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Transcript of Group B9_Co-Movement of Various Global Stockmarket Indices Couses and Implications
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Co-Movement of Global
Capital Markets
Group No: 9, Section B
Ankit Mangla 11P123
Dipesh Kaien 11P134
Prakash Kumar 11P154
Varun Garg 11P179
Vishal Gupta 11P180
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ContentsCO-MOVEMENT OF GLOBAL CAPITAL MARKETS .......................................................................... 2
Introduction.............................................................................................................................................. 2
Definition.............................................................................................................................................. 3
Motivation ............................................................................................................................................ 3
Global Stock Market Integration and the factors affecting Co-movements............................................. 4
Bilateral Trade relationships ................................................................................................................. 7
Macro-economic factors & Financial markets Integration ................................................................... 8
Other Factors ........................................................................................................................................ 9
Implications ........................................................................................................................................... 10
Research Methodology ............................................................................................................................... 12
Index Description.................................................................................................................................. 13Results ......................................................................................................................................................... 15
Analysis of results obtained ........................................................................................................................ 16
Additional Research .................................................................................................................................... 18
References ................................................................................................................................................ 20
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CO-MOVEMENT OF GLOBAL CAPITAL MARKETS
Introduction
In this world of globalization, companies and individuals are more and more affected by
what is happening in the world as compared to earlier times. Companies operate in tens
of countries and sell products around the globe. Liberalizations of capital flows and
financial innovations supported by the improvements in information and communication
technologies enabled such rapid international growth of businesses.
In this article, we will talk about the co-movements of the global capital markets. We
often see that any event happening in any country affects the markets in that country
immediately. But most of the times, depending upon the event, we see the cascading
effects on other major markets of the world. Sometimes the impact is huge which the
whole world notices while in other cases, it is minor and goes without attracting much
public attention.
The most recent financial crisis of 2008-09 during which nearly all world stock markets
experienced severe downturns, may fill us with feeling that there is just one big
integrated marketplace. However, to assess the actual degree of co-movements
between the international stock markets empirical investigation over longer period on a
day to day basis is necessary.
Knowing the inter dependence on different markets can help the community in different
ways:
Advantage of international diversification of portfolio
Development in other markets and its influence on value of stocks in our industry
Correlation patterns between the national stock markets and their implications for
the stability of the financial system
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Definition
1. Co-movement
Refers to the tendency of two or more stock markets to move simultaneously
together, so their price movements are positively correlated
2. Spillover
Effects of an activity which have spread further than were originally intended or any
type of impact on other countries financial markets
3. Contagion
It refers to the significant increase in cross market linkages during crisis
Motivation
Earlier the only motivation to study co-movements of markets was for portfolio
construction purposes. But crisis that have happened at different points in history could
not be explained by contemporary knowledge and theories. Some examples are:
Crash of 1987: Began in Hong Kong, spread throughout Europe and finally hit
US. The stock markets around the world (e.g. Australia, New Zealand, Spain,
United Kingdom, Germany, Japan and Canada) fell by tens of per cent by the
end of October
Asian crisis of 1997: Started in Thailand as a currency crisis. Spread to East Asia
and Russia and subsequently to Brazil.
Events like these forced the world to turn their attention towards possible reasons for
the spread of such crisis form their host country to other parts of the world. The issue of
ripple co-movement among stock markets has become an important substance in the
modern finance. The degree of linkages, co-movements or interdependencies among
the stock markets provides important implications on potential benefits of international
portfolio diversification and on financial stability of a country. The emerging markets are
also found to be more closely integrated with markets in the rest of the world, although
their integration progress has been far less than the industrial countries. This further
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implies that the potentialities of portfolio diversification benefits across the world stock
markets in the long run tend to diminish. In addition, an escalating integration among
the national stock markets further implies that international financial instabilities are
easily transmitted to domestic financial markets, a phenomenon called as financial
contagion.
Global Stock Market Integration and the factors affecting Co-movements
The movement towards a synchronized stock market landscape has gained momentum
during the past two decades, where tighter economical and financial linkages among
developed economies have grown stronger. However, the rises of many important
emerging markets, which have been a major driver of global growth the past decades,have opened up additional sources for cross-border relations. Other causes behind the
rapid increase in world trade, capital movements, and foreign investments between
world economies are due to market liberalization/deregulation, technological advances
and removals of statutory controls. Many of these factors have contributed to more
interlinked economies, which in turn, are said to have given rise to a higher degree of
stock market synchronization, especially in volatile time periods, e.g. eruption of a
financial crisis, war, or political instability (Mobarek,2011). The impact of 2008 financial
crisis, have opened up a tremendous interest for determining the underlying factors that
might explain how stock markets are correlated with one and other for better
understanding their causes. To investigate the intensity of one country to be affected by
global shocks have enormous value for preventing future crises. The extent of financial
and economical integration between a country-pair may indeed be reflected by the
degree of stock markets co-movement that they exhibit. In fact, the macro-economic
structures of international economies have clearly intensified the complexity behind
stock market performances. As countries become more economically interlinked,
explaining the formation of price co-movement between stock markets on an
international level is significant for better understanding this higher interdependency and
integration. However, the contemporary research in stock market integration has not
sufficiently focused on determining the driving forces behind co-movement although this
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information would be most effective for policy-makers and investors that are keen to
know how economic linkages may influence the countries financial stability,
diversification possibilities and what types of common and specific shocks stock
markets are most vulnerable against (Mobarek,2011).
Greater degree of co-movements in stock prices is seen as a reflection of greater stock
market integration. Research till date indicate that a reasonably clear time trend is
identified, where the extent of contemporaneous co-movement across markets has
intensified over time, especially for emerging countries, which consequently suggests
that greater market efficiency is being fostered at the international level. There are also
evidences that some markets are more likely to lead other markets. These alterations
might be due to possible changes in a countrys economy and market conditions, but
also the stability of global markets. Nevertheless, the highly sophisticated market of the
US and the emerging markets of Brazil and Russia appear to affect other rather than be
affected. Some of the research works focused on trying to identifying the
explanatory variables affecting the stock market co-movements include import
dependence, stock markets size differential and their relative size, difference in
annual GDP growth rate as well as the time trend (Mobarek,2011).
A few research works conclude that the degree of interdependence among the Asian-
Pacific markets increased substantially after the 1987 & 1997 stock market crash and
where the U.S market possesses an influential role affecting these markets. In addition,
the risk reduction benefits of international portfolio diversification have been reduced
due to the higher interdependence that has been observed in these markets. However,
Longin & Solnik (1995) examined the correlation for seven major European countries
over the period 1960-90 indicating that not only is the international covariance and
correlation matrices unstable over time, but that correlation rises in periods when the
conditional volatility of markets is large. Karolyi & Stulz (1996) explores the co-
movements between the Japanese and U.S stock markets from 1988-1999, showing
that correlation and covariance are high when markets move a lot, hence demonstrating
the shortcomings of international diversification in times of high volatility which is when it
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is most needed. In light of the benefits of international portfolio diversification ( Solnik,
1995), there is a range of studies that deals with emerging stock markets, which are
said to have lower exposure to world factors, thus having lower levels of integration and
therefore may offer greater opportunities for risk diversification across countries.
Moreover, Ampomah (2008) presents evidence that African stock markets are still
segmented from global markets offering strong diversification benefits. Another type of
studies has provided evidence on which markets dictates over other markets. An early
study by Eun & Shim (1989) highlights the influence and power that the U.S stock
market has on the stock markets of eight other developed countries. Findings
indicate that a substantial amount of interdependence exists, where the U.S stock
market represents the most influential world economy having by far a dominant position
when it comes to producing valuable information that affects world stock markets.
Empirically they found that innovations in the US stock market were rapidly transmitted
to the rest of the world, whereas innovations in other markets did not have much effect
on the US market. Thus US market domination in co-movements of other markets is
obvious.
A very few studies evidence on the determinants of stock market co-movement
has been presented by Pretorius (2002), which examined ten emerging stock
markets for the period 19952000 by employing a cross-section and a time-series
model. The major findings showed that only bilateral trade and the industrial
production growth differential were significant for explaining the correlation
between two countries on a cross-sectional basis. Similar results were achieved by
the time-series regression. They conclude that the higher levels of market co-movement
during the observed periods are mostly due to interdependence, which depend on the
linkages that economies have with each other. Results show that trade and financial
integration contributes positively to stock market synchronization, while a fixed
exchange rate regime increases co-movements. Other factors such as the
similarity of economic structure across countries, informational asymmetries and
a common language also contribute to stock market synchronization. Morgado &
Tavares (2007) examined the impact of bilateral indicators of economic integration on
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the correlation of stock return of 40 developed and emerging markets for the period
19701990. Results showed that bilateral trade intensity affects the correlation
positively, whereas the asymmetry of output growth, the dissimilarity of export structure
and the real exchange rate volatility have negative effects on stock return correlation. A
few research results indicate that the degree of international integration (measured as
the magnitude of the correlation structure) is positively associated with (1) world market
volatility and (2) trend; while it is negatively related to (3) exchange rate volatility, (4)
term structure differential across markets, (5) real interest rate differential. A few main
empirical results from these studies show that (Bracker et al., 1999) Several
macroeconomic factors are significantly associated with the extent of stock market
integration over time, e.g. trade, geographic distance, the nature and extent of the
bilateral trade relationships, GDP Annual growth rate differential, stock market size
differential, time trend, and real interest differential (Johnson & Soenen, 2002).
Asian stock markets became more integrated with the Japanese stock market over
time, especially since 1994, where increased export share from Asian economies to
Japan and greater foreign direct investment from Japan to other Asian economies
contributes to greater co-movement; (Johnson & Soenen, 2003) indicating that a high
share of trade with the US has a strong positive effect on stock market co-movements
for equity markets of the America, whereas increased bilateral exchange rate volatility
and a higher ratio of stock market capitalization relative to the US contribute to lower co-
movement.
In nutshell following are some of the factors which have been focused upon till date in
the research works considered as the driving forces behind the co-movement of the
stock markets:
Bilateral Trade relationships
The importance of the bilateral trade relationships lies in the relative size of the export of
a country with the other country relative to its total export size in value. Similarly the size
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of the import relative to its total import affects the co-movement of the market index. The
higher export or import dependence on any country will impact the co-movements. If
there is any severe impact affecting the bilateral trade relationships, this will reflect in
the relative returns of the stock-markets. Although the contribution of international trade
in goods is usually recognized as increasing the extent of business cycle
synchronization, its overall effects on co-movements of stock index remain ambiguous
(Walti, 2005). On the demand side, higher aggregate demand in one country will
partially fall on imported goods, thereby raising output and income in trading partners
economies and inducing output co-movements across countries. On the supply side,
however, there are two opposite effects which relate to two different approaches to
modeling international trade. Intra-industry models of trade emphasize economies with
similar production structures and factors. To the extent that trade occurs mostly within
industries, an expansion in some industries will raise output co-movements across
countries. However, trade integration may also lead economies to specialize in the
production of goods for which they have a comparative advantage, hence reducing co-
movements. The net impact of international trade on co-movements is therefore
ambiguous.
Macro-economic factors & Financial markets Integration
The macroeconomic factors include, inflation rate differential, real interest rate
differential and GDP annual growth rate differential, are expected to be negatively
related to the co-movements in a stock markets pair. Indeed, the larger these
differences become, the larger the divergence between the economy linkages of the two
countries and hence the less their stock markets will be influenced by each other (Walti,
2005). The degree of other financial market integration such as money market, debt
market, foreign exchange, credit markets also affects the correlation of the equitymarkets. However, the exact trend of their specific correlation is not clear due to the
complexities involved in the linkages among these markets themselves.
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The nature of the exchange rate regime is likely to have implications for the degree of
covariation between national economies. The argument in favour of floating exchange
rates is that they act as a shock absorber for external disturbances. A greater flexibility
of the exchange rate should therefore reduce the effects coming from the transmission
of country-specific real shocks, thereby delivering lower output co-movements across
countries. Similarly, provided that capital flow mobility is high, maintaining a fixed
exchange rate requires a high degree of coordination of monetary policies, thereby
inducing greater co-movements in economic conditions and consequently greater co-
movement among the index. In the extreme case of a currency union like EU, the
stance of monetary policy would in fact be identical for all participating countries.
Other Factors
The other group of variables affecting the co-movement of the index includes indicators
of the stage of stock markets development, such as stock markets size differential and
relative size. More specifically, the stock market capitalization of a country may be a
measure of the ease or difficulty, in terms of liquidity and costs, of trading on that stock
market. While a large difference in market size for a pair of countries may determine
less co-movements between their respective stock markets, the relative size of the two
markets in the pair have opposite effects.
Lastly, a time trend has been identified in a few research works including the possibility
that stock market inter-dependence has increased over time, due to the advanced
communications technology, the eased flow of information, trade and capital across
borders and the increasing cross-listing of stocks and mergers between stock markets
of different countries (Forbes & Rigobon, 2002).
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Implications
In an era of mounting Liberalization, Privatization and globalization in the emerging
economies, the transmission of movements in international financial markets and assets
are important issues for cross border investments, especially in periods where marketsare highly volatile. The determination of diversification strategies by an international
investor also depends crucially on the nature and magnitude of the relationships (co-
movement) existing between different stock markets especially emerging markets. Thus
it becomes important for international investors to understand the co-movement and
interdependence among the various markets to diversify their portfolio risks and to
derive high return.
Most of the studies on the international capital market are based on the segmented
market approach. According to it, different national capital markets act as separated
entities, hardly related to each other. If this is true, comparable capital assets may differ
in their on different national markets.
Alternately, another theory says that prices of capital assets in the international capital
markets behave as if there is one multinational perfect capital market. According to it,
returns of comparable capital assets must not be substantially different in different
national capital markets.
Thus we see that how markets co-move has significant impact on the returns that
different capital assets may provide. The results of this study have important
implications for the benefits of international portfolio diversification strategies and the
convergence of the cost of capital across countries. Changes in co-movement across
world markets has important implications for the degree of diversification that can be
achieved by including international stocks in portfolios and for the cost-of-capital for
firms seeking to lower their cost of capital by issuing securities in different world
markets.
Knowledge about the co-movement of capital markets can permit verification of the
simultaneous occurrence of booms or crashes in two markets, and the intensity of the
effect. For portfolio managers to carry out their international asset allocation strategies,
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it is important for them to assess the probability of the occurrence of extreme events
with negative repercussion in the markets under consideration
The knowledge about co-movement can also help the investors who believe that the
twenty-first century may well be the time when the balance of power shifts to Brazil,
Russia, India and China, nations collectively referred to as BRICs economies. Foreign
Investors are investing considerably in the emerging economies with mainly two
objectives:
To enhance the portfolio growth
To reduce portfolio risk through efficient international portfolio diversification
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Research Methodology
1. Literature review was done for reasons of co-movement between various stock
indices the reason for that and implications.
2. 9 indexes were selected, which represented countries across the globe.
Countries covered different continents, Developed and developing countries,
some different indices with in the countries
3. Index closing data for these 9 index was collected for past 11 years (2000-11)
4. Data was adjusted for holiday, we considered all the weekdays falling during the
study period
5. Where data was missing for individual indexes we considered last day closing
index level
6. Day wise return was calculated for selected index for the study period considered
7. Pair wise correlation and covariance matrix was constructed for these 9 indexes
for the return data found in previous step
8. The result obtained was analyzed in light of literature review and macroeconomicconditions
Following indices were considered which are described in report also
NIFTY S&P CNX
CAC Index
Nikkei 225
NASDAQ
Brazil Bovespa Index
S&P500 Russell 2000 Index
FTSE Index
DAX Index
The index description is given in the below
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Index Description
Index Representing
Number
of Stocks
Weighting
of Index
Special
CharacteristicsDrawbacks/Comments
Dow Jones
Industrial
Average
U.S. blue chip
companies30
Price-
weighted
The oldest and
most widely
followed U.S.
equity index
30 stocks chosen by
Wall Street Journal
editors; large, mature
blue chip companies
NikkeiStock
Average
Japanese bluechip
companies
225Modifiedprice-
weighted
Originally
formulated by
Dow Jones &Company, using
essentially the
same method as
the DJIA
Also known as the
Nikkei 225. There is a
huge variation in
share price levels of
the componentcompanies, and some
high-priced shares are
weighted at a fraction
of their share price.
Some component
stocks are illiquid.
S&P TSX
Composite
Broad market
cap stocks
listed on the
Toronto Stock
Exchange
VariesFloat-
weighted
Very
comprehensive
index
Widely used Canadian
equities benchmark
CAC 40
French blue
chip
companies
40Float-
weighted
Chosen from the
100 largest
market cap
stocks on the
Paris Bourse
(Euronext Paris)
DAX 30
German blue
chip
companies
30Float-
weighted
Published by the
Frankfurt Stock
Exchange
Widely used German
equities benchmark
FTSE 100
The 100
largest
publicly
traded stocks
on the London
Stock
Exchange
100Float-
weighted
A large-cap
index,
pronounced
Footsie 100
There are also a FTSE
Mid 250 for mid-cap
stocks and a small-cap
index.
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Russell2000
The smallest
2,000 stocksin the Russell
3000
2,000 on the
day the new
composition
is
determined
Float-weighted
"A small-capindex"
The many U.S. small-
cap index funds
tracking this index and
the consequent
annual reconstitution
costs and possible tax
consequences make
this a relatively high-
cost benchmark
for a U.S. small-cap
index fund.
S&P 500
Predominantly
large-cap
companiesrepresentative
of the U.S.
stock market
500 Float-weighted
Membership
determined by acommittee of
S&P employees
Its popularity with
indexers
causes new
components to earnaverage positive
abnormal returns on
the announcement
that they are joining
the index
(Lynch, 1997) and Malkiel and Radisich (2001)
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Results
Covariance Matrix
Covariance
matrix
NIFTY S&P
CNX
CAC
Index
Nikkei
225
NASDA
Q
Brazil
Bovespa
Index
S&P500
Russell
2000
Index
FTSE
Index
DAX
Index
NIFTY S&P
CNX0.00027 0.00008 0.00008 0.00005 0.00007 0.00004 0.00005 0.00007 0.00007
CAC Index 0.00008 0.00025 0.00007 0.00014 0.00014 0.00012 0.00013 0.00018 0.00022
Nikkei 225 0.00008 0.00007 0.00024 0.00003 0.00005 0.00002 0.00003 0.00006 0.00006
NASDAQ 0.00005 0.00014 0.00003 0.00033 0.00020 0.00022 0.00027 0.00010 0.00016
Brazil
BovespaIndex
0.00007 0.00014 0.00005 0.00020 0.00036 0.00016 0.00019 0.00011 0.00014
S&P500 0.00004 0.00012 0.00002 0.00022 0.00016 0.00019 0.00021 0.00009 0.00013
Russell 2000
Index0.00005 0.00013 0.00003 0.00027 0.00019 0.00021 0.00028 0.00010 0.00014
FTSE Index 0.00007 0.00018 0.00006 0.00010 0.00011 0.00009 0.00010 0.00017 0.00016
DAX Index 0.00007 0.00022 0.00006 0.00016 0.00014 0.00013 0.00014 0.00016 0.00026
Correlationmatrix
NIFTY S&PCNX
CACIndex
Nikkei 225
NASDAQ
BrazilBovespa
Index
S&P500
Russ
ell
2000
Inde
x
FTSEIndex
DAX Index
NIFTY S&P CNX 1.00 0.30 0.32 0.17 0.23 0.19 0.19 0.31 0.28
CAC Index 0.30 1.00 0.29 0.48 0.46 0.56 0.50 0.89 0.87
Nikkei 225 0.32 0.29 1.00 0.11 0.17 0.12 0.10 0.30 0.26
NASDAQ 0.17 0.48 0.11 1.00 0.57 0.88 0.88 0.44 0.53
Brazil
Bovespa
Index
0.23 0.46 0.17 0.57 1.00 0.62 0.59 0.47 0.47
S&P500 0.19 0.56 0.12 0.88 0.62 1.00 0.90 0.53 0.60
Russell 2000
Index0.19 0.50 0.10 0.88 0.59 0.90 1.00 0.47 0.53
FTSE Index 0.31 0.89 0.30 0.44 0.47 0.53 0.47 1.00 0.79
DAX Index 0.28 0.87 0.26 0.53 0.47 0.60 0.53 0.79 1.00
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Analysis of results obtained
We have taken 9 indices covering various countries namely India, USA, Germany, UK,
Brazil. Japan and France. Based on the correlation matrix above, we can conclude to
certain extent the reasons and justifications of co-movements of various markets. There
may be various reasons for two markets two co-move and the extent of co-movement.
Nifty (India) does not have a substantially high value of co-relation with any of the
indices. The highest is in range of 0.3-0.32 for France, Japan and UK. This can be
attributed to the fact that India is a developing country and most of the indices in the
table are of developed countries (with the exception of Brazil).
CAC Index (France) is highly co-related and co-moves with the indices of other
European countries like UK (0.89) and Germany (0.87). It may be due to similar
levels of development of economies. Also their geographical proximity results in any
effect in any market to quickly reach the neighboring markets.
CAC is also co-related to US indices (NASDAQ- 0.48, Russell-0.45 and S&P-0.56)due to significant financial market integration such as money market, credit market,
debt and bond market. Since both the US and EU are developed, so we expect
some co-movement among indices of these countries.
Similar characteristics are also shown by other European indices.
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Nikkei (Japan) has highest value of co-relation with Nifty. This may be because of
the fact that both the countries are part of Asian sub continent and thus geographical
location is an important factor. It is also co-related to a similar extent to European
indices. However, it has very low value of co-relation with US indices.
US indices have high co-relation with indices within the country and Brazil (due to
geographical factors). It is also significantly co-related to European indices as both
US and Europe are developed economies and thus markets tend to co-move.
Brazilian index tends to have high co-relation with US indices due to location factors.
Next it has high co-relation values with European indices due to information
integration.
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Additional Research
Efficient Portfolio using Index Funds
An efficient portfolio is one that lies on the efficient frontier.
An efficient portfolio provides the lowest level of risk possible for a given level of
expected return. If a portfolio is efficient, then it is not possible to construct a portfolio
with the same, or a better level, of expected return and a lower volatility.
An efficient portfolio also provides the best returns achievable for a given level of risk. If
a portfolio is efficient it is not possible to construct a portfolio with a higher expected
return and the same or a lower level of volatility with the securities available in the
market, excluding risk free assets. Adding the latter allows one to construct portfolios
that lie on the securities market line.
(moneyterms.co.uk)
We constructed a sample efficient portfolio using Jan 2000 to Mar 2000 data.
Imitations to considering such a short horizon .a short portfolio is constructed due to the
hardware constraint as the hardware that we have was not able to process 10 years f
data
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-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 0.5 1 1.5 2 2.5
Return(%)
Systematic Risk (Standard Deviation)
Efficient Frontier and Capital Market Line
Efficient Portfolio of Index Funds using Markowitz Optimizer
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