Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
The Effect of Foreign Institutional Investors before and After
QFII Elimination: Evidence from Taiwan
Ming-Chang Cheng*, Chien-Chi Lee**, Ching-Hwa Lee*** and Chao-Tan
Tseng****
In this article, we found that Taiwan benefited from the elimination of the QFII (Qualified
Foreign Institutional Investors) system and that the destabilizing effects of institutional
investors were diminished. Using 10 years of daily data, we concluded the following. (1)
Foreign investors do have a stabilizing effect on the stock market, except during a crisis
period. (2) There is no strong evidence to prove that foreign investors destabilized the
currency market. (3) Foreign investors had a slight stronger impact than domestic investors
on the stock market during the financial tsunami. (4) The role of foreign investors in Taiwan
has changed following the elimination of the QFII system. The empirical results have policy
implications for those countries that still impose regulations on foreign capital flows and shed
light on the question of whether a small, flat, and export-oriented economy could benefit from
financial deregulation.
JEL Codes: F65, G15, G18
1. Introduction
1.1 Research motivation
In last few decades, financial liberalization has become the mainstream in many emerging
markets, including Taiwan. As an emerging country, the Taiwanese government has
removed restrictions on international capital flows step by step, loosening limitations on the
degree of foreigners’ participation in Taiwan’s stock market.
However, before the Taiwanese government completely eliminated the Qualified Foreign
*
Department of Business Administration, National Chung Cheng University, Taiwan
**Department of Business Administration, National Chung Cheng University, Taiwan; Department of
Hospitality, Taiwan Shoufu University, Taiwan
***Department of Business Administration, National Chung Cheng University, Taiwan
****Department of Business Administration, National Chung Cheng University, Taiwan
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Institutional Investor system (QFII) and prepared to enjoy the benefits of liberalization,
we witnessed the damaging effects of powerful international capital flows in emerging
countries such as the Mexican peso crisis (1994/1995), the Asian financial crisis
(1997/1998), the Russian financial crisis (1998), and the Brazilian crisis (1999). Though
Taiwan was not heavily affected by these crises, we still suffered moderately 1. Radelet and
Sachs (1998) concluded that large-scale movements of international capital flow were at
the core of the Asian crisis. That is, the behavior of international capital flows may offset
some of the advantages of liberalization and internationalization. Hence, this study focused
on the influence of foreign capital flows. We examined the effect of foreign investors in the
stock market and currency market. The objective of this paper is to determine the role of
foreign investors before and after the elimination of the QFII system in Taiwan, including
during the time of 2008 Financial Tsunami.
1.2 Research background
International capital flows from country to country. These cross-border transactions not only
take advantage of high-return markets but also reduce the risk to their portfolios by
diversification. At the end of 2009, Taiwan’s stock market had accumulated approximately
1.768 trillion TWD capital inflows from foreign investors since the elimination of the QFII
system; 470 billion TWD net sales in 2008 were included.
Foreign investors held only 15.06% of the market capitalization of Taiwan’s stock market in
2000, but the number increased to 31.64% in 2009. Furthermore, this figure is
approximately 15 times greater than that for domestic institutions, which held 1.79% for
investment trusts and 0.31% for securities dealers, respectively. This market share
suggests the increasing importance of foreigners to Taiwan’s stock market.
Because of the huge capital flow from foreign investors, when a crisis influenced the
emerging market economies, foreign investors, as the providers of international capital
flows, were blamed for destabilizing stock prices and currency values. However, not all
crises are bad. A crisis might serve as a reflection of the flaws of the country’s existing
policies. Policy flaws could be the reason why an economy was attacked by international
speculators, which were blamed for triggering financial disasters. For example, Mundell
(2000) pointed out that the pegged rate may serve as a useful measure under certain
circumstances, but not as a perpetual measure, because it causes policy conflicts and
eventually leads to a currency crisis. The time bombs started counting down when the
government adopted pegged rate policy, as it did in Mexico, Thailand, and Indonesia.
As a shallow economy, Taiwan exhibited large fluctuations in the stock and exchange
1
Taiwan performed well compared with what other countries suffered during the Asian crisis period. Exchange rate: TWD dropped by 20%, Thai
Baht by 55%, South Korean Won by 50%, Malaysian Ringgit by 42%, and Indonesian Rupiah by 70%. Stock market: Taiwan rose by 18.1%, South
Korea and Southeast Asia dropped by 30% to 55%.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
market, even in the non-crisis period (from 2000 to 2007). The foreign exchange rate
(TWD/$) ranged from 30.302 to 35.168, and the stock index ranged from 3446.26 to
10202.2. As a free market, the capital flows from foreign investors in Taiwan are considered
one of the most influential market powers. By 2001, foreigners owned 15.06% of the stock
market capitalization and 5.89% of trading volume compared with 34.5% of the stock
market capitalization and 17.62% of trading volume in 2007, respectively. Thus, there is
evidence that foreign investors had demonstrated their forceful growth in Taiwan before the
crisis.
Table 1. Net purchases of institutional investors (in 100 million NTD)
2000
Foreign
Investors
2001
2002
2003
2004
2005
2006
5,489.56 2,839.66 7,194.15 5,581.09
2007
741.29
2008
2009
-4,700.02 4,801.40
Total(100
m)
1,466.65 3,062.51
278.97
26,755.26
Investment
Trusts
-160.25
-90.14
68.98
-56.73
-141.07
-863.28
-542.55
1,553.86
441.62
-289.26
-78.82
Securities
Dealers
-693.82
90.46
25.25
297.84
-143.41
169.13
69.01
159.54
437.77
100.08
511.85
Source: Taiwan Economic Journal
Table 1 shows the 10-year net purchases of the three major institutional investors in the
stock market. We can see the magnitude of foreign investors’ capital flow in Taiwan and
find they only act as net sellers during the crisis. Net purchases of domestic institutions
displayed less volume than foreigners most of time, and investment trusts seemed to be the
opposite of foreign investors. Given the differences in magnitude between foreigners and
locals, we would like to know whether foreign investors’ trading behavior, such as feedback
trading2, exacerbates stock and exchange market volatility.
1.3 Research objectives
Taiwan is a good example of crisis effects when capital restrictions are removed. The
difference between the 1997 Asian crisis and the 2008 Financial Tsunami is telling,
because the former occurred during a period of capital control regulation on foreign
investors and the latter did not. Past studies could not provide evidence regarding whether
free international flows stabilize or destabilize the stock market and currency market during
a catastrophe because they lacked such similar events after deregulation. In this study, we
aim to determine the effect of the elimination of the QFII system. Did the role of foreign
investors in Taiwan change after financial liberalization? What role did foreign investors
play during the crisis? Is the effect of foreign investors on stock and currency markets
2
Feedback trading: Trading conduct based on historical data. There are two types of feedback trading: negative and positive. Negative feedback
trading means selling when prices rise and buying when they fall. Positive feedback trading means buying when prices rise and selling when they fall.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
stabilizing or destabilizing before and after the elimination of QFII?
Our hypotheses and explanations are stated as follows: (1) foreign net purchases
destabilize the stock market, which means market returns are affected by foreign net
purchases; (2) foreign net purchases destabilize the currency market; that is, currency
returns are affected by foreign net purchases. Further, we compare foreign institutional
investors with domestic institutional investors regardless of the existence of the QFII
system and during the 2008 financial tsunami: (3) foreign net purchases destabilize the
stock market and currency market more than domestic institutions. Then, we examine
foreigners’ role in Taiwan: (4) has the role of foreign investors changed after the elimination
of the QFII system? In other words, have foreign investors demonstrate different trading
behaviors after deregulation? These hypotheses will be investigated using a vector
autoregressive (VAR) model.
The rest of this paper is organized as follows: Section 2 discusses the possible relationship
between foreign investors and the local market. This section also introduces the
methodology that we used and explains why it applies to this study. Section 3 describes the
data and the methodology. Section 4 presents the empirical results and analyzes the
phenomena that we found during each period. Section 5 concludes the study and makes
suggestions to further studies.
2. Literature review
The stock market in Taiwan is different from other countries; the majority is made up of
individual investors. Thus far, individual investors own approximately 70% of daily trading
volume. Three major institutional investors (securities dealers, investment trusts and
foreign investors) only own approximately 30%. Institutional investors can be separated into
two types: domestic and foreign. Since 2005, foreign institutions have exceeded domestic
institutions in daily trading volume percentage, as Figure 1 shows. In 2008, foreign
institutions reached a high of 22% of daily trading volume; their share has increased nearly
10 times in past 10 years.
We found the trading volume of foreign institutional investors is still much less than that of
individuals (22% vs. 61%). However, their investment strategies and behavior are
considered to be indicators to individual investors. Unlike individual investors, foreign
institutional investors have professional backgrounds and deep pockets. Arbel et al. (1983)
and Brous and Kini (1994) concluded that institutional investors use their own information
and professionals, collecting and analyzing data before devising strategies. Bacmann and
Bolliger (2001) found that foreign analysts produce more timely and accurate forecasts than
local analysts. Thus, individual investors are always concerned with the actions and
strategies of foreign institutional investors. In addition, media reports always focus on the
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
movements of foreign institutional investors. Thus, it is even easier for individual investors
to follow foreign institutional investors’ strategies.
This section discusses the effects of foreign investors on the stock market and foreign
exchange market. Past studies did not come up a certain conclusion regarding whether
foreign investors destabilize these two markets. Thus, the role that foreign investors play,
disturbing and enhancing the volatility of local market, is still a controversial issue for
academics. In the remaining sections, we discuss the negative effects of foreign investors
first, followed by their positive effects on the markets. We also describe the reason that we
chose to study Taiwan. Lastly, we explain why we use a vector autoregressive (VAR) model
as our methodology at the end of the section.
Figure 1. Trading volume of institutional investors in TSE (%)
%
25.00
20.00
15.00
10.00
5.00
0.00
2000
2001
2002
2003
2004
Domestic Institution
2005
2006
2007
Foreign Institution
2008
2009
Year
Source: Taiwan Stock Exchange
2.1 The negative side
The Central Bank in Taiwan3 (CBC) held the opposite attitude toward foreign investors
because they were thought to cause instability and fluctuations in the economy. Taiwan is
a shallow economy; both the stock market and currency market are easily affected by
international and local events. In addition, firms rely on importing and exporting so much
that fluctuations in exchange rates can cause severe damages to them. Thus, the CBC
preferred a step-by-step capital deregulation process to open the market.
Because of the large capital flows from foreign investors, it is also said that foreign
investors may cause market volatility and abnormal returns. Scholes (1972) indicated that
3
The Central Bank in Taiwan (CBC) is the Central Bank of the Republic of China
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
the trading behaviors of foreign investors often cause market instability and abnormal
returns because of their large capital flows. Some research has focused on the trading
behaviors of foreign investors. Bekaert et al. (2002) studied 20 emerging countries and
found that foreign investors were prone to ―capital flight‖: when capital leaves, it leaves
faster than it entered most of these countries. The authors also suggested that foreign
investors’ trading behavior often causes stock market instability and disturbs the exchange
rate.
Foreign investors’ strategies were also examined. Dornbusch and Park (1994) argued that
foreign investors’ trading strategies allow stock prices overreact to changes in
fundamentals and, thus, lead to higher volatility. Eswar et al. (2003) also indicated that
because foreign investors employ herding4 and feedback trading strategies, which can
move stock prices away from fundamentals, fickle capital flows could increase the volatility
of equity returns and destabilize equity markets.
There are also studies of the role of foreign investors during the 1997 East Asian Crisis.
Krugman (1998) argued that speculative capital flows are responsible for the virulence of
these crises and their contagion effects; he suggested that imposing temporary capital
controls could be an effective way to help stabilize the economy during a crisis. Nofsinger
and Sias (1999) and Stiglitz (1998) indicated that positive feedback trading by foreign
investors can partially explain an excess of volatility in stock prices, driving markets away
from fundamentals and destabilizing them. Lin (2006) showed that the strategies of foreign
investors, such as feedback trading strategies, did affect Taiwanese stock market volatility.
The crisis led to a breakdown of international financial transactions and resulted in costly
adjustments for the economies of emerging markets. Previous studies seem to support the
CBC’s claim about the instability caused by foreign investors.
2.2 The positive side
One of the reasons that the government opened the market was that such a policy helps
stabilize, enlarging the domestic stock market and enhancing market efficiency. The Taiwan
Securities and Exchange Commission 5 (TSEC) emphasized the advantage of foreign
investors through, for example, stabilization and demonstration effects. The TSEC
encouraged the government to accelerate the speed with which it opened the stock market
to foreign investors. Wang and Shen (1999) suggested that foreign investors have only mild
influence on the volatility of stock returns and had a positive influence on exchange rate
4
Herd behavior is the tendency for individuals to mimic the actions (rational or irrational) of a larger group.
The Taiwan Securities and Exchange Commission (TSEC), which was renamed the Taiwan Securities and Futures Commission in 1997 and the
Taiwan Securities and Futures Bureau in 2004. The operation of the TSEC is overseen by the Securities & Futures Bureau (SFB), which has the
exclusive authority to inspect the securities market operations and processes of the TSEC.
5
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
volatility during the process of Taiwan’s financial liberalization. Bekaert and Harvey (1997)
studied 21 emerging countries and found that capital market liberalizations significantly
decreased market volatility in emerging countries, especially in Taiwan, Mexico, Brazil, and
Argentina. Bohn and Tesar (1996) pointed out that foreign investors were not the main
cause of stock market instability. Hamao and Mei (2001) found no evidence to support the
claim that trading by foreign investors tends to increase market volatility more than
domestic investors. De Long et al. (1990) offered an analysis of foreign investors’ behavior,
suggesting that, though positive feedback trading is detectable, it did not necessarily
exacerbate the destabilization of the market.
Some studies even suggest that foreign investors did not cause instability during the Asian
financial crisis period. Choe et al. (1999) showed that foreign equity investors did practice
herding during the Korean crisis period, but they found no convincing evidence that foreign
investors played a destabilizing role in the stock market. Kim and Wei (2002) also
suggested that the offshore funds in Korea are the wrong group to blame for exacerbating
the volatility in the emerging markets. Karolyi (2002) pointed out that, though foreigners
became net sellers in the period that Japan was suffering the Asian financial crisis, there is
no evidence to confirm that foreign investors destabilized Japanese markets. Wang (2007)
suggested that increasing the investor-base as a result of liberalization actually decreased
total return volatility. Because each investor has only limited information and a set of
available securities, foreign investors make the investor base larger, which fosters liquid
information. Khanna and Palepu (1999) showed that foreign investors are better monitors
of corporate management than domestic institutions. These studies all concluded that
foreign investors were not the crucial factor in the instability of the stock market, which
means that TSEC’s claims were also supported by scholars.
2.3 Why study Taiwan?
From the above literature review, we can say the role of foreign investors varies from
country to country. As Johnson and Mitton (2003) stated, the benefits of free capital flows
are still debated among policymakers and scholars. Corsetti et al (2001) pointed out that
large institutional investors exacerbated market pressure on smaller- and medium-sized
emerging economies in the crisis period, but large-sized markets were not affected. Thus,
the effect of foreign investors on the stock market and currency market is also controversial
in different periods. Furthermore, the proportion of foreign investors in the stock market and
the magnitude of foreign investors in the economic system are not the same in each country.
Taiwan is a worthy country to study for the following three reasons. First, thus far, it has
been the severest financial crisis Taiwan has encountered after the elimination of QFII
system. Thus, it addresses a key question: does financial liberalization help stabilize the
stock market and currency market? Second, the proportion of daily trading volume of
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
foreign investors (Figure 1) has doubled since the government abandoned the QFII system.
Does this cause damage along with the increase of foreign holding? Finally, as Taiwan is a
shallow economy, how influential could foreign investors be on the Taiwan stock market and
exchange market now that their capital can flow without limitation?
We focus on the role of foreign investors through daily data from 2000 to 2009. Do our
empirical results reveal what foreign investors’ role was during these years, and are there
any different effects between domestic institutions and foreign investors after the
elimination of the QFII system or during the Financial Tsunami? Answering this question
can give policymakers in countries with their own QFII system, such as China, a reference
with which to decide whether to speed up the capital market liberalization process.
2.4 Why a vector autoregressive model?
According to data from the Taiwan Stock Exchange (TSE), in our sample period, we found
that foreign investors were net purchasers, with an average of 1,552 million purchases per
day before crisis. After the crisis, they became net sellers, with an average of 1,888 million
sales per day in 2008. However, foreign investors turned back into net purchasers,
averaging 1,913 million purchases per day as the stock market boomed. We used a VAR
model to examine the joint relationship of stock market returns and currency market returns
and foreign investors’ net flows. Sims (1980) invented the VAR model, which allows every
included variable to be endogenous in order to avoid setting too many inappropriate
limitations on variables. Karolyi (2002) employed a tri-variable VAR model, allowing market
returns and currency returns to depend not only on past returns but also on past net flows
of foreign investment. The ―net purchase‖ variable in this study was highly serially
correlated. Thus, it also depends on past net purchases and past returns in order to test for
foreign investors’ feedback trading behavior. As Lin (2006) suggested, feedback trading of
foreign investors can be detected in Taiwan by employing market returns and net purchase
of foreign investors in the VAR model. Because of the characteristics of the VAR model,
we adopted it as the methodology in this study. Moreover, we use Granger causality to
find the relationship among market returns, currency returns, and net purchases. We
used impulse response function to analyze the effect of a random shock on our variables.
To be certain, variance decomposition is also adopted to show the magnitude of the effect
of other variables.
3. Data and methodology
Our main data source is the Taiwan Economic Journal (TEJ). The daily data comprise
returns of the stock market and currency market and net purchases of foreign investors,
investment trusts, and securities dealers. In calculating the stock market return, we use
ln(Indext / Indext-1), which was also provided by the TEJ database. Currency return was
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Proceedings of 10th Global Business and Social Science Research Conference
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calculated as -ln(ERt / ERt-1); we add a minus to adjust the value. Because of differences in
European terms (TWD/$), when the value of the TWD appreciates, the exchange rate
decreases. This calculation provides a more intuitive way for us to distinguish the change in
value of the TWD. Net purchase was computed by purchases minus sales. In this study, we
divided the entire sample into three sub-samples: sub 1 represents the period before the
elimination of the QFII system, from January 4, 2000 to October 1, 2003; sub 2 represents
the period without crisis after the elimination of the QFII system, from October 2, 20036 to
July 18, 2007; and sub 3 represents the period since the crisis, from July 19, 20077 to
December 31, 20098. The entire sample covers the period between January 4, 2000 to
December 31, 2009. That is, our sample covers four years before the elimination of the
QFII system and four years after its elimination, with 948 and 941 trading days, respectively.
This large sample allows us to find differences in the relatively long run. The crisis after
the elimination of the QFII system lasted 2 years, with 615 trading days.
Figure 2 Daily foreign investment in Taiwan stock market: 2000-2009
12,000
4,800
Market index
10,000
Cumulate FI’s
net purchases
Exchange rate
4,000
8,000
3,200
6,000
2,400
TSE volume
4,000
1,600
2,000
800
0
0
1/
20
09
7/
20
09
-800
1/
20
08
7/
20
08
1/
20
07
7/
20
07
1/
20
06
7/
20
06
1/
20
04
7/
20
04
1/
20
03
7/
20
03
1/
20
02
7/
20
02
1/
20
01
7/
20
01
1/
20
00
7/
20
00
1/
20
05
7/
20
05
FI’s net purchases
-2,000
Left axis (-2,000~12,000): Market index: Taiwan weighted stock index.
Right axis (-800~4,800): Exchange rate: (TWD/$)*100. Cumulative foreign investors’ net purchases (billion, TWD).
Foreign investors’ net purchases (100 million, TWD). TSE volume (100 million, TWD)
Source: Taiwan Economic Journal
We provide Figure 2 to show Taiwan’s weighted stock index and currency values for the
entire sample; net purchases of foreign investors were also included. The time series
6
October 2, 2003 was the day that the Taiwanese government abandoned the QFII system.
July 19, 2007 was the day that Bear Stearns Companies’ two funds collapsed (Bear Stearns High-Grade Structured
Credit Fund and Bear Stearns
High-Grade Structured Credit Enhanced Leveraged Fund). The same day, the Dow Jones Industrial Average Index climbed to 14,000 points. From
that point, the U.S. stock market started entering a bear market.
8
December 31, 2009 was the end-point for the available data we could collect.
7
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
figure helps us to observe the trend of our variables across the entire sample period. During
the crisis, the stock index reached 9,000 in July 2007 and achieved its peak of 9,859.65 in
October 2007. However, the index experienced sustained decline to below 4,500 in late
2008. TSE volume presented significant decline along with market index. TWD/$ also
showed dramatic depreciation, from its peak of 30.1 to its bottom of 35.174. However, local
policy9 should be considered to partly explain this decrease. Cumulative foreign investors’
net purchases demonstrate selling strategies, a trading strategy inverse to the one
previously used. Moreover, we find that net purchases of foreign investors tend to be more
volatile than before, possibly suggesting that these foreign flows are fickle and speculative.
3.1 Methodology
To analyze the destabilizing effect of foreign net purchases on stock or currency returns,
and to determine whether foreigners used feedback trading, we adopt the VAR model,
which employs market returns, currency returns and net purchases. Because there are
strong positive correlations among variables and autocorrelation within variables
themselves, it is difficult to determine their relationship. Sims (1980) invented the VAR
model to solve these problems by establishing a dynamic structural model. The VAR
model is a structural model that enables the variables in this study be driven by the lags of
other variables. Using Schwarz criterion (SC) and evaluating lag structure in the dynamic
system, we determined that four lags10 were enough to capture most of the effects of
institutional investors’ net purchases. Let market returns, m; currency returns, x; net
purchases, n; then the tri-variate equations can be written as follows:
mt  C10  B111mt 1  B112 xt 1  B113nt 1  ...  Bi11mt i  Bi12 xt i  Bi13nt i  e yt
xt  C 20  B121mt 1  B122 xt 1  B123nt 1  ...  Bi21mt i  Bi22 xt i  Bi23nt i  e xt
nt  C30  B131mt 1  B132 xt 1  B133nt 1  ...  Bi31mt 4  Bi32 xt i  Bi33nt i  e zt
…(1)
To make it easier to read, Eq. (1) can be derived as follows:
L
yt      sYt  s  et , E(et et' )  
s 1
…(2)
Where yt is a 3×1 vector of daily observations of market returns, currency returns and net
purchases at time t; α is a 3×1 vector of constant parameters; βs is a 3×3 matrix of
parameters at time s. L represents the lags for the model and μt is a 3×1 vector of errors
9
Similar to other export-oriented countries, the Taiwanese government intended to depreciate the TWD in order to boost export volume in the early
crisis.
10
SC and AIC suggested one lag is the most parsimonious model. However, one lag is not enough for our purposes. We then tested our model from
one lag to ten lags and found that four lags could reflect most of the information that we want. Lags above four only needlessly extend the model
with lags carrying less information.
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Proceedings of 10th Global Business and Social Science Research Conference
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of yt given all the past y’s. The elements of βs measure the direct effect that a change in
the return on the j-th variable would have on the i-th variable in s days. Each investor’s
net purchases series was used to estimate this model.
According to Sims (1980), the coefficients in the VAR model do not effectively reveal
economic implications. Three important applications were developed to interpret the
meanings of the VAR results. Using a causality test, an impulse response analysis, and
variance decomposition makes the VAR model more readable and meaningful in its
analysis. A Granger causality test was used to determine whether the lags of past returns or
purchases are jointly significant predictors of future returns or purchases. We tested
market returns, currency returns, and net purchases of each institutional investor. Eq. (1)
can be written in the following form:
k
k
k
i 1
k
i 1
k
i 1
k
i 1
k
i 1
k
i 1
k
i 1
i 1
i 1
mt  C10   Gi11mt i   Gi12 xt i   Gi13 nt i  e yt
xt  C 20   Gi21mt i   Gi22 xt i   Gi23 nt i  e xt
…(3)
nt  C30   Gi31mt i   Gi32 xt i   Gi33 nt i  e zt
Where G is the set of all coefficients of a certain variable. For example, x Granger causes m
if and only if the coefficients in G12 are not all equal to zero, which rejects the null
hypothesis of Granger causality that G = 0. There are two types of causality:
unidirectional causality and bidirectional causality. In the former, one variable Granger
causes the other variable. In the latter, two variables Granger cause each other.
To trace the cross-equation feedbacks through Eq. (2), an alternative method is to use the
multiplier analysis or innovative accounting technique based on the moving average. The
moving average form can be written as follows:
yt 

 s et  s
s 0
…(4)
Where yt is a linear combination of current and past one-step-ahead forecast innovations.
The (i,j)-th element of matrix Φs measures the response of the i-th variable in s days to a
unit random shock in the j-th variable and none in the other variables. However, the vector
of et is serially correlated, a serious flaw because we would like to determine the distinct
response patterns of the VAR model. Thus, we adopted Choleski factorization. The Σε
can be decomposed into VV’ by using Choleski factorization to remove the problem we
faced. The orthogonalizing transformation proceeds as follows:
E(et et' )    ADA'  AD1/ 2 D1/ 2 A'  VV '
…(5)
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Proceedings of 10th Global Business and Social Science Research Conference
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Where A is a square matrix of the sample covariance matrix Σε, D is a diagonal matrix, and
V is a nonsingular, lower triangular matrix with positive elements on the diagonal.
Then, Eq. (4) can be rewritten as follows:
yt 

 Cs kt , kt  V 1et
s 0
…(6)
Where Cs is a square matrix, the (i,j)-th elements of Cs stands for the impulse response of
the i-th variable in s periods to a shock of one standard error in the j-th variable. The
series of kt is serially uncorrelated error. We plot the impulse responses and 95%
confidence intervals using standard errors from the Monte Carlo integration technique.
Through Choleski factorization, the moving average form of Eq. (4) can be used to allocate
the forecast variance of each element in y to different sources of shocks.
The error of the
optimal T+1 ahead forecast is:
yt 
T 1
 Rij2, h
h 0
…(7)
The element of forecast error variance in the T+1 step ahead forecast of yi, which is
accounted for by innovations in yj. That is, R2ij,h represents the contribution by innovation
in the j-th variable to variance of the i-th variable at time h. Variance decomposition
analysis provides a measure of the overall relative importance of the markets in generating
the fluctuations in daily net purchases from past net flows, stock market returns ,and
currency returns. We report the percentage of total forecast error variance for stock
market returns, TWD/dollar exchange rates returns and net purchases up to 5, 10 ,and 15
days ahead accounted for by innovations of past market returns, exchange rates returns
and net purchases.
4. Empirical results
4.1 Summary statistics
Table 2 presents summary statistics for market returns, currency returns, and net
purchases by each investor group during the QFII, QFII-eliminated, crisis, and entire
sample. Over the entire period, foreign investors were the most aggressive net purchasers
of Taiwanese equity, at 1,000 million per day. Foreign investors’ net purchases increased in
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sub 2 compared with sub 1, indicating that deregulation was successful at attracting
international capital flows (from 9.57 to 21.5). However, the shift to negative net purchases
by foreign investors and negative market returns suggests that the expectation that foreign
investors tend to sell in crisis is true. Domestic institutions did not show the same pattern as
foreign investors. Investment trusts revealed opposite strategies from foreign investors
across the entire sample. Securities dealers apparently had their own approach. However,
domestic institutions became net purchasers during the crisis. Comparing sub 1 with sub 2,
we found that, as the investor base broadened and information liquidation increased, the
standard deviation of stock market returns decreased following liberalization. This result11
met the government’s goal for opening the stock market. Though the standard deviation of
currency returns slightly increased, the change is not strongly statistically significant (10%
level). The result for the currency market suggested that the CBC was too concerned with
the consequences of deregulation. It is also possible that the CBC still has a visible hand in
the currency market.
However, when the U.S. subprime mortgage crunch tolled the bell for the crisis in late 2007,
the bloom of deregulation started fading in Taiwan. The standard deviation of market
returns increased to the same level as it had been before. Has deregulation helped stabilize
these two markets? Or are there other reasons that offset the advantages of liberalization?
Table 2. Summary statistics
Entire sample (2000/01/04~2009/12/31)
Market returns
TWD/$ returns
Foreign Investors’ net
Investment
Trusts’ net
purchases(100m)
Securities
Dealers’
purchases(100m) net
Mean
-0.001252
-0.000664
10.68501
-0.031478
0.204413
S.D.
1.620951
0.24679
72.81381
11.97509
11.6345
Maximum Minimum
6.5246
-6.9123
2.898567 -2.956315
12
13
1252.88
-623.98
64.53
-88.76
63.79
-111.77
Trading
2504
days
2504
2504
2504
2504
Subpurchases(100m)
1 : QFII (2000/01/04~2003/10/01)
Market returns
TWD/$ returns
Foreign Investors’ net
Investment
Trusts’ net
purchases(100m)
Securities
Dealers’ net
purchases(100m)
Mean
-0.043727
-0.007458
9.574473
-0.256983
-0.38192
S.D.
1.88326
0.227434
33.79942
13.09837
9.079974
Maximum Minimum
6.1721
-6.7745
2.898567 -2.956315
175.82
-154.6
64.53
-60.13
42.19
-38.31
Trading
948
days
948
948
948
948
Subpurchases(100m)
2 : QFII-eliminated
Market returns
(2003/10/02~2007/07/18)
TWD/$ returns
Foreign Investors’ net
Mean
0.056351
0.003155
21.50359
S.D.
1.101084
0.241843
79.07887
Maximum Minimum
5.4189
-6.9123
1.006908 -1.006487
1252.88
-549.37
Trading
941
days
941
941
purchases(100m)
11
The standard deviation of the stock index significantly decreased from sub 1 (52.97) to sub 2 (26.33). The currency
market also benefited from liberalization, as the standard deviation of the exchange rate significantly decreased from
sub 1 (0.051) to sub 2 (0.026). However, both eventually rose to 62.99 and 0.044, respectively.
12
The largest net purchases of foreign investors, due to block trading when Philip Taiwan transferred all of its TSMC equity to its headquarters,
Philip Deutschland. (December 28, 2005)
13
The largest net sales of foreign investors caused by Dow Jones slump. (July 27, 2007)
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Investment Trusts’ net
Securities
Dealers’ net
purchases(100m)
-1.102221
0.349458
10.10109
13.96242
Subpurchases(100m)
3 : In the crisis
Market returns
(2007/07/19~2009/12/31)
TWD/$ returns
Foreign Investors’ net
Investment
Trusts’ net
purchases(100m)
Securities
Dealers’ net
purchases(100m)
Source: Taiwan Economic Journal
purchases(100m)
Mean
-0.023915
0.003967
-4.156455
1.954455
0.886293
S.D.
1.837494
0.280795
99.37631
12.56904
11.18761
52.72
63.79
-88.76
-111.77
Maximum Minimum
6.5246
-6.7351
1.500921 -1.222136
14
579.09
-623.98
50.01
-49.39
41.58
-47.8
941
941
Trading
615
days
615
615
615
615
Table 3 shows the correlation coefficients of daily net purchases for each of the investment
sectors, with market returns and currency returns. Foreign investors shared the same
investment behavior as domestic institutional investors; the correlation coefficients between
net purchases of foreign investors and market returns, net purchases of investment trusts
and market returns, and net purchases of securities dealers and market returns are all
strongly positive. The result is consistent with the correlation table in Lin (2006), which uses
daily data from 1994 to 2003. The significant positive correlation coefficients among
institutional investors suggest that herding behavior may exist in the stock market, similar to
Kim and Wei’s (1999) and Kaminsky and Schmukler’s (1999) findings. The significant
positive correlation coefficients among institutional investors suggest that herding behavior
may exist in the stock market, similar to Kim and Wei’s (1999) and Kaminsky and
Schmukler’s (1999) findings. There is a strong positive relation between each institutional
investors’ trading activity.
Table 3. Correlation coefficients
Market
returns
14
TWD/$
returns
Foreign
Investors
Investment
Trust
Entire sample
Market returns
TWD/$ returns
Foreign Investors’ net
Investment Trusts’ net
purchases
Securities Dealers’ net
purchases
1.000
0.276 ***
0.401 ***
0.397 ***
0.565 ***
1.000
0.270 ***
0.101 ***
0.161 ***
1.000
0.175 ***
0.354 ***
1.000
0.406 ***
Sub
1 : QFII
purchases
Market returns
TWD/$ returns
Foreign Investors’ net
Investment Trusts’ net
purchases
Securities Dealers’ net
purchases
1.000
0.183 ***
0.362 ***
0.465 ***
0.584 ***
1.000
0.170 ***
0.087 ***
0.150 ***
1.000
0.280 ***
0.364 ***
1.000
0.496 ***
Sub
2 : QFII-eliminated
purchases
Market returns
1.000
The virtually largest net purchases of foreign investors triggered by Ma’s victory in presidential election. (March 22, 2008)
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TWD/$ returns
Foreign Investors’ net
Investment Trusts’ net
purchases
Securities Dealers’ net
purchases
0.301 ***
0.395 ***
0.373 ***
0.694 ***
1.000
0.259 ***
0.082 **
0.202 ***
1.000
0.162 ***
0.346 ***
1.000
0.396 ***
Sub
3 : In the crisis
purchases
Market returns
1.000
TWD/$ returns
0.400 *** 1.000
Foreign Investors’ net
0.565 *** 0.364 ***
1.000
Investment
Trusts’
net
0.325
***
0.144
***
0.224 ***
1.000
purchases
Securities Dealers’ net
0.568 *** 0.111 ***
0.423 ***
0.374 ***
purchases
Note: When we adjusted the calculation of currency return to –ln(ERt/ERt-1), the correlation became positive.
purchases
*** = 1% significance level; ** = 5% significance level; * = 10% significance level
Source: Taiwan Economic Journal
Nevertheless, foreign investors showed increasing magnitude in both stock and currency
markets during the crisis while domestic institutional investors’ magnitude diminished in the
stock market. We also found that the correlation coefficients between market returns and
currency returns are higher since deregulation, which means the frequent inflow and
outflow of international capital flows enhanced the relationship between the stock market
and the exchange market.
The correlation coefficients between market returns and currency returns reached their
highest in the crisis period compared to the non-crisis period. Similarly, the correlation
coefficients between net purchases of foreign investors and currency returns increased
following deregulation. Domestic institutions showed lower coefficients, which suggests
lower demand in exchange than for foreign investors. We assume that domestic institutions
would have less effect than foreign investors on the exchange market, no matter the status
of the QFII system.
4.2 VAR model results
Before we implement VAR in this study, we have to check whether the VAR model is
suitable. Unit root tests were used to ensure that all the series that we employed are
stationary. An augmented Dickey-Fuller test and a Kwiatkowski-Phillips-Schmidt-Shin test
were adopted. As Table 4 shows, each variable series we employed is stationary. In this
case, we do not have to implement a vector error correction model (VECE). The VAR is
good enough for us to analyze the phenomena in Taiwan. Table 5 reports the estimation
results by the VAR model for each of four different sectors in three periods. For each
sector, the first four coefficients represent the lagged market returns, currency returns and
net purchases. The Granger causality test shows that one variable can be forecasted by
the lags of the other variable. In this section, we focus on the difference between sub 1 and
sub 2 first. Then, we compare the changes in sub 2 with sub 3. Last, we will provide a
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summary of our overall empirical results.
Table 4. Unit root test
ADF
Variable
T-Stat.
KPSS
Critical value (1%) LM-Stat. Critical value (10%)
Market returns
-47.369***
-3.433 0.179
0.347
TWD/$ returns
-46.859***
-3.433 0.115
0.347
Foreign Investors’ net
-20.195***
-3.433 0.185
0.347
Investment
purchases Trusts’ net
-33.018***
-3.433 0.337
0.347
-34.467***
-3.433 0.462*
0.347
Securities
purchases Dealers’ net
Series of securities dealers’ net purchases did not reject the null hypothesis at a 5% significant level in the
purchases
KPSS
test. (critical value: 0.463).
4.2.1 Sub 1 vs. Sub 2
The results show that there is a little evidence to support the idea that foreign investors’ role
in the stock market has changed following the elimination of the QFII system (see Table 5.a
and Table 5.b). The first-lag coefficients of foreign investors’ net purchases in the two
periods for market returns, 0.0065 and 0.0017 respectively, are significant and positive.
There is only a slight change because all the coefficients of foreign investors’ purchases for
market returns in sub 2 had diminished. Foreign investors still adopted positive feedback
trading strategies; most coefficients of market returns for net purchases are positive and
include statistically significant positive coefficients (3.556 in sub 1 and 15.191, 5.576 in sub
2). A Granger causality test also showed that net purchases are affected by market returns;
the chi-square values are 47.426 and 44.771 in the two periods. Information contributions
were also detected for net purchases of foreign investors. Granger caused market returns
(11.659 and 9.441, respectively) with bidirectional causality, which indicates future net
purchases have an impact on market returns and vice-versa. The results show only a small
difference after the QFII system was eliminated. We thought that the step-by-step
liberalization process effectively avoided drastic changes. However, net purchases of
foreign investors Granger caused currency returns from an insignificant 6.623 to a
significant 20.029, indicating that, as the volume of international capital flows increases,
foreigners gained greater impact on currency market.
In contrast, domestic institutions became less influential on the stock market. Both
investment trusts and securities dealers were more powerful than foreigners in sub 1, as
Table 5.a shows. Domestic institutions’ first-lag and third-lag coefficients were significant:
0.013 and -0.014 for investment trusts 0.027 and -0.013 for securities dealers. However,
their influence diminished to only one significant coefficient: first-lag 0.009 for investment
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trusts and third-lag -0.006 for securities dealers (see Table 5.b). Investment trusts
revealed themselves to be negative feedback traders in two periods, as most of the
coefficients are negative and were Granger caused by market returns (89.045 and
173.474). However, they had only a bidirectional causality with market returns in sub 1. In
other words, feedback trading and information contribution were both detected only in sub 1.
We found that investment trusts tended to sell stock when the TWD appreciated in two
periods, though the coefficients are not statistically significant. Securities dealers turned
into positive feedback traders in sub 2; the first-lag coefficient (2.127) and third-lag
coefficient (1.048) are significant and positive, with an inverse causality from 1.952 to
19.112. We also found that currency returns had an increasing impact on securities
dealers in sub 2 for the significant first-lag coefficient (3.393) and third-lag coefficient (4.315)
and Granger caused net purchases of securities dealers. The results suggest that
securities dealers tended to purchase stock when the TWD appreciated, unlike investment
trusts.
The results show that all institutional investors were feedback traders in sub 2, consistent
with Froot et al. (2001). We found no strong evidence that foreign institutional investors
destabilized the stock market more than domestic institutions in sub 1 and sub 2. However,
we did find that foreign institutional investors have had an increasing impact on the
currency market since the elimination of the QFII system. Though domestic institutions
once had greater influence, it disappeared after deregulation. Liberalization was beneficial
to Taiwan in the non-crisis period.
4.2.2 Sub 2 vs. Sub 3
The role of foreign investors changed during the crisis; they demonstrated a destabilizing
effect on stock market, as Table 5.c shows. The coefficients of foreign investors’ net
purchases for market returns became stronger than ever, from a first-lag coefficient of only
0.002 in sub 2 to a second-lag coefficient of -0.002 and a third coefficient of 0.003 in sub 3.
Foreign investors’ positive feedback trading disappeared. The first-lag coefficient (3.505) of
market returns to net purchases of foreign investors became less significant, and this time
only foreign investors Granger caused market returns, a unidirectional causality (11.067).
In other words, information contribution remains. This result shows that market returns
were not able to predict net purchases of foreign investors but that net purchases of foreign
investors still predicted market returns across the entire sample. The results were
opposite those of Lin (2006), who found the existence of foreign investors’ feedback trading
behavior but no information contribution in Taiwan during the Asian crisis. Apparently, the
magnitude of shock and the degree of regulation on foreign investors may lead to different
consequences in crisis.
The influence of currency returns on net purchases of foreign investors has risen in the
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crisis; the first-lag (21.663), third-lag (-31.881), and fourth-lag (29.523) coefficients of
currency returns on the net purchases of foreign investors are statistically significant.
However, we cannot tell whether foreign investors prefer buying stock when the TWD
appreciates or depreciates because the signs were both positive and negative. The
causality between currency returns and foreign investors’ net purchases has changed its
direction. In sub 2, net purchases of foreign investors Granger caused currency returns. In
sub 3, currency returns Granger caused the net purchases of foreign investors. It is also
worth mentioning that we found a bidirectional causality between currency returns and
market returns, which always happens alongside a financial crisis like the Asian crisis.
This result is consistent with Granger et al. (2000), indicating that the impact of currency in
crisis in a flatter economy was exacerbated by outside shocks.
Domestic institutions also changed their behavior and showed asymmetry in the crisis:
investment trusts and securities dealers embraced different strategies. Investment trusts
still hold on to negative feedback trading strategies, with significant negative coefficients
and net purchases Granger caused by market returns (85.08). Still, we found no
information contribution in sub 3. In other words, only a unidirectional causality is
detectable since the elimination of the QFII system. Investment trusts still tend to sell their
stock holdings when TWD appreciates. Securities dealers reversed their trading behavior
again; feedback trading strategies disappeared in sub 3. For the coefficients of market
returns to net purchases of securities dealers, only the second-lag coefficient, (-0.441) is
significant, and net purchases were not Granger caused by market returns (3.157). As for
investment trusts, information contribution was not found after deregulation—the only
pattern they shared in sub 3. Securities dealers’ preference for purchasing stock according
to change in the exchange rate was unclear. The coefficients showed no significance and
Granger causality also vanished. One thing worth to noticing is that securities dealers had a
more slight destabilizing effect on market returns than foreign investors (for the third-lag,
0.018 and for the fourth-lag, -0.011; coefficients are significant.)
The results show the differences between foreign investors’ and domestic institutions’
behavior and impact in the crisis. Foreign investors had a stronger impact than domestic
institutions on the stock market and were affected by currency market more (see Table 5.c).
It is worth pointing out that securities dealers also had a slight effect on the stock market
regardless of the small volume they trade in sub 3. Investment trusts are less likely to be
destabilizing than those for foreign investors and securities dealers. Finally, compared with
foreign investors, domestic institutions showed a weaker relationship with the currency
market.
4.2.3 Summary of VAR analysis
In this section, we found that, despite the small scale (see Table 1), securities dealers also
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demonstrated some destabilizing effects similar to foreign investors. The asymmetry in
scale of the two institution investors resulted in similar consequences, suggesting that the
amount of capital flows does not necessary stand for the influence on the stock market.
Overall, the elimination of the QFII system does benefit Taiwan, as is evident in Table 2 and
from the VAR results. The destabilizing effects of institutional investors were also reduced,
as shown in Table 5.b. Foreign investors were feedback traders until the crisis hit Taiwan,
but their net purchases constantly affect the next day of stock market returns across the
entire sample. These findings were different from those for domestic institutional investors:
investment trusts were negative feedback traders and securities dealers frequently
changed strategies. The results of positive feedback traders for foreign investors and
negative feedback traders for investment trusts are similar to Karolyi’s (2002) findings in
Japan. For domestic institutions, the information contribution effects have disappeared
since deregulation. The currency market was influenced only by foreign investors; domestic
institutional investors showed no effect on it. In the VAR results, we could say that foreign
investors still have greater influence, as their stock holding has increased and restrictions
have been removed.
4.3 Impulse response function analysis
In this section, we discuss relations among market returns, currency returns and
institutional investors. Then the results will be compared with the VAR results and the
Granger causality test to ensure the robustness of the study. The impulse responses in
each plot are the coefficients from the former moving average function in Eq. (3). A Monte
Carlo integration procedure is used to display the 95% confidence intervals around the
impulse response coefficients. From Figure 3, we can see that each variable exhibited
strong autocorrelation patterns. In short, the VAR model is appropriate for this study.
During the crisis (Figure 3.c), relations between variables were amplified and revealed
different, discontinuous movement compared with previous periods (Figures 3.a, 3.b),
showing the impact of the crisis shock.
First, we check the relation between market returns and net purchases of foreign investors
(FI) in three periods. The response of FI to market returns (bottom-left) is affected by
market returns from the beginning and the existence of positive feedback trading strategies.
The response of market returns to FI (top-right) was suppressed in sub 2, indicating that
deregulation did reduce foreign investors’ impact on the stock market. However, as we
mentioned before, benefits of deregulation faded in the crisis period; the response of
market returns to FI became larger and fluctuated more as Granger causality turned to
unidirectional and only FI Granger caused market returns.
Second, we examined market returns and currency returns. The response of market
returns to currency returns (top-center) decreased in sub 2, and the impact shortened to the
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third day. As the plot shows, it became smoother than in sub 1. But, the impact turned more
volatile in sub 3. The response of currency returns to market returns (middle-left) increased
only in the first day yet became smoother in sub 2, compared with sub 1. However, the
response of currency returns to market returns also fluctuated more and became
discontinuous during the crisis. Both of the results showed that the relationship between
market returns and currency returns became volatile in crisis and are consistent with the
bidirectional causality in the Granger causality test.
Currency returns and net purchases of foreign investors were checked last. The response
of FI to currency returns (bottom-center) gradually grew larger over time, especially in
crisis-time, as currency returns Granger caused FI in sub 3. The response of currency
returns to FI (middle-tight) had become larger since deregulation, as FI Granger caused
currency returns and began to be fluctuated more in sub 3.
For domestic institutions, net purchases of investment trusts (IT) and net purchases of
securities dealers (SD) were put into one figure and plotted for three periods as Figures 4.a,
4.b, and 4.c. We did so because of the similar scope of the two sectors and because doing
so made it easier to compare the differences between them. The response of IT to market
returns (bottom-left) exhibited negative feedback trading strategies for IT; the coefficients
went below 0 in two days and stayed negative for several days. The response of SD to
market returns (bottom-left) had reached its peak in sub 2 (Figure 4.b), only became
feedback traders supported SD in this period. We found that the magnitude of the response
of market returns to IT (top left) had decreased after deregulation, as for SD. Though their
magnitude to market returns increased in the crisis (Figure 4.c), the impact was smaller
than in sub 1.
The response of IT to currency returns (bottom-left) showed negative coefficients, just as
we found with VAR that IT tended to sell stock when the exchange rate rose. The response
of SD to currency returns, however, presented different patterns. The coefficients were
larger than IT’s and changed through time. The magnitude was around zero in sub 1,
became positive in sub 2, and went negative in sub 3. This result supports the claim that SD
were Granger caused by currency returns in sub 2, as in the VAR results. The response of
currency returns to IT and SD (top-right) showed little impact in sub 3 (Figure 4.c), also
similar to the VAR results.
To summarize the results of the impulse response analysis, the elimination of the QFII
system reduced not only foreign investors’ but also domestic institutions’ influence on the
stock market. Yet, it also increased the influence of the currency market, especially when
Taiwan was hit by a crisis. Foreign investors had a greater impact than locals, but were
not that powerful compared with their scope. Most of the results of the impulse response
analysis are consistent with the VAR model, suggesting that the results of this study are
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robust.
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Table 5.a QFII (2000/01/04~2003/10/01)
Foreign Investors
Investment Trusts
Market
TWD/$
Net
Market
TWD/$
Net
Market
TWD/$
Net
return
return
purchase
return
return
purchase
return
return
purchase
Market
-1
0.0143
return
-2
-0.0084
0.0070 **
3.5562 ***
0.0126
0.0092 **
1.2439 ***
-0.0182
0.0084 **
0.1757
0.0020
0.1386
0.0179
0.0066 *
-1.5846 ***
-0.0028
0.0067 *
-0.1566
0.1196 ***
0.0050
-0.6843 ***
-0.6763 ***
-3
0.0519 *
0.0012
0.3975
-4
-0.0657 **
-0.0037
-0.7841 *
-0.0431
-0.0020
TWD/$
-1
0.1793
0.0194
-5.6372
0.2532
0.0262
return
-2
0.0505
0.0409
4.6297
0.0565
-3
0.2383
-0.0032
-2.3405
0.2026
-4
0.4355 *
0.0565 **
1.1553
0.4344 *
0.0551 **
Net
-1
0.0065 ***
0.0004 *
0.3800 ***
0.0132 **
0.0002
purchase
-2
0.0021
0.0003
0.1173 ***
-0.0014
-0.0003
-3
-0.0033
0.0000
-0.0140 ***
-0.0000
-4
0.0004
-0.0001
0.0947 ***
0.0054
-0.0961 *
-0.0144 **
4.3273 ***
0.0287
0.0295
Constant
Adj. R-square
Securities Dealers
-0.0225
0.3260
0.0822 **
-0.0007
-0.0471
-0.0645 *
-0.0044
-0.0484
-0.6926
0.2614
0.0271
1.2959
0.0424
1.1745
0.0438
0.0414
1.0790
-0.0022
-0.4433
0.2125
0.0008
-1.4668
-0.8050
0.4307 *
0.0577 **
-0.0715
0.2623 ***
0.0267 ***
0.0005
0.0984 ***
0.0030
-0.0008
0.0266
-0.0427
-0.0132 *
0.0021
-0.0357
-0.0003
0.0175
0.0066
-0.0002
0.0183
-0.0340
-0.0078
-0.2468
-0.0292
-0.0073
-0.2821
0.0266
0.0232
0.2019
0.0273
0.0268
0.2130
6.0309
1.9522
0.4349 ***
Granger causality tests (Chi-sq)
Market return
4.3096
TWD/$ return
3.6549
Net purchase
11.6588 **
47.4261 ***
3.0221
6.6525
8.8498 **
3.8571
89.0449 ***
0.6885
9.6217 **
0.5967
*** = 1 % significance level; ** = 5 % significance level; * = 10 % significance level.
22
3.7996
10.2849 **
2.9458
4.0444
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Table 5.b QFII eliminated (2003/10/02~2007/07/18)
Foreign Investors
TWD/$
Net
Market
TWD/$
Net
Market
TWD/$
Net
return
return
purchase
return
return
purchase
return
return
purchase
-1
-0.0264
return
-2
-0.1051 ***
0.0533
0.0001
15.1911 ***
-0.0069
1.9243
-0.0040
5.5761 **
-0.0156
0.0112 *
-0.0970 ***
0.0780 **
-4
-0.0955 ***
-0.0132 *
-2.3418
TWD/$
-1
0.3777 ***
0.0300
7.7828
return
-2
0.1070
-0.0159
-1.6031
0.1290
-3
-0.0492
-0.0012
1.7061
-4
-0.0415
0.0232
-0.5879
Net
-1
purchase
-2
Securities Dealers
Market
Market
-3
Investment Trusts
0.0017 ***
0.0004 ***
0.2142 ***
-0.0002
0.0001
0.1037 ***
-3
-0.0001
0.0001
-4
0.0000
Constant
Adj. R-square
-0.0585 *
0.4496 ***
1.8671 ***
-0.0107
0.0196 **
0.0082
-2.7302 ***
-0.0755 *
0.0174 *
0.0074
-1.1261 ***
-0.0009
-1.1512 ***
-0.0831 **
0.0085
-0.0015
0.0516 *
-0.5593
1.0480 **
-0.4902
-0.3479
0.4342 ***
-0.0065
-0.0712
0.1065
-0.0054
-0.0324
0.0057
1.4687
-0.0592
0.0121
-0.3222
-0.0308
0.0324
-1.0286
-0.0195
0.0402
-1.0374
0.0088 **
0.0525 **
0.1202 ***
2.1265 ***
3.3925 **
4.3148 ***
0.0000
0.3728 ***
0.0031
-0.0010
0.2162 ***
-0.0016
-0.0009
0.1276 ***
-0.0013
-0.0010
0.0434
0.0902 ***
-0.0002
0.0000
0.1299 ***
-0.0062 *
0.0001
-0.0492
0.0001
0.0516 *
-0.0044
-0.0009
0.0544 **
0.0031
-0.0001
0.0018
0.0287
-0.0110 *
10.2701 ***
0.0218
0.0180
0.2197
3.1426
44.7707 ***
0.0576 **
0.0287
-0.0008
-0.1610
0.0531 *
0.0011
0.1138
0.0125
0.3628
0.0150
-0.0001
0.1471
3.1311
173.4744 ***
Granger causality tests (Chi-sq)
Market return
TWD/$ return
6.5057
Net purchase
9.4411 **
0.6475
20.0294 ***
9.2759 **
4.0268
2.4499
2.7562
*** = 1 % significance level; ** = 5 % significance level; * = 10 % significance level.
23
5.0373
8.5292 *
3.0706
19.1121 ***
9.4145 **
2.9291
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Table 5.c In the crisis (2007/07/19~2009/12/31)
Foreign Investors
Investment Trusts
Market
TWD/$
Net
Market
TWD/$
Net
Market
TWD/$
Net
return
return
purchase
return
return
purchase
return
return
purchase
Market
-1
return
-2
0.0692 *
-3
-0.1083 **
-0.0038
-0.4330
-0.0629
-0.0033
-4
-0.0683 *
-0.0037
-1.0219
-0.0518
-0.0269
TWD/$
-1
0.8985 ***
return
-2
0.3722
-3
-0.315
-4
0.451 *
Net
-1
0.0010
purchase
-2
-0.0021 **
-3
-4
Constant
Adj. R-square
Securities Dealers
0.0032 ***
-0.0191 ***
3.5025 *
-0.0104
-0.0126 **
1.5992 ***
-0.0367
-0.0138 **
0.2779
0.0119 *
4.5356 **
0.0176
0.0100 *
-1.4606 ***
0.0465
0.0143 **
-0.4405 *
-1.2806 ***
-0.1008 **
0.0010
-0.3070
-0.0356
0.2301 ***
-0.1739
0.9966 ***
0.2184 ***
-0.0061
-0.1730
0.0014
0.2208
0.2382 ***
0.1314
21.6633 *
0.9260 ***
-0.0068
-5.0908
0.3439
-0.0063
-0.6050
0.3084
-0.0069
1.1425
-0.0529
-31.8805 **
-0.1620
-0.0468
-0.5888
-0.0931
-0.0410
-0.2468
0.0185
29.5232 **
0.4500 **
0.0172
1.4738
0.4289 *
0.0150
-1.2500
0.0008
0.0006
0.2470
0.0090
0.0008
0.0029
0.0013
0.2317 ***
-0.0091
-0.0004
0.0076
0.0003
0.0603 *
0.0003 **
-0.0001
0.0001
0.3483 ***
-0.0137
0.0914 **
0.0183 **
0.0017 *
0.2282 ***
0.0779 *
0.1099 **
-0.0007
0.0000
-0.0273
-0.0077
-0.0004
0.0745 **
-0.0109 *
-0.0010
-0.0799 *
-0.0298
0.0044
-2.6192
-0.0423
-0.0004
0.6101 *
-0.0426
0.0021
0.5817 *
0.0293
0.0421
0.1874
0.0150
0.0397
0.2770
0.0228
0.0390
0.0717
8.7956 *
5.1293
5.8018
85.0797 ***
6.8182
3.1567
Granger causality tests (Chi-sq)
Market return
TWD/$ return
14.6874 ***
Net purchase
11.0673 **
9.8266 **
4.3822
14.6900 ***
2.2310
*** = 1 % significance level; ** = 5 % significance level; * = 10 % significance level.
24
0.9764
2.8486
15.4486 ***
6.9999
0.9865
2.4556
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Figure 3.a Sub 1, QFII
Response of MARKET to FI
Response of MARKET to CURRENCY
Response of MARKET to MARKET
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
0.0
-0.5
-0.5
-0.5
2
4
6
8
10
12
2
14
Response of CURRENCY to MARKET
4
6
8
10
12
2
14
Response of CURRENCY to CURRENCY
.25
.25
.20
.20
.20
.15
.15
.15
.10
.10
.10
.05
.05
.05
.00
.00
.00
-.05
2
4
6
8
10
12
14
Response of FI to MARKET
4
6
8
10
12
14
2
60
60
60
40
40
40
20
20
20
0
0
0
-20
6
8
10
12
14
10
12
14
6
8
10
12
14
12
14
Response of FI to FI
80
4
4
Response of FI to CURRENCY
80
2
8
-.05
2
80
-20
6
Response of CURRENCY to FI
.25
-.05
4
-20
2
4
6
8
10
12
14
2
4
6
8
10
4.4 Variance decomposition analysis
Variance decomposition analysis reveals how much of the forecast error variance of one
variable can be explained by exogenous shocks to the other variables. In other words, it
reveals the amount of information that one variable contributes to the other in a VAR model.
Table 6 shows the results of variance decomposition of the three periods in entire sample
period. Because there are five trading days per week in Taiwan, we adopted 5 days (1
week), 10 days and 15 days ahead for each variable. On average, 30% of variance for
institutional investors can be associated with innovation in the market returns. In detail, the
proportion of net purchases of foreign investors by market returns increased gradually and
soared to approximately 40% during the crisis (Table 6.c). However, the proportion of net
purchases of investment trusts by market returns decreased gradually from 30% to 20%.
The proportion of net purchases of securities dealers, showed a distinct track: an inverse
25
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U-shape. In sub 2, the proportion reached 60%, but it was 30% and 35% in sub 1 and sub 2,
respectively.
Table 6.a Variance decomposition by N-days ahead forecasts (%). Sub 1, QFII
Foreign Investors
Market
returns
TWD/$
returns
5
10
15
5
10
15
Net
5
purchas
10
es
15
Investment Trusts
Securities Dealers
Market
returns
98.33
98.29
98.28
0.39
0.39
0.39
TWD/$
returns
3.76
3.77
3.77
95.35
95.28
95.28
Net
purchases
22.75
22.83
22.84
0.36
0.39
0.39
Market
returns
98.65
98.62
98.62
0.39
0.40
0.40
TWD/$
returns
4.18
4.20
4.20
95.76
95.7
95.74
Net
Purchases
33.26
33.70
33.70
0.11
0.16
0.17
Market
returns
98.54
98.53
98.53
0.35
0.36
0.36
TWD/$
returns
4.11
4.13
4.13
95.38
95.35
95.35
Net
purchases
38.17
38.16
38.16
0.24
0.24
0.24
1.26
0.88
76.90
0.93
0.045
66.64
1.097
0.50
61.59
1.29
0.93
76.78
0.94
0.054
66.14
1.10
0.51
61.60
1.29
0.93
76.77
0.94
0.054
66.13
1.10
0.51
61.60
Table 6.b Variance decomposition by N-days ahead forecasts (%). Sub 2, QFII eliminated
Foreign Investors
Investment Trusts
Securities Dealers
Market
returns
TWD/$
returns
Net
purchases
Market
returns
TWD/$
returns
Net
purchases
Market
returns
TWD/$
returns
Net
purchases
Market
5
98.07
8.71
27.08
98.65
9.48
26.50
98.69
9.49
58.04
returns
10
98.06
8.85
27.20
98.64
9.49
26.17
98.69
9.49
58.07
15
98.06
8.85
27.20
98.64
9.49
26.07
98.69
9.49
58.07
TWD/$
5
0.90
89.25
1.29
0.96
90.33
0.24
0.90
90.17
1.35
returns
10
0.90
88.97
1.30
0.96
90.21
0.36
0.90
90.17
1.35
15
0.90
88.96
1.30
0.96
90.19
0.370
0.90
90.17
1.35
5
1.02
2.03
71.62
0.37
0.17
73.24
0.39
0.32
40.60
0.32
40.57
Net
purchases 10
1.02
2.17
0.38
0.28
73.46
Table 6.c Variance
decomposition
by71.49
N-days ahead
forecasts
(%). Sub
3, In the0.40
crisis
15
1.02
2.18
71.49
Foreign Investors
0.38
0.30
73.55
Investment Trusts
0.40
0.32
40.57
Securities Dealers
Market
returns
TWD/$
returns
Net
purchases
Market
returns
TWD/$
returns
Net
purchases
Market
returns
TWD/$
returns
Net
purchases
Market
5
95.89
14.39
38.66
97.22
14.40
21.03
96.44
14.19
34.90
returns
10
95.85
14.39
38.59
97.21
14.42
20.72
96.43
14.20
34.90
15
95.85
14.39
38.59
97.21
14.42
20.69
96.43
14.20
34.90
TWD/$
5
2.35
84.83
2.81
2.40
85.07
0.10
2.48
85.38
1.63
returns
10
2.36
84.80
2.94
2.40
84.99
0.19
2.48
85.37
1.65
15
2.36
84.80
2.94
2.40
84.99
0.20
2.48
85.37
1.65
5
1.75
0.77
58.52
0.36
0.51
78.86
1.07
0.41
63.45
purchases 10
1.78
0.80
58.46
0.38
0.57
79.07
1.07
0.42
63.44
15
1.78
0.80
58.46
0.38
0.57
79.10
1.07
0.42
63.44
Net
26
Proceedings of 10th Global Business and Social Science Research Conference
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Figure 3.b Sub 2, QFII eliminated
Response of MARKET to MARKET
Response of MARKET to CURRENCY
Response of MARKET to FI
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
0.0
-0.5
2
4
6
8
10
12
14
-0.5
-0.5
2
Response of CURRENCY to MARKET
4
6
8
10
12
14
2
Response of CURRENCY to CURRENCY
.25
.25
.20
.20
.20
.15
.15
.15
.10
.10
.10
.05
.05
.05
.00
.00
.00
-.05
2
4
6
8
10
12
14
Response of FI to MARKET
4
6
8
10
12
14
2
60
60
60
40
40
40
20
20
20
0
0
0
-20
6
8
10
12
14
10
12
14
6
8
10
12
14
12
14
Response of FI to FI
80
4
4
Response of FI to CURRENCY
80
2
8
-.05
2
80
-20
6
Response of CURRENCY to FI
.25
-.05
4
-20
2
4
6
8
10
12
14
2
4
6
8
10
This inverse U-shape track can be explained in the previous VAR analysis, which shows
that securities dealers turned into feedback traders in sub 2. On the contrary, all institutional
investors’ effects on market returns had shrank in sub 2, but rose again in sub 3, showing
that there were benefits of deregulation. The proportions of institutional investors by
currency returns also rose following the elimination of the QFII system, especially for
foreign investors and securities dealers, with 3-fold and 5-fold increases in sub 2.
Moreover, net purchases of foreign investors and currency returns presented a
growth-and-decline relation, which was similar to the directional change in Granger
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causality. In the crisis period, the proportion of currency returns by market returns had
increased to its peak of 14%. The proportion of market returns by currency returns also
increased, doubling compared to sub 2.
Figure 3.c Sub 3, In the crisis
Response of MARKET to FI
Response of MARKET to CURRENCY
Response of MARKET to MARKET
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
0.0
-0.5
-0.5
-0.5
2
4
6
8
10
12
2
14
Response of CURRENCY to MARKET
4
6
8
10
12
2
14
Response of CURRENCY to CURRENCY
.25
.25
.20
.20
.20
.15
.15
.15
.10
.10
.10
.05
.05
.05
.00
.00
.00
-.05
2
4
6
8
10
12
14
Response of FI to MARKET
4
6
8
10
12
14
2
60
60
60
40
40
40
20
20
20
0
0
0
-20
6
8
10
12
14
10
12
14
6
8
10
12
14
12
14
Response of FI to FI
80
4
4
Response of FI to CURRENCY
80
-20
8
-.05
2
80
2
6
Response of CURRENCY to FI
.25
-.05
4
-20
2
4
6
8
10
12
14
2
4
6
8
10
The results of the innovation of market returns on net purchases of foreign investors and
currency returns are similar to Karolyi (2002). Karolyi (2002) found that the proportions of
net purchases of foreign investors and currency returns by market returns went up in Japan
during the Asian crisis. Investment trusts and securities dealers exhibited different patterns
in this analysis as well. The results of this analysis coincide with the VAR model and
impulse response analysis, indicating the robustness of this study.
28
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Figure 4.a Sub 1, QFII (domestic institutions)
Response of MARKET to Cholesky
One Standard Deviation Innovations
Response of CURRENCY to Cholesky
One Standard Deviation Innovations
1.5
.25
.20
1.0
.15
0.5
.10
.05
0.0
.00
-0.5
-.05
2
4
6
8
10
12
14
2
Response of IT to Cholesky
One Standard Deviation Innovations
4
6
8
10
12
14
Response of SD to Cholesky
One Standard Deviation Innovations
10
10
8
8
6
6
4
4
2
2
0
0
-2
-2
2
4
6
8
10
12
14
2
MARKET
IT
29
4
CURRENCY
SD
6
8
10
12
14
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Figure 4.b Sub 2, QFII-eliminated (domestic institutions)
Response of MARKET to Cholesky
One Standard Deviation Innovations
Response of CURRENCY to Cholesky
One Standard Deviation Innovations
1.5
.25
.20
1.0
.15
0.5
.10
.05
0.0
.00
-0.5
-.05
2
4
6
8
10
12
14
2
Response of IT to Cholesky
One Standard Deviation Innovations
4
6
8
10
12
14
Response of SD to Cholesky
One Standard Deviation Innovations
10
10
8
8
6
6
4
4
2
2
0
0
-2
-2
2
4
6
8
10
12
14
2
MARKET
IT
30
4
CURRENCY
SD
6
8
10
12
14
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Figure 4.c Sub 3, In the crisis (domestic institutions)
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Response of MARKET to Cholesky
One Standard Deviation Innovations
Response of CURRENCY to Cholesky
One Standard Deviation Innovations
1.5
.30
.25
1.0
.20
.15
0.5
.10
.05
0.0
.00
-0.5
-.05
2
4
6
8
10
12
14
2
Response of IT to Cholesky
One Standard Deviation Innovations
4
6
8
10
12
14
Response of SD to Cholesky
One Standard Deviation Innovations
10
10
8
8
6
6
4
4
2
2
0
0
-2
-2
2
4
6
8
10
12
2
14
MARKET
IT
4
6
8
10
12
14
CURRENCY
SD
5. Conclusion
In this study, we investigated market returns, currency returns and net purchases of
institutional investors before QFII was eliminated, after QFII was eliminated and in the crisis.
We used a VAR model and then impulse response analysis and variance decomposition to
ensure robustness. Our hypotheses were tested and stated as follows: (1) Foreign
investors do have stabilizing effect on Taiwanese stock market, except during the crisis
period. (2) There is no strong evidence to prove that foreign investors destabilized the
currency market, but their impact on currency market did increase.
(3) Compared with domestic institutions, foreign investors were slightly stronger than
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securities dealers and investment trusts on the Taiwanese stock market during the financial
tsunami. (4) The role of foreign investors has changed twice after the elimination of the QFII
system. One change occurred following deregulation, when the impact on currency market
increased; the other change occurred during the financial tsunami, when a destabilizing
effect was detected on the stock market. However, we should note that domestic
institutions also had the ability to destabilize the stock market before deregulation.
Securities dealers even cast a destabilizing effect during the crisis period, similar to foreign
investors. The destabilizing effect of foreign investors only became stronger in the crisis.
Foreign investors adopted feedback trading strategies until the crisis hit Taiwan, but they
still have a greater impact than domestic institutions on the stock market because
information contribution exists in entire sample. For all institutional investors, trading
behavior did not stand for the potential to destabilize the stock or currency markets.
Though foreign investors had demonstrated their destabilizing effect on the stock market
during the crisis, we should understand the reason why they came to Taiwan.
International capital flows seek profit all around the world; it is their nature to escape from a
downturned economy. Moreover, the flows returned after 2009, indicating that the flight was
a temporary move. Our findings are similar to Bohn and Tesar (1996) and Hamao and Mei
(2001). Foreign investors are not the main factor because we find that domestic institutions
such as securities dealers also had a destabilizing effect during the crisis, even with smaller
scale of daily trading volume. We thought that the CBC’s exchange rate management
diminished the foreign investors’ impact on the currency market, resulting in an increase in
international flows, but the data showed a slight influence.
We think that once we know the nature of international flows and the ways to deal them
with proper financial supervisory mechanisms, international capital flows can have a
positive effect on a shallow economy country such as Taiwan. If we do have a good
financial environment with a strong economic foundation, the flows are not too difficult to
manage. The government has currency policy, money policy, and fiscal policy to protect the
economic system from ―hot‖ money. What we should worry about is the rising fiscal deficit,
which could swell to cripple the flexibility of these economic balancing policies. The case
of Taiwan could provide policy implications to those countries that still impose regulations
on foreign capital flows and shed light on an important question: could a small, flat, and
export-oriented economy benefit from financial deregulation? We think the answer is a
resounding ―Yes.‖
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Reference
Bacmann, JC, & Bolliger, G (2001). Who are the best? Local versus foreign analysts on the
Latin American stock markets. University of Neuchâtle, working paper,
Bekaert,G, & Harvey, CR (1997). Emerging equity market volatility. Journal of Financial
Economics, 43, 29-77
Bekaert,G, & Harvey, CR (2000). Foreign Speculators and Emerging Equity Markets,
Journal of Finance, Vol. 55, No. 2, 565-613
Bekaert, G, Harvey, CR & Lumsdaine, R (2002). The dynamics of emerging market equity
flows. Journal of International Money and Finance, 21, 295–350.
Bohn, H., & Tesar, L (1996). U.S. equity investment in foreign markets: Portfolio
rebalancing or return chasing? American Economic Review, 86, 77–81.
Brous, PA, & Kini, O (1994). The Valuation Effects of Equity Issues and the Level of
Institutional Ownership: Evidence from Analysts' Earnings Forecasts, Financial
Management, Vol. 23, No. 1, 33-46
Choe, H, Kho, B & Stulz, RM (1999). Do foreign investors destabilize stock markets? The
Korean experience in 1997. Journal of Financial Economics, 54, 227–264.
Corsetti, G, Pesenti, P & Roubini, N (2001). The role of large players in currency crises.
NBER working paper, w8303
DeLong, B, Shleifer, A, Summers, L & Walkmann, R (1990). Positive feedback investment
strategies and destabilizing rational speculation, Journal of Finance, 45, 379– 395.
Dornbusch, R & Park, YC (1994). Financial integration in a second-best world: are we still
sure about our classical prejudices? Korea Institute of Finance, Policy issue series,
94-1, February
Eswar, P, Kenneth, R, Wei, SJ & Kose, MA (2003). Effects of financial globalization on
developing countries: some empirical evidence. International monetary fund.
Froot, K., O’Connell, P & Seasholes, M. (2001). The portfolio flows of international investors.
Journal of Financial Economics, 59, 151–193.
Granger, CWJ, Huang, BN & Yang, CY (2000). A Bivariate Causality between Stock Prices
and Exchange Rates: Evidence from Recent Asian Flu. Quarterly Journal of
Economics and Finance, 40, 337–354.
Hamao, Y., & Mei, J (2001). Living with the enemy: an analysis of foreign investment in
Japanese equity market. Journal of International Money and Finance, 20, 715–735.
Johnson, S & Mitton, T (2003). Cronyism and capital controls: Evidence from Malaysia.
Journal of Financial Economics, 67,351–382.
Kaminsky, G, Lyons, R & Schmukler, S. (2001). Mutual fund investment in emerging
34
Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
markets: an overview. World Bank Economic Review,15, 315– 340.
Kaminsky, GL & Schmukler, SL (1999). What triggers market jitters? A chronicle of the
Asian crisis. Journal of International Money and Finance, 18, 537–560.
Karolyi, GA (2002). Did the Asian financial crisis scare foreign investors out of Japan?
Pacific-Basin Finance Journal, 10, 411–442.
Khanna,T & Palepu, K (1999) Emerging Market Business Groups, Foreign Investors, and
Corporate Governance. National Bureau of Economic Research, working paper 6955.
Kim, W & Wei, S (2002). Foreign portfolio investors before and during a crisis. Journal of
International Economics, 56, 77–96.
Krugman, P (1998). Saving Asia: it is time to get radical. Fortune, September
Lin, AY (2006). Has the Asian crisis changed the role of foreign investors in emerging equity
markets: Taiwan’s experience. International Review of Economics and Finance,
15,364-382.
Mundell, R. (2000). Currency areas, exchange rate systems and international monetary
reform. Journal of Applied Economic, Vol. III, No. 2, 217-256
Scholes, MS (1972). The market for securities: Substitution versus price pressure and the
effects of information on sharpe prices. The Journal of Business, 45(2), 170.
Sims, C (1980). Macroeconomics and reality. Econometrica, 48, 1 –48.
Stiglitz, J (1998). Boat, blain and capital flows. Financial Times, March 25.
Wang, LR & Shen, CH (1999). Do foreign investments affect foreign exchange and stock
markets—the case of Taiwan. Applied Economics, v31, i11, p1303.
Wang, J (2007) Foreign equity trading and emerging market volatility: Evidence from
Indonesia and Thailand, Journal of Development Economics, 84, 798-811.
35