period, the Korean market is highly influenced by foreignshocks and ADR trading reflects innovations in the listing market. Therefore, an investigation of pricetransmission under different U.S. market situations appears worthwhile.

2.3. Methodology

To examine the reversal mechanisms of price transmission between Korean and U.S. markets, the WPCmethod can specify how one market movement affects the opening price of the other. If after observing thedaytime price changes in ADRs, investors use the closing prices as a guideline to determine the opening pricesof the underlying Korean stocks, the price information of ADRs appears to lead that of underlying stocks.Similarly, price discovery occurs in the homemarket if the ADRs' opening prices approach the Korean closingprices. TheWPC formula to measure the influence of U.S.-related trading information on the Korean openingprices for ADR i is given by:

WPC ¼XTt¼1

RNKi; tþ1

��� ���∑T

t¼1 RNKi; tþ1

��� ���0B@

1CA� RDA

i; t

RNKi; tþ1

!; i ¼ 1;2;…5; and t ¼ 1;2;…T : ð1Þ

Thefirst item on the right-hand side indicates theweighted average of the aggregated overnight returns ofunderlying stocks during the sample period. The second item is the contribution percentage of the tradingreturns of ADRs to the overnight returns of underlying stocks. TheWPCs of the Korean daytime returns on theADR overnight returns similarly can be obtained by substituting RNi, t+1

K and RDi, tA for RNi, t

A and RDi, t+1K ,

respectively.

3. Results

3.1. WPC results

This study tests the effect of the daytime returns of ADRs (underlying stocks) on overnight returns forunderlying stocks (ADRs) under different market conditions. Table 4 presents the results. Panel A shows that

1166 M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

the WPC values of the daytime returns of ADRs to the overnight returns of underlying stocks range from50.33% to 84.74%,with an average contribution of 68.67%. Thedaytime price changes infive Korean underlyingstocks contribute to 63.27%–68.09% of the price changes in the correspondingADRs,with an average of 65.56%,similar to that of the underlying stocks. The WPC of ADRs does not dominate the underlying stocks, or vice

Table 4Weighted price contribution (WPC) results for ADRs and underlying Korean stocks under different U.S. market conditions.

This table summarizes WPC as measured by Eq. (1) for various stocks under different U.S. market conditions. Panel A reports theresults of the full sample period. Panel B reports the results when the DJIA movements are stable. Panel C reports the results duringthe period of large movements in the DJIA. WPC (A) is the weighted price contribution for the ADRs' daytime returns on theunderlying Korean stocks' overnight returns; WPC (K) is the weighted price contribution for the Korean daytime returns on theADRs' overnight returns. The average of WPCs for each stock is also presented in this table. The dominant markets are shown inboldface type. The stable period is from 2004/1/1 to 2006/7/17; the more stable period is from 2004/4/21 to 2004/10/29; the risingperiod (I) is from 2006/7/18 to 2007/10/9; the falling period is from 2007/10/10 to 2009/3/9; the rising period (II) is from 2009/3/10to 2010/12/31; the rapid rising period is from 2007/3/16 to 2007/10/9; the sharp drop period is from 2008/5/15 to 2008/10/28; andthe recovering period is from 2009/3/10 to 2009/10/15.

Panel A: Full sample period

Company WPC (A) WPC (K)

KB 76.45% 68.09%KEP 64.61% 67.33%KTC 67.25% 63.76%PKX 84.74% 65.34%SKM 50.33% 63.27%

Average 68.67% 65.56%

Panel B: DJIA in stable and more stable periods

Market condition Stable period More stable period

Company WPC (A) WPC (K) WPC (A) WPC (K)

KB 39.13% 91.47% 15.46% 91.29%KEP 48.55% 77.13% 50.51% 77.43%KTC 36.51% 83.77% 11.52% 99.89%PKX 54.79% 79.10% 54.62% 83.58%SKM 29.95% 93.03% 0.04% 119.97%

Average 41.79% 84.90% 26.43% 94.43%

Panel C: DJIA in rising and falling periods

Market condition Rising period (I) Falling period Rising period (II)

Company WPC (A) WPC (K) WPC (A) WPC (K) WPC (A) WPC (K)

KB 72.62% 75.14% 75.68% 55.76% 63.01% 67.48%KEP 54.72% 63.95% 113.84% 60.71% 60.45% 66.29%KTC 81.33% 78.22% 90.80% 59.86% 55.19% 62.59%PKX 65.77% 69.66% 103.81% 62.49% 80.41% 52.40%SKM 45.03% 75.73% 74.18% 48.43% 75.15% 47.59%

Average 63.89% 72.54% 91.66% 57.45% 68.01% 58.15%

Market condition Rapid rising period Sharp drop period Recovering period

Company WPC (A) WPC (K) WPC (A) WPC (K) WPC (A) WPC (K)

KB 82.39% 54.94% 73.96% 41.14% 77.31% 67.78%KEP 60.05% 50.81% 112.12% 66.38% 84.51% 66.10%KTC 110.28% 61.02% 104.88% 52.95% 74.54% 56.36%PKX 63.64% 65.27% 104.18% 66.39% 90.27% 47.96%SKM 41.67% 58.18% 70.82% 45.47% 87.70% 47.94%

Average 71.61% 58.04% 93.19% 54.47% 82.87% 57.23%

1167M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

versa. This result therefore does not reveal which market makes a greater contribution to price discovery. Thenext step is to investigate the dynamics of information transmission under different market conditions.

Panel B of Table 4 shows that when the U.S. market is steady, the average values for Korean daytimetrading are as high as 84.90%. In contrast, the average WPCs of the ADRs' daytime price changes inresponse to the overnight returns of the underlying stocks is 41.79%, just half that of the underlying stocks.The results indicate that for the Korean market, price discovery occurs largely in the home market; thisconclusion is similar to that obtained in previous empirical studies.

In the more stable period, the average of the daytime changes in the underlying stocks (ADRs) to theovernight changes in the ADRs (underlying stocks) increases (decreases) to 94.43% (26.43%), and the differencesare particularly obvious for KB, KTC, and SKM. Therefore, the WPCs of the underlying stocks dominate those ofthe ADRs. Furthermore, Korean ADRs do not appear to have price discovery ability in this period.

However, in volatile period, the WPC values of the two markets reverse. According to Table 4, Panel C, inrising period (I), the WPC values of the ADRs remain less than those of the underlying stocks, but the overallvalue increases to 63.89%, while the WPC of underlying stocks drops to 72.54%. In the rapid rising, falling, andsharp drop periods, the daytime returns of the five ADRs show higher price contributions than their underlyingstocks. Consider, for example, the case of KTC: the WPCs of the ADR and underlying stock change from 11.52%and 99.89% (more stable period) to 104.88% and 52.95% (sharp drop period), respectively. Even though themean and volatility of the KOSPI are higher than those of the DJIA after the financial crisis (i.e., rising period IIand recovering period), the two WPC values of ADRs' daytime trading (68.01% and 82.87%) are much greaterthan those of the underlying stocks' daytime trading (58.15% and 57.23%) during both periods. In this panel, thedifference in average WPCs ranges from 10% (rising period II) and goes as high as 40% (sharp drop period),indicating that ADR trading has the highest price discovery ability during the sharp drop period. Overall, thecomparisons of the WPC values indicate that U.S. trading might explain Korean overnight returns. In otherwords, price information is transmitted from the NYSE to the home market.

The WPC results prove that when there are large movements in the U.S. market, especially during thefinancial crisis, U.S. tradingprovides a dominant source for pricing. Agarwal et al. (2007) indicate that daytimechanges in Hong Kong stocks can largely capture the overnight returns of their cross-listed stocks in London,which shows that London trading is driven by liquidity. Our results suggest a similar mechanism for Koreancross-listed U.S. stocks, but the price transmission reverses when U.S. market movements are volatile.

3.2. WPC results under U.S. and Korean market conditions

The preceding results indicate a reversal effect for the trading of Korean ADRs in different U.S. marketconditions. To provide additional evidence about the generality of price transmission dynamics between thetwo markets, this study further investigates international flows by incorporating Korean market conditions.There are four market conditions that are necessary to consider: (1) both the Korean market and the U.S.market are volatile; (2) the Korean market is volatile and the U.S. market is stable; (3) the Korean market isstable and the U.S. market is volatile; and (4) both the Korean market and the U.S. market are stable.9 Byconsidering these conditions, we can clarify the ADRs' price discovery process and help investors forecastopeningpricesmore accurately. Tomeasure theWPCwhen the Koreanmarket is volatile or stable, we examinetheprice dynamics of KOSPI, as shown in Fig. 1. Koreanmarketmovements clearly aremore active than those ofthe U.S. market, which makes it difficult to identify a sufficiently long period of stability in our sample.10

To replicate the four market conditions, we therefore first calculate the daily movements of KOSPI andDJIA (in absolute value) and divide the full sample into two groups, according to the daily movements ofKOSPI, with the volatile Korean market in the first group and the stable Korean market in the secondgroup. Next, we divide each group into two subgroups, according to the corresponding daily movementsof DJIA. Thus, we derive four groups to represent the U.S. market as volatile or stable when the Koreanmarket is volatile, and the U.S. market as volatile or stable when the Koreanmarket is stable. In parallel, weconsider DJIA movements first and then add KOSPI movements to obtain four additional groups thatrepresent the Korean market as volatile or stable when the U.S. market is volatile; and the Korean market

9 We are grateful to the referee for this useful suggestion.10 We tried to extend the sample period from 2000/1/1 to 2010/12/31, but the price dynamics of KOSPI remained similar to those inFig. 1.

Table 5Summary statistics of DJIA and KOSPI daily movements and WPC values.

Panel A reports the mean (in absolute value) and standard deviation (in parentheses) of DJIA and KOSPI daily returns. These groupsare derived by first dividing the sample according to KOSPI daily movements and then adding DJIA movements (in absolute value),and vice versa. Conditions A and E denote the situation that both the U.S. and the Korean markets are volatile; B and G denote thatthe U.S. market is volatile and the Korean market is stable; C and F denote that the US market is stable and the Korean market isvolatile; and D and H denote that both the U.S. and Korean markets are stable. Panel B presents the average of WPC values under thespecified condition that is obtained from Panel A. The dominant markets are shown in boldface type.

Panel A: Summary statistics of DJIA and KOSPI daily movements

Market condition DJIA KOSPI DJIA KOSPI Market condition

U.S. market is volatile(condition A)

1.65%(2.12%)

2.03%(2.49%)

Korean market isvolatile

U.S. market isvolatile

1.57%(2.08%)

2.11%(2.55%)

Korean market is volatile(condition E)

U.S. market is stable(condition B)

0.25%(0.31%)

1.57%(1.74%)

1.25%(1.51%)

0.41%(0.48%)

Korean market is stable(condition F)

U.S. market is volatile(condition C)

1.16%(1.43%)

0.34%(0.41%)

Korean market isstable

U.S. market isstable

0.21%(0.26%)

1.47%(1.65%)

Korean market is volatile(condition G)

U.S. market is stable(condition D)

0.20%(0.23%)

0.35%(0.41%)

0.23%(0.27%)

0.31%(0.35%)

Korean market is stable(condition H)

Panel B: Average of WPCs for ADRs and underlying Korean stocks under different U.S. and Korean market conditions

Market condition WPC (A) WPC (K) WPC (A) WPC (K) Market condition

U.S. market is volatile(condition A)

79.13% 62.11% Korean market isvolatile

U.S. market isvolatile

83.66% 62.55% Korean market is volatile(condition E)

U.S. market is stable(condition B)

55.66% 72.48% 86.08% 66.45% Korean market is stable(condition F)

U.S. market is volatile(condition C)

73.09% 61.71% Korean market isstable

U.S. market isstable

60.21% 74.03% Korean market is volatile(condition G)

U.S. market is stable(condition D)

48.36% 67.86% 40.47% 75.56% Korean market is stable(condition H)

1168 M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

as volatile or stable when the U.S. market is stable. The advantage of this approach is that it enables us toexamine whether the price discovery process reverse when we change one market condition (volatile orstable), while holding the other market condition steady. Finally, we calculate theWPC values according tothe given overnight and daytime returns of ADRs and underlying stocks.

Table 5 contains the summary statistics of KOSPI and DJIA markets (Panel A) andWPC values (Panel B)for the four market conditions.11 As Panel A shows, the mean and volatility of the two markets in eachstate are similar,12 indicating the two approaches capture the same market condition, regardless ofwhether we arrange the Korean or U.S. market movement first. Panel B of Table 5 reports the dynamics ofWPC under corresponding condition; the average WPCs of ADRs and underlying stocks reveal two keyfindings. First, when the U.S. market is volatile in a situation with a volatile Korean market (condition A),the average WPCs for the effect of ADR daytime returns on the Korean overnight returns (79.13%) issubstantially greater than that of Korean daytime trading (62.11%). Therefore, U.S. closing prices play animportant role in determining Korean opening prices. However, if the U.S. market condition is stable(condition B), then the information transmission reverses, and price discovery moves from the U.S. to theKorean market (55.66% versus 72.48%). Second, when a volatile U.S. market combines with a stable Koreanmarket (condition C), trading of Korean ADRs reveals a U.S. influence; however, if the U.S. market is stable inthis scenario (condition D), then a reversal occurs, and the Korean market provides the price information.

11 In this study, conditions A and E denote the situation whereby both the U.S. and the Korean markets are volatile; B and G denotethat the U.S. market is volatile and the Korean market is stable; C and F denote that the U.S. market is stable and the Korean market isvolatile; and D and H denote that both the U.S. and Korean markets are stable.12 For example, the mean (volatility) of DJIA is 1.65% (2.12%) in condition A and 1.57% (2.08%) in condition E, respectively. Themean (volatility) of KOSPI is 1.57% (1.74%) in condition B and 1.47% (1.65%) in condition G, respectively.

1169M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

This study also investigates the dynamics of WPC values according to our other approaches to definethe conditions. Consistent with the previous discussion, the WPC results again indicate two main findings.First, in a volatile U.S. market regardless of whether the Korean market is volatile or stable, theWPC valuesof the ADRs dominate the underlying stocks (condition E: 83.66% versus 62.55%; condition F: 86.08%versus 66.45%). Second, if the U.S. market is stable, again regardless of whether the Korean is volatile orstable (conditions G and H), the price transmission reverse, and trading of underlying stocks hasexplanatory power for determining ADR prices. Therefore, in both classifications, the WPC results for thefour market conditions are virtually identical, even in the highly volatile of Korean market. The WPC teststhus confirm the role of U.S. trading.

As expected, the empirical results provide convincing evidence that theNewYorkmarket plays an importantrole in the price discovery process. The reversal in price transmission does not indicate market inefficiency, butrather reflects the existence of arbitrage opportunities between the two markets. The trading of ADRs showsthat investors' expectations regarding Korean market performance and the closing prices in the United Statescan serve as a benchmark for predicting the opening prices of underlying stocks, particularly during periods of asharp decline in the former. Thus, the main hypothesis has explanatory power regarding the process of pricediscovery and the formation of opening prices for Korean ADRs and their underlying stocks.13

3.3. Regression model

This study also includes a regression model to measure the relative contribution to the price discoveryprocess from the ADR and underlying stock, because if the ADR return has a greater impact on theunderlying stock return, then the price information transfers from the U.S. to the Korean market. Tomeasure the contribution of ADR (underlying stock) to the price discovery of underlying stock (ADR), weuse the following model:

wherestocksincludby sum

where17, 20the dechangmarkeADR tcoeffic

13 Wesame pedespiteare ava

RAt ¼ β0 þ β1 R

Kt þ β2 R

Ktþ1 þ εt ; and ð2Þ

RKtþ1 ¼ γ0 þ γ1 R

At þ γ2 R

Atþ1 þ ε′t ; ð3Þ

RtA and Rt

K represent the daily equally weighted portfolio returns for the ADRs and underlyingat day t, respectively. To control for non-synchronous trading, Rt+1

K and RtA are respectively

ed in Eqs. (2) and (3). The underlying stock's contribution to the price discovery of ADR is measuredming the coefficients β1 and β2. Similarly, the sum of the coefficients γ1 and γ2 measures the ADR's

bution to the price discovery of the underlying stock.

contriIn order to identify the change of the price discovery contribution under different U.S. and Korean

market conditions, a dummy variable added to Eqs. (2) and (3) produces the following regression model:

RAt ¼ β0 þ β1R

Kt þ β2R

Ktþ1 þ β�

0Dt þ β�1DtR

Kt þ β�

2DtRKtþ1 þ εt ; and ð4Þ

RKtþ1 ¼ γ0 þ γ1R

At þ γ2R

Atþ1 þ γ�

0Dt þ γ�1DtR

At þ γ�

2DtRAtþ1 þ ε′t ; ð5Þ

Dt is a dummy variable, such that Dt=1 for the period when the U.S. market is rising or falling (July06–December 31, 2010), but Dt=0 for all other times. The division of the two sub-periods followsfinition in Section 3.1. In Eqs. (4) and (5), the coefficients (β1*+β2*) and (γ1*+γ2*) measure thee of contribution to price discovery from the underlying stock and ADR, respectively, when the U.S.t is volatile. TheWPC results in Section 3.1 imply that the U.S. market should have a greater effect onrading in this period. Therefore, we predict that the coefficient (β1*+β2*) will be negative and theient (γ1*+γ2*) will be positive.

also use the WPC method to examine the international flows of Taiwan and Hong Kong cross-listed U.S. stocks during theriods and find that the reversal effect is unique for the Korean market. The trading of Taiwan ADRs reveals the U.S. influence,large changes or stability in the U.S. market. However, we find the opposite result in Hong Kong ADRs. The detailed outputsilable upon request from the author.

1170 M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

To confirm the results, this study also creates two dummy variables to denote conditions when boththe U.S. and Korean markets are volatile or when both markets are stable, as obtained in Section 3.2(conditions A and D, respectively). The regression equation is thus written as:

wheremarkeinforminform(β′11+

whereand Rrespecmovem

RAt ¼ β0 þ β1R

Kt þ β2R

Ktþ1 þ

X2i¼1

β′i0 þ β′

i1 RKt þ β′

i2 RKtþ1

� �Di; t þ εt ; and ð6Þ

RKtþ1 ¼ γ0 þ γ1R

At þ γ2R

Atþ1 þ

X2i¼1

γ′i0 þ γ′

i1 RAt þ γ′

i2 RAtþ1

� �Di; t þ ε′t ; ð7Þ

D1, t and D2, t are two dummy variables, such that D1, t=1 indicates that both the U.S. and Koreants are volatile, and D2, t=1 implies they are both stable. The WPC results show that the priceation is transmitted from the U.S. to the Korean market when both markets are volatile, and theation transmission is reversed when both markets are stable. We also predict that the coefficientsβ′12) and (γ′21+γ′22) will be negative and the coefficients (β′21+β′22) and (γ′11+γ′12) will bee.

positiv

Table 6 contains the regression results based on daily returns of theunderlying stock andADR. According toPanel A, in Eqs. (2) and (3), the sum of coefficients β1 and β2 is 0.96 and the sum of coefficients γ1 and γ2 is0.84, and all are significantly different from zero. This indicates that the underlying stock (ADR) returns affecttheADR (underlying stock) returns, but the underlying stock hasmore explanatory power on the ADR returns.

Panels B and C contain results for dummy variables. When a dummy variable is incorporated into Eqs. (4)and (5), in Panel B, the beta coefficient γ2* is negative and significant, but the other three coefficients β1*,β2*,and γ2* are not significantly different from zero. The result does not obtain a significantly positive (negative)change in contribution of the ADR (underlying stock) to the price discovery of the underlying stock (ADR)during the period when the U.S. market is volatile. In Panel C, out of the eight estimated coefficients thatmeasure the change of contribution in Eqs. (6) and (7), the coefficients β′11, β′12, and γ′11 are significant andpositive, but the other coefficients are insignificant. Therefore, this study also rejects the assumption that theADR becomes more (less) correlated with the underlying stock when both markets are volatile (stable), andthe underlying stock becomes less (more) correlatedwith the ADRwhen bothmarkets are stable (volatile). Insum, the regression results based on daily returns of the ADR and underlying stock do not find the reversaleffect by incorporating the dummy variables, which are inconsistent with the WPC results.

To explore the inconsistent results, we suggest that U.S. (Korean) market movements may be one reasonto explain the ADR (underlying stock) daily returns. Several studies support that stock returns are affected bythe market movements where the stocks are traded. Bodurtha et al. (1995) find that the stock prices offoreign country funds traded in the U.S. are heavily influenced by the U.S. market movement although theirnet asset values are not. Chan et al. (2003) study equity trading on the Jardine Group whose share listingswere delisted from the Hong Kong Stock Exchange and moved to the Singapore Stock Exchange. They findreturns are correlated less (more) with the Hong Kong (Singapore) market after the delisting even thoughJardine's core business remained inHongKong. Chen et al. (2009) show thatU.K. ADR returns are drivenmoreby U.S. market returns than by U.K. market returns and that trading location impacts equity pricing behavior.We next identify the change in co-movement of ADR (underlying stock) returns with the U.S. (Korean)market and examinewhether the price information transmission reverses under differentmarket conditions.

Applying Chan et al.'s (2003) formula, we regress each stock portfolio returns on the U.S. and Koreanmarket returns to investigate the influence of the two market movements. The regression model ispresented as follows:

RAt ¼ β0 þ β1R

DJIAt þ β2RD

KOSPIt þ β3RN

KOSPItþ1 þ εt ; ð8Þ

RKtþ1 ¼ γ0 þ γ1R

KOSPIt þ γ2RD

DJIAt þ γ3RN

DJIAtþ1 þ ε′t ; ð9Þ

RtDJIA and Rt

KOSPI denote the DJIA and KOSPI daily returns, respectively; RDtKOSPI and RNt+1

KOSPI (RDtDJIA

Nt+1DJIA ) denote the KOSPI (DJIA) daytime return at day t and overnight return at day t+1,

tively. The coefficient β1 measures the co-movement of ADR with the U.S. market while the co-ent of ADR with the Korean market is measured by summing the coefficients β2 and β3.

Table 6Regression results of the Korean ADR (underlying stock) returns on the underlying stock (ADR) returns.

This table represents the regression results showing the influence of the Korean ADR (underlying stock) on the underlying stock (ADR) returns under different market conditions. Panel Asummarizes the results of Eqs. (2) and (3). R t

A and RtK represent the daily equally weighted portfolio returns for the ADRs and underlying stocks at day t, respectively. Panel B summarizes the results

of Eqs. (4) and (5). Dt is the dummy variable to proxy for the U.S. market condition, which equals one for the period from 2006/7/18 to 2010/12/31 and zero otherwise. Panel C summarizes theresults of Eqs. (6) and (7). D1, t is the dummy variable that equals one for when both the U.S. and the Korean markets are volatile; and D2, t is the dummy variable that equals one for when both theU.S. and the Korean markets are stable. Intercepts and dummy intercepts are not reported.

Panel A: Regression model:

R tA=β0+β1Rt

K+β2Rt+1K +εt R t+1

K =γ0+γ1RtA+γ2R t+1

A +εt′

β1 β2 γ1 γ2

ADR return 0.63***(29.69)

0.33***(15.54)

Underlying stock return 0.31***(19.15)

0.53***(32.36)

Panel B: Regression model:

R tA=β0+β1R t

K+β2Rt+1K +β0*Dt+β1*DtRt

K+β2*DtRt+1K +εt R t+1

K =γ0+γ1RtA+γ2Rt+1

A +γ0*Dt+γ1*DtRtA+γ2*DtRt+1

A +εt′

β1 β2 β1* β2* γ1 γ2 γ1* γ2*

ADR return 0.61***(13.26)

0.31***(6.88)

0.03(0.63)

0.02(0.40)

Underlying stock return 0.35***(8.76)

0.64***(16.10)

−0.04(−1.08)

−0.13***(−3.19)

Panel C: Regression model:

RAt ¼ β0 þ β1R

Kt þ β2R

Ktþ1 þ

P2i¼1

β′i0 þ β′

i1 RKt þ β′

i2 RKtþ1

� �Di; t þ εt RK

tþ1 ¼ γ0 þ γ1RAt þ γ2R

Atþ1 þ

P2i¼1

γ′i 0 þ γ′

i1 RAt þ γ′

i2 RAtþ1

� �Di; t þ ε′t

β1 β2 β11′ β12′ β21′ β22′ γ1 γ2 γ11′ γ12′ γ21′ γ22′

ADR return 0.54***(14.03)

0.19***(6.41)

0.12***(2.78)

0.33***(7.53)

0.08(0.76)

−0.06(−0.91)

Underlying stock return 0.26***(10.76)

0.49***(16.21)

0.11***(3.32)

0.03(0.94)

−0.08(−1.48)

0.08(0.92)

Notes: the symbol *** denotes significance at the 1% level. The t-statistics are included in parentheses.

1171M-C.W

ang/Pacific-Basin

FinanceJournal21

(2013)1160

–1174

1172 M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

Unlike the setting of Eqs. (2) and (3), the above equations consider the daytime return at day t andovernight return at day t+1, but not the daily returns at days t and t+1, for the Korean and U.S. markets.The motivation for this analysis is because the two returns are more related to the daily returns of thecorresponding foreign stock portfolio (see Fig. 2). In addition, this setting is useful to examine whetherprice information is released by daytime trading.

To examine whether the co-movement of stock returns with the two market returns changes when theU.S. market is volatile, we extend Eqs. (8) and (9) by using a dummy variable that is defined in Eqs. (4) and(5). The regression model is rewritten as:

RAt ¼

RKtþ1

RAt ¼ β0 þ β1R

DJIAt þ β2RD

KOSPIt þ β3RN

KOSPItþ1 þ β�

0 þ β�1R

DJIAt þ β�

2RDKOSPIt þ β�

3RNKOSPItþ1

� �Dt þ εt ; ð10Þ

RKtþ1 ¼ γ0 þ γ1R

KOSPItþ1 þ γ2RD

DJIAt þ γ3RN

DJIAtþ1 þ γ�

0 þ γ�1R

KOSPItþ1 þ γ�

2RDDJIAt þ γ�

3RNDJIAtþ1

� �Dt þ ε′t : ð11Þ

Similarly, this study also creates two dummy variables that are defined in Eqs. (5) and (6) to denoteconditions when both markets are volatile or when both markets are stable. The equations are thuswritten as:

β0 þ β1RDJIAt þ β2RD

KOSPIt þ β3RN

KOSPIt þ

X2i¼1

β′i0 þ β′

i 1 RDJIAt þ β′

i 2 RDKOSPIt þ β′

i3 RNKOSPItþ1

� �Di; t þ εt ; ð12Þ

¼ γ0 þ γ1RKOSPIt þ γ2RD

DJIAt þ γ3RN

DJIAtþ1 þ

X2i¼1

γ′i 0 þ γ′

i1 RKOSPIt þ γ′

i 2 RDDJIAt þ γ′

i3 RNDJIAtþ1

� �Di; t þ ε′t : ð13Þ

Table 7 presents the regression results. The results of Eqs. (8) and (9) in Panel A reveal the impacts ofthe two market returns on the stock returns. For the ADR return, the beta coefficients are positive andsignificantly different from zero, indicating that the movements of both the home market and the tradingmarket affect the ADR returns. Comparing the co-movements shows that the DJIA returns have moreexplanatory power on the ADR returns, and the aggregate influence of the daytime and overnight returnsof KOSPI (β2+β3) is lower than β1. The result also reveals that the influence of KOSPI overnight returns islimited. For the underlying stock returns, the KOSPI daily returns (γ1) best explain the stock returns.However, the change in the DJIA overnight returns (γ3) also has an impact on the underlying stock returns.In sum, the regression results show that both market movements influence the pricing behavior of KoreanADRs and underlying stocks, but the trading market provides the highest explanatory power for stockpricings.

Panel B contains the results for Eqs. (10) and (11), showing that the DJIA returns have a significantinfluence on ADR returns, with an estimated coefficient of 0.73. The aggregate influence of the KOSPIreturns on the ADR returns has a significant coefficient of 0.87, higher than the corresponding coefficientfor the DJIA. This finding indicates the Korean market movements have more explanatory power for ADRs.In contrast, including a dummy variable in the regression causes the coefficient β1* to become significantlypositive through the DJIA movements and the coefficient β2*+β3* to become negative through the KOSPImovements. In other words, compared with the stable condition, the aggregate influence of the DJIA onADR returns can be as high as 1.11 (β1+β1*), which offers a higher degree of explanatory power than theKOSPI, showing that ADR returns are more correlated with the U.S. market and are less correlated with theKorean market for this period. However, underlying stock returns are strongly (weakly) affected byKorean (U.S.) market movements, and the coefficients γ1*, γ2*, and γ3* that measure the change in the co-movement of underlying stock with either the U.S. or Korean market are insignificant when the U.S.market is volatile.

Panel C reports the results for Eqs. (12) and (13). Briefly, the U.S. and Korean movements affectADR returns, and the aggregate influence of the Korean market reveals that its effect is lower than thatof the trading market. When both markets are volatile, the effect of KOSPI overnight returns (β′13) onADR returns is significant but relatively limited, and the influence of DJIA returns is larger than KOSPIreturns. If both markets are stable, then it is possible to argue that the U.S. market movements have anegative effect on ADR returns through a significantly negative β′21 coefficient (−0.46), and the sum

Table 7Regression results of stock returns on market index returns of the DJIA and KOSPI.

This table represents the regression results showing the influences of the Korean and U.S. market index returns on the stock returnsunder different conditions. Panel A summarizes the results of Eqs. (8) and (9). Rt

A and RtK represent the daily equally weighted

portfolio returns for the ADRs and underlying stocks at day t, respectively; RDtDJIA, RDt

DJIA and RNtDJIA (RtKOSPI, RDt

KOSPI, and RNtKOSPI) are

the DJIA (KOSPI) daily return, daytime return and overnight return at day t, respectively. Panel B summarizes the results of Eqs. (10)and (11). Dt is the dummy variable to proxy for the U.S. market condition, which equals one for the period from 2006/7/18 to 2010/12/31 and zero otherwise. Panel C summarizes the results of Eqs. (12) and (13). D1, t is the dummy variable that equals one for whenboth the U.S. and the Korean markets are volatile; and D2, t is the dummy variable that equals one for when both the U.S. and theKorean markets are stable. Intercepts and dummy intercepts are not reported.

Panel A: Regression model:

RtA=β0+β1Rt

DJIA+β2RDtKOSPI+β3RNt+1

KOSPI+εt Rt+1K =γ0+γ1Rt+1

KOSPI+γ2RDtDJIA+γ3RNt+1

DJIA +εt′

β1 β2 β3 γ1 γ2 γ3

ADR return 1.08***(32.23)

0.72***(25.88)

0.09***(3.84)

Underlying stock return 0.84***(73.80)

0.09***(4.86)

0.22**(2.02)

Panel B: Regression model:

RtA=β0+β1Rt

DJIA+β2RDtKOSPI+β3RNt+1

KOSPI+(β0*+β1*RtDJIA+β2*RDtKOSPI+β3*RNt+1

KOSPI)Dt+εt

Rt+1K =γ0+γ1Rt+1

KOSPI+γ2RDtDJIA+γ3RNt+1

DJIA +(γ0*+γ1*Rt+1KOSPI+γ2*RDt

DJIA+γ3*RNt+1DJIA)Dt+εt′

β1 β2 β3 β1* β2* β3*

ADR return 0.73***(8.36)

0.62***(14.12)

0.25***(4.05)

0.38***(4.02)

0.14**(2.51)

−0.17**(−2.49)

γ1 γ2 γ3 γ1* γ2* γ3*

Underlying stock return 0.81***(32.99)

0.10*(1.94)

0.01(0.06)

0.03(1.21)

−0.01(−0.19)

0.26(0.98)

Panel C: Regression model:

RAt ¼ β0 þ β1R

DJIAt þ β2RD

KOSPIt þ β3RN

KOSPIt þP2

i¼1β′i 0 þ β′

i1 RDJIAt þ β′

i2 RDKOSPIt þ β′

i3 RNKOSPItþ1

� �Di;t þ εt

RKtþ1 ¼ γ0 þ γ1R

KOSPItþ1 þ γ2RD

DJIAt þ γ3RN

DJIAtþ1 þP2

i¼1γ′i0 þ γ′

i1 RKOSPItþ1 þ γ′

i2 RDDJIAt þ γ′

i3 RNDJIAtþ1

� �Di;t þ ε′t

β1 β2 β3 β11′ β12′ β13′ β21′ β22′ β23′

ADR return 1.01***(18.57)

0.67***(15.02)

0.05(1.50)

0.06(0.81)

0.09(1.54)

0.11**(2.11)

−0.46*(−1.86)

−0.1(−1.25)

0.03(0.35)

γ1 γ2 γ3 γ11′ γ12′ γ13′ γ21′ γ22′ γ23′

Underlying stock return 0.83***(37.06)

0.07**(2.28)

0.35*(1.94)

0.02(0.64)

0.04(1.05)

−0.25(−1.10)

−0.11(−1.12)

0.03(0.53)

0.45(0.72)

Notes: the symbols ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. The t-statistics are included inparentheses.

1173M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

of β1 and β′21 reduces to 0.55. The KOSPI returns affect the ADR returns more than the DJIA and thereversal effect is found in this period. Finally, the co-movement of the underlying stock with the twomarkets does not change under the two conditions, because the coefficient estimates of γ′i1, γ′i2, andγ′i3 in Eq. (13) are not significantly different from zero. This result is consistent with Panel B of thistable, and the home market movements also have a strong ability to affect the underlying stockreturns.

Comparisons of the co-movement of stock returns with the two market index returns show that theDJIA returns have a significant influence on the ADR portfolio returns and that U.S. trading providesrelevant information on ADRs, particularly in periods marked by large U.S. market movements. The

1174 M-C. Wang / Pacific-Basin Finance Journal 21 (2013) 1160–1174

evidence is consistent with Chan et al. (2003) and Chen et al. (2009). In contrast, the influence of theKOSPI movements is more informative than that of the DJIA during periods of stability in the U.S. market.The ADR returns reflect not only the performance of the U.S. market, but also investors' expectations ofpossible changes in the underlying prices. The reversal effect is in line with the WPC result. Therefore, thefinding confirms this study's prediction and concludes that the information transmission for Korean ADRsand their underlying stocks is efficient.

4. Conclusions

Many empirical studies have shown that trading in cross-listed stocks depends on price informationprovided by the home market. The present study further investigates price transmission dynamicsbetween the U.S. and Korean markets and extends prior research by showing how U.S. market movementsaffect the price discovery of ADRs. Korean ADRs and their underlying stocks exhibit price discoverycapabilities under some market conditions. In particular, during the 2008 global financial crisis, priceinformation primarily originated from the U.S. market when the ADRs were traded.

Reversal effects support a close economic linkage between Korea and the United States, and they alsohelp explain the mixed information flows that have appeared in previous literature. These findingsconfirm the efficient use of price information in the ADR market through arbitrage, by both Korean andforeign investors, and inform investors about how to forecast changes in their investments moreaccurately. To reach these findings, this research has assumed that investors' expectations of stock pricechanges in foreign markets could explain the formation of the price discovery process. Follow-up researchshould determine how the pricing factors can explain the ADR price structure more clearly.

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