Do domestic investors really make more money? The trading performance of investor groups in an emerging market Abstract We investigate the trading performance of domestic versus foreign investors using high-frequency data. Our sample involves over 270 million observations from companies listed on the Egyptian stock exchange (EGX) from 2004-2009. The transaction data includes unique trade identifiers allowing us to classify investors into six categories: Domestic, Arab and Non-Arab (foreign) and each into: individual and institutional. We assess trading performance: (1) short term using relative price ratios and (2) long term using both cumulative returns and total profits. We find that in the short term, domestic institutions trade at significantly better prices than other categories for single listed stocks, but only outperform the non-Arab foreign investor category for cross-listed stocks. This trend is reversed in longer term performance, since non-Arab foreign institutional investors show more superior cumulative returns and profits. The domestic individual category is the loser on all accounts despite their domination of the overall size and value of trading on the stock market consistent with prior literature. This sheds light on the importance of expertise over local knowledge in trading performance. JEL Classification: G10 Keywords: Foreign investors; domestic investors, Information Asymmetry; Trading Performance; Ownership 1. Introduction The question of whether foreign or domestic investors perform better in national stock markets is an open one. On one side, 1
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Do domestic investors really make more money? The trading performance of investor
groups in an emerging market
Abstract
We investigate the trading performance of domestic versus foreign investors using high-frequency data. Our sample involves over 270 million observations from companies listed on the Egyptian stock exchange (EGX) from 2004-2009. The transaction data includes unique trade identifiers allowing us to classify investors into six categories: Domestic, Arab and Non-Arab (foreign) and each into: individual and institutional. We assess trading performance: (1) short term using relative price ratios and (2) long term using both cumulative returns and total profits. We find that in the short term, domestic institutions trade at significantly better prices than other categories for single listed stocks, but only outperform the non-Arab foreign investor category for cross-listed stocks. This trend is reversed in longer term performance, since non-Arab foreign institutional investors show more superior cumulative returns and profits. The domestic individual category is the loser on all accounts despite their domination of the overall size and value of trading on the stock market consistent with prior literature. This sheds light on the importance of expertise over local knowledge in trading performance.
JEL Classification: G10
Keywords: Foreign investors; domestic investors, Information Asymmetry; Trading Performance; Ownership
1. Introduction
The question of whether foreign or domestic investors perform better in national stock markets is
an open one. On one side, empirical support that foreigners underperform domestic investors,
due to better local knowledge, appears in Brennan and Cao (1997), Hau (2001), Choe et al
(2005), Dvorak (2005), Kalev et al (2008) and Agarwal et al (2009). On the other side, studies by
Grinblatt and Keloharju (2000), Seasholes (2004) and Froot and Ramadorai (2008) make a case
that foreigners do better than local investors, since foreign investors have a significant amount of
investment experience.
These ‘seemingly contradictory findings’ (Froot & Ramadorai, 2008) usually rely on one
main argument: information asymmetry. Whichever investor group knows more about the stocks
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makes more money. If domestic investors know more about the local market, then they are at an
advantage in timing their investment decisions. This is justified by their linguistic, cultural and
geographic proximity to the local market (Hau, 2001), their better evaluation of the local firms’
governance structure (Leuz et al, 2010) and lower costs of trade (Parwada et al, 2007). However,
if foreign investors have more expertise in trading and larger portfolios, they can employ this to
profit from trading in small or emerging markets, where domestic investors are not very
sophisticated or experienced.
The discussion on information asymmetry between foreign and domestic investors is an
important one in finance. Gehrig (1993), Brennan and Cao (1997), Kang & Stulz (1997) and Van
Nieuwerburgh & Veldkamp (2009) all emphasize informational asymmetry in their theoretical
model explaining the concentration of portfolio investment in domestic assets resulting in what is
known as the "home equity bias”. The underlying assumption behind these theoretical models is
that domestic investors apriori know more about stocks trading on their local exchanges, and
even choose to remain uninformed about foreign stocks, ‘since they profit more from knowing
information others do not know’. (Van Nieuwerburgh and Veldkamp, 2009). However, assuming
an information structure in which domestic investors know more than foreign one is not
undisputed, as Seasholes (2004) stands against the underlying assumption that domestic investors
are better informed in theoretical models of information asymmetry.
This study seeks to contribute to this line of research by answering the research question of
which investor group, domestic or foreign, performs better, and directly testing the information
asymmetry hypothesis as the reason behind this performance. As Hau (2001) warns, although
‘information asymmetry between investors can be indirectly inferred from asset allocation
decisions ……to learn more about information asymmetry, we must look directly at investment
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profitability’. Thus, in this study we perform a direct empirical analysis involving high frequency
intraday dataset from the Egyptian Stock Exchange (EGX). The EGX provides an ideal setting
for answering this controversial question: it is a good example of a country where information
asymmetry might be high: the local language is Arabic, the political setting is different than
foreign ones, it has different trading days than international stock markets and finally it is an
open and highly liberal stock market that has no impediment to foreign investment.
Several aspects of the data and methodology employed in this study contribute to the
existing literature on trading performance. First, our dataset overcomes the data limitations of
previous studies by including both transaction and ownership records for a large number of
observations (over 270 million) across over 50 different companies and a considerably long time
frame (2004-2009). We have company level high frequency transaction data which includes
prices, volumes, anonymous but unique code for each buyer and seller for each trade as well as
their nationality, type and broker. The transaction level data is combined with ownership records,
overcoming the data limitations in Hau (2001) and Dvorak (2005) and allowing us to track actual
trading profits of the various investor groups across the sample period.
When assessing trading performance we try to complement our current understanding of
the link between trading performance and information asymmetry. So far little studies provide a
concrete link between performance and information asymmetry, since this can only be achieved
by isolating the nature of differential information between the two groups. Kalev et al (2008)
attempt to do so by dividing stocks into different information levels depending on whether they
are foreign-listed or not. In addition to dividing our companies to cross-listed and foreign listed,
we further extend that by dividing our investor groups into Domestic, Arab and Non-Arab
(foreign). Our categorization allows us to capture any differences in trading performance that can
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arise from cultural, geographical or language barriers by dividing foreign investors to Arab and
Non-Arab. We further divide them by type into individual or institutional which can affect the
information level and processing capability of the investor (Aragon et al, 2007). Studies so far
suggest that individual investors are generally poor performers of equity trading (Barber et al.,
2009) suggesting that a complete analysis on whether foreigners or domestic investors perform
better should control for the type of investor.
In measuring trading performance, we combine two main methodologies in the literature
that assess performance on different levels. We measure short term trading performance using
the ‘relative price ratios’ introduced in Cho et al (2005). In this methodology, an investor group
is considered better in trading if they can on average buy (sell) stocks at lower (higher) prices
than the value weighted average price of the trading day. However, this measure short term
trading performance is in no way an indication of the ability of a certain group in making more
money over their investment horizon. We therefore use two established methods for measuring
long term performance: cumulative returns (Grinblatt and Titman, 1993) and trading profits
(Dvorak, 2005). Both measures can better assess the market timing capabilities of certain
investor groups in managing their investments to achieve higher (lower) cumulative returns
(losses) or trading profits (losses).
Our results so far indicate the following. Domestic institutional investors in the short term
trade at significantly better prices than all other categories for single listed stocks, but only
outperform foreign investors for cross-listed stocks. Domestic individuals and Non-Arab
investors are at the most disadvantage in executing trades at favorable prices, especially for
single listed stocks, in comparison to Domestic institutions and Arab investors. The result of the
long term analysis shows a different picture. Foreign institutional investors show significantly
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more superior cumulative returns in the long term for both cross-listed and single listed stocks.
Moreover, foreign and domestic institutions rank the higher in terms of making on average the
most (least) total profits (loss) per year from trading both stock categories. This shows the
importance of expertise over local knowledge in trading performance.
This paper is organized as follows. Section 2 presents the methodology employed to assess
trading performance across various investor groups. Section 3 gives an overview of the dataset
and descriptive statistics. The main results of the paper are presented in Section 4. Finally,
Section 5 gives the main conclusions of this study and possible extensions to the current work.
2. Methodology
In assessing trading performance we use three standard measures from the literature: Relative
Price Ratios (Choe et al, 2005), Cumulative Returns (Grinblatt and Titman, 1993) and Trading
Profits (Dvorak, 2005). The analysis will further use a set of ‘identifying variables’ to
distinguish between investor performance and profits by the size of broker they use, as well as by
the information environment of the company invested in by identifying companies on single or
cross-listed (Kalev et al, 2008). The identifying variables help us test the information hypothesis,
such as whether clients of big brokerages have an information advantage because of the superior
advice that they receive and thus perform better or whether foreign investors’ performance is
better for international cross-listed versus solely local single listed companies due to the better
information coverage on the former group of stocks.
2.1. Relative Price Ratios
Our first level of analysis will use the intraday trading data to compare between the short term
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trading performances of the different investor groups. We use the relative price ratio used by
Choe et al (2005) and Kalev et al, (2008). This measures compares the daily volume weighted
price by which each investor group buys (sells) relative to the overall value weighted average
price of that day to compute a relative price ratio. The investor group that buys (sells) at a
lower(higher) price to the overall VWAP of the day is assumed to have better trading
performance. Calculating relative price ratios for each group involves the following steps:
(1) Calculate a Daily Volume Weighted Average Price (VWAP) by computing a volume
weighted average price for each stock using the intraday transaction data as follows:
Aid=
∑t
Pidt V i
dt
∑t
V idt where
Pidt
refers to the price for stock i on day d for trade t, and
Vidt
refers to the
volume for stock i on day d for trade t.
(2) Calculate the Buying & Selling Weighted Average Price by each Investor group on each
stock
Bi , jd =
∑t
Pijdt V ij
dt
∑t
V ijdt where j refers to the investor group
Si , jd =
∑t
Pijdt V ij
dt
∑t
V ijdt where j refers to the investor group
(3) Compute Buy/Sell Relative Price Ratio for each investor group as follows
Bi , jd
A i ,d is a buy ratio calculated for each investor group j for each day for every stock.
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S i , jd
A i ,d is a sell ratio calculated for each investor group j for each day for every stock.
A buy (sell) ratio of less (more) than 1 indicates that an investor group pays (receives) less
(more) than the average daily price and vice versa. Everything else equal, a group of investor is
disadvantaged relative to another for purchases if investors of the first group buy at a higher
price than the investors of the second group. Similarly, a group of investor that sells at a lower
price than another group of investor is at a disadvantage relative to that group of investor. The
difference between the buy (sell) price ratios of each investor group is then computed and subject
to a t-test for significance to assess whether a certain group of investor trade at statistically
different price to the other for purchases and sales.
We calculate the relative price ratios on two levels. First, by comparing investor groups by their
nationality: Domestic, Arab, Non-Arab (j=3) and second, by comparing investor groups by both
nationality and investor type (institutional, individual) (j=6).
2.2. Cumulative Returns
To compare trading performance of the different investor across a longer time horizon, we
employ the cumulative return measure of Grinblatt & Titman (1993):
CR j=∑t=1
T
(Purc h asest−1−Salest−1
Purc hasest−1+Salest−1¿) Rt ¿
CRi is cumulative return over each period (taken as one year) by investor group j, Purchasest-1
indicates the amount bought each day by a specific investor group, Salest-1 indicates amount sold
each day by a specific investor group and Rt is return on stock following the trade ratio. This
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measure of cumulative return indicates the market timing capability of an investor group in
predicting future market movements to point out the ultimate winners and losers from trading.
2.3. Trading Profits
We finally use the trading profits of Hau (2001) and Dvorak (2005) to measure trading profits
that each group of investor makes to assess which group performs better in the market. Trading
profits ∏t+1 are calculated per transaction as:
∏t+1 = Qt ∆Pt+1
where, Qt is the stock holdings at time t, ∆Pt+1 is the share price increase between t and t+1,
where t refers to a market transaction.
The profits estimated from this equation can be market to market each time a stock trades, and
can be used to estimate the profits for each group of investor. The trading profits are then
aggregated across different spectrums from short (daily), medium (weekly) and long-run
(monthly) trading. As Dvorak (2005) explains: “This decomposition may help in understanding
the nature of the information asymmetries that may exist. For example, when domestic investors
have short-lived inside information, they should have greater short-term profits, while superior
stock picking ability should lead to long-term profits.”
One major limitation of the works of Hau (2001) and Dvorak (2005) in using the above measure
of trading profits is their lack of data on initial portfolio holding , Q0 which helps them to
properly track trading profits. We overcome this limitation in the current study by combining
intraday transaction data with initial ownership holdings for the companies analyzed.
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3. Data and Descriptive Statistics
The setting for the study is the Egyptian Stock Exchange (EGX), an emerging market that in the
past 2 decades has undergone several liberalization measures making it attractive for foreign
investment for many years up until the Arab spring uprising in January 2011. The time period of
this study cover the 6 years from 2004-2009, a period in which the EGX was considered one of
the world’s best performing stock exchanges (Saleh, 2004) and is the Middle East's second-
largest stock market by market capitalization after Saudi Arabia. (Guest, 2008), averaging
around $90billion at the end of our sample in 2009, which compares well with other emerging
markets studied in the literature.
The EGX presents a good setup for our research question for several notable reasons. First of all,
EGX attracts a large number of foreign investors who contribute as high as 32% of trading value
during our sample period, which insures sufficient foreign presence in the data to warrant
reliable results. Second, the Egyptian stock exchange does not have any restrictions on foreign
ownership of local stocks and provides several incentives for foreign ownership including tax
breaks on capital gains and dividends. Third, the EGX provides a good setup to dissect foreign
investors into more groups, mainly Arab foreign investors as well as other foreign investors. This
provides an interesting point of analysis since Arabs have similar language and culture to
Egyptians, making them a good mediating group to compare their performance to other Non-
Arab foreign investors and analyze the information asymmetry hypothesis. Finally, some of the
stocks trading on the EGX are foreign listed as global depository receipts (GDRs) on the London
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Stock Exchange (LSE) providing an excellent opportunity to distinguish the information
characteristics of the different stocks as in Kalev et al (2008).
The dataset used in our study presents a unique chance to test our research question since it is a
complete and comprehensive one. It relies on a large data set for 46 stocks1 that make up over
40% of the market capitalization of the EGX during the sample period which are presented in
Table (1). One of the merits of the dataset is its relative balance. It covers a long period of time
from January 2004-October 2009, and thus takes into consideration two important periods: 2004-
2005 when EGX was considered one of the best performing exchanges worldwide and 2008, the
crisis year, and thus time period is sufficiently spread to take into consideration different cycles
in the life of any exchange.
This complete dataset for that we will analyze relies on two major sources of data obtained from
the Egyptian Clearing House (MCSD). First, we obtain EGX’s complete intraday transaction
records for the 46 stocks for the approximately 6 year period from Jan 2004-October 2009 with
close to 30 million transactions. A typical transaction record of the data set includes the
following 14 vector columns: Date of Trade, Sequence of Trade, Stock Name, Price, Quantity,
but anonymous code, Buyer Nationality, Buyer Type, Buyer Broker. This is one of the most
comprehensive data sets in the literature, containing information on type of investor (as in Choe
et al (2005)), the broker of each investor (as in Dvork (2005) and Agrawal et al (2008)) and the
specific nationality of each investor. Moreover, the dataset is complemented by having data on
1 Stocks selected fulfilled the following criteria (1) most active by numbers and size of trade (2) were listed for at least 3 out of the 6 years and (3) traded by three nationalities of investors groups
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the holdings of all investor groups of each stock in the sample at the onset of our analysis period.
This allows us to overcome the problems in Hau (2001) and Dvorak (2005) of assuming specific
ownership values and thus to be able to calculate accurate profits for each investor group rather
than hypothetical/assumed ones.
Table (2) presents the percentage ownership of the different investor groups in our sample across
the sample period, for all the sample as well as by classification of companies into cross-listed
and single listed. Non-Arab (foreign) institutions are the largest group outside of domestic
investors owning Egyptian listed companies. On average, over 50% of cross-listed companies
are owned by Non-Arab (foreign) institutions, while single listed companies are mostly owned
by domestic institutions and only 9% owned by Non-Arab (foreign) institutions. In light of the
apparent difference in ownership between the cross-listed and single listed companies, we would
like to compare the trading performance between the investor groups across both stock
categories.
Table (3) presents a summary of the trading statistics across the sample. Domestic individuals
dominate the number of trades and proportion of value trading. This is consistent with individual
trader behavior presented in Barber et al (2009). On the other hand, Arab and Non-Arab
institutional investors, while trading less frequently than domestic investors, their average trade
size is much larger. This gives an indication of the trading behavior of institutional investors in
general, and foreign ones in particular, who execute more strategic trades reflecting their
disciplined portfolio management approach.
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4. Main Results
4.1. Relative price ratio
Tables 4 and 5 present the results across the sample for buy and sell ratios. Domestic institutions
outperform all other investor groups by buying at significantly lower prices and selling at
significantly higher prices than all other investor groups. Foreign institutional investors are the
worst performers, buying at higher and selling at lower prices than other groups. They even
perform worse than foreign individuals, an observation that goes contrary to Arab and Domestic
investors, in which institutions perform much better than individual investors. There seems to be
no consistent differences amongst short term trading performance between individual investors
of different nationalities.
How can we explain the results of the short term trading performance? Since the result is
consistent in both stock categories, this indicates that the difference in trading at better prices
across the trading day is due to short term dynamics and factors. One of those is the size of
broker being used to execute the trade. We divide the trades by broker, by classifying brokers
into two categories: Large and Small. The large brokers are those that consistently dominate a
third of market activity. Foreign institutions mostly rely on those brokers in executing their
trades. On the other hand, we find that domestic institutions and individuals rely mostly on small
brokers. The reliance of domestic institutions on small brokers for trade execution is a result of a
legal requirement by the Egyptian financial services authority to limit the trades of domestic
financial institutions though their own brokerages to 30% of their executed trades.
12
We find that for cross-listed companies, Table (6), there are no significant differences in
performance between large and small brokers. However, the interesting result is for single listed
companies, in which small brokers buy (sell) at significantly lower (higher) prices than large
brokers. This might be attributed to the ability of small brokers to execute smaller and better
timed deal within the trading day.
4.2. Cumulative returns
The results of short term trading performance using price ratios can at best indicate the ability of
certain investor groups to use their market knowledge and presence as well as technical trading
rules to execute deals at favorable prices and profit from short term trades. This is in no way a
conclusive indication of their better trading performance since foreign institutional investors like
pension funds and international money managers can have longer term horizons and rely on
more fundamental trading strategies and therefore their trading performance assessment
measures are usually longer term metrics. We use in this section cumulative returns and in the
next section trading profits to assess longer term trading performance.
Table (7) present the cumulative returns for different investor groups across stock categories and
years. The result is very different from the short term performance dynamics. Foreign institutions
attain on average much higher aggregate cumulative performance for both cross-listed and single
listed stocks and for the majority of sample period. On average, the make an approximate annual
cumulative return of 23.52% for cross-listed stocks and 13.65% for single listed stocks from
2004-2009, outperforming all other investor group categories. Domestic individual and
institutions are surprisingly the worst performers across the sample period and across stock
categories, having on average cumulative loss of -5.36% and -9.22%.
13
4.3. Trading profits
In terms of size of profits, Table (8) presents the annual total profits by each investor group
across stock categories and sample period and Table (9) presents the profits per trade executed.
Individual investors on average achieve the least profits with the exception of 2006 and 2008, in
which individual investors outperformed institutional investors. This shows that individual
investors hold better during crisis periods. Non-Arab Foreign institutional investors achieved the
largest profits across the sample followed by Arab investors, but the result is asymmetric across
time periods.
5. Conclusions
This paper studies the trading performance of various investor groups on an emerging markets.
We find that in the short term, domestic institutional investors trade at significantly better prices
than all other categories for single listed stocks, but only outperform the non-Arab foreign
investor category for cross-listed stocks. This trend is reversed in longer term performance, since
non-Arab foreign institutional investors show more superior cumulative returns and profits. The
domestic individual category is the loser on all accounts despite their domination of the overall
size and value of trading on the stock market consistent with evidence in [Barber, B. M., Lee, Y.
T., Liu, Y. J., & Odean, T. (2009). Just how much do individual investors lose by trading?
Review of Financial Studies, 22(2), 609-632.] This sheds light on the importance of expertise
over local knowledge in trading performance. One important outcome of our results is to
encourage the role of market authorities in education of small domestic investors on rational
trading since they are the group with the most consistent losses both short and long term.
14
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Table 1 Sample Companies
Stock Ticker IndustryExchanges
listedProportion of Total Market Cap
2004 2005 2006 2007 2008 2009Egyptians Abroad ABRD Financial services EGX 0.01% 0.01% 0.01% 0.01% 0.01% 0.02%Arab Cotton Ginning ACGC Household products EGX 0.10% 0.26% 0.13% 0.07% 0.05% 0.07%Al Ahly for Development and Investment AFDI Financial services EGX 0.03% 0.08% 0.06% 0.04% 0.10% 0.10%Alexandria Mineral Oils co AMOC Oil and Gas EGX 1.47% 1.27% 0.87% 0.87% 0.65%Arab Polvara Spinning and Weaving Company APSW Household products EGX 0.10% 0.14% 0.07% 0.05% 0.04% 0.07%Credit Agricole Egypt CIEB Banks EGX 0.08% 0.13% 0.03% 0.07% 0.04% 0.05%Commercial International Bank (Egypt) COMI Banks EGX, LSE 2.10% 1.65% 2.11% 2.27% 2.26% 3.44%Canal Shipping Agencies Co CSAG Industrial Goods EGX 0.37% 0.38% 0.38% 1.09% 0.35% 0.51%National Bank for Development (Egypt) DEVE Banks EGX 0.08% 0.06% 0.09% 0.13% 0.06% 0.10%El Ezz Ceramics and Porcelain Co (Gemma) ECAP Construction EGX 0.07% 0.15% 0.07% 0.08% 0.04% 0.06%Egyptian Financial and Industrial EFCO Chemicals EGX 0.11% 0.14% 0.08% 0.13% 0.05% 0.06%Egyptian Resorts Company EGTS Travel EGX 0.04% 0.02% 0.13% 0.06% 0.03% 0.04%Egyptians for Hous, Develop and Recon. EHDR Real estate EGX 0.00% 0.01% 0.01% 0.03% 0.05% 0.10%Egypt Kuwait Holding Co (SAE) EKHO Financial services EGX, KSE 0.65% 0.10% 0.14% 0.11% 0.11% 0.19%Electro Cable Egypt Co ELEC Industrial Goods EGX 0.03% 0.03% 0.05% 0.00% 0.01% 0.01%El Kahera for housing and Development ELKA Real estate EGX 0.02% 0.04% 0.03% 0.03% 0.02% 0.04%El Shams Housing and Urbanization SAE ELSH Real estate EGX 0.03% 0.05% 0.04% 0.04% 0.02% 0.07%
Egyptian Company for Mobile Services EMOBTelecommunications EGX 5.46% 4.43% 3.40% 2.62% 3.06% 4.22%
Egypt for Poultry EPCO Food and beverages EGX 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%Al Ezz Steel Rebars Company SAE ESRS Basic resources EGX 0.85% 1.30% 0.86% 0.74% 0.48% 0.76%
Housing and Development Bank HDBK Financial services EGX 0.04% 0.07% 0.18% 0.08% 0.09% 0.08%Heliopolis Co for Housing & Development HELI Real estate EGX 0.15% 0.33% 0.34% 0.99% 0.40% 0.55%EFG Hermes Holding SAE HRHO Financial services EGX, LSE 0.24% 1.18% 0.96% 1.46% 0.68% 1.35%Egyptian Iron and Steel Company IRON Basic resources EGX 0.02% 0.02% 0.02% 0.02% 0.02% 0.03%El Nasr Clothes and textiles Co Kabo KABO Household products EGX 0.03% 0.07% 0.02% 0.01% 0.01% 0.01%Misr Chemical Industries Co. MICH Chemicals EGX 0.10% 0.16% 0.07% 0.07% 0.05% 0.10%Nasr City Company for Housing & Development MNHD Real estate EGX 0.34% 0.39% 0.33% 0.70% 0.59% 0.65%Egyptian Media Production City co MPRC Media EGX 0.96% 0.52% 0.37% 0.26% 0.16% 0.22%Nile Cotton Ginning NCGC Household products EGX 0.01% 0.05% 0.04% 0.03% 0.10% 0.15%Sixth of October Development and Investment OCDI Real estate EGX 0.02% 0.45% 0.41% 0.46% 0.13% 0.30%Orascom Construction Industries OCIC Construction EGX, LSE 4.40% 6.85% 7.58% 10.77% 4.31% 7.71%Orascom Hotels and Development ORHD Travel EGX 0.20% 0.51% 0.66% 1.01% 0.35% 0.62%
Al Watany bank of egypt WATA Banks EGX 0.13% 0.20% 0.23% 0.64% 0.62% 0.86%Extracted Oils and Derivatives Co ZEOT Oil and Gas EGX 0.01% 0.01% 0.02% 0.01% 0.02% 0.02%
Total 34.24
% 48.87% 46.79%44.68
% 34.88% 46.29%
18
Table 2 Ownership by Investor Group (%) across sample period 2004-2009 (detailed table in appendix)