Working Paper

2008-WP-09 July 2008

Return Spread and Liquidity on Chinese ADRs Malay K. Dey and Chaoyan Wang

Abstract: We empirically investigate the role of host (U.S.) and home (Hong Kong) (HK) security market returns as common determinants of security returns for Chinese American Depository Receipts (ADRs) and their underlying H-shares.1 We also empirically determine the relation between return spread (difference between the returns on ADR and the corresponding underlying security) and security specific liquidity for ADRs and their underlying HK shares after controlling for U.S. and Hong Kong market returns. We use multiple proxies for liquidity and find evidence that trading volume and liquidity spread (the difference between trading volumes) for ADRs and their underlying HK securities are consistent determinant of return spread for Chinese ADRs with primary listing in Hong Kong stock exchange (SEHK). We use a switching regression model and find the model parameter estimates are not stationary and change over three distinct time periods, before 2000, 2000-2003, and after 2003.

About the Author: Malay K. Dey is an Associate Professor of Finance at the Cotsakos School of Business, William Paterson University, New Jersey. He was formerly a faculty member at the University of Bridgeport and Morgan State University and a Visiting Faculty at the Indian Institute of Management Calcutta. Dey received his Ph.D. (Finance) degree from the University of Massachusetts Amherst in 2001. His primary research interests are market microstructure, international financial markets, and financial econometrics with a secondary interest in financial technology. Chaoyan Wang is a Ph.D. candidate in the Economics Department, Stirling University.

Keywords: American Depository Receipts, Stock Exchange of Hong Kong, arbitrage, liquidity, return spread.

JEL Codes: G15

This paper was previously circulated under a different title. The authors gratefully acknowledge the comments and support of Dipak Ghosh and have also benefited from the comments of Bruce Resnick, Thomas Flavin and the participants at Xiamen University, the Southern Finance Association, the Global Finance Association, and INFINITY annual conferences. The views expressed are those of the individual authors and do not necessarily reflect official positions of Networks Financial Institute. Please address questions regarding content to Malay Dey at at [email protected]. Any errors or omissions are the responsibility of the author. NFI working papers and other publications are available on NFI’s website (www.networksfinancialinstitute.org). Click “Research” and then “Publications/Papers.”

1 An H-share is a share of a company based on the Chinese mainland that is listed on the Hong Kong Stock Exchange or other foreign exchange. http://www.investopedia.com/terms/h/hshares.asp. Return Spread and Liquidity on Chinese ADRs Malay K. Dey and Chaoyan Wang

1. Introduction

As of October 2006, 33 Hong Kong (HK) (home) common stocks trade in the American stock exchanges (host) as American Depository Receipts (ADRs). We determine the home and host market betas associated with the returns on those securities at home (Hong

Kong) and their corresponding ADRs in the host country (U.S.A.). We also estimate the magnitude of return spread (difference between ADR and the corresponding Hong Kong security returns) associated with those ADRs and further determine through a multivariate time series regression model the relation between return spreads and liquidities for ADR and Hong Kong securities, after controlling for the effect of host and home market returns on returns spreads.

ADRs and their underlying home country securities prices have long been the subject of research in finance and are compared to test whether the law of one price, which stipulates identical prices for securities with similar payoffs under no-arbitrage conditions, is violated, since any such violation may indicate arbitrage opportunities. However, most empirical studies on dually listed securities in home and host markets (e.g., Kato et al [1991], Park and Travakkol [1994], Miller and Morey [1996], and Karolyi and Stulz

[1996]) report that there are no significant differences between home and host securities prices and thus conclude that there are very few profitable arbitrage opportunities between any of those markets and the U.S. market. Wahab et al (1992) use a portfolio approach and find small but significant differences between prices of some ADRs and their underlying securities. Grossman et al (2007) find that multiple prices in home and

2 host markets cannot be arbitraged away due to excessive costs, while Arquette et al (2008) find convincing evidence that firm specific and market wide sentiments determine ADR premium for Shanghai listed firms. This literature thus concludes that identical host and home securities are priced differently, either due to different market specific risks or to excessive costs of arbitrage arising from market segmentation due to ownership restrictions (Eun and Janakiraman, 1986), asynchronous trading hours (Kim et al, 2000), discriminatory tax laws (Kato et al, 1991), and low liquidity (Bekaert [1995], Bailey and

Chung [1999]) in home markets.

Rabinovitch et al (2003) find that liquidity and not capital account controls is the primary driver of transaction costs, which accounts for some of the differences in return spread, including the difference between returns on ADRs and their underlying securities denoting arbitrage profits between Chile and Argentina, two emerging financial markets.

Grossman et al (2007) also report a significant relation between transaction costs and

ADR premium. In addition, Costa et al (2005) report that local market betas are the primary determinants of ADR premium (percentage difference in prices between ADRs and their underlying securities and is equivalent to return spread) in Brazil. Gagnon and

Karolyi (2004) find return spread is an increasing function of both home and host country market returns.

In this paper, we compare the return distributions for 33 Chinese securities listed and

traded in home (Hong Kong) and host (U.S.) markets. We focus on the return

distributions rather than only the means, since Rabinovitch et al (2003) find that even

when the means are not different there may still be differences in higher moments for

securities return distributions. We investigate the impact of home and host country

3 market betas on individual security returns. Finally we consider return spread and investigate the role of liquidity of ADR and that of the underlying HK security as determinants of return spread after adjusting for home and host country risk premiums.

Investigating potential arbitrage opportunities involving Chinese ADRs is interesting due to the following reasons. First, China is an emerging financial market and, because of the presumed low level of integration between the U.S. and Chinese securities markets, there is a potential for arbitrage profit. Second, besides being an emerging financial market, China is one of the fastest growing economies of the world and undergoing liberalization of its equity markets aimed at increasing the liquidity of the domestic securities markets. Such increases in liquidity may have an impact on return spread, denoting arbitrage profits (Rabinovitch et al, 2003; Gagnon and Karolyi, 2004). Third,

China’s securities market is a dichotomy- it suffers from poor corporate governance and low institutional safeguards, yet some of its market segments (e.g., IPOs) handily outperform a majority of other markets. Fourth, China has a unique geopolitical and economic relation with Hong Kong, stemming from Hong Kong’s colonial past and the transfer of sovereignty from the British to the Chinese government in July 1997. This unique historical event provides a laboratory for studying arbitrage opportunities in an evolving dualistic securities market -- one in Hong Kong that is well developed and fairly integrated with a broad section of the global markets, and the fledgling ones in mainland

China which have just recently begun their operations.

Our empirical results confirm the findings of Rabinovitch et al (2003) that even when the means returns of ADRs and their underlying HK securities are not different, the variances are. We also find support for Gagnon and Karolyi (2004) that both home and

4 (host) market betas are significant determinants of securities returns and return spreads.

Finally, the results from our regression models provide strong evidence that firm liquidity

denoted by log (trading volume) and liquidity spread are significant determinants of

return spread, after controlling for home and host markets risk premiums. However, the

coefficient estimates for the regression model are non-stationary and we identify two

distinct regime changes in 2000 and 2003 during the sample period.

The paper proceeds as follows: Section 2 contains a brief literature review; Section 3 provides a description of the data and an empirical analysis of home and host country security returns; Section 4 introduces liquidity as a risk factor and tests the implications of a multi factor asset pricing model where liquidities of ADRs and that of their underlying securities are determinants of return spread; Section 5 concludes.

2. Literature review

The following is a brief review of the literature on the impact of home and host country market returns on ADR and the underlying securities returns, measurement and distributional properties of arbitrage profits denoted by ADR premium or return spread, and the determinants of such arbitrage profits. These questions have been addressed in the literature with respect to ADRs from a single country, a few specific countries with a common link, and multiple developed and/or emerging market economies. Karolyi

(1998) and Gande (1997) provide comprehensive reviews of the literature on ADRs.

Recent empirical evidence indicates the following: 1) home country securities witness a significant reduction in local market risk and only a slight increase in global market risk, leading to a net reduction in overall risk; 2) only respective local market

5 betas have significantly positive relations with ADR and home securities returns as documented by Chakrovarty (2005) for India and Alberton et al (2006) for Brazil; 3) positive announcement effects in home country due to ADR listing (Domowitz et al,

1997; Miller, 1998, Forester and Karolyi, 1999); and 4) generally positive effects on liquidity in the home market (Chan et al, 2006), thus reducing the cost of capital (Forester and Karolyi, 1999). However, the debate continues whether the increased liquidity in the home market for a security is due to investors switching from other domestic securities, which then suffer a loss in their shareholders base, or due to a broadening of the investor pool where marginal investors are attracted due to the increased attention of the security or the security market in general (Karolyi, 2004).

More recently, the research on ADRs has focused on determining the existence and extent of and the factors leading to ADR premium, which is the percentage difference in home and ADR prices after adjusting for exchange rates; this indicates deviation from the law of one price and, consequently, arbitrage opportunities. Kato et al (1991) examine

ADR prices from Australia, England, and Japan and report that there are no significant differences between the prices of ADRs and their respective underlying shares and hence there are no obvious profitable arbitrage opportunities between any of these three markets and the U.S. market. Park and Tavakkol (1994), Miller and Morey (1996), and Karolyi and Stulz (1996) also report similar findings that ADRs do not offer arbitrage opportunities. On the contrary, Wahab et al (1992) study ADRs from several countries and report evidence of limited arbitrage profits measured by return differentials between

ADRs and underlying securities in some countries. They find return differentials ranging between 1.23% and 4.44%, accruing to an equally weighted and a mean-variance

6 efficient portfolio respectively. Further, Rabinovitch et al (2003) study ADRs from

Chile and Argentina, both of which are emerging financial markets with several common characteristics, and find that while mean returns of home country shares and their corresponding ADRs were not different in either country, the return distributions for home country shares and their corresponding ADRs are similar in the case of Argentina but different in the case of Chile. They attribute the difference between the return spreads in Chile and Argentina to the restrictions on foreign investment as well as a flexible exchange rate in one country and not the other; further, they report that differences in costs of liquidity between the home and host securities markets account for some of the differences in return spreads between ADRs and their underlying securities in

Chile and Argentina. Arquette et al (2008) find firm and market wide sentiments denoted by the respective P/E ratios are significant determinants of ADR premium/discount for Shanghai listed stocks. Grossman et al (2007) find evidence that both the high cost of arbitrage and consumer sentiment partially account for price deviations for ADRs.

Amihud and Mendelson (1991) posit that liquidity is not directly observable and has a number of aspects, not all of which can be captured by a single measure. Several empirical studies (e.g., Datar et al [1998], Chordia et al [2001], and Amihud [2002]) relate asset returns to different firm specific liquidity measures such as spread, depth, turnover, turnover volatility, and trading volume.

There is indirect evidence in the literature linking asset prices with liquidity in cross-listings. Kadlec and McConnell (1994) and Forester and Karolyi (1999) document a reduction in a security’s expected return after its international listing, which

7 they attribute to the superior liquidity associated with the international listing. Chan et al (2006) document changes in liquidity due to cross-listing. Jain et al (2004) find that the premium on closed-end country funds corresponds to differences in liquidity between the funds’ host (country where the funds invest) and home (U.S.) markets.

Gagnon and Karolyi (2004) note a host of factors resembling the fundamental valuation approach -- economy, market, and firm including liquidity -- as determinants of the betas, denoting the relation between return spread and home and host market returns.

Kim et al (2000) show that asynchronous trading hours in the host and home markets recede arbitrage activities.

3. Data, methodology, and empirical results

3.1. Data

Our data consists of daily prices and trading volumes on Chinese ADRs listed on the U.S. stock exchanges, namely the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotation System (NASDAQ), and their corresponding underlying securities listed on the Stock Exchange of Hong Kong (SEHK). Some of those HK securities are also subsequently listed on the Shanghai Stock Exchange (SHSE).

The shares listed on the SEHK are H-shares, which allow foreign ownership and are traded in Hong Kong currency, while the shares listed on SHSE are A-shares available only to Chinese citizens and traded in Chinese currency.2 Because of this restriction on

A-shares, international investors participate in the growing Chinese stock market by investing in H-shares. From the issuers’ standpoint, H-shares allow Chinese firms to

2 Guo (2006) investigate return spread between H and A-shares listing in Hong Kong and Shanghai respectively.

8 raise global capital.

As of October 2006, there are 33 Chinese ADRs listed in U.S. markets; another 26 are traded on the OTC. Among the listed ADRs, 16 are listed on the NYSE and 17 on the

NASDAQ. For the purpose of this study, we form three portfolios from the listed ADRs:

Portfolio I consists of nine stocks listed on NYSE and SEHK; Portfolio II of six stocks listed on NYSE, SEHK, and SHSE; and finally, Portfolio III of 17 stocks listed only on

NASDAQ.3 ADRs listed on the NASDAQ do not have underlying shares in Hong

Kong or in other stock exchanges in China and thus our analysis of those ADRs in

Portfolio III is limited.

Table 1 contains a list of Chinese ADRs listed on the NYSE and NASDAQ, the corresponding underlying securities, if available on Hong Kong (SEHK) and additional listing on Shanghai (SHSE) stock exchanges, along with the respective listing month/year in each exchange. We observe that the listing month/year and particularly the years in which the ADRS were issued reveal some strategic decisions on the part of the issuing

Hong Kong firms to enlist their ADRs. First, as Graph 1 indicates, ADR listings by

Hong Kong firms during our sample period do not show any trend or other discernible regularities; strikingly though, more than 50% (8/15) of ADRs were listed between 2000 and 2003. A reasonable hypothesis for this structural shift in 2000 may be the market collapse in U.S.A., particularly the IPO market, which led some Hong Kong firms, to offer their securities to U.S. investors as alternative investment options. Before 2000,

1997 was an exceptional year in which three, a larger than usual number of firms issued

ADRs on the NYSE. A careful observation reveals that CEA and ZNH, two firms from

3 Portfolio I includes nine stocks. STP is excluded because this ADR was first listed in Dec. of 2005 and its time period is less than one year. Further, because of the problem of ascertaining an index for OTC stocks, we exclude OTC ADRs.

9 the Travel and Leisure industry, issued ADRs in January and July 1997 respectively.

These two firms are the final two ADRs listed on the NYSE from an unlikely industry and were perhaps motivated by the success of GSH that enlisted its ADR in May 1996.

CHL is the first mobile telecommunication firm in Hong Kong to enlist its ADR on the

NYSE in 1997. Based on our observation from Graph 1, we will later use 2000 and

2003 as two structural shift points for our regression analysis.

Second, almost all listings at the SEHK and at the NYSE were done almost simultaneously (within a month) and pre-date listing on the SHSE.4 Since the United

Kingdom transferred sovereignty of Hong Kong to China in 1997, a few Hong Kong firms enlisted their stocks in Shanghai (SHSE) as well. Note that Hong Kong firms cross-list in Shanghai, a domestic market, after they cross-list in the U.S While the motives of Hong Kong firms to cross-list in the U.S.A. to attract more capital is obvious, the implications for Hong Kong firms cross-listing in the Shanghai stock market, which has far less liquidity, depth, and width than that of Hong Kong, is not clear since the conventional wisdom is that firms cross-list to achieve lower cost of capital and greater liquidity. Recently Guo et al (2006) find evidence that for dually listed H (Hong Kong) and A (Shanghai) shares, the cost of capital is lower and liquidity is greater for A-shares than their H-shares counterparts. This evidence justifies cross-listing in the Shanghai stock market but raises the question of whether a mature market is necessarily more liquid and/or less costly.5 Further it raises questions about those Hong Kong firms

4 HNP is an exception that issued H-shares in 1998, A-shares in 2001, and ADR in 2003. This order of listing for all securities except HNP (i.e., listing as ADRs prior to or simultaneously on Hong Kong exchange) prevents us from investigating the announcement effect around cross-listing. 5 The evidence first raises a question about the presumed linear relation between liquidity and cost of liquidity. The evidence also raises a political economy question -- does cross-listing help or hurt emerging market economies? Dey (2005) finds turnover is higher for countries with multiple domestic listing facilities. Karolyi (2004) and Karoyli and Li (2004) investigate the impact of cross-listing on financial market development and economic growth in home countries.

10 which do not cross-list in Shanghai Stock Exchange and those NASDAQ listed ADRs which do not list in any of their home country exchanges. The answer may lie in the ownership structure of firms, since the Hong Kong firms with additional cross-listing in the U.S.A. and Shanghai are state-owned enterprises (SOEs) with political ties. Third, while all the early ADRs were listed on the NYSE, after 2003 all ADRs are listed on

NASDAQ. Finally, the securities in Portfolio II have a longer history and thus might have reaped the benefits of ADR premium early on and have since cross-listed on

Shanghai. Both of these trends may be due to the recognition by Hong Kong companies of, first, the shrinking ADR underpricing for latecomers on the NYSE as reported by

Gande (1997) and, second, the expectation of a cross-listing premium for the HK shares at the home market.6

Table 1 also lists the corresponding industries which these securities belong to. We find that all NYSE listed ADRs belong to four industries: utilities; communication; travel and leisure; and heavy engineering and industrial products. To the contrary, the

NASDAQ lists securities for the most part that belong to information technology and communication industries. Compared to the large industrial base of China, the ADRs from China seem to be from a very selective industry core.

3.2. Descriptive statistics on host and home market return

We compute continuously compounded returns on the ADR listings in U.S. exchanges and their corresponding underlying stocks listed in the Hong Kong Stock Exchange (and

6 Another possible reason for the timing of Chinese IT firms to list on the NASDAQ is related to the ‘NASDAQ surge.’ While the Chinese IT industry missed the first surge in 1999 due to its infancy, it decided to pursue the second ‘NASDAQ surge’ in 2003. However, most of those privately owned firms merged with shell companies in the U.S. and register in the free-tax haven in Bermuda or the Cayman Islands before listing on the NASDAQ. The Chinese government issued regulations in 2005 banning Chinese private firms from listing abroad in this way.

11 iii i Shanghai Stock Exchange as applicable) as rPPttt= ln( /−1 ) , where Pt denotes price of security i on day t. The sample period for each pair of ADR and the underlying security begins from the initial listing dates of the ADR for a HK security and ends in October

2006. The actual number of observations for each security used in our analysis is reported in Table 2A. In Portfolio I, the number of observations vary from 627 (CEO) to 2214

(CHL), while in Portfolio II, the minimum and maximum observations are 1465 (SNP) and 3248 (SHI) respectively. Obviously Portfolio II securities have a longer ADR listing history. Further, we compute return spread for day t as the difference between the returns on each ADR and that of its corresponding underlying security respectively.

Returns spread denotes arbitrage profit and Rabinovitch et al (2003) show that it is equivalent to ADR premium, or the percentage difference in prices between an ADR and its underlying security.

Table 2A provides descriptive statistics on returns on ADR and the underlying securities listed at SEHK. The annualized returns on the ADRs (Hong Kong shares) range from -13.25% (-10.5%) to 42.5% (46.25%), with a mean of 12.25% (14.77%); however, after splitting the ADRs and the underlying HK shares into Portfolios I and II, the means of ADR (HK shares) break down as follows: 16.19% (18.19%) for Portfolio I and 6.33% (9.63%) for Portfolio II respectively. Thus while HK shares provide consistently although not significantly higher returns than ADRs during the sample period, the average returns for Portfolio I for the underlying H-shares and ADRs are significantly higher than those of Portfolio II. Further the HK shares in Portfolio II seem to be performing significantly above their ADR counterparts. The standard deviations of returns for ADRs and the underlying HK shares are almost identical in Portfolio II but the

12 standard deviation of returns is higher for ADRs than that of the underlying H-shares in

Portfolio I. Thus Portfolios I and II seem to have different risk profiles, which explain the differences in their mean returns. Ironically, the H-shares in Portfolio II offer higher return and lower risk than those of their ADR counterparts.

Table 2B contains descriptive statistics on return spreads for 15 Hong Kong securities grouped into Portfolios I and II, which have their corresponding ADRs listed at the NYSE. The mean return spread is negative for all securities except CBA in Portfolio

I and CEA in Portfolio II. Note that CBA’s return spread has a maximum value of 0.58, which clearly seems like an outlier. We compute means excluding this observation and the conclusion does not change, implying that this extremely high positive return spread related to CBA does not affect any test performance, perhaps due to the large number of sample observations. Wang (2007) indicates how CBA is unique by virtue of its history and the geopolitical status of its founder.

We conduct several tests to determine whether the distributions of returns for ADR and their underlying HK securities are identical. Table 3A reports the test results. First we conduct a paired t-test for differences in means. Column 2 contains the t-statistics for the difference in means test. Based on the t-statistics, we cannot reject the null hypothesis that there is no difference in means between the ADR and the corresponding underlying security returns at 10% or lower significance level for any of the 15 ADR-HK security pairs.

Second, in column 3 in Table 3A, we report results from a non-parametric Wilcoxon sign rank test. On the basis of the signed rank test results, also, we cannot reject the null hypothesis on the equality of medians between ADR and HK security returns for any of

13 the 15 ADR-HK security pairs.

Third, in column 4 in Table 3A, we report Kolmogorov-Smirnov test for testing the null hypothesis that the distributions ADRs and HK returns are identical. As the reported test statistics, KSa show we reject the null hypothesis that ADR and underlying security returns distributions are identical at less than 5% significance level for seven out of nine pairs of securities (each ADR and its underlying HK security) from Portfolio I and reject the equality of the return distributions for all ADRs and their underlying securities in Portfolio II. In addition to the differences in means and standard deviations in portfolio returns as stated earlier, here again we find evidence of a structural difference between Portfolios I and II; however, the number of securities in each portfolio is too small to conduct a conclusive statistical test on the difference between the two portfolios.

Fourth, we test the null hypothesis that ρ (rrADR, HK ) = 0 (i.e., ADRs and underlying

H-shares returns are uncorrelated). Since ADRs are claims on cash flows generated by their respective underlying shares, in the long run, the returns of both securities should move in the same direction, so we expect a positive correlation between those returns.

However, in the short run, differences in volatilities between the two markets may create temporary profitable arbitrage opportunities where the holders of underlying shares

(ADRs) can convert into ADRs (HK shares) as required, leading to a negative correlation between returns on ADRs and local securities listed on the SEHK (Kim et al, 2000). In

Table 3B, we report Pearson and Spearman correlation coefficients and the corresponding significance levels between a given ADR and the underlying security return. Except

CBA and HNP, both of which show a significantly positive correlation, for all other

ADRs and their corresponding home securities, we cannot reject the null hypothesis of

14 zero correlation between the corresponding returns. Wang (2007) contains a profile of the sample firms from which she reports that these two firms are different from other firms in several ways. HNP is the largest utility company run by the family of former

Chinese premier Li Peng, which obviously provides enormous name recognition for the overseas investors in the U.S.A.. As noted earlier CBA is unique because of the way it was incorporated and that a Chinese expatriate in the U.S.A. founded the company.

We also test whether correlation between U.S. and HK market returns is zero as well.

Once again a Pearson correlation test indicates U.S. and HK market returns have a significant positive correlation while HK and Shanghai shows a significant negative correlation. These results are consistent with similar findings in the literature. In comparison, for individual securities, the correlation between returns on ADR and HK are mostly zero and the mean correlation between returns on ADRs and HK securities in

Portfolio I is 0.029 and in Portfolio II is 0.079. Evidently these results suggest that the

ADR portfolios are quite different that the S&P 500 (U.S.) and Hang Seng index (Hong

Kong) market portfolios.

Finally we do a joint means and variance test as in Rabinovitch et al (2003), based on

Bradley and Blackwood (1989), who assume a joint parametric distribution of the means and variances. We report the results in Table 3C. On the basis of the reported F-tests, we reject equality of both means and variances for four out of nine securities in Portfolio

I and five out of six securities in Portfolio II. The results confirm a structural difference between Portfolio I and Portfolio II and that, when return variances are considered, the return distributions for a majority of ADRs and HK shares are not identical.

We summarize the test results in Tables 3A through 3C in Table 4. The mixed

15 evidence from Tables 3A-3C summarized in Table 4 indicates the following. First, standard locations tests are inadequate in capturing the differences between ADR and the underlying security returns. Second, the distributions appear to be different primarily due to differences in variances and their tail masses (we provide Q-Q plots of normalized returns in Graph 2 to show how the tail areas are different from those of identical distributions). Third, there are structural differences between Portfolio I and Portfolio II, which lead to differences in their return distributions. The differences may be due to firm (e.g., institutional/government ownership), industry (e.g., one portfolio may have a high level of investment in utilities or transportation), or market (e.g., attracting liquidity in both the Shanghai and Hong Kong exchanges). Fourth, even within Portfolio I, for two securities (almost 20% of the sample), the equality of return distributions cannot be rejected. Thus we find support for Gagnon and Karolyi (2004) that beside country factor, there are firm and market (trading venue) level factors, which determine asset returns in multiple locations.

As mentioned earlier, in Graph 2 we provide Q-Q (quantile-quantile) plots of the normalized returns for ADRs and their underlying HK securities. The Q-Q plots generally point to the deviations from the 450 lines in the left and right tail areas.

3.3. Impact of home and host market return on security return

The zero correlations between ADR and HK security returns for the majority of ADR-HK security pairs indicate that security returns in each country may depend on local rather than remote market conditions (i.e., for ADRs (HK securities) affected by U.S. (Hong

Kong) market performance) (Grubel, 1968; Hilliard, 1979). We test this hypothesis in a

16 regression framework. We propose the following regression models for ADR and underlying HK security return from Karolyi and Stulz (2003), Chakrovarty (2005), and

Alberton et al (2006):

ADR ADR S& P HS ADR rRRtt=+αβ12 + βt +et…….(1)

HKHKHSSPH& K rRRtt=+αβ45 + βt +et………(2)

i where rt = return for security i (Hong Kong or ADR) on day t SP& Rt = return on host (U.S.) market on day t HS Rt = return on home (Hong Kong) market on day t.

While Portfolio I consists of ADRs listed only in Hong Kong exchange, Portfolio II contains ADRs listed on Hong Kong and Shanghai stock exchanges. Thus the regression model for Portfolio II securities has one additional explanatory variable,

Shanghai market return. We include Shanghai market return as an explanatory variable in spite of the evidence that the Chinese market and the global market in general and U.S. market in particular are not correlated. Yet, the Shanghai market may have an indirect impact on the Chinese ADR return. This is because empirical evidence suggests an inverse correlation between the two market returns. Nevertheless, the inverse correlation is still surprising given the markets in Hong Kong and Mainland China are integrated due to their common culture, interdependence, and cooperation in business.

They also have seven overlapping opening hours of stock exchanges during which political and economic news in China are likely to impact H-shares’ performance in Hong

Kong, which may then transmit from the H-shares to the ADRs in the U.S., due to the integration between U.S. and Hong Kong markets. A possible explanation for the inverse correlation is that returns are driven by fund flows and the Hong Kong and

Shanghai markets both compete for funds. Thus one market can gain only at the cost of

17 the other.

Tables 5A and 5B report parameter estimates for NYSE and NASDAQ listings for the above regression model. The results confirm similar findings from several studies that, on a contemporaneous level, local market effects are more pronounced than security/portfolio effects transmitted through economic activities.7 However, while the effects of the local market index return on security returns are consistently significant for both ADRs and the home securities, for a few ADRs there is evidence of isolated home country effect. Our sample is too small and the number of firms displaying such anomalies is even smaller. Thus, we refrain from generalizing the exceptions; however, we note that similar evidence of scattered security specific effects are documented in

Chakrovarty (2005) and Alberton et al (2006).

We estimate the parameters of equations (3) and (4) by ordinary least squares (OLS), which assumes the error terms in equations (3) and (4) are independent and normally distributed, and thus the OLS estimates are unbiased and efficient. However, since both equations contain two common independent variables, we also consider the possibility that the error terms are correlated. This calls for estimating the parameters of a system of equations through the seemingly unrelated regressions (SUR) method. SUR estimates are identical to OLS estimates when the independent variables are identical throughout the system. To get around this problem, we include a first order autoregressive component (i.e., one lag local market return in each equation). The autoregressive component is significant in both equations for only one pair; it is significantly positive for HK return in two cases, and significantly negative (positive) for

7 We acknowledge that the effects are not completely contemporaneous and there is some intertemporal effect, since there is time difference between Hong Kong and the U.S. in terms of trading hours. However the intertemporal effect is likely to be most pronounced at the beginning and would even out over the entire trading hours in a day.

18 two ADR returns. Due to the low frequency and inconsistency of significant parameter estimates, we do not report the coefficients for the first order autoregressive components of ADR and the underlying HK security returns. Otherwise the SUR and the OLS estimates of the parameters and their corresponding standard errors appear to be similar in most instances for both ADR and the underlying securities. The correlation between the errors is either zero or very low, and thus the OLS estimates are unbiased and efficient.

We conduct the Breusch-Pagan test and report the Chi-square test statistics for testing the null hypothesis that the error correlation is zero along with the OLS estimates. The

Chi-square test results are reported in Table 5C and confirm our findings in Table 2B.

The results and the ensuing discussion above establish that a) although means and medians are not different, the distributions of ADR (host) and (HK) home security returns are not identical and b) local market (U.S. and HK market) returns are the primary determinants of expected returns for home and host country securities.

4. Return spread and its determinants

In this section we shift our attention to return spread instead of return, and particularly the role that security specific liquidity plays in the determination of return spread. Since

ADRs and the underlying securities are essentially identical securities traded in two different locations, the spread between returns represents arbitrage profit. Rabinovitch et al (2003) contend that return spread indicates arbitrage payoff and is identical to ADR premium, or the percentage difference between ADR and the underlying security prices.

Gagnon and Karolyi (2004) conduct an extensive study of the nature and determinants of

19 return spread in a two-stage model for a large group of ADRs.8 They categorize the determinants of return spread into three groups: market, country, and firm specific factors including firm level liquidity.

We extend Gagnon and Karolyi (2004) first stage regression model and obtain an equation for return spread against firm level liquidity after controlling for market returns from home (HK) and host (U.S.) markets as follows:

US HK ADR HK Return spreadttttt=+αβ12 R + β R + β 3 LP + β 4 LP + et…..(6).

In the above equation, return spread is the difference between returns on an ADR and

HK US its underlying security, Rt and Rt denote Hong Kong (home) and U.S. (host) market returns respectively, and LP is the security specific liquidity for the ADR and the corresponding underlying security in the U.S. and HK markets respectively. We also determine if liquidity spread, which is the difference between liquidity for an ADR and the corresponding underlying security in the U.S. and HK markets respectively, explains any part of the variance in return spread.

There are several security or firm specific measures of liquidity used in the finance literature. Chordia et al (2001) use share turnover, dollar volume of trade, coefficients of variation of share turnover and dollar volume of trade, price inverse, and standard deviation of dollar volume of trade as measures of liquidity. They report high correlations between the quoted bid ask spread and those liquidity measures. Datar et al (1998) criticize trading volume as a liquidity measure since it is an absolute and not a relative measure and suggest turnover (also turnover ratio), a ratio between dollar value of shares traded and market value of those shares as a preferred alternative. They document that

8 Gagnon and Karolyi (2004) first estimate the coefficients of a two-factor beta model (i.e., regress home and host market return on return spread) and then estimate the coefficients of a large set of firm, market, and country specific variables from a regression model, where the dependent variables are the betas obtained from the previous model.

20 turnover explains a significant portion of the cross-sectional variation in stock returns even after controlling for the well-known determinants of stock returns like firm size, book-to-market ratio, and firm beta. However, Dey (2005) finds turnover to have a random component and Lo and Wang (2003) report that both trading volume and turnover are non-stationary time series. Amihud (2002) introduces illiquidity, a ratio measuring the absolute return per dollar of daily trading volume, or the daily price impact of the order flow as a proxy for liquidity and report a positive relationship between return and illiquidity. However, Keene and Paterson (2007) criticize Amihud’s (2002) liquidity measure as incomplete, since it does not account for other known risk measures such as book-to-market and size on liquidity.

Based on a review of the existing literature, we predict that the coefficients of U.S.

(HK) market return will be positive (negative) and the liquidity for U.S. (HK) securities will be positive (negative) as well. We use two proxies for liquidity: liquidity denoted by log (trading volume) and illiquidity denoted by the absolute return per dollar of trading volume in home and host markets. Hence we have two measures of liquidity spreads as well, the differences between host and home measures of liquidity. Table 6 provides descriptive statistics on the liquidity and liquidity spread measures we use in this paper.

We report the parameter estimates along with the t-statistics for the regression model in equation 6 in Table 7A. From Table 7A we find that U.S. (Hong Kong) market returns bear a significantly (at less than 5% level) positive (negative) relation with return spread. This relation is true for all securities pairs in the sample except LFC, which shows inverse relations with both U.S. and HK market returns. Gagnon and Karolyi

21 (2004) also report similar findings of the generally direct (inverse) relation between return spread and U.S. (home) market returns. The model results are consistent across securities in Portfolio I and Portfolio II, although Portfolio I mean coefficient is higher than that of Portfolio II. It obviously begs the question whether this difference between the portfolios is due to among others the industry breakdown of the portfolios or ownership structure of the firms. Our limited data does not allow us to investigate this question any further.

Table 7A also reports the coefficients of the two liquidity measures (log (trading volume) and illiquidity) for each of the underlying (home) and ADR (host) securities.

Only 6 of the 9 securities in Portfolio I, but all securities in Portfolio II, exhibit liquidities in home and host securities measured as log (trading volume) as significant determinants of return spread. The signs are consistent across the board with ADR (HK) liquidity showing a positive (negative) relation. The magnitude of the coefficients ranges from

0.001 to 0.110 for ADR liquidity and -0.001 to -0.009 for HK liquidity. One ADR, CHL, finds ADR trading volume to be significant but HK volume to be not. The coefficients for illiquidity are far less significant; none is significant for ADR illiquidity and only four are significant for HK illiquidity. Thus the two illiquidity measures seem to have very little explanatory power and also the signs are both positives and negatives, which make it difficult to conclude one way or the other. We suspect illiquidity to be a more noisy measure of liquidity than trading volume, since it is measured as return per unit of volume.

We modify the regression model above by including liquidity (illiquidity) spread; in addition, we include Shanghai market return for Portfolio II securities. Liquidity

22 (illiquidity) spread is the difference between trading volumes (absolute return per unit of trading volume) for ADRs and the underlying Hong Kong securities. We report the parameter estimates in columns 9 and 10 in Table 7A. Liquidity spread is significantly positive for seven out of nine Portfolio I securities, and for all six Portfolio II securities.

Illiquidity spread is again significant for only eight out of 15 securities. Further, while for six securities the signs are negative, indicating an inverse relation with return spread, for two securities the coefficients are positive. Such counterintuitive results raise doubt about illiquidity as a proxy for liquidity. The adjusted R-squared (R2)9 for the models range from 4% to 34% for securities in Portfolio I but range from 4% to 8% for securities in Portfolio II.

We note the obvious difference between the two ADRs for which liquidity premium is insignificant and the rest in terms of cross-listing (U.S.) and IPO (HK) dates -- these were listed on or after 2003. This difference raises the question of whether there is a structural shift in the regression coefficients between pre- and post-2003. The cause of this possible structural shift is evident from Graph 1 and the ensuing remarks -- the shift may be due more to perception about the U.S. market than the security market reforms in

China. Econometrically, we test this structural break/shift via a Chow test that requires the break periods to be known a priori. Table 7B contains the parameter estimates from the regression model in equation 6 and the corresponding Chow test statistic for the following two sub periods: prior to 2003 and post 2003. The Chow tests are statistically significant at less than a 1% level. The parameter estimates are also mostly significant at less than a 10% level. The parameter estimates for each sub period provide a

9 R-squared (R2) is “a statistical measure that represents the percentage of a fund or security's movements that can be explained by movements in a benchmark index. For fixed-income securities, the benchmark is the T-bill. For equities, the benchmark is the S&P 500.” http://www.investopedia.com/terms/r/r-squared.asp

23 narrative for a dynamic relation between return spread and its determinants, particularly liquidity.

Further we explore if there are other possible structural breaks, particularly if there is a similar structural break in 2000. In this case, we break the sample period, where available, into three sub periods, prior to 2000, 2000-2003, and post 2003. As before, the high values of the modified Chow test (accounting for multiple breaks) clearly indicate multiple structural shifts during the sample period, which causes the regression parameters to significantly differ in values from one period to another. This points to the obvious unreliability of pooled estimates since the regression coefficients seem to be non-stationary.

The overarching theme from Table 7B is that the regression coefficients vary more with time than with respect to models. However some parameter estimates vary more than others. For example, all securities show a higher level of variability in the parameter estimates associated with HK market return than with those associated with

U.S. market return. Second, Shanghai market return has either a direct or an inverse relation with return spread but that relation exists only after 2003 (in the last sub period).

Third, the relation between return spread and liquidity is also not uniform across all time periods. While liquidity measured as volume seems to have more explanatory power than illiquidity measured as absolute return per unit of volume, the parameters associated with those significantly change from one period to the other. Such changes cause large differences in the adjusted R2 as reported in the table. This is also true for the parameters associated with liquidity and illiquidity spread.

24 5. Conclusion

We study returns and return spreads on Chinese ADRs listed with primary listing in the

Hong Kong stock exchange. We investigate the relation between the returns on the

Chinese ADRs and their underlying H-shares, and further how host and home market returns affect the returns and the return spread, which is the difference between returns on

ADR and the underlying security. We also investigate how an ADR and its underlying

HK liquidity and their spread (the difference between liquidity of host and home market securities) affects return spread. We measure liquidity by trading volume and illiquidity, absolute return per unit of trading volume for each security, and ADR and its underlying

HK security. We find liquidity and liquidity spread (denoted by trading volume) and their difference explains a significant part of the variation in return spread after controlling for U.S. (host) and HK (home) market returns. However, we find that the parameters related to the determinants of return spread significantly change over time.

We identify two structural shifts, in 2000 and in 2003, which significantly affect the relation between return spread and its determinants including liquidity.

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28 Dissertation, Stirling University

29 Table 1: Chinese ADRs listed on the NYSE and NASDAQ

NYSE NY HK HK Home SH Industry- NASDAQ Effective ADR Listing Underlying Listing shares Listing NYSE ADR ADR date Symbol Date Shares Date (SH) Date Symbol CEA Jan/97 0670 Jan/97 600115 Nov/97 Travel & JOBS 10/2004 Leisure SNP Oct/00 0386 Oct/00 600028 Aug/01 Oil & Gas ACTS b 12/2005 Producers ZNH Jul/97 1055 Jul/97 600029 Jul/03 Travel & BIDU 8/2005 Leisure HNP a Aug/03 0902 Jan/98 600011 Dec/01 Electricity JRJC 10/2004 SHI Jul/93 0338 Jul/93 600688 Nov/93 Chemicals CMED 8/2005 YZC Mar/98 1171 Mar/98 600188 Jul/98 Mining CNTF 5/2005 ACH Dec/01 2600 Dec/01 N/A N/A Industrial CTRP 12/2003 Metals CBA a Apr/00 1114 Oct/99 N/A N/A Automobiles LONG 11/2004 & Parts LFC Dec/03 2628 Dec/03 N/A N/A Life FMCN 7/2005 Insurance CHL Oct/97 0941 Oct/97 N/A N/A Mobile HRAY 2/2005 Telecom CHA Nov/02 0728 Nov/02 N/A N/A Fixed Line LTON 3/2004 Telecom CHU Jun/00 0762 Jun/00 N/A N/A Mobile NTES 6/2000 Telecom CEO Feb/01 0883 Feb/01 N/A N/A Oil & Gas NINE 12/2004 Producers GSH May/96 0525 May/96 N/A N/A Travel & SNDA 5/2004 Leisure PTR Mar/00 0857 Apr/00 N/A N/A Oil & Gas NCTY 12/2004 Producers STP b Dec/05 N/A N/A N/A N/A Electro.& TOMO 3/2004 Electric Eq VIMC b 11/2005 a. HNP and CBA are different from other Chinese ADRs. These two firms issued IPOs on the NYSE in 1994 and 1992 respectively, and issued H-shares on the SEHK in 1998 and 1999 respectively. They finally issued ADRs in 2003 and 2000 respectively. b. STP, ACTS, and VIMC are excluded from our empirical study because their listing dates are after our cut off date, October 2005.

30

Graph 1: Yearly ADR listing by Hong Kong firms during the sample period.

# of ADR

3

2

1

0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Table 2A: Descriptive statistics on daily returns on Chinese ADRs listed on U.S. exchanges and their underlying shares listed on the Stock Exchange of Hong Kong. Return is computed as iii i rPPttt= ln( /−1 ) , where Pt denotes price of security i on day t.

Symbol Obs. Mean Std. Dev Min Max Skewness Kurtosis Portfolio I ACH 1166 0.112% 3.02% -0.1039 0.1618 0.3313 4.5507 [2600.hk] (0.116%) (3.13%) (-0.1488) (0.1441) (0.0952) (4.3958) CBA 1606 0.007% 3.60% -0.2478 0.3730 0.7991 13.7708 [1114.hk] (-0.008%) (3.63%) (-0.2191) (0.2208) (0.2870) (7.8776) LFC 701 0.170% 2.26% -0.1046 0.1155 0.2038 6.5607 [2628.hk] (0.185%) (2.09%) (-0.0810) (0.0999) (0.1994) (5.7222) CHL 2214 0.073% 2.93% -0.1577 0.1854 0.2970 6.6720 [0941.hk] (0.078%) (2.76%) (-0.1652) (0.1561) (0.2537) (7.0759) CHA 951 0.073% 1.93% -0.0876 0.0821 0.1535 4.6979 [0728.hk] (0.074%) (1.90%) (-0.0834) (0.0752) (0.0169) (4.2728) CEO 627 0.104% 1.93% (-0.0756) 0.0874 0.1793 4.2225 [0883.hk] (0.106%) (2.02%) (-0.0770) (0.0902) (0.0336) (3.9058) GSH 2566 0.033% 2.88% -0.2074 0.2684 0.5561 10.6413 [0525.hk] (0.010%) (3.09%) (-0.2029) (0.1849) (0.0026) (7.6805) PTR 1534 0.130% 2.03% -0.0922 0.1124 0.1274 5.8477 [0857.hk] (0.136%) (2.03%) (-0.1076) (0.0995) (0.1554) (5.3402) CHU 1550 -0.053% 2.82% -0.1280 0.1594 0.2274 6.2551 [0762.hk] (-0.042%) (2.67%) (-0.1309) (0.1431) (0.0780) (5.3384) Portfolio II CEA 2317 -0.002% 3.32% -0.1942 0.2360 0.6894 8.4124 [0670.hk] (-0.005%) (3.94%) (-0.3756) (0.2668) (0.2248) (10.0132) SNP 1465 0.072% 2.45% -0.1691 0.1823 0.0801 8.3341 [0386.hk] (0.095%) (2.37%) (-0.0902) (0.1039) (-0.0632) (4.2485) ZNH 2070 0.004% 3.67% -0.3277 0.2415 0.3016 9.6722 [1055.hk] (0.007%) (3.85%) (-0.3269) (0.2967) (0.2597) (9.9477) HNP 2108 0.005% 3.29% -0.6775 0.1689 -3.7780 85.8508 [0902.hk] (0.063%) (3.19%) (-0.1594) (0.2231) (0.3126) (6.7165) SHI 3248 0.036% 3.37% -0.1945 0.2709 0.5098 8.90506 [0338.hk] (0.022%) (3.80%) (-0.2393) (0.3042) (0.3619) (8.0904) YZC 1929 0.037% 3.76% -0.4540 0.2378 -0.4539 17.4782 [1171.hk] (0.049%) (3.81%) (-0.4874) (0.2678) (-0.7881) (17.6775)

31

Table 2B: Descriptive statistics on return spread defined as ADR’s return minus H-share’s return Symbol Min First quartile Median* Third quartile Max Mean* Std. Dev Portfolio I ACH -0.1473 -0.0256 -3.267 0.0255 0.1618 -0.413 0.0426 CBA -0.3496 -0.0262 2.12 0.0255 0.5876 1.52 0.0496 LFC -0.1062 -0.0164 2.894 0.0170 0.1234 -1.516 0.0311 CHL -0.2434 -0.0220 5.385 0.0217 0.3100 -7.011 0.0500 CHA -0.1272 -0.0161 -8.527 0.0150 0.0966 -0.052 0.0279 CEO -0.0984 -0.0171 -1.147 0.0180 0.0929 -0.176 0.0275 GSH -0.2233 -0.0213 0 0.0215 0.3144 2.389 0.0423 PTR -0.1361 -0.0165 4.39 0.0168 0.1124 -0.556 0.0280 CHU -0.1600 -0.0226 5.892 0.0224 0.1861 -1.11 0.0372 Portfolio II CEA -0.2542 -0.0239 1.922 0.0260 0.3805 0.342 0.0507 SNP -0.1885 -0.0190 -12.51 0.0188 0.1770 -2.32 0.0343 ZNH -0.3770 -0.0279 -6.154 0.0271 0.3740 -2.95 0.0528 HNP -0.7047 -0.0134 -2.128 0.0138 0.2366 -.5.75 0.0346 SHI -0.3250 -0.0262 0 0.0274 0.3264 1.32 0.0515 YZC -0.4690 -0.0234 0 0.0224 0.4725 -1.18 0.0538 * multiplied by 104

Table 3A: Comparisons between daily return distributions for underlying traded shares and their NYSE ADRs. Wilcoxon signed-rank test is to test the null hypothesis that median return of ADRs = median return of H-shares; reported Z score (P value in parentheses). The value for the Kolmogorov-Smirnov statistic, KS, was calculated; its asymptotic statistic, KSa, is reported (P value in parentheses).

Symbol Two sample t-test Wilcoxon signed-rank test Kolmogorov-Smirnov test Portfolio I ACH [2600.hk] -0.0331(0.9736) -0.207 (0.8362) 1.1959 (0.115) CBA [1114.hk] 0.1227(0.9736) 0.183 (0.8544) 1.6527 (0.010)* LFC [2628.hk] -0.1289(0.8975) -0.242 (0.8091) 1.0062 (0.001)* CHL [0941.hk] -0.0577(0.9540) 0.042 (0.9663) 0.9193 (0.369) CHA.[0728.hk] -0.0058(0.9954) -0.011 (0.9912) 2.4433 (0.000)* CEO [0883.hk] -0.0161(0.9872) 0.148 (0.8820) 1.4069 (0.038)** GSH [0525.hk] 0.2861(0.7748) -0.123 (0.9018) 1.8961 (0.001)* PTR [0857.hk] -0.0779(0.9379) 0.143 (0.8866) 2.5826 (0.000)* CHU [0762.hk] -0.1173(0.9066) 0.038 (0.9693) 1.3721 (0.046)** Portfolio II CEA [0670.hk] 0.0324(0.9741) 0.550 (0.5824) 2.0422 (0.000)* SNP [0386.hk] -0.2595(0.7953) -0.610 (0.5416) 2.6330 (0.000)* ZNH [1055.hk] -0.0254(0.9797) -0.304 (0.7614) 1.5380 (0.032)** HNP [0902.hk] -0.7638(0.4451) -0.470 (0.6383) 2.0749 (0.000)* SHI [0338.hk] 0.1454(0.8844) 0.592 (0.5540) 1.9546 (0.001)* YZC [1171.hk] -0.0959(0.9236) -0.630 (0.5285) 1.5287 (0.041)**

Table 3B: Pearson and Spearman correlation test with p-value Symbol Pearson P value (p>t) Spearman P value (p> |t|) Correlation Correlation Portfolio I ACH 0.0395 0.1779 0.0268 0.3606 CBA 0.0588 0.0184** 0.0628 0.0118** LFC -0.0127 0.7378 -0.0261 0.4908 CHL 0.0184 0.3861 -0.0005 0.9805 CHA -0.0239 0.4621 -0.0280 0.3882 CEO 0.0313 0.4334 0.0046 0.9086 GSH 0.0045 0.8183 0.0320 0.1054 PTR 0.0594 0.0201** 0.0203 0.4261 CHU 0.0872 0.0006* 0.0129 0.6108 Portfolio II CEA 0.0284 0.1720 0.0377 0.0699*** SNP -0.0074 0.7779 -0.0022 0.9328 ZNH 0.0197 0.3702 0.0019 0.9322 HNP 0.4369 0.0000* 0.5188 0.0000* SHI -0.0246 0.1602 -0.0082 0.6391 YZC 0.0180 0.4305 0.0346 0.1292

32

Table 3C: F-test for joint means and variance tests

Symbol Equality test Intercept Slope F-test ACH -4.13e-5(-0.03) -0.034 (-1.21) 1.46 CBA 1.41e-4(0.11) -0.007 (-0.30) 0.09 LFC -1.03e-4(-0.09) 0.077**(2.25) 5.07** CHL -4.91e-5 (-0.06) 0.063*(3.05) 9.30* CHA -3.82e-5 (-0.04) -0.008 (-0.25) 0.06 CEO -1.76e-5 (-0.02) -0.042 (-1.10) 1.20 GSH 2.22e-4(0.27) -0.065*(-3.31) 10.99* PTR -5.56e-5 (-0.08) 0.010 (0.43) 0.18 CHU -1.11e-4(-0.12) 0.063*(2.75) 7.56* CEA 3.42e-5 (0.03) -0.166*(-8.35) 69.73* SNP -2.32e-4(-0.26) 0.044***(1.68) 2.84*** ZNH -2.95e-5 (-0.03) -0.044**(-2.05) 4.21** HNP -5.75e-4(-0.76) 0.034*(2.50) 6.26* SHI 9.65e-5 (0.10) -0.130*(-7.06) 49.84* YZC -2.91e-4(-0.27) -0.018 (-1.00) 1.00

Table 4: Summary of results of hypotheses testing from Tables 3A-3C. ‘Yes’ indicates rejection of the null hypothesis while ‘no’ indicates null hypothesis cannot be rejected at less than 10% significance.

Symbol Ksa Wilcoxon signed-rank test Two sample t-test Joint means and variance tests Portfolio I ACH No No No No CBA Yes No No No LFC Yes No No Yes CHL No No No Yes CHA Yes No No No CEO Yes No No No GSH Yes No No Yes PTR Yes No No No CHU Yes No No Yes Portfolio II CEA Yes No No Yes SNP Yes No No Yes ZNH Yes No No Yes HNP Yes No No Yes SHI Yes No No Yes YZC Yes No No No

33

Graph 2: Q-Q (Quantile-Quantile) plots of ADR and underlying Hong Kong security returns

1. ACH 2. CBA 3. LFC return hk return hk return hk

.161317 .370752 .115324 return return return

-.148815 -.246886 -.104528 -.148815 .144075 -.219054 .220788 -.081016 .099875 hk hk hk Quantile-Quantile Plot Quantile-Quantile Plot Quantile-Quantile Plot 4. CHL 5. CHA 6. CEO return hk return hk return hk

.185039 .081914 .090151 return return return

-.165185 -.087326 -.076961 -.165185 .156106 -.083382 .075223 -.076961 .090151 hk hk hk Quantile-Quantile Plot Quantile-Quantile Plot Quantile-Quantile Plot 7. GSH 8. PTR 9. CHU return hk return hk return hk

.2673 .112368 .159379 return return return

-.206824 -.107631 -.130906 -.202941 .184922 -.107631 .09953 -.130906 .143101 hk hk hk Quantile-Quantile Plot Quantile-Quantile Plot Quantile-Quantile Plot 10. CEA 11. SNP 12. ZNH return hk return hk return hk

.26604 .181571 .296732 return return return

-.373481 -.168463 -.327086 -.373481 .26604 -.091152 .102191 -.326903 .296732 hk hk hk Quantile-Quantile Plot Quantile-Quantile Plot Quantile-Quantile Plot 13. HNP 14. SHI 15. YZC return hk return hk return hk

.223144 .304211 .267801 return return return

-.667466 -.23923 -.487413 -.159428 .223144 -.23923 .304211 -.487413 .267801 hk hk hk Quantile-Quantile Plot Quantile-Quantile Plot Quantile-Quantile Plot

Table 5A: Firm specific regression of the return of the Chinese ADRs and their respective underlying H-shares on S&P500, ^HSI, ^SHA returns. ^HSI is Hang Seng Index and ^SHA is Shanghai index. t statistics are inside parentheses.

Symbol S&P 500 ^HIS ^SHA R2 Symbol S&P 500 ^HSI ^SHA R2 Portfolio I ACH 0.321* 0.121 0.0141 HK2600 0.063 1.060 * 0.1170 (3.80) (1.39) (0.76) (12.37) CBA 0.150** 0.080 0.0031 HK1114 -0.043 0.789* 0.0734 (1.87) (1.12) (-0.56) (11.26) LFC 1.171 * -0.137 0.1210 HK2628 0.049 0.789* 0.1160 (9.75) (-1.53) (0.44) (9.53) CHL 0.387 * -0.016 0.0119 HK0941 -0.092* 1.251* 0.5980 (5.15) (-0.32) (-2.94) (57.25) CHA 0.977 * -0.032 0.1640 HK0728 -0.052 1.109* 0.2888 (13.63) (-0.52) (-0.79) (19.61) CEO 0.531 * 0.159** 0.0378 HK0883 0.063 1.310* 0.3297 (4.66) (1.86) (0.64) (17.52) GSH 0.603 * 0.024 0.0570 HK0525 0.003 0.489* 0.0697 (12.43) (0.72) (0.07) (13.86) PTR 0.421 * 0.023 0.0535 HK0857 0.054 0.563* 0.1174 (9.21) (0.57) (1.24) (14.06) CHU 1.276* 0.044 0.2503 HK0762 -0.056 1.519* 0.4834 (22.62) (0.86) (-1.30) (38.04) Portfolio II CEA 0.695* 0.016 0.062 0.0597 HK0670 -0.111 0.711* 0.024 0.0985 (11.85) (0.41) (1.42) (-1.63) (15.58) (0.47) SNP 0.237* -0.013 0.026 0.0117 HK0386 0.074 0.857* -0.09 0.1909 (4.03) (-0.24) (0.55) (1.45) (17.84) (-2.34) ZNH 0.927* -0.028 0.199* 0.0898 HK1055 0.105 0.958* 0.009 0.1450 (13.35) (-0.53) (3.49) (1.54) (17.91) (0.16) HNP 0.634* 0.370* 0.067 0.0875 HK0902 0.034 0.566* 0.028 0.0745 (10.05) (7.61) (1.31) (0.58) (12.23) (0.58) SHI 0.783* 0.045 0.083* 0.0611 HK0338 0.030 0.703* -0.032 0.0923 (13.72) (1.25) (3.11) (0.49) (17.51) (-1.09) YZC 0.370* 0.127* 0.233* 0.0228 HK1171 0.036 0.760* -0.090 0.0888 (4.76) (2.18) (3.72) (0.48) (13.03) (-1.44)

* Indicating significant at 1% level, ** indicating significant at 10% level.

^HSI is Hang Seng Index; this is the most representative index of Stock Exchange of Hong Kong (SEHK) and is active from 1969. The composite index of SEHK is active from 2001.

^SHA is the index of all A-shares on the Shanghai Stock Exchange (SSE). The reason not to use ^SSEC, the composite index of SSE, is because ^SSEC also includes all B-shares on the SSE, however A-shares and B-shares are two segmented markets.

The intercepts are quite small, in the scale of 10-3 to 10-6, and not significant.

36 Table 5B: Firm specific regression results of NASDAQ firms return on market return

The coefficient of each firm’s return on the market return is reported (t ratio in parentheses).

Symbol Intercept S&P500 Adjusted R2 JOBS -1.4E-3 1.5222* 0.0401 (-0.69) (4.65) BIDU -1.7E-3 1.6567* 0.0511 (-0.65) (4.04) JRJC -1.61E-3 0.3320 0.0032 (-0.95) (1.28) CMED 9.28E-4 1.4255* 0.0447 (0.37) (3.74) CNTF -2.21E-3 0.7250* 0.0119 (-1.01) (2.10) CTRP 9.75E-4 1.2383 * 0.0664 (0.84) (7.16) LONG -3.14E-4 0.9380* 0.0313 (-0.21) (4.01) FMCN 2.78E-3 1.2359* 0.0542 (1.50) (4.85) HRAY -1.25E-3 0.4036** 0.0074 (-0.85) (1.79) LTON -2.27E-3** 1.5160* 0.0700 (-1.57) (8.93) NTES 1.07E-3 0.3100* 0.0040 (0.79) (2.51) NINE -1.93E-3 0.0692 0.0002 (-1.40) (0.33) SNDA -1.72E-3 1.6446* 0.0903 (-0.12) (7.80) NCTY -5.95E-5 0.7843* 0.0207 (-0.04) (3.13) TOMO -9.09E-4 1.5888* 0.0896 (-0.69) (8.05)

Table 5C: Results from residuals tests. Model: Return = a + b1* Market returns&p + Market returnHK + e. We add an appropriate local market return lag to the model for each security and estimate the parameters via SUR method. We then test whether the errors are correlated and report Breusch-Pagan test of independence of errors.

Symbol Correlation between Chi-square (p value) Pearson correlation Spearman correlation ADR and HK returns between ADR return and between ADR return and residuals one-lag HK return one-lag HK return ACH 0.025 0.760( 0.38) 0.024(0.41) 0.019(0.50) CBA 0.109 19.198*( 0.00) 0.035(0.16) 0.026(0.29) LFC 0.060 2.588( 0.10) -0.012(0.75) -0.024(0.51) CHL 0.022 1.106( 0.29) 0.004(0.81) -0.014(0.49) CHA -0.011 0.118( 0.73) -0.018(0.57) -0.015(0.63) CEO -0.018 0.218( 0.64) -0.019(0.63) -0.031(0.43) GSH -0.001 0.001(0.97) 0.162(0.00)* 0.128(0.00)* PTR 0.048 3.640**( 0.05) 0.140(0.00)* 0.123(0.00)* CHU 0.103 16.456*( 0.00) 0.350(0.00)* 0.264(0.00)* CEA 0.039 3.576**( 0.05) 0.037(0.07)*** 0.057(0.06)*** SNP -0.017 0.442( 0.50) -0.005(0.82) -0.013(0.62) ZNH 0.002 0.016(0.90) 0.072(0.00)* 0.018(0.38) HNP 0.403 332.120*( 0.00) 0.366(0.00)* 0.293(0.00)* SHI 0.002 0.014(0.90) -0.018(0.31) -0.007(0.69) YZC 0.200 79.079*(0.00) 0.121(0.00)* 0.086(0.00)*

* Indicates significance at 1%, ** indicates significance at 5%, *** indicates significance at 10%.

37

Table 6: Descriptive statistics on proxies for liquidity and liquidity spread, illiquidity and illiquidity spread, spearman correlation of liquidity and illiquidity of ADR/HK shares, all Spearman correlation statistics are significant at less than 1% level.

Symbol Log(volume) Log(volume) ILLIQ ILLIQ Diff Diff Diff Diff Corr. Of Corr. Of mean St. Dev mean St. Dev Log(volume) Log(volume) ILLIQ ILLIQ liquidity liquidity mean St. Dev mean St. Dev and and ILLIQ ILLIQ (ADR) (HK) ACH 5.099 0.751 7.12e-07 3.92e-06 -0.340 0.712 -1.16e-07 8.93e-07 -0.596 -0.130 5.431 0.299 8.28e-07 8.18e-06 CBA 4.213 0.512 2.029e-06 3.84e-06 -0.918 0.650 1.64e-06 4.82e-06 -0.593 -0.449 5.144 0.446 3.83e-07 2.826e-06 LFC 5.10 0.525 1.29e-07 1.465e-07 -1.10 0.486 1.20e-07 1.25e-07 -0.498 -0.138 6.20 0.287 8.66e-09 7.83e-09 CHL 5.460 0.426 1.02e-07 1.68e-07 -1.113 0.432 9.67e-07 1.73e-07 -0.532 -0.157 6.567 0.233 5.22e-09 5.46e-09 CHA 4.948 0.337 1.52e-07 1.27e-07 -0.940 0.416 1.34e-07 2.45e-07 -0.292 -0.213 5.897 0.255 1.79e-08 1.67e-08 CEO 5.157 0.256 1.04e-07 8.90e-08 -0.655 0.305 1.01e-07 9.65e-08 -0.356 -0.189 5.808 0.226 2.39e-09 2.26e-09 GSH 4.146 0.577 2.01e-06 4.05e-06 -0.864 0.645 1.75e-06 5.08e-06 -0.518 -0.333 5.011 0.392 2.63e-07 6.73e-07 PTR 5.198 0.592 1.94e-07 3.50e-07 -0.744 0.525 1.78e-06 2.86e-07 -0.700 -0.143 5.939 0.272 1.59e-08 1.50e-08 CHU 5.459 0.399 8.68e-08 1.31e-07 -0.769 0.366 8.57e-08 1.19e-07 -0.484 -0.123 6.227 0.257 1.06e-09 9.15e-10 CEA 3.735 0.705 6.73e-06 1.59e-05 -1.347 0.841 6.64e-06 1.89e-05 -0.603 -0.429 5.081 0.468 8.33e-09 1.51e-09 SNP 4.953 0.575 4.09e-07 7.88e-07 -0.870 0.485 3.78e-07 6.98e-07 -0.676 -0.389 5.813 0.332 3.08e-08 3.93e-07 ZNH 3.948 0.605 5.69e-06 1.58e-05 -1.188 0.765 5.62e-06 1.75e-05 -0.608 -0.397 5.135 0.419 6.53e-08 2.71e-07 HNP 5.069 0.397 1.93e-07 3.36e-07 -1.507 0.764 1.07e-07 5.45e-07 -0.348 -0.618 6.668 0.608 8.55e-08 1.61e-08 SHI 4.249 0.528 1.87e-06 7.30e-06 -0.892 0.656 1.74e-06 7.38e-06 -0.567 -0.419 5.145 0.449 1.32e-07 3.89e-07 YZC 3.838 0.731 9.87e-06 2.64e-05 -1.390 0.696 9.65e-06 2.23e-05 -0.628 -0.458 (5.223) 0.375 2.14e-07 3.93e-07

38

Table 7A: Regression results from the model, Return spread = a + b1*U.S. Market return + b2*HK Market return + b3*ADR liquidity + b4*HK security liquidity. Return spread=ADR return-H-share return, U.S. Market return = S&P500 return, HK return = HSI return, Liquidity = Log(volume)ADR or Log(volume)HK. Illiquidity is the ratio between abs (daily return) and dollar volume times 105. t statistics are reported in parenthesis.

Symbol Market Return Market Return Market Log(volume) Log(volume) ILLIQ ILLIQ Log(volume) ILLIQ Adj R2 (U.S.) (HK) Return (ADR) (HK) (ADR) (HK) Diff Diff (SH) ACH 0.43(3.88)* -1.53(-13.35)* 0.004(2.74)* -0.014(-3.51)* 0.1546 0.41(3.67)* -1.54(-13.36)* 0.005(2.87)* 0.1497 0.40(3.59)* -1.54(-13.32)* -0.074(-0.25) -0.13(-0.97) 0.1444 0.40(3.63)* -1.54(-13.32)* 0.099(0.77) 0.1441 CBA 0.71(6.24)* -0.80(-7.72)* 0.008(3.37)* -0.01 (-3.56)* 0.0699 0.71(6.23)* -0.80(-7.73)* 0.009(4.69)* 0.0697 0.71(6.18)* -0.82(-7.91)* -0.031(-0.92) 0.014(3.02)* 0.0627 0.71(6.19)* -0.82(-7.91)* -0.014(-3.13)* 0.0625 LFC -0.37(-2.29)** -0.57(-4.67)* 0.002(1.10) -0.002(-0.54) 0.0406 -0.38(-2.29)** -0.57(-4.67)* 0.002(1.10) 0.0406 -0.39(-2.38)* -0.58(-4.77)* 0.05(0.58) -0.032(-2.32)* 0.0469 -0.36(-2.22)** -0.58(-4.74)* 0.013(1.74)*** 0.0432 CHL 0.44(7.27)* -1.23(-29.32)* 0.003(1.69)*** -0.003(-1.08) 0.2955 0.44(7.30)* -1.23(-29.35)* 0.003(1.72)*** 0.2954 0.44(7.26)* -1.24(-29.35)* 0.03(0.66) -0.015(-1.15) 0.2950 0.44(7.26)* -1.24*(-29.46) 0.03(0.75) 0.2947 CHA 0.93(8.91)* -0.76(-8.62)* 0.005(2.00)** -0.004(-1.81)*** 0.1383 0.93(8.92)* -0.76(-8.62)* 0.005(2.77)* 0.1384 0.93(8.94)* -0.77(-8.67)* -0.01(-0.15) 0.017(0.34) 0.1331 0.93(8.95)* -0.77(-8.67)* -0.015(-0.36) 0.1331 CEO 0.47(3.07)* -1.14(-9.96)* -0.003(-0.66) -0.0001(-0.04) 0.1542 0.47(3.08)* -1.15(-10.01)* -0.001(-0.43) 0.1539 0.48(3.15)* -1.16(-10.06)* 0.14(1.23) -0.03(-0.67) 0.1581 0.48(3.11)* -1.16(-10.16) 0.042(0.97) 0.1575 GSH 0.51(7.67)* -0.21(-4.72)* 0.001(1.89)*** -0.006(-2.35)* 0.0583 0.51(7.66)* -0.21(-4.71)* 0.003(2.44)* 0.0584 0.51(7.65)* -0.21(-4.71)* 0.06(0.04) 0.016(1.74)*** 0.0575 0.51(7.65)* -0.21(-4.71)* -0.016(-1.85)*** 0.0574 PTR 0.50(8.67)* -0.45(-8.63)* 0.002(1.70)*** -0.008(-2.98)* 0.0805 0.50(8.64)* -0.45(-8.68)* 0.002(1.69)*** 0.0768 0.51(8.80)* -0.45(-8.70)* 0.48(0.26) -0.87(-2.19)** 0.0780 0.51(8.84)* -0.45(-8.70)* 0.86(2.19)** 0.0780 CHU 1.19(19.54)* -1.24(-22.53)* 0.003(1.98)*** -0.004(-2.01)*** 0.3389 1.19(19.54)* -1.24(-22.53)* 0.003(2.04)** 0.3387 1.19(19.53)* -1.24(-22.53)* -0.024(-0.48) -0.048(-0.66) 0.3381 1.19(19.53)* -1.24(-22.53)* -0.026(-0.52) 0.3378

CEA 0.56(6.63)* -0.62(-10.96)* -.0009(-0.11) 0.004(3.01)* -0.01(-4.55)* 0.0808 0.56(6.63)* -0.62(-10.96)* -.0009(-0.11) 0.006(5.00)* 0.0789 0.55(6.48)* -0.63(-11.08)* -.0009(-0.15) -0.05(-0.85) 0.07(1.72) 0.0695 0.56(6.49)* -0.62(-11.09)* -.0009(-0.14) -0.06(-1.88)*** 0.0705 SNP 0.15(1.95)** -0.88(-11.96)* 0.11(1.79)*** 0.004(2.30)** -0.004(-1.36) 0.0977 0.15(1.95)** -0.88(-11.97)* 0.11(1.79)*** 0.004(2.31)** 0.0977 0.15(1.88)** -0.87(-11.87)* 0.11(1.78)*** -0.019(-1.64)*** 0.049(0.21) 0.0960 0.15(1.88)** -0.87(-11.89)* 0.11(1.78)*** -0.018(-1.65)*** 0.0960 ZNH 0.81(8.30)* -0.97(-12.90)* 0.19(2.39)* 0.006(3.03)* -0.005(-1.73)*** 0.1149 0.81(8.31)* -0.98(-12.91)* 0.19(2.41)* 0.005(3.62)* 0.1149 0.81(8.24)* -0.98(-12.94)* 0.19(2.38)* -0.09(-1.27) 0.10(0.14) 0.1096 0.81(8.24)* -0.98(-12.95)* 0.19(2.38)* -0.09(-1.38) 0.1098 HNP 0.59(10.21)* -0.19(-4.40)* 0.044(0.89) 0.004(2.91)* -0.003(-2.92)* 0.0665 0.59(10.21)* -0.19(-4.40)* 0.044(0.89) 0.003(2.97)* 0.0664 0.59(10.20)* -0.19(-4.42)* 0.044(0.87) -0.018(-0.90) -0.009(-2.13)** 0.0598 0.59(10.20)* -0.19(-4.43)* 0.044(0.87) 0.007(1.90)** 0.0589 SHI 0.73(8.44)* -0.73(-12.20)* 0.094(1.95)** 0.006(3.54)* -0.011(-5.35)* 0.0898 0.73(8.44)* -0.73(-12.16)* 0.092(1.93)** 0.008(5.82)* 0.0886 0.75(8.62)* -0.73(-12.20)* 0.097(1.93)** -0.002(-1.61)*** 0.006(2.68)* 0.0805 0.75(8.62)* -0.73(-12.24)* 0.097(1.93)** -0.002(-1.78)*** 0.0803 YZC 0.26(2.60)* -0.46(-6.18)* 0.11(1.79)*** 0.004(2.66)* -0.008(-2.49)* 0.0510 0.26(2.61)* -0.47(-6.24)* 0.11(1.79)*** 0.005(2.90)* 0.0502 0.27(2.68)* -0.47(-6.24)* 0.11(1.85)*** -0.0003(-0.68) 0.0003(1.17) 0.0465 0.27(2.68)* -0.47(-6.24)* 0.11(1.85)*** -0.0003(-1.28) 0.0465

39

Table 7B: Regression results from the model, Return spread = a + b1*U.S. Market return + b2*HK Market return + b3*ADR liquidity + b4*HK security liquidity. Return spread=ADR return-H-share return, U.S. Market return = S&P500 return, HK return = HSI return, Liquidity = Log(volume)ADR or Log(volume)HK. Illiquidity is the ratio between abs (daily return) and dollar volume times 105. t statistics are reported in parenthesis. The Chow test statistic reported on the first row of a sequence of two/three consecutive sub periods denotes a structural break and relates to the identical regression model for.

Symbol Market Return Market Return Market Log(volume) Log(volume) ILLIQ ILLIQ Log(volume) ILLIQ Adj R2 Chow Start (U.S.) (HK) Return (ADR) (HK) (ADR) (HK) Diff Diff test date (SH)

ACH 0.59(3.98)* -0.98(-4.68)* -0.0001(-0.04) -0.01(-1.27) 0.1567 2.77 12/01 0.58(3.93)* -1.76(-12.89)* 0.005(2.37)* -0.017(-3.49)* 0.1663 0.58(3.89)* -1.04(-4.95)* 0.056(0.13) -0.24(-1.16) 0.1555 2.57 0.58(3.93)* -1.76(-12.77)* -0.069(-0.18) 0.069(0.34) 0.1534 0.58(3.94)* -1.00(-4.85)* 0.001(0.37) 0.1509 3.11 0.58(3.93)* -1.75(-12.78)* 0.005(2.81)* 0.1604 0.58(3.93)* -1.03(-4.94)* 0.2(1.10) 0.1550 3.30 0.58(3.93)* -1.76(-12.93)* -0.069(-0.39) 0.1534 CBA 0.57(3.45)* -0.65(-4.22)* 0.011(2.36)* -0.01(-2.16)* 0.0562 1.69 04/00 0.99(5.92)* -1.07(-7.61)* 0.005(2.00)* -0.008(-2.50)* 0.1041 0.57(3.43)* -0.68(-4.32)* -0.012(-0.25) 0.013(2.35)* 0.0477 2.31 0.97(5.78)* -1.07(-7.48)* -0.051(-1.02) 0.10(1.24) 0.0980 0.57(3.46)* -0.66(-4.22)* 0.011(3.41)* 0.0562 2.05 0.99(5.89)* -1.07(-7.63)* 0.006(2.87)* 0.1036 0.57(3.44)* -0.68(-4.33)* -0.013(-2.38)* 0.0477 1.95 0.97(5.77)* -1.07(-7.51)* -0.064(-1.51) 0.0977 CHL 0.52(6.61)* -1.21(-23.05)* 0.002(1.00) -0.004(-0.87) 0.3019 3.71 10/97 0.39(2.01)* -1.34(-16.66)* 0.006(1.78)*** -0.001(-0.94) 0.2976 0.52(6.62)* -1.22(-23.18)* 0.03(0.57) -0.01(-0.69) 0.3015 3.64 0.39(2.01)* -1.34(16.65)* 0.006(0.62) -0.04(-1.17) 0.2660 0.52(6.66)* -1.21(-23.08)* 0.002(1.13) 0.3018 2.28 0.39(2.01)* -1.34(-16.66)* 0.004(1.58) 0.2769 0.52(6.63)* -1.22(-23.18)* 0.013(0.77) 0.3014 2.51 0.39(2.01)* -1.34(-16.67)* 0.039(1.16) 0.2660 GSH 0.53(3.86)* 0.05(0.71) 0.003(0.91) -0.001(-1.50) 0.0205 13.30 05/96 0.38(4.28)* -0.52(-6.47)* 0.0005(1.23) -0.001(-1.53) 0.0691 0.73(5.75)* -0.80(-7.47)* 0.004(1.98)*** -0.006(-1.86)*** 0.0949 0.52(3.80)* 0.050(0.72) 0.14(0.03) 0.015(1.09) 0.0210 12.75 0.38(4.27)* -0.52(-6.48)* -0.21(-0.10) 0.044(1.69)*** 0.0695 0.73(5.72)* -0.81(-7.57)* 0.63(0.16) 0.058(1.96)*** 0.0898 0.53(3.86)* 0.05(0.71) 0.002(1.51) 0.0204 16.49 0.38(4.28)* -0.52(-6.47)* 0.0001(1.69)*** 0.0688 0.73(5.75)* -0.80(-7.47)* 0.004(2.42)* 0.0946 0.52(3.82)* 0.050(0.72) -0.015(-1.13) 0.0210 15.95 0.38(4.27)* -0.52(-6.48)* -0.04(-1.72)*** 0.0694 0.73(5.74)* -0.81(-7.59)* -0.057(-1.75)*** 0.0897 PTR 0.33(4.56)* -0.30(-4.34)* 0.002(0.67) -0.008(-2.18)** 0.0608 5.53 04/00 0.85(8.69)* -0.71(-8.66)* 0.0037(1.64)*** -0.009(-2.35)** 0.1227 0.34(4.63)* -0.30(-4.36) -0.016(-0.73) -0.002(-0.39) 0.0545 6.93 0.82(8.39)* -0.68(-8.29)* 0.16(1.89)** -0.026(-3.32)* 0.1300 0.33(4.69)* -0.30(-4.39)* 0.004(1.62)*** 0.0575 7.39 0.85(8.65)* -0.71(-8.67)* 0.004(1.99)** 0.1208 0.34(4.63)* -0.30(-4.36) 0.001(0.25) 0.0535 7.90 0.82(8.65)* -0.68(-8.67)* 0.026(3.36)* 0.1276 CHU 1.16(13.45)* -1.22(-15.14)* -0.0016(-0.53) 0.003(0.51) 0.2790 2.38 06/00 1.28(13.34)* -1.26(-15.82)* 0.006(2.84)* -0.009(-2.78)* 0.2943 1.16(13.47)* -1.23(-15.12)* -0.016(-0.20) -0.009(-0.06) 0.2787 2.37 1.28(13.38)* -1.26(-15.78)* -0.036(-0.54) -0.06(-0.75) 0.2870 1.16(13.55)* -1.23(-15.15)* -0.001(-0.57) 0.2790 2.50 1.27(13.33)* -1.26(-15.85)* 0.007(3.14)* 0.2937 1.16(13.50)* -1.23(-15.14)* -0.01(-0.14) 0.2786 2.52 1.29(13.42)* -1.28(-15.75)* 0.002(0.04) 0.2862

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Table 7B (continued) Symbol Market Return Market Return Market Log(volume) Log(volume) ILLIQ ILLIQ Log(volume) ILLIQ Adj R2 Chow Start (U.S.) (HK) Return (ADR) (HK) (ADR) (HK) Diff Diff test date (SH)

CEA 1.12(5.57)* -0.54(-5.40)* -0.030(-0.24) 0.009(1.93)** -0.009(-2.41)* 0.1026 9.38 01/97 0.21(1.93)** -0.57(-5.39)* 0.20(1.51) 0.01(3.87)* -0.019(-4.11)* 0.0899 0.55(4.67)* -0.85(-8.50)* -0.15(-2.06)* 0.003(2.14)** -0.007(-2.56)* 0.1045 1.10(5.45)* -0.55(-5.48)* -0.031(-0.24) -0.026(-2.40)* 0.0007(0.51) 0.0973 9.47 0.20(1.97)** -0.57(-5.33)* 0.20(1.51) -0.0005(-0.72) 0.002(2.77)* 0.0634 0.53(4.44)* -0.87(-8.58)* -0.15(-2.06)* -0.0004(-0.69) 0.005(2.91)* 0.0967 1.12(5.58)* -0.54(-5.41)* -0.030(-0.24) 0.009(3.19)* 0.1026 12.09 0.22(1.91)** -0.57(-5.48)* 0.20(1.56) 0.012(4.80)* 0.0854 0.55(4.63)* -0.86(-8.58)* -0.14(-2.05)* 0.004(2.87)* 0.1033 1.09(5.40)* -0.55(-5.54)* -0.031(-0.19) -0.013(-0.93) 0.0903 7.65 0.20(1.96)** -0.57(-5.40)* 0.20(1.60) -0.058(-1.27) 0.0522 0.53(4.44)* -0.87(-8.61)* -0.15(-2.07)* -0.049(-1.50) 0.0967 SNP 0.22(2.22)** -0.53(-5.31)* 0.32(2.91)* 0.008(2.19)** -0.009(-1.94)** 0.0824 10.28 10/00 -0.019(-0.14) -1.41(-12.91)* -0.029(-0.37) -0.0003(-0.13) 0.0002(0.05) 0.1606 0.22(2.13)** -0.52(-5.16)* 0.34(3.07)* -0.019(-1.35) 0.028(1.00) 0.0761 10.67 -0.027(-0.21) -1.41(-12.91)* -0.026(-0.33) 0.067(1.52) -0.071(-1.46) 0.1640 0.22(2.21)** -0.53(-5.33)* 0.32(2.91)* 0.008(2.48)* 0.0823 12.86 -0.019(-0.14) -1.42(-12.92)* -0.029(-0.37) -0.0003(-0.12) 0.1606 0.21(2.09)** -0.52(-5.19)* 0.34(3.07)* -0.019(-1.36) 0.0745 12.67 -0.019(-0.21) -1.41(-12.91)* -0.026(-0.33) 0.057(1.31) 0.1622 ZNH 0.87(3.52)* -0.74(-5.20)* 0.078(0.82) 0.028(3.27)* -0.003(-0.53) 0.1100 6.79 04/98 0.82(5.64)* -1.19(-8.88)* 0.19(1.29) 0.01(3.07)* -0.013(-2.31)** 0.1620 0.78(4.86)* -1.06(-7.81)* 0.33(1.74)*** 0.002(2.76)* -0.005(-2.51)* 0.1565 0.78(3.09)* -0.75(-5.22)* 0.08(0.84) -0.03(-1.38) -0.003(-0.37) 0.0901 4.33 0.81(5.50)* -1.19(-8.78)* 0.20 (1.31) -0.09(-0.99) 0.07(1.66)*** 0.1500 0.78(4.91)* -1.06(-7.85)* 0.33(1.72)*** -0.13(-0.95) 0.074(2.35)* 0.1545 0.87(3.51)* -0.74(-5.12)* 0.074(0.77) 0.012(2.44)* 0.0989 6.34 0.82(5.65)* -1.19(-8.90)* 0.19(1.29) 0.011(3.59)* 0.1617 0.78(4.84)* -1.06(-7.83)* 0.33(1.91)*** 0.003(2.35)* 0.0960 0.82(3.28)* -0.77(-5.33)* 0.07(0.77) -0.002(-0.29) 0.0859 3.09 0.81(5.57)* -1.19(-8.82)* 0.20 (1.25) -0.09(-1.01) 0.1465 0.78(4.87)* -1.07(-7.91)* 0.34(1.78)*** -0.12(-0.85) 0.0948 HNP 0.98(4.96)* 0.069(1.60)*** 0.04(0.25) 0.007(1.45) -0.01(-3.05)* 0.0644 6.62 01/98 0.38(6.58)* -0.19(-3.64)* -0.01(-0.17) 0.008(3.80)* -0.007(-2.28)** 0.0719 0.76(8.31)* -0.65(-8.45)* 0.01(1.90)** 0.0002(2.08)* -0.0002(-2.05)** 0.1406 0.99(5.00)* 0.068(1.62)*** 0.038(0.24) 0.038(0.24) 0.013(1.19) 0.0600 7.01 0.38(6.55)* -0.20(-3.73)* -0.07(-0.12) -0.005(-0.30) -0.075(-1.61)*** 0.0740 0.76(8.39)* -0.65(-8.40)* 0.01(1.92)** -0.029(-0.47) -0.037(-1.33) 0.1428 0.97(4.96)* 0.059(1.60)*** 0.04(0.25) 0.005(2.32)** 0.0606 7.61 0.38(6.58)* -0.19(-3.64)* -0.01(-0.19) -0.001(-2.09)** 0.0718 0.76(8.33)* -0.65(-8.45)* 0.01(1.90)** -0.0001(-1.98)** 0.1405 0.98(4.99)* 0.068(1.62)*** 0.037(0.23) -0.012(-1.14) 0.0596 8.01 0.38(6.50)* -0.20(-3.64)* -0.01(-0.24) -0.005(-1.30) 0.0703 0.76(8.36) -0.65(-8.45)* 0.01(1.89)** -0.004(-1.67)*** 0.1410 SHI 0.96(6.00)* -0.57(-6.94)* 0.046(0.68) 0.008(2.52)* -0.011(-3.25)* 0.0758 9.67 07/93 0.53(4.12)* -0.84(-6.87)* 0.14(1.08) 0.014(3.42)* -0.024(-5.15)* 0.1327 0.77(4.69)* -1.39(-9.81)* 0.26(2.69)* 0.004(1.34) -0.017(-3.44)* 0.1542 0.97(6.03)* -0.58(-6.99)* 0.046(0.62) -0.012(-1.16) 0.05(1.81)*** 0.0688 8.04 0.54(4.15)* -0.84(-6.74)* 0.14(1.01) -0.001(-1.29) 0.039(3.20)* 0.1067 0.78(4.74)* -1.37(-9.62)* 0.26(2.84)* -0.008(-1.09) 0.085(3.01)* 0.1507 0.97(6.03)* -0.57(-6.92)* 0.046(0.68) 0.01(3.64)* 0.0754 11.20 0.53(4.17)* -0.84(-6.92)* 0.12(1.08) 0.018(5.39)* 0.1287 0.77(4.64)* -1.39(-9.71)* 0.26(2.76)* 0.007(2.81)* 0.1486 0.97(6.00)* -0.58(-6.99)* 0.041(0.60) -0.001(-1.75)*** 0.0675 8.16 0.54(4.08)* -0.86(-6.90)* 0.14(1.02) -0.002(-1.62)*** 0.0942 0.77(4.66)* -1.39(-9.75)* 0.29(2.90)* -0.011(-1.60) 0.1431 YZC 0.30(2.26)* -0.14(-2.05)** 0.23(1.26) 0.015(3.26)* -0.009(-1.14) 0.0690 11.72 04/98 0.46(3.92)* -0.43(-4.03)* 0.26(2.14)** 0.0027(0.85) -0.014(-2.76)* 0.0734 -0.32(-1.62)*** -1.30(-7.90)* -0.091(-0.78) 0.0004(0.14) -0.007(-1.63)*** 0.0737 0.30(2.25)* -0.17(-2.25)* 0.22(1.20) -0.001(-1.04) 0.00009(0.21) 0.0548 15.22 0.48(4.12)* -0.46(-4.31)* 0.24(1.97)** 0.0002(0.39) 0.0009(1.09) 0.0627 -0.33(-1.70)*** -1.30(-8.00)* -0.075(-0.78) -0.003(-1.32) 0.008(0.81) 0.0684 0.30(2.24)* -0.14(-2.00)** 0.23(1.26) 0.014(3.22)* 0.0681 13.69 0.46(3.95)* -0.44(-4.10)* 0.24(2.02)** 0.005(1.75) 0.0655 -0.32(-1.62)*** -1.30(-7.90)* -0.091(-0.78) 0.001(1.48) 0.0603 0.30(2.25)* -0.18(-2.20)* 0.24(1.28) -0.0002(-0.51) 0.0527 17.71 0.48(4.08)* -0.46(-4.31)* 0.24(1.97)** -0.0001(-0.25) 0.0604 -0.33(-1.66)*** -1.31(-8.09)* -0.082(-0.72) -0.007(-1.45) 0.0650

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