# ADR Effects on Domestic Latin American Financial Market. los efectos adr en los mercados domésticos financieros de Latinoamérica

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4 48 Journal of Economics, Finance and Administrative Science June 2010 a sample selection bias. To control for this possibility, the implement Heckman s procedure has been implemented. 10 λ i = φ (Z i ) 1 Φ (Z i ) (4) This sample selection problem can be summarized as follows 11 : Consider a random sample of I observations. For each observation i the following equations can be defined: Y 1i = X 1i β l + U 1i (1) Y 2i = X 2i β 2 + U 2i (2) 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 two-stage 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. Heckman s 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 cross-listed or singlylisted). The independent variables included in this regression represent the general characteristics of all the domestically-listed firms such as market capitalization, leverage ratio, asset turnover and return on equity. 2. Following Heckman s 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 ) (error-terms of equations 1 and 2) is bivariate normal, the following parameters (for each of the domesticallylisted stock) can be estimated: Zi = X 2i β 2 (3) σ 22 where λ i is known as the inverse of Mill s ratio. This ratio is a correction term that is used to control for the bias that arises from the non-random sample selection. As the probability of being in the sample (i.e. cross-listed share) increases, the cumulative density function approaches one and the probability density function approaches zero, so the Inverse Mill s ratio approaches zero. 3. For the estimation of equation 1 coefficients, the Inverse Mill s 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 Heckman s procedure. His results show that, in the absence of collinearity, a full information maximum likelihood estimator is preferable to the limited-information two-step method of Heckman If, however, collinearity problems prevail, subsample OLS (or the twopart model) is the most robust amongst the simple-to-calculate estimators (p. 54). As previously indicated, Heckman s procedure is a two-stage procedure. In this sub-section, the first step is implemented (i.e. the estimation of the Inverse Mill s 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 firm s size. Larger firms are believed to be the most important in their home countries and should tend to be crosslisting targets. 10 Refer to Heckman (1979) and Puhani (2000). 11 This summary is from Heckman.

5 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 49 Return on equity (ROE) as a profitability measure of a shareholder s investment 12. Profitable firms should tend to be cross-listing 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 firm s financial risk 13. The rationale is that a higher leverage ratio should lower cross-listing 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 firm s operational efficiency 14. The most efficient firms should tend to be cross-listing targets. Another (opposite) argument is that inefficient firms will cross-list to precipitate changes that will improve asset turnover. Dummy for utility firms (D utility ). A significant proportion of the cross-listed firms correspond to this economic sector (electricity and telecommunication firms). Dummy for financial sector (DFinancial). Banks are believed to be important cross-listing 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 Heckman s procedure (Step 1). The information is subdivided across singly- and crosslisted stocks. As expected, the firms with cross-listed shares are bigger and more profitable, if measured by the return of equity ratio. Finally, to implement Step 1 of the Heckman s procedure, the following probit regression equation 12 Return on equity = ROE = After tax net income / Shareholder s equity 13 Leverage ratio = LR = Total liabilities / Total assets 14 Asset turnover = ATu = Total sales / Total assets. is estimated: D i crosslisted = β 0 + β 1 Mkt_ Capitalization i + β 2 ROE i + β 3 Leverage_Ratio i + β 4 Asset_Turnover i + β 5 D i utility + β 6 D i financial + U 2i (5) where D i cross_listed is a dummy variable that takes a value of 1 (0) if firm i stock is cross- (singly-) listed. Regarding the implementation of the Heckman s 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 cross-listed 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 cross-listed stocks. Secondly, in the paper the implementation of Heckman s procedure is neither directed toward examining any differences in the firms with singly- and cross-listed shares nor in the characteristics of the firms that cross-list 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 cross-listed 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 firm-year information is 3,134, of which 765 correspond to firms with cross-listed shares. The Market Capitalization coefficient is significantly negative. The return on equity (ROE) coefficient is non-significant. The coefficients for the Leverage Ratio and Asset Turnover are significant and evidence that the firm s financial risk and operational efficiency are taken into consideration to the define the possibility of ADR-listing 15. The utility sector dummy 15 The Pearson correlation coefficient across these two variables is small and significant.

11 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 55 Smith, K., & Sofranos G. (1997). The Impact of an NYSE Listing on the Global Trading of Non-US Stocks. (Working Paper w97-02). New York: NYSE. Stultz, R. (1981). On the Effects of Barriers to International Investment. Journal of Finance, 36(4), Viswanathan, K. G. (1996). Listing in the U.S. Markets by Foreign Firms: Evidence on Return and Risk. Advances in International Banking and Finance, 2,

12 56 Journal of Economics, Finance and Administrative Science June 2010 Table 1. Number of Firm-Shares Proportion of trading days Country Total listed shares Cross-listed firm shares Singly-listed firm shares Total number of listed shares Proportion of Cross-listed firm shares Singly-listed firm shares All the sample 30% of available trading days 40% of available trading days 50% of available trading days Argentina % 9% 9% Brazil % 59% 59% Chile % 13% 26% México % 19% 6% Total % 100% 100% Argentina % 11% 14% Brazil % 55% 47% Chile % 16% 33% México % 18% 6% Total % 100% 100% Argentina % 11% 14% Brazil % 57% 48% Chile % 16% 32% México % 16% 6% Total % 100% 100% Argentina % 12% 13% Brazil % 57% 49% Chile % 16% 31% México % 15% 6% Total % 100% 100% Note. Number of listed firms in each of the seven Latin American exchanges included in the sample: Argentina, Brazil, Chile, Colombia, México, Peru and Venezuela. The proportion of trading days defines a benchmark to include firm shares in the sample. For example, a proportion equal to 30% means that the firm shares included in the sample were traded in 30% o more of the total available trading days. The proportion of listed stocks is equal to proportion (for each country) of the listed shares.

13 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 57 Table 2. Pearson Correlation Coefficients of the Firm-Characteristic Variables Included in Probit Regression of Heckman Procedure - Step 1 Market Capitalization Return on Equity Leverage Ratio Asset Turnover Utility Sector Dummy Return on Equity (0.3299) Leverage Ratio (0.4838) (0.3741) Asset Turnover (<0.0001) (0.0189) (0.0023) Utility Sector Dummy (0.0017) (0.6469) (0.3071) (<0.0001) Financial Sector Dummy (0.0002) (0.1450) (0.0011) (<0.0001) (<0.0001) Note. Market capitalization correspond to end-of-year stock price times the outstanding shares. Leverage ratio is equal to Total Liabilities divided by Total Assets. Return on Equity is equal to Net Income divided by Common Stock. Asset Turnover is equal to Total Sales divided by Total Assets. Utility Sector and Financial Sector dummies are included. All the information has been collected from DataStream Table 3. Average Values of the Firm Characteristic Variables Singly-listed stocks Cross-listed stocks Difference Number of observations 2, Market Capitalization Return on Equity Leverage Ratio Asset Turnover Stock Price * Outstanding shares Net Income / Common Stock Total Liabilities / Total Assets Total Sales / Total Assets Million dollars Yes (204.03) (224.33) % Yes (2.24) (63.29) No (0.55) (0.18) No (0.59) (0.37) Note. This table presents the average values of the market and accounting information included in the determination of the Inverse Mills Ratio (Heckman procedure - Step 1). The information is presented for the cross- and singly-listed stocks. Standard deviations are shown in parenthesis. The column labeled difference indicates the results of a t-test of the mean difference. The differences across the dummy variables are not included.

14 58 Journal of Economics, Finance and Administrative Science June 2010 Table 4. Heckman s Procedure: Probit Estimates (1st step) Panel (A): Probit Regression Results - All Information Variable Coefficient Pr > ChiSq Number of observations 3,134 Singly listed stocks 2,369 Cross listed stocks 765 Market capitalization MC < Return on equity ROE Leverage ratio LR Asset turnover ATu Dummy: Utility sector firm DuUtil < Dummy: Financial sector firm DuFin Note. The probit regression equation used is: D i cross_listed = β 0 + β 1 Mkt_Capitalization i + β 2 ROE i + β 3 Leverage_Ratio i + β 4 Asset_Turnover i + β 5 D utility i + β 6 Dfinancial i + U 2i D cross_listed i takes a value of 1 is firm i stock is cross-listed. ROE is firm i return on equity and is equal to Net Income (After Taxes) / Shareholders Equity. Leverage Ratio is equal to Total Liabilities / Total Assets. Asset Turnover is calculated as Total Sales / Total Assets. D Utility takes a value of 1 if firm i belongs to a utility sector. D Financial takes a value of 1 if the firm i belongs to the financial sector. The total number of firm-observations included is 3134, of which 2369 correspond to singly-listed firms and 765 to cross-listed firms. The estimated coefficients for the probit regression are as explained in the above table.

15 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 59 Table 4 (continued). Panel (B): Probit regression Results by Country Argentina Brazil Coefficient Pr > ChiSq Coefficient Pr > ChiSq Number of observations Singly listed stocks 194 1,431 Cross listed stocks Market capitalization MC < Return on equity ROE Leverage ratio LR Asset turnover ATu Dummy: Utility sector firm DuUtil < < Dummy: Financial sector firm DuFin < Chile Mexico Coefficient Pr > ChiSq Coefficient Pr > ChiSq Number of observations Singly listed stocks Cross listed stocks Market capitalization MC < Return on equity ROE < Leverage ratio LR < Asset turnover ATu Dummy: Utility sector firm DuUtil Dummy: Financial sector firm DuFin

16 60 Journal of Economics, Finance and Administrative Science June 2010 Table 5. Cross-Listed Stock Returns: Pre- and Post-Cross Listing Differences (I) (II) (III) (IV) (V) Intercept (0.18) (1.08) (0.30) (0.79) (0.93) Return - Local Index (47.50) (47.70) (47.69) (47.44) (47.65) Return - World Index (3.34) (3.15) (3.14) (3.32) (3.15) Cross listing week - Dummy variable (0.49) (0.48) (0.47) (0.48) (0.47) Post Cross listing week - Dummy variable Return Local Index * Post cross listing week dummy Return World Index * Post cross listing week dummy (0.01) (0.66) (0.63) (0.20) (0.59) (3.64) (3.67) (3.66) (3.62) (3.65) (2.37) (2.34) (2.33) (2.36) (2.32) Inverse Mills Ratio Yes Yes Yes Yes Yes Year dummies Yes Yes Country dummies Yes Yes Yes Pre 1997 dummy Yes Yes R-Square Note. The sample includes information from Argentina, Brazil, Chile and Mexico for the period January 01, 1992 to December 31, The stocks traded in less than 30% of the available trading days are not included in the sample. The reported coefficients correspond to the following International Asset Pricing Model (IAPM): r it = α k pre + β kd pre r kt + β kw pre r t world + α k list list + α k +β kd r kt + β kw r t world + β k λ k + ε it where r it refers to the weekly excess returns for stock i in period t. The variables r kt and r t world refer to the excess return in week t on the k-th domestic stock exchange (were stock i is listed) and the world market portfolio, respectively. list and are dummy variables to control for the cross-listing and -cross-listing weeks. λ k is the average Inverse Mills ratio for each stock, and is included to control sample selection bias. Following Foerster and Karolyi (1999), 24 months of information around the cross-listing event (12 months before and after the cross listing week) is used to estimate the IAPM coefficients. Dummy variables are included to control for possible country differences and time trends. Robust t-statistics are included in parenthesis.

17 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 61 Table 6. Singly-Listed Stock Returns: Pre- and Post-Cross Listing Differences (I) (II) Intercept (0.54) (0.40) Return - Local Index (25.72) (25.71) Return - World Index (0.35) (0.36) Cross-listing week - Dummy variable (0.20) (0.21) Post-Cross-listing week - Dummy variable (2.43) (2.47) Return - Local Index * Post-cross-listing week dummy (3.70) (3.71) Return - World Index * Post-cross-listing week dummy (2.56) (2.56) 2nd ADR listing dummy (0.42) 3rd ADR listing dummy (0.43) Inverse Mills Ratio Yes Yes Country dummies Yes Yes R-Square Note. The sample includes information from Argentina, Brazil, Chile and Mexico for the period January 01, 1992 to December 31, The stocks traded in less than 30% of the available trading days are not included in the sample. The reported coefficients correspond to the following International Asset Pricing Model (IAPM): r it = α k pre + β kd pre r kt + β kw pre r t world + α k list list + α k +β kd r kt + β kw r t world + β k λ k + ε it where r it refers to the weekly excess returns for stock i in period t. The variables r kt and r t world refer to the excess return in week t on the k-th domestic stock exchange (were stock i is listed) and the world market portfolio, respectively. list and are dummy variables to control for the cross-listing and -cross-listing weeks. λ k is the average Inverse Mills ratio for each stock, and is included to control sample selection bias. Consistent with the analysis done for the cross-listed stocks, the implementation of this regression includes 24 months of information around the first three ADR listing. Dummy variables are included to control for possible country differences and differences across the first, second and third ADR listing. Robust t-statistics are included in parenthesis.

18 62 Journal of Economics, Finance and Administrative Science June 2010 Table 7. Singly Listed Stock Returns: Persistence in the Pre- and Post-Cross Listing Differences 4th 5th 6th Intercept (1.67) (1.72) (1.79) Return - Local Index (29.01) (28.76) (28.75) Return - World Index (1.06) (1.04) (1.00) Return - Local Index * Dummy (1.80) (0.44) (0.35) Return - Local Index * Dummy (0.04) (0.11) (0.87) Return - Local Index * Dummy (5.47) (5.38) (5.34) Return - Local Index * Dummy (3.89) (3.82) (3.76) Return - Local Index * Dummy (4.44) (4.35) (4.31) Return - Local Index * Dummy (4.44) (4.93) (4.90) Return - Local Index * Dummy (3.11) (3.16) (3.26) Return - Local Index * Dummy (6.25) (6.16) (6.12) Return - Local Index * Dummy (4.60) (4.50) (4.46) Return - Local Index * Dummy (1.42) (1.34) (1.28) Note. The sample includes information from Argentina, Brazil, Chile and Mexico for the period January 01, 1992 to December 31, The stocks traded in less than 30% of the available trading days are not included in the sample. The reported coefficients correspond to the following regression equation: r it = α k + β kd r kt + β kw r t world + β k λ k + Σ β y kd r kt D y + Σ β y kw r world t D y + ε it y=1993 y=1993 where r it refers to the weekly excess returns for stock i in period t. The variables r kt and r t world refer to the excess return in week t on the k-th domestic stock exchange (were stock i is listed) and the world market portfolio, respectively. D yt is a dummy variables 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 4th, 5th and 6th ADR listing. Robust t-statistics are included in parenthesis.

19 Vol. 15 Nº 28 Mendiola: ADR effects on domestic Latin American financial markets 63 Table 7 (Continued) 4th 5th 6th Return - World Index * Dummy (0.43) (0.06) (0.09) Return - World Index * Dummy (1.67) (1.67) (1.10) Return - World Index * Dummy (2.85) (2.87) (2.85) Return - World Index * Dummy (1.52) (1.50) (1.49) Return - World Index * Dummy (0.78) (0.76) (0.74) Return - World Index * Dummy (1.04) (1.01) (1.00) Return - World Index * Dummy (1.18) (1.19) (1.23) Return - World Index * Dummy (0.07) (0.09) (0.13) Return - World Index * Dummy (2.08) (2.06) (2.02) Return - World Index * Dummy (1.11) (1.09) (1.05) Inverse Mills Ratio Yes Yes Yes Country dummies Yes Yes Yes F-Test: All dummies = F-Test: All world dummies = R-Square

20 64 Journal of Economics, Finance and Administrative Science June 2010 Table 8. Singly- and Cross-Listed Stock Returns: Long-Run Differences (I) (II) (III) (IV) (V) Intercept (2.68) (3.21) (6.35) (2.38) (3.81) Return - Local Index (147.53) (148.93) (148.86) (147.43) (148.75) Return - World Index (3.19) (4.12) (4.19) (3.17) (4.17) Cross-listing week - Dummy variable Post-Cross-listing week - Dummy variable Return - Local Index * Post-cross-listing week dummy Return - World Index * Post-cross-listing week dummy (2.35) (2.37) (2.39) (2.35) (2.40) (2.93) (17.89) (5.38) (3.85) (17.71) (5.62) (3.48) (17.71) (5.61) (2.63) (17.93) (5.37) (3.19) (17.74) (5.60) Inverse Mills Ratio Yes Yes Yes Yes Yes Year dummies Yes Yes Country dummies Yes Yes Yes Post-1997 dummy Yes Yes R-Square Note: The sample includes information from Argentina, Brazil, Chile and Mexico for the period January 01, 1992 to December 31, The stocks traded in less than 30% of the available trading days are not included in the sample. The reported coefficients correspond to the following International Asset Pricing Model (IAPM): r it = α k pre + β kd pre r kt + β kw pre r t world + α k list list + α k +β kd r kt + β kw r t world + β k λ k + ε it where r it refers to the weekly excess returns for stock i in period t. The variables r kt and r t world refer to the excess return in week t on the k-th domestic stock exchange (were stock i is listed) and the world market portfolio, respectively. list and are dummy variables to control for the cross-listing and -cross-listing weeks for the cross-listed stocks; for the singly-listed stocks this dummy variables will always be equal to zero. λ k is the average Inverse Mills ratio for each stock, and is included to control for any difference across singly- and cross-listed stocks. All the information of singly- and cross-listed stocks is pooled to estimate the regression coefficients. Additional dummy variables are included to control for possible country differences, time trends and events. Robust t-statistics are included in parenthesis.

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