Conditions For Foreigners’ Trade Executions in The KOSPI
- What Are The Determinants of Portfolio Flows of Foreign Origin? -
Written By
Minseok Lee
Senior Honors Thesis (2011), Economics Department, Tufts University
Read By
Professor Heiwai Tang
Professor
Professor Patrick Schena
Abstract
This paper observes the historical price to earnings ratio (PER) and the trading data of the
four major investor groups in the Korea Composite Stock Price Index (KOSPI): foreign
investors, institutional investors, individual investors, and other corporations. The paper
analyzes foreign investors’ role in setting market valuation-norms in the KOSPI from
July 2002 to December 2010, and examines whether or not the foreign investors are most
value-oriented among the four groups. This paper finds that it is unclear whether or not
valuation-cause have been a meaningful and reliable metric of purchases and sales of
securities for foreign investors who participate in creating the portfolio flows. However,
this paper finds that foreign investors sensitively react to the anticipation of the VIX and
the won-dollar exchange rate.
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Index
I. Introduction……………………………………………………………………. 2
II. Backgrounds
1. Valuation and Trading Flow, and Value Investors……………………
4
2. The KOSPI and Hot Money…………………………………………..
5
III. Analyses of The Residual PER’s Impact on Trading Flow …………..……..
1. Design………………………………………………………..……….
(1) Big Picture………………………………………………….
(2) Detailed Scene……………………………………..…………
2. Result……………………………………………..…………..…….....
IV. Conclusion……………………………………………………………………
1
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I. Introduction
In the Korea Composite Stock Price Index (KOSPI), investors are frequently
categorized into the following four groups: foreign investors, local institutional investors,
individual investors, and other corporations. 1 Korea Exchange (KRX) compiles and
discloses each investor group’s trading flow data at both high and low frequencies.
Because there is a public access to such detailed data at high precision, each investor
group’s behaviors are repeatedly tracked and characterized by journalists and financial
analysts.
In the KOSPI, foreign investors are generally believed to be the most
sophisticated investors with the most abundant monetary supplies and best human capital.
Due to the common placed dollar shortage in the won-dollar segment of the exchange
(currency) market, foreign investors almost always enjoy the luxury of not having to
suffer Korea’s domestic dollar shortage, and thus they often are considered as the richest
investors. As foreign investors are mostly from larger economies, the scale of their capital
base is much larger than that of most Korean institutions or individuals (Financial
Supervisory Service, 2011). As a result, foreign investors are believed to be able to afford
to execute trades with more confidence.
Many contemporary investment strategy reports popularize the stereotype of
foreign investors as managing the trading flow’s momentum in order to maximize the
unrealized profit from the positions that they have previously made (De Long, Shleifer,
1
Except for the foreign investors, the location of trading account is in Korea. More detailed elaboration on
the classification of each investor group is made in Background 2.
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Summers, and Waldmann, 1990). Consequently, foreign investors often face public
criticism that their portfolio flows do not carry enough valuation-cause. While there are
specific funds of foreign origin that are characterized as long term value investors,
foreign portfolio flows as a whole are often depicted as if they have intention to
manipulate the market with their capability and sophistication. This characterization gives
the impression to the members of investment community, including regulators, that
foreign investors do not take a constructive role in the KOSPI. The recent sanction on
Deutsche Bank by Korea’s Prosecutors’ Office and the public criticism on the incident
was one good example that showed the negative image of the portfolio flows of foreign
origin (Lee and Tudor, 2011). Viewers of the incidents similar to Deutsche Bank’s case
could have made a mistake of hasty generalization.
In this paper, the author sought to find out whether or not foreign investors are
significantly value-oriented investor group. Despite the research, the author finds that the
degree of value orientation of each investor group is unclear. However, he identifies the
risk appetite and the won-dollar exchange as clearly significant factors that interact with
foreign investors’ portfolio flows. In the period from Jul. 2002 to Dec. 2010, foreign
investors were sensitive to the VIX, and they paid more attention to the risk management
than other players did. In addition, currency valuation appear not to be perceived by
foreign investors as a major consideration of equity investment in Korea, unless the
currency made an excessively depressive deviation from the norm in the short term.
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II. Background
II. 1. Valuation and Trading Flow, and Value Investors
Valuations and trading flows2 inevitably influence each other in the traded equity
markets. Securities’ valuations provide a logical foundation to the value-oriented
investors and encourage them to take positions. As more long (buy) positions or fewer
short (sell) or no positions are made, the securities’ value appreciates or depreciates. This
invites the question of which, between the valuations or the trading flows, takes a more
autonomous role in determining the other factor.
Value investors believe that securities have correct values that at some point
should be converged upon, because “owners and buyers of common stocks are generally
anxious to arrive at an intelligent idea of their value” and human nature seeks to wrap the
speculative greed with “a screen of apparent logic and good sense” (Graham and Dodd,
2008).3 While value investors have historically enjoyed significant returns, those who pay
close attention to the trading flows and seek to exploit the opportunities that arise from
interpreting the trading flows have also been thriving, as proven by the presence of a
significant number of arbitragers and quant-hedge funds.4 Many market participants sit
somewhere in the middle of the two investment styles.
In this paper, ‘valuations’ means justifiable values given to the securities through security analyses of
various sort by market participants, and ‘trading flows’ means every disclosed transaction made by buyers
and sellers of respective securities in the market. Block deals are not taken into account.
3
Variations within value investment and the reason for selecting PER as valuation metric is elaborated in
the latter part of this paper.
4
Value investors call this investor group as speculators (Klarman, 2008); however, this paper defines
investors as: anyone (including machines) who buys and/or sells in the market.
2
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Although different investors have various areas of focus in terms of their
investment analysis, the executions of their trades exhibit only two forms: either long or
short.5 Value investors seek to buy an asset at a discount and sell it at a premium, and
when these intentions are combined with the fact that buyers and sellers determine the
price of any traded security, it is evident that even purely value-oriented investors add
and reduce momentum. Buying at bid price or selling at offer price represents the cases of
‘adding momentum to the desired direction’. Buying at offer price or selling at bid price
represents the cases of ‘reducing momentum from the opposite direction’. This is done
through unintentionally adding momentum to the desired direction or reducing
momentum from the opposite direction in order to set the valuation level that they desire
to achieve in the investment process.6
II. 2. The KOSPI and Hot Money
The Korean equity market has developed in scale and sophistication as the listed
companies and the capital markets have grown (Figure 3). The KOSPI comprises of
many significant companies, most of which have long history and large market
capitalizations. The saturation in terms of market capitalization is relatively high due to
Korea’s economic development, which was dominated by large conglomerates (Figure 1
and 2).
5
In this paper, positions made by arbitragers who often seek to eliminate their risks are not discussed in
detail. However, purchased-sold history of equities enables the author to successfully include the
arbitrageurs either directly or indirectly.
6
A true distinction between value investor and momentum investor can only be drawn from their approach
and attitude rather than from their investment executions. When the size of investment is large relative to
the market depth, it becomes more difficult to differentiate value investors from a trader or a momentum
investor. This is because it is easier for value investors to set the valuation norm when purchased-sold
amount per total traded amount is high. Support this with analysis: each region’s various markets’ turnover
versus their volatilities.
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Figure 1. KOSPI Saturation by Market Capitalization
(Mar. 2011)
Market
Capitalization
Sort
Large Cap.
(Million
Total Market
Capitalization
Largest In The
Group
Smallest In The
Group
Korean Won)
Average
Market
Capitalization
Weight
Number of
Companies
908,683,988
139,197,873
961,174
9,086,840
87%
100
Mid Cap.
109,989,064
3,483,149
71,307*
555,500
10%
198
Small Cap.
30,562,647
487,140*
2,719
70,747
3%
432
All
1,049,235,699
139,197,873
2,719
1,437,309
100%
730
Source: KRX
* This discrepancy attributes to the abnormal short-term price fluctuation during the period of analysis.
** The Sorting was done on the 4th of Mar. 2011.
Figure 2. KOSPI Saturation by Market Capitalization
(Mar. 2011)
Category
Market Capitalization
(Million Korean Won)
Weight
1*
139,197,873
13.3%
1~10
396,230,295
37.8%
1~50
767,705,199
73.2%
1~100
908,683,988
86.6%
All
1,764,989,556
100%
Source: KRX
*Samsung Electronics Corporation
Figure 3. KOSPI Market Capitalization
(Jan. 2001 – Dec. 2010, Daily)
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KRW
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1.20E+09
1.00E+09
8.00E+08
6.00E+08
4.00E+08
2.00E+08
01.1.2
01.5.15
01.9.20
02.1.31
02.6.17
02.10.25
03.3.6
03.7.15
03.11.25
04.4.8
04.8.16
04.12.22
05.5.4
05.9.9
06.1.17
06.5.26
06.10.9
07.2.14
07.6.26
07.11.6
08.3.19
08.7.29
08.12.4
09.4.15
09.8.20
09.12.24
10.5.6
10.9.10
0.00E+00
Source: Bloomberg LP
*KRW: Korean Won
Major shareholding investor groups in KOSPI include foreign investors,
institutional investors, individual investors, and other corporations. 7 Foreign investors
include those without foreigner investment registration identification as well as ones with
the identification. Institutional investors includes local mutual funds, investment banks,
foundations, government pension funds, insurance companies, commercial banks,
merchant banks and private equity funds whose accounts are located in South Korea.
Technically, individual investors include individuals who trade with personal stock
accounts; however, ‘individual investors’, as it is colloquially used, also add and reduce
direct momentum to the mutual funds by initiating fund flows from and into the mutual
7
Major shareholding investor groups in some literature are foreign investors, institutional investors,
individual investors, government bodies, and other corporations. The distinction among institutional
investors, government bodies and other corporations has not been very strict in the KRX’s record; one or
two types of sub categorical investor groups such as pension funds have been included under institutional
investors or government bodies at different periods of time (Korea Exchange, 2011). To eliminate the
confusion, the author merges government bodies and institution into one, and calls it institutional investors.
In addition, the author’s focus on the foreign investors is partly attributable to this categorical issue in the
data compilation.
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funds. Other corporations include venture capitals, and medium-size corporations that do
not specialize in investment. All of those who are identified with their business
registration identifications, due to their lack of institutional investor registration
identifications, are categorized under other corporations.
Each player within other corporations has various trading purposes that does not
directly relate to the profit seeking; hence other corporations’ intentions and behaviors
are often not generalized. The above-mentioned four players’ inflows and outflows
during the same period sum to zero without an exception.
The foreign investors hold large portions of KOSPI (Figure 4 and 5), and they
create the bulk of trading flow (Figure 6), while individual investors’ turnover has
historically been significantly higher than the rest (Figure 7). The total turnover has also
been growing (Figure 8). Just like other global indices, the KOSPI shows high correlation
with every other major stock index around the world (Figure 9). In May of 1998, the
universal cap on foreign shareholding in KOSPI was removed by Korea’s Financial
Supervisory Service, while very few strategically important sectors that provide public
services remained exceptions.8 There is no additional tax applicable for foreigners. With
very few exceptions,9 the daily limit for appreciation or depreciation of any share prices
is ±15%.
8
The shareholding limits for foreigners range from 33% to 50% for companies such as: Korea Telecom,
SK Telecom, LG U Plus, Korea Tobacco & Ginseng, KoGas, Korea Electricity Power Corporation, Korean
Air, Asiana Air, and Some broadcasting systems. Foreigners’ purchase of the shares above the limit is
possible with the use of derivative products, though foreigners hardly hit the shareholding limits in practice
(Korea Exchange, 2011).
9
Periods of the initial public offering (IPO) and delisting fall into the exceptions.
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Figure 4. KOSPI shareholding by investor group
(2000 – 2009, Yearly)
Year
(Billion Korean Won)
Investor
Group
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
GOV
26,714
20,824
14,645
16,127
18,153
26,060
30,227
29,713
16,767
16,441
INS
29,475
40,297
40,991
59,328
72,765
128,375
154,723
201,589
71,144
111,087
OTH
36,493
43,914
52,123
67,354
74,246
119,658
130,667
204,996
166,287
194,811
IND
37,314
57,117
57,760
70,020
74,266
120,718
126,437
207,465
155,703
275,253
FOR
56,209
93,698
93,161
142,534
173,158
260,263
262,534
308,181
165,655
289,724
Year
(%)
Investor
Group
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
GOV
14.35
8.14
5.66
4.54
4.40
3.98
4.29
3.12
2.91
1.85
INS
15.83
15.75
15.85
16.70
17.64
19.60
21.96
21.18
12.36
12.52
OTH
19.60
17.16
20.15
18.95
18.00
18.27
18.55
21.53
28.89
21.96
IND
20.04
22.32
22.33
19.70
18.00
18.43
17.94
21.79
27.05
31.02
FOR
30.19
36.62
36.01
40.11
41.97
39.73
37.26
32.37
28.78
32.65
Source: KRX
* GOV: Government & Public Bodies, INS: Institution, OTH: Other Corporations, IND: Individual, FOR: Foreigners.
Figure 5. KOSPI shareholding by investor group
(2000 – 2009, Yearly)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Foreigners
Individual
Other
Corporations
Institution
Government &
Public Bodies
Source: KRX
Figure 6. KSFINET Index
(Jan. 2003 – Dec. 2010, Daily)
Ticker
Max Inflow (Million USD)
Max Outflow (Million USD)
Variance
Standard Deviation
KSFINET Index*
1204.02
-1175.95
54313.83
233.05
Source: Bloomberg LP
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* KSFINET Index: Daily Net Flows in South Korean equities by foreign investors in KOSPI & Korean securities
dealers automated quotations (KOSDAQ). Foreign investors include one without foreigner investment registration ID
as well as one with ID.
**The numbers in the table above largely shows the portfolio flows.
Figure 7. Each Investor Group’s Weight of Monthly Purchased-Sold Amount
(Jul. 2002 – Dec. 2010, Monthly)
80
70
60
50
40
30
20
10
0
200207
200211
200303
200307
200311
200403
200407
200411
200503
200507
200511
200603
200607
200611
200703
200707
200711
200803
200807
200811
200903
200907
200911
201003
201007
201011
%
Dat
FOREI
INSTI
INDIV
OTHER
Source: KRX
*Each investor group’s weight of monthly purchased-sold amount = (purchased amount + sold amount) / Turnover / 2
Figure 8. KOSPI Turnover
(Jul. 2002 – Dec. 2010, Monthly)
2.00E+11
Korean Won
1.80E+11
1.60E+11
1.40E+11
1.20E+11
1.00E+11
8.00E+10
6.00E+10
4.00E+10
2.00E+10
Date
200207
200211
200303
200307
200311
200403
200407
200411
200503
200507
200511
200603
200607
200611
200703
200707
200711
200803
200807
200811
200903
200907
200911
201003
201007
201011
0.00E+00
Source: Bloomberg LP
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Figure 9. Correlations Among Major Equity Indices Around The World
(Jan. 2002 – Dec. 2010, Daily)
KOSPI
S&P500
DAX
FTSE
CAC
HSI
STI
NKY
IBOV
KOSPI
1.0000
S&P500 0.5869
1.0000
DAX
0.9133
0.8154
1.0000
FTSE
0.7611
0.9300
0.9229
1.0000
CAC
0.5412
0.9396
0.7965
0.9150
1.0000
HSI
0.9536
0.6416
0.9033
0.7585
0.5663
1.0000
STI
0.9360
0.7903
0.9691
0.8895
0.7304
0.9403
1.0000
NKY
0.4542
0.8648
0.6977
0.8175
0.9198
0.4831
0.6254
1.0000
IBOV
0.9511
0.4362
0.8167
0.6192
0.3665
0.9314
0.8633
0.2725
1.0000
Source: Bloomberg LP
* Acronyms in the table indicate the following: the Standard & Poor’s 500 (S&P500), the Deutscher Aktien Index
(DAX), the FTSE 100 Index (FTSE), the CAC 40 (CAC), the Hang Seng Index (HSI), the Straits Times Index (STI),
the Nikkei 225 (NKY), and the Bovespa Index (IBOV).
The majority of companies listed in KOSPI report their annual earnings around
April. The earnings’ releases’ impact on the PER is observed in Figure 10, as most
conspicuous adjustments in the PER occurred in Aprils of 2003, 2004, 2005, 2009, and
2010. One notable take away from Figure 10 and 12 is that the adjustments in PER at the
time of the earnings release are not necessarily made toward the historical trend. The
downward adjustments in 2003, 2005, and 2010 represent the cases where the aboveexpectation earnings were released or optimistic speculations with momentum-cause
were in place, before the announcement of the earnings. The upward adjustments in 2004
and 2009 represent the cases where the below-expectation earnings were released or
pessimistic speculations with momentum-cause were in place (De Long, Shleifer,
Summers, and Waldmann, 1990). The adjustments at the point of earnings releases do not
necessarily minimize the deviation of PER from the historically trending PER. This
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phenomenon is likely to be attributable to those who execute trades after reaffirming their
own convictions on the traded shares.
Figure 17. PER of KOSPI Based on The Annually Reported Earnings
(Jan. 2001 – Dec. 2010, Monthly)
50
45
200103
40
200204
35
200909
30
200905
25
20
15
10
5
201003
200503
200107
200304
200305
200505
200710
200704
200903
201005
200101
200105
200109
200201
200205
200209
200301
200305
200309
200401
200405
200409
200501
200505
200509
200601
200605
200609
200701
200705
200709
200801
200805
200809
200901
200905
200909
201001
201005
201009
0
Source: Bloomberg LP
*Reminder: Earnings’ releases occur in Aprils.
In the foreign exchange market, any large amount of currency flows with profitcause is described as hot money, as there is no clear measure for the fair values of
currencies (Razin and Rosefielde, 2011). Short-term speculative capital flows from
abroad, or hot money, are believed to increase volatility, as seen in China (Guo and
Huang, 2010). Being one of the less developed markets with relatively small capital pool
and high domestic economic growth, the KOSPI and won-dollar exchange markets are
not exceptions in terms of being affected significantly by foreign investment flows. For
Korea’s case, insufficient amount of analysis and discussion has been made on whether
or not the portfolio flow of foreign origin in aggregate is hot money.
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III. Analysis of The Residual PER (RPER)’s Impact on Trading Flow
In the KOSPI, there is a popular stereotyping of foreign investors that they are
predatory and driven by momentum. This analysis observes how true this perception has
been in practice. More specifically, the author analyzes the behavior of each investor
group to find out whose value-orientation is the strongest by measuring how much each
investor group rely their trades on the gap between the historically trending PER and the
PER of the time, with an anticipation that the deviation will disappear. This analysis
measures the impact of the current PER’s deviation from the historically prevailing PER
on each investor group’s trading behaviors. Those who add momentum toward the
historically prevailing PER are seen as more value oriented investors.
III. 1. Design
III. 1. (1) Big Picture
Various approaches at different dimensions are possible in the analysis of the
traded equity markets. While the author could have used the variables at different facet,
such as: interest rates, unemployment rates, and base commodity prices, he chooses to
focus on a dimension that simplifies the analysis significantly. The author designs the
experiment with a reasonable assumption that trade executions are necessarily made
when the investors want, willingness, and are capable of, constraint, making the trades
(Figure 13).
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Figure 13. Conditions For Trade Executions
Willingness includes (1) a controllable willingness (valuation) and (2) an
uncontrollable willingness (risk appetite).10 The above distinction between controllable
and uncontrollable willingness must be made in this analysis, as it is clear that two
different sorts of willingness exists. Valuation-levels are identified after investors’
research and analysis, and the investors are likely to choose whether or not to purchase or
sell shares with logic; however, risk appetites carry emotional component in it, and
therefore it is not modulated by the investing selves (Lee, Shleifer and Thaler, 1990). For
the measure of the risk appetites, Chicago Board Options Exchange (CBOE) Market
Volatility Index (VIX) is included.11
Constraints include (1) availability of funds (market liquidity) and (2) regulatory
factors. To analyze foreign investors’ situation, the availability of funds is taken into
account with two variables: won-dollar exchange rate (KRWD) and USD London
Interbank Offered Rate (LIBOR) spreads between two different maturities. Won-dollar
exchange rate is paired with USD LIBOR yield spreads between 1 months’ and 3
months’ (SPREAD). The USD LIBOR yield spreads represents anticipated short-term
10
Both valuation and risk appetite are motivations for trading; however, unlike valuation that serves as an
opportunity that the investors intend to exploit, risk appetite is not managed by the investors’ will.
11
VIX “is a good indicator of the level of fear or greed in US and Global capital markets. When investors
are fearful, the VIX level is significantly higher than normal” (Traub, Ferreira, McArdle, and Antognelli,
2000). Two additional reasons support the use of VIX in this context: (1) Korea’s economy’s structural
vulnerability to overseas markets due to its focus on exports, and (2) the high correlation among major
equity indices around the world (Figure 9).
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liquidity in the market. USD LIBOR 1-month yield and 3-month yield are commonly
believed to be sensitive to almost the identical variables, except that the uncertainties for
the two-month-long gap makes the 3 months’ rate slightly higher in the normal situations.
The won-dollar exchange rate displays the currency impacts, as when the won weakens
vis-à-vis the USD South Korean equities seem discounted in the foreign investors’
perspective, and vice versa. No significant enough regulatory factors exist, as elaborated
in Background 3; therefore, the impact of regulatory factors is zero. While the point made
by numerous researchers that risk appetite and macroeconomic uncertainties are two
major variables that affect asset pricing, are well taken,12 macroeconomic uncertainties
whose impact is directly reflected on the reported earnings was not included in this model
as the PER is calculated from the reported earnings.
The author adds the KOSPI’s total market turnover, as an additional control
variable. Total market turnover is the sum of the products of each share’s price and the
volume, and the unit is Korean Won. The inclusion of the turnover enables the author to
better capture the trades’ impact on the KOSPI’s fluctuation than otherwise, as the traded
amount can differ significantly in different periods.
Figure 14. Summary of Variables Used in Panel A
N
=
=
Net Purchase Monetary Amount of Each Investor Group in KRW
ƒ (RPER, VIX*, SPREAD, KRWD, Turnover)
RPER
=
SPER** – IPER***
SPREAD
=
USD 3M LIBOR Yield – USD 1M LIBOR Yield
KRWD
=
Exchangeable Amount of KRW per 1 USD
*Detailed description of the VIX can be found in CBOE’s white paper (CBOE, 2011).
**SPER: adjusted and smoothed PER. calculation of SPER is described in detail in III. 1. (2) Detailed Scene.
***IPER: Index PER. The calculation of IPER is described in detail in III. 1. (2) Detailed Scene.
12
Bansal and Yaron (2004), and Bekaert, Engatrom, and Xing (2009)
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III. 1. (2) Detailed Scene
First, the author identifies the net purchased-sold monetary amount in Korean
Won of each investor group (NET) as a dependent variable.13 Then, the residual PER
(RPER) is calculated by calculating and deducting the index PER (IPER) from the
adjusted/smoothed PER (SPER) (Figure 100, 11 and 12).14 The residual PER is used as
the independent variable that represents the adjusted/smoothed PER (SPER)’s deviation
from the historically prevailing SPER (IPER).
The adjustments and smoothing of PER is a necessary step, as the use of SPER
enables the analysis to capture the effects of prices and earnings, evenly. This adjustment
is necessary because companies’ annual earnings are officially announced once a year,
while the price fluctuates much more frequently. In the KOSPI, the time of earnings
announcements are concentrated around April, and rapid adjustments in PER occur in
Aprils, as described in Background 3 and in Figure 16 and 17.
For each investing entity, value-orientation is based on their knowledge on the
earning’s capability of the target company for the investment and the share valuation is
supposed to be at the reasonable level at the individual level. Under such circumstances,
because these investors’ view and knowledge can change dramatically over time, the use
of SPER is once more justified. Due to the lack of market wide earnings’ data at a higher
frequency, estimation of monthly earnings based on the annual change and the smoothing
of earnings were conducted.
13
The resulting terms are NETFOREI (Nf), NETINSTI (Ni), NETINDIV, and NETOTHER (No) (Figure 15).
Among various valuation metrics, the PER, one of the two (the other being the price to book ratio) most
widely accepted standard of valuation in contemporary financial industry in Korea, was chosen. Also, the
fact that PER is the only financial ratio (valuation metric) audited by accounting firms makes it more
reliable. The need for the adjustments and smoothing on the PER is explained in III. 1. (2) Detailed Scene.
14
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‘Estimation of monthly earnings based on the annual change’ of reported earnings
looks at the path of earnings based on the earnings in Mays. As shown in figure 16, the
updated earnings’ impacts are in place from May to the April of the next year, as the
perceived earnings’ levels are set in Mays, as the earnings tend to level out from May to
April. Also, it is logical to assume that the earnings numbers obtained in April is
gradually outdated. Therefore, the earnings’ number of May is most reliable. Adjusted
earnings (A_E) are the resulting values of this estimation (Figure 144). In the estimation
process, the author makes an assumption that the amount of earnings of the next year
(May) becomes clearer as the next May approaches, and gave weights according to this
logic (Figure 100). The weights are given as in Figure 100 because there is a high
likelihood that investors will have better understanding of the earnings’ situation based
on either information leakage or research, as the time of earnings announcement
approaches.
After identifying the adjusted earnings (A_E), the author generates smoothed
earnings (S_E) that is more useful for the anlysis than A_E is. In the smoothing process
with average, the author included 2 months prior to the month and the following 10
months. Such design is made to reflect the fact that equity investors in general are
forward-looking and there are a lot more focus on the predictions than on the past record,
in their research. Inclusion of 2 months prior to the month is made because the past
earnings can affect the investors’ confidence in the short term. This attributes to the fact
that the history of companies could be a barometer of the quality of the business for some
investors.
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Minseok Lee
Smoothed PER (SPER) is calculated using the monthly market capitalization of
the KOSPI and the smoothed earnings (S_E), and Index PER is calculated as shown in
Figure 11.
Figure 100. Weights Used in The Estimation of Adjusted Earnings (A_E)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
5
6
7
8
9
Weight on the May of Y
10
11
12
1
Weight on the May of Y+1
2
3
4
Month
5
Figure 16. Perceived Earnings* of KOSPI
(Jan. 2001 – Dec. 2010, Monthly)
8.00E+07
KRW
7.00E+07
6.00E+07
5.00E+07
4.00E+07
200109
200505
200705
200107
200305
1.00E+07
200103
201005
200804
201003
200503
200101
200105
200109
200201
200205
200209
200301
200305
200309
200401
200405
200409
200501
200505
200509
200601
200605
200609
200701
200705
200709
200801
200805
200809
200901
200905
200909
201001
201005
201009
0.00E+00
200405
200303
200204
200208
200904
200905
200403
3.00E+07
2.00E+07
200704 200805
Source: Bloomberg LP
*KOSPI’s Perceived Earnings = Total Market Capitalization / KSPEKOSP Index
**KSPEKOSP Index measures the PER based on the reported earnings. However, because the earnings were estimated
with help of total market capitalization in the equation above, KOSPI’s earnings perceived by investors is calculated.
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Minseok Lee
Figure 144. Adjusted Earnings and Smoothed Earnings Based on Aprils’ Reports
(May 2003 – May 2010, Monthly)
7.00E+13
6.00E+13
5.00E+13
4.00E+13
3.00E+13
2.00E+13
1.00E+13
200305
200309
200401
200405
200409
200501
200505
200509
200601
200605
200609
200701
200705
200709
200801
200805
200809
200901
200905
200909
201001
201005
0.00E+00
A_E
S_E
*A_E: Adjusted Earnings, S_E: Smoothed Earnings
Figure 11. Calculation of Index PER (IPER) and Residual PER (RPER)
(Jul. 2003 – Aug. 2009, Monthly)
Given that:
Time Index (TINDEX): 1 to 74, respectively, from Jul. 2003 to Aug. 2009,
Solving for:
SPER = Slope × TINDEX + SPER-intercept,
Slope
SPER-intercept
Coefficients
0.0824472
8.861607
t
8.07
20.11
Index PER (IPER) = 0.0824472× TINDEX + 8.861607
Residual PER (RPER) = SPER - IPER
19
P > abs (t)
0.000
0.000
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Minseok Lee
Figure 12. Smoothed PER (SPER) and Index PER (IPER)
(Jul. 2003 – Aug. 2009, Monthly)
18
16
14
12
SPER
IPER
10
8
200307
200311
200403
200407
200411
200503
200507
200511
200603
200607
200611
200703
200707
200711
200803
200807
200811
200903
200907
6
Source: Bloomberg LP
*SPER = Monthly KOSPI Total Market Capitalization / S_E
**Calculation of IPER can be found in Figure 11.
Figure 1000. Residual PER (RPER)
5
4
3
2
1
-1
-2
200307
200310
200401
200404
200407
200410
200501
200504
200507
200510
200601
200604
200607
200610
200701
200704
200707
200710
200801
200804
200807
200810
200901
200904
200907
0
-3
-4
Source: Bloomberg LP
*Reminder: RPER = SPER - IPER
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Minseok Lee
Figure 15. Panel A Structure
Time
0
.
.
.
t
0
.
.
.
t
0
.
.
.
t
0
.
.
.
t
Dependent
Variable
Independent
Variable
Control Variables
NFOREI (Nf)
RPER
VIX
SPREAD
KRWD
Turnover
NINSTI (Ni)
RPER
VIX
SPREAD
KRWD
Turnover
NINDIV
RPER
VIX
SPREAD
KRWD
Turnover
NOTHER (No)
RPER
VIX
SPREAD
KRWD
Turnover
The author recognizes that there is a high probability of individual investors’
positive or negative sentiments having an impact on the local mutual funds due to the fact
that mutual funds are commonly known to adjust their positions based on the fund flows
that are initiated by individuals who hold accounts in the mutual fund. This possibility is
supported by the correlation (-0.4165) between the daily net purchase-sold amounts of
individuals and mutual funds, in the period from 2001 January to 2010 December.
Strongly negative correlation between the mutual funds’ and individuals’ net purchasesold amount can be explained by the fact that there are two accessible ways for
individuals to invest in equities: either buying mutual funds (recorded as mutual funds) or
trading with personal trading accounts (recorded as individuals). Negative correlation
implies that the author’s original conjecture, that mutual funds’ trading behavior might be
affected by the individual investors’ sentiments (positive or negative), is likely to be
invalid. Monthly net purchased-sold amounts of the two show significantly low
correlations (0.0614). The drastic change in correlations between the mutual funds and
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Minseok Lee
the individuals from daily and monthly data sets is likely attributable to the fact that the
mutual funds increase or decrease their cash holdings to minimize the possible shock
from the retail investors’ purchases and sales of the mutual fund account. As the
correlations: -0.4165 and 0.0614 15 indicate, the longer time the mutual funds have, the
better prepared they are for the shock from the individual investors who add or reduce
their mutual fund accounts. The regressions in this paper use monthly data; therefore, the
author decides that including the mutual funds under the institutional investors makes a
better analysis.
When determining the time duration of the analysis, the author chose to include
only the period from Jul. 2002 to Dec. 2010, despite the availability of a complete panel
data in the first half of 2002. Such decision was made in order to eliminate the noise, as
the PER deviates excessively from the historical trend during the first half of 2002
(Figure 10 and 17). In addition, in the process of adjusting and smoothing of the earnings
data, the observation period shrinks again from, Jul. 2002 to Dec. 2010, to, Jul. 2003 to
Aug. 2009.
The author recognizes that one of the most important motivations of value
investment is the expectation for growth in future earnings. The reported-earnings-based
PER is the data used for this analysis, and some readers may be concerned that the PER
used in this paper does not capture the expectation for the earnings growth. However, the
smoothing process used in the generation of SPER eliminated such concern. The author
assumes that those investors who base their valuation analysis on the prediction of future
earnings get the numbers correct with marginal errors. This assumption justifies the use
of the reported-earnings-based PER as a valid valuation metric.
15
Source: KRX via Daewoo Securities Co. Ltd.
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Minseok Lee
III. 2. Results
III. 2. (1) Preliminary Results
The preliminary regression for foreign investors’ investment flow is made in
Figure 18. The result of the preliminary regression shows that investment flows of
foreign origin (KSFINET)16 is sensitive to the VIX, KRWD and Turnover. The reason
why the impact from RPER is not clearly observed is likely to be attributable to the fact
that the investors within each group are not monolithic. SPREAD’s unclear relevance to
the net foreign portfolio flow is due to the variety of investor characters in terms of
location and investment goals (Figure 19 and 20). What we see in the result section is the
aggregate outcome. Also, the result of the preliminary regression represents the situation
in both KOSPI and KOSDAQ, unlike the actual regression (only KOSPI).
Figure 18. Estimation Results for KOSPI & KOSDAQ
(Jul. 2003 – Aug. 2009, Collapsed to Monthly from Daily, Robust Regression)
Parameters and the Estimates
Time
Series A:
Variable
KSFINET = π0 +π1RPER +π2VIX +π3SPREAD +π4KRWD + π5Turnover
N/A
π0
-2.00e+10
(-6.73)
RPER
π1
-9.76e+07
(-0.68)
VIX
π2
-2.14e+08
(-5.71)
SPREAD
π3
-1.57e+09
(-0.75)
KRWD
π4
2.18e+07
(7.38)
Turnover
π5
5.63e-06
(0.63)
Source: Bloomberg LP
*KSFINET’s unit is Million USD.
**The values in the parenthesis indicate the t-statistics.
***Statistically significant
Figure 90. Means and Elasticities From The Preliminary Run
(Jul. 2003 – Aug. 2009, Collapsed to Monthly from Daily, Robust Regression)
KSFINET
RPER
VIX
SPREAD
KRWD
Turnover
Mean
-6.08E+08
1.78E-06
20.1324
0.170958
1075.092
8.27E+13
Elasticity
N/A
2.86E-07
7.09E+00
4.41E-01
-3.85E+01
-7.66E-01
Figure 19. Composition of Foreign Investors by Number of Accounts
16
KSFINET Index: Daily Net Flows in South Korean equities by foreign investors in KOSPI and
KOSDAQ.
23
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Minseok Lee
Category
Individuals
Institutions
(Group Investments)
(Pension Funds)
(Brokerages)
(Banks)
(Insurers)
(Unclear)
End of 2008
End of 2009
End of 2010
29.29%
70.71%
41.44%
6.32%
2.37%
2.25%
1.34%
17.00%
29.01%
70.99%
41.27%
6.04%
2.30%
2.11%
1.28%
17.99%
28.76%
71.24%
41.85%
5.78%
2.21%
1.96%
1.24%
18.21%
Source: Financial Supervisory Service
Figure 20. Composition of Foreign Investors by Location
(Jan. 2011)
Location
US
Saudi Arabia
China
Cayman Is.
Ireland
Switzerland
Luxemburg
Hong Kong
Denmark
UAE
Sweden
Kuwait
Germany
Malaysia
Virgin Is.
Australia
Canada
Singapore
Spain
Japan
France
Netherlands
UK
Etc
Purchased
71005
8916
4482
21756
11508
8349
19385
3800
1194
1132
1561
1424
9644
1860
1215
2917
3184
7593
396
2180
8444
4720
62514
6963
Sold
43869
5420
1528
19839
9651
6934
18577
3095
967
1023
1506
1526
9777
2136
1692
3467
3736
8814
1646
4218
13594
11456
74505
6866
Net
27136
3496
2954
1917
1857
1415
808
705
227
109
55
-102
-133
-276
-477
-550
-552
-1221
-1250
-2038
-5150
-6736
-11991
97
Source: Financial Supervisory Service
*Unit for Purchased, Sold, and Net: 100 Million KRW.
**Transaction’s Weight = (Purchased + Sold) / (Sum of all of Purchased and Sold)
III. 2. (2) Thorough Analysis
24
Transaction's Weight
22.0%
2.7%
1.2%
8.0%
4.1%
2.9%
7.3%
1.3%
0.4%
0.4%
0.6%
0.6%
3.7%
0.8%
0.6%
1.2%
1.3%
3.1%
0.4%
1.2%
4.2%
3.1%
26.2%
2.6%
20110429
Minseok Lee
In Figure 21, the author extensively utilizes dummy variables and interaction
terms. Row 1 enables us to identify which investor trade on the other side of one another.
Although Row 2 did not show statistically significant results, the row was meant to
enable us to gauge each investor group’s trade execution’s sensitivity to RPER. In
addition, rows 5 and 6 look at the KRW’s and Turnover’s impact on the NET,
respectively.
Because data used for the control variables were the same across the investor
groups, use of interaction terms were applied for the control variables, as well. The VIX’s
interaction terms with FOREI, INSTI, and OTHER: VIXf, VIXi and VIXo, read the VIX’s
influence on foreign and institutional investors and other corporation, respectively (Row
3). Although not discussed afterwards due to the result’s statistical insignificance,
SPREAD’s interaction terms with FOREI, INSTI, and OTHER: SPREADf, SPREADi,
and SPREADo, were meant to measure the 1 Month and 3 Month LIBORs’ yield spread’s
impact on each investor group’s trade executions (Row 4).
To test for the presence of autocorrelation, Durbin-Watson d-statistics was used.
As the resulting value (1.987788) is close to 2, we conclude that this regression has no
autocorrelation issue (Figure 21).
Figure 21. Estimation Results for KOSPI
(Jul. 2002 – Dec. 2010, Monthly, Robust Regression)
Parameters and the Estimates
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Minseok Lee
Panel A:
N
Variable
= α1
+ β1RPER
+ γ1VIX
+ δ1SPREAD
+ ε1KRWD
+ ζ1Turnover
N/A
α1
2.50e+12
(0.96)
Interactions
With FOREI
Interactions
With INSTI
Interactions
With OTHER
αf
-2.31e+13
(-5.59)
αi
1.14e+13
(2.82)
αo
1.68e+12
(0.62)
+ αfFOREI
+ βfRPERf
+ γfVIXf
+ δfSPREADf
+ εfKRWDf
+ ζfTurnoverf
RPER
β1
8.39e+10
(0.81)
βf
-1.50e+11
(-0.83)
βi
-1.10e+11
(-0.73)
βo
-7.24e+10
(-0.67)
VIX
γ1
4.94e+10
(1.99)
γf
-2.69e+11
(-5.93)
γi
8.97e+10
(2.26)
γo
-1.77e+10
(-0.64)
+ αiINSTI
+ βiRPERi
+ γiVIXi
+ δiSPREADi
+ εiKRWDi
+ ζiTurnoveri
SPREAD
δ1
1.38e+11
(0.08)
δf
-1.72e+12
(-0.62)
δi
8.08e+11
(0.32)
δo
3.91e+11
(0.22)
+ αoOTHER
+ βoRPERo
+ γoVIXo
+ δoSPREADo
+ εoKRWDo
+ ζoTurnovero
KRWD
ε1
-3.70e+09
(-1.52)
εf
2.60e+10
(6.55)
εi
-1.08e+10
(-2.86)
εo
-3.62e+08
(-0.14)
(Row 1)
(Row 2)
(Row 3)
(Row 4)
(Row 5)
(Row 6)
Turnover
ζ1
0.0035631
(0.47)
ζf
0.0063798
(0.57)
ζi
-0.0156617
(-1.29)
ζo
-0.0054136
(-0.69)
Durbin-Watson d-statistics (NEW VALUE, NEW VALUE) = NEW VALUE
*The values in the parenthesis indicate the t-statistics.
**Statistically significant
Figure 22. Means and Elasticities
P
RPER
(Mean: 1.78e-06)
VIX
(Mean: 2.01e+01)
SPREAD
(Mean: 1.71e-01)
KRWD
(Mean: 1.08e+03)
Turnover
(Mean: 8.27e+13)
Q
Q’s Elasticity on P
NFOREI
NINSTI
NINDIV
NOTHER
-1.27E-04
-5.01E-05
1.61E-04
2.21E-05
NFOREI
NINSTI
-4.77E+03
3.02E+03
NINDIV
NOTHER
1.07E+03
6.88E+02
NFOREI
NINSTI
NINDIV
NOTHER
-2.92E+02
1.74E+02
2.54E+01
9.76E+01
NFOREI
NINSTI
NINDIV
NOTHER
2.59E+04
-1.68E+04
-4.29E+03
-4.71E+03
NFOREI
NINSTI
8.87E+02
-1.08E+03
3.18E+02
-1.65E+02
NINDIV
NOTHER
*Shaded parts indicate the results that are more reliable than the rest, due to the statistical significance. The darker, the more reliable.
** Calculations of the elasticity were done by the following equation: Elasticity = Coefficient × (Mean of RPER, VIX, SPREAD,
KRWD, or Turnover) / (Mean of N). Mean of N is 9.27e+08.
The result from the row 1 shows: α1 + αf (-2.06e+13) < 0 < α1 (2.50e+12) < α1 + αi
(1.39e+13). The negative sign on α1 + αf indicates that the foreigners are on the other side
26
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Minseok Lee
of individual investors’ trade, while institutional investors and other corporations are on
the same side of individual investors’ trade in aggregate.
We intended to distinguish from this result which group has been trading in order
to achieve the historically prevailing PER. The result shows no statistically significant
results for the row 2.
From the elasticity table (Figure 22), we identify that the foreign investors on
average increased long positions (or decreased short positions) in the KOSPI by 684%
when the VIX hiked 1%, while institutional investors increased short positions (or
decreased long positions) in the KOSPI by 157%. Individual investors increased short
positions (or decreased long positions) in the KOSPI by 6.65% when the VIX hiked 1%,
ceteris paribus. In addition, the elasticity of KRWD’s NFOREI show that foreigners on
average decreased long positions in the KOSPI by -3360% as KRW depreciated by 1%,
ceteris paribus. KRWD’s NINSTI shows that 825% increase in long positions (or decrease
short positions) were made by institutional investors for KRW’s every 1% depreciation
vis-à-vis USD. As NINDIV indicates, individual investors increased long positions (or
decreased short positions) by 24.6% for every 1% depreciation of KRW against USD.
The result from the VIX might be misinterpreted to mean that foreign investors
are risk-loving. More thorough observation on the trading flow and VIX can give more
correct picture. In Figure 24, we can see that the VIX has been relatively high from 2007
to 2009 (and 2010).17 From the beginning of the subprime mortgage crisis of 2007 to the
post-Lehman period of the end of 2009, individual investors and foreign investors show
two distinct behaviors. Until the Oct. 2008, the VIX hiked rapidly (Figure 24), and we
observe that the foreigners have aggressively reduced the KOSPI in 2007 and 2008
17
Figure 27 does not contain the data in 2010, so I exclude 2010 from the analysis.
27
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Minseok Lee
(Figure 27). Unlike the foreign investors, domestic individual investors were net buyers
of the KOSPI in 2007 and 2008. In 2009, although the VIX stayed at a high level, it
headed down at a fast pace (Figure 24). During this period, foreign investors added
significant amount of the KOSPI in aggregate. This observation indicates that the
direction of the VIX movement is a more important indicator than the level of the index,
as stabilizing events encourage investors to make optimistic speculation.18 This finding,
with help of knowledge acquired from the regression of the Panel A, indicates that
foreign investors do not mind adding the KOSPI even as the VIX rises, as long as they
expect the VIX to be lower in future. This means that foreign investors are more
concerned about the risk managements than individual investors.
The result of this analysis weakens the argument that foreign investors utilize
depreciations of won as buying opportunities. What this analysis implies is that
foreigners’ investment flows caused by factors other than currencies, such as liquidity
squeeze, affects the currency market. LIBOR spread’s spike in the period between Nov.
2008 and Jul. 2009 (Figure 23), and its relation to the spike in the foreign exchange
market is worth noting (Figure 25). This provides a good explanation with regard to the
simultaneous local currency depreciation and the outflow of the portfolio flow of foreign
origin: as foreigners sell Korea, they abandon Korean Won at the same time. Figure 17
shows that the PER level hiked during the same period, and this implies that foreign
investors’ purchases of the KOSPI were caused by depressed Korean Won.
The observations in Figure 25 and 28 show that under the circumstances where
the local currency (KRW) is extremely depressed, foreign investors’ investment inflow
The author classifies this as a speculation, as the KOSPI’s earnings perceived by investors was extremely
poor in 2009 (Figure 16).
18
28
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Minseok Lee
occurs to Korea. The period from Sept. 2008 to Jul. 2009 can be classified as the time
period where KRW was extremely depressed (Figure 25), and its impact on the portfolio
flows of foreign origin is very clear during the same period (Figure 28).
Figure 23. LIBOR Yield Spread between 1 Months’ and 3 Months’ (SPREAD)
(Jul. 2002 – Dec. 2010, Averaged Monthly From Daily)
0.9
200901, 0.827411
0.8
0.7
0.6
0.5
201006,
0.1881623
0.4
0.3
0.2
0.1
-0.1
200207
200211
200303
200307
200311
200403
200407
200411
200503
200507
200511
200603
200607
200611
200703
200707
200711
200803
200807
200811
200903
200907
200911
201003
201007
201011
0
Source: Bloomberg LP
*Calculation of SPREAD is explained in Figure 14.
Figure 24. VIX
(Jul. 2002 – Dec. 2010, Averaged Monthly From Daily)
29
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Minseok Lee
70
200810
60
50
201005
40
30
20
10
201011
201007
201003
200911
200907
200903
200811
200807
200803
200711
200707
200703
200611
200607
200603
200511
200507
200503
200411
200407
200403
200311
200307
200303
200211
200207
0
Source: Bloomberg LP
*For the VIX, the magnitude of swing is the major concern, as oppose to the numerical level (vertical axis) of the VIX.
Figure 25. Won-dollar Exchange Rate (KRWD)
(Apr. 2000 – Dec. 2010, Daily)
1800
10/24/2008,
1454.5
1600
2/27/2009,
1538.3
1400
1200
9/29/2008,
1184.9
1000
800
4/25/2007, 926.8
600
7/9/2009, 1279
400
200
0
Source: Bloomberg LP
*KRWD = Exchangeable Amount of KRW per 1 USD
Figure 26. Change in Shareholding of KOSPI by Investor Group
(2001 – 2009, Annually)
30
5/25/2010,
1271.68
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Minseok Lee
Year
2001
2002
2003
2004
2005
2006
2007
2008
(%)
2009
6.43
-6.29
2.28
-0.61
-2.38
0.01
4.1
-0.27
-2.63
1.86
0.8
-1.7
-2.24
1.54
0.43
-2.47
2.67
-0.49
-4.89
-1.95
3.85
-3.59
-9.03
5.26
3.87
-0.9
3.97
OTH
-2.44
Source: KRX
2.99
-1.2
-0.95
0.27
0.28
2.98
7.36
-6.93
Investor
Group
FOR
INS
IND
Figure 27. Change in Shareholding of KOSPI by Investor Group
(2001 – 2009, Annually)
10
%
8
6
4
2
0
2001
2002
2003
2004
2005
2006
2007
2008
-2
-4
-6
-8
-10
FOR
INS
IND
Source: KRX
Figure 28. KSFINET Index USD
(Jan. 2001 – Dec. 2010, Monthly)
31
OTH
2009
20110429
Minseok Lee
6E+09
200704,
2781500000
4E+09
201004,
4866220000
200805,
878080000
2E+09
-2E+09
200101
200105
200109
200201
200205
200209
200301
200305
200309
200401
200405
200409
200501
200505
200509
200601
200605
200609
200701
200705
200709
200801
200805
200809
200901
200905
200909
201001
201005
201009
0
-4E+09
-6E+09
-8E+09
-1E+10
-1.2E+10
200708,
-9597800000
201005,
-5423890000
200801,
-9474960000
Source: Bloomberg LP
*KSFINET Index: Daily Net Flows in South Korean equities by foreign investors in KOSPI and KOSDAQ.
Figure 26. KOSPI Index
(Jul. 2002 – Dec. 2010, Daily)
2500
2000
1500
1000
500
200207
200211
200303
200307
200311
200403
200407
200411
200503
200507
200511
200603
200607
200611
200703
200707
200711
200803
200807
200811
200903
200907
200911
201003
201007
201011
0
Source: Bloomberg LP
Above interpretations of results are valid within the period from Jul. 2002 to Dec.
2010; however, the result from the following analysis indicates that each investor group’s
32
20110429
Minseok Lee
trading behavior changes over time. The author examined the varying investment
approach of each investor group over time by running the regression of Panel A (Figure
21) with additional dummies for two periods: the first half (FH=1) and the second half
(FH=0).
In Figure 30, FOREIfh, INSTIfh, OTHERfh, RPERfh, RPERfh,f, RPERfh,i, RPERfh,o,
VIXfh, VIXfh,f, VIXfh,i, VIXfh,o, SPREADfh, SPREADfh,f, SPREADfh,i, SPREADfh,o,
KRWDfh, KRWDfh,f, KRWDfh,i, KRWDfh,o, Turnoverfh, Turnoverfh,f, Turnoverfh,i, and
Turnoverfh,o indicate the interaction terms between FH and FOREI, INSTI, OTHER,
RPER, RPERf, RPERi, RPERo, VIX, VIXf, VIXi, VIXo, SPREAD, SPREADf, SPREADi,
SPREADo, KRWD, KRWDf, KRWDi, KRWDo, Turnover, Turnoverf, Turnoveri, and
Turnovero, respectively.
The F-test’s result in Figure 30 shows that each investor’s characteristics that are
identified from Figure 21 are applicable only for the period from Jul. 2002 to Dec.
2010. 19 This is likely to be attributable to two major reasons: the changes in the
composition of each investor group and each investing selves’ behaviors. The author was
concerned that the outliers’ impacts were not interpreted in detail; hence, more tests
followed (Figure 31). Both KRWD and VIX,20 appear to change their degree of impact
on the trading behavior of foreign investors, as the hypothesis 2 and 4 were rejected in
Figure 31. The elasticities of the two variables in the first and the second half also differ
significantly (Figure 32).
Figure 30. Test of Change in Character within Each Investor Group Over Time
(Jul. 2002 – Dec. 2010, Monthly, Robust Regression)
Parameters and Estimators
19
This is in line with the findings from Cheo, Kho, and Stulz (1998) and Kim and Wei (1999).
Two variables with statistically significant results from Figure 21, among those that affected the result of
the F-test in Figure 30, are the VIX and KRWD.
20
33
20110429
Panel A:
Variable
Minseok Lee
N
= _α1
+ _β1RPER
+ _γ1VIX
+ _δ1SPREAD
+ _ε1KRWD
+ _ζ1Turnover
+ ω00FH
+ ω10RPERfh
+ ω20VIXfh
+ ω30SPREADfh
+ ω40KRWDfh
+ ω50Turnoverfh
N/A
_α1
Interactions
With FOREI
Interactions
With INSTI
Interactions
With OTHER
Interactions
With FH
Interactions
With FH, F
Interactions
With FH, I
Interactions
With FH, O
2.07e+12
(0.40)
_αf
-2.74e+13
(-4.25)
_αi
1.64e+13
(2.43)
_αo
2.88e+12
(0.55)
+ _αfFOREI
+ _βfRPERf
+ _γfVIXf
+ _δfSPREADf
+ _εfKRWDf
+ _ζfTurnoverf
+ ω01FOREIfh
+ ω11RPERfh,f
+ ω21VIXfh,f
+ ω31SPREADfh,f
+ ω41KRWDfh,f
+ ω51Turnoverfh,f
RPER
_β1
2.76e+10
(0.13)
_βf
-4.65e+11
(-0.92)
_βi
3.80e+11
(1.00)
_βo
-3.93e+10
(-0.17)
+ _αiINSTI
+ _βiRPERi
+ _γiVIXi
+ _δiSPREADi
+ _εiKRWDi
+ _ζiTurnoveri
+ ω02INSTIfh
+ ω12RPERfh,i
+ ω22VIXfh,i
+ ω32SPREADfh,i
+ ω42KRWDfh,i
+ ω52Turnoverfh,i
VIX
_γ1
4.13e+10
(1.00)
_γf
-3.48e+11
(-5.42)
_γi
1.90e+11
(3.15)
_γo
-6.23e+09
(-0.14)
SPREAD
_δ1
-8.74e+10
(-0.03)
_δf
-3.58e+12
(-1.05)
_δi
2.90e+12
(0.86)
_δo
1.14e+12
(0.43)
+ _αoOTHER
(Row 1)
+ _βoRPERo
(Row 2)
+ _γoVIXo
(Row 3)
+_ δoSPREADo (Row 4)
+ _εoKRWDo
(Row 5)
+ _ζoTurnovero (Row 6)
+ ω03OTHERfh (Row 7)
+ ω13RPERfh,o
(Row 8)
+ ω23VIXfh,o
(Row 9)
+ ω33SPREADfh,o (Row 10)
+ ω43KRWDfh,o (Row 11)
+ ω53Turnoverfh,o (Row 12)
KRWD
_ε1
-3.54e+09
(-0.59)
_εf
3.29e+10
(4.32)
_εi
-1.71e+10
(-2.22)
_εo
-1.87e+09
(-0.31)
Turnover
_ζ1
0.0086209
(1.08)
_ζf
0.0038968
(0.20)
_ζi
-0.0314433
(-2.01)
_ζo
-0.0061355
(-0.71)
ω00
ω10
ω20
ω30
ω40
ω50
-2.27e+12
(-0.38)
1.35e+11
(0.55)
2.31e+11
(2.33)
8.54e+12
(2.12)
-1.68e+09
(-0.26)
-0.0047034
(-0.39)
ω01
ω11
ω21
ω31
ω41
ω51
2.32e+13
(2.67)
5.99e+11
(1.07)
-2.85e+11
(-1.53)
-1.22e+13
(-1.82)
-1.64e+10
(-1.83)
-0.025298
(-0.89)
ω02
ω12
ω22
ω32
ω42
ω52
-1.42e+13
(-1.81)
-9.33e+11
(-2.22)
-4.75e+11
(-3.30)
-1.34e+13
(-2.36)
1.98e+10
(2.40)
0.044402
(2.06)
ω03
ω13
ω23
ω33
ω43
ω53
-2.17e+10
(-0.00)
-1.87e+11
(-0.70)
-1.73e+11
(-1.62)
-8.66e+12
(-2.05)
3.64e+09
(0.55)
-0.0011949
(-0.09)
Hypothesis
H0: ω00 ~ ω53=0
Result
F (24, 248) = 2.93
Prob > F = 0.0000
Figure 31. Examination of Change in Character within Each Investor Group Over Time
Hypotheses
F-Test Results
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20110429
Minseok Lee
(0) H0: ω00=ω01=ω02= ω03=0
F (4, 248) = 5.80
Prob > F = 0.0002
(Variable: N/A)
(1) H0: ω10=ω11=ω12= ω13=0
F (4, 248) = 0.02
Prob > F = 0.0915
(Variable: RPER)
(2) H0: ω20=ω21=ω22= ω23=0
F (4, 248) = 3.29
Prob > F = 0.0119
(Variable: VIX)
(3) H0: ω30=ω31=ω32= ω33=0
F (4, 248) = 1.61
Prob > F = 0.1719
(Variable: SPREAD)
(4) H0: ω40=ω41=ω42= ω43=0
F (4, 248) = 5.49
Prob > F = 0.0003
(Variable: KRWD)
(5) H0: ω50=ω51=ω52= ω53=0
F (4, 248) = 1.98
Prob > F = 0.0985
(Variable: Turnover)
Figure 300. Variables’ Means
MEAN
N
RPER
VIX
SPREAD
KRWD
Turnover
9.27E+08
1.78E-06
20.1324
0.1709575
1075.092
8.27E+13
Figure 32. Comparison of the Elasticity of The First and The Second Half of The Period
IV or CV**
FOREI
RPER
VIX
SPREAD
KRWD
Turnover
-8.40E-04
-6.66E+03
-6.76E+02
3.41E+04
1.12E+03
First Half of The Period
INSTI
INDIV
7.83E-04
5.02E+03
5.19E+02
-2.39E+04
-2.04E+03
-8.93E-04
8.97E+02
-1.61E+01
-4.11E+03
7.69E+02
OTHER
FOREI
-2.25E-05
7.62E+02
1.94E+02
-6.27E+03
2.22E+02
1.41E-03
-1.17E+03
-6.75E+02
-2.10E+04
-2.68E+03
Second Half of The Period
INSTI
INDIV
-1.53E-03
-5.30E+03
-8.96E+02
2.10E+04
3.54E+03
2.59E-04
5.02E+03
1.57E+03
-1.95E+03
-4.20E+02
OTHER
-9.98E-05
1.26E+03
-2.21E+01
2.27E+03
-5.26E+02
**IV: Independent Variable, CV: Control Variable
* indicates reliable results whose coefficients are statistically significant.
IV. Conclusion
Listed and traded equities are important segments of contemporary financial
markets. Yet, it is unclear which factor validates the price level of the listed shares. In the
absence of unclear universal guideline in equity markets, the answer for the following
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Minseok Lee
question: which factors determine most sophisticated investors’ (foreign investors)
portfolio flow, is sought in this paper.
This paper does not identify clear evidences that value-orientation is a major
concern for all investors, and concludes that it is unclear whether foreign investors’ trade
executions are momentum or value-caused. However, in the process, we have positively
identified that foreign investors react sensitively to the VIX. The risk-averse
characteristic of foreign investors identified in this paper shows that claims that foreign
investors manipulate the market with their funding capability are falsifiable. The variety
in sources of funds in terms of character and location also makes the manipulation
argument weak. From this aspect, foreign investors take a constructive role for the
domestic equity market’s development, as their risk-averse attitude and intelligent
estimates on the future risk add value to the KOSPI.
Additionally, this paper concludes that foreign investors’ sales-purchases of
Korean equities and the Korean Won are simultaneously done, and when the Korean
Won depreciates excessively, foreign investors’ trade executions are affected by their
anticipation on the Korean Won that the historical norm of the Korean Won is
maintained. This is stability that foreign investors add to the won-dollar market, albeit
contributing to the elevated KOSPI’s valuation in the short term.
The conclusions above are applicable to the specified window of Jul. 2002 to Dec.
2010. While the preferences and composition of investors shift over time, the analyses
demonstrated in this paper provides an accurate snapshot of the motivations of each
investor group from the substantive period of Jul. 2002 to Dec. 2010. Thus, this paper
36
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Minseok Lee
frames the assumptions that drive decisions made by foreign investors in the KOSPI to
facilitate a clearer understanding of the developments to date.
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