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  • 1. Large Foreign Ownership and Stock Return Volatility in Emerging Markets1 Donghui Li Quang Ngoc Nguyen Peter Kien Pham School of Banking and Finance The University of New South Wales Sydney, NSW Australia and 2 Steven X. Wei School of Accounting and Finance Hong Kong Polytechnic University, Hung Hom, Kowloon Hong Kong This version: May 2006 1 We appreciate the helpful comments of Mike Firth, Toan Pham, Wilson Tong, and Yong Wang. Li, Nguyen and Pham acknowledge financial support from the University of New South Wales and Wei acknowledges financial support from the Hong Kong Polytechnic University Research Grant A- PG31. We bear responsibility for any mistakes and inaccuracies. 2 The corresponding author: Tel: (853) 27667056; fax: (852) 23309845. E-mail address: afweix@inet.polyu.edu.hk (Steven X. Wei).
  • 2. 2 Large Foreign Ownership and Stock Return Volatility in Emerging Markets Abstract This paper documents a robust negative relationship between large foreign ownership (LFO) and firm-level stock return volatility by using a unique data set from 32 emerging markets. We demonstrate that our large foreign ownership measure substantially differs from the investibility measure used in the literature, so that our major finding makes an incremental contribution to the literature. We interpret this finding along three directions: (1) LFO stabilizes stock return as it can be viewed as an approximation to foreign direct investment (FDI), confirming the prediction about FDI made by Stiglitz (1999, 2000 and 2004); (2) LFO plays a role on improving firms’ corporate governance. This role stabilizes firm’s performances so as to reduce stock return volatility. In addition, we find that this role strongly exists in good corporate governance environments but it disappears in bad ones; and (3) LFO implies a broadened investor base so as to lower stock return volatility as theoretically predicted from Merton’s (1987) model of capital market equilibrium with incomplete information. JEL classification: G15, O16 Keywords: large foreign ownership, stock return volatility
  • 3. 3 It has been well established that capital market liberalization delivers substantial benefits to emerging economies. Various international capital asset pricing models (Stapleton and Subrahmanyan (1977), Errunza and Losq (1985), Eun and Janakiramanan (1986), Alexander, Eun, and Janakiramanan (1987), and Stulz (1999a and 1999b)) and supporting empirical evidence (Chari and Henry (2004), Henry (2000 and 2003), Bekaert and Harvey (2000), Kim and Singal (2000)) show that stock market liberalization reduces the cost of equity capital by allowing for risk sharing between domestic and foreign agents. The financial development literature (Boyd and Smith (1996), Levine and Zervos (1996 and 1998), and Rajan and Zingales (1998)) demonstrates that a reduction in the cost of capital facilitates investments in the economy. Bekaert, Harvey, and Lundblad’s (2005) find significant increase in economic growth following equity market liberalizations. Milton (2006) finds that are open to foreign investors experience better operating performances in terms of growth, investment, profitability, and efficiency. However, past financial crises in emerging economies also initiated grave concerns about capital market liberalization. Foreign investors were widely blamed for the severity of capital flight out of crisis countries, which further amplifies stock return volatility and worsen the crises. On the one hand, event studies such as Kim and Singal (2000) and Bekaert and Harvey (1997 and 2000) document a decrease in the volatility of stock returns following capital market liberalizations. From a theoretical point of view, Merton (1987) implies that an increase in a stock’s investor-base would lead to a decrease in the stock return volatility. His theory is supported by empirical evidence from the Mexican stock market (Clark and Berko (1997)). On the other hand, Bae, Chan, and Ng (2004) find a positive relation between foreign investment restrictions (which reflects the extent or degree of liberalization) and stock return volatility. They argue that when stocks are highly accessible to foreign investors, the stock returns are subject to large world market exposure and therefore are vulnerable to world market risk. Stiglitz (1999, 2000 and 2004) further argues that premature financial market liberalization, which occurs without the support of well-functioning institutions and appropriate regulations, makes the liberalizing country vulnerable to financial crisis.
  • 4. 4 Most of previous studies of the impact of capital market liberalization on stock market volatility focus on the degree of regulatory restrictions on foreign investment. They ask what would happen when a country eases restrictions and allows foreign investors to enter the domestic stock market. The nature of capital flows (short-term versus long-term) and its impact on volatility in emerging capital markets remain not clearly understood. Although the economic development literature has extensively discussed the stability aspect of foreign investment and its effect on emerging economies (Stiglitz (1999, 2000 and 2004)), the capital market liberalization literature seems to ignore the important role of foreign investment stability on emerging markets. Further, investability, as a proxy for the extent of liberalization (Edison and Warnock (2002), Bae et al. (2004), and Chari and Henry (2004)), has its own problems. First, it fails to account for the fact that foreign investors might not invest up to the limit allowed by the regulation due to firms not belonging to the foreign investors’ areas of interests. Second, stocks that do not pass the size and liquidity screenings will be given a value of 0 for the investability measure by the Emerging Market Database (EMDB) even though there might be foreign investment in those stocks. In this study, we collect firm-level large foreign ownership (holding 5% or more of a firm’s share) data from the OSIRIS and Lexis/Nexis databases and merge it with the Emerging Market Database to form a unique annual large foreign ownership data set for 1485 firms from 32 developing countries. Using the country-effect model, we document a negative relation between stock return volatility and large foreign ownership after controlling for firm size, turnover, and industry. This negative relation is robust to using alternative measures of stock return volatility and large foreign ownership, and dealing with the endogeneity problem to some extent. Our study appears to be related to Bae, Chan, and Ng (2004). Looking at the cross- section of individual stock return volatility over the period January 1989 – September 2000, Bae et al. (2004) find a positive relation between return volatility and the investability of individual stocks. In detail, they classify stocks into three groups: non- investable (foreigners may not own any of the stock), partially investable (foreigners may own up to 50% of the stock) and highly investable (foreigners may own more than 50% of the stock). They find that stocks in the highly investable group exhibit higher return volatility than those in the non-investable group. This result leads us to
  • 5. 5 investigate the difference between the large foreign ownership variable used in our study and the investability variable used in Bae et al.’s. Our analysis indicates that investablity and large foreign ownership are two very different concepts: while investability indicates how much of a local firm foreigners can legally own and are subject to some screening criteria as defined by EMDB3, large foreign ownership measures the actual shareholdings of all large foreign investors in a local firm, regardless of the degree of investability of that firm4. The correlation between the two variables in our sample is close to zero. Therefore, our major finding makes an incremental contribution to the literature. We further analyse and interpret our major finding along three directions. First, we link our finding to Stiglitz’s (2000) foreign direct investment (FDI) story5. Though Stiglitz strongly criticizes short-run speculative foreign capital flows to emerging markets, he hypothesizes a stabilizing role of FDI in developing economies. Stiglitz argues that FDI provides resources, technology, and the training of human capital. All of these would increase a firm’s operating efficiency and reduce its total risk, resulting in lower stock return volatility. Using 10% as a cut-off point for foreign shareholding to construct a new large foreign ownership (LFO) variable, which at least resembles FDI, we find that so-defined LFO is still negatively related to stock return volatility, so as to confirm the prediction about FDI made by Stiglitz. This new LFO variable, mimicking FDI, has a strong correlation coefficient of 0.98 with our large foreign ownership defined in the beginning. In this sense, our large foreign ownership variable can be viewed as an approximation to FDI. This partially explains why large foreign ownership is negatively related to stock return volatility. 3 For stocks to be included in the investable series, not only must they be able to be legally held by foreigners, but they also have to meet size and liquidity screening criteria. The size criterion requires a stock to have a minimum investable market capitalisation of $50 million or more over the 12 months prior to the addition of the stock to the investable index. The investable market capitalization is determined after applying the foreign investment rules and after any adjustments due to cross-holdings or government ownership. The size criteria require that stock must have at least $20 million in trade over the prior year, and that it must be traded on at least half of the local exchange's trading days. Therefore, even when a stock can legally be held by foreigners, it will still be classified as non- investable according to the EMDB if it fails either the size or liquidity criteria. 4 The degree to which foreign investors could invest in a local firm may not accurately reflect the reality of foreign investment in that firm as foreign investors may not invest up to the limit legally allowed by local governments. This problem with investability is acknowledged by Bae et al. (2004). 5 The OECD (1999) defines a foreign direct investment as a foreign ownership of 10% or more of the ordinary shares or voting power of a local enterprise.
  • 6. 6 Second, our study can be explained by an improvement in corporate governance following participation by foreign investors. Stulz (2005) argues that foreign investors provide firms in emerging markets with the tools and incentives to improve corporate governance. In a similar line, Kelley and Woidtke (2006), Rossi and Volpin (2004) finds that investment in a firm by foreign investors leads to increased probability that good investor protection mechanisms be implemented in the firm. This could be the result of foreign investors’ demand for higher transparency, improved disclosure rules, accountability of management, and better shareholder rights (Kim and Singal (2000)). The improved corporate governance in a firm due to large foreign ownership should lead to a reduction in the firm’s return volatility. Further investigations of the role of large foreign ownership in different corporate governance environments produce very interesting results. Large foreign ownership is related to return volatility only in better corporate governance environments. There is no relation between large foreign ownership and stock return volatility in worse corporate governance environments. These findings hold for country-level as well as firm-level corporate governance. We interpret the results as evidence that the quality of corporate governance affects the foreign investors’ role in reducing stock return volatility. Foreign investors have more flexibility to improve a firm’s risk in better corporate governance environments, whereas their influence on the firm’s governance and operation is weak in bad corporate governance environments. Last, the negative relation between large foreign ownership and stock return volatility could be explained by Merton’s (1987) investor-base broadening hypothesis. His hypothesis implies that in a market with incomplete information, expanding investor base would lead to lower stock return volatility. Increasing large foreign ownership could be roughly understood as expanding the investor base of a firm. The advantage of using the foreign ownership variable is that it measures the actual presence of foreign investors in emerging markets. Most of previous papers, which investigate the openness of foreign investment regulation, could only measure the prospect of foreign presence in these countries. Therefore, our study contributes to the capital market liberalization literature by providing new evidence on the benefits of capital market liberalization. In addition, the differentiation between the “actual foreign presence” and the “prospect of foreign presence” helps solve the liberalization
  • 7. 7 debate by implying that liberalization is not the only (and effective) path to reduce the riskiness of firms in developing countries (and to ultimately promote economic growth). The evidence in our paper further shows that a relaxation in foreign investment restrictions does not automatically guarantee actual foreign capital flows into emerging markets. Policies or reforms designed to improve investor protection, enhance transparency, and better reporting regulations are needed as they provide the real incentives for foreign investors to invest in the emerging markets. Shleifer and Wolfenzon (2002) argue that countries with better investor protection have higher interest rates6 and are consequently more attractive to international capital flows. The outline of the paper is as follows. Section I describes the data and the summary statistics. Section II explores the relationship between foreign ownership and volatility. Section III compares the two variables of large foreign ownership and investability. Section IV provides the economic explanations of our major finding. Section V concludes the paper. I. Data and Descriptive Statistics A. Data sources and sample To measure the large foreign ownership variable, we collect firm-level ownership data from two main sources, the OSIRIS database provided by Bureau Van Dijk and Lexis/ Nexis (through which ownership data are obtained from Worldscope, Major Companies Database and Thompson Financial’s Extel Cards)7. From these sources, we obtain shareholder names and percentage shareholdings reported in the year 2002 for listed domestic firms in 33 developing countries, where detailed ownership data are available8. We measure large foreign ownership as the sum of foreign block holdings, where a block is defined as a holding larger than or equal to 5% of the 6 In their model, total output is determined by the production technology and by agency costs (the waste or fines resulting from diversion). Even though firms in different countries have access to the same production technology, they differ in the severity of agency costs. In countries with better investor protection, the agency problem is less severe, so the effective production technology (net of agency costs) is more efficient. Countries with better investor protection then have a higher marginal product of capital and consequently higher interest rates. 7 Multiple data sources allow outlying observations to be cross-checked. To further improve our coverage, we also obtain ownership data for many firms in Mexico, Malaysia, Pakistan, Sri Lanka, Singapore and Thailand directly from their annual reports, and for firms in India and Chile from their stock exchanges’ websites.
  • 8. 8 firm’s issued shares. In their paper, Li, Moshirian, Pham and Zein (2006) also use the same threshold to define a block holding. We later increase the threshold to 10% as a robustness check. The firm-level data, which are used to calculate stock return volatility, size, turnover, and investability, are from Standard & Poor’s Emerging Market Database (EMDB). The firms in analysis are the constituents of the EMDB’s S&P/IFCG indices, which are the core of Standard & Poor's family of emerging markets indices. These indices are intended to represent the performance of the most active stocks in their respective markets and to be the broadest possible indicator of market movements. In 2002, the aggregate market capitalization of S&P/IFCG index constituents ranged between 60% and 75% of the total capitalization of all domestic listed shares on the local stock exchange. The S&P/IFCG Composite Index included 33 emerging markets and covered 1941 firms. In their study of the relation between stock investability and stock return volatility, Bae et al. (2004) analysed firms across 33 emerging markets. Although the S&P/IFCG Composite Index in 2002 covered the same number of emerging markets, it did not include Greece and Portugal due to these countries’ graduation to the “developed economy” status9. Replacing Greece and Portugal in the S&P/IFCG Composite Index are Bahrain and Oman. The main reason for us to use the EMDB is that it has the investable weights for individual firms across emerging markets. One important finding of this paper is based on the analysis of the difference between large foreign ownership (and as extension, actual foreign ownership) and a stock’s investability. EMDB applies two tests to measure the investability of a stock. The first test of a stock’s investability is to determine whether the market is open to foreign institutions. It assesses the extent 8 During our data collection process, OSIRIS and Worldscope gradually updated their ownership database. As a result, some firms with no ownership data in 2002 were updated with such data in 2003. For the sake of providing better coverage, especially for countries with a small number of firms, we also include these firms in the sample. 9 Portugal was removed from EMDB in March, 1999 and Greece was removed from the database in May, 2001. EMDB specifies two criteria for a market to graduate from S&P/IFCG coverage: i) GNP per capita must be in the high income economy range for three consecutive years, and ii) the investable market capitalization-to-GDP ratio must be in the top 25% in the emerging markets universe for three consecutive years.
  • 9. 9 to which and the mechanisms foreign institutions can use to buy and sell shares on local exchanges and repatriate capital, capital gains, and dividend income without undue constraint. If foreign institutions can invest in the listed shares, then the second test is applied by determining whether there is any corporate by-law or corporate charter or industry limitation on foreign ownership of the stock. It then creates a variable called the “degree open factor” with value from zero to one that indicates the amount of the security foreigners may legally own (0.00 indicates that no share of the stock is legally available to foreigners). Rouwenhorst (1999) documents two problems associated with the EMDB. One problem is the survivorship bias as EMDB select stocks based on firm size and liquidity, which are probably correlated with the past performance of the companies. The other is data error due to the existence of unreasonable figures in the database. Bae et al. (2004) argue that for cross-sectional studies, the first problem is irrelevant because they do not seek to explain the performance of the companies over time. Neither do we in this paper. The second problem, however, is important as a few return outlier observations could significantly bias the volatility upward. We follow the procedure used in Bae et al.’s study to detect dubious return and volume observations. The basic idea behind Bae et al.’s procedure in identifying the extreme observations is that stock return and trading volume observations have to be compatible because it is well documented that stock return volatility is positively related to trading volume. For instance, if the absolute return of a stock in a month falls into the top 0.1% tail of the distributions of all firm-month return observations in a country, but the monthly turnover for the stock in that month is not in the top 10% tail of the distributions of all firm-month turnover observations in the country, we will cross-check the stock’s return in the month with the figure from Datastream. If the two deviate by more than 5% or if the stock data are not available from Datastream, the observation of the stock in that month will be discarded10. After deleting the outliers identified in the process described above, we merge the data from the EMDB with those from the OSIRIS database. The merged sample contains annual data for 2002 and covers 1485 firms across 32 emerging markets11. 10 For a detailed description of how the error-fixing process works, see Bae et al. (2004). 11 Saudi Arabia is missed out compared with the EMDB coverage.
  • 10. 10 B. Variables We discuss the variables to be used in the analysis of this paper in order. For the consideration of robustness, both the dependent variable, stock return volatility, and the major independent variable, large foreign ownership, are measured in two different ways. Control variables are also explained. Stock return volatility: We calculate the return volatility of individual stocks by using two alternative methods: the traditional standard deviation and the logarithm of squared returns as also used in Bae et al. (2004). • Method 1: Volatility is the sample standard deviation of monthly stock returns. 1 n 2 Volatility = å=1 ( Rt - R) n- 1 t where n is the number of monthly observations for each stock in 200212; Rt is the stock’s monthly return (after adjusting for dividends, stock-splits, etc.); and, R is the average of monthly stock returns over n months. • Method 2: Volatility is the logarithm of squared returns: 1 n Volatility = å ln ( Rt 2 ) n t =1 where n and Rt are defined as above. Large foreign ownership (LFO): We measure large foreign ownership as the sum of foreign block holdings, where a block is defined as a holding larger than or equal to 5% of the firm’s issued shares. The large foreign ownership varies from 0% to 100%. Large foreign ownership dummies: Stocks are classified into three groups by defining the following two large foreign ownership dummy variables. DZO is a dummy variable taking value 1 if LFO = 0 and taking value 0 otherwise. DPO is a dummy variable taking value 1 if 0 < LFO ≤ 50% and assuming value 0 otherwise. If both dummies take value 0, LFO for the corresponding observations are higher than 50%. Using 12 Normally, n equals to 12, which is the number of months in a year. However, for some stocks there are less than 12 monthly observations in a year due to delisting or late listing to the respective stock markets.
  • 11. 11 dummy variables is intended to captures a qualitative or non-linear relationship between large foreign ownership and stock return volatility. Size: As the market capitalizations of stocks in emerging markets are highly skewed, the logarithm of market capitalization is used to proxy for the size variable in regressions. 1 n Size = å ln ( mktcapt ) n t =1 where mktcapt is the stock’s monthly market capitalization. Turnover: The monthly turnover of a share is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. The turnover variable used in this paper is the average of monthly turnover: 1 n Number Of Shares Traded t Turnover = å n t =1 Shares Outstandingt - 1 Industry dummies: A more appropriate name should be Sector dummies as we use the 10 sectors in the Global Industry Classification System (GICS) to identify asset classes. These sectors are Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, and Utilities. There are nine industry dummies as one category of stocks is dropped out to avoid colinearity. The industry dummy variables are set to 1 if the observation belongs to the relevant category. Investability: The investability value of a stock is the average of the stock’s monthly investable weights, which range from 0 to 1: 1 n Investability = å Investable Weightt n t =1 Investability dummies: Stocks are classified into three groups based on their investability. Zero investability group includes stocks whose investability measures are zero. Partial investability group consists of stocks with investability being higher than 0 and lower or equal to 0.5. High investability group consists of stocks with
  • 12. 12 investability being higher than 0.5. There are two investability dummies as one category of stocks is dropped out to avoid colinearity. In this study, the high investability dummy is dropped out. The investability dummy variables are set to 1 if the observation belongs to the relevant category. C. Descriptive statistics Table 1 shows the summary statistics of the emerging stock markets covered in the sample. There are a total of 32 countries spanning across Asia, Europe, Latin America, Africa, and Middle East. The number of stocks in each country ranges from 9 in Slovakia to 189 in China. The volatility measure, the standard deviation of monthly stock returns in 2002, ranges from 4.95% in Morocco to 28.9% in Zimbabwe. The median market capitalization is lowest in Sri Lanka with US$ 12.62 million and highest in Russia with US$ 3,920.96 million. With the exception of Korea, Taiwan, and Turkey, all other emerging markets have median turnover of less than 10% per month. South Africa and Korea are the two countries with the highest average investable weight (0.79 and 0.73, respectively) whereas Bahrain, Colombia, Jordan, Nigeria, Oman, Pakistan, Slovakia, Sri Lanka, Venezuela, and Zimbabwe have an average investable weight of zero. It is noted that the statistics on the large foreign shareholding of domestic firms are quite different from those on the investability. The two countries with the highest average foreign shareholding are Slovakia (43.84%) and Argentina (40.21%) and none of the countries in the sample have the average foreign ownership of zero. II. Regression analysis This section documents a negative relationship between stock return volatility and large foreign ownership. To do so, we form two country-effects regression models: 9 Volatilityi , j = b LFOi , j + g Sizei , j + dTurnoveri , j + å k =1 t k Industryk ,i , j + u j + ei , j (1)
  • 13. 13 Volatilityi , j = b1 D ZO i , j + b 2 D PO i , j + g Sizei , j + dTurnoveri , j 9 (2) + å t k Industryk ,i , j + u j + ei , j k =1 where i represents stock i, j represents country j, and all the variables are as defined in part B of Section I. The reason for us to use the country-effect regression technique versus simple cross- section technique is that stocks in the same country are more homogeneous than stocks from different countries. This is because different countries have different sets of law and regulation, various stages of financial development, and different degrees of corporate governance and macro-economic policies, etc. In addition, Stulz (2005) argues that country attributes are critical to financial decision-makings because of the “twin agency problems”, which are different across countries. It is well-known that fixed effects (here, country-effects) models can deal with the heterogeneity issue well, as they still produce consistent estimates of the mode parameters even if there are latent and/or omitted country-related variables correlated with the explanatory variables of the models. A. Analysis using the standard deviation of return volatility In this section we run regressions (1) and (2) with the volatility calculated using Method 1 (the sample standard deviation of monthly stock returns). The results of regression (1) are reported in columns (1a), (2a), and those of regression (2) are in column (3a) of Table 2. When large foreign ownership is the only explanatory variable, the coefficient on the large foreign ownership variable is negative and significant at the 1% level (t-statistic of -3.65 in Column (1a)). With the inclusion of control variables such as size, turnover, and industry dummies, the negative relation between return volatility and large foreign ownership still exists but it weakens. The estimated coefficient on large foreign ownership is significant at 10% level (Column (2a)). In column (3a), where the large foreign ownership dummies are used, the results show that stocks belonging to the high – large foreign ownership group (i.e. large foreign ownership is greater than 50%) have lower volatility than those belong to the other two large foreign
  • 14. 14 ownership categories. The coefficient on the zero – large foreign ownership is positive and significant at the 5% level (t-statistic of 2.23), and the coefficient on the partial – large foreign ownership is positive and significant at the 10% level (t-statistic of 1.69). B. Analysis using the logarithm of squared returns Volatility measured by the standard deviation of monthly returns is highly skewed and has very high kurtosis. The skewness and kurtosis of the volatility measured in this way are 1.9764 and 10.1630, respectively. In this section, we estimate volatility by using the logarithm of squared monthly returns (Method 2). The distribution of volatility measured in this way is closer to normality with the skewness of -0.2871 and kurtosis of 3.8888. In their paper, Bae et al. (2004) also use the logarithm of squared returns as a measure of stock return volatility. The results of regression (1) are reported in columns (1b), (2b), and those of regression (2) are in column (3b) of Table 2. The results in columns (1b), (2b), and (3b) are quantitatively the same but are much stronger than those in columns (1a), (2a), and (3a) in terms of the significance level. For instance, column (2b) shows that the coefficient on large foreign ownership is negative and significant at the 1% level. The value and t-statistic of the coefficient are -0.0036 and -2.82, respectively. In column (2a), the value and t-statistic of the coefficient on large foreign ownership are -0.0001 and -1.69, respectively. C. Endogeneity C.1 Instrumental variables regressions As with studies on the effect of financial liberalization on economic growth, our study also faces the endogeneity issue. Is the investment decision by foreign investors exogenous or do they make investment in firms based on the firms’ corporate governance? These concerns are highly relevant because investment environment is an important factor in the investment decision-making process.
  • 15. 15 Addressing the endogeneity issue is difficult in our context as there is no prior theory or empirical evidence that could help us identify a suitable instrument for large foreign ownership. Therefore, we choose an ad-hoc approach, using the corporate governance factors that are highly correlated with the large foreign ownership variable. We employ the following corporate governance factors as instruments. The first corporate governance variable is the ownership of the largest domestic shareholders. These shareholders tend to be become controlling shareholders when their ownership in firms surpasses certain levels. La Porta et al. (2002) argue that controlling shareholders have the power to expropriate minority shareholders within the constraints imposed by the law. Facing with the possible expropriation by the largest domestic shareholder, foreign investors may choose to invest in firms where the largest domestic shareholder has weak power of expropriation. Dahlquist and Robertsson (2001) find that foreigners tend to underweight firms with a dominant owner. Initial analysis shows that large foreign ownership and the largest domestic shareholder’s ownership are highly negatively correlated (coefficient correlation of -0.4898). The second corporate governance variable is the type of the controlling shareholder, who is defined as the largest domestic shareholder among those with more than 20% ownership13. The type of the controlling shareholder variable is a dummy one, taking a value of 1 if the controlling shareholder is family or government, and 0 otherwise. This correlation between this variable and large foreign ownership is – 0.3964. The final corporate governance variable is the pyramidal structure of the controlling shareholder’s ownership. We call this variable pyramid dummy, which takes a value of 1 if the controlling shareholder owns shares through a pyramid and 0 otherwise. La Porta et al. (1999) and Wolfenzon (1998) find that controlling shareholders use pyramids to acquire power disproportionate to their cash flow rights. Therefore the pyramidal structure of the controlling shareholder’s ownership could be an important factor which foreign investors take into consideration before making an investment. The correlation efficient between pyramid dummy and large foreign ownership in our sample is -0.1488. We run six instrumental variable (IV) regressions of stock return volatility on large foreign ownership. With the first three IV regressions, the volatility is estimated by 13 La Porta et al. (1999) also uses the 20% cut off point. We follow La Porta et al.’s (2002, 1999) assumption of one controlling shareholder.
  • 16. 16 standard deviation of monthly returns. With the last three IV regressions, the volatility is estimated by logarithm of squared monthly returns. The results are reported in Table 3. Columns (1a) and (1b) of Table 3 shows the IV regression results when the instrument is largest domestic shareholder’s ownership. The coefficient on large foreign ownership in each column is statistically smaller than 0. The t-statistic for the coefficient is -2.29 in column (1a) and -2.36 in column (1b). Moving to the regression results in columns (2a) and (2b) where the instrument is type of the controlling shareholder, the relation between large foreign ownership and stock return volatility is also negative (t-statistic of -2.90 and -2.27, respectively). Columns (3a) and (3b) report the IV regression results when two instruments are used. The two instruments are type of the controlling shareholder and pyramid dummy. The reason we do not use pyramid dummy as the only instrument in our IV regressions is that pyramid dummy has very low correlation with large foreign ownership. This suggests the pyramidal structure of the controlling shareholder’s ownership is not a stand-alone factor in the foreign investors’ investment decision making process. La Porta et al. (1999) find that controlling shareholders in large corporations are usually State or families. These shareholders largely use the pyramidal ownership structure to gain control rights in firms in excess of their cash flow rights. A combination of the type of the controlling shareholder and the pyramidal property of the controlling shareholder’s ownership would strongly affect foreign investment in the firm. The results in columns (3a) and (3b) confirm the findings in columns (1a), (2a), (1b), and (2b). The t-statistic for the coefficient on large foreign ownership is -2.36 in column (3a) and -2.04 in column (3b), suggesting that the negative relation between large foreign ownership and stock return volatility is significant at 5% level. In summary, the consistent theme across all six columns in Table 3 is that large foreign ownership leads to a reduction in stock return volatility in emerging market. C.2 The relation between 2002 large foreign ownership and 2003 stock return volatility
  • 17. 17 As another attempt to deal with the endogeneity issue, we conduct a test of the relation between large foreign ownership and stock return volatility where LFO is the 2002 data and stock return volatility is calculated based on the 2003 data. This is an ad-hoc approach to solving the endogeneity issue. The intuition is that foreign investors as at 2002 could not observe the 2003 stock return volatility and thus did not make investment based on the 2003 stock return volatility14. Table 4 reports the regression results where all the variables are measured as described in part B of section I. The volatility, size, turnover, and industry dummies are the 2003 data, whereas large foreign ownership and the large foreign ownership dummies are the 2002 data. Columns (1a), (2a), and (3a) report the regression results where volatility is estimated by standard deviation of monthly returns. Columns (1b), (2b), and (3b) report the regression results where volatility is estimated by logarithm of squared monthly returns. In column (1a), the coefficient on large foreign ownership is -0.0004. Its t-statistic of -3.67 indicates that it is significant at 1% level. When size, turnover, and industry dummies are present, the coefficient on large foreign ownership is -0.0002 and significant at 5% level (t-statistic of -2.07, column (2a)). These results are similar to those in columns (1a) and (2a) of Table 2, except that the coefficient on large foreign ownership in column (2a) of Table 2 is significant at 10% level. Moving to column (3a) of Table 4, we find that the coefficient on non- large foreign ownership dummy is positive and significant at 10% level (t-statistic of 1.81), while the coefficient on partial-large foreign ownership is insignificant (t- statistic of 1.16). This is different from the result in column (3a) of Table 2, where the coefficient on partial-large foreign ownership is significant at 10% level (t-statistic of 1.69). The slightly quantitative difference between the results in column (3a) of Table 4 and column (3a) of Table 2 may be due to the excessive skewness of return volatility measured by standard deviation of monthly returns. When volatility is measured by logarithm of squared monthly returns, the coefficient on the partial-large foreign ownership dummy is positive and significant at 5% level. Its value and t-statistic are 0.2076 and 2.05, respectively (column (3b)). The coefficient on the non-large foreign 14
  • 18. 18 ownership is also positive and significant at 1% level. These results imply that stocks in the high-large foreign ownership group have lower volatility than those in the non- large foreign ownership or partial-large foreign ownership groups. This finding is consistent with that for the 2002 sample. The regression results when the continuous large foreign ownership variable is used also show support for the conclusion that large foreign ownership is negatively related to return volatility. The coefficient on large foreign ownership in both columns (1b) and (2b) is negative and significant at 1% level. The corresponding t-statistics are -4.74 and -3.23. In summary, Table 4 shows that the 2002 LFO is negatively related to the 2003 stock return volatility. This result strengthens the causal relationship between large foreign ownership and stock return volatility. III. The Comparison of Large Foreign Ownership and Investability A popular measure related to foreign investment in emerging markets is the investability of a stock (Bae et. al. (2004), Chari and Henry (2004), and Edison and Warnock (2002)). This measure indicates the percentage of a firm’s capitalization that is available to foreign investors. It is fair to ask whether our finding is driven by or related to investability. We address this issue in this section. Before we proceed, the major points and results are summarized below: • Large foreign ownership measures the actual presence of large foreign investors in a firm, while investability measures the degree the firm is accessible to foreign institutional investors. • Investability is not a good measure of accessibility to foreign investors. • Using annual data, investability is not related to stock return volatility. The first point is obvious from the definitions of large foreign ownership and investability. As defined in Part B of Section I, large foreign ownership is the sum of foreign block holdings, which have to be in existence for large foreign ownership to be higher than 0. On the other hand, investability is determined based on the limit on the amount of company capital foreign investors may hold, regardless of whether
  • 19. 19 foreign investors actually invest in the company. Thus, the investability of a stock could take a value of 1 (that is, the stock is fully investable) while there is no foreign investment in the stock. Panel B of Table 5 shows 225 cases where the investability of a stock is higher than 0.5, i.e. stocks are highly accessible to foreign investors, but its large foreign ownership is 0. Although large foreign ownership, which excludes foreign block shareholdings of less than 5%, does not exactly represent the actual total foreign ownership, Panel B gives an indication that in reality, there could exist stocks with high investability and zero foreign ownership. In other words, Panel B implies that easing foreign investment restrictions does not necessarily attract foreign investment in domestic firms. Panel A of Table 5 provides further evidence with only 16 out of 1485 stocks actually having more than 50% foreign ownership and high investability (that is, investable weights larger than 0.5). The second point is evidenced through Panel C of Table 5. There are a total of 55 stocks where large foreign ownership in each stock is higher than 50% but the investability for each stock is 0. There are two possible reasons for the mismatch between large foreign ownership and investability. Firstly, some stocks are currently owned by foreign investors however they do not pass the size and liquidity tests as specified by EMDB, they are assigned investable weights15 of 0 (see footnote 3). Secondly, some stocks are owned by a local firm, which is a wholly-owned subsidiary of a foreign entity. EMDB excludes the local firm’s ownership of those stocks in their calculation of the stocks’ investability, whereas our large foreign ownership variable regards the local firm as if it was a foreign firm, and accordingly, the firm’s ownership of those stocks as foreign ownership. In addition, investability does not exclude family or individual domestic block shareholdings. Failing to take into account these block shareholdings overestimates the investable market capitalization that is available to foreign investors. The third point comes from the regression results in Table 6. In columns (1a) and (2a) of Table 6, which correspond to regression (1) and where investability is treated as a continuous variable (see part II), the coefficients on investability in both columns are insignificant. The corresponding t-statistics are 0.36 and 0.76, respectively. When volatility is measured by logarithm of squared returns, the coefficient on investability 15 We use investability and investable weight interchangeably.
  • 20. 20 is also insignificant. Its t-statistic is 0.74 in column (1b) and 0.73 in column (2b). The regression results in columns (1a), (2a), (1b), and (2b) show that investability is not related to stock return volatility. Bae et al. (2004), however, find that investability is positively related to stock return volatility. In their paper, they do not use the continuous investability. Instead, they use three investability dummies, non-investability where investable weight is equal to 0, partial-investability where investable weight ranges from above 0 and up to 0.5 (inclusive), and high-investability where investable weight is higher than 0. To examine whether our findings in columns (1a) and (2a) are driven by the use of continuous investability, we also create three investability dummies based on Bae et al.’s methodology. The regression results are reported in column (3a) and (3b). Note that the high-investability dummy is dropped out to avoid linearity among the investability dummies. In column (3a), the coefficient on the non-investability dummy is -0.006. Its t-statistic is only -1.28, indicating that stocks in the high-investability and non-investability groups have similar return volatility. The coefficient on the partial-investability dummy and its t-statistic are -0.0048 and -1.05, respectively. The low absolute value of t-statistic shows that stocks in the high-investability and partial- investability groups have similar return volatility. Column (3b) reveals similar findings to those in column (3a). In summary, the results in column (3a) and (3b) imply that investability is not related to return volatility16. Overall, the results from Table 2 and Table 6 prove that large foreign ownership and investability are two different variables. The next issue is whether investability will take away all (or part) of large foreign ownership’s explanatory power of the variation in stock volatility if both variables appear in the same regressions. We study this issue by including both investability and foreign ownership as independent variables in regressions. The volatility measure used in the regressions is calculated using logarithm of monthly return17. 16 Using monthly data for the period Jan 1989 – Sep 2000, Bae et al. find a positive relation between investability and stock return volatility. This prompts us to test the investability – volatility relation using the monthly data from firms in our sample. The regression result confirms the positive relation between stock investability and stock return volatility. This, coupled with the results from Table 4, means that the relation between investability and stock volatility is significant at smaller interval (monthly) rather than larger interval (yearly). 17 Although we choose to demonstrate the regression results where the logarithm of squared monthly return is used as a measure of volatility, those regression results where the standard deviation of
  • 21. 21 Table 7 reports four different regressions with different combinations of large foreign ownership and investability variables. Looking across the first two columns (1) & (2) where large foreign ownership is a continuous variable, the coefficient on large foreign ownership is negative and significant at the 1% level. The t-statistic for the coefficient on large foreign ownership is -2.78 in column (1) and -2.80 in column (2). The values of the coefficient on large foreign ownership in those two columns are both -0.0036. This figure is the same as the coefficient on large foreign ownership in column (2b) of Table 2, suggesting that the investability measures, whether continuous or dummies, do not diminish the explanatory power of large foreign ownership. The results in columns (3) and (4) support the conclusion above. In these columns, we use large foreign ownership dummies instead of continuous large foreign ownership. The coefficient on non-large foreign ownership in column (3) is 0.3016 and significant at 1% level (t-statistic of 3.24). Its counterpart in column (4) has similar value (0.3005) and t-statistic (3.21). The coefficient on partial-large foreign ownership in column (3) is 0.2192 and significant at 5% level (t-statistic of 2.17). Its counterpart in column (4) also has similar value (0.2143) and t-statistic (2.11). Comparing the values of the coefficients on non-large foreign ownership and partial- large foreign ownership dummies in columns (3) and (4) of Table 7 with their counterparts in column (3b) of Table 2, we find that they almost the same. This suggests that the presence of investability measures in the regression models do not weaken the power of large foreign ownership in explaining stock return volatility. IV. Economic Explanations Given the negative relation between large foreign ownership and stock return volatility, it is important and interesting to ask why this should be the case. In this section, we analyse this issue along three directions. First, we interpret our finding as to be consistent with the foreign direct investment (FDI) story by Stiglitz (2000). Second, we investigate the influence of large foreign ownership on stock return volatility under different scenarios of corporate governance environments. Third, we monthly returns is used as a measure of volatility are quantitatively similar.
  • 22. 22 interpret the finding through Merton’s (1987) model of capital market equilibrium with incomplete information. However, we admit that there might be other possible explanations and likely we raise more questions than what we are trying to answer in this section. Consequently, we invite more future research on these issues. A. Link to Stiglitz’s (2000) FDI story A.1 Stiglitz’s FDI story The experience of the 1997 Asian financial crisis leads many people to blame foreign investors for the excessive volatility. Stiglitz (1998) says that “developing countries are more vulnerable to vacillations in the international flows than ever before”. Bae et al. (2004) provide supporting evidence, which is based on the measure of investability. The argument against foreign investment is that short-term capital flows, or “hot money”, come and goes quickly, generating large price fluctuations. However, Stiglitz (2000) makes a strong distinction between (short-term) speculative foreign capital flows and (long-run) foreign direct investment. An FDI represents a strategic investment by a foreign investor in a local firm and “brings with it not only resources, but technology, access to markets, and (hopefully) valuable training, an improvement in human capital” (Stiglitz (2000)). Stiglitz develops a hypothesis on the stabilizing role of the FDI. We first examine his hypothesis within our sample. A.2 Constructed FDI and stock return volatility To align with the discussion of FDI above, we change the cut-off point for a block shareholding from 5% to 10%. By this way, a block shareholding will resemble a foreign direct investment18. The large foreign ownership variable, under the new definition of block shareholding, is likely more stable than the old one as an FDI is renowned for its stability. With the new large foreign ownership variable, we repeat the country-effect regressions (1) and (2) and report the results in Table 8. It is clear to see that Table 8 18 We do not call a block shareholding defined in this way a foreign direct investment because in many cases, block shareholdings are owned by foreign portfolio managers. We notice, however, that standard definition of foreign direct investment implies a foreign block shareholding a foreign direct investment.
  • 23. 23 is almost a mirror of Table 2 with the coefficients and the relevant statistics in each column are similar to those in the counterpart column. In other words, Table 8 confirms the earlier results that large foreign ownership is negatively related to stock return volatility. B. Corporate governance role The results in section III are also consistent with Stulz’s (2005) theory of twin agency problems. Stulz argues that foreign investment will lower the agency costs of corporate insider discretion and those of state ruler discretion by providing the governance mechanisms and incentives for firms to improve their corporate governance19. In this section, we investigate how the role of foreign investors varies varies in different macro corporate governance environments. B.1 Firm-level governance environments We examine the role of large foreign ownership in three different scenarios. In the first scenario, we classify firms into two groups based on the presence of the controlling shareholders in the firms. One group includes firms with a controlling shareholder and the other consists of firms without a controlling shareholder. We then run the country-effect regressions of stock return volatility on large foreign ownership for each group. The regression results for the non-controlling shareholder group are reported in column (1a) of Panel A and Panel B (Table 9). The results for the other group are reported in column (1b) of these panels. Similarly, in the second scenario, we classify firms into two groups based on the pyramidal structure of the controlling shareholder’s ownership. If the controlling shareholder owns the shares through a pyramid, the firm is assigned to the pyramid group. All other firms, including those which do not have a controlling shareholder, 19 The first problem occurs when corporate insiders, or those who control firms, expropriate private benefits to maximize their own welfare at the expense of the firms’ investors. The second problem occurs when state rulers use the powers of the state to expropriate investors, such as confiscating the firms’ assets, changing regulations in favour of the constituents of the current rulers of the state, etc. In the environment of severe twin agency problems, ownership concentration becomes common to counteract the problems. Claessens, Djankov, and Lang (2000), Faccio and Lang (2002) show that outside the United States and United Kingdom, firms are typically controlled by large shareholders.
  • 24. 24 are assigned to the non-pyramid group. We then run the country-effect regressions of stock return on large foreign ownership for each group. The regression results for the non-pyramid group are reported in column (2a) of Panel A and Panel B. The results for the pyramid group are reported in column (2b) of these panels. In the final scenario, we classify firms into two groups based on the type of the controlling shareholder. Firms belong to the family group if the controlling shareholders are individuals, families or the State. All other firms belong to the non- family group. We then run the country-effect regressions of stock return on large foreign ownership for each group. The regression results for the non-family group are reported in column (3a) of Panel A and Panel B. The results for the family group are reported in column (3b) of these panels. Panel A shows that the coefficient on large foreign ownership is statistically indifferent from 0 across almost all groups. The exception is with the non-pyramid group, in which the coefficient on large foreign ownership is negative and significant at the 10% level (t-statistic of -1.80, column (2a)). However, moving to Panel B, where stock return volatility is estimated by logarithm of squared returns, we find that the coefficient on large foreign ownership is negative and significant for one group and insignificant for the other group in each scenario. The regression results for firms in the non-controlling shareholder group show that large foreign ownership is negatively related to stock return volatility, while the results for firms in the other group show no relation between those two variables. The t-statistic for the coefficient on large foreign ownership is -2.67 in column (1a) and 0.14 in column (1b). These results imply that when there is a presence of a controlling shareholder, the influence of foreign investment is weakened. Columns (2a) and (2b) show that large foreign ownership is negatively related to stock return volatility in the non-pyramid group, but not in the pyramid group. The regression for the non-pyramid group produces a large foreign ownership coefficient of -0.0043 and a corresponding t-statistic of -3.39. In contrast, the regression for the pyramid group returns a large foreign ownership coefficient of -0.0023 and a corresponding t-statistic of -0.22. These results indicate that for cases in which
  • 25. 25 controlling shareholders employ pyramids to gain control in firms, the influence of large foreign ownership is weakened. In the third scenario, in which stocks are sorted based on the type of the controlling shareholder, we find that large foreign ownership is negatively related to stock return volatility for the non-family group. The t-statistic of the coefficient on large foreign ownership is -2.22 (column (3a)). In contrast, we find no significant relation between large foreign ownership and return volatility for the family group. The relevant t- statistic is only -0.57 (column (3b)). These results suggest that for cases in which the controlling shareholder is individuals, families, or the State, the influence of large foreign ownership is weakened. The results in Panel B are consistent with findings in La Porta et al. (2002, 1999) and Wolfenzon (1999). These authors find that controlling shareholders have the power to expropriate minority shareholders. The power of expropriation is higher through the use of pyramids. Controlling shareholders are often families, who participate in the management of the firms they own and whose control of firms is unchallenged by other equity holders. We show that in those cases the role of large foreign ownership almost disappears. Why do Panel A and Panel B give different implications on the role of foreign investors in different governance environment? We argue that it is because volatility estimated using the standard deviation method is far from normally distributed. The skewness and kurtosis of the volatility measured in this way are 1.9764 and 10.1630, respectively. In contrast, the distribution of the volatility estimated using the logarithm method is closer to normality with a skewness of -0.2871 and kurtosis of 3.8888. B. Country-level governance environments In this section, we analyse four different country-level corporate scenarios based on four alternative measures of macro corporate governance environment. First, we use the Minority Rights index to measure both the existence and the degree of enforcement of shareholder rights. This index is formulated based on the survey of
  • 26. 26 world business leaders in the World 14 Economic Forum’s Global Competitiveness Report 2003. Second, we use the Financial Disclosure index to measure the ability to access sufficient, accurate, and timely corporate information by shareholders. This index is also from the survey data from the World Economic Forum’s Global Competitiveness Report 2003. Third, we use La Porta anti-director rights index to measure the degree to which a macro governance environment protects voting rights of minority shareholders and offers them avenues to challenge insiders in the corporate decision making process. This measure is labelled La Porta 2002 and taken from Pagano and Volpin (2005). Final, we use a law-origin indicator to differentiate between countries that have civil law origin and those that have common law origin. This indicator is denoted Common Law and taken from La Porta et al. (1998, 2000). With the first three governance indices, a higher score means better macro corporate governance environment. With the last index, the common law countries are considered to have better corporate governance environment than the civil law ones (La Porta et al. (2000)). In the first scenario, we assign countries to two corporate governance groups based on the median Minority Rights index. We then run country-effect regressions of stock return volatility on large foreign ownership for firms in the high Minority Rights group and for those in the low Minority Rights group separately. We repeat the same procedure with Financial Disclosure index, La Porta 2002 index, and Common Law indicator take turn to be the basis for division of countries into high versus low governance groups. The country-effect regression results are reported in Panel A and Panel B of Table 10. For each scenario in Panel A, the regression results consistently show that in better corporate governance environment, large foreign ownership is negatively related to stock return volatility. The t-statistics for the coefficient on large foreign ownership in high Minority Rights, high Financial Disclosure, high La Porta 2002, and Common Law group are -2.26, -2.35, -1.97, and -3.10, respectively. Nevertheless, in weaker corporate governance environment, large foreign ownership is not related to stock return volatility. The t-statistics for the coefficient on large foreign ownership in low Minority Rights, low Financial Disclosure, low La Parta 2002, and Civil Law group are -0.12, 0.47, 0.28, and 0.25, respectively.
  • 27. 27 The first three scenarios in Panel B show a similar picture to the four scenarios reported in Panel A quantitatively. Large foreign ownership is negatively related to stock return volatility in better corporate governance environment, but there is no relation between these two variables in weaker corporate governance environment. The fourth scenario exhibit an exception to the conclusion above. Not only large foreign ownership is negatively related to stock return volatility in Common Law countries, but also it is negatively related to stock return volatility in Civil Law countries. The t-statistic for the coefficient on large foreign ownership in the Civil Law group is -1.87, indicating the coefficient is statistically significant lower than 0 at the 10% level. As argued above, when the regression results are different due to alternative measures of stock return volatility, we will base our conclusion on the results where stock return volatility is estimated by logarithm of squared returns. In this section, we therefore use the results from Panel B to reach a conclusion on the relation between stock return volatility and large foreign ownership under different macro corporate governance scenarios. In summary, the role of foreign investors disappears in weak corporate governance environments, where the quality of corporate governance is measured based on Minority Rights, Financial Disclosure, and La Porta 2002 indices. Nevertheless, we find that the influence of foreign investors on firms’ risks exists in both common law and civil law countries. C. Relation to Merton’s (1987) Model The negative relation between large foreign ownership and stock return volatility is also consistent with Merton’s (1987) investor-base broadening hypothesis, which implies that a larger foreign investor base would lead to lower stock return volatility. Our findings are consistent with the prediction of Merton’s model if we measure the increase of investor base by the actual large foreign ownership.
  • 28. 28 V. Conclusion Past financial crises have raised concerns on the impact of capital market liberalization on the market volatility. Many studies have investigated this issue but do not reach a conclusion on what effect capital market liberalization might have on emerging market volatility. This paper studies the issue by looking at the role played by large foreign investors on stock return volatility. We find a negative relation between large foreign ownership and stock return volatility. The result is robust to alternative definitions of stock return volatility as well as alternative definition of large foreign ownership. We have three stories to understand our major finding. Firstly, in many cases, a large foreign ownership is a foreign direct investment (FDI). Stiglitz (2000) argues that coming with FDI are resources, technology, and valuable training of human capital. All of these would increase the firm’ operating efficiency and reduce its specific risk, resulting in lower stock return volatility. Secondly, foreign investors of large investment demand higher transparency, improved disclosure rules, accountability of management, and better shareholder rights (Kim and Singal (2000)). In addition, financial liberalization benefits the liberalizing countries by reducing the cost of the twin agency problems proposed by Stulz (2005). It provides the governance mechanisms and incentives for firms to improve their corporate governance. Finally, Merton’s (1987) model implies that a larger foreign investor base would lead to lower stock return volatility. Roughly speaking, our finding is consistent with the prediction of Merton’s model if we measure the increase of investor base by the actual large foreign ownership. The use of large foreign ownership in our paper helps address two problems that many of previous research face. First, they focus on regulatory barriers and ignore the nature (stability versus non-stability) of investment. Second, previous studies ignore the fact that opening markets is not sufficient for foreign investors to make investment in domestic stock markets. Our study therefore provides a new dimension of studying capital market liberalization.
  • 29. 29 The study also shows that large foreign ownership of a domestic firm is different from the degree of openness, or investability of that firm. Firms with high foreign ownership may have very low investable weights, while firms with high investable weights may have low foreign investment. This is due to two possible reasons. Firstly, there are problems with the investability measures, such as it does not reflect the true investability of a stock when the stock is small and illiquid. Secondly, foreign investors may not invest up to the legal limit in companies that are not their targets. Furthermore, our paper reveals the varying role of foreign investors in different corporate governance environments. We find that the role of foreign investors is strong in better corporate governance environments, whether at firm-level or country- level corporate governance, but it disappears in weaker corporate governance environments. Except for the case of civil law versus common law countries, the role of foreign investors exists in both groups of countries. The results in this paper have important implications for policy makers and international financial theorists alike. For policy makers, designing policies that could attract foreign investors, such as better regulations, more investor protection, more transparency, etc. is more important than just opening up the markets. For international financial theorists, foreign investment factor needs to be taken into account in their model of international investment and risk.
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  • 33. Table 1. Summary Statistics of the Foreign Ownership Sample In 2002 observations from the Standard and Poor’s Emerging Markets Database are merged with those from the OSIRIS database. Standard deviation, investable weight, and foreign ownership are the means of firms’ standard deviation, investable weight, and foreign ownership across all firms in each country. Size and turnover are the medians of firm size and turnover across all firms in each country. A firm’s standard deviation, investable weight, foreign ownership, size, and turnover are computed as followed. A firm’s standard deviation is the standard deviation of monthly U.S dollar stock returns. A firm’s size, turnover and investable weight are the (time series) averages of monthly market capitalization, monthly turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month, and investable weight, respectively. A firm’s foreign ownership is the annual percentage of block shareholdings by all foreign investors. Country No. of Standard Size Turnover Foreign Investable Stocks Deviation (mil. USD) (%) Ownership Weight (%) (%) Argentina 18 26.75 67.24 4.94 40.21 0.45 Bahrain 12 6.95 120.46 0.33 18.93 0.00 Brazil 50 19.92 258.64 2.44 19.44 0.54 Chile 37 8.17 515.64 0.70 21.38 0.43 China 189 9.72 358.46 6.40 1.77 0.18 Colombia 11 9.52 253.61 0.43 7.33 0.00 Czech Republic 15 12.43 132.98 0.10 30.06 0.17 Egypt 49 7.36 32.17 0.99 6.79 0.14 Hungary 17 9.77 122.43 3.22 35.03 0.50 India 119 12.83 172.79 4.31 12.18 0.16 Indonesia 54 17.50 54.29 1.62 16.65 0.19 Israel 43 12.43 275.37 2.93 7.47 0.54 Jordan 28 8.04 55.19 2.22 4.70 0.00 Korea 98 15.20 352.44 25.82 5.89 0.73 Malaysia 105 10.29 308.77 1.56 4.61 0.40 Mexico 49 11.81 578.34 1.40 13.37 0.55 Morocco 20 4.95 300.25 0.49 23.37 0.28 Nigeria 26 10.12 102.05 0.62 28.98 0.00 Oman 14 11.12 48.39 0.93 9.40 0.00 Pakistan 34 15.58 49.72 9.65 10.31 0.00 Peru 19 14.87 34.42 0.67 21.73 0.23 Philippines 53 14.22 114.02 0.88 7.37 0.09 Poland 25 12.57 224.20 2.19 36.83 0.46 Russia 14 12.59 3920.96 1.77 2.24 0.39 Slovakia 9 13.10 56.66 3.94 43.84 0.00 South Africa 56 13.60 493.77 4.00 8.25 0.79 Sri Lanka 41 11.87 12.62 1.83 19.47 0.00 Taiwan 91 16.02 928.80 19.84 2.63 0.43 Thailand 55 13.95 238.04 8.36 11.04 0.21 Turkey 22 19.12 43.70 13.41 20.94 0.33 Venezuela 11 16.94 88.01 0.36 11.03 0.00 Zimbabwe 20 28.90 130.20 2.01 5.05 0.00 Average 44 13.38 326.39 4.07 15.88 0.26
  • 34. Table 2. Country-effect Regressions of Volatility on Large Foreign Ownership Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of stock monthly returns over the 12 months in 2002. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2002. Large foreign ownership is sum of block shareholdings in a firm. Three large foreign ownership dummies are created: zero- large foreign ownership dummy takes a value of 1 if large foreign ownership is equal to 0% and 0 otherwise; partial-large foreign ownership dummy takes a value of 1 if large foreign ownership is higher than 0% but less than and up to 50% and 0 otherwise; high-large foreign ownership dummy takes a value of 1 if foreign ownership is higher than 50% and 0 otherwise. Only two large foreign ownership dummies are used in order to avoid the collinearity problem. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Wald test tests the null hypothesis that the coefficients on the zero-large foreign ownership and partial-large foreign ownership dummies are the same. Dependent variable Standard Deviation of Monthly Stock Returns Logarithm of squared returns (1a) (2a) (3a) (1b) (2b) (3b) Independent variables Constant 0.1304 0.1761 0.1645 3.6085 4.4570 4.1494 t-stat 80.76 27.64 19.92 121.73 38.06 27.52 Large foreign ownership -0.0003 -0.0001 -0.0061 -0.0036 t-stat -3.65 -1.69 -4.63 -2.82 Large foreign ownership dummies Foreign ownership = 0% 0.0114 0.3041 t-stat 2.23 3.27 0% < Foreign ownership <= 50% 0.0093 0.2273 t-stat 1.69 2.27 Size -0.0082 -0.0081 -0.1488 -0.1472 t-stat -7.78 -7.69 -7.65 -7.55 Turnover 0.0234 0.0233 0.4913 0.4876 t-stat 4.31 4.29 4.81 4.78 Industry dummies Yes Yes Yes Yes Wald test 0.3700 0.4500 p-value 0.54 0.23 R-squared 0.0097 0.0937 0.0951 0.0146 0.1074 0.1093
  • 35. 35 Table 3. Instrumental variable regressions of volatility on large foreign ownership Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of monthly stock returns over the 12 months in 2002. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2002. Large foreign ownership is the sum of block shareholdings in a firm. Three instruments for the foreign ownership are Largest domestic shareholder’s ownership, Controlling shareholder type, and Pyramid dummy. Largest domestic shareholder’s ownership is the ownership of the largest shareholders in firms (excluding foreign shareholders). Controlling shareholder type is a dummy variable. It takes a value of 1 if the controlling shareholder is family or government, and takes a value of 0 otherwise. The controlling shareholder is defined as the largest domestic shareholder among those with more than 20% ownership of a firm. Pyramid dummy takes the value of 1 if the controlling shareholder owns shares through a pyramid, and 0 otherwise. Columns (1a) and (1b): Instruments are Largest domestic shareholder’s ownership, Size, Turnover, and Industry dummies Columns (2a) and (2b): Instruments are Controlling shareholder type, Size, Turnover, and Industry dummies Columns (3a) and (3b): Instruments are Controlling shareholder type, Pyramid dummy, Size, Turnover, and Industry dummies Dependent variable Standard Deviation of Monthly Returns Logarithm of squared monthly returns (1a) (2a) (3a) (1b) (2b) (3b) Independent variables Constant 0.1713 0.1714 0.1715 4.4566 4.4603 4.4602 t-stat 26.84 26.59 26.66 38.05 37.99 38.03 Large foreign ownership -0.0003 -0.0005 -0.0005 -0.0054 -0.0070 -0.0062 t-stat -2.29 -2.90 -2.69 -2.36 -2.27 -2.04 Size -0.0079 -0.0075 -0.0076 -0.1452 -0.1425 -0.1440 t-stat -7.38 -6.76 -6.87 -7.33 -7.04 -7.13 Turnover 0.0229 0.0225 0.0226 0.4857 0.4825 0.4838 t-stat 4.20 4.08 4.11 4.75 4.71 4.72 Industry dummies Yes Yes Yes Yes Yes Yes R-squared 0.0909 0.0748 0.0792 0.1061 0.1033 0.1052
  • 36. 36 Table 4. Country-effect Regressions of Volatility on Large Foreign Ownership – 2003 volatility Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of monthly stock returns over the 12 months in 2003. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2003. Large foreign ownership is the sum of block shareholdings in a firm. Three large foreign ownership dummies are created (but only two are used in order to avoid the collinearity problem): zero-foreign ownership dummy takes a value of 1 if foreign ownership is equal to 0% and 0 otherwise; partial- foreign ownership dummy takes a value of 1 if foreign ownership is higher than 0% but less than and up to 50% and 0 otherwise; high foreign ownership dummy takes a value of 1 if foreign ownership is higher than 50% and 0 otherwise. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time- series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Wald test tests the null hypothesis that the coefficients on the zero foreign ownership and partial foreign ownership dummies are the same. Dependent variable Standard Deviation of Monthly Stock Returns Logarithm of squared returns (1a) (2a) (3a) (1b) (2b) (3b) Independent variables Constant 0.1331 0.2156 0.2009 3.6115 4.4407 4.1481 t-stat 51.83 19.10 14.45 119.33 33.52 25.48 Large foreign ownership -0.0004 -0.0002 -0.0065 -0.0043 t-stat -3.67 -2.07 -4.74 -3.23 Large foreign ownership dummies Foreign ownership = 0% 0.0146 0.2903 t-stat 1.81 3.06 0% < Foreign ownership <= 50% 0.0100 0.2076 t-stat 1.16 2.05 Size -0.0165 -0.0165 -0.1724 -0.1722 t-stat -9.28 -9.27 -8.28 -8.25 Turnover 0.0456 0.0455 0.8171 0.8161 t-stat 3.97 3.96 6.04 6.03 Industry dummies Yes Yes Yes Yes Wald test 0.7400 0.6900 p-value 0.39 0.19 R-squared 0.0105 0.1177 0.1172 0.0173 0.1287 0.1282
  • 37. 37 Table 5. Stocks and their countries of origins in different combinations of investability and foreign ownership High investability group is the group of stocks where the stocks’ investable weights > 0.5. Partial investability group is the group of stocks where the stocks’ investable weights are higher than 0 but less than or equal to 0.5. Non investability group is the group of stocks where the stocks’ investable weights equal to 0. High foreign ownership group is the group of stocks where the stocks’ large foreign ownership is higher than 50%. Partial foreign ownership group is the group of stocks where the stocks’ large foreign ownership is higher than 0% but less than or equal to 50%. Zero foreign ownership group is the group of stocks where the stocks’ large foreign ownership equal to 0. Panel A Panel B Panel C High investability and high foreign ownership High investability but zero foreign ownership Zero investability but high foreign ownership Markets No. of Pct Markets No. of Pct Markets No. of Pct Stocks Stocks Stocks Argentina 1 6.25 Argentina 2 0.89 Argentina 4 7.27 Brazil 5 31.25 Brazil 19 8.44 Chile 2 3.64 Chile 1 6.25 Chile 8 3.56 Czech Republic 1 1.82 Egypt 1 6.25 China 21 9.33 Egypt 1 1.82 Hungary 1 6.25 Egypt 2 0.89 Hungary 3 5.45 Indonesia 1 6.25 Hungary 4 1.78 India 4 7.27 Mexico 4 25 India 1 0.44 Indonesia 4 7.27 Poland 2 12.5 Indonesia 3 1.33 Morocco 2 3.64 Total 16 100 Israel 14 6.22 Nigeria 10 18.18 Korea 51 22.67 Oman 1 1.82 Malaysia 35 15.56 Pakistan 4 7.27 Mexico 14 6.22 Peru 3 5.45 Morocco 2 0.89 Philippines 1 1.82 Peru 2 0.89 Slovakia 3 5.45 Poland 3 1.33 Sri Lanka 9 16.36 Russia 5 2.22 Thailand 1 1.82 South Africa 37 16.44 Turkey 1 1.82 Turkey 2 0.89 Venezuela 1 1.82 Total 225 100 Total 55 100
  • 38. 38 Table 6. Country-effect Regressions of Volatility on Investability Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of monthly stock returns over the 12 months in 2002. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2002. Investability is the legal limit on foreign investment in a domestic firm and is reported by EMDB. Three investability dummies are created (but only two are used in order to avoid the collinearity problem): non-investability dummy takes a value of 1 if investability is equal to 0 and 0 otherwise; partial-investability dummy takes a value of 1 if investability is higher than 0 but less than and up to 0.5 and 0 otherwise; high investability dummy takes a value of 1 if investability is higher than 0.5 and 0 otherwise. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Wald test tests the null hypothesis that the coefficients on the non investability and partial investability dummies are the same. Dependent variable Standard Deviation of Monthly Stock Returns Logarithm of squared returns (1a) (2a) (3a) (1b) (2b) (3b) Independent variables Constant 0.1268 0.1753 0.1806 3.5188 4.4438 4.5469 t-stat 57.27 27.18 23.97 88.38 37.46 32.89 Investability 0.0020 0.0041 0.0748 0.0705 t-stat 0.36 0.76 0.74 0.73 Investability dummies Investability = 0 -0.0060 -0.1108 t-stat -1.28 -1.32 0 < Investability <= 0.5 -0.0048 -0.0642 t-stat -1.05 -0.77 Size -0.0085 -0.0085 -0.1570 -0.1592 t-stat -8.09 -7.50 -8.06 -7.61 Turnover 0.0234 0.0233 0.4921 0.4900 t-stat 4.29 4.29 4.80 4.78 Industry dummies Yes Yes Yes Yes Wald test 0.0700 0.3100 p-value 0.79 0.58 R-squared 0.0001 0.0922 0.0930 0.0004 0.1028 0.1036
  • 39. Table 7. Country-effect Regressions of Volatility on Large foreign ownership and Investability Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of monthly stock returns over the 12 months in 2002. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2002. Large foreign ownership is the sum of block shareholdings in a firm. Investability is the legal limit on foreign investment in a domestic firm and is reported by EMDB. Three large foreign ownership dummies are created (but only two are used in order to avoid the collinearity problem): zero-foreign ownership dummy takes a value of 1 if foreign ownership is equal to 0% and 0 otherwise; partial-foreign ownership dummy takes a value of 1 if foreign ownership is higher than 0% but less than and up to 50% and 0 otherwise; high foreign ownership dummy takes a value of 1 if foreign ownership is higher than 50% and 0 otherwise. Similarly, three investability dummies are created (but only two are used in order to avoid the collinearity problem): non-investability dummy takes a value of 1 if investability is equal to 0 and 0 otherwise; partial-investability dummy takes a value of 1 if investability is higher than 0 but less than and up to 0.5 and 0 otherwise; high investability dummy takes a value of 1 if investability is higher than 0.5 and 0 otherwise. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Wald test tests the null hypothesis that the coefficients on the two relevant dummies are the same. Dependent variable Logarithm of squared returns (1a) (2a) (3a) (4a) Independent variables Constant 4.5468 4.4460 4.1413 4.2458 t-stat 32.96 37.57 27.37 25.07 Large foreign ownership -0.0036 -0.0036 t-stat -2.78 -2.80 Investability 0.0625 0.0652 t-stat 0.65 0.67 Large foreign ownership dummies Foreign ownership = 0% 0.3016 0.3005 t-stat 3.24 3.21 0% < Foreign ownership <= 50% 0.2192 0.2143 t-stat 2.17 2.11 Investability dummies Investability = 0 -0.1036 -0.1046 t-stat -1.23 -1.23 0 < Investability <= 0.5 -0.0408 -0.0376 t-stat -0.49 -0.44 Size -0.1541 -0.1502 -0.1485 -0.1527 t-stat -7.36 -7.67 -7.58 -7.29 Turnover 0.4840 0.4869 0.4827 0.4795 t-stat 4.73 4.76 4.72 4.69 Industry dummies Wald test of the coefficients on the foreign 1.7900 ownership dummies being equal p-stat 0.18 Wald test of the coefficients on the 0.5600 1.6300 0.6400 investability dummies being equal p-stat 0.45 0.20 0.42 R-squared 0.1084 0.1077 0.1095 0.1102
  • 40. Table 8. Country-effect Regressions of Volatility on Large Foreign Ownership: Alternative definition of large foreign ownership Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. In Regressions 1a, 2a, and 3a, volatility is the standard deviation of stock monthly returns over the 12 months in 2002. In Regression 1b, 2b, and 3b, volatility is the average of logarithm of squared monthly returns in 2002. Large foreign ownership is sum of block shareholdings in a firm, with a block now defined as an ownership of 10% or more. Three large foreign ownership dummies are created: zero-large foreign ownership dummy takes a value of 1 if large foreign ownership is equal to 0% and 0 otherwise; partial-large foreign ownership dummy takes a value of 1 if large foreign ownership is higher than 0% but less than and up to 50% and 0 otherwise; high-large foreign ownership dummy takes a value of 1 if foreign ownership is higher than 50% and 0 otherwise. Only two large foreign ownership dummies are used in order to avoid the collinearity problem. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Wald test tests the null hypothesis that the coefficients on the zero-large foreign ownership and partial-large foreign ownership dummies are the same. Dependent variable Standard Deviation of Monthly Returns Logarithm of squared monthly returns (1a) (2a) (3a) (1b) (2b) (3b) Independent variables Constant 0.1301 0.1761 0.1604 3.6031 4.4566 4.1667 t-stat 82.36 27.65 19.39 124.08 38.06 27.59 Large foreign ownership -0.0003 -0.0001 -0.0062 -0.0038 t-stat -3.70 -1.81 -4.67 -2.90 Large foreign ownership dummies Foreign ownership = 0% 0.0107 0.2884 t-stat 2.08 3.08 0% < Foreign ownership <= 50% 0.0105 0.2138 t-stat 1.77 1.97 Size -0.0082 -0.0082 -0.1492 -0.1489 t-stat -7.80 -7.80 -7.68 -7.67 Turnover 0.0234 0.0234 0.4908 0.4888 t-stat 4.31 4.31 4.81 4.79 Industry dummies Yes Yes Yes Yes Wald test 0.0000 0.9800 p-value 0.96 0.32 R-squared 0.0100 0.0940 0.0948 0.0148 0.1077 0.1085
  • 41. 41 Table 9. The varying role of foreign shareholders under different micro corporate governance environments Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. The table reports the country-effect regressions of volatility on large foreign ownership and controlled variables. We analyse three different scenarios. First, firms are classified into two groups based on the presence of controlling shareholders. Second, firms are classified into two groups based on the pyramidal structure of the controlling shareholder’s ownership. Third, firms are classified into two groups based on the type of controlling shareholders. In particular: Column (1a): The controlling shareholder is not present Column (1b): The controlling shareholder is present Column (2a): There is no pyramid ownership structure Column (2b): There is a pyramid ownership structure Column (3a): The controlling shareholder is not present or is a widely held corporation Column (3b): The controlling shareholder is family/individual or government Controlling shareholder is the largest domestic shareholder with an ownership of more than 20% of the firm’s equity. Pyramidal structure of the controlling shareholder’s ownership means indirect ownership obtained through one or many third parties. Large foreign ownership is sum of block shareholdings in a firm. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Panel A. Dependent variable Standard deviation of monthly returns (1a) (1b) (2a) (2b) (3a) (3b) Independent variables Constant 0.1826 0.1571 0.1732 0.1597 0.1677 0.1721 t-stat 18.16 18.87 25.27 8.33 17.99 19.08 Large foreign ownership -0.0002 0.0000 -0.0001 -0.0002 0.0000 -0.0001 t-stat -1.53 0.13 -1.80 -0.35 -0.41 -0.50 Size -0.0090 -0.0072 -0.0084 -0.0085 -0.0074 -0.0092 t-stat -5.41 -5.22 -7.31 -2.78 -4.82 -6.12 Turnover 0.0190 0.0732 0.0216 0.1820 0.0204 0.0357 t-stat 3.04 4.97 3.91 3.16 3.18 3.11 Industry dummies Yes Yes Yes Yes Yes Yes R-squared 0.1310 0.0956 0.0986 0.1481 0.0957 0.0961
  • 42. 42 Panel B. Dependent variable Logarithm of squared monthly returns (1a) (1b) (2a) (2b) (3a) (3b) Independent variables Constant 4.6644 4.2797 4.4154 4.2571 4.6246 4.2754 t-stat 25.01 27.96 37.62 12.93 26.99 26.10 Large foreign ownership -0.0049 0.0007 -0.0043 -0.0023 -0.0037 -0.0031 t-stat -2.67 0.14 -3.39 -0.22 -2.22 -0.57 Size -0.1540 -0.1474 -0.1341 -0.1588 -0.1547 -0.1346 t-stat -4.96 -5.79 -6.84 -3.03 -5.39 -4.95 Turnover 0.3677 1.5582 0.4385 3.5706 0.4173 0.7375 t-stat 3.09 5.72 4.63 3.61 3.43 3.50 Industry dummies Yes Yes Yes Yes Yes Yes R-squared 0.1412 0.1189 0.1283 0.1753 0.1345 0.0868
  • 43. 43 Table 10. The varying role of foreign shareholders under different macro corporate governance environments Data are from the Foreign Ownership database and in the panel data format: one dimension is across countries and the other across individual stocks within each country. Panel A reports the country-effect regressions where the dependent variable is measured by the standard deviation of monthly returns. Panel B reports the country-effect regressions where the dependent variable is measured by the logarithm of squared monthly returns. Minority rights, financial disclosure, and LaPorta 2002 are the corporate governance scores. The sample is divided into high versus low corporate governance score groups based on the median value for each score. In the common law column, yes refers to a group of countries which have a common law system and no refers to the other countries. Large foreign ownership is sum of block shareholdings in a firm. Size is the (time-series) average of monthly market capitalization for each firm. Turnover is the (time-series) average of monthly stock turnover, which is the number of shares traded in a month divided by the number of shares outstanding at the beginning of the month. Industries are classified based on the GICS methodology. Panel A. Dependent variable Standard deviation of monthly returns Minority Rights Financial disclosure LaPorta 2002 Common Law High Low High Low High Low Yes No Independent variables Constant 0.1673 0.1751 0.1792 0.1523 0.1769 0.1158 0.1833 0.1643 t-stat 19.89 17.67 20.84 15.98 25.98 6.12 17.51 20.39 Large foreign ownership -0.0002 0.0000 -0.0002 0.0000 -0.0002 0.0000 -0.0004 0.0000 t-stat -2.26 -0.12 -2.35 0.47 -1.97 0.28 -3.10 0.25 Size -0.0088 -0.0071 -0.0085 -0.0077 -0.0090 -0.0006 -0.0088 -0.0080 t-stat -6.51 -4.12 -6.22 -4.63 -7.99 -0.19 -4.90 -6.09 Turnover 0.0362 0.0131 0.0224 0.1151 0.0222 0.0870 0.0248 0.0223 t-stat 4.53 1.76 3.92 3.48 4.02 2.70 2.87 3.16 Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes R-squared 0.1127 0.0922 0.1087 0.1046 0.1074 0.1057 0.1512 0.0795
  • 44. 44 Panel B. Dependent variable Logarithm of squared monthly returns Minority Rights Financial disclosure LaPorta2002 Common Law High Low High Low High Low Yes No Independent variables Constant 4.2920 4.6876 4.5374 4.2015 4.5008 4.0001 4.5260 4.4356 t-stat 26.72 27.30 28.22 24.94 35.35 13.14 21.98 31.23 Large foreign ownership -0.0047 -0.0015 -0.0047 -0.0014 -0.0039 -0.0011 -0.0054 -0.0029 t-stat -2.86 -0.69 -2.61 -0.77 -2.71 -0.41 -2.33 -1.87 Size -0.1549 -0.1436 -0.1449 -0.1527 -0.1551 -0.0864 -0.1752 -0.1360 t-stat -5.93 -4.85 -5.63 -5.22 -7.36 -1.66 -4.93 -5.87 Turnover 0.7168 0.2985 0.4531 2.2973 0.4626 1.2452 0.6616 0.3612 t-stat 4.57 2.29 4.12 3.92 4.37 2.37 3.81 2.85 Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes R-squared 0.1216 0.0968 0.1102 0.1376 0.1112 0.1294 0.1334 0.0951