Download Does the difference in valuation between domestic and foreign

Journal of Banking & Finance 34 (2010) 2886–2896
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Journal of Banking & Finance
journal homepage: www.elsevier.com/locate/jbf
Does the difference in valuation between domestic and foreign investors
help explain their distinct holdings of domestic stocks?
Hyung Cheol Kang a, Dong Wook Lee b,*, Kyung Suh Park b
a
b
Department of Business Administration, The University of Seoul, Seoul 130-743, Republic of Korea
Korea University Business School, Seoul 130-701, Republic of Korea
a r t i c l e
i n f o
Article history:
Received 14 July 2009
Accepted 13 November 2009
Available online 22 November 2009
JEL classification:
G11
G12
G15
Keywords:
Investor heterogeneity
Foreign investors
Valuation difference
Domestic stock holdings
a b s t r a c t
This paper proposes an investor heterogeneity approach to the different domestic stock holdings between
domestic and foreign investors. Specifically, we hypothesize that domestic and foreign investors evaluate
domestic stocks via different models and thus arrive at different valuations for them; consequently, the
two investor groups are attracted to different sets of domestic stocks. Using panel data from Korea, we
find strong support for our hypothesis. More precisely, we find that the foreign ownership of a stock
increases with foreigners’ valuation for the stock in excess of that of domestic investors. As we control
for various firm characteristics known to be correlated with foreign ownership, our results indicate that
the valuation difference between domestic and foreign investors can help explain the allocation of
domestic stocks between the two groups over and above the existing explanations.
Ó 2009 Elsevier B.V. All rights reserved.
1. Introduction
The conventional approach to the coexistence of domestic and
foreign investors in a domestic market is based on information
asymmetries, in which foreign investors are typically depicted as
being informationally disadvantaged (e.g., Kang and Stulz, 1997;
Brennan and Cao, 1997). In this framework, the two investor groups
are predicted to become similar, since the assumed information
asymmetries should shrink as the information of the better-informed is revealed through their trades. However, the stock holdings
pattern of foreigners in a domestic market shows a continued difference from that of domestic investors, thereby raising concerns over
this asymmetric information view (e.g., Karolyi and Stulz, 2003).
In this paper, we propose an alternative approach to the different domestic stock holdings between domestic and foreign investors. Specifically, we adopt an investor heterogeneity perspective.
This alternative approach is well motivated, since it allows for differences among investors to persist over time.1 Moreover, the usual
* Corresponding author. Tel.: +82 2 3290 2820; fax: +82 2 3290 1307.
E-mail addresses: [email protected] (H.C. Kang), [email protected] (D.W. Lee),
[email protected] (K.S. Park).
1
See, e.g., Harrison and Kreps (1978), Varian (1985), Harris and Raviv (1993),
Kandel and Pearson (1995), Allen and Gale (1999), Basak (2000), Coval and Thakor
(2005), or Boot et al. (2006).
0378-4266/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.jbankfin.2009.11.020
problems associated with investor heterogeneity—i.e., its measurability and the aggregability into a ‘‘composite” individual (see,
e.g., Anderson et al., 2005)—are less of an issue in the context of locals vs. foreigners. It is because distinguishing between the two
investor groups is fairly straightforward and the imperfect risk-sharing between them—often known as the home bias—suggests that the
domestic market is incomplete and thus aggregating the two groups
into a representative investor is not appropriate.2
The key to implementing this alternative approach is to identify
and model an inherent difference between domestic and foreign
investors. We do so by building on two stylized facts in international finance. One is that foreign investors are a return-chaser
across countries3 and the other is that domestic investors prefer
home-country stocks to overseas ones.4 Given that foreigners in a
domestic market are international investors who invest in multiple
countries and thus their performances are likely to be assessed in
a global context, the former empirical regularity implies that for-
2
For the home bias, see, e.g., Lewis (1999) or Karolyi and Stulz (2003). For the
aggregation of individual investors in relation to market completeness, see, e.g.,
Rubinstein (1974), Constantidies, 1982, Ingersoll (1987), or Cochrane (2001).
3
See, e.g., Bohn and Tesar (1996), Brennan and Cao (1997), Choe et al., 1999, 2005,
Grinblatt and Keloharju (2000), Froot et al. (2001), or Froot and Ramadorai (2008).
4
See, e.g., French and Poterba (1991), Tesar and Werner (1995), or Lewis (1995,
1999).
H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
eigners are attracted to domestic stocks when those stocks outperform stocks outside the domestic market. In other words, foreigners
evaluate domestic stocks via a global benchmark. In contrast, the latter stylized fact suggests that domestic investors use a local benchmark to evaluate domestic stocks. While some domestic institutions
may be investing abroad as well as at home, they are oftentimes an
agent of domestic individuals and thus are compelled to pay attention to the domestic market. Consequently, we hypothesize that,
due to the benchmark difference, the two investor groups have different valuations for domestic stocks and thus are attracted to different sets of domestic stocks.
We test this hypothesis using data from Korea. This country has
several features that qualify it as an instructive experimental
setting. First, the preference of domestic investors for their
home-country stocks is known to be more pronounced in emerging
markets than in developed markets (e.g., Chan et al., 2005). As an
emerging market that has been frequently examined by prior studies (e.g., Choe et al., 1999, 2005), Korea thus makes a good setting.
Second, this market became fully open to foreigners following the
1997 financial crisis. Consequently, by focusing on the post-crisis
period, we can ensure that the results are not driven by any formal
barriers to foreigners investing in the market. Third, Korea uniquely offers insider ownership information for a large number of
stocks and thus allows us to measure float-based foreign ownership. This data availability is not trivial, since shares held by insiders are not available to foreign investors and thus mechanically
affect their stock holdings pattern (Dahlquist et al., 2003; Kho
et al., 2006).
Using the panel data from 2000 to 2004, we find strong support
for our hypothesis. Specifically, we find that the valuation difference—foreign minus domestic—for a domestic stock is significantly
and positively related to its foreign ownership. This means that foreigners hold the stocks for which their valuation is higher than that
of domestic investors. In obtaining this result, we explicitly control
for the valuation level, so that our finding can be correctly attributed to the valuation differential.
To obtain a more instructive result, we cross-sectionally orthogonalize the foreign valuation to the domestic valuation (and vice
versa). The resultant orthogonalized valuation measure is effectively the valuation difference that is unrelated to the cross-sectional pattern common to both valuation levels. Consistent with
the earlier valuation difference result, we find that the orthogonalized foreign valuation is positively related to foreign ownership,
whereas the orthogonalized domestic valuation has a negative
relation to foreign ownership. In sum, the results clearly support
our hypothesis that the allocation of domestic stocks between
domestic and foreign investors is affected by their valuation
difference.
Before proceeding further, we stress that our goal is not to rule
out any existing explanations but to ‘‘rule in” a new explanation for
the distinct stock holdings between domestic and foreign investors
in a domestic market.5 For this reason, we employ a large set of control variables and demonstrate that the valuation difference explains
the allocation of domestic stocks over and above the existing explanations. More precisely, what is unique to our explanation is its
emphasis on the interaction between domestic and foreign investors,
in particular, the manner in which foreign investors as global returnchasers interact with domestic investors who are biased toward
their home-country stocks.
Of course, our analysis does not explain those trading and holdings patterns per se. As they persist over time, however, we hope to
contribute to the literature by putting such persistent behavior of
the two investor groups into the investor heterogeneity framework
and clarifying its consequences for domestic stock allocation. In
fact, it is conceivable—and even likely—that the hypothesized
benchmark/valuation difference feeds back and reinforces the global return-chasing behavior and the preference for home-country
stocks. Such a feedback channel definitely merits further investigation but seems to be beyond the scope of our paper. We believe
that the focus of our paper—namely, the stock allocation among
investors—is itself a crucial topic and our results serve as an important step toward other broader issues. Our results are also consistent with a recent theoretical work by David (2008) in which
agents with heterogeneous beliefs employ different valuation
models and compete with each other. Such competition makes
risk-sharing imperfect and gives rise to a risk factor that commands a risk premium independent of the traditional market risk
premium. In this regard, our results suggest that the country of residence is one useful dimension of heterogeneity that can help better understand the risk-sharing capacity of a market, especially
that of an emerging market.6
This paper proceeds as follows. In the next section, we develop
the testing hypotheses. Section 3 describes the sample and data,
and Section 4 reports the empirical results. Section 5 concludes
the paper.
2. Hypothesis
2.1. Background
It is standard among economists to explain any difference in
behavior or belief among investors using different information
(e.g., Morris, 1995). It is thus natural to explain the observed difference in domestic stock holdings between domestic and foreign
investors by their difference in the information set. The conventional approach in this spirit is the asymmetric information view,
in which the information set of one investor group encompasses
that of the other.
One immediate implication of this approach is that such an
informational (dis)advantage should shrink over time as the information of the better-informed is revealed, albeit imperfectly,
through their trades. However, even after the massive financial
integration that occurred in the 1980s and 1990s and the resultant
reduction in barriers to information acquisition across countries,
the difference in stock holdings between locals and foreigners persists (e.g., Karolyi and Stulz, 2003). What’s worse, empirical evidence regarding the information asymmetry between domestic
and foreign investors is quite mixed (Grinblatt and Keloharju,
2000; Seasholes, 2000; Froot et al., 2001; Choe et al., 2005; Froot
and Ramadorai, 2008; Kalev et al., 2008; Chang et al., 2009; Chen
et al., 2009). Hence, an alternative approach is definitely
warranted.
One viable alternative is the investor heterogeneity perspective
in which investors with the same information endowment behave
differently because of their focus on different aspects of the common information. Specifically, this alternative approach postulates
that investors either have different priors or employ different models to process information.7 This approach seems to be particularly
promising in international finance, since many of the differences between locals and foreigners are in fact exogenous and thus make the
two groups behave differently over a long time. It is thus worth asking whether domestic and foreign investors hold different domestic
stock portfolios because they use information differently, not because their information endowments are different. To make this ap6
A recent study by Jung et al. (2009) is one such attempt.
Earlier writers adopting this approach include Harrison and Kreps (1978), Varian
(1985), Harris and Raviv (1993), Kandel and Pearson (1995), Allen and Gale (1999),
Basak (2000), Coval and Thakor (2005), and Boot et al. (2006).
7
5
We borrow the expression ‘‘rule in” from Baker et al. (2009).
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
proach operational, we need to specify the manner in which investors on an equal informational footing agree to disagree. The next
section explains our specification.8
the accumulation of daily ownership changes occurring in response to the daily valuations.
Specifically, we summarize the valuation during a given ownership updating interval by estimating the following equation:
2.2. Hypothesis development
Ri;t ¼ ai þ bi RB;t þ ei;t ;
2.2.1. Idea
To specify an inherent difference between domestic and foreign
investors, we use two stylized facts in international finance, one of
which concerns foreign investors and the other concerns domestic
investors. First, foreign investors are known to be a return-chaser
across countries. Given that they are international investors who
also invest in other markets and thus their performances are likely
to be assessed in a global context, their global return-chasing
behavior suggests that foreign investors are attracted to domestic
stocks when those stocks outperform stocks outside the domestic
market. This notion leads us to postulate that foreign investors
evaluate domestic stocks using a global benchmark.
The other empirical regularity we use is that domestic investors
have a strong preference for their home-country stocks. Such a
preference is also known to be more pronounced in emerging markets than in developed markets.9 It thus follows that, unlike foreign
investors, domestic investors—especially those in an emerging market—employ a local benchmark to evaluate local stocks. Of course,
some of the domestic institutions may also be investing in overseas
stocks. However, to the extent that domestic institutions manage
funds on behalf of domestic individuals, the performance of those
institutions will be evaluated against a local benchmark.
Consequently, an instructive contrast can be made between
domestic and foreign investors in terms of their benchmarks. Specifically, our idea is that domestic and foreign investors evaluate
domestic stocks via different benchmarks and thus arrive at different valuations for domestic stocks. This implies that the two investor groups may be attracted to different sets of domestic stocks
even though both groups have the same information endowment.
We thus hypothesize that the allocation of domestic stocks between domestic and foreign investors is affected by their valuation
difference. Below we discuss how we detect the hypothesized valuation differential and its impact on the allocation of domestic
stocks between the two investor groups.
2.2.2. Setup for empirical predictions
While the hypothesized valuation differential is likely to occur
on a continual basis, its consequences—i.e., the allocation of
domestic stocks between domestic and foreign investors—are in
fact observed at a much lower frequency. For this reason, an empiricist cannot perfectly reconstruct the valuation process. As an alternative, we use a summary of these valuations, which is the average
over the stock ownership updating interval. For example, suppose
that investors evaluate domestic stocks on a daily basis but the
ownership information is updated only annually. In such a case,
we first obtain the average daily measure of foreigners’ valuation
(relative to the local valuation) during a certain year and then associate it with the ownership information at the year-end, which is
8
Some prior studies note the cross-country differences in uncertainty with regard
to consumption opportunities, inflation, or non-traded assets. Consequently, they
correctly raise the possibility that locals and foreigners hold different stock portfolios
because they use stocks to hedge against different types of uncertainty (e.g., Cooper
and Kaplanis, 1994; Baxter and Jermann, 1997; Glassman and Riddick, 2001).
However, none of these studies attempt to address the difference between locals and
foreigners from the investor heterogeneity perspective. Moreover, these studies are
focused exclusively on the overall underweighting of domestic stocks by foreigners,
not the difference in stock selection between domestic and foreign investors.
9
For example, Chan et al. (2005) report that none of their sample mutual funds in
emerging markets invest in overseas stocks (p. 1501).
ð1Þ
where Ri,t and RB,t are the returns on stock i and on benchmark portfolio B, respectively, and are both in excess of the risk-free rate.
Here, the return measurement interval is the frequency at which
investors evaluate the stock relative to the benchmark.
This statistical model is well suited for our purpose, since it sep i;t Þ into the one that
arates the average performance of a stock ðR
B;t Þ and
^i R
was actually replicable with the benchmark portfolio ðb
^
the one that was not ðai Þ. To account for the estimation precision,
we scale the estimated alpha with the volatility of the residuals
ðei;t Þ. The resultant scaled alpha is an ex-post look at how much
the stock could have contributed to a higher Sharpe ratio over
and above what could have been achieved by the benchmark portfolio alone (e.g., Treynor and Black, 1973; Gibbons et al., 1989; Pontiff, 2006; Bodie et al., 2008).
Before deriving empirical predictions from this setup, we
emphasize two things. One is that Eq. (1) is not introduced as an
asset pricing model. As mentioned above, this is a statistical model
that helps us understand the return on a stock that is not related to
its benchmark. The other is that we do not argue that investors
estimate Eq. (1) and change their position in the stock in accordance with the alpha estimate. Rather, our view is that investors
constantly evaluate the stock in relation to the benchmark and
the scaled alpha from Eq. (1) is an ex-post summary of those evaluations occurring during each of the return measurement intervals. Put simply, we estimate Eq. (1) to summarize the valuations
to which investors continually responded over our estimation
period.
2.2.3. Empirical predictions
We summarize the valuation of foreign investors by estimating
the following equation:
Ri;t ¼ aFi þ bFi RF;t þ eFi;t ;
ð2Þ
where RF,t is the return on a global benchmark portfolio. Similarly,
we estimate the following equation to summarize the valuation of
domestic investors:
Ri;t ¼ aDi þ bDi RD;t þ eDi;t ;
ð3Þ
where RD,t is the return on a domestic benchmark portfolio.
Our hypothesis is that the allocation of domestic stocks between domestic and foreign investors is determined by their valuation difference. More precisely, a stock is held by whoever has a
higher valuation for that stock. Since the estimated scaled alpha
represents such a valuation averaged over our estimation period,
we predict the following:H1: The scaled alpha difference (scaled
foreign alpha – scaled domestic alpha) is positively related to foreign ownership.
When testing H1, we control for the valuation level by including
the domestic or foreign alpha along with the alpha difference.
Otherwise, we cannot guarantee that the result is indeed driven
by the valuation differential. Another way of ensuring such an
attribution is to use a difference measure that is unrelated to the
valuation level. To this end, we cross-sectionally orthogonalize
one scaled alpha to the other. This procedure purges the common
cross-sectional variation in the alpha level from the orthogonalized
alpha, and thus allows us to examine the role of the alpha difference independent of the effect of the alpha level. Specifically, we
have the following prediction:
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
H2: The orthogonalized foreign alpha—i.e., the foreign alpha
unrelated to the common alpha level—is positively related to
foreign ownership. The orthogonalized domestic alpha—i.e.,
the domestic alpha unrelated to the common alpha level—is
negatively related to foreign ownership.
Table 1
A rundown of sample. This table reports the run-down of our sample. Our sample
includes all non-financial Korean companies listed on the Korean Stock Exchange
(KSE) whose foreign ownership, accounting information, and daily stock return data
are available. Foreign ownership information is compiled by the KSE, while
accounting data are obtained from TS2000, a database maintained by the Korea
Listed Companies Association. Stock return data are from the Korea Securities
Research Institute.
3. Sample and data
Our sample includes all non-financial Korean companies listed
on the Korean Stock Exchange (KSE) for which foreign ownership,
accounting information, and daily stock return data are available.
Foreign ownership information is compiled by the KSE, while the
accounting data are from TS200, a database maintained by the Korea Listed Companies Association. Stock return data are from the
Korea Securities Research Institute. We focus on a period during
which the country’s stock market is fully open to foreign investors—namely, the period from 2000 to 2004. Together with the preceding data requirements, this sample period provides us with
2798 firm-year observations.10 Table 1 shows that our sample is
fairly complete, including more than 70% of the KSE-listed
companies.
In measuring foreign ownership, we use the public float rather
than the total number of shares outstanding. This is motivated by
the fact that shares held by insiders are not available to foreign
investors, nor to other outside investors (e.g., Dahlquist et al.,
2003; Kho et al., 2006). Given that corporate ownership is concentrated in most countries except for the US and the UK (La Porta et
al., 1999), it is crucial to control for insider ownership in analyzing
the stock selection by foreign investors. Otherwise, foreign ownership would be biased downwards with its cross-section potentially
distorted as well. As the KSE provides only the outstanding sharebased foreign ownership, the float-based foreign ownership is calculated by dividing the original foreign ownership by (1 – insider
ownership), wherein the insider ownership is the number of shares
held by controlling shareholders, related parties, and treasury
stock, divided by the total number of shares outstanding.
The first line of Table 2 reports the summary statistics of foreign
ownership. On average, approximately 11% of the public float is
held by foreign investors in the Korean stock market. However,
the distribution seems to be positively skewed, as the median is
at a much lower level of 1%. Not surprisingly, some stocks have
no foreign investors while other stocks are held virtually entirely
by them. These two patterns together—i.e., foreigners’ overweighting of some domestic stocks and their simultaneous underweighting of other domestic stocks—are often called the foreign bias, as
opposed to the domestic bias that refers to the preference of
domestic investors for domestic stocks (Chan et al., 2005).
4. Empirical results
4.1. Scaled alphas
The variable of our interest is the scaled alpha. The scaled foreign alpha is obtained by estimating Eq. (2) in which the MSCI
World Index is the benchmark portfolio.11 A minimum of 200 US
dollar-denominated daily returns in excess of the Eurodollar deposit
rate are used for this estimation. We conduct this estimation every
year because foreign ownership data are updated annually. The
10
These are the observations after we eliminate the top and bottom 0.5 percentiles
of the initial sample by the book-to-market ratios (28 observations). Without this
truncation, the ratio ranges between 347.95 and +97.71, thereby posing a serious
outlier problem. An earlier version of the paper contained some of those extreme
values and found virtually the same results as those reported in this paper.
11
Alternatively, we used the MSCI All Country Index and found results similar to
those reported in this paper. The results are available from the authors upon request.
Year-by-year
Total
2000
2001
2002
2003
2004
Companies listed on the
Korean Stock Exchange
(Financial companies)
(Non-financial companies
with insufficient data)
704
689
683
684
683
3443
(76)
(101)
(65)
(82)
(63)
(55)
(61)
(34)
(56)
(52)
(321)
(324)
Final sample
527
542
565
589
575
2798
Fraction of the total (%)
74.9
78.7
82.7
86.1
84.2
81.3
scaled domestic alpha is estimated every year via Eq. (3) using a
minimum of 200 daily returns and with the Korea Composite Stock
Price Index (KOSPI) as the benchmark. For the latter estimation, we
use the Korean Won-denominated return in excess of the domestic
risk-free rate (daily yield on the Monetary Stabilization Bond). Both
scaled alphas are estimated over a certain calendar year, and then
are associated with float-based foreign ownership at the end of that
year.
Note that the use of the dollar return for Eq. (2) amounts to
assuming that foreign investors are not perfectly hedged against
domestic currency movements. This may also imply that the
uncertainty associated with domestic currency (e.g., currency risk
premium) is one reason for foreigners to invest in the domestic
market. In a later robustness check, we replace the dollar-denominated return with the domestic-currency return, an approach that
assumes that foreign investors are perfectly hedged against
domestic currency movements and thus they take a given domestic-currency return as is.12
We estimate Eqs. (2) and (3) using daily data. As detailed in Section 2.2.2, this means that the resultant alpha estimate is the average daily return on the stock unrelated to the benchmark, or in the
context of our analysis, the average daily valuation in response to
which investors change their position in the stock. This approach is
justified by the finding in the literature that foreigners’ returnchasing behavior is evident at daily frequency. Interestingly, this
behavior is known to be short-lived. For example, Griffin et al.
(2004) show that the domestic return-foreign equity capital flow
relationship disappears at weekly frequency. Other studies, such
as Froot and Ramadorai (2008), confirm the absence of the return-flow relationship at weekly frequency in the Korean market.
The later robustness check thus examines weekly data as well.
Other things being equal, the weekly data are expected to produce
a weaker or no result.
Table 2 provides the summary statistics of the estimated raw
(i.e., unscaled) alphas. As they are estimated with daily stock returns, the magnitude is quite small: the domestic and foreign
scaled alphas are, respectively, 0.04% and 0.05%. The summary statistics of the residual volatilities, the scaled alphas, and the scaled
alpha difference are also provided in the table. Finally, we report
the orthogonalized alphas, which are the residuals from the yearby-year cross-sectional regression of one scaled alpha on the other.
12
In a world in which the uncovered interest rate parity holds, the currency of
denomination would not be much of an issue. It is because
(Ri,D rf,D) (Ri,F rf,F) (Ri,D rf,D) (Ri,D fx rf,F) = fx (rf,D rf,F) = 0, where Ri,D
is a stock’s domestic-currency return, Ri,F is its foreign-currency return, rf,D is the
domestic risk-free rate, rf,F is the foreign risk-free rate, and fx is the change in the
domestic-currency price for one unit of foreign currency.
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
Table 2
Summary statistics of float-based foreign ownership and explanatory variables. This table reports the summary statistics of float-based foreign ownership and its explanatory
variables. Alphas (a), betas (b), and residual volatilities (r) are estimated through Eqs. (2) and (3). EBITDA represents the earnings before interests, taxes, depreciations, and
amortizations. BM is the book-to-market ratio and DivYld stands for dividend yield. Turnover is the total number of shares traded during the year, divided by the number of
shares outstanding. Liquidity is the ratio of current assets to current liabilities. Export is the fraction of export in total sales.
Variable
n
Standard deviation
Min
Foreign ownership
aD
aF
r(eD)
r(eF)
aD/r(eD)
aF/r(eF)
2798
2798
2798
2798
2798
2798
2798
Mean
0.112
0.00039
0.00054
0.037
0.039
0.009
0.016
Median
0.009
0.00028
0.00052
0.034
0.037
0.008
0.016
0.192
0.00216
0.00232
0.015
0.015
0.053
0.053
0.000
0.01453
0.01684
0.009
0.010
0.193
0.189
Max
0.992
0.01107
0.01164
0.099
0.100
0.186
0.209
aF/r(eF) aD/r(eD)
aD/r(eD)ORTH
aF/r(eF)ORTH
2798
2798
2798
0.008
0.000
0.000
0.013
0.000
0.000
0.024
0.011
0.011
0.106
0.075
0.077
0.105
0.072
0.058
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
2798
2798
2798
2798
2798
2798
2798
2798
17.644
0.051
0.845
0.501
0.030
5.458
1.296
0.285
17.331
0.055
0.850
0.496
0.023
2.634
0.947
0.179
1.615
0.095
0.839
0.205
0.038
8.306
1.387
0.305
12.196
2.084
7.676
0.031
0.000
0.001
0.036
0.000
24.951
0.458
7.963
0.999
0.668
87.926
31.725
1.000
Table 3
Correlation coefficients between float-based foreign ownership and explanatory variables. This table reports the correlation coefficients between float-based foreign ownership
(FO) and its explanatory variables. The variables are defined in Table 2. Numbers in parentheses are p-values.
(1) aD/r(eD)
(2) aF/r(eF)
F
F
D
D
(3) a /r(e ) a /r(e )
(4) aD/r(eD)ORTH
(5) aF/r(eF)ORTH
(6) ln(mktcap)
(7) EBITDA
(8) BM
(9) Leverage
(10) DivYld
(11) Turnover
(12) Liquidity
(13) Export
FO
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
0.178
(0.000)
0.202
(0.000)
0.053
(0.006)
0.029
(0.120)
0.057
(0.003)
0.669
(0.000)
0.278
(0.000)
0.118
(0.000)
0.168
(0.000)
0.010
(0.599)
0.217
(0.000)
0.040
(0.033)
0.057
(0.003)
0.895
(0.000)
0.228
(0.000)
0.217
(0.000)
0.000
(1.000)
0.210
(0.000)
0.263
(0.000)
0.069
(0.000)
0.022
(0.250)
0.080
(0.000)
0.020
(0.297)
0.010
(0.604)
0.074
(0.000)
0.230
(0.000)
0.000
(1.000)
0.205
(0.000)
0.232
(0.000)
0.249
(0.000)
0.072
(0.000)
0.084
(0.000)
0.063
(0.001)
0.056
(0.003)
0.016
(0.397)
0.081
(0.000)
0.475
(0.000)
0.448
(0.000)
0.048
(0.011)
0.032
(0.090)
0.005
(0.783)
0.135
(0.000)
0.036
(0.058)
0.080
(0.000)
0.056
(0.003)
0.016
(0.400)
0.970
(0.000)
0.015
(0.434)
0.054
(0.004)
0.015
(0.441)
0.099
(0.000)
0.099
(0.000)
0.167
(0.000)
0.038
(0.044)
0.000
(0.998)
0.035
(0.067)
0.105
(0.000)
0.002
(0.900)
0.105
(0.000)
0.114
(0.000)
0.165
(0.000)
0.043
(0.025)
0.028
(0.136)
0.316
(0.000)
0.175
(0.000)
0.159
(0.000)
0.004
(0.829)
0.210
(0.000)
0.023
(0.216)
0.081
(0.000)
0.061
(0.001)
0.183
(0.000)
0.223
(0.000)
0.312
(0.000)
0.086
(0.000)
0.124
(0.000)
0.003
(0.859)
0.025
(0.184)
0.021
(0.268)
0.065
(0.001)
0.008
(0.682)
0.229
(0.000)
0.193
(0.000)
0.401
(0.000)
0.029
(0.131)
0.222
(0.000)
0.003
(0.861)
0.088
(0.000)
0.044
(0.019)
0.038
(0.047)
0.059
(0.002)
Table 3 shows the correlation coefficient between these variables. The scaled domestic and foreign alphas are highly correlated
(q = 0.895), thereby warranting the use of the orthogonalized alphas when both are to be in one regression. As mentioned in Section 2.2.3, the orthogonalized alpha is effectively the alpha
difference: the alpha difference (foreign minus domestic) has a correlation coefficient with the orthogonalized foreign alpha of 0.449
(p-value less than 0.0001), while its correlation coefficient with the
orthogonalized domestic alpha is 0.475 (p-value less than
0.0001). The orthogonalized alpha is instructive, since it contains
the information of the original alpha while being unrelated to
the other alpha. For example, the orthogonalized foreign alpha is
reliably correlated with the original foreign alpha (q = 0.205 with
a p-value less than 0.0001) but it is unrelated to the scaled domestic alpha by construction.
4.2. Control variables
A variety of firm characteristics have been identified to affect
foreign ownership (e.g., Kang and Stulz, 1997; Dahlquist and Robertsson, 2001). We thus employ a large set of control variables to
correctly attribute our results to the scaled alpha difference. (Note
that corporate insider ownership is already controlled by the use of
the float-based foreign ownership.) Specifically, we use firm size,
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
profitability, book-to-market ratio, leverage, dividend yield, stock
turnover, asset liquidity, and export-to-sales ratio. Like the scaled
alphas, all the control variables are measured at the end of a given
year and then are associated with foreign ownership at the end of
that year. The summary statistics of those control variables are reported in Table 2 and their correlation coefficients appear in Table
3.
Table 3 is particularly useful in confirming their known relationship with foreign ownership univariately. Market value of
equity is the most widely accepted firm characteristic that explains
foreign ownership, and its positive relationship with foreign ownership is confirmed in our data. Corporate profitability, as measured by the earnings before interests, taxes, depreciations, and
amortizations (EBITDA), also has a positive relation to foreign ownership. Book-to-market ratio (BM) is generally known to be negatively related to foreign ownership, since foreigners tend to hold
growth stocks; this relationship is confirmed in our data. Leverage
is the ratio of total debt to total assets, and we confirm its negative
relationship with foreign ownership.
Dividend yield (DivYld) can speak to the governance-related
preference of foreign investors, because well-governed companies
tend to pay their shareholders more (La Porta et al., 2000) and foreigners prefer companies with shareholder-friendly governance
(Lins and Warnock, 2006). Viewed this way, dividend yield will
be positively related to foreign ownership. However, this prediction may be too naïve, since dividend can serve as a governance
proxy only after the growth opportunities are taken into account
(e.g., Chae et al., 2009). In our data, dividend yield alone is not significantly related to foreign ownership. To control for investor
horizon, turnover (Turnover) is also examined, which is the total
number of shares traded during the year divided by the number
of shares outstanding; we find that stocks held more by foreigners
are traded less frequently. Finally, we use the ratio of current assets
to current liabilities (Liquidity) and the fraction of export in total
sales (Export), both of which are positively related to foreign
ownership.
In brief, virtually all of our control variables are highly correlated with foreign ownership, thereby justifying themselves as valid controls. Although dividend yield is not significantly correlated
with foreign ownership in this univariate setting, it is correlated
with other variables and thus needs to be controlled. While our
control variables are also highly correlated with each other, that
does not pose a problem so long as they are not highly correlated
with the variables of our interest, namely, the alpha difference
and the orthogonalized alphas. As Table 3 shows in columns (3)–
(5), their correlations are oftentimes statistically significant but
the magnitude is never greater than 0.167 in absolute terms.
4.3. Main results – tests of H1 and H2
We now test H1 by estimating the following equation:
FOi;t ¼ a0 þ b1 Alphai;t þ b2 AlphaDiffi;t þ
K
X
ck X k;i;t þ fi þ yt
k¼1
þ ei;t ;
ð4Þ
where FOi,t is the float-based foreign ownership of stock i at the end
of year t, Alphai,t is the scaled alpha (either domestic or foreign) for
stock i estimated during year t, AlphaDiffi,t is the scaled alpha difference (foreign – domestic), and Xk,i,t represents one of the control
variables for stock i that are measured at the end of year t. Finally,
fi and yt are, respectively, firm and year fixed-effects.13 With firm
fixed-effects, our results can reveal the within-firm variation in for13
Our choice of the fixed-effect model over the random-effect model is based on the
Hausman (1978) test.
eign ownership. Any pattern that is common to all firms and is
attributable to a certain year will then be taken out by year fixedeffects. Throughout the paper, we use the Rogers standard errors
to control for both heteroscedasticity and the correlation among
same-firm observations (see Petersen 2009).
We do not use the Tobit regression here, since zero foreign
ownership does not necessarily indicate that foreigners wanted
to sell the stock short but were unable to do so (see, e.g., Maddala, 1992, p. 342).14 Our view is that zero foreign ownership is simply an indication that foreigners are not interested in the stock. On
the upper side, the maximum foreign ownership in our sample is
less than one; thus, it is not truncated at all. In a later analysis,
however, we employ the Tobit regression to ensure the robustness
of our results.
Models (1) and (2) in Table 4 show that the alpha difference enters the regression with a significant and positive coefficient, after
controlling for the level of the domestic or foreign alpha and other
firm characteristics. This finding indicates that stocks that are more
valuable to foreigners than to locals are indeed held by foreigners,
thereby confirming H1. The positive coefficient on the domestic or
foreign alpha itself may reflect the return-chasing behavior of foreigners beyond what the alpha difference captures. However, another possibility is that foreigners prefer stocks with certain
characteristics that happen to be correlated with alpha. A third
possibility is that foreigners have private information so they have
a greater position in stocks that will subsequently rise in value
(regardless of the benchmark). Since we control for the alpha level
to obtain the alpha difference result, these versions of endogeneity
are not much of a concern. In Section 4.4, however, we address
endogeneity more carefully.
We test H2 by estimating the following equation:
DðFÞ
FðDÞ
FOi;t ¼ a0 þ b1 Alphai;t þ b2 OrthAlphai;t þ
K
X
ck X k;i;t þ fi þ yt þ ei;t ;
k¼1
ð5Þ
F(D)
where OrthAlphai,t
is the scaled foreign (domestic) alpha that is
orthogonalized each year to the scaled domestic (foreign) alpha.
Note that when the scale domestic alpha, AlphaD, is in the regression, it is accompanied by the orthogonalized foreign alpha, OrthAlphaF; and vice versa. As the latter is unrelated to the former by
construction, we can obtain a clearer picture of the role of the valuation difference.
Consistent with the hypothesis, models (3) and (4) in Table 4
show that the orthogonalized foreign alpha is positively related
to foreign ownership, whereas the orthogonalized domestic alpha is negatively related to foreign ownership. This result evidently supports our idea that the valuation difference between
locals and foreigners is an important determinant of the allocation of domestic stocks between the two investor groups. Again,
we stress that this orthogonalized alpha result is particularly
convincing, since the differing effects of the domestic and foreign valuations on foreign ownership are revealed quite clearly.
Thus, we employ these two models in the following robustness
checks.
4.4. Robustness checks
In this section, we ensure the robustness of our alpha difference
results by applying a number of different specifications to our
analysis.
14
If zero foreign ownership were a result of a short sale constraint, then the stocks
not held by foreigners should have a lower return subsequently (e.g., Chen et al.
2002). However, Jung et al. (2009) report exactly the opposite result in the Korean
stock market.
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
Table 4
Panel regressions of float-based foreign ownership. This table reports the panel regressions of float-based foreign ownership on firm characteristics including the scaled alphas in
several different forms. Specifically, we estimate Eq. (4) (for models (1) and (2) below) and (5) (for models (3) and (4) below). The variables used for the regressions are defined in
Table 2. Numbers in brackets are the t-statistics that take into account both heteroscedasticity and correlation among same-firm observations.
Dependent variable: float-based foreign ownership
(1)
aF/r(eF)
aD/r(eD)
aF/r(eF) aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Control variables
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
(2)
Coeff.
t-Stat.
0.139
[2.86]
0.286
[2.15]
0.043
0.003
0.001
0.015
0.032
0.001
0.003
0.036
Coeff.
0.139
0.425
[6.66]
[0.12]
[0.24]
[0.47]
[0.46]
[3.27]
[1.41]
[1.92]
0.043
0.003
0.001
0.015
0.032
0.001
0.003
0.036
(3)
t-Stat.
(4)
Coeff.
t-Stat.
0.128
[2.65]
[2.86]
[2.95]
[6.66]
[0.12]
[0.24]
[0.47]
[0.46]
[3.27]
[1.41]
[1.92]
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
R2 (%)
44.5
44.5
# of obs.
2798
2798
0.389
[2.77]
0.043
0.004
0.000
0.016
0.032
0.001
0.003
0.035
[6.66]
[0.15]
[0.21]
[0.48]
[0.46]
[3.14]
[1.39]
[1.90]
44.5
2798
Coeff.
t-Stat.
0.103
[2.28]
0.516
[3.52]
0.043
0.004
0.000
0.015
0.032
0.001
0.003
0.035
[6.64]
[0.13]
[0.21]
[0.45]
[0.46]
[3.17]
[1.38]
[1.90]
44.6
2798
Table 5
Panel regressions of float-based foreign ownership – Robustness checks. This table reports the panel regressions of float-based foreign ownership on firm characteristics including
the scaled alphas in several different forms. The variables used for the regressions are defined in Table 2. Numbers in brackets are the t-statistics that take into account both
heteroscedasticity and correlation among same-firm observations.
Dependent variable: float-based foreign ownership
Alpha and residual volatility separated
F
a
r(eF)
aD
r(eD)
aF/r(eF)
aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Coeff.
t-Stat.
1.529
0.063
[1.20]
[0.15]
Beta difference also controlled for
Coeff.
t-Stat.
1.614
0.058
[1.18]
[0.13]
bF bD
Control variables
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
0.044
0.007
0.000
0.021
0.029
0.001
0.003
0.036
[6.82]
[0.26]
[0.22]
[0.64]
[0.40]
[2.22]
[1.52]
[1.97]
0.044
0.007
0.000
0.020
0.029
0.001
0.003
0.036
[6.74]
[0.23]
[0.23]
[0.64]
[0.39]
[2.16]
[1.52]
[1.98]
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
R2 (%)
44.4
44.4
# of obs.
2798
2798
4.4.1. Omitted variables
4.4.1.1. Residual volatility. Thus far, we have scaled the raw alpha
by the residual volatility. Some may argue that such scaling induces a spurious effect, since the residual volatility itself is a proxy
for some important firm characteristics such as information asymmetry. To address this criticism, we separate the residual volatility
from the alpha, and include both in the same regression. As shown
in the left-half panel of Table 5, the residual volatility alone is
never significantly related to foreign ownership. This result means
that the residual volatility helps explain foreign ownership only in
conjunction with the alpha estimate. In other words, the residual
volatility is related to foreign ownership only as a precision measure for the alpha. The result thus implies that persistently positive
Coeff.
t-Stat.
0.130
[2.67]
Coeff.
t-Stat.
0.104
0.535
[2.29]
[3.61]
0.408
0.007
[2.89]
[1.04]
0.007
[1.02]
0.043
0.004
0.001
0.015
0.032
0.001
0.003
0.035
[6.66]
[0.15]
[0.27]
[0.47]
[0.45]
[3.02]
[1.36]
[1.92]
0.043
0.003
0.001
0.015
0.031
0.001
0.003
0.035
[6.64]
[0.13]
[0.28]
[0.45]
[0.45]
[3.05]
[1.35]
[1.92]
44.6
2798
44.6
2798
or negative valuations induce foreigners to change their position in
the stock more than do a few large valuation shocks.
4.4.1.2. Beta difference. Another possible criticism is that our results
are driven by beta, not alpha. Note that the beta in Eq. (2) and (3)
represents the covariance risk with the benchmark portfolio. Consequently, the beta difference can represent the international
diversification benefits (see, e.g., Alexander et al., 1987). To the extent that foreigners, unlike locals, seek low foreign beta stocks to
reduce their portfolio risks, the beta difference (foreign – domestic)
will be negatively related to foreign ownership. Due to the inherent
negative relationship between the alpha and beta estimates, the al-
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H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
pha difference may be spuriously related to foreign ownership
with a positive coefficient.
The right-half panel of Table 5 disproves this story. The beta difference itself is only insignificantly related to foreign ownership
and, further, the sign of the coefficient is the opposite of what this
story predicts. The alpha difference results, on the other hand, remain virtually unchanged: the orthogonalized foreign alpha is positively related to foreign ownership, whereas the orthogonalized
domestic alpha is negatively related to foreign ownership.
4.4.2. Sub-sample analysis
In this section, we split the sample to further address endogeneity. A sub-sample analysis is a straightforward way of detecting
any discontinuities in the explanatory power of a variable in relation to others. Hence, this analysis will help us understand the
sources of the explanatory power of alpha difference. To create
sub-samples, we use the median value of a given sorting variable
identified each year.
4.4.2.1. By market capitalization. One possible challenge to our alpha difference results is that the buying pressure of foreigners
boosts the domestic market as a whole, as well as the individual
stocks, because the stocks preferred by foreigners are typically
large and thus account for a large proportion of the domestic port-
folio. If this were the case, then the domestic alpha estimate would
be lower than the foreign alpha estimate, since the global benchmark is likely to remain unaffected by the buying pressure on
domestic stocks. As a result, a spurious positive relationship can
arise between alpha difference and foreign ownership.
Table 6 (Panel A) vitiates this possibility. While this endogeneity story predicts a stronger alpha difference result in the larger
market capitalization sub-sample, the data point to the opposite.
More precisely, the orthogonalized alphas are reliably related to
foreign ownership only in the smaller market capitalization subsample. It is also worth noting that firm size, ln(mktcap), has
remarkably different explanatory power between the two subsamples: its coefficient in the smaller market capitalization subsample is less than one fifth of the coefficient in the other sub-sample. Together with the weaker significance of the other control
variables, this explains the low R2 in the smaller market capitalization sub-sample. The stronger orthogonalized alpha results in this
sub-sample can thus be interpreted as the valuation difference
being useful in explaining the allocation of domestic stocks when
other explanations are not.
4.4.2.2. By absolute value of scaled alpha. Perhaps a better way of
splitting the sample to gauge the effects of foreigners’ buying/selling pressure on our results is to directly use the magnitude of the
Table 6
Panel regressions of float-based foreign ownership – Sub-sample analysis. This table reports the panel regressions of float-based foreign ownership on firm characteristics
including the scaled alphas in several different forms. Specifically, we estimate Eq. (5) for each sub-sample created by the median value of the sorting variable identified each year.
The variables used for the regressions are defined in Table 2. Numbers in brackets are the t-statistics that take into account both heteroscedasticity and correlation among samefirm observations.
Dependent variable: float-based foreign ownership
Below median ln(mktcap) each year
Panel A
aF/r(eF)
aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Control variables
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
Coeff.
t-Stat.
0.016
[0.41]
0.281
[2.54]
0.014
0.019
0.002
0.002
0.024
0.000
0.001
0.003
[3.33]
[1.52]
[1.27]
[0.15]
[0.99]
[0.68]
[0.70]
[0.45]
Above median ln(mktcap) each year
Coeff.
t-Stat.
0.026
0.243
[0.67]
[2.48]
0.014
0.019
0.002
0.002
0.024
0.000
0.001
0.003
[3.33]
[1.51]
[1.27]
[0.14]
[0.99]
[0.74]
[0.72]
[0.42]
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
R2
5.3%
5.3%
# of obs.
1,401
1,401
Below median |aF/r(eF)| each year
Coeff.
t-Stat.
0.047
[0.46]
0.333
[1.49]
0.084
0.079
0.002
0.149
0.199
0.002
0.002
0.057
[5.36]
[0.70]
[0.33]
[1.56]
[1.06]
[2.10]
[0.58]
[1.36]
42.0%
1,397
Coeff.
t-Stat.
0.038
0.376
[0.41]
[1.41]
0.083
0.076
0.002
0.150
0.195
0.002
0.002
0.058
[5.32]
[0.67]
[0.33]
[1.57]
[1.04]
[2.15]
[0.58]
[1.36]
42.1%
1,397
Above median |aF/r(eF)| each year
Panel B
aF/r(eF)
aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Control variables
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
0.061
[0.49]
0.224
0.080
0.887
0.845
[3.93]
0.032
0.052
0.004
0.013
0.053
0.000
0.002
0.030
[3.73]
[1.21]
[1.44]
[0.36]
[0.76]
[0.66]
[0.84]
[1.65]
0.032
0.051
0.003
0.012
0.054
0.000
0.002
0.029
[3.06]
[0.72]
[3.31]
[3.75]
[1.18]
[1.43]
[0.34]
[0.76]
[0.69]
[0.82]
[1.64]
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
R2 (%)
43.1
43.1
# of obs.
1401
1401
0.150
[0.58]
0.040
0.020
0.000
0.003
0.227
0.001
0.006
0.058
[4.45]
[0.44]
[0.18]
[0.06]
[1.77]
[3.59]
[1.22]
[1.58]
42.8
1397
0.211
0.345
[2.92]
[1.32]
0.040
0.019
0.000
0.001
0.227
0.001
0.006
0.059
[4.36]
[0.42]
[0.19]
[0.03]
[1.78]
[3.61]
[1.20]
[1.59]
42.7
1397
2894
H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
Table 7
Panel regressions of float-based foreign ownership – Alternative alpha estimates. This table reports the panel regressions of float-based foreign ownership on firm characteristics
including the scaled alphas in several different forms. Specifically, we estimate Eq. (5). The variables used for the regressions are defined in Table 2. Numbers in brackets are the tstatistics that take into account both heteroscedasticity and correlation among same-firm observations.
Dependent variable: float-based foreign ownership
aF/r(eF)
aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Cumulative benchmark-adjusted return in
place of alpha
Foreign alpha estimated with local-currency return
on domestic stocks
Foreign and domestic alphas estimated at
weekly frequency
Coeff.
t-Stat.
Coeff.
t-Stat.
Coeff.
t-Stat.
0.0006
[3.06]
0.116
[2.42]
0.043
[2.18]
Coeff.
0.0004
0.002
0.002
[3.77]
Control variables
ln(mktcap)
0.042
EBITDA
0.001
BM
0.001
Leverage
0.015
DivYld
0.032
Turnover
0.001
Liquidity
0.003
Export
0.035
[6.62]
[0.02]
[0.28]
[0.46]
[0.47]
[3.00]
[1.44]
[1.86]
0.042
0.001
0.001
0.015
0.032
0.001
0.003
0.035
t-Stat.
Coeff.
[2.42]
[3.95]
[6.61]
[0.04]
[0.26]
[0.45]
[0.46]
[3.13]
[1.45]
[1.88]
0.103
0.305
0.233
[1.67]
0.043
0.003
0.000
0.016
0.032
0.001
0.003
0.036
[6.58]
[0.11]
[0.20]
[0.50]
[0.46]
[3.46]
[1.46]
[1.93]
0.042
0.003
0.000
0.016
0.032
0.001
0.003
0.036
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
R2 (%)
44.6
44.6
44.4
# of obs.
2798
2798
2798
alpha. If the alpha is caused by foreigners’ purchases and sales,
then the alpha difference result should be stronger when such demand/supply shocks are more evident, i.e., when the absolute value of the alpha is greater.
As shown in Table 6 (Panel B), the alpha difference turns out to
be significant when the scaled foreign alpha is small in absolute
terms. In contrast, the alpha itself is significant only in the other
sub-sample. We therefore reject this alternative explanation for
our results. Recall that, in our hypothesis, the foreign alpha of a
domestic stock represents the average signal to which foreigners
respond. Consequently, these results are interpreted as follows.
When foreigners receive a strong signal on a stock in either direction (i.e., when the absolute value of the scaled foreign alpha is
large), they obtain what they want and domestic investors yield
(i.e., foreign ownership is positively related to the scaled foreign alpha itself).15 On the other hand, when foreign investors do not have
such a strong signal (i.e., when the absolute value of the scaled foreign alpha is small), the scope for interaction grows and the valuation difference becomes relevant (i.e., foreign ownership is
positively related to the scaled alpha difference).16
4.4.3. Alternative specifications
In this section, we modify the alpha or regression specifications
to further investigate our hypothesis.
4.4.3.1. Cumulative daily valuation. We have thus far used the
scaled alpha difference obtained from Eqs. (2) and (3). As explained
in Sections 2.2.2 and 2.2.3, those equations are to produce a summary of daily valuations over a certain year, so that it can be related to the annually reported ownership information. An
alternative way of obtaining a valuation measure that corresponds
to the annually updated stock ownership information is to cumulate daily valuations during the year. Specifically, we cumulate
15
Of course, domestic investors need a reward to make this concession. As shown
by Jung et al. (2009), the existence of a higher average return accruing to the stocks
held less by foreign investors is consistent with this interpretation. That is, this
premium can be considered to be a compensation that domestic investors receive in
return for giving up stocks that foreigners demand and instead holding other stocks to
clear the market.
16
Consistent with this interpretation, when the sub-samples are created by the
absolute value of the scaled domestic alpha, neither the alpha itself nor the alpha
difference are significant in the regressions.
44.5
2798
t-Stat.
[2.27]
[2.06]
[6.56]
[0.10]
[0.21]
[0.48]
[0.46]
[3.48]
[1.46]
[1.94]
0.024
[0.49]
0.043
0.004
0.001
0.018
0.035
0.001
0.003
0.037
[6.67]
[0.17]
[0.28]
[0.56]
[0.50]
[3.56]
[1.46]
[1.98]
44.4
2798
Coeff.
t-Stat.
0.042
0.008
[2.26]
[0.16]
0.043
0.004
0.001
0.018
0.036
0.001
0.003
0.037
[6.63]
[0.15]
[0.27]
[0.56]
[0.50]
[3.59]
[1.46]
[2.00]
44.4
2798
the daily benchmark-adjusted returns (i.e., return on a stock minus
return on its benchmark) during a given year. To account for precision and persistency, we then scale the cumulated daily valuations
by their standard deviation during that year. Finally, the valuation
difference is obtained by deduction the scaled cumulative domestic benchmark-adjusted return from the scaled cumulative foreign
benchmark-adjusted return. The first two models in Table 7 show
that our results are robust to this alternative way of obtaining a
valuation measure.
4.4.3.2. Currency of denomination. Recall that the foreign alpha is
estimated using the US dollar-denominated return on domestic
stocks. This reflects the perspective of an investor who is not
hedged against domestic currency movements, or that of an investor who is motivated to invest in the domestic market by the
uncertainty associated with the domestic currency. Given that
the domestic alpha is estimated using the domestic currencydenominated return, the alpha difference may contain the differing
perspectives on the domestic currency. To gauge the importance of
this component in the alpha difference, we use an alternative foreign alpha, which is estimated using the domestic currencydenominated return.
The middle two models in Table 7 show that the results are
somewhat sensitive to using this alternative foreign alpha. Specifically, the orthogonalized domestic alpha becomes marginally
insignificant with a t-statistic of 1.67, while the sign remains correct. The orthogonalized foreign alpha continues to enter the
regression significantly with a positive coefficient. Note that the
different benchmarks and the different perceptions on the domestic currency are not mutually exclusive. For example, it is plausible
that domestic investors use a domestic benchmark because their
consumption basket is denominated in the domestic currency.
Thus, the somewhat weaker results with this alternative specification should not be taken as evidence against our hypothesis.
4.4.3.3. Estimation using weekly data. As mentioned earlier in Section 4.1, the positive domestic return-foreign equity capital flow
relationship disappears at weekly frequency in some markets
including Korea (e.g., Griffin et al., 2004; Froot and Ramadorai,
2008). Given that we use the regression alpha as a proxy for the
average signal to which investors respond, the lack of such responses at weekly frequency suggests that the alpha difference
2895
H.C. Kang et al. / Journal of Banking & Finance 34 (2010) 2886–2896
Table 8
Regressions of float-based foreign ownership – Alternative regression specifications. This table reports the Tobit or panel regression of float-based foreign ownership on firm
characteristics including the scaled alphas in several different forms. Specifically, we estimate Eq. (5). The variables used for the regressions are defined in Table 2. Numbers in
brackets are the t-statistics that take into account both heteroscedasticity and correlation among same-firm observations (only for panel regressions).
Dependent variable: float-based foreign ownership
Tobit regressions
aF/r(eF)
aD/r(eD)
aF/r(eF)ORTH
aD/r(eD)ORTH
Control variables
ln(mktcap)
EBITDA
BM
Leverage
DivYld
Turnover
Liquidity
Export
Coeff.
t-Stat.
0.092
[2.34]
Observations with zero foreign ownership are excluded
Coeff.
0.077
0.385
0.312
[2.02]
0.054
0.008
0.001
0.011
0.061
0.001
0.003
0.037
[14.14]
[0.29]
[0.25]
[0.50]
[0.89]
[2.04]
[1.94]
[2.20]
0.054
0.009
0.001
0.010
0.061
0.001
0.003
0.037
t-Stat.
Coeff.
t-Stat.
0.126
[2.11]
[2.08]
[2.37]
[14.07]
[0.33]
[0.25]
[0.46]
[0.89]
[2.10]
[1.94]
[2.22]
An intercept, year fixed-effects, and firm fixed-effects are in the regressions but not reported
ln(likelihood) of R2 (%)
2673.42
2673.42
# of obs.
2798
2798
estimated using weekly data is not useful in explaining foreign
ownership. Another relevant observation is that the domestic and
global benchmarks become remarkably similar in weekly data.
Specifically, while their correlation coefficient at daily frequency
is 0.281, it increases to 0.525 at weekly frequency. Thus, the scope
for valuation difference due to benchmark difference shrinks at
this longer interval. Consistent with these two observations, the final two models of Table 7 show that the relationship between the
alpha difference (i.e., orthogonalized alphas) and foreign ownership disappears when the alphas are estimated using weekly
data.17
4.4.3.4. Alternative regression specifications. As mentioned in Section
4.3, zero foreign ownership is unlikely to be a sign of data truncation; hence, the Tobit regression is not warranted. However, we
repeat the analysis using this alternative specification to ensure
the robustness of our results. The results in the left-half panel of
Table 8 show that our results are robust to this alternative regression specification.
Another possible alternative regarding data truncation is to use
only the observations with non-zero foreign ownership. That way,
we can ensure that we are not simply picking up the variation between the zero foreign ownership case and the rest. The results in
the right-half panel of Table 8 show that the valuation difference is
still useful in explaining the dispersion among non-zero foreign
ownership cases.
5. Conclusions
In this paper, we propose an investor heterogeneity approach to
the distinct domestic stock holdings between domestic and foreign
investors. Specifically, we hypothesize that domestic and foreign
investors evaluate domestic stocks via different models and the
resultant difference in valuation leads them to hold different sets
of domestic stocks. To make this hypothesis empirically testable,
we specify the model difference in terms of benchmark: that is,
domestic investors utilize a domestic benchmark, whereas foreign
investors employ a global benchmark. This particular specification
0.334
[2.06]
0.051
0.008
0.001
0.008
0.071
0.001
0.003
0.035
[6.42]
[0.26]
[0.49]
[0.18]
[0.76]
[2.43]
[1.12]
[1.57]
44.2
2384
Coeff.
t-Stat.
0.105
0.452
[1.92]
[2.57]
0.051
0.009
0.001
0.008
0.071
0.001
0.003
0.035
[6.41]
[0.29]
[0.49]
[0.20]
[0.76]
[2.47]
[1.11]
[1.57]
44.2
2384
is motivated by two of the most well-established empirical regularities in international finance: namely, that foreigners are a return-chaser across countries and that domestic investors—
especially those in emerging markets—prefer home-country stocks
to overseas ones.
Using data from Korea, we find strong empirical support for our
hypothesis. More precisely, we find that foreigners hold stocks for
which their valuation is higher than that of domestic investors. As
we control for a variety of firm characteristics known to be correlated with foreign ownership, our results indicate that the valuation difference between domestic and foreign investors can help
explain the allocation of domestic stocks between the two groups
over and above the existing explanations. Our results thus suggest
that risk-sharing within a market can be better understood when
heterogeneity among investors is explicitly recognized and, further, the country of residence provides one useful dimension for
gauging such heterogeneity.
Acknowledgements
Previous versions of the paper were circulated under several
different titles including: ‘‘Do different interpretations of the same
information help explain the distinct stock holdings of foreign
investors?” and ‘‘Do different interpretations of the same information help explain the home bias?” We are genuinely grateful to an
anonymous referee for many of the constructive comments. We
also thank Kee-Hong Bae, Joon Chae, Jay Chung, Gwang Heon Hong,
Sung Wook Joh, Dae Il Kang, Bong-Chan Kho, Joonghyuk Kim, Woojin Kim, Kuan-Hui Lee, Hyoung-Jin Park, and the seminar participants and discussants at Korea University Business School, Seoul
National University, the SKK GSB, the Korea Institute of Finance,
the 2008 joint conference by five Korean finance associations, the
2008 KFMA meetings, the 2008 KFA meetings, the 2008 CAFM conference, the 2009 INFINITI conference, and the 2009 CICF conference for comments. Any remaining errors are our own
responsibilities. Financial support from Korea University is gratefully acknowledged (Kyung Suh Park).
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17
Note that this weaker result with weekly data is not attributable to imprecise
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residual volatility is smaller and the regression R2 is higher with the weekly
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