Herding by foreign investors and emerging market equity returns: Evidence from Korea

Herding by foreign investors and emerging market equity returns: Evidence from Korea

International Review of Economics and Finance 19 (2010) 698–710 Contents lists available at ScienceDirect International Review of Economics and Fina...

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International Review of Economics and Finance 19 (2010) 698–710

Contents lists available at ScienceDirect

International Review of Economics and Finance j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i r e f

Herding by foreign investors and emerging market equity returns: Evidence from Korea Jin Q Jeon a,⁎, Clay M. Moffett b a b

Dongguk Business School, Dongguk University, 3ga 26, Phil-dong, Jung-gu, Seoul, Korea Department of Economics and Finance, University of North Carolina Wilmington, NC, United States

a r t i c l e

i n f o

Article history: Received 10 May 2009 Received in revised form 23 February 2010 Accepted 24 February 2010 Available online 4 March 2010 JEL classification: G11 G14 G15

a b s t r a c t This paper studies the effect of herding by foreign investors on stock returns in the Korean market. We conduct both pre and post-liberalization analyses and utilize a three-stage least squares analysis in order to control for the simultaneous relationship. We find evidence of a significant impact of foreign investor herding on stock returns in addition to intra-year positive feedback trading by foreign investors. However, changes in domestic institutional ownership do not have any significant effect on stock returns. In addition, foreign investors tend to buy/ sell shares that domestic institutions sell/buy in the herding year. © 2010 Elsevier Inc. All rights reserved.

Keywords: Herding Feedback trading Foreign investment Emerging markets

1. Introduction Much has been written about the explanatory potential of herding and the associated feedback trading with various phenomena including stock price movements, momentum and even volatility.1 However, to the best of our knowledge, there has been little research on the impact of foreign investors in newly liberalized markets. Over the last few decades, one of the most important trends in international markets is the liberalization of financial markets in emerging economies. Financial market liberalization has provided global investors with new investment opportunities to invest in what were restricted domestic securities. We believe this resultant growth of foreign ownership in emerging markets is of great significance to researchers interested in understanding the impact of trading behaviors of global investors on local markets as well as to investors — foreign and domestic, individual and institutional. One of the most successful emerging markets is the Korean market which has several unique characteristics that make it of great interest to those curious about investment behavior. Normally domestic institutional investors are recognized as the most important investment group — particularly in more established markets.2 However, they may not be the most influential class in some emerging markets. In Korea foreign investors, most of whom are U.S. and European institutional investors, hold more than 40% of the total market capitalization while domestic ⁎ Corresponding author. Dongguk Business School, Dongguk University, Seoul, Korea. Tel.: +82 02 2260 8880; fax: +82 02 2260 3684. E-mail address: [email protected] (J.Q Jeon). 1 Section 2 discusses and compares the various definitions of herding and feedback trading detailed in the previous literature as well as our applicable usage. 2 Gompers and Metrick (2001) report that large institutional investors with at least $100 million under management almost doubled their share of the U.S. equity market from 1980 and 1996. In contrast, Khanna and Palepu (1999) document foreign ownership has a positive and significant effect on firm value in India, while domestic institutional investors have a negligible effect on firm value. 1059-0560/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2010.03.001

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institutional investors hold approximately 17% (as of 2003) with the remainder held by individuals or non-financial companies. This structure is common in emerging markets, particularly in East-Asian markets with the recent innovation of foreign investment. We add to the literature by concentrating on four issues. First, we investigate the cross-sectional relationship between changes in foreign or domestic institutional ownership and stock returns. This seeks to assess the relative importance of herding by foreign or domestic institutional investors in the Korean market. We generally follow Nofsinger and Sias' (1999) ownership change portfolio approach. We then extend the work by performing a three-stage least squares regression (3SLS) analysis controlling for simultaneity between changes in foreign or domestic institutional ownership and abnormal stock returns. Further, we consider the change of economic regime by dividing the sample period into two sub-periods, pre- and post-1998, when the Korean government abolished the limits on foreign equities ownership. Second, we examine whether changes in foreign or domestic institutional ownership are related to positive feedback trading. Third, we examine whether changes in foreign or domestic institutional ownership are consistent with information cascades.3 Finally, we investigate the possible existence of information asymmetries between foreign and domestic institutions. Even though a number of papers focus on herding by institutions or retail investors, to our knowledge, this is the first study that investigates herding by foreign investors and its impact on emerging market stock returns. We find a strong and positive relation between changes in foreign ownership and stock returns. After 1998, when the foreign ownership limit was abolished in the Korean market, the relationship becomes even stronger. Since the ownership data is observed once at the end of the year, the significant correlation between changes in foreign ownership and abnormal returns may come from either the positive impact of changes in foreign ownership on stock returns or intra-year positive feedback trading by foreign investors. We further investigate both hypotheses using three-stage least squares (3SLS) analysis and find that the results are supportive of both hypotheses. We also find little evidence that changes in domestic institutional ownership have a significant effect on stock returns. Neither foreign nor domestic institutional herding is consistent with information cascades — ownership changes during the herding year are not positively correlated with those during the pre-herding year. In addition, we find evidence of information asymmetries between foreign and domestic institutions. This suggests that foreign institutions tend to buy/sell shares that domestic institutions sell/buy in the herding year. The study is organized as follows. In Section 2, we introduce the theory and empirical evidence on institutional herding. Section 3 describes the data and methodology used in this study and addresses general findings on foreign and domestic institutional herding in Korea. Section 4 examines the effect of herding by foreign investors on stock prices. In Section 5, we discuss additional issues related to pre-herding behavior by foreign investors. In Section 6, we examine informational cascades and information asymmetries and Section 7 concludes. 2. Herding and feedback We address the issue of the price impact of foreign investors in emerging markets by focusing on their herding behaviors. The literature both theoretically as well as empirically confirms that herding and feedback trading have the potential to explain the behaviors of institutional investors (Lakonishok, Shleifer, & Vishny, 1992; Nofsinger & Sias, 1999; Wermers, 1999; and Dennis & Strickland, 2002). Since institutional investors are regarded as sophisticated investors in the capital markets, researchers have studied extensively whether institutional investors have the ability to identify mispriced stocks and outperform the market. Some authors consider institutional investors to engage in “herding” or “feedback trading” — the trading of securities without appropriate fundamental information. Banerjee (1992) argues that herding is a rational behavior because in following others' decisions, it may “reflect information that they have and we do not.” Bannerjee goes on to point out that as this process is extended, it offers less and less information to those with an increasingly distant view of the leader. He identifies this as the “head externality” as fewer and fewer use their own information in making a decision, but base it upon others preceding (the head) which serves to actually impede the flow of information as investors act sequentially. Avery and Zemsky (1998) identify this sequential investing where the group of investors follows the lead of the market while ignoring or failing to gather private information as an “informational cascade.” We avail ourselves of their distinction, examining herding as the concurrent investing, feedback trading as the reaction to the returns of the risky assets and informational cascades as the sequential response of agents following the lead of other investors completely independent of private information. We hold these distinctions as important due to the evidence in the literature that local institutional herding has a significant impact on stock returns in, particularly in non-U.S equity markets (Kim & Nofsinger, 2005; Chen & Hong, 2006). We seek to clarify the specific nature of these general effects on stock market returns. Why would investors herd? Finance theories offer several explanations of why institutional investors might trade together. Wermers (1999) summarizes four popular theories as to why institutional investors herd. First, institutional managers are subject to reputational risk. This is the risk of acting differently from other managers, with different results, so that managers may ignore private information and trade with the crowd (Sharfstein & Stein, 1990) in order to insure more consistent results. Second, institutional managers may receive similar private information because they analyze the same price factors (Froot, Scharfstein, & Stein, 1992; Hirshleifer, Subrahmanyam, & Titman, 1994). Third, Wermers suggests institutional mangers infer private information from trades of other managers, resulting in informational cascades (Bihkchandani, Hirshleifer, & Welch, 1992; Avery & Zemsky, 1998). Finally, 3 An information cascade arises when decisions are made by each investor sequentially, but investors begin to ignore their private signals in favor of the observed actions of previous investors (Banerjee (1992), Welch (1992)).

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institutional managers may share similar aversion to stocks with particular characteristics, such as lower liquidity or higher risk (Falkenstein, 1996). Our study is closely related to the literature which examines the correlations between institutional herding and contemporaneous stock returns. These studies provide insight on why institutions herd and its impact on stock prices. Lakonishok et al. (1992) first develop a model to examine whether institutions herd. Using a sample of pension funds, they find that herding and positive feedback trading are two main facets of trading by money managers. Even though this is related to the argument that herding institutions potentially destabilize stock prices, they do not find evidence that the institutions' practices are necessarily destabilizing to market equilibriums, they find it may in fact promote it. Nofsinger and Sias (1999) examine the price impact of institutional herding. They argue that institutional herding and positive feedback trading are more common than herding by individual investors. They find stocks purchased by institutions outperform stocks sold by institutions and institutional herding is correlated with lag returns. We utilize this information in focusing our study on the impact of institutional investors, both foreign and domestic for Korea. Dennis and Strickland (2002) utilize an event study with event dates determined by large market swings. They find that both abnormal returns and turnover rates are related to the percentage of institutional ownership and in particular both mutual and pension funds utilize feedback trading and herding. Sias (2003) reexamines why institutional investors herd. He does not support the hypothesis that institutional herding is related to positive feedback trading. Rather, he finds that institutions herd as a result of inferring information from each other's trades, (i.e., information cascades). We seek to identify these trading patterns and their significance in the emerging market of Korea. Recent literature provides international evidence on institutional herding. Kim and Nofsinger (2005) use a sample of Japanese firms. They argue that characteristics of Japanese institutions are somewhat different from those of U.S. institutions, since institutions in Japan usually have strong long-term relationship with the firms in which they hold stock. This allows for Japanese institutions to have better private information on the firms. They find that when institutional herding in Japan occurs to a lesser extent than is common in the U.S, the price impact of herding in Japan is greater. Chen and Hong (2006) examine institutional herding in Taiwan with daily institutional holding data. In particular, they investigate the relationship between institutional ownership changes and returns localized around release events of earnings forecasts by analysts. They find that the relationship between changes in institutional ownership and contemporaneous returns are the results of the price impact of herding and they find institutional investors more informed in buying behavior than in their selling behavior. 3. Data, variable descriptions and methodology 3.1. Sample This study is based on a sample of Korean firms listed on the Korea Exchange (KRX) from 1992 through 2003. Monthly stock returns, annual foreign ownership and domestic institutional ownership, and financial data come from the KIS-Value database provided by the Korea Investor Service (KIS). Macroeconomic data including the exchange rate and interest rate are obtained from the Bank of Korea's online statistical database, BOK Economic Statistic System. The Korean security market was initially opened to foreign investors in 1992. The Korean government, however, maintained a strict limit on equity holdings for foreign investors. This limit was set at 10% in 1992, raised to 20% in 1996 and then 55% in 1997. After the financial crisis in 1997–1998, the Korean government abolished the foreign equity holding restriction. This effort was an attempt to attract more foreign investors and provide greater liquidity for the Korean markets. The net result was a dramatic increase in foreign investment as shown in Table 1. To address this significant structural change, we divide the sample period into two sub-periods, pre-1998 (from 1992 to 1997), the

Table 1 Ownership structure in Korea. This table reports annual ownership by foreign investors, domestic institutions, and domestic individuals from 1992 to 2003 in Korea. All firms in the sample are listed on the Korean Stock Exchange (KRS). Each type of ownership shows the market-value weighted mean and median of the ratios of the number of shares held by each investor to the number of shares outstanding at the end of the year. Year

N

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

438 444 466 490 527 549 552 567 572 586 611 628 6430

Foreign ownership

Local Inst. ownership

Local Indiv. ownership

Mean

Median

Stdev

Mean

Median

Stdev

Mean

Median

Stdev

0.079 0.086 0.103 0.115 0.116 0.127 0.195 0.225 0.273 0.371 0.330 0.403 0.264

0.080 0.080 0.089 0.110 0.134 0.106 0.199 0.187 0.261 0.357 0.371 0.435 0.224

0.084 0.073 0.080 0.077 0.084 0.105 0.176 0.149 0.174 0.218 0.226 0.216 0.209

0.239 0.247 0.261 0.247 0.219 0.186 0.163 0.124 0.105 0.081 0.177 0.173 0.163

0.216 0.256 0.274 0.266 0.203 0.204 0.172 0.044 0.044 0.064 0.150 0.162 0.131

0.145 0.148 0.153 0.149 0.147 0.126 0.122 0.142 0.129 0.116 0.131 0.117 0.142

0.315 0.342 0.335 0.304 0.315 0.331 0.283 0.269 0.222 0.226 0.217 0.197 0.253

0.308 0.320 0.308 0.276 0.300 0.307 0.234 0.212 0.151 0.201 0.157 0.124 0.188

0.253 0.228 0.230 0.223 0.236 0.236 0.250 0.264 0.219 0.204 0.192 0.188 0.227

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period of existence of foreign ownership limits, and post-1998 (from 1999 to 2003) when the restrictions were removed.4 We exclude the year 1998 from analysis, due to the abnormal volatility of stock returns for Korean firms during that year as a result of the Asian economic crisis. This results in 6340 firm-year observations. There are several advantages in using Korean data to investigate the impact of herding by foreign investors. First, foreign investment in Korea comprised 40.3% of total market capitalization in 2003 which was fourth in the world behind Hungary (72.6%), Finland (55.7%), and Mexico (46.4%) and first among Asian countries.5 Second, most foreign investors in Korea are institutional investors. The Korean Financial Supervisory Service (KFSS) reports that as of 2003 99.8% of foreign investors in the Korea Stock Exchange are institutions such as investment companies (50.9%), investment banks (20.9%) and pension funds (10.4%).6 The characteristics of foreign investors, however, are likely different from those of domestic institutions. These foreign investors are predominately from well-established markets such as the U.S or UK, and have well-established global standards and practices. This paper seeks to understand how these firms act and whether they impact markets differently from domestic investors. 3.2. Variable descriptions 3.2.1. Key variables of interest The key variables of this study are foreign and domestic institutional shareholdings, changes in shareholdings, and abnormal herding-year returns. Foreign ownership (Local Inst Ownership) is defined as the ratio of number of shares held by foreign investors (domestic institutional investors) to the number of shares outstanding. We define domestic institutions as domestic banks, securities companies and insurance companies headquartered in Korea. Changes in foreign ownership (Δ Foreign Ownership) are defined as foreign ownership at the end of the year minus ownership at the beginning of the year with the same formula applied to domestic changes. Abnormal herd-year returns is a firm's excess returns over (or under) monthly compounded market returns (Korea Composite Stock Price Index — KOSPI returns) during the year. 3.2.2. Firm characteristics Chaebol is a dummy variable which equals one if a firm in the sample is a member of the 30 largest Chaebols, Korean conglomerate business groups, as defined by the Korea Fair Trade Commission (KFTC) or 0 otherwise.7 In Korea, the Chaebol system dominates the market and the controlling shareholders of the Chaebol have full control over its affiliated firms using pyramid ownership structure. This implies that Chaebol-affiliated firms have weak corporate governance (Joh, 2003; Bae, Kang, & Kim, 2002). On one hand, foreign investors who are active monitors of management would not prefer Chaebol-affiliated firms due to their weak governance. One the other hand, due to their large firm size, Chaebol-affiliated firms are more visible to foreign investors (Kang & Stulz, 1997). Following previous literature, we include several control variables for firm characteristics. Beta (proxy for systematic risk) is calculated using daily returns with the market portfolio, (KOSPI). Size is the natural logarithm of annual sales. M/B is the market to book ratio calculated by dividing the market value of equity by book value of equity and serves as a proxy for growth potentials. Dividend yield is the amount of dividends per share divided by the stock price at the end of the year. Leverage is the ratio of total debt to total equity. Finally, ROA is net profit (before interest, tax, and exceptional items) divided by the book value of assets and is the proxy for profitability. 3.2.3. Macroeconomic variables We also consider the effects of political risk and macroeconomic conditions on foreign ownership or on abnormal returns (Desai, Foley, & Hines, 2008; Hau & Rey, 2006; Cauchie, Hoesli, & Isakov, 2004). We employ two variables corresponding to measurable aspect of macroeconomic conditions. Exchange rate volatility is the standard deviation of the dollar-denominated exchange rate measured at a weekly frequency during the herding year. Interest rate volatility is the standard deviation of 3-year corporate bond yields using a weekly frequency during the herding year. 3.2.4. Instrumental variables In order to ensure identification in our three-stage least squares analysis, we specify six potential instruments for foreign ownership. Average_Δ Foreign is defined as the industry-average of changes in foreign ownership during the herding year. Pre_Δ Foreign is the change in foreign ownership during the pre-herding year. We expect that Average_Δ Foreign accounts for industry characteristics and Pre_Δ Foreign captures the preference of foreign investors. Ln_age is the natural logarithm of the number of years since the firm was set up. Capitol is a dummy variable which equals one if a firm headquarters is located in Seoul, the capitol of Korea. Gov_ownership is defined as the ratio of the number of shares held by the government to the number of shares outstanding. Cash ratio is the value of cash and marketable securities divided by total assets. 4

Lin (2006) and Pan, Fok and Liu (2007) document changes in the trading behaviors of foreign investors in the Asian market after the Asian financial crisis. High foreign ownership in Finland is due mainly to the more than 90% of foreign ownership in Nokia. The major portion of foreign ownership in Hungary is due to the presence of 45 of the world's top 50 multinational companies. In Mexico, foreign owned assembly plants, referred to as “maquiladora industry”, comprise the major portion of foreign investment. 6 KFSS's Annual report, “The stock transaction trend of foreign investors during 2003,” 2004. 7 Each year, the KFTC announces the list of top 30 Chaebol groups based on total assets. 5

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We also use two potential instruments for domestic institutional ownership defined as the following: Chaebol_inst is a dummy variable which equals one if a firm is affiliated with Chaebols which own financial institutions as their subsidiaries and Average_Δ Local is the industry-average of changes in domestic institutional ownership during the herding year.8 3.3. Methodology We examine the herding behavior of foreign investors by following the ownership change portfolio approach used in Nofsinger and Sias (1999). First, firms are sorted into 10 portfolios based on foreign or domestic institutional ownership at the beginning of the year. The firms in each decile are then further sorted into 10 portfolios based on the change in ownership over the year. This leaves 10 change portfolios within each of the 10 initial ownership portfolios. Then, firms are re-aggregated based on their changes in ownership decile rank resulting in 10 initial ownership stratified, ownership change portfolios, that have similar foreign or domestic institutional ownership at the beginning of the year but experience different changes in ownership over the year. Nofsinger and Sias (1999) point out there is a limitation of this analysis. Since it focuses on changes in the fraction of shares held by institutions, the change in institutional ownership may not reflect herding. This is due to the possibility the changes result from one or only a few institutional investors taking a large position in a security. However, they assume that firms with the largest institutional ownership changes occur as a result of herding. Kim and Nofsinger (2005) address the same concerns by arguing the number of shares in each decile is sufficiently large casting doubt on the idea that only a few institutions are making these trades. We find in our data the mean market values from decile 1 to decile 10 are approximately 132 million to 779 million U.S dollars (not reported in the table). Since equity funds with a portfolio of more than $10 million are classified as large funds in Korea, it stretches credulity to hold that only a few institutions trade these large amounts. Thus, the assumption by Nofsinger and Sias (1999) is deemed valid for purposes of this study. While this analysis is helpful in assessing the relative importance of herding by foreign investors, it is possible that the results may be driven by some other factors such as firm characteristics and industry effects. In other to control for the variables that might significantly impact the change of foreign ownership and stock returns, we employ a more rigorous model of regression analysis. We also address the potential simultaneous relationship between the change in foreign ownership and abnormal stock returns. Demsetz (1983) and Demsetz and Lehn (1985) illustrate that ownership structure is endogenously determined to reach a trade-off between several cost advantages and disadvantages in the firm. Their arguments are reinforced by Demsetz and Villalonga (2001), who provide both a review of discussion and empirical evidence showing the ownership structure is determined by past performance and firm characteristics. In our study there are several possible explanations for the positive relationship between the change in foreign ownership and abnormal returns. One could be simply foreign investors are attracted to firms already exhibiting some positive abnormal returns. Another possible explanation is that higher foreign ownership leads firms to perform better. Therefore, we need to control for possible endogeneity of the relationship. To address a simultaneous relation between foreign ownership and stock returns, we employ three-stage least squares (3SLS) regressions.9,10 The system of equations to jointly estimate the effect of changes in foreign or domestic institutional ownership on stock returns is: Δ foreign ðlocal inst:Þ ownershipi = β1 abnormal herding−year returnsi + β2 xi + β3 z1 + υi

ð1Þ

abnormal herding−year returnsi = δ1 Δforeign ðlocal inst:Þ ownershipi + δ2 xi + δ2 z2 + ϖi where xi is the set of control variables, z1 and z2 are the set of instrumental variables, and υi and ϖi are the set of error terms which are possibly contemporaneously correlated (i.e., Cov(υi,ϖi) ≠ 0).11 3.4. Descriptive statistics Table 1 reports descriptive statistics for the overall period, 1992–2003. In 1992 when Korea first opened its financial markets to foreign investors, the mean market capitalization of foreign ownership was 7.9%. After the Korean government abolished the foreign ownership limit, foreign ownership dramatically increased from 19.5% in 1998 to 40.3% by the end of 2003. The mean market capitalization of domestic institutional ownership was 23.9% in 1992, which significantly decreases after the economic crisis in 1997.12 This domestic selling continued after 2000, when Korea had largely recovered from the 1997–1998 financial crisis. This selling by domestic institutions was apparently fueled by the bursting of the hi-tech bubble in 2000–2001. However, after 2002, domestic firms again increased holdings from 8.1% in 2001 to 17.3% in 2003. Prior to 1998 individual investor ownership was very high, at 33.1%. After the market crash individual holdings went from 28.3% in 1998 to 19.7% in 2003. Over the life of the study, 8

The validity of instrumental variables is discussed in Section 4.2. To address the endogenous problem, both two-stage least squares (2SLS) and 3SLS are typically used. However, we believe that 3SLS is better suited for this study due to the error terms in the system of equations are possibly contemporaneously correlated. This is due to many foreign or domestic institutional investors holding multiple equity stakes in different firms. 10 The typical method utilized to control for simultaneity is to determine the direction of causality. However, due to the relatively short sample period and low frequency (annual) data, we employ the cross-sectional specification (3SLS) that can address the contemporaneous relationship. 11 All variables are described in Section 3.2. 12 The Korean Composite Stock Price index (KOSPI) was at 700 by the end of 1997, and fell to 298.01 by the end of 1998. 9

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Table 2 Characteristics of ownership change portfolios from 1992 to 2003. This table reports the characteristics of ownership change portfolio from 1992 to 2003. Each year, firms are sorted into 10 portfolios based on initial foreign or institutional ownership at the beginning of the year. The firms in each initial ownership deciles are then further sorted into 10 portfolios based on the change in foreign or institutional ownership over the year. Then, firms are re-aggregated based on their changes in ownership deciles rank. The ownership change is ownership at the end of the year minus ownership at the beginning of the year. The symbols a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. Panel A: Foreign ownership change portfolios and stock returns Change decile

Initial foreign ownership

Δ Foreign ownership

Abnormal herd-year returns

Market-cap

ROA

BETA

Volatility

Dividend yield

1 2 3 4 5 6 7 8 9 10 F-test

0.082 0.078 0.083 0.083 0.077 0.083 0.086 0.084 0.077 0.078 0.83

− 0.072 − 0.043 − 0.028 − 0.017 − 0.007 0.002 0.012 0.024 0.045 0.131 248.87a

0.047 0.063 0.114 0.021 0.081 0.126 0.140 0.209 0.207 0.453 9.87a

17.204 17.610 17.874 17.877 17.727 18.169 18.210 18.386 18.597 18.730 52.58a

− 0.001 0.024 0.000 0.014 0.005 0.022 0.018 0.029 0.038 0.042 3.12a

0.725 0.803 0.775 0.703 0.717 0.770 0.758 0.765 0.808 0.797 3.01a

0.521 0.502 0.447 0.446 0.432 0.415 0.420 0.428 0.432 0.493 3.45a

0.035 0.038 0.035 0.038 0.040 0.041 0.050 0.045 0.040 0.048 2.12b

Panel B: Domestic institutional ownership change portfolios and stock returns Change decile

Initial local inst. ownership

Δ Local inst. ownership

Abnormal herd-year returns

Market-cap

ROA

BETA

Volatility

Dividend yield

1 2 3 4 5 6 7 8 9 10 F-test

0.158 0.142 0.146 0.142 0.143 0.144 0.146 0.141 0.148 0.144 0.82

− 0.122 − 0.079 − 0.060 − 0.040 − 0.023 − 0.007 0.010 0.033 0.067 0.170 623.45a

0.119 0.116 0.113 0.081 0.077 0.202 0.156 0.173 0.235 0.284 4.58a

17.115 17.291 17.469 17.600 17.802 17.949 17.889 18.033 18.134 18.057 39.55a

− 0.020 0.000 0.000 0.004 0.016 0.031 0.017 0.019 0.032 0.039 5.87a

0.689 0.731 0.731 0.718 0.746 0.760 0.741 0.775 0.769 0.765 2.95a

0.480 0.481 0.445 0.464 0.434 0.429 0.426 0.416 0.441 0.472 3.12a

0.033 0.039 0.043 0.044 0.043 0.043 0.043 0.038 0.041 0.043 1.56

foreign ownership increased from 7.9% in 1992 to 40.3% in 2003 while domestic institutional declined from 23.9% to 17.3%, a rather significant change. Overall, the weighted averages of foreign ownership, domestic institutional, and domestic individual ownership over the study period are 26.4%, 16.3%, and 25.3%, respectively. 4. Empirical results 4.1. Ownership changes and herding-year returns Table 2 presents the cross-sectional mean initial ownership by foreign and domestic institutional investors and ownership changes for firms in each ownership change portfolio. Note that our procedure is designed to have similar foreign or domestic institutional ownership at the beginning of the year but experience various ownership changes over the course of the year for each of the 10 portfolios. The last row presents a test of the null hypothesis in the form of F-statistics, which test that each variable is not different across the ownership change portfolio. The F-statistics both for initial ownership of foreign investors and domestic institutions are 0.83 and 0.82 respectively indicating that initial ownership is not significantly different with respect to the Herdyear Returns. Of particular interest to our study are the F-statistics both for changes in foreign ownership and domestic ownership which are 248.87 and 623.45, both significant at the 1% level. These results strongly indicate the procedure was successful in identifying a significant contributor to the abnormal returns since initial ownership is not statistically different among the portfolios. This occurs while the change in each ownership decile is statistically different and actually monotonically increasing across the 10 portfolios. The cross-sectional mean of abnormal herd-year returns over the herding year is also reported in Table 2. As expected, Panel A shows changes in foreign ownership and abnormal herd-year returns are positively correlated. Firms in the tenth decile experience the largest increase in foreign ownership, 13.1%, and enjoy abnormal returns of 45.3%, while firms in the first decile suffer low abnormal returns of 4.7% and show a change in foreign ownership of −7.2%. It should be noted the overall relationship is not monotonic. For example, the Abnormal Herd-year returns of firms in the third decile are 11.4%, which are much higher than those of firms in the fourth decile with a 2.1% abnormal return. A contributor to this non-monotonic relationship is likely the existence of the strict foreign ownership limit before 1998. This limit was set at 10% in 1992, which was raised to 20% in 1996 and 55% in 1997 and finally abolished in 1998. To address these concerns we later perform Nofsinger and Sias' test and regression analysis after excluding the period 1992–1999 from the sample in Table 2.

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Firm characteristic variables by decile of foreign ownership changes are reported in Table 2. First, we reject the null hypothesis that each characteristic variable is not different across the ownership change portfolio, since the F-statistics are significant at the 5% level or better. However, only market capitalization monotonically increases with changes in foreign ownership. ROA, Volatility, and Dividend yield are significantly different across the ownership change portfolio with no apparent monotonic relationship between those variables and foreign ownership changes. Panel B shows the results of domestic institutional ownership changes and abnormal stock returns. Abnormal stock returns of firms in the tenth decile are the highest at 28.4%, but the magnitude of domestic institutional ownership changes is substantially less than that of the portfolios with foreign ownership changes at 45.3%. Table 3 details the results in changes of foreign and domestic institutional ownership and abnormal herding-year returns after excluding the period 1992–1998. The initial foreign ownership, again, does not significantly appear different across the foreign ownership change portfolios, since the F-statistics for the null hypothesis is 0.28. The results demonstrate the strong and monotonic relation between changes in foreign ownership and abnormal herding-returns. Firms in the lowest decile experiencing the biggest decrease in foreign ownership suffer −1.0% of Abnormal Herding-year returns while firms in the highest decile enjoy a 56.2% abnormal return. Thus, the range of returns between the first decile and the tenth decile is 57.2% for foreign investors, as

Table 3 Ownership change portfolios and stock returns from 1999 to 2003. This table reports the characteristics of ownership change portfolio from 1999 to 2003. Each year, firms are sorted into 10 portfolios based on initial foreign or institutional ownership at the beginning of the year. The firms in each initial ownership deciles are then further sorted into 10 portfolios based on the change in foreign or institutional ownership over the year. Then, firms are re-aggregated based on their changes in ownership deciles rank. The ownership change is ownership at the end of the year minus ownership at the beginning of the year. The symbols a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. Panel A: Foreign and domestic ownership change portfolio and stock returns, 1999–2003 Change decile

Initial ownership

Δ Foreign ownership

Abnormal herd-year returns

Market-cap

1 2 3 4 5 6 7 8 9 10 F-test

0.083 0.094 0.091 0.090 0.087 0.091 0.093 0.092 0.082 0.091 0.28

− 0.069 − 0.046 − 0.026 − 0.015 − 0.006 0.004 0.013 0.026 0.050 0.148 134.52a

− 0.010 − 0.015 − 0.003 − 0.063 0.016 0.015 0.071 0.194 0.195 0.562 8.25a

17.133 17.756 17.953 17.935 17.813 18.173 18.173 18.643 18.820 19.038 24.52a

ROA 0.012 0.070 0.010 0.026 0.009 0.032 0.017 0.044 0.048 0.054 2.05b

Volatility

Dividend yield

0.730 0.682 0.618 0.625 0.617 0.596 0.622 0.605 0.650 0.711 3.1a

0.035 0.039 0.029 0.043 0.043 0.043 0.046 0.043 0.032 0.041 2.05b

Panel B: Domestic institutional ownership change portfolios and stock returns, 1999–2003 Change decile

Initial ownership

Δ Local inst. ownership

Abnormal herd-year returns

Market-cap

ROA

Volatility

Dividend yield

1 2 3 4 5 6 7 8 9 10 F-test

0.123 0.096 0.107 0.095 0.103 0.100 0.103 0.097 0.106 0.103 0.95

− 0.110 − 0.075 − 0.065 − 0.041 − 0.027 − 0.012 0.005 0.027 0.062 0.202 250.23a

0.038 0.032 0.004 − 0.041 − 0.015 0.161 0.051 0.135 0.224 0.261 2.98a

17.099 17.355 17.636 17.605 17.796 18.071 18.036 18.088 18.305 18.045 15.58a

− 0.008 0.004 0.011 0.010 0.030 0.059 0.026 0.018 0.042 0.069 3.14a

0.713 0.716 0.688 0.685 0.650 0.653 0.634 0.633 0.692 0.727 2.12b

0.033 0.034 0.047 0.041 0.042 0.049 0.040 0.041 0.037 0.029 1.85c

Panel C: Institutional ownership change portfolio and stock returns in Japan and U.S Japan results 1975–2001 (Kim and Nofsinger 2005)

U.S. results 1977–1995 (Nofsinger and Sias 1999)

Decile

Initial inst. ownership

Δ Institutional ownership

Herding-year returns

Initial inst. ownership

Δ Institutional ownership

Herding-year returns

1 2 3 4 5 6 7 8 9 10 F-stats

0.636 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.639 0.12

− 0.062 − 0.027 − 0.016 − 0.004 − 0.009 0.002 0.008 0.017 0.030 0.074 6948.20a

− 0.051 − 0.061 − 0.061 − 0.053 − 0.043 − 0.033 − 0.016 0.000 0.032 0.128 145.70a

0.376 0.370 0.367 0.365 0.366 0.366 0.367 0.367 0.366 0.364 0.02

− 0.160 − 0.071 − 0.042 − 0.025 − 0.011 0.002 0.017 0.037 0.070 0.183 624.82a

− 0.131 − 0.082 − 0.060 − 0.029 − 0.014 − 0.008 0.011 0.043 0.084 0.184 68.94a

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Table 4 Test for the validity of instrumental variables. This table reports validity tests for potential instrumental variables initially employed. Six potential instruments for a change in foreign ownership are considered: Average_Δ Foreign, Pre_Δ Foreign, Ln_Age, Capitol, Gov_ownership, Cash ratio. For a change in domestic institutional ownership, two possible instruments are considered: Chaebol_inst and Average_Δ Local. The symbols a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. Endogenous variables

Instruments

t-test

F-test

Sargan test

Δ Foreign ownership

Average_Δ Foreign Pre_Δ Foreign Ln_Age Capitol Gov_ownership Cash ratio Chaebol_inst Average_Δ Local

4.83a − 9.67a − 3.07a 0.27 0.28 0.01 1.87b 5.18b

8.73a

1.225

7.03a

0.099

Δ Local inst. ownership

contrasted to only 22.3% for domestic institutions. The result is consistent with the notion that foreign investors are the most influential ownership class in the Korean market. Given that the range of the effect of foreign institutional herding is even larger than that of domestic institutional herding in Japan at 18%, and in the U.S at 31.4% (shown in Panel C), foreign institutional herding has a stronger effect on stock prices than does domestic institutional herding in Korea, and a stronger effect than institutional herding in Japan and the U.S. These results evidence foreign investors using well developed techniques and global experiences can obtain higher returns commensurate with the higher risk of global investing. The relationship between the changes in domestic institutional ownership and abnormal herding-returns is examined in Panel B. The abnormal herding-returns of local institutions in the fourth and fifth deciles experience negative returns of −4.1% and −1.5% respectively, while firms in the third decile enjoy positive returns of 0.4%. Also, abnormal returns in the sixth decile are even greater than those in the seventh and eighth deciles. These results are consistent with the hypothesis that the impact of domestic institutions on emerging markets is not as strong as that of foreign investors from more established markets.

4.2. Regression analysis In this section, we investigate whether this positive relationship between changes in foreign ownership and abnormal herdingreturns comes from (1) intra-year positive feedback trading by foreign investors or (2) positive impact of foreign investors on returns. Note that Table 3 does not inform as to whether stock returns change before or after foreign investors herd during the year. It is possible that foreign ownership changes and stock return changes occur contemporaneously. In order to examine this, we employ three-stage least squares (3SLS) regressions to control for this simultaneity of the two variables.13 Before proceeding to 3SLS analysis, we check the validity of the instrumental variables chosen. In order to ensure identification, we initially specify six potential instruments for foreign ownership: Average_Δ Foreign, Pre_Δ Foreign, Ln_Age, Capitol, Gov_ownership, and Cash ratio. We also use two potential instruments for domestic institutional ownership: Chaebol_inst and Average_Δ Local. The definitions of these variables are discussed in Section 3.2. The results of validity tests for instrumental variables are shown in Table 4.14 Among six potential instrumental variables for foreign ownership, only the three variables Average_Δ Foreign, Pre_Δ Foreign, and Ln_age are statistically relevant to the change in foreign ownership with significant t-stats (all at the 1% level). The two instruments for domestic institutional ownership, Chaebol_inst and Average_Δ Local, are also significantly related to domestic institutional ownership though the confidence in the Chaebol_inst variable is at the 5% level. Next, in an effort to strengthen these results, we perform Sargan tests for the joint test of the model specification and the validity of the instruments. 15 Excluding weak instruments, Capitol, Gov_ownership, and Cash ratio, we obtain a χ2-statistic of 1.225 for the change in foreign ownership and 0.099 for the change in domestic institutional ownership and, thus, fail to reject the null that instruments are valid. Table 5 reports the results of 3SLS regressions. In the first regression of 3SLS (1), abnormal herding-returns are positively correlated to changes in foreign ownership. The results show that foreign investors are more likely to be attracted to firms with greater abnormal returns which suggest foreign investors engage in feedback trading. In the second regression of 3SLS (1), the coefficient of a change in foreign ownership is also positive and significant at the 1% level on abnormal herding-year returns, which suggests that changes in foreign ownership significantly impact or drive abnormal herd-year returns. The coefficients of three instrumental variables, Average_Δ Foreign, Pre_Δ Foreign, and Ln_age are significant in the first regression and are validated as good instruments. While most of the control variables are generally not significantly related to changes in foreign ownership changes, Ln_Sales (proxy for firm size), M/B, and Exchange rate volatility matter. The positive effect of 13

See Section 3.3 for a detailed discussion of the model. Instrumental variables must satisfy two requirements: First, every instrument must be highly correlated with endogenous variables. Second, every instrument is orthogonal to the error term of the second stage equation. 15 The Sargan test (1958) is one of the over-identifying restriction tests to test whether instruments are orthogonal to the error term. In particular, first, we obtain a IV-residual on all exogenous variables (instruments + control variables). Then, we regress the residual on instrument variables and obtain R2. The test statistics is S = nR2, where n is the number of observations. 14

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Table 5 Simultaneous equation analysis of ownership changes and abnormal returns. This table reports estimation results for simultaneous equation analysis using the 3stage least squares (3SLS) regressions. The z-statistics are within parentheses below the estimated coefficients. a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. 3SLS (1) Δ Foreign ownership returns Abnormal returns

a

0.021 [8.03]

Pre_Δ Foreign Ln_Age

1.702 [8.66] a

Pre_abnormal returns Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations X2

− 0.003 [− 0.45] − 0.001 [− 0.04] 0.012a [6.12] − 0.010c [− 1.92] − 0.030 [− 1.01] − 0.001 [− 1.08] 0.004 [0.42] − 0.080a [− 2.67] − 0.007 [− 1.23] Yes 1.105b [2.06] 1782 311.58a

Chaebol_inst

0.779a [10.59] − 0.049a [− 2.33] 0.002 [0.08] − 0.421a [− 7.28] 0.092a [6.44] 0.526a [9.02] 0.112 [0.98] − 0.001 [− 0.13] 0.005 [0.05] − 0.099 [− 1.06] − 0.253b [− 1.99] Yes − 1.001a [4.89] 1782 992.20a

0.162 [0.12] a

1.008 [5.01] − 0.002 [− 0.28]

Average_abnormal returns Pre_abnormal returns Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations X2

Abnormal returns

0.002 [0.03]

Δ Local inst. ownership Average Δ Local

0.061 [0.40] − 0.201a [− 9.35] − 0.007a [− 2.60]

Average_abnormal returns

Δ Local inst. ownership Abnormal returns

a

Δ Foreign ownership Average_Δ Foreign

3SLS (2) Abnormal returns

0.001 [0.03] − 0.005 [0.06] − 0.001 [− 0.02] − 0.005 [− 0.42] − 0.015 [− 0.33] − 0.001a [− 2.88] 0.042b [2.33] 0.071 [0.58] − 0.021 [− 1.03] Yes 0.009 [0.22] 1756 172.50a

0.956a [13.99] − 0.050 [− 1.90]b 0.044 [0.98] − 0.288a [− 5.98] 0.085a [5.77] 0.522a [11.45] 0.285 [1.33] 0.001 [0.04] 0.029 [0.34] − 0.079 [− 0.21] − 0.311b [− 2.12] Yes − 1.557a [− 5.78] 1756 910.33a

firm size is consistent with a notion that larger firms are more visible and, so, more attractive to foreign investors (Kang & Stulz, 1997; Dahlquist & Robertsson, 2001). The negative effect of the market to book ratio suggests that foreign investors are less likely to choose a growth firm. In addition, any exchange rate fluctuation significantly reduces foreign investment, consistent with a notion that exchange rate volatility is a disincentive for foreign investors because it increases the dimension of risk inherent in foreign investing. In comparison, 3SLS (2) shows that there is no evidence that domestic institutional herding is related to positive feedback trading or that it impacts abnormal herding-year returns. The coefficients of abnormal herding-returns in the first regression (0.002) and of changes in local ownership (0.162) in the second regression of 3SLS (2) are not significant. This indicates that many institutions in the Korean market have a long-term relationship with the firms in which they invest, they usually neither engage in feedback trading, nor have a short-term focus. Kim and Nofsinger (2005) discuss this issue using their sample of institutions in Japan. Similar to the case of Japan, numerous financial institutions in Korea are Chaebol-affiliated and have reasons for a strong relationship with other Chaebol-affiliated firms in their portfolio that extends beyond current returns. In addition, as distinct from foreign investors, domestic institutions significantly reduce their ownership in highly leveraged firms, while their ownership increases for highly profitable firms with high ROA. In summation, the evidence supports both hypotheses that foreign investors engage in positive feedback trading and that changes in foreign ownership significantly impact abnormal returns in the Korean market. We find no evidence that domestic institutional ownership changes have a significant effect on stock returns. 5. Pre-herding-year returns and ownership changes In this section, we consider whether foreign or domestic institutional investors engage in positive feedback trading in a more direct way. The portfolios are sorted based on previous herding-year returns, then examined as to the subsequent changes in

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Table 6 Pre-herding-year returns and ownership change. This table shows the relation between pre-herding-year returns and ownership changes. Each year, firms are sorted into 10 portfolios based on previous herding-year returns. The ownership change is ownership at the end of the year minus ownership at the beginning of the year. Pre-herding abnormal returns are monthly compounded market-adjusted returns during the previous herding year. a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. Panel A: Foreign ownership changes by decile of pre-herding annual returns Whole period: 1999–2003

Sub-period: 1999–2003

Decile

Pre_herding abnormal returns

Δ Foreign ownership

Decile

Pre_herding abnormal returns

Δ Foreign ownership

Loser 2 3 4 5 6 7 8 9 Winner F-stats

− 0.4097 − 0.2486 − 0.1791 − 0.1173 − 0.0422 0.0838 0.2045 0.3655 0.5466 1.3211 434.18a

− 0.0148 − 0.0156 − 0.0057 − 0.0050 0.0022 0.0056 0.0074 0.0077 0.0059 0.0121 4.64a

Loser 2 3 4 5 6 7 8 9 Winner F-stats

− 0.5689 − 0.3199 − 0.2247 − 0.1884 − 0.0730 0.2201 0.2488 0.3753 0.6382 1.8685 238.96a

− 0.0166 − 0.0161 − 0.0047 0.0029 − 0.0009 0.0113 0.0144 0.0154 0.0119 0.0164 3.27a

Panel B: Local institutional ownership changes by decile of pre-herding annual returns Whole period: 1999–2003

Sub-period: 1999–2003

Decile

Pre_herding abnormal returns

Δ Local inst. ownership

Decile

Pre_herding abnormal returns

Δ Local inst. ownership

Loser 2 3 4 5 6 7 8 9 Winner F-stats

− 0.4189 − 0.2607 − 0.1864 − 0.1264 − 0.0358 0.0918 0.2013 0.3636 0.5365 1.3719 571.15a

− 0.0049 0.0000 − 0.0201 − 0.0118 − 0.0117 − 0.0061 − 0.0164 − 0.0090 − 0.0019 − 0.0015 1.89b

Loser 2 3 4 5 6 7 8 9 Winner F-stats

− 0.5882 − 0.3388 − 0.2398 − 0.2118 − 0.0746 0.2310 0.2418 0.3655 0.6441 1.9532 296.19a

0.0091 0.0069 − 0.0250 − 0.0190 − 0.0093 0.0109 − 0.0135 − 0.0015 − 0.0029 − 0.0182 2.52b

foreign or domestic institutional ownership. Foreign or domestic institutions exhibit feedback trading patterns if they increase ownership in firms with higher previous returns and decrease ownership in firms with lower previous returns. The results are reported in Table 6. Panel A shows that firms with previous higher performance experience a subsequent increase in foreign ownership with an approximately monotonic change in ownership through the progression of the deciles. That is, foreign investors tend to buy shares with higher previous performance (winners) and sell shares with lower previous performance (losers). This evidence looks more apparent in the full sample period, but is still consistent, though somewhat weaker in the sub-period post-1998. Panel A generally supports the hypothesis that foreign investors exhibit patterns of positive feedback trading. Panel B suggests that domestic institutions are not likely to buy previous winners or sell previous losers. As discussed in the previous section, the fact that many domestic institutions are Chaebol-affiliated and have a strong relationship with other Chaebol-affiliated firms in their portfolios is likely a significant contributor to these results. 6. Additional issues 6.1. Informational cascades In this section, we examine whether foreign or domestic institution herding is consistent with informational cascades. Banerjee (1992) and Bihkchandani et al. (1992) suggest that herding arises from informational externalities. Institutional managers ignore their noisy information while they infer private information from other managers' trades. If herding is related to informational cascades, there should be a positive relationship between ownership changes in the pre-herding year and those in the herding year. Table 7 shows the results of OLS regressions of ownership changes in the pre-herding year on ownership changes in the herding year. It is modestly surprising to see that the coefficients of foreign ownership change in the pre-herding year are significantly negative in OLS (1).16 The result is similar for the case of local institutional ownership in OLS (2), but the negative coefficients are not statistically significant. It is possible that a year may be too long of a horizon to detect actual trading activities 16 We also control for the initial level of foreign ownership in year t. While not reported in the paper, the results are qualitatively the same as those reported in the table.

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Table 7 Ownership changes during herding year and pre-herding year. This table reports estimation results for OLS regressions. The dependent variable in OLS (1) is a change in foreign ownership, while it is a change in local institutional ownership in OLS (2). The t-statistics are within parentheses below the estimated coefficients. a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. OLS (1)

OLS (2)

Dependent variable

Δ Foreign inst. ownership

Period

1999–2003

1992–2003

Period

1999–2003

1999–2003

Pre_Δ Foreign

− 0.198a [− 2.98] 0.793a [6.01] − 0.003 [− 1.43] − 0.001 [− 0.14] − 0.011c [− 1.92] 0.009a [6.78] − 0.022 [− 1.12] 0.014 [0.76] 0.001 [0.48] 0.007 [0.29] − 0.010b [− 2.01] − 0.007 [− 0.98] Yes − 0.155a [− 5.20] 2516 0.123

− 0.192a [− 2.83] 0.721a [4.01] − 0.003c [1.69] − 0.002 [− 0.04] − 0.006 [− 1.03] 0.013a [6.88] 0.008 [0.89] − 0.021 [− 0.11] − 0.001 [− 0.05] 0.003 [0.17] − 0.992a [− 2.69] − 0.010 [− 1.06] Yes − 0.165a [− 4.12] 1782 0.118

Pre_Δ Local

− 0.008 [− 0.34] 1.233a [8.01] 0.001 [0.01] 0.001 [0.09] − 0.012 [1.63] − 0.001 [− 0.68] 0.002 [0.46] 0.033 [1.13] − 0.001b [− 2.11] 0.028b [2.44] 0.003 [0.78] − 0.020 [− 1.55] Yes − 0.009 [0.10] 2516 0.115

− 0.007 [− 0.25] 1.023a [4.99] − 0.002 [− 0.24] 0.001 [0.02] − 0.011 [− 0.12] 0.001 [0.01] − 0.002 [− 0.38] − 0.014 [− 0.33] − 0.001a [− 3.01] 0.021c [1.93] 0.025 [1.12] − 0.058c [− 1.77] Yes 0.001 [0.01] 1782 0.103

Average_Δ Foreign Ln_Age Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations R2

Dependent variable

Average_Δ Local Chaebol_inst Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations R2

Δ Local inst. ownership

by institutional managers, particularly if many of their trades are short-term positions.17 The results from our data, however, suggest that neither foreign nor domestic institutions increase nor decrease shares that they have increased or decreased during the previous year. 6.2. Informational asymmetries between foreign and domestic institutions It is intuitive that there is information asymmetry between foreign investors and domestic investors. Literature finds that domestic investors have an advantage of languages and short distances, while foreign investors have well developed technologies and may be more practiced at analyzing information. For example, Dvorak (2005) finds that domestic investors have an advantage on short-lived information, but foreign investors have an advantage on long-term information. Grinblatt and Keloharju (2000) argue domestic investors are less sophisticated and take the opposite position of that of more sophisticated foreign investors. In this section, we examine whether foreign investor herd differently from domestic institutional investors. If there is asymmetry in terms of firm value, we expect changes of foreign ownership are not positively correlated with changes of domestic institutional ownership. The more severe the information asymmetry that exists between foreign investors and domestic institutions, the more negative the correlation. To control for a simultaneity bias, we again employ 3-stage least squares (3SLS) regressions by estimating the following system of simultaneous equations: Δforeign ðlocal inst:Þ ownershipi = β1 Δ local inst: ownershipi + β2 xi + β3 z1 + υi

ð2Þ

Δlocal inst: ownershipi = δ1 Δ foreign ownershipi + δ2 xi + δ2 z2 + ϖi where xi is the set of control variables, z1 and z2 are the set of instrumental variables, and υi and ϖi are error terms which are possibly contemporaneously correlated.

17 Chen and Hong (2006) discuss that using infrequent data may not reveal institutional herding if it occurs over a shorter time interval. For this reason, they use the unique dataset from the Taiwan Stock Exchange which provides daily institutional trading information.

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Table 8 Simultaneous equation analysis of foreign and domestic ownership changes. This table reports estimation results for simultaneous equation analysis using the 3stage least squares (3SLS) regressions. The z-statistics are within parentheses below the estimated coefficients. a, b, and c represent statistical significance at the 1%, 5%, and 10% levels, respectively. The descriptions of variables are provided in Section 3.2. Dependent variables

Δ Foreign ownership

Dependent variables

Δ Local inst. ownership

Δ Local Inst ownership

− 0.076a [− 3.99] 0.855a [6.09] − 0.212a [9.87] − 0.004b [− 1.99] − 0.001 [− 0.43] − 0.004 [− 0.77] 0.010a [5.92] − 0.042 [− 1.04] − 0.028 [− 0.88] − 0.001 [− 0.69] 0.005 [0.55] − 0.091a [− 2.78] − 0.006 [− 1.18] Yes − 0.165a [− 4.83] 1756 278.04a

Δ Foreign ownership

− 0.120a [− 4.34] 1.003a [4.76] 0.020c [1.76]

Average_Δ Foreign Pre_Δ Foreign Ln_Age Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations X2

Average_Δ Local Chaebol_inst

Chaebol Beta Ln_Sales M/B Dividend yield Leverage ROA Exchange rate volatility Bond rate volatility Year dummy Intercept Observations X2

0.001 [0.02] − 0.006 [− 0.89] 0.001 [0.48] − 0.002 [− 0.25] − 0.017 [− 0.45] − 0.001a [− 2.83] 0.033b [2.34] 0.069 [0.49] − 0.019 [− 0.99] Yes − 0.178a [− 3.90] 1756 221.43a

Table 8 shows the results of 3SLS regressions. In the first stage, the coefficient of changes in local institutional ownership in is significant and negative (− 0.076) based on the z-statistic. This is consistent with the argument of informational asymmetry between foreign and domestic investors. Foreign investors would buy or sell stocks with domestic institutions taking the reciprocal position of selling or buying the stocks in the previous year. The same story appears in the second stage. The negative coefficient of changes in foreign ownership indicates that domestic institutions subsequently buy what foreign investors sold. Three instruments for changes in foreign ownership, Average_Δ Foreign, Pre _Δ Foreign, and Ln_age, are statistically significant at the 5% level or greater, implying that they are valid instruments. Also, instruments for changes in domestic institutional ownership, Average_Δ Local and Chaebol_inst, appear to be significant. Consistent with results in Table 6, changes in foreign ownership in the previous year are negatively correlated with changes in the herding year with a coefficient of −0.212 and a z-statistic of −9.87 which is significant at the 1% level. The coefficient indicates firms that experienced higher changes in foreign ownership in the previous year are more likely to have lower (or more negative) changes in foreign ownership. This argument is related to our previous discussion of whether foreign investors possess informational cascade behaviors discussed in Table 7. This negative relationship indicates that they do not increase shareholdings of stocks that they have increased in the previous year. The significance of these results dovetails into Choe et al. (2001) who use Korean intraday data (from 1996 to 1998) to show that the domestic investors in Korea have a private information advantage over foreign investors, which creates a “substantial disadvantage for foreign investors” with regard to buying/selling securities which then have large positive/large negative abnormal returns. While due to our data infrequency, we can't be as specific with regard to results on individual trades, we can confirm that this opposite pattern of trading behavior is clear and for the duration of our data (1999 through 2003) continues to exist. 7. Conclusion The liberalization of financial markets in emerging economies usually results in the growth of foreign ownership of domestic securities. In Korea, foreign investors, most of whom are institutional investors from the U.S and Europe, hold more than 40% of the total market capitalization while domestic institutional investors hold only about 17%. This has attracted the attention of researchers who seek to understand the impact of trading behaviors of global investors on emerging markets.

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In this paper, we extend the literature on institutional herding and feedback trading and explore how foreign ownership changes are related to stock returns. More specifically, this study focuses on four issues. First, it examines the cross-sectional relation between changes in foreign and domestic institutional ownership and stock returns. This serves to assess the importance of herding by foreign and domestic institutional investors in the Korean market. In addition, we consider the change of economic regimes by dividing the sample period into two sub-periods, pre- and post-1998 when the Korean government abolished the limit on foreign ownership. Secondly, we examine whether changes in foreign and domestic institutional ownership are related to positive feedback trading. Third, we examine whether ownership changes are consistent with information cascades and further test for information asymmetries between foreign and domestic institutions. We find a strong and positive relation between changes in foreign ownership and stock returns. After 1998, when the foreign ownership limit was abolished in the Korean market, the relationship became even stronger. Since the ownership data is observed once at the end of the year, the significant correlation between changes in foreign ownership and abnormal returns may come from either the positive impact of changes in foreign ownership on stock returns or intra-year positive feedback trading by foreign investors. We further investigate both hypotheses using three-stage least squares (3SLS) analysis and find that the results are supportive of both hypotheses. We also find little evidence that changes in domestic institutional ownership have a significant effect on stock returns. Neither foreign nor domestic institutional herding is consistent with information cascades — ownership changes during the herding year are not positively correlated with those during the pre-herding year. 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