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1
Saudi Stock Market Historical View and Crisis Effect: Graphical and Statistical Analysis Abdulrahman A. Al-Twaijry
Associate professor, Accounting Department, College of Business & Economics,
Qassim University
[email protected], [email protected]
Abstract
During the past year of 2006, the Saudi stock market experienced a rigorous
crash after the stock price index collapsed and lost 65% of its value. The aim
of this study is to review the stock market from its formal initial in 1985 until
2006. Both graphical and statistical analysis were used to highlight the stock
market behavior. The results illustrated that the sharp increase in the share
prices started early 2003 and until they reached their highest level by end of
February 2006. Shares of the banking sector were the most profitable stock
with a significant positive return mean. The cross-sectional regression results
revealed that EPS and DPS are not always good predictors of the changes in
the stock price index, however, the daily number of trades, turnover, and
values were found to be better forecasting the stock prices even during the
market crisis
Saudi Stock Market Historical View and Crisis Effect: Graphical and Statistical Analysis
Introduction
2
During 2006, the Saudi Stock Market collapsed and the price index lost over
13000 points (65% of its top level). This disaster had an effect on large
number of the population and in several cases, death was recorded and in
some others people became ill, this had occurred for the first time in Saudi
stock history. Since there was no sudden event leading to this heavy decline
in the share prices, studies are now warranted to investigate this issue more
deeply.
Saudi Stock Market is recent and only developed as a recognized
market within the last five years although the first stock company in the
Kingdom of Saudi Arabia was established about 70 years ago. The number of
joint stock companies which existed in 1985 were approximately 50 which
then doubled in 1995 and reduced to be around 90 by the end of 2000. Now
(15-2-2007) the publicly held companies consist of 88 firms, representing
eight sectors: banking (10), manufacturing (34), cement (8), service (23),
electricity (1), telecom (2), insurance (1), and agricultural (9).
The stock market in Saudi Arabia only formally regulated in 1984 and
after on year (in 1985) the Saudi Shares Registration Company was
established (Al-Rumaihi, 1997, p.182). In 1990 an Electronic Securities
Information System was introduced by the Saudi Arabian Monetary Agency
[the Saudi Central Bank] to facilitate multi-location trading (Azzam, 1993).
In October 2001, the Saudi Stock Company (knowing as TADAWUL) was
initiated. Recently, TADAWUL introduced a modern system for facilitating
3
investment environment and control. During the last four years, many of the
stock market existing regulations were reviewed and upgraded and also
several new regulations were promulgated. The top of these new regulation is
"Capital Market Law", which was introduced 31/7/2003 for restructuring the
capital market in the country taking advantage of international stock market
standards. The reason for issuing this law was to protect the investors' rights
and to ensure the reliability and confidence in the Saudi Stock Market.1
Although the number of Saudi Joint Stock Companies is small, they represent
about 60% of the invested capital in the country, and even though the Saudi
Arabian stock market is relatively new, it is now the largest market in the
entire Arab World. Initially, only Saudi (and GCC) nationals could own
shares, but this restriction was relaxed in 1997 and relaxed again with some
constraints in 2006.
In this study, however, the historical development of the Saudi stock
market will be reviewed. Both graphical and statistical analysis are employed
to closely investigate the behavior of the of the market with more focus on the
recent dramatic changes.
Literature Review
Most researches on stock market use the past to predict future. For example,
Damir (2005) analyzed the US stock market during the past 25 years, from
1980 to 2005, to predict the future of stock market behavior. He found that
4
the political situations, volatility in international trade, and foreign exchanges
have significant negative impact on the US stock market prices. Similarly,
Fair (2002), and Liu (2006) looked at the historical stock market behavior in
the US during 1980s and 1990s. Liu focused on daily data while Fair used
short time intervals data. Siegel and Schwartz (2006) went back further to the
original of S&P 500 index which was launched in 1957.
The historical stock data were used by the great majority of stock
market literature for studying the relationship between share price or return
and share performance. Campbell and Shiller (1988), Bulkley and Tonks
(1989), Goetzmann and Jorion (1995), Chiang et al. (1997), and more
recently, Batchelor and Orakcioglu (2003), Kanas (2005), Lettau and
Ludvigson (2005), Lee (2006) are examples of these studies. These research
examined either gross indexes (aggregate data) or individual markets
(industries or companies) utilizing in most cases time-series (short or long
horizon or both) and/or cross-sectional data from developed nations. On the
other hand, some studies such as: Crouch (1970), Rogalski (1978), Smirlock
and Starks (1985), Hiemstra and Jones (1995), Silvapulle and Choi (1999)
Lee and Rui (2000), Llorente et al. (2002), and Groenewold (2004) focused
on the relationship between share prices (or returns) and the volume of trades.
Positive relations, negative relation, and weak relations were all reported.
Some researchers; for example: Ray and Tsay (2000), Areal and Taylor
(2002), and Cochran and Mansur (2002); focused on the stock market
5
volatility. Cochran and Mansur used monthly basis and various five-year
intervals from January 1928 through June 2001 and found that stock volatility
is much larger in recent time (January 1998-June 2001).
The great majority of stock market literature have investigated markets
in developed countries. The outcomes of these studies may not be applicable
for developing stock market since these later markets have their unique
characters. Thus, the purpose of this study is to bridge the gap existing in the
stock market literature.
Saudi Stock Market Historical View
The earliest data about Saudi Stock Market can be traced to 1985. 28th of
February 1985 was the first day of the stock index which started with 1000
points. Each of the six sectors existing by that time received 1000 points as
well. Graph (1) explains the behavior of the general stock price index from its
birth until end of 1990.
Graph (1) Index weekly behavior from its birth up to end of 1990
0
200
400
600
800
1000
1200
1400
28/02/198509/05/198525/07/198510/10/198519/12/1985
27/02/198608/05/198624/07/198609/10/198618/12/198626/02/198707/05/198723/07/198708/10/198717/12/1987
25/02/198805/05/198828/07/198806/10/198815/12/198823/02/198904/05/198913/07/198921/09/198930/11/1989
08/02/199019/04/199028/06/199012/09/199021/11/1990
6
From its origin, the index was going down until it reached its lowest level
630.41 (decreased by 37%) during the month of September 1986 and then
returned to grow until it reached its highest point 1182.37 (increased by 88%
from its lowest level) during the month of June 1990, then it went down
again to end very near to where it stated. Graph (2) shows the second stage
(next 5 years) of the index.
Graph (2) Index weekly behavior from 1991 until 1995
0
500
1000
1500
2000
2500
02/01/1991
24/04/1991
14/08/1991
27/11/1991
11/03/1992
08/07/1992
21/10/1992
03/02/1993
26/05/1993
15/09/1993
29/12/1993
20/04/1994
11/08/1994
24/11/1994
16/03/1995
06/07/1995
19/10/1995
As the above graph depicts, the index started again from nearly 1000 points
and kept increasing sharply until it reached its peak 2298.89 (increased by
230%) during the month of April 1992. After that it went down to near its
root and by the end of 1995 (December 28), the index closed at 1370.82
points. The third stage of the index history, which is the second half of the
1990s, is illustrated in Graph (3).
Graph (3) Index weekly behavior from 1996 until 2000
7
0
500
1000
1500
2000
2500
3000
04/01/1996
28/03/1996
20/06/1996
05/09/1996
21/11/1996
06/02/1997
01/05/1997
17/07/1997
02/10/1997
18/12/1997
05/03/1998
28/05/1998
13/08/1998
29/10/1998
14/01/1999
15/04/1999
01/07/1999
16/09/1999
02/12/1999
24/02/2000
11/05/2000
27/07/2000
12/10/2000
28/12/2000
During the last 5 years of the 20th century, where most of the well known
international stock market was booming to their highest points (Damir 2005,
Shiller 2005), the Saudi stock market index was ranging between 1250 and
2350 points. The behavior of the index line, as shown above, seems not to be
affected by the international stock market escalation. Graph (4) however,
shows the trend of the index during the next five years (4th stage).
This era (2001-2005) can be, as it is clear in the graph, divided into two
equal periods. In the first one, the index was almost stable, however, in the
next half, stock market jumped up. The prices of the shares doubled many
times during this period. The share index started at about 2000 and end up to
about 17000 points (8.5 times) with no major breakdowns.
Graph (4) Index weekly behavior from 2001 until 2005
8
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
04/01/2001
22/03/2001
31/05/2001
09/08/2001
18/10/2001
27/12/2001
07/03/2002
16/05/2002
25/07/2002
03/10/2002
12/12/2002
20/02/2003
01/05/2003
10/07/2003
18/09/2003
04/12/2003
19/02/2004
29/04/2004
08/07/2004
16/09/2004
02/12/2004
17/02/2005
28/04/2005
07/07/2005
15/09/2005
24/11/2005
The only possible reason for this boom is the large increase in the demand
side since a huge number of people started investing in the stock market either
directly or through various types of portfolios provided mainly by banks.
Graph (5) illustrates the index movement during the last 7 moths (1-1-2006 to
31-7-2006)
Graph (5) Index daily behavior from 1st of January to 1st of August 2006
0
5000
10000
15000
20000
25000
01/0
1/20
06
16/0
1/20
06
24/0
1/20
06
01/0
2/20
06
09/0
2/20
06
18/0
2/20
06
26/0
2/20
06
06/0
3/20
06
14/0
3/20
06
22/0
3/20
06
01/0
4/20
06
10/0
4/20
06
19/0
4/20
06
29/0
4/20
06
07/0
5/20
06
15/0
5/20
06
23/0
5/20
06
31/0
5/20
06
08/0
6/20
06
18/0
6/20
06
27/0
6/20
06
08/0
7/20
06
17/0
7/20
06
26/0
7/20
06
Until the end of February, the index kept growing to reach its highest peak
ever (20624.84 points). The last week of February 2006 (exactly the days
from 21 to 25) is the only week in the entire history of Saudi stock market
where the stock price index had been staying above 20000 points. Saudi
9
media kept stressing on this extraordinary event in the stock market and
probably participated on creating fear in the investor' mind. As depicted in
the above graph, the stock market price index starts falling after reaching its
boom (over 20000 points) to come down to 15000 points (25%) within three
weeks, then lasted almost above the 15000 points for about one month, then
started decreasing for the second time until it was very close to the 10000
points within less than a month. In total the index lost 50% of its highest level
during two months and a half. This crash was the worst in the entire history
of Saudi stock market and the consequences on thousands of investors was
severe.
By looking to the gross stock market return during the last 25 years, we can
understand when the risk started to exist. Graph(6) exhibits the index return
volatility from its birth until recent.
Graph (6) Index weekly return behavior from 1985 until 2006
INDEX(R)
-3000
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
07/03/1985
07/03/1986
07/03/1987
07/03/1988
07/03/1989
07/03/1990
07/03/1991
07/03/1992
07/03/1993
07/03/1994
07/03/1995
07/03/1996
07/03/1997
07/03/1998
07/03/1999
07/03/2000
07/03/2001
07/03/2002
07/03/2003
07/03/2004
07/03/2005
07/03/2006
The above return graph shows that the return volatility was low and near the
zero throughout the time from the start of the index (1985) until the year 2003
10
Return
-1500
-1000
-500
0
500
1000
1500
2000
01/01/2006
07/01/2006
19/01/2006
25/01/2006
31/01/2006
06/02/2006
12/02/2006
18/02/2006
23/02/2006
01/03/2006
07/03/2006
13/03/2006
19/03/2006
25/03/2006
01/04/2006
08/04/2006
15/04/2006
22/04/2006
27/04/2006
03/05/2006
09/05/2006
15/05/2006
21/05/2006
27/05/2006
01/06/2006
07/06/2006
13/06/2006
20/06/2006
27/06/2006
04/07/2006
11/07/2006
18/07/2006
25/07/2006
01/08/2006
then the volatility began to increase and this year (2006) received the largest
volatility ever. By looking closer to the stock market daily return volatility
during 2006, as plotted in graph (7), it can be seen that the gross return went
up to 1500 points (positive) and down to 1000 points (negative) with total
difference between these two levels 2500 points which reflects the high
existing risk in the stock market.
Graph (7) Index daily return behavior from 1-1 to 1-8 year 2006
Since the Saudi society was classified according to Hofstede's (1991)
diminutions as risk averse, stock holders may not like the occurring situation
and probably many will draw back from the market. The price daily volatility
(high-low) for the year 2006 is shown in graph (8).
Graph (8) Index daily volatility behavior from 1-1 to 1-8 year 2006
11
Daily volatility
0
200
400
600
800
1000
1200
1400
1600
1800
01/0
1/20
06
07/0
1/20
06
19/0
1/20
06
25/0
1/20
06
31/0
1/20
06
06/0
2/20
06
12/0
2/20
06
18/0
2/20
06
23/0
2/20
06
01/0
3/20
06
07/0
3/20
06
13/0
3/20
06
19/0
3/20
06
25/0
3/20
06
01/0
4/20
06
08/0
4/20
06
15/0
4/20
06
22/0
4/20
06
27/0
4/20
06
03/0
5/20
06
09/0
5/20
06
15/0
5/20
06
21/0
5/20
06
27/0
5/20
06
01/0
6/20
06
07/0
6/20
06
13/0
6/20
06
20/0
6/20
06
27/0
6/20
06
04/0
7/20
06
11/0
7/20
06
18/0
7/20
06
25/0
7/20
06
01/0
8/20
06
Graphs (9, 10, 11) below depict the trend of the number of daily trades, the
daily total turnover (value), and the daily total volume.
Graph (9) Daily number of trades from 1-1 to 1-8 year 2006
# of Trades
0
100000
200000
300000
400000
500000
600000
700000
800000
01/0
1/20
06
17/0
1/20
06
26/0
1/20
06
05/0
2/20
06
14/0
2/20
06
23/0
2/20
06
05/0
3/20
06
14/0
3/20
06
23/0
3/20
06
03/0
4/20
06
15/0
4/20
06
25/0
4/20
06
04/0
5/20
06
14/0
5/20
06
23/0
5/20
06
01/0
6/20
06
11/0
6/20
06
21/0
6/20
06
03/0
7/20
06
15/0
7/20
06
25/0
7/20
06
Graph (10) Daily total traded shares value from 1-1 to 1-8 year 2006
Total Turnover
05000000000
100000000001500000000020000000000250000000003000000000035000000000400000000004500000000050000000000
01/0
1/20
06
16/0
1/20
06
24/0
1/20
06
01/0
2/20
06
09/0
2/20
06
18/0
2/20
06
26/0
2/20
06
06/0
3/20
06
14/0
3/20
06
22/0
3/20
06
01/0
4/20
06
10/0
4/20
06
19/0
4/20
06
29/0
4/20
06
07/0
5/20
06
15/0
5/20
06
23/0
5/20
06
31/0
5/20
06
08/0
6/20
06
18/0
6/20
06
27/0
6/20
06
08/0
7/20
06
17/0
7/20
06
26/0
7/20
06
12
Graph (11) Daily total traded share volume from 1-1 to 1-8 year 2006
Total Volume
0
100000000
200000000
300000000
400000000
500000000
600000000
01/0
1/20
06
16/0
1/20
06
24/0
1/20
06
01/0
2/20
06
09/0
2/20
06
18/0
2/20
06
26/0
2/20
06
06/0
3/20
06
14/0
3/20
06
22/0
3/20
06
01/0
4/20
06
10/0
4/20
06
19/0
4/20
06
29/0
4/20
06
07/0
5/20
06
15/0
5/20
06
23/0
5/20
06
31/0
5/20
06
08/0
6/20
06
18/0
6/20
06
27/0
6/20
06
08/0
7/20
06
17/0
7/20
06
26/0
7/20
06
Before the crash started, the number of trades and total value (turnover) were
generally drifting up, but total volume was stable below 100 million. During
the crash, the daily number of trades and values slowed down and after the
crash total volume starts drifting up, whilst the number of daily trade and total
turnover remain slow for a while then went up with cautious.
Individual markets
Saudi stock market comprise eight individual sectors: banking, manufacturing,
cement, service, electricity, telecom, insurance, and agricultural. All these
sectors, except telecom and insurance were existing when the market index
was set forth early 1985 and each sector indexed with 1000 point bases.3
Telecom sector was added to the market index by end of January 2003 and
also started with 1000 point basis whilst insurance sector was added at the
begging of March 2005. Graph (12) plots the weekly index of each of the
eight sectors from 1985 to 2006.
Graph (12) Each sector price week index from 1985 to 2006
13
0
10000
20000
30000
40000
50000
60000
26/0
1/1994
02/0
5/1994
26/0
7/1994
09/1
0/1994
22/1
2/1994
13/0
3/1995
04/0
6/1995
16/0
8/1995
30/1
0/1995
11/0
1/1996
02/0
4/1996
23/0
6/1996
04/0
9/1996
17/1
1/1996
29/0
1/1997
28/0
4/1997
12/0
7/1997
24/0
9/1997
07/1
2/1997
25/0
2/1998
18/0
5/1998
30/0
7/1998
12/1
0/1998
24/1
2/1998
16/0
3/1999
07/0
6/1999
19/0
8/1999
01/1
1/1999
26/0
1/2000
16/0
4/2000
28/0
6/2000
10/0
9/2000
22/1
1/2000
12/0
2/2001
03/0
5/2001
16/0
7/2001
27/0
9/2001
11/1
2/2001
07/0
3/2002
20/0
5/2002
01/0
8/2002
14/1
0/2002
01/0
1/2003
24/0
3/2003
05/0
6/2003
18/0
8/2003
30/1
0/2003
19/0
1/2004
10/0
4/2004
22/0
6/2004
04/0
9/2004
24/1
1/2004
14/0
2/2005
28/0
4/2005
11/0
7/2005
22/0
9/2005
13/1
2/2005
02/0
3/2006
20/0
5/2006
All indexes remained under 10000 points until the end of 2003 when they
stated rising up sharply especially banking and manufacturing sectors. 23rd of
February 2006 was the day all eight sectors indexes were in their highest level
ever. Table (1) shows these top points and the percentage increases from the
original basis.
Table (1) The highest points achieved in all eight sectors (23-2-2006) and the total increase from basis
Both banking and manufacturing industries jumped up largely (nearly 50
times) from their original points, whilst insurance was the lowest sector to
increase, followed by electricity and telecom then service. Graph (13)
exhibits all sectors' stock weekly returns during the last 26 years.
Graph (13) all sectors stock weekly returns during the last 26 years.
the Sector Banking Manu. Cement Services Electricity Telecom Insurance Agric.
Top point 47723.92 48861.95 13179.91 8464.91 5449.09 6952.11 2829.71 13343.45
Times of increases from the original basis
47.7239 48.862 13.18 8.46491 5.44909 6.95211 2.82971 13.34345
14
-20000
-15000
-10000
-5000
0
5000
10000
07/03/1985
28/11/1985
14/08/1986
30/04/1987
21/01/1988
13/10/1988
22/06/1989
01/03/1990
14/11/1990
07/08/1991
22/04/1992
06/01/1993
29/09/1993
23/06/1994
09/03/1995
23/11/1995
15/08/1996
01/05/1997
08/01/1998
24/09/1998
17/06/1999
02/03/2000
09/11/2000
26/07/2001
04/04/2002
12/12/2002
21/08/2003
13/05/2004
03/02/2005
13/10/2005
BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R)ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R)
The return of all indexes was very close to zero until year 2004 when its
volatility started getting larger. To look closer to the behavior of the sectors'
returns, we used data from the last six years (2001 to 2006), which are showed
in Graph (14).
Graph (14) All sectors' stock weekly returns during the last 6 years (1-2001 to 2006)
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
04/01/2001
15/03/2001
17/05/2001
19/07/2001
20/09/2001
22/11/2001
24/01/2002
28/03/2002
30/05/2002
01/08/2002
03/10/2002
05/12/2002
06/02/2003
10/04/2003
12/06/2003
14/08/2003
16/10/2003
25/12/2003
04/03/2004
06/05/2004
08/07/2004
09/09/2004
11/11/2004
20/01/2005
31/03/2005
02/06/2005
04/08/2005
06/10/2005
08/12/2005
09/02/2006
13/04/2006
BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R)
ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R)
The above graph explains that the returns were almost stable until 2004 when
the volatility getting higher and mostly moving above zero but in 2006 it
reversed and was mostly moving under zero. To find out more about the
recent situation of the eight sectors included in the general index, we have
calculated the means of the daily trades during the month of July 2006. Graph
(15) compare the means of close index prices of these sectors.
15
Graph (15) Means of close index prices of all sectors (July 2006)
0
5000
10000
15000
20000
25000
30000
35000
Bank
ing
Manufac
turin
g
Cemen
t
Serv
ice
Elec
tricit
y
Teleco
m
Insu
ranc
e
Agric
ultura
l
The mean price of the banking sector is the highest (over 30000 points)
followed by manufacturing. The mean of close price indexes are far smaller
in the other sectors where electricity and insurance were the lowest. Graph
(16) compares these sectors in terms of total volume of the traded stock during
July (the mean).
Graph (16) % of total volume of all sectors (means of July 2006)
16
Banking2%
Manufacturing36%
Cement2%
Service36%
Electricity10%
Telecom2%
Insurance0%
Agricultural12%
As depicted in the above graph, service and manufacturing sectors each
captures 36% (both captures 72%) of the trade volume in the Saudi stock
market. Cement, banking, and telecom each represents only 2% (all 6%) of
the total volume. In terms of total turnover (value), graph (17) compares the
means of the sectors. Manufacturing sector received the highest daily
turnover which exceeded, an average, 8 billion Saudi Riyals. The second
largest sector in terms of value is the service with, on average, about 5.6
billion SR daily turnover.
Graph (17) Means of total volume of all sectors (July 2006)
17
0
1000000000
2000000000
3000000000
4000000000
5000000000
6000000000
7000000000
8000000000
Bank
ing
Manuf
actu
ring
Cem
ent
Service
Elec
tricit
y
Teleco
m
Insu
ranc
eAg
ricultu
ral
Agricultural comes the third and all the other sectors' daily mean turnover is
less than one billion SR. Graph (18) compares these sectors in terms of the
number of daily trades (mean of the month).
Graph (18) % of the number of daily trades of all sectors (means of July 2006)
Service34%
Telecom2%
Cement3%
Agricultural14%
Manufacturing40%
Electricity3%
Insurance0%
Banking4%
Manufacturing sector again received the largest number of daily trades (40%
of the whole market). Service sector comes next capturing 34% of the total
number of daily trades, while agricultural sector acquires 14%. The
18
remaining sectors altogether do not represent more than 12% of the total
number of daily trades.
Stock performance and stock prices
The link between stock performance (earning and dividends) and stock price
(and return) should be strong. The previous studies confirmed, using data
from Western stock market, that stock price (and return) is strongly correlated
with the stock performance. However, in Saudi market the case may not be
the same since people are not educated enough yet about how, when, and
where to invest their money in the stock market. People's decisions on this
mater are mostly directed by factors different from the company performance.
These factors include friends and family influence, company and government
announcements, and stock price past behavior.
Table (2) presents the companies performance during the past three
years (2003, 2004, 2005). By comparing these figures, we notice that the
mean of earning per share (EPS) was SR9 in 2003, SR12.19 in 2004 and
SR14.64 in 2005 and similarly the standards deviation increased from 11.68 in
2003 to 16.46 in 2005.
Table (2) shares performance during 2003, 2004, and 2005 Year N Minimum Maximum Mean Std. Deviation
2003 69 -11.49 45.29 9.004058 11.6809273 2004 70 -14.53 65.24 12.18629 14.7141934 Earning
Per Share 2005 73 -11.94 62.59 14.64055 16.46109993
2003 69 0 36.15 6.721884 8.405158828 2004 70 0 36.65 7.557571 8.899705977
Dividend Per Share
2005 73 0 31 6.343288 8.390891234
19
2003 69 0 221.66 25.86928 33.53536642 2004 70 0 1000 80.27586 174.2533933 Price/Earning 2005 73 0 1000 101.5044 200.2505196
Year 2006 was excluded because its data was not comparable due to the
dramatic changes introduced to the market, such as splitting all shares (one to
five).
On the other hand, the mean of dividends per share (DPS) was SR6.72
in 2003, SR7.56 in 2004, and SR 6.34 in 2005. The mean of price/earning
25.89 times in 2003 and heavily increased to 80.28 times in 2004 and
increased again to be more than 100 times in 2005. This clearly shows how
the prices were highly overestimated.
By comparing stock market activities during these three years (in daily
basis) we find that, as presented in table (3), the mean of number of trades was
489.87 in 2003 and increased by more than three times in 2004, and it
decreased to be slightly over 1000 in 2005, and increased again by nearly four
times in 5-2-2006.2 Within two years (2003-2005), the means of trade
volume and value increased by 2.3 times and 3.5 times, respectively.
Table (3) Daily stock trade comparison for three years: 2003, 2004, and 2005
Year N Minimum Maximum Mean Std. Deviation 2003 69 1 4385 489.8696 774.4801846 2004 70 16 12922 507.114 2287.474999 Trades 2005 73 2 9148 006.753 2097.283135
2003 69 100 7066585 533542 1215619.13 2004 70 2254 7119353 692747.2 1317172.025 Volume2005 73 16 4581600 37893.9 548274.0934
2003 69 16000 8.52E+08 9402871 137193022.2 Value 2004 73 18416 9.8E+08 2963232 175318705.4
20
2005 70 1693993 2.65E+09 .11E+08 464378769.9
To investigate the impact of the crisis on the efficient market hypothesis, we
examine the correlation between stock prices (dependent variable) and stock
earnings and dividends (explanatory variables), as shown in the following
mode:4
P = a + b1 EPS + b2 DPS + e
To estimated the above OLS model, we used cross-sectional data for three
years 2004, 2005, 2006. Since the companies financial report take an average
2-3 months to be published to the public, we had to choose the dates after
financial reports were released to measure the impact of disclosed information
on the behavior of stock prices. Table (4) exhibits the regression analysis of
the above estimation:
Table (4) Ordinary Least Square estimation regression results of the first model
2004 2005 2006 (Before the crisis)
2006 (During the crisis)
2006 (After the crisis)
Constant (a) 108.506 157.402 666.213 343.894 74.875 (.000) (.000) (.000) (.000) (.000) EPS (b1) 6.764 21.776 22.377 14.917 1.345 (.005) (.000) (.009) (.010) (.292) DPS (b2) 9.508 -5.398 -1.769 8.063 .764 (.004) (.409) (.913) (.465) (.759) R2 (adjusted) 0.80 0.62 0.20 0.29 0.04 (.000) (.000) (.000) (.000) (.087) Figures in parentheses reflect the significance.
The results exhibited in table 4 show that the constant was slightly over 100 in
2004 and increased by about 50% in 2005 and doubled by more than four
times during 2006 before the crisis and reduced to nearly half during the crisis
and reduced again to be 75 after the crisis. All these constants are significant
21
at the 1% level. EPS had a strong (p < .05) positive effect on the share prices
in years 2004, 2005, and 2006 before and during the crisis but after the crisis,
the EPS became insignificant. The Coefficient of EPS was 6.8 in year 2004
and doubled three times in 2005 and was nearly the same in 2006 before the
beginning of the crisis and declined to reach 1.34 after the crisis ended. DPS
has only significant impact on the share prices in 2004 but after that it became
insignificant and the coefficient even negative in 2005 and 2006 before the
crisis. Adjusted R2, which measure the quality of the model, was high (0.80)
in year 2004 and reduced to 0.62 in year 2005 and reduced further to be as
low as 0.20 in year 2006 before the crisis and 0.04 after the crisis. From this
we may infer that year 2005 witnessed flow in of huge speculators to the stock
market since speculators are not very much concern about future long-run
share performance.
Because EPS and DPS , as found above, might not be always good
predictors of the changes in the share prices, other variables might be better
for forecasting future prices. The daily number of trades, volume, and value
might have good correlation with stock prices. We exam this hypothesis via
regressing stock prices against these variables as in the following model:5
P = b1 log(Trades) + b2 log(Volume) + b3 log(Value) + e
The regression results are revealed in table (5).
Table (5) Ordinary Least Square estimation regression results of the second model
2004 2005 2006 (Before the crisis)
2006 (During the crisis)
2006 (After the crisis)
22
logTrades (b1) 3.062 4.211 3.218 2.629 3.298 (.000) (.000) (.000) (.000) (.000) logVolume (b2) -12.488 -14.301 -11.502 -6.025 -12.158 (.000) (.000) (.000) (.000) (.000) logValue (b3) 10.233 10.844 9.065 4.181 10.271 (.000) (.000) (.000) (.000) (.000) R2 (adjusted) 0.90 0.91 0.87 0.78 0.83 (.000) (.000) (.000) (.000) (.000)
Note that constant was excluded from this model because we assume that the regression line goes through the zero point as long as the explanatory variables equal to zero. Coefficients were standardized. Figure in parentheses reflects the significance.
The relationship between stock prices and number of daily trades is positively
significant. The standardized coefficient of the natural logarithm (logTrades)
was 3.06 in year 4 and increased by 40% in year 2005. In 2006 before the
crisis, it was 3.22 and reduced by about 20% during the crisis and after the
crisis return to near its value before the crisis. The effect of the daily stock
volume on the share prices is negative and significant (p = .000). The biggest
impact was in 2005 (standardized coefficient of logVolume = 14.30) and
reduced to its lowest level (logVolume = 6.03) during the 2006 crisis. In
contrast to the volume effect on stock prices, the value of the daily traded
shares has positive significant relationship with stock prices. The strongest
effect (logValue = 10.84) was in 2005 however during the crisis the effect
reduced by more than 50% (4.18) and returned back to be above 10 after the
crisis. The R adjusted square is relatively significantly high (R2 > 0.75) in all
situation. This properly means that in a developing speculating market such
as Saudi stock market, the daily disclosed data (No. of trades, turnover, and
value) can be better predictors of changes in the stock prices.
Return hypothesis
23
In the long run, the return of stock share should not be different from zero.
Graphs (19), (20) and (21) exhibit this fact. The return of the share index was
fluctuating up and down with the zero line.
Graph (19) Stock market return between 1985 and 2006
ALL INDEX(R)
-3000.00
-2500.00
-2000.00
-1500.00
-1000.00-500.00
0.00
500.00
1000.00
1500.00
07
/03
/19
85
07
/03
/19
86
07
/03
/19
87
07
/03
/19
88
07
/03
/19
89
07
/03
/19
90
07
/03
/19
91
07
/03
/19
92
07
/03
/19
93
07
/03
/19
94
07
/03
/19
95
07
/03
/19
96
07
/03
/19
97
07
/03
/19
98
07
/03
/19
99
07
/03
/20
00
07
/03
/20
01
07
/03
/20
02
07
/03
/20
03
07
/03
/20
04
07
/03
/20
05
07
/03
/20
06
Graph (20) Stock market return for all sectors between 1985 and 2006
-20000.00
-15000.00
-10000.00
-5000.00
0.00
5000.00
10000.00
07/0
3/19
85
07/0
3/19
86
07/0
3/19
87
07/0
3/19
88
07/0
3/19
89
07/0
3/19
90
07/0
3/19
91
07/0
3/19
92
07/0
3/19
93
07/0
3/19
94
07/0
3/19
95
07/0
3/19
96
07/0
3/19
97
07/0
3/19
98
07/0
3/19
99
07/0
3/20
00
07/0
3/20
01
07/0
3/20
02
07/0
3/20
03
07/0
3/20
04
07/0
3/20
05
07/0
3/20
06BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R)ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R)
Graph (21) Stock market return for all sectors between 2001 and 2006
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
04/0
1/20
01
04/0
4/20
01
04/0
7/20
01
04/1
0/20
01
04/0
1/20
02
04/0
4/20
02
04/0
7/20
02
04/1
0/20
02
04/0
1/20
03
04/0
4/20
03
04/0
7/20
03
04/1
0/20
03
04/0
1/20
04
04/0
4/20
04
04/0
7/20
04
04/1
0/20
04
04/0
1/20
05
04/0
4/20
05
04/0
7/20
05
04/1
0/20
05
04/0
1/20
06
04/0
4/20
06
BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R)
ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R)
As it is clear in these graphs, the volatility of stock returns (and prices) was
small until the last three years (2004-2006) when the indexes (General and
24
industrial) started shifting up and down. Since volatility means risk
(Premaratne and Bala 2004), the Saudi stock market is becoming more riskier
than before and because in Saudi society, as measured by Hofstede (1980)'s
dimensions, uncertainty avoidance is higher and most investors in stock
market are risk averse the problem is expected to become worse.
To statistically test for the hypothesis that the mean of the returns for
the whole market and individual industries does not different from zero, we
use both weekly (from March 1985 to May 2006 containing 1077 weekly
observations) and daily (from February 1994 to June 2006 containing 3676
daily observations) data for gross and industrial individual returns. Tables (6)
and (7) confirm that the gross return and individual industrial returns were not
significantly (p > .10) different from zero except in the banking sector where
the return is significantly higher than zero at the 5% when using weekly data
and at the 10% when using daily data.
Table (6) Testing for long-run return equality of zero (weekly data) One-Sample Statistics Test Value = 0 95% Confidence Interv
the Difference
The Index N Mean Std. Deviation
Std. Mean t df Sig.
tailed) Lower Upper
ALL INDEX(R) 1077 8.714 202.321 6.165 1.414 1076 0.158 -3.382 20.811 Banks(r) 1077 27.371 447.004 13.621 2.009 1076 0.045 0.644 54.097 Industrial(r) 1077 18.706 583.016 17.765 1.053 1076 0.293 -16.153 53.564 Cement(r) 1077 5.310 163.798 4.991 1.064 1076 0.288 -4.483 15.104 Services(r) 1077 1.977 131.062 3.994 0.495 1076 0.621 -5.860 9.813 Electricity(r) 1077 0.655 102.344 3.119 0.210 1076 0.834 -5.464 6.775 Telecom(r) 170 17.287 212.642 16.309 1.060 169 0.291 -14.908 49.483 Insurance(r) 66 16.142 164.722 20.276 0.796 65 0.429 -24.352 56.636 Agriculture(r) 1077 3.274 190.360 5.801 0.564 1076 0.573 -8.108 14.656
25
As shown in table 6, Banks' stocks achieved the best performance (average >
27 SR) followed by Industrial sector (18.61SR) and Telecom (17.29SR) whilst
Electricity and Service sectors received the lowest return (< 2 SR). Industrial
stocks are the most riskier return followed by Banking sector whereas
Electricity stocks were the lowest riskier return.
Table (7) Testing for long-run return equality of zero (daily data) One-Sample Statistics Test Value = 0 95% Confidence Interv
the Difference
The Index N Mean Std. Deviation
Std. EMean t df Sig.
tailed) Lower Upper
ALL INDEX(R) 3676 2.800 110.280 1.819 1.540 3675 0.124 -0.766 6.367 Banks(r) 3676 7.388 269.375 4.443 1.663 3675 0.096 -1.323 16.099 Industrial(r) 3674 6.759 332.030 5.478 1.234 3673 0.217 -3.981 17.499 Cement(r) 3676 1.647 145.078 2.393 0.688 3675 0.491 -3.044 6.338 Services(r) 3676 0.744 48.294 0.797 0.934 3675 0.350 -0.818 2.306 Electricity(r) 3674 0.488 42.298 0.698 0.700 3673 0.484 -0.880 1.856 Telecom(R) 1011 3.217 107.683 3.387 0.950 1010 0.342 -3.429 9.862 Insurance(R) 402 3.094 67.770 3.380 0.915 401 0.361 -3.551 9.738 Agriculture(r) 3676 1.415 84.307 1.391 1.018 3675 0.309 -1.311 4.142
By comparing the figures in tables 6 and 7, we notice that the means of stock
returns (gross and individual) are much smaller in the daily return than in the
weekly return and this might means, for the speculators point view, that
keeping the share for a week is better off, but on the other hand, it is riskier
(measured by Std. Deviation).
Summary and Conclusion
Saudi stock market is relatively recent sine stock price index can be only
traced back to 1985 which means that the age of the market is about 22 years.
The number of joint stock companies less than 100 companies representing
26
eight sectors. During 2006, Saudi stock market witnessed severe collapse
since the index lost about 65% of its highest level.
The stock price index started in 1985 with 1000 points and lasted with
no much changes for the following 10 years (In 1995, the index was below
1500 points). Also the index was reasonable for the following seven years (in
2002, the index was below 3000 points). The fast boom stated in the
beginning of year 2003 and the stock price index kept sharply growing
without major breakdowns to reach its top level (over 20600 points) by the
end of February 2006. After that the direction of the index changed to down
to reach 10000 points by May 2006 and then staid over 10000 point for about
five months. By the end of year 2006, the index went down again to below
7000 points and remained below 8000 point during the beginning of 2007.
The stock market index return was almost stable from its initial (1985)
until year 2003 since it started fluctuating to reach its highest variation in year
2006, in practically between February and May. However, the general index
weekly and daily return was not significantly different from zero. The average
of EPS was 9 SR in year 2003 and increased to 12.19 and 14.64 in years 2004
and 2005, respectively. Nevertheless, the mean of DPS in these three years
has not changed much (ranging between 6.34 and 7.56). The mean of
price/earning ratio was 25.87 in year 2003 and jumped to be 80.28 in year
2004 and 101.50 in year 2005 and this may suggest that the heavy increase in
the share prices was not adjusted by increase in earnings which is a sign of
27
danger. The mean of the number of daily trades doubled thrice between 2003
and 2004, while the mean of the volume increased by less than 30% and the
mean of the value of daily traded shares increased from 69.4 millions in 2003
to 211 million in year 2005.
Although each individual market's (banking, manufacturing, cement,
service, electricity, telecom, insurance, and agricultural) index started with
1000 points, they vary in their trends. Both manufacturing and banking
sectors' price indexes increased dramatically to be more than 47 times of its
origin during the month of February 2006 (top levels), followed by
agricultural and cement sectors (> 13 times) whilst the remaining sectors'
indexes total increase were less than 10 times. In terms of the number and
volume of stock daily trades, both manufacturing and service achieved the
largest turnover. Banking sector achieved the highest individual index return
during the last 25 years (daily mean = 7.39 and weekly mean = 27.37).
The cross-sectional regression analysis suggested that EPS and DPS
were good predictors during 2004 (before the influence the speculators on the
market) but less powerful in 2005 and even worse in 2006. On the other
hand, daily number of trades, turnover, and values can help forecasting the
stock prices even during the market crises.
The Saudi stock market was performing sensibly from its formal initial
(1985) until the beginning of year 2003 in which the Saudi Telecom company
was converted into a joint stock company and the conversion was
28
accompanied by huge media acknowledgment which encouraged people, who
were not previously aware of stock market, to invest in the new joint stock
company. During the following two years (2004 and 2005) several companies
were added to the stock market where the majority of the Saudi population
bought from their shares. In the mean time, the idea of entering into stock
market trades was accepted by many people who acted upon and started new
journey. The consequence of this development is the huge increase in the
demand side (buying) which led the share prices to boom until they collapsed.
The responsibility of Saudi stock market crisis is shared by three
parties: government (shares agency), media, and traders themselves. Many
important decisions which should have been taken by TADAWEL on time
either were not made or came late. In addition to this, people should have
been educated either directly or indirectly about when and how to invest in the
stock market. Media played negative role since there was no real warning
about the possible collapse and writers about stock market were not specialist
and indirectly encouraged people to continue speculating in the sock market
even though the share prices were unreasonably high. People, who are the
victims of the crash, are also blamed because they have jumped into the
market to join the crew without rational thinking. In general terms, people in
developing counties do not base their financial decisions on a sensible study
but rather they get advices from their families and friends and also they are
strongly affected by rumors. After the crisis many things were corrected and
29
people now are better aware of the problem and thus we do not expect the
share prices to unreasonably boom again at least during the next few years.
Taking all different factors into account, we expect the stock price general
index to range between 7000 and 9000 points at least for the short-run unless
unexpected even occurs.
Future studies should focus on the cultural impact on stock market and
vise versa. This is particularly important in the developing nations since
culture plays significant role in people's life including the way of thinking and
decision making.
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Endnotes
1 See http://www.tadawul.com.sa 2 We took beginning of February instead of end of march because the crisis started end of
February 2006. 3 See http://www.tadawul.com.sa 4 This model was initially introduced by Gordon (1959). 5 This equation was developed based on previous studies.