Saudi Stock Market Historical View and Crisis Effect:
             Graphical and Statistical Analysis
              Abdulr...
During 2006, the Saudi Stock Market collapsed and the price index lost over

13000 points (65% of its top level). This dis...
investment environment and control. During the last four years, many of the

stock market existing regulations were review...
the political situations, volatility in international trade, and foreign exchanges

have significant negative impact on th...
volatility. Cochran and Mansur used monthly basis and various five-year

intervals from January 1928 through June 2001 and...
From its origin, the index was going down until it reached its lowest level

630.41 (decreased by 37%) during the month of...
3000

    2500

    2000

    1500

    1000

     500

      0
           04 28 20 05 21 06 01 17 02 18 05 28 13 29 14 15...
18000
    16000
    14000
    12000
    10000
     8000
     6000
     4000
     2000
       0
            04
            ...
media kept stressing on this extraordinary event in the stock market and

probably participated on creating fear in the in...
then the volatility began to increase and this year (2006) received the largest

volatility ever. By looking closer to the...
Daily volatility

      1800

      1600

      1400

      1200

      1000

       800

       600

       400

       2...
Graph (11)
Daily total traded share volume from 1-1 to 1-8 year 2006
                                  Total Volume
 60000...
60000



      50000



      40000



      30000



      20000



      10000



            0




                24 /...
10000

     5000

         0
             07/03/1985
                                 28/11/1985
                         ...
Graph (15)
Means of close index prices of all sectors (July 2006)
     35000

     30000

     25000

     20000

     150...
Insurance
                                   0%
                                Te leco m
                                ...
8000000000
         7000000000
         6000000000
         5000000000
         4000000000
         3000000000
         20...
remaining sectors altogether do not represent more than 12% of the total

number of daily trades.

Stock performance and s...
2003        69     0          221.66        25.86928   33.53536642
 Price/Earning      2004        70     0          1000 ...
2005      70       1693993           2.65E+09       .11E+08          464378769.9



To investigate the impact of the crisi...
at the 1% level. EPS had a strong (p < .05) positive effect on the share prices

in years 2004, 2005, and 2006 before and ...
logTrades (b1)     3.062       4.211       3.218             2.629             3.298
                    (.000)      (.000...
In the long run, the return of stock share should not be different from zero.

Graphs (19), (20) and (21) exhibit this fac...
industrial) started shifting up and down.                       Since volatility means risk

(Premaratne and Bala 2004), t...
As shown in table 6, Banks' stocks achieved the best performance (average >

27 SR) followed by Industrial sector (18.61SR...
Saudi Stock Market Historical View and Crisis Effect ...
Saudi Stock Market Historical View and Crisis Effect ...
Saudi Stock Market Historical View and Crisis Effect ...
Saudi Stock Market Historical View and Crisis Effect ...
Saudi Stock Market Historical View and Crisis Effect ...
Saudi Stock Market Historical View and Crisis Effect ...
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Saudi Stock Market Historical View and Crisis Effect ...

  1. 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 atwaijry@lycos.com, atwaijry@yahoo.com 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 1
  2. 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 2
  3. 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 3
  4. 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 4
  5. 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 1400 1200 1000 800 600 400 200 0 28 0 9 2 /1 2 5 5 /1 5 1 0 7 /1 5 1 9 0 /1 5 2 7 2 /1 5 0 8 2 /1 5 2 4 5 /1 6 0 9 7 /1 6 1 8 0 /1 6 2 6 2 /1 6 0 7 2 /1 6 2 3 5 /1 7 0 8 7 /1 7 1 7 0 /1 7 2 5 2 /1 7 0 5 2 /1 7 2 8 5 /1 8 0 6 7 /1 8 1 5 0 /1 8 2 3 2 /1 8 0 4 2 /1 8 1 3 5 /1 9 2 1 7 /1 9 3 0 9 /1 9 0 8 1 /1 9 1 9 2 /1 9 2 8 4 /1 0 1 2 6 /1 0 2 1 9 /1 0 /0 /0 9 8 /0 9 8 /1 9 8 /1 9 8 /0 9 8 /0 9 8 /0 9 8 /1 9 8 /1 9 8 /0 9 8 /0 9 8 /0 9 8 /1 9 8 /1 9 8 /0 9 8 /0 9 8 /0 9 8 /1 9 8 /1 9 8 /0 9 8 /0 9 8 /0 9 8 /0 9 8 /1 9 8 /0 9 8 /0 9 9 /0 9 9 /0 9 9 /1 9 9 1/ 0 19 90 5
  6. 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 2500 2000 1500 1000 500 0 02 24 14 27 11 08 21 03 26 15 29 20 11 24 16 06 19 /0 1/1 /04/1 /08/1 /11/1 /03/1 /07/1 /10/1 /02/1 /05/1 /09/1 /12/1 /04/1 /08/1 /11/1 /03/1 /07/1 /10/1 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 1 1 1 1 2 2 2 3 3 3 3 4 4 4 5 5 5 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 6
  7. 7. 3000 2500 2000 1500 1000 500 0 04 28 20 05 21 06 01 17 02 18 05 28 13 29 14 15 01 16 02 24 11 27 12 28 /0 /0 /0 /0 /1 /0 /0 /0 /1 /1 /0 /0 /0 /1 /0 /0 /0 /0 /1 /0 /0 /0 /1 /1 1/ 3/ 6/ 9/ 1/ 2/ 5/ 7/ 0/ 2/ 3/ 5/ 8/ 0/ 1/ 4/ 7/ 9/ 2/ 2/ 5/ 7/ 0/ 2/ 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 96 96 96 96 96 97 97 97 97 97 98 98 98 98 99 99 99 99 99 00 00 00 00 00 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 7
  8. 8. 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 04 22 1/ 31 3/ 01 09 5/ 01 18 8/ 01 27 0/ 01 07 2/ 01 16 3/ 01 25 5/ 02 03 7/ 02 12 0/ 02 20 2/ 02 01 2/ 02 10 5/ 03 18 7/ 03 04 9/ 03 19 2/ 03 29 2/ 03 08 4/ 04 16 7/ 04 02 9/ 04 17 2/ 04 28 2/ 04 07 4/ 05 15 7/ 05 24 9/ 05 /0 /0 20 /0 20 /0 20 /1 20 /1 20 /0 20 /0 20 /0 20 /1 20 /1 20 /0 20 /0 20 /0 20 /0 20 /1 20 /0 20 /0 20 /0 20 /0 20 /1 20 /0 20 /0 20 /0 20 /0 20 /1 20 1/ 05 20 05 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 25000 20000 15000 10000 5000 0 6 1/ 6 6 6 6 2/ 6 6 6 6 4/ 6 6 6 6 5/ 6 6 6 6 6/ 6 6 6 6 7/ 6 7/ 6 06 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 20 16 /2 24 /2 2 09 /2 18 /2 26 /2 2 14 /2 22 /2 01 /2 2 19 /2 29 /2 07 /2 2 23 /2 31 /2 08 /2 2 27 /2 08 /2 17 /2 2 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 7 /0 01 01 06 10 15 18 26 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 8
  9. 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) 1500 1000 500 0 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 -500 -1000 -1500 -2000 -2500 -3000 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 9
  10. 10. 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 Return 2000 1500 1000 500 0 -500 -1000 -1500 01 07 1/ 19 1/ 06 25 1/ 06 31 1/ 06 06 1/ 06 12 2/ 06 18 2/ 06 23 2/ 06 01 2/ 06 07 3/ 06 13 3/ 06 19 3/ 06 25 3/ 06 01 3/ 06 08 4/ 06 15 4/ 06 22 4/ 06 27 4/ 06 03 4/ 06 09 5/ 06 15 5/ 06 21 5/ 06 27 5/ 06 01 5/ 06 07 6/ 06 13 6/ 06 20 6/ 06 27 6/ 06 04 6/ 06 11 7/ 06 18 7/ 06 25 7/ 06 01 7/ 06 /0 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 /0 20 8/ 06 20 06 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 10
  11. 11. Daily volatility 1800 1600 1400 1200 1000 800 600 400 200 0 19 1/2 6 25 1/2 6 31 1/2 6 06 1/2 6 12 2/2 6 18 2/2 6 23 2/2 6 01 2/2 6 07 3/2 6 13 3/2 6 19 3/2 6 25 3/2 6 01 3/2 6 08 4/2 6 15 4/2 6 22 4/ 6 27 4/2 6 4/ 6 09 5/2 6 15 5/2 6 21 5/2 6 27 5/2 6 01 5/2 6 07 6/2 6 13 6/2 6 20 6/2 6 27 6/2 6 04 6/2 6 11 7/2 6 18 7/2 6 25 7/2 6 01 7/2 6 8/ 6 06 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 0 /0 00 /0 200 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 20 20 07 1/2 /0 01 03 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 800000 700000 600000 500000 400000 300000 200000 100000 0 06 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 006 20 /2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1/ 1 1/ 2/ 2/ 2/ 3/ 3/ 3/ 4/ 4/ 4/ 5/ 5/ 5/ 6/ 6/ 6/ 7/ 7/ 7/ /0 7/0 6/0 5/0 4/0 3/0 5/0 4/0 3/0 3/0 5/0 5/0 4/0 4/0 3/0 1/0 1/0 1/0 3/0 5/0 5/0 01 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 Graph (10) Daily total traded shares value from 1-1 to 1-8 year 2006 Total Turnover 50000000000 45000000000 40000000000 35000000000 30000000000 25000000000 20000000000 15000000000 10000000000 5000000000 0 6 6 2/ 6 6 6 6 6 6 3/ 6 6 6 4/ 6 6 6 5/ 6 6 6 6/ 6 6 08 6/2 6 6 6 7/ 6 6 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 0 20 16 1/2 24 /2 01 1/2 2 18 2/2 26 /2 06 2/2 14 /2 22 3/2 2 10 /2 19 4/2 2 07 4/2 15 /2 2 31 /2 08 5/2 2 27 6/2 17 /2 26 7/2 1 2 3 4 5 5 7 /0 01 09 01 29 23 18 11
  12. 12. Graph (11) Daily total traded share volume from 1-1 to 1-8 year 2006 Total Volume 600000000 500000000 400000000 300000000 200000000 100000000 0 24 1/2 6 01 1/2 6 6 6 6 6 14 3/2 6 22 3/2 6 01 3/2 6 10 4/2 6 6 6 6 6 6 5/ 6 08 5/2 6 18 6/2 6 6 6 17 7/2 6 6 7/ 6 06 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 /0 00 20 16 1/2 09 / 2 18 2/2 26 2/2 06 2/2 19 / 2 29 4/2 07 4/2 15 5/2 23 5/2 2 27 / 2 08 6/2 26 7/2 2 4 6 /0 01 31 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 12
  13. 13. 60000 50000 40000 30000 20000 10000 0 24 /0 05 /0 18 /0 30 /1 19/0 10 /0 22 /0 04 /0 24 /1 14 /0 28 /0 11 /0 22 /0 13/1 02 /0 20 /0 24 /1 16/0 07 /0 19 /0 01 /1 26 /0 16 /0 28 /0 10 /0 22 /1 12 /0 03 /0 16 /0 27 /0 11 /1 07/0 20 /0 01 /0 14 /1 01 /0 26 /0 02 /0 26 /0 09 /1 22 /1 13 /0 04/0 16 /0 30/1 11 /0 02 /0 23 /0 04 /0 17 /1 29 /0 28/0 12 /0 24 /0 07 /1 25 /0 18 /0 30 /0 12 /1 1/2 00 3/2 00 6/2 00 8/2 00 0/2 0 1/2 00 4/20 6/2 00 9/20 1/2 00 2/2 00 4/2 00 7/2 00 9/2 0 2/2 00 3/20 5/2 00 2/1 9 3/1 99 6/1 99 8/1 99 1/1 99 1/2 0 4/2 00 6/2 0 9/2 00 1/2 0 2/2 00 5/2 00 7/2 00 9/2 00 2/2 0 3/2 00 5/2 0 8/2 00 0/20 1/19 5/1 99 7/1 99 0/1 99 2/1 99 3/1 9 6/1 99 8/1 9 0/1 99 1/19 4/1 99 6/1 99 9/1 99 1/1 99 1/1 9 4/1 99 7/1 99 9/1 99 2/19 2/1 99 5/1 99 7/1 9 0/1 99 03 04 04 05 06 98 00 00 00 01 02 02 94 95 95 96 97 97 98 3 3 3 3 4 4 4 5 5 5 5 6 9 9 9 9 0 0 1 1 1 1 2 2 4 4 4 4 5 5 6 6 6 6 7 7 7 8 8 8 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 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 47.7239 48.862 13.18 8.46491 5.44909 6.95211 2.82971 13.34345 original 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. 13
  14. 14. 10000 5000 0 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 -5000 -10000 -15000 -20000 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) 6000 4000 2000 0 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 -2000 -4000 -6000 BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R) -8000 ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R) -10000 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. 14
  15. 15. Graph (15) Means of close index prices of all sectors (July 2006) 35000 30000 25000 20000 15000 10000 5000 0 g ng ity e ce m l t ra en in c co an ri vi ric tu k em tu er an le ul r ct su ac Te S C B ic e In El uf gr an A M 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) 15
  16. 16. Insurance 0% Te leco m 2% Agricultural 12% Banking Electricity 2% 10% Manufa cturing 36% Service 36% C em ent 2% 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) 16
  17. 17. 8000000000 7000000000 6000000000 5000000000 4000000000 3000000000 2000000000 1000000000 0 g l ity g m t ce e ra en in in c co an ic ltu vi r nk m tu tr er le r cu Ce Ba ec su ac Te S ri El In uf Ag an M 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) Service 34% Electricity 3% Cement Telecom 3% 2% Insurance 0% Agricultural 14% Manufacturing 40% Banking 4% 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 17
  18. 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 Earning 2004 70 -14.53 65.24 12.18629 14.7141934 Per Share 2005 73 -11.94 62.59 14.64055 16.46109993 2003 69 0 36.15 6.721884 8.405158828 Dividend Per Share 2004 70 0 36.65 7.557571 8.899705977 2005 73 0 31 6.343288 8.390891234 18
  19. 19. 2003 69 0 221.66 25.86928 33.53536642 Price/Earning 2004 70 0 1000 80.27586 174.2533933 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 Trades 2004 70 16 12922 507.114 2287.474999 2005 73 2 9148 006.753 2097.283135 2003 69 100 7066585 533542 1215619.13 Volume 2004 70 2254 7119353 692747.2 1317172.025 2005 73 16 4581600 37893.9 548274.0934 Value 2003 69 16000 8.52E+08 9402871 137193022.2 2004 73 18416 9.8E+08 2963232 175318705.4 19
  20. 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 2006 (Before 2006 (During 2006 (After 2004 2005 the crisis) the crisis) 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 20
  21. 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 2006 (Before 2006 (During 2006 (After 2004 2005 the crisis) the crisis) the crisis) 21
  22. 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 22
  23. 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) 1500.00 1000.00 500.00 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 0.00 -500.00 -1000.00 -1500.00 -2000.00 -2500.00 -3000.00 Graph (20) Stock market return for all sectors between 1985 and 2006 10000.00 5000.00 0.00 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 -5000.00 -10000.00 -15000.00 -20000.00 BANKS(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 6000 4000 2000 0 4 1 01 4 4 01 4 7 01 4 0 01 4 1 02 4 4 02 4 7 02 4 0 02 4 1 03 4 4 03 4 7 03 4 0 03 4 1 04 4 4 04 4 7 04 4 0 04 4 1 05 4 4 05 4 7 05 4 0 05 4 1 06 4 4 06 0 /0 /2 0 0 /0 /2 0 0 /0 /2 0 0 /1 /2 0 0 /0 /2 0 0 /0 /2 0 0 /0 /2 0 0 /1 /2 0 0 /0 /2 0 0 /0 /2 0 0 /0 /2 0 0 /1 /2 0 0 /0 /2 0 0 /0 /2 0 0 /0 /2 0 0 /1 /2 0 0 /0 /2 0 0 /0 /2 0 0 /0 /2 0 0 /1 /2 0 0 /0 /2 0 0 /0 /2 0 -2000 -4000 -6000 BANKS(R) INDUSTRIAL(R) CEMENT(R) SERVICES(R) -8000 ELECTRICITY(R) Telecom(R) Insurance(R) AGRICULTURE(R) -10000 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 23
  24. 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) 95% Confidence Interv One-Sample Statistics Test Value = 0 the Difference Std. Std. Sig. The Index N Mean t df Lower Upper Deviation Mean tailed) 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 24
  25. 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) 95% Confidence Interv One-Sample Statistics Test Value = 0 the Difference Std. Std. E Sig. The Index N Mean t df Lower Upper Deviation Mean tailed) 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 25

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