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Journal of Economics, Finance and Administrative Science
versión impresa ISSN 20771886
Journal of Economics, Finance and Administrative Science v.15 n.28 Lima jun. 2010
ADR Effects on Domestic Latin American Financial Market
Los efectos adr en los mercados domésticos financieros de Latinoamérica
Alfredo Mendiola^{1}
^{1}. Professor Alfredo Mendiola is Ph. D. in Management Finances from Cornell University, USA. He also holds a MBA from the University of Toronto, Canada; a MBA from Esan Business School, Peru. He is a B.of Sc. in Systems Engineer from the Universidad Nacional de Ingeniería del Peru.
Abstract
The purpose of this paper is to revisit and extend previous research work that examines the ADRlisting effects on the trading process of all the domesticallylisted stocks in the main Latin American exchanges. The most important result is consistent with the idea of a greater isolation (from global markets) of the singlylisted stocks in the postcrosslisting period. These results persist over the crosslisting months. As expected, the crosslisted stocks become more integrated in the postcross listing period.
Keywords: International finance, economic integration.
Resumen
El propósito de este artículo es revisar y extender trabajos de investigación en que se examinan los efectos de emitir ADRs en el proceso de negociación de las acciones listadas en los mercados de valores latinoamericanas. El resultado más importante es consistente con la idea de un mayor aislamiento (de mercados financieros internacionales) de las acciones listadas únicamente en el mercado doméstico en el período posterior a la emisión de los ADRs. Estos resultados son persistentes en el tiempo. Como era de esperarse, las acciones sobre las que se han emitido ADRs se encuentran más integradas con mercados financieros internacionales en períodos posteriores a la emisión de este.
Palabras claves: Finanzas internacionales, integración económica.
Research has established that cross listing significantly affects the ADRs underlying share1 trading process in the domestic exchange. Examples of these effects include higher valuations and improvements in an investors appreciation of the firms information (Coffee, 1999; Reese & Weisbach, 2002; Doidge, Karolyi, & Stulz, 2003); declines in cost of capital (Errunza & Miller, 2000; Foerster & Karolyi, 1993, 1999; Domowitz, Glen, & Madhavan, 1998); positive abnormal stock returns in the precrosslisting period (Foerster & Karolyi, 1993, 1999; Jayaraman, Shastri, & Tandon, 1993; Viswanathan, 1996; Miller, 1999; Errunza & Miller, 2000; Kim & Singal, 2000); improvement in firm visibility and information environment (Baker, Nofsinger, & Weaver, 2002; Lang, Lins, & Miller, 2003; Bailey, Karolyi, & Salva, 2006); spillover of crosslisting effects to singlylisted stocks (Fernandes, 2003; Melvin & ValeroTonone, 2003; Lee, 2003); a migration of trading volume (Smith & Sofianos, 1997; Pulatkonak & Sofianos, 1999; Levine & Schmukler, 2003; Domowitz, Glen, & Madhavan, 1998). The purpose of this paper is to revisit and extend previous research work that examines the ADRlisting effects on the stock returns of all domesticallylisted stocks in Latin American exchanges. Initially, the analysis is done considering the singly and crosslisted stocks separately; next, all the information of the domesticallylisted stocks is pooled to determine possible differences in the trading process across the two groups of securities.
This approach builds on previous research work2 and, additionally, takes into consideration three important factors affecting ADR listings. First, including only Latin American stocks ensures that the time zone differences across local and US exchanges are, at most, two hours.3 Second, to facilitate the identification of spillovers4, the examination of ADRlisting effects is done separately on singly and crosslisted stocks; in a subsequent step, all the information (from the singly and crosslisted stocks) is pooled to determine whether differences exist across these two groups of securities. Third, Heckmans (1979) procedure is used to control for the differences in the characteristics of the firms with cross and singlylisted stocks; without this procedure, a non random sample selection occurs given that the behavior of cross and singlylisted stocks is examined separately.
The main results of this paper are as follows: ADRlisting effects on the domesticallylisted stocks are significant and affect singly and crosslisted stocks in different ways. As expected, ADRlisting results in an increase in the importance of the world exchange index in explaining the behavior of crosslisted shares. However, for the singlylisted shares, ADRlisting induces a significant increase in the importance of the domestic exchange variables to explain the trading behavior of this group of stocks. I interpret this finding as an increase in the isolation (from international markets) of singlylisted shares in the postcrosslisting period.
This paper is organized as follows: The first section includes a summary of the sources and characteristics of the data used for empirical tests. Section two presents a discussion of Heckmans technique and its empirical implementation to determine the Inverse Mills Ratio (probability of crosslisting) for each stock. The behavior of the stock returns is included in the third section.
DATA
The information collected includes firm and exchange related information from four Latin American countries: Argentina, Brazil, Chile and Mexico.5 The period analyzed extends from January 1, 1992 to December 31, 20026. The total number of firm / shares considered in the sample is 926, of which 203 (22%) have crosslisted securities (See Table 1).
To minimize the possibility of a nonsynchronous trading bias, I exclude the securities that trade in less than 30% of the available trading days.7 As Campbell, Lo and MacKinlay (1997) indicate the nonsynchronous trading or nontrading effect arises when time series, usually asset prices, are taken to be recorded at time intervals of one length when in fact they are recorded at time intervals of other, possibly irregular, lengths... (p. 84). For example, this problem may occur if it is assumed that daily closing prices are recorded at the end of the trading day. As Campbell et al. specifically indicate, this effect may introduce biases in the moments and comoments of asset returns such as their means, variances, betas and autocorrelations (p. 84). Scholes and Williams (1977) examine this problem and show that for actively traded stocks, any adjustment to control for nontrading effects are generally small and unimportant. Consequently, limiting the sample to include only the most liquid stocks minimizes the possibility of biasing the results due to nontrading effects, improving the quality of the empirical results.8
If we exclude the securities traded in less than 30% of the available trading days, the total number of firmshares drops from 926 to 453 (51% reduction in the sample size). Furthermore, with this control the number of singlylisted firmshares included in the sample decreases from 723 to 292 (60% reduction); for the crosslisted firmshares the sample size decreases from 203 to 161 (21% reduction). When trading in, more than 40% and 50% of the possible days is considered as a benchmark, the total sample size is reduced to 395 and 347 firmshares, respectively. The proportion of singly and crosslisted firmshares excluded from the sample is in line with the previously indicated information (see Table 1).
Daily stock information has been collected from DataStream and includes closing prices, traded volume, and market capitalization. This information was collected in the countrys domestic currency and then converted to US Dollars to facilitate crosssectional analysis9. The firms accounting information, necessary for the implementation of Heckmans procedure, was obtained from the WorldScope database available through DataStream. All information has been collected in home country currency. Exchange related information has also been collected from DataStream and includes the domestic stock exchange index and the MSCI World Stock exchange index. To facilitate the crosssectional analysis across exchanges, all the information has been converted to US dollars.
HECKMAN S PROCEDURE TO CONTROL FOR SAMPLE SELECTION BIASES
The firms that cross list stocks are believed to be the largest and most successful organizations in their home countries. As such, examining the behavior of the singly and crosslisted stocks separately induces a sample selection bias. To control for this possibility, the implement Heckmans procedure has been implemented.10
This sample selection problem can be summarized as follows11: Consider a random sample of I observations. For each observation i the following equations can be defined:
where X_{ji} is a (1 x K_{j}) vector of regressors and β_{j} is a (K_{j} x 1) vector of parameters. Suppose that data is available for Y_{1i} if Y_{2i} ≥ 0; if Y_{2i} = 0 then there are no observations for Y_{1i}. The general idea is to develop a twostage estimator to overcome any possible bias related to the non random sample selection due to limitations in the information on Y_{1i}. In this dissertation, Y_{2i} = 0 (1) if the stock is singly (cross) listed.
Heckmans procedure is implemented as follows:
1. Use the full sample of listed stocks to estimate a probit regression to determine the probability that Y_{2i} ≥ 0 (the stock is crosslisted or singlylisted). The independent variables included in this regression represent the general characteristics of all the domesticallylisted firms such as market capitalization, leverage ratio, asset turnover and return on equity.
2. Following Heckmans notation, define φ() as the density function and Φ() as the distribution function of a standard normal variable. Using the coefficients estimated in the probit regression and assuming that h(U_{1i},U_{2i}) (errorterms of equations 1 and 2) is bivariate normal, the following parameters (for each of the domesticallylisted stock) can be estimated:
where λ_{i} is known as the inverse of Mills ratio. This ratio is a correction term that is used to control for the bias that arises from the nonrandom sample selection. As the probability of being in the sample (i.e. crosslisted share) increases, the cumulative density function approaches one and the probability density function approaches zero, so the Inverse Mills ratio approaches zero.
3. For the estimation of equation 1 coefficients, the Inverse Mills ratio (λ_{i}) is included as one of the independent variables. Heckman demonstrates that under the previously indicated assumptions the regression estimators (coefficients of X_{1i} and λ_{1} in equation 1) are consistent. Puhani (2000) conducts different Monte Carlo exploratory studies around Heckmans procedure. His results show that, in the absence of collinearity, a full information maximum likelihood estimator is preferable to the limitedinformation twostep method of Heckman If, however, collinearity problems prevail, subsample OLS (or the twopart model) is the most robust amongst the simpletocalculate estimators (p. 54).
As previously indicated, Heckmans procedure is a twostage procedure. In this subsection, the first step is implemented (i.e. the estimation of the Inverse Mills ratio for each stock). This ratio is included as one of the independent variables in different regressions to be implemented in later sections of this chapter.
To implement step 1 probit regression, the following independent variables that characterize the domesticallylisted firms (X_{2i} in equation 2) are included:

Market capitalization (MC) to proxy for firms size. Larger firms are believed to be the most important in their home countries and should tend to be crosslisting targets.

Return on equity (ROE) as a profitability measure of a shareholders investment12. Profitable firms should tend to be crosslisting targets. Another possible argument is that firms with a low ROE cross list to force an improvement in their performance.

Leverage ratio (LR) to proxy for the firms financial risk13. The rationale is that a higher leverage ratio should lower crosslisting possibilities. Another possible interpretation of this factor is that highly levered firms will cross list to redefine their capital structure.

Asset turnover (ATu) to measure the firms operational efficiency14. The most efficient firms should tend to be crosslisting targets. Another (opposite) argument is that inefficient firms will crosslist to precipitate changes that will improve asset turnover.

Dummy for utility firms (D^{utility}). A significant proportion of the crosslisted firms correspond to this economic sector (electricity and telecommunication firms).

Dummy for financial sector (DFinancial). Banks are believed to be important crosslisting targets.
The Pearson Correlation Coefficients across the previously indicated variables are presented in Table 2. The general picture is consistent with the idea that no strong correlations are observed across these variables.
Table 3 presents the average values of the firm characteristic variables that are included in the determination of probit regression of Heckmans procedure (Step 1). The information is subdivided across singly and crosslisted stocks. As expected, the firms with crosslisted shares are bigger and more profitable, if measured by the return of equity ratio.
Finally, to implement Step 1 of the Heckmans procedure, the following probit regression equation is estimated:
Regarding the implementation of the Heckmans procedure two final points must be noted. Firstly, as demonstrated by Heckman, including the Inverse Mills Ratio as an independent variable in subsequent regression estimations should control for any possible differences across singly and crosslisted stocks that may bias the results. In other words, including this ratio as one of the independent variables will control for the previously indicated differences in size and profitability across singly and crosslisted stocks. Secondly, in the paper the implementation of Heckmans procedure is neither directed toward examining any differences in the firms with singly and crosslisted shares nor in the characteristics of the firms that crosslist shares. Instead, this procedure is implemented to estimate a variable (Inverse Mills Ratio) that will be used to control for possible differences across the firms with singly and crosslisted stocks.
Table 4  Panel A reports equation (5) estimated coefficients after pooling all the information from the four exchanges included in the sample: Argentina, Brazil, Chile and México. The total number of firmyear information is 3,134, of which 765 correspond to firms with crosslisted shares. The Market Capitalization coefficient is significantly negative. The return on equity (ROE) coefficient is nonsignificant. The coefficients for the Leverage Ratio and Asset Turnover are significant and evidence that the firms financial risk and operational efficiency are taken into consideration to the define the possibility of ADRlisting15. The utility sector dummy coefficient (D^{utility}) is significant and negative. The coefficient for the financial sector dummy (D^{Financial}) is not significant. These results are included in equations 3 and 4 to determine the Inverse Mills Ratio of each firm.
Similarly, Table 4  Panel B reports equation (5) estimated coefficients for each of the four countries included in the sample. Even though most of these coefficients have the same sign and significance as the ones presented in Table 4 – Panel A, some differences can be appreciated. For example, for Chile and Brazil, the asset turnover and leverage ratio coefficients respectively are not statistically significant from zero.
Taking into consideration the country differences in the estimation of the probit regression (equation (5)) coefficients, Table 4 – Panel B coefficients will be used to estimate the Inverse Mills Ratio for each firm (Equations (3) and (4)). These ratios will be used in the regression analysis described in latter sections.
STOCK RETURNS
Research has established that there are significant differences in the pre and postcrosslisting excess returns of the ADRs underlying shares. Miller (1999) reports significant crosssectional differences across pre and postcrosslisting ADRstock returns; at the same time, he argues that these results are consistent with the idea that ADRlisting limits the negative effects of trading barriers, facilitates risk diversification and, consequently, reduces the investors required returns. Errunza and Miller (2000) report a significant decline in buyandhold ADRstock returns across the ADRs pre liberalization period (months 36 to 7 before crosslisting) and the post liberalization period (months +7 to +36).
Foester and Karolyi (1999) state that the reduction in the ADRs underlying share returns for the postcrosslisting period are explained by a decrease in the risk perceived by investors, as they have access to better information about the ADR issuer. These arguments are consistent with Mertons (1987) incomplete information asset pricing model, Amihud and Mendelsons (1986) liquidity analysis, and Kladec and McConnells (1994) examination of the reactions in the stock trading process to changes of trading venue.
Fernandes (2003) uses a sample of individual firms from 27 emerging markets to examine the spillover effects of the first ADRlisting. He finds a spillover effect (as predicted by Alexander et al. (1985) asset pricing model) that results in a decrease in the expected returns across all domesticallylisted stocks. Melvin and ValeroTonone (2003) report that rivals of an ADRissuing firm that list in the local market are negatively affected by crosslisting: there is a reduction in the rival firms excess return around the announcement and listing day16.
To implement the empirical tests, when necessary, the daily stock information is summarized into weekly periods17. For each week, the last available daily price is considered to be the endoftheweek closing price (CP). The weekly stock return (Ri_{,t}) for stock i in week t is defined as:
To estimate weekly stock excess returns (r_{i,t}), the US TBill (30 day maturity) return (Rf_{t}) is considered to be riskfree, such that:
The procedure used to calculate the endoftheweek index returns is similar to that used for stock returns.
The international asset pricing model (IAPM) implemented by Foerster and Karolyi (1999) is used to determine whether there are crosssectional differences across the pre and postcrosslisting weekly excess returns of the singly and crosslisted stocks.18 This model relates the excess returns on the stocks, domestic market exchange index, and world exchange index for the pre, during and postcrosslisting weeks, such that:
where r_{it} refers to the weekly excess returns of stock i in period t. The variables r_{kt}^{local} and r_{t}^{world} correspond to the weekly excess return of the k^{th} domestic stock exchange index (where stock i is listed) in period t and the world stock exchange index, respectively. D_{it}^{list} and D_{it}^{post} are dummy variables to control for the listing and postcrosslisting periods, respectively. λ_{i} is stock i average Inverse Mills ratio, and it is included to control for any possible problem related to nonrandom sample selection. To control for potential countrydifferences and time trends, the corresponding dummy variables are included. Additionally, a post1997 dummy variable is included to control for possible differences across the pre and postAsian crisis.19
As previously indicated, the examination of ADRlisting effects on singly and crosslisted stocks is done separately for each. For the crosslisted stocks, equation (8) estimates coefficients using 24 months of stock and exchange information around the ADRs crosslisting date.20 To examine the crosslisting spillover effects on singlylisted stocks, equation (8) estimates coefficients considering 24 months of information around the first three ADRlisting days.21 The statistical significance of α_{k}^{post}, α_{k}^{post}, β_{kd}^{post} and β_{kw}^{post} coefficients is used to examine the ADRlisting effects.
To examine whether the crosslisting effects spread uniformly to the singly and crosslisted stocks, all the information (of singly and crosslisted stocks) is pooled to estimate the IAPM coefficients (Equation (8)). In this case, the dummy variables D_{it}^{list} and D_{it}^{post} are equal to 1 for the ADRstocks in the crosslisting and postcrosslisting periods, respectively. Similarly, the statistical significance of α_{k}^{list}α_{k}^{post}, α_{k}^{post}α_{k}^{post}, β_{kd}^{post }and β_{kw}^{post} will provide evidence of the existence of the previously indicated differences.
Table 5 reports the equation (8) estimated coefficients for the crosslisted stocks traded in more that 30% of the available trading days. The reported regression coefficients correspond to five different combinations of the year, country and post1997 control variables. In all five regressions, the coefficients of the interactive terms22 are significant, have the expected sign, and are consistent with the idea that crosslisting determines an increase (decrease) in the importance of the world (domestic) stock exchange index to explain the ADRs underlying stock returns in the postcrosslisting period23.
Table 6 reports the equation (8) estimated coefficients for the singlylisted stocks traded in more that 30% of the available trading days. Dummy variables are included to control for possible country differences. In addition, dummy variables are included to control for possible differences across the first, second and third ADR listing effects. As all the first three ADRlistings occurred before 1997, the inclusion of this control variable is not relevant. Similarly, year dummies are not included as they are strongly correlated with the first, second and third ADRlisting dummy. The results highlight significant positive abnormal returns for the postcross listing period. In addition, the coefficients of the interactive terms emphasize that the singlylisted stocks become more isolated from world markets for the postcrosslisting period (i.e. the importance of the world index returns to explain the stock returns decreases for the postcrosslisting period)24.
Given the nature of the results (i.e. isolation of singlylisted stocks) it is important to examine the longrun persistence of these effects. To examine this possibility the following regression is estimated:
where r_{it} refers to the weekly excess returns for singlylisted stock i in period t. The variables r^{local} and r^{world} refer to the excess return in week t on the kth domestic stock exchange (were stock i is listed) and the world market portfolio, respectively. D^{y} is a dummy variable that takes a value of 1 for year y. λ_{k} is the average Inverse Mills ratio for each stock, and is included to control sample selection bias. The information corresponds to the stock and exchange information after the fourth, fifth and sixth ADR listing.
Table 7 reports equation (9) estimated coefficients considering the singlylisted stocks traded on more than 30% of the available trading days. The results indicate that the return on the local index, if compared with the 1993return on the world index, explains a larger portion of the singlylisted stock return. From 1995 to 2001, all the local index return coefficients are significant; for the same period only two world index return coefficients are significant. An FTest is implemented to determine if the local index and world index return coefficients were significantly different from zero. Although the results indicate that both sets of coefficients25 are significantly different from zero, the results for the local index return coefficients are much stronger. All of this evidence is consistent with the idea that in the longrun, a significant portion of the singlylisted returns can be explained by changes in the local index returns that provides evidence for a continuous isolation of this type of stock from global markets26.
Table 8 reports equation (8) estimated coefficients that correspond to the longrun differences in the returns of the singly and crosslisted stocks traded in more than 30% of the available trading days. The coefficients for five different regressions are reported and correspond to different combinations of the year, country and post1997 control variables. These results provide evidence of significant differences in the excess returns behavior of these two groups of securities. As expected, crosslisted stocks returns are larger (smaller) for the crosslisting (postcrosslisting) week if compared with singlylisted stock returns. The coefficient for the crosslisting (postcrosslisting) week dummy variable is significant and positive (negative); this is evidence that the crosslisted stock returns increase (decrease) during (after) this period. The interactive term Return on World Index * PostCrosslisting week dummy (significant and positive) provides evidence of a greater integration of crosslisted stocks with world financial markets as compared to singlylisted stocks. In a somewhat unexpected result, in all five regressions, the interactive term Return on Local Index *
Postcrosslisting dummy is significant and positive. This result could be related to a greater integration of the exchange with world markets. This possibility will be addressed in future research work27.
CONCLUSIONS
Overall, these results are consistent with the idea that ADRlisting significantly affects the returns of the domesticallylisted stocks. The evidence supports the assertion that cross listed stocks become more integrated with world markets. However, in contrast, there is a significant decrease in the importance of world market returns to explain the behavior of singlylisted stock returns. Consequently, as singly listed stocks become more isolated from world market, investors will demand a returnpremium to compensate for additional risk28. These results contradict those of Alexander et al. (1987), as they reveal that cross listing effects do not evenly spread to all domesticallylisted stocks29.
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Notas de pie
1 The underlying share refers to the ADRs share traded in the local (nonUS) exchange. For example, Teléfonos de México (Telmex) traded in the Mexican stock exchange.
2 For example, you may refer to Foerster and Karolyi, 1999; Miller, 1999; Lee, 2003; Fernandes, 2003; Karolyi, 2004.
3 The rationale behind this argument follows from Pulatkonak and Sofianos (1999). These authors find a strong relation between time zone differences (across domestic and US exchanges) and the strength of volume migration associated with stock crosslistings. Given the fact that the time zone difference across Latin American and US exchanges is, at the most, two hours, the crosslisting effects across these exchanges would tend to be similar.
4 The direction of these spillover effects are believed to be from the crosslisted to the singlylisted stocks.
5 Information from Colombia, Peru and Venezuela stock exchanges was collected. The small market capitalization and limited liquidity of these markets determined their exclusion from the sample.
6 This sample period is consistent with postliberalization periods included in Blair (2000) in all four countries.
7 The empirical implementation of the different tests is done considering 40% and 50% as benchmark. The final conclusions are not significantly affected.
8 A similar control was implemented by Bailey and Chung (1995).
9 DataStream provides the following definitions for each data item:

Closing price (CP): latest price available to us (Datastream) from the appropriate market in primary units of currency.

Traded volume (Vol): number of shares traded for a stock on a particular day. The figure is always expressed in thousands.

Market capitalization (MCap): Share price multiplied by the number of ordinary shares in issue displayed in millions of units of local currency.
10 Refer to Heckman (1979) and Puhani (2000).
11 This summary is from Heckman.
12 Return on equity = ROE = After tax net income / Shareholders equity
13 Leverage ratio = LR = Total liabilities / Total assets
14 Asset turnover = ATu = Total sales / Total assets.
15 The Pearson correlation coefficient across these two variables is small and significant.
16 Melvin and ValeroTonone argue that this situation may evidence that investors see rivals as less transparent, less informative and with poorer growth prospects relative to the listing firm. (Working Paper, Tempe, Arizona State University).
17 The IAPM coefficients were also estimated using monthly information. The statistical significance of the results was low. A possible explanation for this situation refers to the high price variability that is observed in these exchanges. Apparently, Foerster and Karolyi (1999) had a similar problem, as they used weekly information to estimate a similar IAPM.
18 Fernandes (2003) uses monthly information to implement a similar IAPM. Foerster and Karolyi (1999) use weekly returns.
19 Levine and Schumukler (2003) find evidence that the intensity of information flows across Asian and US exchanges increased after the 1997 Asian crisis. OHara (2001) considers that there are significant changes in the performance of Latin American exchanges after 1994.
20 12 months before and after the crosslisting week.
21 For the singlylisted stocks, additional dummy variables are included to control for the 2nd and 3rd ADR listing.
22 Return Domestic Exchange Index * Dummy for the postcrosslisting period, and Return World Exchange Index * Dummy for the postcrosslisting period.
23 Similar regressions considering the crosslisted stocks traded in more than 40% and 50% of the available trading days were estimated. The previously indicated conclusions are not affected by this sample change.
24 Similar regressions considering the singlylisted stocks traded on more than 40% and 50% of the available trading days were estimated. The conclusions previously indicated are not affected by this sample change.
25 Local index return coefficients and world index return coefficients.
26 Similar regressions considering the singlylisted stocks traded on more than 40% and 50% of the available trading days were estimated. The previously indicated conclusions are not significantly affected by this sample change.
27 Similar regressions considering all the domesticallylisted stocks traded on more than 40% and 50% of the available trading days were estimated. The conclusions previously indicated are not affected.
28 See Stulz (1981), Errunza and Losq (1985).
29 A possible explanation for these differences is that the assumptions behind Alexander et al. model are not satisfied in Latin American exchange markets. In particular, short sales and fixed exchange rates are not available in Latin American markets.