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Do ADR Investors Herd?
Evidence from Advanced and Emerging Markets
Rıza Demirer, Ali M. Kutan and Huacheng Zhang †
First draft: May 2008
This draft: March 2012
Abstract
This paper extends the research on investor herds to American Depository Receipts
(ADRs). Using daily price data on 305 ADRs traded in U.S. exchanges issued by
corporations from 19 countries, we examine herding behavior in the market for ADRs within
country-based portfolios. We also provide evidence from sector-based portfolios. There is
significant evidence of herding behavior in the market for ADRs from Chile only regardless
of alternative model specifications. On the other hand, we find significant effect of the Asian
crisis and the recent credit market crisis on herding behavior in ADR issues from Korea and
the U.K., respectively, suggesting a link between market crisis periods and herding behavior.
Furthermore, we find no significant effects of currency rates (except Korea) or the
performance of the market of origin on herding behavior among ADR issues. In the case of
sector-based ADR portfolios, evidence of herding behavior exists in Basic Industries, Capital
Goods, Food & Tobacco, and Textile & Trade, but only during periods of large market
downturns. We next discuss implications for ADR investors.
Key words: American Depository Receipts, Herding Behavior, Return Dispersions, Market
Efficiency
JEL Classification: G14, G15
† Rıza Demirer. Department of Economics and Finance, School of Business, Southern Illinois University
Edwardsville, Edwardsville, IL 62026-1102. E-mail: [email protected];
Ali M. Kutan. Department of Economics and Finance, School of Business, Southern Illinois University
Edwardsville; The William Davidson Institute, University of Michigan Business School; and The Emerging
Markets Group, Sir Cass Business School, London. E-mail: [email protected].
Huacheng Zhang. Department of Finance, Eller College of Management, University of Arizona, Tucson, AZ,
85721. E-mail: [email protected].
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1. Introduction
Herding behavior in financial markets has attracted much attention over the past
decade. The literature, in general, defines herding behavior as the tendency of investors to
mimic the actions of other investors, moving in and out of particular securities, industries or
markets in general as a group (Bikhchandani and Sharma, 2000). One implication of herding
behavior is that it drives security prices away from equilibrium values supported by
fundamentals, potentially leading to market bubbles and subsequent crashes. An increasing
number of published works in the literature has tested the existence of investor herds in a
number of domestic and global markets. In general, the literature provides market specific
results with the strongest support for herding behavior in mostly Asian markets. Interestingly,
the analysis of investor herds has not yet been extended to American Depository Receipts
(ADRs). Therefore, the main goal of this paper is to extend the research on investor herds to
the market for ADRs. To our best knowledge, this is the initial study testing herd behavior in
the market for foreign stocks traded in the U.S. market.
Several studies including Christie and Huang (1995), Wermers (1999) and Chang et
al. (2000) have examined herding behavior in the U.S. market; however, these studies have
only focused on securities issued by U.S. firms for which information is easily accessible
relative to those issued by foreign firms. Studying herding behavior in the market for ADRs
is different from prior studies on herding that focus on securities traded in a single market
and also interesting for several reasons. First, unlike domestic securities traded in the U.S.,
ADR returns are affected by not only the risk factors specific to the U.S. market where the
ADR is traded, but also potentially driven by additional uncertainties related to exchange rate
movements as well as the developments in the home market where the ADR is based on. One
can argue that compared to investors focusing on domestic securities only, investors in ADRs
are exposed to a wider array of risk factors which may create additional uncertainty, thus
potentially leading to a greater tendency to suppress their own beliefs and act as a herd, in
particular during periods of market stress. Second, focusing on ADR returns allows us to
examine if herding behavior is more prevalent among investors in ADR issues from
particular countries, providing us with clues on what might be driving investors towards such
behavior. Such an analysis could provide valuable insight to fund managers focusing on
country specific portfolios as evidence for herding in a particular market would suggest
greater challenges for diversification due to correlated actions of market participants. Third,
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in addition to country-based ADRs, we also analyze ADRs within sector-based portfolios in
order to investigate possible sector effects on herding behavior. For example, ADR investors
might be interested in foreign firms in particular industries, say telecommunication,
regardless of the country of origin. This may be an important source of herding behavior as
one would have a more homogeneous group of ADR investors not only facing similar
uncertainties specific to those industries but also facing a significant disadvantage regarding
access to information relative to domestic investors in the corresponding home markets.
Finally, herding behavior among ADR investors may be influenced by a number of factors
including the risk factors in the underlying stock market (in our case, the U.S. market), as
well as uncertainties in the home stock market and the currency market. It is therefore
possible to test whether it is the market stress in the underlying market, in the country of
origin or shocks in the currency market which drives such behavior among investors of that
country’s ADRs.
Overall, this paper contributes to the literature by examining herding behavior in the
market for ADRs which, to our best knowledge, has not been studied in the literature. Using
daily price data on 305 ADRs traded in U.S. exchanges issued by corporations from 19
countries, we examine the cross-sectional ADR behavior with respect to movements in the
U.S. market index and test for possible herding behavior across country-based as well as
sector-based ADR portfolios. In a recent study, Chiang and Zheng (2010) use daily sector
returns from 18 advanced and emerging markets and find evidence on herding in some Asian
and advanced markets (except for the U.S.). Our study examines herding behavior from a
different angle by focusing on the securities of foreign firms from a wide range of advanced
and emerging markets.
Our tests of country-based ADR portfolios indicate evidence of herding behavior in
the ADRs from Chile, Korea and the U.K. The robustness tests suggest that the results are
robust to alternative model specifications and that shocks in the currency market and the
market of origin have no significant impact on herding behavior in the ADR market. Overall,
we find that herding behavior is more prevalent for ADRs from Chile and the U.K whereas,
we observe asymmetry in herding behavior in the case of Korea where such behavior occurs
during periods of large market losses only. We also run similar tests after classifying ADRs
into different sectors based on the North American Industry Classification System (NAICS)
codes and test whether herd behavior exists within sector-based ADR groups. We find
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evidence consistent with herding behavior in Basic Industries, Capital Goods, Food &
Tobacco, and Textile & Trade during periods of large market downturns only. The finding of
herding behavior during large negative market shocks is consistent with several studies
including Kahneman and Tversky (1979) and Kahneman et al. (1990) which suggest that
investors are more concerned with potential losses than gains, further suggesting
asymmetries in utility functions or assessment of risk by investors.
This paper is organized as follows. Section 2 provides a brief review of the literature
on ADRs and tests of herding behavior. Section 3 provides the methodological details.
Section 4 presents empirical results on country and sector based ADR portfolios. Finally,
Section 5 concludes the paper.
2. Related literature review
ADRs have been studied extensively from different angles in the literature. A major
research area has been to identify the explanatory variables for the price premium on ADRs
relative to the corresponding securities traded in their home markets [e.g. Kadiyala and
Kadiyala (2004), Grossman et al. (2007), Arquette et al. (2008), Chan et al. (2008)]. Another
line of research has focused on the different risk factors, including those related to the
underlying securities at the home market, exchange rate movements, home market index,
driving ADR returns [Kim et al. (2000), Patro (2000), Bin and Morris (2003), Kutan and
Zhou (2006)]. Other studies examine why investors would prefer ADRs over other
investments [Alaganar and Bhar (2001), Arnold et al. (2004), Aggarwal et al. (2007)]. Given
the findings in these studies, one may conclude that ADR investors behave differently from
investors who focus only on domestic securities. Although a number of studies in the
literature have proposed rational or irrational explanations to herding behavior, most of them
have been explained in the context of domestic markets and therefore it will be interesting to
see whether such behavior applies to ADR investors who may be exposed to a wider variety
of uncertainties.1
Tests to detect herding behavior have been applied to a number of advanced and
emerging markets. Some of the prior studies provide support for rational asset pricing models
whereas a number of studies find significant evidence of herding behavior. Christie and
Huang (1995) study U.S. stock returns classified into sectors and find no evidence of herding
1 For further discussion, see Levy (2004), Devenow and Welch (1996), and Scharfstein and Stein (1990).
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in the U.S. market. Wermers (1999) uses trade data to examine the mutual fund industry in
the U.S. and finds that herd behavior exists in small cap and growth oriented funds. Chang et
al. (2000) use firm level data to examine stock returns in the U.S., Hong Kong, Japan, South
Korea, and Taiwan and find significant evidence of herding in Taiwan and South Korea.
Demirer and Kutan (2006) investigate Chinese stock returns and report no herding behavior.
However, in a later study, Tan et al. (2008) examine Chinese A and B shares separately and
find evidence of herding in this market. In another study on Asian markets, Demirer et al.
(2010) study sector-based portfolios and find evidence of herding in the Taiwanese stock
market. Chiang and Zheng (2010) offer a more comprehensive study of investor herds and
examine sector returns in a number of advanced and emerging markets. Their tests yield
evidence of herding in Asian and advanced markets (except for the U.S. market). Using a
different testing methodology, Carpenter (2007) finds evidence of herding propensity among
non-bank financial institutions (NBFIs) in the Australian foreign exchange market. Uchida
and Nakagawa (2007) report similar findings when they examine the Japanese loan market
where they find irrational herding among Japanese banks.
As noted earlier, however, the tests on herding have not yet been extended to
securities of firms that are traded in foreign markets, in this case the U.S. market. As
suggested in prior studies in the ADR literature, ADR returns should be explained by a wider
array of risk factors, including international interest rates, exchange rate movements, risk
premiums in the home market, as well as the risk premiums in the market where these
securities are traded. Therefore, challenged with greater uncertainties, investors in the ADR
market might have a greater tendency to follow each others’ trades or the market consensus.
We contribute to this literature by providing the very first evidence from the market for
ADRs. In the next section we explain our methodology.
3. Methodological Considerations
Two broad groups of methodologies have been proposed in the literature to test the
existence of investor herds. In the first group, herding behavior is measured using
trading/holding data (e.g. buy/sell order activity) whereas the methodologies in the second
group are based on herding measures that are estimated using price data. Within the first
group of herding tests, Lakonishok, Shleifer and Vishny (1992) propose a methodology
where they use an adjusted ratio of net buyers in a security over the sum of net buyers and
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net sellers and examine the probability distribution of this ratio in order to make inferences
on herding.2 Methodologies in the second group utilize price data and examine cross
sectional behavior of returns within a group of securities to test for the existence of investor
herds. In this paper, we employ the testing methodology originally proposed by Chang et al.
(2000) and applied in a number of studies to developed and emerging stock markets.3 This
methodology uses the conditional form of CAPM and focuses on the relation between return
dispersion and market return in order to come up with inferences on herding in a market. For
this purpose, they first define return dispersion as the cross sectional absolute deviation
(CSAD) of security returns within a portfolio. Let tir , be the return on ADR i on day t. The
return dispersion is then formulated as
||1
,
1
, tm
n
i
tit rrN
CSAD
(1)
where tmr , is the return on the equally-weighted ADR portfolio and on day t, and N is the
number of securities in the ADR portfolio. Following the conditional CAPM specification,
Chang et al. (2000) derive a positive and linear relation between return dispersion and the
market return. Considering a portfolio of stocks with different sensitivities to the market risk
factor, one expects the cross-sectional variation in stock betas to lead to a greater variation in
stock returns as each stock would differ in their sensitivity to the market. Therefore, from a
rational asset pricing framework, one would expect a positive and linear relation between
dispersion and the absolute value of the market return. However, Chang et al. (2000) argue
that, in a market where herding behavior exists, the positive and linear relation would no
longer hold. Therefore, they propose the following model which is based on a general
quadratic relationship between return dispersion and the market return
ttmtmt rrCSAD 2
,2,1 || (2)
where tmr , is the equally weighted average of the ADR returns in the portfolio on day
t. In this specification, rational assets pricing models predict a significantly positive value for
α1 (due to the cross-sectional variation in security betas) and an insignificant value for the
coefficient of the non-linear term (α2). However, considering a market where herding
2 Several applications of this methodology include Wermers (1999), Bow and Domuta (2004), Carpenter and
Wang (2007), Uchida and Nakagawa (2007), and more recently, Lin (2008). 3 See Chang, Cheng, and Khorana (2000), Lin and Swanson (2003), Gleason, et al. (2004), Demirer and Kutan
(2006), Tan et al. (2008), and more recently Demirer et al. (2010), and Chiang and Zheng (2010).
7
behavior exists, investors’ correlated actions of moving in and out of markets as a herd would
lead to greater directional similarity in stock returns across the portfolio, leading to lower
return dispersions. Therefore, a significant and negative estimate for α2 is used as support for
the presence of herding behavior. In this study, we follow an alternative specification
originally suggested by Duffee (2000) and estimate
ttmtmtmt rrrCSAD 2
,3,2,10 (3)
where is the equally weighted realized return of all ADRs in the portfolio on day
t. In this alternative specification, (2+1) and (2-1) capture the relation between the
CSAD term and the market return for positive and negative realizations of rm,t, respectively.
Similarly, a significant and negative value for the non-linear term (3) will be consistent with
herding behavior.
In order to check the robustness of the findings, we perform two additional tests.
First, we include additional variables in the model in order to control for the effects of the
market of origin where the ADR is based on and the currency rates. This is based on the
literature on ADRs suggesting that ADR returns can be explained by a wider array of risk
factors, including risk premiums in the home market, risk premiums in the market where
these securities are traded as well as uncertainties regarding exchange rate movements (e.g.
Bin and Morris, 2003, Kutan and Zhou, 2006). This suggests that herding behavior among
ADR investors can be driven by additional risk factors including the stock market
performance in the home country as well as shocks in the currency market. For this purpose,
we perform robustness tests by estimating an augmented model specified as
ttFXtHtmtmtUSt rrrrrCSAD 2
,5
2
,4
2
,3,2,10 (4)
where tHr , and tFXr , are the return on the home market index and the exchange rate on
day t, respectively. This specification allows us to control for effects of the home market
index and exchange rate on the non-linear relation between return dispersion and market
return beyond what is explained by the market factor in the conditional CAPM specification.
A similar specification is used by Chiang et al. (2010) in order to examine the effect of the
U.S. market on herding behavior in global markets.
Following a number of studies reporting asymmetries in equity returns and return
dispersions with respect to market conditions (e.g. Duffee, 2000, Longin and Solnik, 2000,
Ang and Chen, 2002, among others), we next perform additional tests by conditioning our
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estimations on market returns. For this purpose, following the applications of asymmetries to
herding tests in Tan et al. (2008) and Chiang and Zheng (2010), we examine the effect of
market gains and losses on herding behavior by dividing the data into two groups using a
market dummy variable and modify Equation (3) as
ttmtmtmtmtmtmt rrrrCSAD 2
,,4
2
,,3,2,10 )1( (5)
where tm, is a dummy variable that equals one if the market return on day t is
negative and zero otherwise. This specification allows us to test whether herding behavior is
more prevalent during periods of market gains or losses. Next, we present the empirical
findings.
4. Data and Empirical Results
4.1. Data
The data set used in this study contains daily prices for on 305 ADRs issued by
corporations from 19 countries which have at least five ADR issues traded in the U.S. Daily
ADR data and stock market index data are obtained from CRSP and Datastream,
respectively. The data period analyzed is from January 1995 to January 2011, totaling 4,030
listing days for most ADR issues. As suggested by Bikhchandani and Sharma (2000), one
would be more likely to observe herding behavior within sufficiently homogeneous groups of
investors in which they face similar uncertainties and decision problems. For this purpose,
we classify ADRs into two categories: based on the country of origin and industry
classification. Table 1 provides the summary statistics for the cross-sectional absolute
deviations (CSAD) of ADR returns across ADR portfolios as formulated in Equation (1).
Panels A and B in the table report the summary statistics for country and sector based ADR
portfolios, respectively. Note that the number of ADRs for a given country or sector
classification changes over time. The average number of ADRs for each classification over
the sample period is listed in the second column of the table.
Examining country-based ADR portfolios in Panel A, we observe that ADR issues
from India and Ireland have the highest mean level of return dispersion, suggesting higher
variability across ADR returns for these countries. This may also suggest that returns on
ADR issues from these countries had unusual cross-sectional variations due to unexpected
news or shocks, either in their markets or in the U.S. market that they are listed on. On the
9
other hand, the lowest level of cross-sectional dispersion is observed in Chilean ADRs,
suggesting relatively greater directional similarity in the ADR issues from this country.
Following the suggestion by Bikhchandani and Sharma (2000) that it is more likely to
observe herding behavior within homogeneous groups of investors, we also classify ADRs
into sector-based portfolios based on their NAICS classification regardless of their country of
origins. The rationale behind this classification is that ADR investors, in particular emerging
market investors, might be especially interested in foreign firms in specific industries
regardless of the country of origin and this might be another source of herding behavior as
this would lead to homogeneous investor groups facing similar uncertainties specific to those
industries. For this purpose, we assign each ADR to one of fifteen industries including
Agriculture & Forestry, Basic Industries, Capital Goods, Construction, Consumer Durables,
Finance & Real Estate, Food & Tobacco, Information, Mining, Oil & Gas, Petroleum &
Chemicals, Professional Services, Textile & Trade, Transportation, and Utility. We then
compute cross-sectional average deviation of ADR returns based on an equally-weighted
portfolio of all ADRs within each industry classification. Panel B in Table 1 presents
summary statistics for industry-based ADR portfolios. The mean level of cross-sectional
return dispersion ranges between a high of 2.316% for the Service sector and a low of
1.140% for Oil & Gas. The low level of return dispersion across ADRs for Oil & Gas
suggests the presence of a common risk factor, possibly the price of crude oil, driving these
returns and leading to greater directional similarity across these ADRs. Next, we provide the
results of herding tests.
4.2. Herding across country-based ADR portfolios
We begin our analysis with herding tests across country-based ADR portfolios. Table
2 provides the regression estimates for Equation (3). Note that the dependent variable is the
return dispersion measured by the CSAD of ADR returns within each country classification.
As predicted by the conditional CAPM, the regressions yield positive and significant
estimates for 1 and 2 due to cross-sectional variation in asset betas, leading ADR returns
react in different degrees to the market return, thus leading to greater cross-sectional
dispersion. Focusing on the non-linear term in the model, consistent with earlier studies [e.g.
Christie and Huang, 1995 and Chang et al. (2000)] on U.S. stocks, we generally observe
insignificant and non-negative values for 3 for most countries. The only exceptions are
10
ADR issues from Chile, Korea and, interestingly, UK where cross-sectional dispersions are
found to be significantly lower during periods of large market movements indicated by
significant and negative estimates for 3, providing support for herding behavior among ADR
issues from these countries. In order to further examine the different results obtained for
Korea, Chile and the U.K., we have included a dummy variable in herding regressions for
these countries in order to control for major financial crisis experienced in Korea (the 1997
Asian crisis) and the recent credit market crisis that started in the U.S. in 2007. Following
Chiang and Zheng (2010), we used the time periods (7/1/1997-12/31/1998) for the Asian
crisis and (3/1/2008-3/31/2009) for the credit market crisis. The results showed no herding
for the UK once the recent credit market crisis is controlled for in the regressions, suggesting
that financial crises (market crashes) can be a key factor driving herding behavior as
emphasized in the literature. Similarly, in the case of Korea, controlling for the Asian crisis
led to no herding, suggesting that finding of herding behavior in Korean ADRs is
significantly driven by the Asian crisis. Interestingly, the results for Chile are found to be
robust even after controlling for these market crises, suggesting that herding behavior in
Chilean ADRs is more prevalent, and not a statistical result driven by market crisis periods.
4.3. Robustness tests
In order to check the robustness of our findings, we perform two additional tests.
First, following prior findings from the ADR literature, we include two additional variables
in order to control for the effects of the home market index and exchange rate and estimate an
augmented model as specified in Equation (4). Table 3 provides the estimates for the
augmented model. We find that the findings for Chile, Korea and the U.K. are robust even
after controlling for exchange rate effects as well as the shocks in the home market.
However, as explained earlier, once the Asian and credit market crises are accounted for, we
find no evidence of herding behavior in Korea and the U.K., respectively.4 However, we
observe that large swings in the exchange rates have impact on herding behavior among
Korean ADRs. Overall, our tests suggest no significant effect of exchange rates and home
market performance on herding behavior among ADRs, with the exception of Korean ADRs.
Hence, there is some evidence that herding behavior reported in Table 2 for Korea may be
due to exchange rate movements. It is possible that the effect of exchange rates on herding
4 The results of additional tests for these countries are available upon request.
11
among Korean ADRs is largely driven by the high volatility experienced during the Asian
crisis period.
Next, we distinguish between market gains and losses and test for asymmetric effects
of market returns on herding behavior among ADR investors. Table 3 provides the regression
estimates for Equation (5). Note that significant and negative estimates for 3 (4) suggests
that herding behavior is more prevalent during periods of market losses (gains). We find no
asymmetries in herding among ADR issues from Chile and the U.K., suggesting that herding
behavior is more prevalent among ADRs from these countries, regardless of the direction of
the market. However, in the case of Korea, we find that herding behavior occurs during
periods of large market losses only. A similar asymmetry is observed for South African
ADRs where herding behavior is found to occur during periods of large market losses. The
finding of herding behavior during periods of large market losses for Korea and South Africa
is consistent with earlier studies including Kahneman and Tversky (1979) and Kahneman et
al. (1990) suggesting that investors display asymmetries in the way they react to gains and
losses. These findings overall suggest that investors in the ADRs from Chile, Korea, South
Africa and the U.K. will have greater challenges during market downturns or financial crises
as ADRs from these countries will behave more similarly during such periods, eroding
diversification benefits. This means that investors will need to augment their portfolios with
additional assets during such periods. On the other hand, ADR portfolios from Chile and the
U.K. will provide better results than predicted by standard diversification models as these
securities will behave more similarly during periods of market gains, leading these securities
to move in the same direction which will improve portfolio performance.
4.4 Herding across sector-based ADR portfolios
Having found some evidence of herding behavior among ADRs within country
groups, we next focus on sector-based ADR groups in order to investigate possible herding
among ADR investors concentrating on the same sector. In the case of the ADR market,
focusing on foreign securities within the same sector would make it more likely to observe
homogeneous groups of investors facing similar uncertainties, thus leading to greater
tendency to herd. For this purpose, we run similar tests on ADRs classified into sectors.
Table 5 provides the regression estimates for Equation (3). The dependent variable is
the cross-sectional return dispersion measured by the cross-sectional absolute deviation of
12
ADR returns within each sector classification. Once again, consistent with rational asset
pricing models, all regressions in general yield insignificant or non-negative nonlinear term
coefficients (α3), providing no support for herding. We then distinguish between periods of
market gains and losses, as formulated in Equation (5). The findings presented in Table 6
suggest asymmetry in herding behavior in Basic Industries, Capital Goods, Food & Tobacco,
and Textile & Trade, with significant and negative coefficients for the non-linear term during
large market losses only. This suggests that ADRs in these four industries display greater
directional similarity during large negative market shocks beyond what can be explained by
the market return. Once again, the finding that herding behavior is more prevalent during
periods of large market losses is consistent with studies including Kahneman and Tversky
(1979) and Kahneman et al. (1990) which suggest that investors are more concerned with
potential losses than gains. Further examining the findings for these sectors by controlling for
the market crises suggests that the finding of herding behavior in Textile & Trade is largely
due to the recent credit market crisis, leading to no herding after controlling for the effect of
the crisis. However, the finding of herding in Basic Industries, Capital Goods, and Food &
Tobbaco is robust and significant even after controlling for crisis periods, suggesting that
herding behavior is more prevalent in these sectors.
A detailed analysis of observed herding behavior in certain sectors only is beyond the
scope of this study but would be an interesting research agenda in the future. However,
Wemers (1999) summarizes four possible reasons for possible herding within the same
sector. First, investors in the same sector may herd because they want to forestall the
reputational risk of acting differently from others. Second, they may receive correlated
private information, perhaps from analyzing the same indicators. Third, they may infer
private information from the prior trades of better-informed managers and trade in the same
direction. Finally, the fourth reason is that institutional investors like mutual funds may share
an aversion to stocks with certain characteristics. Future studies could provide a detailed
analysis of the above factors in each sector studied here to identify the potential factors
driving herding behavior in certain sectors.
5. Conclusions and suggestions for further research
13
This paper extends the research on investor herds to the market for American
Depository Receipts (ADR). We find evidence consistent with herding behavior in the
market for ADRs from Chile and Korea. Our tests suggest that herding behavior is more
prevalent for Chilean ADRs whereas herding behavior in Korean ADRs is found to be
asymmetric where herding occurs during periods of large market losses only. There is some
evidence that herding in Korea may be driven by exchange rate movements. These findings
suggest that investors in these two countries’ ADRs must exert extra caution in order to
achieve diversification in their country portfolios as ADR returns are likely to display greater
directional similarity during periods of market stress, eroding the potential benefits from
diversification.
We also run similar tests after classifying ADRs into different sectors based on their
NAICS codes and test whether herd behavior exists within sector based ADR groups. Once
again, we find evidence of herding behavior in Basic Industries, Capital Goods, Food &
Tobacco, and Textile & Trade during periods of large market losses. The finding of herding
behavior during large market downturns is consistent with a number of studies in the
literature suggesting asymmetry in utility functions where investors are more concerned
about potential gains than potential losses.
Unlike Chiang and Zheng (2010) who focus on the stock markets of 18 advanced and
emerging economies, we find no evidence of herd formation among investors of ADRs from
developed nations including Australia, France, Germany, Ireland, Italy, Japan, Netherlands,
and Switzerland. The only exception to this is the U.K. where herding behavior is heavily
observed during the recent financial crisis period only. Similarly, unlike Tan et al. (2008)
who document evidence of herding behavior in the Chinese markets, we find no evidence
supporting such behavior in the market for Chinese ADRs.
We suggest four avenues for future research. First, we have provided evidence from
ADRs traded in the U.S. only. It would be interesting to see whether similar results hold for
firms that are cross listed in other major financial centers (i.e., London) or traded in different
foreign currencies (i.e. the euro, the British pound, and the Japanese yen). Second, we have
found that the performance of the market of origin and shocks in currency rates do not have
any significant effect on the cross-sectional dispersion of ADR returns. Therefore, it is
possible that ADR portfolios can be augmented by positions in the currency futures or in the
home market index in order to enhance risk-return tradeoffs of ADR portfolios. Providing
14
empirical evidence for potential diversification benefits is beyond the scope of this paper, but
it could be considered in further studies. Third, potential reasons for differences in herding
behavior among different sectors would be an interesting research agenda. Finally, this study
has used a well-known herding test in the literature originally offered by Chang et al. (2000).
Although our additional tests suggest that the findings are robust to alternative specifications,
the results may still be sensitive to using other alternative herding tests. Future studies need
to apply alternative methods to see whether our findings hold well under different
methodologies which test the existence of investor herds.
15
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18
Table 1. Summary Statistics: Cross-sectional absolute deviations (CSAD) of ADR portfolios.
Average number
of ADRs N Mean Std. Dev. Maximum Minimum
Panel A: Country-Based ADR portfolios Argentina 10.65 4030 1.865% 1.325% 28.876% 0.160% Australia 9.19 4030 2.260 1.579 39.553 0.143 Brazil 5.89 3418 1.492 0.906 12.786 0.032 Chile 12.13 4030 1.241 0.658 8.367 0.175 China 17.06 3819 1.876 1.004 20.019 0.002 France 11.54 4028 2.195 1.557 40.563 0.047 Germany 7.96 4030 1.657 1.178 25.785 0.107 Hong Kong 4.70 3501 2.222 2.130 55.427 0.002 India 10.21 4030 2.338 1.858 37.803 0.007 Ireland 8.89 4030 2.309 1.440 15.968 0.236 Italy 6.56 4030 1.521 1.181 21.923 0.076 Japan 22.67 4030 1.495 0.673 7.964 0.245 Korea 7.43 4012 1.966 1.382 16.898 0.001 Mexico 12.22 4030 1.613 0.867 16.871 0.344 Netherlands 12.01 4030 1.576 0.912 12.598 0.206 South Africa 6.67 4026 1.958 1.227 14.973 0.044 Switzerland 6.23 2676 1.390 0.983 11.114 0.049 Taiwan 6.06 2593 1.554 0.946 12.251 0.109 U.K. 29.13 4030 1.590 1.246 24.364 0.076
Panel B: Sector-Based ADR portfolios Agriculture &
Forestry 2.84 3336 1.425% 1.204% 21.535% 0.002%
Basic Industries 10.35 4030 1.845 1.183 36.847 0.122 Capital Goods 14.27 4030 1.814 0.834 9.254 0.353 Construction 2.42 3971 1.497 1.425 18.244 0.003 Consumer
Durables 25.87 4030 1.931 0.856 7.012 0.233
Fin & Real Estate 24.85 4030 1.553 0.855 10.980 0.218 Food & Tobacco 9.01 4030 1.192 0.687 11.946 0.122 Information 36.78 4030 1.902 0.767 8.152 0.475 Mining 12.03 4030 1.832 1.185 43.954 0.162 Oil & Gas 7.87 4029 1.140 0.597 6.444 0.051 Petro & Chemicals 22.85 4030 2.017 0.953 10.745 0.499 Service 14.53 4030 2.316 1.498 27.110 0.003 Textile & Trade 15.98 4030 2.202 1.146 24.966 0.297 Transportation 7.54 4028 1.794 1.117 15.750 0.013 Utility 10.12 4030 1.589 0.830 11.459 0.249
Note: Panels A and B report the summary statistics for the daily cross-sectional absolute deviation of
ADR returns (formulated in Equation 1) within each country and sector classification, respectively. The
second column reports the average number of ADRs used to calculate the cross-sectional return
dispersions since the number of ADRs for a given country (or sector) changes over time. Note that
sector-based portfolios in Panel B include ADRs from different countries. N is the number of trading
days in each sample.
19
Table 2. Estimates for herding behavior across country-based ADR portfolios (t-ratios in parentheses).
0 1 2 3
Argentina 0.013*** 0.089*** 0.416*** 2.168 (18.405) (4.136) (3.893) (1.028) Australia 0.016*** 0.104*** 0.432*** 3.183*** (49.487) (5.999) (13.911) (7.389) Brazil 0.011*** 0.036*** 0.241*** -0.241 (35.569) (3.622) (8.536) (-0.655) Chile 0.009*** 0.058*** 0.394*** -0.511*** (66.17) (5.853) (23.528) (-4.607) China 0.014*** 0.058*** 0.328*** -0.179 (50.457) (6.011) (10.058) (-0.301) France 0.015*** 0.097*** 0.371*** 3.732*** (31.407) (6.703) (5.922) (3.25) Germany 0.011*** 0.057*** 0.308*** 2.486 (17.238) (3.335) (3.208) (1.346) Hong Kong 0.015*** 0.114*** 0.306*** 2.558** (17.738) (5.798) (3.435) (2.163) India 0.015*** 0.097*** 0.39*** 0.167 (12.044) (4.671) (3.252) (0.131) Ireland 0.015*** 0.053*** 0.589*** 2.207*** (51.647) (3.429) (17.473) (3.419) Italy 0.011*** 0.074*** 0.346*** 3.661** (27.726) (3.403) (5.344) (2.484) Japan 0.012*** 0.034*** 0.295*** -0.266 (53.722) (4.296) (8.597) (-0.344) Korea 0.014*** 0.068*** 0.376*** -0.952* (33.077) (5.403) (9.331) (-1.828) Mexico 0.012*** 0.07*** 0.34*** -0.239 (47.403) (5.768) (10.03) (-0.402) Netherlands 0.011*** 0.047*** 0.389*** 0.24 (50.105) (4.172) (13.911) (0.47) South Africa 0.014*** 0.076*** 0.277*** 0.872* (44.846) (6.661) (8.685) (1.685) Switzerland 0.009*** 0.003 0.321*** 2.614*** (31.961) (0.171) (7.58) (2.797) Taiwan 0.012*** 0.04*** 0.147*** 0.885 (29.855) (4.26) (3.682) (1.507) U.K. 0.012*** 0.045** 0.42*** -3.053***
(42.4) (2.163) (9.101) (-4.234) Notes: The table reports White heteroscedastic-consistent regression estimates for
Equation (3) using daily ADR returns organized into country-based portfolios.
***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively. A
negative and significant estimate for 3 is consistent with herding behavior.
20
Table 3. The effect of the home market index and exchange rate movements on herding behavior
across country-based ADR portfolios (t-ratios in parentheses).
0 1 2 3 4 5
Argentina 0.013*** 0.079*** 0.415*** 2.88 -1.404 1.207* (18.915) (4.199) (4.297) (1.317) (-1.339) (1.958) Australia 0.016*** 0.11*** 0.404*** 3.12*** 2.696* 1.959 (45.341) (5.726) (9.76) (6.073) (1.764) (0.488) Brazil 0.011*** 0.038*** 0.209*** -0.753*** 1.382*** 1.507** (44.013) (4.136) (9.962) (-2.981) (3.438) (2.111) Chile 0.009*** 0.058*** 0.385*** -0.61*** 0.775 -0.33 (64.338) (5.889) (18.729) (-2.757) (0.538) (-0.187) China 0.014*** 0.059*** 0.325*** -0.176 -0.044 7.342 (50.433) (5.899) (9.946) (-0.294) (-0.753) (0.347) France 0.015*** 0.102*** 0.371*** 3.716*** -0.427 6.383* (29.981) (6.568) (5.454) (3.279) (-0.292) (1.767) Germany 0.011*** 0.057*** 0.312*** 2.665 -0.996 2.022 (16.686) (3.39) (3.626) (1.368) (-0.757) (0.633) Hong Kong 0.015*** 0.115*** 0.293*** 2.607** 0.791** 56.63 (17.409) (5.573) (3.223) (2.193) (2.4) (0.281) India 0.015*** 0.099*** 0.381*** 0.239 0.319 -7.496 (11.232) (4.305) (3.095) (0.173) (0.524) (-0.983) Ireland 0.014*** 0.06*** 0.583*** 1.72** 1.183** 9.062*** (46.658) (3.743) (17.203) (2.522) (2.323) (2.907) Italy 0.01*** 0.082*** 0.319*** 3.647** 0.284 12.953*** (25.145) (3.695) (4.72) (2.441) (0.234) (2.589) Japan 0.012*** 0.049*** 0.23*** 0.137 2.483*** 0.652 (62.08) (5.871) (8.219) (0.216) (5.884) (0.507) Korea 0.015*** 0.074*** 0.354*** -0.9* 0.635** -0.671*** (33.651) (5.378) (8.096) (-1.651) (2.278) (-3.368) Mexico 0.012*** 0.071*** 0.313*** -0.546 1.195** -0.218 (46.258) (5.62) (8.507) (-0.738) (2.034) (-0.582) Netherlands 0.011*** 0.051*** 0.375*** -0.091 0.948** 4.046* (45.607) (4.325) (12.494) (-0.15) (2.279) (1.761) South Africa 0.014*** 0.078*** 0.255*** 1.068** 2.221*** -1.326*** (43.723) (6.781) (8.026) (2.019) (3.081) (-3.163) Switzerland 0.009*** 0.001 0.32*** 2.295** 0.79 1.756 (31.017) (0.03) (7.235) (2.291) (0.932) (1.03) Taiwan 0.012*** 0.035*** 0.116** 1.030 1.986*** 9.308 (26.214) (3.775) (2.567) (1.49) (2.862) (1.239) U.K. 0.013*** 0.039* 0.399*** -3.231*** 2.069** -4.044
(41.771) (1.78) (8.066) (-3.64) (2.143) (-1.032) Notes: The table reports White heteroscedastic-consistent regression estimates for Equation (4)
using daily ADR returns organized into country-based portfolios. ***, **, and * denote statistical
significance at 1%, 5%, and 10%, respectively. Negative and significant estimates for 3, 4, and 5
are consistent with herding behavior.
21
Table 4. Asymmetry in herding behavior across country-based ADR portfolios (t-ratios in
parentheses).
0 1 2 3 4
Argentina 0.012*** 0.024 0.442*** 0.226 3.009 (21.289) (0.488) (5.122) (0.193) (1.094) Australia 0.016*** 0.081*** 0.459*** 1.964* 3.207*** (44.739) (3.734) (12.248) (1.903) (7.583) Brazil 0.011*** 0.048*** 0.226*** 0.087 -0.288 (41.941) (3.716) (10.474) (0.329) (-0.682) Chile 0.009*** 0.04*** 0.413*** -1.429** -0.464*** (59.969) (3.166) (19.878) (-2.52) (-3.374) China 0.014*** 0.093*** 0.291*** 1.231 -0.278 (56.361) (5.33) (10.163) (1.552) (-0.463) France 0.015*** 0.049** 0.421*** 1.596** 3.876*** (38.79) (2.031) (9.453) (1.986) (3.49) Germany 0.011*** -0.008 0.333*** 0.582 3.371 (22.214) (-0.196) (4.741) (0.796) (1.5) Hong Kong 0.014*** 0.02 0.414*** -1.119 2.595** (20.184) (0.586) (5.853) (-1.19) (2.199) India 0.015*** 0.085* 0.415*** -0.133 0.197 (14.603) (1.797) (4.17) (-0.13) (0.145) Ireland 0.015*** 0.065*** 0.592*** 2.426*** 1.893 (47.51) (2.765) (15.666) (3.944) (1.572) Italy 0.010*** 0.046 0.354*** 2.842*** 4.121* (29.393) (1.326) (6.123) (3.054) (1.892) Japan 0.012*** 0.048*** 0.284*** 0.432 -0.422 (60.216) (3.011) (9.927) (0.666) (-0.473) Korea 0.014*** 0.095*** 0.355*** -1.08** -0.119 (36.549) (4.707) (9.868) (-2.004) (-0.185) Mexico 0.012*** 0.087*** 0.317*** 0.393 -0.334 (49.167) (4.585) (9.844) (0.592) (-0.436) Netherlands 0.011*** 0.028* 0.392*** -0.266 0.601 (50.34) (1.722) (14.488) (-0.567) (0.798) South Africa 0.014*** 0.016 0.301*** -0.699** 1.565*** (48.848) (1.028) (11.62) (-2.104) (2.587) Switzerland 0.009*** -0.015 0.324*** 2.083 3.001*** (31.946) (-0.586) (7.605) (1.576) (2.703) Taiwan 0.012*** 0.002 0.165*** -0.099 1.234** (35.854) (0.127) (5.504) (-0.243) (1.985) U.K. 0.012*** 0.058** 0.421*** -2.686*** -3.45***
(42.417) (2.013) (9.166) (-2.874) (-3.845)
Notes: The table reports White heteroscedastic-consistent regression estimates for Equation
(5) using daily ADR returns organized into country-based portfolios. ***, **, and * denote
statistical significance at 1%, 5%, and 10%, respectively. Negative and significant estimates
for 3 and 4 are consistent with herding behavior.
22
Table 5. Estimates for herding behavior across sector-based ADR portfolios (t-ratios in
parentheses).
0 1 2 3 Agriculture & Forestry 0.010*** 0.076*** 0.311*** 1.622***
(29.522) (3.265) (9.035) (2.672) Basic Industries 0.014*** 0.089*** 0.334*** 3.903***
(35.598) (4.366) (5.168) (2.893) Capital Goods 0.014*** 0.055*** 0.353*** -0.474
(67.172) (5.534) (12.816) (-1.127) Construction 0.009*** 0.054*** 0.312*** 0.749*
(25.692) (3.256) (9.674) (1.653) Consumer Durables 0.014*** 0.052*** 0.374*** 0.162
(76.532) (6.548) (16.512) (0.407) Finance &Real Estate 0.011*** 0.048*** 0.393*** 0.801**
(64.85) (4.964) (16.782) (2.251) Food & Tobacco 0.009*** 0.063*** 0.359*** 1.091
(38.104) (4.197) (7.366) (0.818) Information 0.015*** 0.078*** 0.391*** 0.158
(76.538) (9.141) (14.098) (0.267) Mining 0.015*** 0.089*** 0.080 4.687**
(16.633) (4.529) (0.611) (2.019) Oil & Gas 0.009*** 0.029*** 0.189*** 0.241
(63.152) (4.062) (11.079) (1.079) Petroleum &Chemical 0.015*** 0.097*** 0.534*** 0.489
(44.035) (6.195) (8.001) (0.287) Service 0.016*** 0.082*** 0.393*** 4.136***
(38.266) (4.733) (6.424) (3.303) Textile & trade 0.017*** 0.084*** 0.447*** 2.829
(37.188) (5.179) (5.674) (1.415) Transportation 0.012*** 0.099*** 0.441*** 0.217
(39.983) (6.653) (11.385) (0.320) Utility 0.012*** 0.079*** 0.387*** 0.267
(68.557) (7.561) (19.456) (0.981) Notes: The table reports White heteroscedastic-consistent regression estimates for
Equation (3) using daily ADR returns organized into sector-based portfolios. ***, **,
and * denote statistical significance at 1%, 5%, and 10%, respectively. A negative and
significant estimate for 3 is consistent with herding behavior.
23
Table 6. Asymmetry in herding behavior across sector-based ADR portfolios (t-ratios in
parentheses).
0 1 2 3 4 Agriculture & Forestry 0.01*** 0.036* 0.290*** 1.186** 2.757***
(30.314) (1.83) (9.128) (2.237) (3.208) Basic Industries 0.014*** -0.022 0.445*** -1.192* 4.350***
(47.12) (-0.981) (11.22) (-1.758) (4.067) Capital Goods 0.014*** 0.054*** 0.354*** -0.486** -0.470
(72.374) (3.785) (15.034) (-2.15) (-0.888) Construction 0.009*** 0.033 0.327*** 0.213 0.878*
(23.325) (1.398) (8.766) (0.237) (1.686) Consumer Durables 0.015*** 0.073*** 0.361*** 1.055* -0.101
(80.24) (5.527) (17.052) (1.81) (-0.265) Finance &Real Estate 0.011*** 0.035** 0.392*** 0.564 1.039
(59.796) (2.197) (15.224) (1.491) (1.628) Food & Tobacco 0.009*** 0.012 0.401*** -1.718** 1.598
(43.451) (0.551) (10.495) (-2.085) (1.028) Information 0.015*** 0.089*** 0.387*** 0.551 -0.033
(82.599) (6.179) (16.087) (0.889) (-0.049) Mining 0.015*** -0.035 0.163** 0.773* 5.693**
(26.077) (-0.859) (2.128) (1.851) (2.393) Oil & Gas 0.009*** 0.018* 0.192*** -0.031 0.408*
(65.428) (1.912) (12.194) (-0.134) (1.703) Petroleum &Chemical 0.014*** 0.204*** 0.485*** 3.164*** 0.002***
(39.347) (6.721) (15.949) (3.668) (4.551) Service 0.016*** 0.071** 0.401*** 3.767*** 4.246***
(43.531) (2.337) (7.765) (3.05) (2.814) Textile & trade 0.016*** -0.007 0.537*** -2.305** 3.272
(42.967) (-0.228) (8.94) (-2.541) (1.455) Transportation 0.011*** 0.097*** 0.474*** -0.522 0.001
(26.537) (3.999) (19.552) (-1.349) (1.15) Utility 0.012*** 0.051*** 0.406*** -0.742 0.559**
(66.768) (3.937) (19.614) (-1.587) (2.414) Notes: The table reports White heteroscedastic-consistent regression estimates for Equation (5) using
daily ADR returns organized into sector-based portfolios. ***, **, and * denote statistical significance
at 1%, 5%, and 10%, respectively. Negative and significant estimates for 3 and 4 are consistent with
herding behavior.
