Page 1 of 16
ANZAM 2012
26th Annual Australian and New Zealand Academy of Management Conference, 2012
Integration of Financial Markets and Overseas Investments
Sriranga Vishnu
Indian Institute of Management Indore, Indore, India
E-mail: f09srirangav@iimidr.ac.in ; srirangavishnu@gmail.com
ANZAM 2012
Integration of Financial Markets and Overseas Investments
Abstract
Capital markets of emerging economies are expected to depict a different level of market efficiency
and stock price volatility in comparison to those of the developed markets. Hence, an international
investor shall gain from portfolio diversification and overseas investments. However, recent waves of
economic reforms, deregulations and globalization have led to greater integration of financial
markets. This study empirically investigates the level of integration of financial markets. Focus is on
emerging economies. For the considered period, findings reveal that the stock returns of major
emerging markets have shown low linkages to the Emerging Market Index and are more closely
correlated with that of the developed markets. The findings support the argument of growing
integration of financial markets world over.
Key Words: - Risk, Global Financial Crisis, Volatility, Flexibility, Financial Market Integration.
Page 2 of 16
Page 3 of 16
ANZAM 2012
Risk management is one of the most extensively studied issues in Corporate Finance. A firm, during
its day-to-day operations, faces certain risks such as financial risk, operational risk, management risk,
etc. Over years, operational risk (physical damage) and management risks (poor governance) have
received lesser attention with the focus shifting on management of financial risks. Loads of theoretical
and empirical literature is devoted to this end.
The three commonly used methods to manage risk are insurance, hedging and diversification (Merton
1993).
While hedging, the hedger takes a particular position in one market in an attempt to
compensate for price fluctuations in another market position. The primary objective is to reduce the
probability of occurrence of unwanted risk. There are many financial instruments available to
facilitate hedging. This includes insurance policies, forward contracts, futures contracts, swaps,
options and other types of over-the-counter and derivative products. Diversification refers to reduction
of risk by investing in an array of asset classes. This diversification can be financial (portfolio
diversification) or strategic (business diversification).
For individual and institutional investors,
portfolio diversification is the most often used tool to manage risk. Generally, such a portfolio yield
higher returns with lower risks.
Diversified investments, especially in terms of overseas investments have been gaining much
importance in recent years. The rationale behind this is the segmentation of the financial markets.
Soenen and Johnson (2008) agree upon partial segmentation of markets after discussion upon
emerging capital markets. They cite reasons such as control over capital, lack of adequate protection
for investors, poorly developed accounting standards, restrictive ownership of enterprise, political
risks, etc. in emerging economies as reasons for persisting segmentation. Various stock markets
remain linked with the local events which lead to information asymmetry. This, in turn, causes price
differentials across markets providing opportunity for international investors. The impact of overseas
investors (in form of Foreign Direct Investment and Foreign Institutional Investment) is huge on most
of the emerging markets where they are the main drivers of traded share prices and volumes.
ANZAM 2012
1. LITERATURE REVIEW
International investors are in constant look out for super normal return on their investment. The
interest for inclusion of emerging economies as a part of portfolio diversification to realize higher
returns has increased (Domowitz, Glen & Madhavan 1997). Economic and financial reforms coupled
with liberalization and deregulations have led to easy movement foreign capital across the globe
(Bekaert & Harvey 2003). Global portfolio diversification is a prudent way to minimize risk on
investment. While it gives the overseas investor an opportunity to participate in investment risk and
returns, the domestic capital market is benefitted by the element of internationalization (Errunza &
Miller 2000).
A pressing concern for international investors is related to the growing integration of financial
markets worldwide (Soenen & Johnson 2008). As the correlation of stock returns among these
markets grows, the international investor is ordained to lose the benefits of diversification of portfolio.
Growing integration of markets will demand for a global perspective to estimate the cost of capital
(Soenen & Johnson 2008). Researchers such as Longin and Solnik (1995) and Jacquier and Marcus
(2001) have reported increased co-movements of the stock markets during instances of turbulence in
capital market. In such cases, the risk mitigation strategy of diversification may become
counterproductive because it will fail to generate expected gains for the overseas investor.
Complete integration of capital markets will ensure similar returns worldwide on assets with same
risk profile (Bekaert & Harvey 1994). The subject matter of market segmentation is important for
managers because it influences their financing and investment decisions. In case of segmented
markets, local market factors will determine the cost of capital of the investment made (Heston,
Rouwenhorst & Wessels 1995).
Empirical and theoretical work on related topics such as co-movement / integration / correlation of
stock markets has been done by quite a few researchers. While some of them have reported about
enhanced integration in the selected market samples (Beaulieu, Gagnon & Khalaf 2009; Bekaert &
Harvey 2003; Fratzscher 2002; Heston et al. 1995; Mohsin 2011; Rizavi, Naqvi & Rizvi 2011;
Page 4 of 16
Page 5 of 16
ANZAM 2012
Siddiqui 2009), others have found decreasing trend in co-movements of their sample stock markets
(Hergis & Mei 2006; Mishra & Mukund 2009). Another stream of research studies has reported
greater market integration within their regional boundaries in comparison to distant markets across
continents (Collins & Abrahamson 2004; Goldberg & Delgado 2001).
Conover, Friday and Sirmans (2002) have reflected upon the diversification benefits for American
investors and have found lower correlation between US stocks and foreign real estate as compared to
foreign stocks. Hence, the investors will be benefitted by the addition of real estate as asset class in
their portfolio. Morana (2008) has studied the stock markets co-movements of G-7 countries and
reports that idiosyncratic stock market shocks have stronger effect on price volatility as compared to
global shocks. This creates a scope for international investor to diversify portfolio in different
geographical regions. Stieglitz (2010) puts forward a range of arguments to discuss why financial
autonomy is preferred over complete integration of stock markets.
To conclude with, the set of literature dealing with integration of stock markets is not unanimous in its
findings. Difference in data sample and chosen time period has resulted in conclusions in favour and
against the market co-movements.
2. RESEARCH PROBLEM
Theoretically, the integration of two capital markets can be assessed by construction of a portfolio
which is in complete correlation. In condition of smooth flow of capital across markets, arbitrageurs
will come into play and the asset prices will equalize. In practice, however, stock market
idiosyncrasies will not permit for perfect correlation. As opined by Gultekin, Gultekin and Penati
(1989:849), “Capital markets are integrated if assets with perfectly correlated rates of return have the
same price regardless of the location in which they are traded.”
The present study is an endeavour to empirically investigate the degree of integration in the capital
markets of emerging economies and world market. Further, the paper attempts to understand how well
the predictability of the world indices vis-a-vis the component indices is. The three research
hypotheses put forward for the same are:-
ANZAM 2012
H1. The stock price movements of the member emerging economies are closely linked with the
World Emerging Market Index.
H2. The stock price movements of the member emerging economies show low linkage with the
World Developed Markets Index.
H3. The major emerging economies are a good predictor of the World Emerging Market
Index.
H4. The major emerging economies are not a good predictor of the World Developed Market
Index.
3. DATA AND METHODOLOGY
The stock markets of the emerging economies show strong correlation with the world markets in the
times of economic turbulence (Longin & Solnik 1995). The period of the recent global economic
crisis is roughly attributed to the years 2007 through 2009. Hence, to test the behaviour of the stock
indices in relatively normal economic conditions, weekly data on ten major emerging economies and
MSCI World Index and Emerging Markets Index has been collected from January, 2010 to 31st
March, 2011. These data have been collected from Yahoo Finance and Google Finance and are
adjusted for stock split and dividend yields, thereby giving a smoother picture of stock market returns.
The ten major emerging economies selected for the study are :- India(IND), China (CHN), Russia
(RUS), Taiwan (TWN), Indonesia (INDO), Malaysia (MAL), South Korea (KOR), Argentina (ARG),
Brazil (BRZ) and Mexico (MEX). ishare MSCI World Index (MSCIW) has been taken as a proxy for
developed markets index and ishare MSCI Emerging Market Index (MSCIEM) has been taken as the
proxy for emerging economies. Data from the prominent indices of these emerging economies has
been considered for this study. The ishare World Index data as provided by MSCI does not include
stock price movements from the US and Canadian markets. This happens to be a limitation of the
data source and consequently, this piece of work.
Page 6 of 16
Page 7 of 16
ANZAM 2012
For the testing of H1 and H2, the weekly stock market returns of the ten emerging economies have
been correlated with that of the ishare Emerging Market Index and ishare World Index respectively.
To address H3, linear regression analysis has been used to predict the strength of relationship between
the stock returns of emerging economies and the Emerging Market Index. For hypothesis H4, the
developed regression equation is used to predict the strength of relationship between the stock returns
of emerging economies and the developed economies. Since the data on stocks returns is in national
currencies, the commonly used method is to use dollar-converted figures. In this study, however,
return figures have been calculated in percentage to adjust for the exchange rate anomalies.
The Regression equation used to address H3 will be as under:MSCIEM = α + β1IND + β2CHN + β3RUS + β4 TWN + β5INDO + β6MAL +β7KOR + β8 ARG +
β9BRZ + β10 MEX +ε
For the fourth hypothesis H4, the regression equation shall be:MSCIW = α + β1IND + β2CHN + β3RUS + β4 TWN + β5INDO + β6MAL +β7KOR + β8 ARG +
β9BRZ + β10 MEX +ε
4. FINDINGS AND DISCUSSIONS
The Table-1 shows the descriptive statistics of the data considered for this study. The stock returns on
MSCI Emerging Market Index, Russian index and Argentinean index have highest variances of 10.23,
13.12 and 11.09 respectively during the concerned period. Stock returns on Bursa Malaysia have
witnessed lowest variance of 1.92. The mean returns for Brazil and China have remained negative for
the same period.
The results of Table-2 show the pair-wise correlation between various emerging markets. Here, the
findings reveal that only the Taiwanese market is significantly correlated with the MSCI Emerging
Market Index with a value of 0.45 with 99% confidence level. No other market shows significance in
ANZAM 2012
correlation at 1% or 5% significance levels. During the same period, only Malaysia and Indonesia,
other than Taiwan, show positive correlation. This leads to the rejection of our first hypothesis H1.
The pair-wise correlation values of emerging markets and MSCI World Index as shown in Table -3
reveal significant correlation of Indian, Russian, Indonesian, Korean, Argentinean, Brazilian and
Mexican markets vis-a-vis developed markets index at 1% significance level. Only Taiwanese market
has shown negative correlation for the period. China and Malaysia do not show correlation with either
the emerging market index or the developed market index at 1% or 5% significance levels. These
findings are in contrast to the proposed hypothesis that stock price movements of the emerging
economies are lowly correlated to the World Developed Markets Index. Hence, the second
hypothesis, H2 is rejected.
As for the third hypothesis that major emerging economies are better predictor of Emerging Market
Index, only Taiwan, with a beta value of .47 has shown significance at 5% level. The R-square value
in Table-4 reveals that Taiwanese market alone accounts for around 22% of the variations in the stock
returns. The next part of the output table is ANOVA (Table- 5) which shows the various sums of
squares the associated degrees of freedom. The f-value is 17.27 and is significant at 1%. In Table– 6,
the intercept is .36 and the beta-value for TWN is .47 which shows that that Taiwanese market is able
to predict 47% of the variations in the returns on Emerging Market Index. Notably, no other emerging
economy has been found to predict the variations in the Emerging Market Index. Hence, the
hypothesis H3 also stands rejected.
The fourth hypothesis proposes that stock price movements of emerging economies should not be a
good indicator of MSCI developed market index. But for Mexico and Russia, no other emerging
economy shows significant predicting power for ishare MSCIW index (Table-7). In the Table -8
(ANOVA), the significant f-values for Mexico and Russia are 68.91 and 42.72 respectively. The
intercept for these two nations has negative values. However, both Mexico and Russia seem to be
strong predictors of MSCI World Index. The hypothesis H4 is not rejected.
Page 8 of 16
Page 9 of 16
ANZAM 2012
5. CONCLUSIONS
The empirical results show that during the period of January, 2010 - March, 2011, the major emerging
markets have shown low level of linkages to the Emerging Market Index and are more closely linked
to the developed markets. This finding reveals that Taiwan shows highest correlation with Emerging
Market Index and consequently, lowest linkage with developed market index. Also, Taiwan is
significantly able to predict the stock returns on the emerging market index. Two economies, China
and Malaysia, do not show significant correlation with either of the indices. Mexico and Russia show
stronger linkage with developed market index in comparison to the other emerging economies.
The result findings support the argument of growing integration of the world capital markets. The
segmentation of developed and emerging markets does not hold good for the concerned period. This
finding leaves lesser incentive for an international investor to diversify portfolio and opt for overseas
investments in emerging markets to derive super normal profits. The finding of closer integration of
world market during economic turbulence can be complemented with significant integration even
during periods of normal economic activity.
The use of ishare MSCI World Index and ishare Emerging Market Index as a proxy for World Index
and Emerging Market Index respectively is bound to affect the results of this study. Future researches
should take care of this. This empirical work can be further extended to study the relationship and
predictability of emerging economies with US indices. This is relevant because ishare MSCI World
Index does not include the US and Canada in its data computation. Another study can focus on the
regional correlation wherein, emerging economies of Asia-Pacific can be correlated with that of
America or Europe.
ANZAM 2012
REFERENCES
Beaulieu, MC, Gagnon, MH & Khalaf, L (2009) A cross-section analysis of financial market
integration in North America using a four factor model, International Journal of Managerial
Finance 5(3), 248-267.
Bekaert, G & Harvey, CR (1994) Time-varying world market integration: National Bureau of
Economic Research.
Bekaert, G & Harvey, CR (2003) Emerging markets finance, [doi: 10.1016/S0927-5398(02)00054-3].
Journal of Empirical Finance 10(1–2), 3-55.
Collins, D & Abrahanson, M (2004) African equity markets and the process of global financial
integration, South African Journal of Economics 72(4), 658-683.
Conover, MC, Friday, H S & Sirmans, GS (2002) Diversification benefits from foreign real estate
investments, Journal of Real Estate Portfolio Management 8(1), 17-25.
Domowitz, I, Glen, J & Madhavan, A (1997). Market segmentation and stock prices: Evidence from
an emerging market. Journal of Finance, 1059-1085.
Errunza, VR & Miller, DP (2000) Market segmentation and the cost of capital in international equity
markets, Journal of Financial and Quantitative Analysis 35(4), 577-600.
Fratzscher, M (2002) Financial market integration in Europe: on the effects of EMU on stock markets,
International Journal of Finance & Economics 7(3), 165-193.
Goldberg, CS, & Delgado, FA (2001) Financial integration of emerging markets: An analysis of
Latin America versus South Asia using individual stocks, Multinational Finance Journal 5(4),
259-301.
Gultekin, M N, Gultekin, N B & Penati, A (1989) Capital controls and international capital market
segmentation: The evidence from the Japanese and American stock markets, Journal of
Finance 44(4), 849-869.
Hergis, K & Mei, J (2006) Is country diversification better than industry diversification? European
Financial Management 12(3), 319–340.
Heston, SL, Rouwenhorst, K G & Wessels, RE (1995) The structure of international stock returns and
the integration of capital markets, [doi: 10.1016/0927-5398(95)00002-C]. Journal of
Empirical Finance 2(3), 173-197.
Jacquier, E & Marcus, A J (2001) Asset allocation models and market volatility, Financial Analysts
Journal 57(2), 16-30.
Longin, F & Solnik, B (1995) Is the correlation in international equity returns constant: 1960-1990?
Journal of International Money and Finance 14(1), 3-26.
Merton, RC (1993) Operation and regulation in financial intermediation: A functional perspective. P.
Englund (Ed.): Operation and Regulation of Financial Markets (The Economic Council,
Stockholm).
Page 10 of 16
Page 11 of 16
ANZAM 2012
Mishra, A K & Mukund, J (2009) Emerging trends in financial markets integration: the Indian
experience, International Journal of Emerging Markets 4(3), 235-251.
Mohsin, HM & Rivers, PA (2011) Financial Market Integration of South Asian Countries: Panel Data
Analysis, International Journal of Economics and Finance 3(2), 65-75.
Morana, C (2008) International stock markets co-movements: the role of economic and financial
integration, Empirical Econ 35, 333–359.
Siddiqui, S (2009) Stock markets integration: Examining linkages between selected world markets,
VISION-The Journal of Business Perspective 13(1), 19-30.
Soenen, L & Johnson, R (2008) The Equity Market Risk Premium and the Valuation of Overseas
Investments, Journal of Applied Corporate Finance 20(2), 113-121.
Stiglitz, J E (2010) Risk and Global Economic Architecture: Why Full Financial Integration May Be
Undesirable, American Economic Review: Papers & Proceedings 100, 388–392.
ANZAM 2012
Page 12 of 16
Table-1
Descriptive Statistics (MSCIEM)
N
Minimum
Maximum
Mean
Std. Deviation
Variance
MSCIEM
65
-9.18
6.47
.28
3.20
10.23
MSCIW
66
-5.21
5.18
.15
2.11
4.47
IND
65
-4.50
5.24
.18
2.47
6.12
CHN
63
-6.66
8.49
-.03
2.89
8.35
RUS
65
-12.90
8.48
.41
3.62
13.12
TWN
64
-6.88
4.32
.11
2.42
5.85
INDO
66
-8.23
6.96
.57
2.67
7.11
MAL
67
-4.00
3.51
.26
1.39
1.92
KOR
67
-5.63
3.68
.38
2.10
4.42
ARG
66
-9.85
11.47
.61
3.33
11.09
BRZ
66
-6.90
6.39
-.05
2.61
6.79
MEX
66
-4.44
3.86
.20
1.96
3.84
Valid N (list wise)
63
Page 13 of 16
ANZAM 2012
Table -2
Correlation (with Emerging Market Index)
MSCIEM
MSCIEM
Pearson Correlation
IND
CHN
RUS
TWN
INDO
MAL
KOR
ARG
BRZ
MEX
1
Sig. (2-tailed)
IND
CHN
RUS
TWN
Pearson Correlation
0.79
Pearson Correlation
-0.05
0.14
Sig. (2-tailed)
0.69
0.28
Pearson Correlation
0.00
.32**
0.18
Sig. (2-tailed)
0.97
0.01
0.15
Pearson Correlation
Pearson Correlation
Sig. (2-tailed)
MAL
KOR
ARG
BRZ
1
1
.45**
0.14
0.04
-0.18
0
0.28
0.73
0.16
1
0.0
.51**
0.1
.55**
-0.11
0.75
0.00
0.32
0.00
0.39
1
Pearson Correlation
0.13
0.17
0.04
0.14
-0.09
0.23
Sig. (2-tailed)
0.31
0.18
0.77
0.27
0.50
0.06
1
Pearson Correlation
-0.08
.65**
0.14
.6**
0.06
.44**
-0.03
Sig. (2-tailed)
0.51
0.00
0.29
0.00
0.63
0.00
0.82
Pearson Correlation
-0.09
.63**
0.17
.47**
0.08
.38**
-0.09
.62**
Sig. (2-tailed)
0.47
0.00
0.19
0.00
0.55
0.00
0.47
0.00
-0.14
.58**
0.21
.61**
-0.15
.37**
0.08
.64**
.69**
0.27
0
0.10
0
0.25
0.00
0.52
0
0
-0.16
.56**
.27*
.56**
-0.13
.3*
0.19
.61**
.70**
.71**
0.22
0.00
0.03
0.00
0.31
0.02
0.13
0.00
0.00
0.00
Pearson Correlation
Sig. (2-tailed)
MEX
1
Sig. (2-tailed)
Sig. (2-tailed)
INDO
-0.03
Pearson Correlation
Sig. (2-tailed)
*p < .05; **p < .01
1
1
1
1
ANZAM 2012
Page 14 of 16
Table -3
Correlation (with World Market Index)
MSCIW
MSCIW
Pearson Correlation
IND
CHN
RUS
TWN
INDO
MAL
KOR
ARG
BRZ
MEX
1
Sig. (2-tailed)
IND
Pearson Correlation
.51**
Sig. (2-tailed)
CHN
Pearson Correlation
RUS
Pearson Correlation
0.21
Sig. (2-tailed)
1
0.10
0.28
.32**
0.18
0
0.01
0.15
-0.20
0.14
0.04
Pearson Correlation
Sig. (2-tailed)
1
-0.18
1
0.12
0.28
0.73
0.16
.41**
.51**
0.13
.55**
Sig. (2-tailed)
0.00
0.00
0.32
0.00
0.39
Pearson Correlation
0.17
0.17
0.04
0.14
-0.09
0.18
0.18
0.77
0.27
0.50
0.06
.59**
.65**
0.14
.59**
0.06
.44**
0
0
0.29
0
0.63
0
0.82
.57**
.63**
0.17
.47**
0.08
.38**
-0.09
0
0
0.19
0
0.56
0.00
0.47
0
.55**
.58**
0.21
.61**
-0.15
.37**
0.08
.64**
INDO
Pearson Correlation
MAL
Sig. (2-tailed)
KOR
0.14
.60**
Sig. (2-tailed)
TWN
1
0
Pearson Correlation
Sig. (2-tailed)
ARG
Pearson Correlation
BRZ
Pearson Correlation
MEX
Pearson Correlation
Sig. (2-tailed)
Sig. (2-tailed)
-0.11
1
0.23
1
-0.03
1
.62**
1
.69**
1
0
0
0.10
0
0.25
0.00
0.52
0
0
.74**
.56**
.27*
.56**
-0.13
.3*
0.19
.61**
.70**
.71**
0
0
0.03
0
0.31
0.02
0.13
0
0
0
Sig. (2-tailed)
*p < .05; **p < .01
1
Table -4
Model Summery (MSCIEM)
Model
1
R
.47a
R Square
0.22
a. Predictors: (Constant), TWN
Adjusted R
Square
0.21
Std. Error
of the
Estimate
2.79
Change Statistics
R Square
Change
0.22
F Change
17.27
df1
1
df2
61
Sig. F
Change
0
Page 15 of 16
ANZAM 2012
Table -5
ANOVAb
Model
1
Sum of
Squares
134.33
Regression
1
Mean
Square
134.33
7.78
df
Residual
474.45
61
Total
608.79
62
a.
Predictors: (Constant), TWN
b.
Dependent Variable: MSCIEM
F
17.27
Sig.
.000a
Table -6
Coefficientsa
Model
Unstandardized Coefficients
1
(Constant)
B
.36
TWN
.60
Std. Error
Standardized
Coefficients
95.0% Confidence Interval for B
Beta
.35
.15
.47
t
1.03
Sig.
.31
Lower Bound
-.34
Upper Bound
1.06
4.16
.00
.31
.89
a. Dependent Variable: MSCIEM
Table -7
Model Summery (MSCIW)
Model
Change Statistics
1
R
.73a
R Square
.53
Adjusted
R Square
.52
2
.77b
.59
.57
a. Predictors: (Constant), MEX
b. Predictors: (Constant), MEX, RUS
Std. Error of
the Estimate
1.46
R Square
Change
.53
F Change
68.91
df1
1
df2
61
Sig. F
Change
.000
1.38
.06
8.29
1
60
.006
ANZAM 2012
Page 16 of 16
Table -8
ANOVAc
Model
1
2
Sum of Squares
145.92
df
1
Mean Square
145.92
Residual
129.17
61
2.12
Total
275.09
62
Regression
161.60
2
80.80
Residual
113.49
60
1.89
Total
275.09
62
Regression
F
68.91
Sig.
.000a
42.72
.000b
a. Predictors: (Constant), MEX
b. Predictors: (Constant), MEX, RUS
c. Dependent Variable: MSCIW
Table -9
Coefficientsa
Model
1
2
Unstandardized
Coefficients
Standardized
Coefficients
Std. Error
.19
Beta
(Constant)
B
-.02
MEX
.81
.10
.73
(Constant)
-.05
.18
t
Sig.
95.0% Confidence
Interval for B
-.09
.929
Lower
Bound
-.39
Upper
Bound
.36
8.30
.000
.62
1.01
-.26
.799
-.40
.31
MEX
.65
.11
.58
5.89
.000
.43
.87
RUS
.16
.06
.28
2.88
.006
.05
.28
a. Dependent Variable: MSCIW