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Abstract

Many psychological studies evidence that weather or moon cycles affects human mood, and then their behaviors, many researchers in the field of behavioral finance tend to investigate if there are relationships between weather, lunar phases and stock market variables. This research tests the relationship between stock market variables (such as Trading Volumes) and the weather in Financial center of Syria ; Damascus city and also tests if there is relationship between stock market variables (index returns and trading volumes) and the month of Ramadan in Two Stock Exchanges in the Middle East ,and finally test If the trading volumes differ from the first two weeks (New-moon cycle) in Ramadan Month vs. the next two weeks (full-moon cycle ) in that month .We have come out with the conclusion that trading volumes and market return Index as ( Market Variables ) of DSE1 showed no statistically significant evidence of either a sunny day or a (rainy or cloudy) day effect ,So our findings do not challenge the efficient market hypothesis, and about the effect of Ramadan on trading volume ; we found that there are significant relationship between trading volume in the month of Ramadan and the average of the annual trading volume in certain year. the most important extraction of our study is the negative relationship between the market return index in last 15-day window and the market return index of new 15-day window in the month of Ramadan.

1 DSE ; Damascus Securities Exchange

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Modern finance suggests that financial markets are informational efficient as they instantly

reflect all available information (Fama ,1965).This mean that markets are driven by rational

individuals who make inferences and rational choices based on available information.(Hammami et

al , 2010).The efficient market hypothesis does not allow behavioral influence in the movements

of stock prices. Indeed, If prices respond to purely behavioral and psychological influences (non

related to intrinsic value), then this press the efficient market hypothesis to be questioned.

It is clear that a lot of people tend to be driven by their mood in their deeds and behaviour. Many

psychological studies confirm the fact that depending on the mood individuals are more

predisposed to either pessimistic or optimistic expectations (Arkes et al, 1988; Etzioni, 1988;

Romer, 2000). Therefore, economic agents including investors and stock market traders also

should be influenced by subjective stances (e. g. mood, feelings etc) when making their decisions.

Moreover, weather influences people’s mood in such a way that sunny days are associated with

positive perception of the world and information while rainy or cloudy days are often associated

with depressed mood and pessimism (Cunningham, 1979; Howarth et al., 1984). The

psychological literature argues that people feel happier during sunny days while lack of sunshine

has an opposite effect (Schwarz et al., 1983; Eagles, 1994). Hence, weather can affect stock

market players like any other people in their decisions through psychological channels of mood

and perception. This in turn might impact stock returns as investors are more willing to buy

stocks during sunny weather and are more predisposed to sell if there are bad weather conditions.

This is known as deficient market hypothesis theory that predicts movements of the stock market

based on psychological factors.

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Although there have been several studies on the psychological patterns of investors behavior and

academic research have continued to bear the effects of day of the week, Effects of January, firm

size effect, etc.., there is insufficient attention given to analyze the effects of lunar cycles (new or

full moon) on the stock markets.

Some researchers in the field of behavioral finance have relied on the evidence of existence of

such abnormal behavior during the lunar phase and analyzed statistically the effect of full moon

separately from that of new moon on the stock markets. The attempts of association between

lunar phases and stock returns date for long time.

The research aims to investigate whether there is any significant impact of weather on Trading

volume and Market return index in Damascus Securities Exchange (DWX Index ), and also aims

to study the lunar phase, by choosing Ramadan month, and try to find whether there are any

change in trading volume or not ,and also to study the effect of full moon separately from that of

new moon on the stock markets return in Ramadan . The importance behind this paper comes

from three sides, the first one is the independent variables in our research. As we know the

weather and climate, nowadays, occupies important position of interesting around the world. Of

course this interesting directed to its effects on the economies energy production even the

international monetary fund (IMF) reacted to the weather effects by creating green fund but what

about its effects on Individuals behaviour. This paper finds this way by studying investors

behavior in stock markets.

And about lunar phases effect in financial markets , really the researcher chooses Ramadan month

to study the effects of lunar phase ,because of characteristics of this month which has particular

Physiological effects at least in The Arab and Muslims' Countries.

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The second one comes from the research results itself .The results of our research can be useful

for all sides which means investors brokerage firms ,and the management of financial market itself

;for example it is useful to know for SEC that there is a specific effect of Ramadan on market

performance, so we found many securities exchanges in Middle East changing the opening hours

of trading and that is a clear evidence of the effect of this month. The third importance of our

research comes from the truth that It is the first time In Middle East that someone tries to test

how weather related variables affect financial markets performance such as Damascus securities

exchange (DWX Index ).

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Literature Review

The literature review discusses two sides, the first one dealing with academic literature about the

effect of weather on stock markets variables (like trading volumes, market returns , etc.. ) and the

second one dealing with the effect of lunar phase on the investors mind in stock markets .

Unlike academic literature existed about Ramadan effects or the effect of lunar phases on stock

markets ,there exists a sizable academic literature on the effect of weather on stock returns.

Researchers argue that good weather impacts investors’ mood and they, in turn, might wrongly

attribute positive feelings as such that indicate about favorable prospects of financial markets

(even though it is just a good weather effect). The opposite reasoning holds for bad weather

conditions. Some researchers have found a significant negative relationship between cloud cover

and returns on stocks (Saunders, 1993; Hirshleifer and Shumway, 2003; Chang, Shao-Chi et al.,

2008; Chang, Tsangyao et al. 2006; Dowling et al., 2005; etc). Others, however, argue that there

is no such a relationship and find it to be insignificant ( Loughran et al., 2004; Jacobsen et al.,

2008; Kramer et al., 1997; etc).

Investors in countries of transition could be influenced more by psychological factors than those

in developed economies due to a lower level of development of financial markets. Iarina (2008)

finds that subjective perception of a current situation has the greatest influence on formation of

positive expectations in developing economies and not that great in developed ones. Hence,

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market players in developing economies could be driven by mood and subjective stances with a

higher probability than those in the developed ones. Therefore, the impact of weather on investors

and traders might be higher in Syria and its neighboring countries under consideration than in

European and North American nations being researched so far.

From here, we will first focus on psychological studies about the impact of weather on human

behaviour, then proceed with the overview of the existing literature about the influence of weather

on stock returns, and finally, conclude with a discussion of empirical methods used by researchers.

The efficient market hypothesis is a theory claiming that given rational behaviour of all investors,

current market prices reflect the discounted future cash flows (Fama, 1970). That is market

players account for all possible events in their decision making and set prices accordingly.

However, Hirshleifer (2001) argues that investors are irrational and their decisions are affected by

different subjective factors. This theory is often referred to as Deficient Market Hypothesis. The

main idea of it is that wrong decisions by market participants cause securities to be priced

incorrectly. In this research we are interested in factors that influence investors’ choices such as

climate and weather; and emotions through which these two operate.

In their research Lo et al. (2001) concentrated on the role of emotions in stock market traders’

behaviour and decision-making. They find a significant correlation between psychological stances

and the way markets move (e.g. upward/downward) and claim that emotions improve traders’

performance and ability to adjust to volatile environment. Another research conducted by Ashbury

at al. (1999) also suggests that people in a good mood perform better as they tend to superimpose

current positive outlook on an assignment being carried out at the moment.

Environmental psychology tries to explain how surroundings affect human behaviour. Weather is

one of the main factors that influence a person’s mood and the way one feels. Experiments of Bell

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et al. (2003) have shown that cold makes people be more predisposed to sadness and melancholy

but its influence is slight and almost insignificant. Scientists (Bell et al., 2003) argue that heat, on

the other hand, has a strong negative impact on human behaviour and claim that violence

increases rapidly during the high temperature periods of a year.

Psychologists also say that people become more optimistic during sunny weather and more

pessimistic during rainy or cloudy days (Eagles, 1994; Rind, 1996). Good mood and positive

outlook in turn positively affect the perception of reality and future. (Herren et al., 1988). Such a

positive feeling affects people’s decisions that are usually made in accord with their mood

(Schwarz, 1990). Thus, investors that are in a good mood are inclined to invest in riskier projects

as they believe in a success of their ventures (Herren et al., 1988).

The pioneer in the field of exploring the impact of weather on stock returns was Saunders (1993)

who investigated how local weather affects New-York City Exchange indices. The author found a

strong negative relationship between cloud cover and returns on stocks. Later on Hirshleifer and

Shumway (2003) have repeated Saunder’s research but using data on stock indices of 26

countries. They have confirmed previous developments and found that sunshine has a significant

positive correlation with stock returns.

However, they have concluded that using weather as a determinant of a pattern of stock trades is

efficient only for low transaction costs investors. Similar results are obtained for the Irish Stock

Market by Dowling and Lucey (2005). These authors have found that a ‘rain’ variable is

significant while estimating the model of weather effects on stock returns.

The impact of weather on trading volume is researched by Loughran et al. (2004) who have found

a negative relationship between the amount of blizzard strokes and trading volumes. Chang et al.

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(2008) research the extent to which cloud cover influenced spread measures of New-York Stock

Exchange during 1994-2004 and conclude that there is little correlation between these variables.

A critical view on Saunder’s findings is expressed by Kramer and Runde (1997). They used

German stock index data and found that local weather does not affect short-term stock returns.

Also Loughran and Schultz (2004) argue that it is better to use data on local weather in home-

cities of investors listed on New-York City Exchanges in Saunder’s study. That is due to the fact

that many investors are located in different parts of the USA even though they trade on New-

York Stock Market. Loughran and Schultz (2004) have found no significant relationship between

the local weather in the homecity of a company and stock returns. Pardo et al. (2003) confirmed

the above argument and found no effect of weather variables (sunshine and humidity) on stock

returns for Madrid Stock Exchange. Worthington (2006) came to the same conclusion using

Australian stock market data as well as Tufan (2004) using data for the Istanbul Stock Exchange.

It is argued by Chang et al. (2008) that it is the intraday weather pattern that influences investors’

behaviour. They have found that cloud cover affects the returns on stocks only at the beginning of

the trading day, specifically, only during the first 12-15 minutes of the working day. They explain

this finding by the fact that traders and investors are impacted by the weather conditions only on

their way to work and, then, while at the office they do not really feel the weather influence due to

the presence of air-conditioners and lack of windows (as is most probably the case). Hence, the

effect of cloud cover declines very quickly.

Locke et al. (2007) suggest that traders’ afternoon behaviour is influenced by morning weather

more than by the weather during other parts of the day. In this research authors found that wind

causes the effective bid-ask spread to widen. They explain this conclusion claiming that imbalance

caused by the wind affects the mood of market players and, as a consequence, market quotes.

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Whether there is any effect of temperature on stock returns was examined by Cao and Wei

(2005). Researchers argue that aggressive behaviour is often a result of low temperature while

both apathy and aggression can be consequences of high temperature. Therefore, they have

hypothesized negative relationship between stock returns and temperature and have actually found

it to be significant.

Using Taiwanese stock market data Chang et al. (2006) have confirmed an existence of a

significant relationship between temperature, cloud cover and stock returns. They included

temperature as an explanatory variable into their model and found that stock returns are higher

when the temperature is within normal bounds; though, they tend to be lower when it is extremely

hot or cold and when the cloud cover is heavier.

The most recent paper of Jacobsen et al. (2008) argue there is no negative effect of temperature

on stock returns, though authors do not reject that there is a strong seasonal anomaly: stock

returns appear to be lower in summer and autumn and higher in winter and spring months. Thus,

seasonality issue is closely related to our debate about the weather impact on stock returns. One

has to account for so-called calendar effects that are thought by some researchers to have a

significant impact on financial markets. In general, winter and spring months are associated with

higher stock market returns than summer and autumn months (Bouman et al., 2002).

Saunders (1993) argues that market usually shows an upward movement in January as investors’

activity increases due to holiday rush. Others explain the significance of January effect by the fact

that at the end of a tax year (December) prices tend to decrease, but then rise again during the

first month of a new year (Al-Khazali et al., 2008). Scientists explain existence of calendar

anomalies due to errors in data and methods used to evaluate the impact, as well as due to micro-

market and information flow effects (Pettengill, 2003) As for the estimation techniques, linear

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models are mostly used to trace the effect of weather on stock returns (Saunders, 1993;

Hirshleifer and Shumway, 2003, Dowling et al., 2005, Krämer et al., 1997). Some researchers use

GARCH technique (Chang et al., 2006).

The literature examined above can be divided into two categories: the one that finds that weather

affects stock returns, and the other that argues that there is no such an effect. However, none of

these research studied middle east financial markets.

Thus, this paper aims at checking if Damascus securities exchange (DSE) falls under the category

of weather sensitive or weather proof ones. An effect of weather on stock market variables is

expected to be found, because psychological factors are expected to impact investors more in

countries with less developed financial markets like Syria.

Although there have been several studies on the psychological patterns of investors behavior and

academic research have continued to bear the effects of day of the week, Effects of January, firm

size effect, etc.., there is insufficient attention given to analyze the effects of lunar cycles (new or

full moon) on the stock markets.

Some researchers in the field of behavioral finance have relied on the evidence of existence of

such abnormal behavior during the lunar phase and analyzed statistically the effect of full moon

separately from that of new moon on the stock markets.

The attempts of association between lunar phases and stock returns date for long time. Rotton

and Rosenberg (1984) seek in a research paper the relationship between lunar phases and the

average closing price of the Dow Jones index, but they find no significant relationships.

The study by Dichev and Janes (2003) differs from Rotton and Rosenberg (1984) insofar as it

examines the returns of the indices rather than prices. In addition, autocorrelation are corrected in

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the series of returns, which gives a more accurate test of the relationship. Dichev and Janes

(2003) reported a significant lunar effect on stock returns.

They focused initially on the U.S. market by studying the association of the lunar effect with

returns of the four major U.S. stock indices, the Dow-Jones Industrial Average (1896-1999), the

Standards & Poors 500 (1928 -2000), NYSE-AMEX (1962-2000), and NASDAQ (1973-2000).

The authors found that the stock returns of all the above cited indices are substantially higher

around the new moon days than around the full moon days as shown in the following figure:

Figure 1: New Moon Versus Full Moon Annualized Returns for Four Major U.S. Stock Indexes

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(Source : Dichev and Janes (2003), p13.)

Specifically, the annual difference between stock returns of new moon days and full moon days is

at 5 to 8%, and probably exceeds the market risk premium. Then, the results of the American

context have prompted the authors to expand the research in an international context using data

from 24 countries mainly covers major markets in the world. The authors find that the effect of

lunar cycles observed in the U.S. market is repeated and it is stronger on other international

markets. Specifically, the new moon returns are higher than those of full moon and for 23 among

24 countries studied, and the average annual difference varies between 7% and 10%. In addition,

the combination of U.S. and international data enabled the authors to construct powerful

statistical tests that reject the null hypothesis assuming no difference in performance between

phases of full and new moon, and this at a high level of statistical significance. However, Dichev

and Janes (2003) have found that the effect of lunar cycles is not statistically significant on the

returns volatility, on transaction volumes, bond returns, and on interest rates changes.

Yuan et al (2006) reported either a significant lunar effect on the stock returns. Their results are

complementary to those found by Dichev et al. (2003). Yuan et al (2006) have examined the

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relationship between lunar phases and stock returns of 48 countries in the world, which

constitutes a more comprehensive and more powerful test. In order to formulate their hypothesis,

Yuan et al (2006) have followed the arguments of Hirshleifer and Shumway (2003) meaning that

good mood is associated with higher stock returns. The results of Yuan et al (2006) are

significant and conform to the presumed assumptions as shown in the following figure:

Figure 2: Average daily logarithmic return of the global portfolio (48 countries) by lunar dates

(Source: Yuan et al.(2006) p :10.)

As shown above, the results of Yuan et al. (2006) indicate that stock returns are lower in the days

surrounding a full moon than the days around a new moon. The magnitude of the difference in

returns is about 3% to 5% per year based on the analysis of two portfolios: one equal-weighted

and the other value-weighted. They found that the returns difference is not due to changes in

market volatility or trading volumes. Their data also show that the lunar effect is not explained by

the announcements of macroeconomic indicators, nor caused by major global shocks. They also

found that the lunar effect is independent of other “anomalies” of the calendar effect, such as the

January effect, the day-of-the-week effect, and the holiday effect. To check the strength of the

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lunar effect, Yuan et al. (2006) use different lengths of the lunar window, alternative ARIMA

specifications, and a random test cycle of 30 days. In all, their result indicates that the lunar effect

on stock returns is robust.

Chandy et al. (2007) have also analyzed the return behavior on market index of the most famous

American (NYSE / AMEX, NASDAQ and S & P500). In contrast with the results found by

Dichev et al. (2003) and Yuan et al (2006), Chandy et al. (2007) showed merely that markets

aren’t influenced by either the full or new moon, and therefore support the efficient market theory.

The authors advise the portfolio managers and investors on the stock market not to expect to

obtain abnormal profit opportunities by giving attention to the lunar cycle.

For its part, Herbst (2007) examined the influence of lunar phases on the performance and

volatility of the Dow Jones Industrial Average (DJIA) from 1980 to 2004. He found only small

effects that are not compatible with the assumptions presupposed and are not predictable only by

information and lunar calendar.

Therefore, Herbst (2007) states that no significant differences for measures of returns or volatility

is observed when all lunar events are considered. like those of Chandy et al. (2007), strongly

supports the concept of rational market and the efficient market theory, which maintains that

prices should be determined by fundamental factors and new events rather than behavioral

influences such as those induced by the cycles lunar.

Dowling and Lucey (2005), from their study of the influence of weather and biorhythms change

on stock returns, among others have studied the effect of lunar cycles. However, they found a

positive relationship and statistically significant. The most recent paper of Hammami et al. (2010)

tested whether the New-moon cycle stock returns are higher than those of a full moon cycle or

not, on the Tunisian stock market in four window specifications from 15-day to 1-day windows

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around the full and the new moon days. They found that the stock market indexes examined

showed globally no statistically significant evidence of either a full moon or a new moon effect.

The literature examined above can be divided into two categories: the one that finds that lunar

phases affect stock returns, and the other that argues that there is no such an effect. However,

none of these researchers studied Ramadan effect and the effect of lunar phases on the market

return in this month.

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Theoretical Framework

The efficient market hypothesis is a theory claiming that given rational behaviour of all investors,

current market prices reflect the discounted future cash flows (Fama, 1970). That is market

players account for all possible events in their decision making and set prices accordingly.

However, Hirshleifer (2001) argues that investors are irrational and their decisions are affected by

different subjective factors. This theory is often referred to as Deficient Market Hypothesis. The

main idea of it is that wrong decisions by market participants cause securities to be priced

incorrectly. In this research we are interested in factors that influence investors’ choices such as

climate and weather; Ramadan effects through which these two operate.

The efficient markets hypothesis (EMH) has been the central proposition of finance for nearly

thirty years. In his classic statement of this hypothesis, Fama (1970) defined an efficient

financial market as one in which security prices always fully reflect the available information.

The efficient markets hypothesis then states that real-world financial markets, such as the U.S.

bond or stock market, are actually efficient according to this definition. The power of this

statement is dazzling. Perhaps most radically, the (EMH) 'rules out the possibility of trading

systems based only on currently available information that have expected profits or returns in

excess of equilibrium expected profit or return' (Fama ,1970).

In the first decade after its conception in the 1960s, the EMH turned into an enormous

theoretical and empirical success. Academics developed powerful theoretical reasons why the

hypothesis should hold. More impressively, a vast array of empirical findings quickly

emerged—nearly all of them supporting the hypothesis. Indeed, the field of academic finance in

general, and security analysis in particular, was created on the basis of the (EMH) and its

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applications. In 1978, Michael Jensen—a Chicago graduate and one of the creators of the

EMH—declared that 'there is no other proposition in economics which has more solid empirical

evidence supporting it than the Efficient Markets Hypothesis' (Jensen 1978, p. 95).

In the last twenty years, both the theoretical foundations of the (EMH) and the empirical

evidence purporting to support it have been challenged. The key forces by which markets are

supposed to attain efficiency, such as arbitrage, are likely to be much weaker and more limited

than the efficient markets theorists have supposed. Moreover, new studies of security prices

have reversed some of the earlier evidence favoring the (EMH).

With the new theory and evidence, behavioral finance has emerged as an alternative view of

financial markets. In this view, economic theory does not lead us to expect financial markets to

be efficient. Rather, systematic and significant deviations from efficiency are expected to persist

for long periods of time. Empirically, behavioral finance both explains the evidence that appears

anomalous from the efficient markets perspective, and generates new predictions that have been

confirmed in the data.

We will describe both the theoretical and the empirical foundations of the EMH, as well as

some of the theoretical and empirical cracks that have emerged in these foundations, and will

discuss the three Form of EMH , then we will discuss the behavioral finance concept as an

alternative view of financial behavioral.

The Theoretical Foundations of The (EMH) :

The basic theoretical case for the (EMH) rests on three arguments which rely on progressively

weaker assumptions. First, investors are assumed to be rational and hence to value securities

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rationally. Second, to the extent that some investors are not rational, their trades are random and

therefore cancel each other out without affecting prices. Third, to the extent that investors are

irrational in similar ways, they are met in the market by rational arbitrageurs who eliminate their

influence on prices. When investors are rational, they value each security for its fundamental

value: the net present value of its future cash flows, discounted using their risk characteristics.

When investors learn something about fundamental values of securities, they quickly respond to

the new information by bidding up prices when the news is good and bidding them down when

the news is bad. As a consequence, security prices incorporate all the available information

almost immediately and prices adjust to new levels corresponding to the new net present values

of cash flows.

But remarkably, the EMH does not live or die by investor rationality. In many scenarios where

some investors are not fully rational, markets are still predicted to be efficient. In one commonly

discussed case, the irrational investors in the market trade randomly. When there are large

numbers of such investors, and when their trading strategies are uncorrelated, their trades are

likely to cancel each other out. In such a market, there will be substantial trading volume as the

irrational investors exchange securities with each other, but the prices are nonetheless close to

fundamental values. This argument relies crucially on the lack of correlation in the strategies of

the irrational investors, and, for that reason, is quite limited. The case for the EMH, however,

can be made even in situations where the trading strategies of investors are correlated.

This case, as made by Milton Friedman (1953) and Fama (1965), is based on arbitrage. It is one

of the most intuitively appealing and plausible arguments in all of economics. A textbook

definition (Sharpe and Alexander 1990) defines arbitrage as 'the simultaneous purchase and sale

of the same, or essentially similar, security in two different markets at advantageously different

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prices.' Suppose that some security, say a stock, becomes overpriced in a market relative to its

fundamental value as a result of correlated purchases by unsophisticated, or irrational, investors.

This security now represents a bad buy, since its price exceeds the properly risk adjusted net

present value of its cash flows or dividends. Noting this overpricing, smart investors, or arbitra-

geurs, would sell or even sell short this expensive security and simultaneously purchase other,

'essentially similar,' securities to hedge their risks. If such substitute securities are available and

arbitrageurs are able to trade them, they can earn a profit, since they are short expensive

securities and long the same, or very similar, but cheaper securities. The effect of this selling by

arbitrageurs is to bring the price of the overpriced security down to its fundamental value. In

fact, if arbitrage is quick and effective enough because substitute securities are readily available

and the arbitrageurs are competing with each other to earn profits, the price of a security can

never get far away from its fundamental value, and indeed arbitrageurs themselves are unable to

earn much of an abnormal return. A similar argument applies to an undervalued security. To

earn a profit, arbitrageurs would buy underpriced securities and sell short essentially similar

securities to hedge their risk thereby preventing the under pricing from being either substantial

or very long-lasting. The process of arbitrage brings security prices in line with their

fundamental values even when some investors are not fully rational and their demands are

correlated, as long as securities have close substitutes.

Arbitrage has a further implication. To the extent that the securities that the irrational investors

are buying are overpriced and the securities they are getting rid of are underpriced, such

investors earn lower returns than either passive investors or arbitrageurs. Relative to their peers,

irrational investors lose money. As Friedman (1953) points out, they cannot lose money forever:

they must become much less wealthy and eventually disappear from the market. If arbitrage

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does not eliminate their influence on asset prices instantaneously, market forces eliminate their

wealth. In the long run, market efficiency prevails because of competitive selection and

arbitrage.

It is difficult not to be impressed with the full range and power of the theoretical arguments for

efficient markets. When people are rational, markets are efficient by definition. When some

people are irrational, much or all of their trading is with each other, and hence has only a limited

influence on prices even without countervailing trading by the rational investors. But such

countervailing trading does exist and works to bring prices closer to fundamental values.

Competition between arbitrageurs for superior returns ensures that the adjustment of prices to

fundamental values is very quick. Finally, to the extent that the irrational investors do manage to

transact at prices that are different from fundamental values, they only hurt themselves and bring

about their own demise. Not only investor rationality, but market forces themselves bring about

the efficiency of financial markets.

The Empirical Foundations of The (EMH):

Strong as the theoretical case for the (EMH) may seem, the empirical evidence that appeared in

the 1960s and the 1970s was even more overwhelming. At the most general level, the empirical

predictions of the (EMH) can be divided into two broad categories.

First, when news about the value of a security hits the market, its price should react and

incorporate this news both quickly and correctly. The ''quickly'' part means that those who

receive the news late should not be able to profit from this information. The ''correctly'' part

means that the price adjustment in response to the news should be accurate on average: the

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prices should neither under react nor overreact to particular news announcements. There should

be neither price trends nor price reversals following the initial impact of the news.

Second, since a security's price must be equal to its value, prices should not move without any

news about the value of the security. That is, prices should not react to changes in demand or

supply of a security that are not accompanied by news about its fundamental value. The quick

and accurate reaction of security prices to information, as well as the non-reaction to non-

information, is the two broad predictions of the efficient markets hypothesis.

The principal hypothesis following from quick and accurate reaction of prices to new

information is that stale information is of no value in making money, as Fama (1970) points out.

To evaluate this hypothesis empirically, researchers needed to define (stale information) and

(making money). The first definition turns out to be relatively straightforward.

Fama distinguishes between three types of stale information, giving rise to three forms of the

(EMH). For the so-called weak form efficiency, the relevant stale information is past prices and

returns. The weak form (EMH) posits that it is impossible to earn superior risk-adjusted profits

based on the knowledge of past prices and returns. Under the assumption of risk neutrality, this

version of the (EMH) reduces to the random walk hypothesis, the statement that stock returns

are entirely unpredictable based on past returns (Fama 1965).

Past returns are not the only stale information that investors have. The semi-strong form of the

(EMH) states that investors cannot earn superior risk-adjusted returns using any publicly

available information. Put differently, as soon as information becomes public, it is immediately

incorporated into prices, and hence an investor cannot gain by using this information to predict

returns. A semi-strong form efficient market is obviously weak form efficient as well, since past

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prices and returns are a proper subset of the publicly available information about a security.

It is still possible that while an investor cannot profit from trading on publicly available

information, he can still earn abnormal risk-adjusted profits by trading on information that is not

yet known to market participants, sometimes described as inside information. The strong form

of the (EMH) states that even these profits are impossible because the insiders' information

quickly leaks out and is incorporated into prices. To be fair, most evaluations of the (EMH)

have focused on weak and semi-strong form efficiency, and have not taken the extreme position

that there is no such thing as profitable insider trading, as would be required if the strong form

(EMH) were to hold. Indeed, the insider traders occupying minimum security prisons for

making illegal profits themselves represent some evidence against the strong form (EMH). But

there is more systematic evidence as well that insiders earn some abnormal returns even when

they trade completely legally (Seyhun 1998, Jeng et al. 1999).

Theoretical Challenges to The (EMH) :

The (EMH) was challenged on both theoretical and empirical grounds. Although the initial

challenges were primarily empirical, it is easier to begin by reviewing some potential difficulties

with the theoretical case for the (EMH) and then turn to the evidence.

To begin, it is difficult to sustain the case that people in general, and investors in particular, are

fully rational. At the superficial level, many investors react to irrelevant information in forming

their demand for securities; as Fischer Black (1986) put it, they trade on noise rather than

information. Investors follow the advice of financial gurus, fail to diversify, actively trade stocks

and churn their portfolios, sell winning stocks and hold on to losing stocks thereby increasing

their tax liabilities, buy and sell actively and expensively managed mutual funds, follow stock

24

price patterns and other popular models. In short, investors hardly pursue the passive strategies

expected of uninformed market participants by the efficient markets theory.

This evidence of what investors actually do is only the tip of the iceberg. Investors' deviations

from the maxims of economic rationality turn out to be highly pervasive and systematic. As

summarized by Kahneman and Riepe (1998), people deviate from the standard decision making

model in a number of fundamental areas. We can group these areas, somewhat simplistically,

into three broad categories: attitudes toward risk, non-Bayesian expectation formation, and

sensitivity of decision making to the framing of problems.

First, individuals do not assess risky gambles following the precepts of von Neumann-

Morgenstern rationality. Rather, in assessing such gambles, people look not at the levels of final

wealth they can attain but at gains and losses relative to some reference point, which may vary

from situation to situation, and display loss aversion a loss function that is steeper than a gain

function. Such preferences first described and modeled by Kahneman and Tversky (1979) in

their 'Prospect Theory' are helpful for thinking about a number of problems in finance. One of

them is the notorious reluctance of investors to sell stocks that lose value, which comes out of

loss aversion (Odean 1998). Another is investors' aversion to holding stocks more generally,

known as the equity premium puzzle (Mehra and Prescott 1985, Benartzi and Thaler 1995).

Second, individuals systematically violate Bayes rule and other maxims of probability theory in

their predictions of uncertain outcomes (Kahneman and Tversky 1973). For example, people

often predict future uncertain events by taking a short history of data and asking what broader

picture this history is representative of. In focusing on such representativeness, they often do

not pay enough attention to the possibility that the recent history is generated by chance rather

25

than by the 'model' they are constructing. Such heuristics are useful in many life situations—

they help people to identify patterns in the data as well as to save on computation—but they

may lead investors seriously astray. For example, investors may extrapolate short past histories

of rapid earnings growth of some companies too far into the future and therefore overprice these

glamorous companies without a recognition that, statistically speaking, trees do not grow to the

sky. Such overreaction lowers future returns as past growth rates fail to repeat themselves and

prices adjust to more plausible valuations.

Perhaps most radically, individuals make different choices depending on how a given problem

is presented to them, so that framing influences decisions. In choosing investments, for

example, investors allocate more of their wealth to stocks rather than bonds when they see a

very impressive history of long-term stock returns relative to those on bonds, than if they only

see the volatile short-term stock returns (Benartzi and Thaler 1995).

A number of terms have been used to describe investors whose preferences and beliefs conform

to the psychological evidence rather than the normative economic model. Beliefs based on

heuristics rather than Bayesian rationality are sometimes called 'investor sentiment.' Less

kindly, the investors whose conduct is not rational according to the normative model are

described as 'unsophisticated' or, following Kyle (1985) and Black (1986), as 'noise traders.'

If the theory of efficient markets relied entirely on the rationality of individual investors, then

the psychological evidence would by itself present an extremely serious, perhaps fatal, problem

for the theory. But of course it does not. Recall that the second line of defense of the efficient

markets theory is that the irrational investors, while they may exist, trade randomly, and hence

their trades cancel each other out. It is this argument that the Kahneman and Tversky theories

26

dispose of entirely. The psychological evidence shows precisely that people do not deviate

from rationality randomly, but rather most deviate in the same way. To the extent that

unsophisticated investors form their demands for securities based on their own beliefs, buying

and selling would be highly correlated across investors. Investors would not trade randomly

with each other, but rather many of them would try to buy the same securities or to sell the same

securities at roughly the same time. This problem only becomes more severe when the noise

traders behave socially and follow each others' mistakes by listening to rumors or imitating their

neighbors (Shiller 1984). Investor sentiment reflects the common judgment errors made by a

substantial number of investors, rather than uncorrelated random mistakes.

Individuals are not the only investors whose trading strategies are difficult to reconcile with

rationality. Much of the money in financial markets is allocated by professional managers of

pension and mutual funds on behalf of individual investors and corporations. Professional

money managers are of course themselves people, and as such are subject to the same biases as

individual investors. But they are also agents who manage other people's money, and this

delegation introduces further distortions into their decisions relative to what fully-informed

sponsors might wish (Lakonishok et al. 1992). For example, professional managers may choose

portfolios that are excessively close to the benchmark that they are evaluated against, such as

the S&P 500 Index, so as to minimize the risk of underperforming this benchmark. They may

also herd and select stocks that other managers select, again to avoid falling behind and looking

bad (Scharfstein and Stein 1990). They may artificially add to their portfolios stocks that have

recently done well, and sell stocks that have recently done poorly, to look good to investors

who are getting end-of-year reports on portfolio holdings. There indeed appears to be some

27

evidence of such window-dressing by pension fund managers (Lakonishok et al. 1991).

Consistent with the presence of costly investment distortions, pension and mutual fund

managers on average underperform passive investment strategies (Ippolito 1989, Lakonishok et

al. 1992). In some situations, they may be the relevant noise traders.

This brings us to the ultimate set of theoretical arguments for efficient markets, those based on

arbitrage. Even if sentiment is correlated across unsophisticated investors, the arbitrageurs—

who perhaps are not subject to psychological biases—should take the other side of

unsophisticated demand and bring prices back to fundamental values. Ultimately, the

theoretical case for efficient markets depends on the effectiveness of such arbitrage.

The central argument of behavioral finance states that, in contrast to the efficient markets

theory, real-world arbitrage is risky and therefore limited. As we already noted, the effectiveness

of arbitrage relies crucially on the availability of close substitutes for securities whose price is

potentially affected by noise trading. To lay off their risks, arbitrageurs who sell or sell short

overpriced securities must be able to buy 'the same or essentially similar' securities that are not

overpriced. For some so-called derivative securities, such as futures and options, close

substitutes are usually available, although arbitrage may still require considerable trading. For

example, the S&P 500 Index futures typically sell at a price close to the value of the underlying

basket of stocks, since if the future sells at a price different from the basket, an arbitrageur can

always buy whichever is cheaper and sell whichever is more expensive against it, locking in a

safe profit. Yet in many instances, securities do not have obvious substitutes. Thus arbitrage

does not help to pin down price levels of, say, stocks and bonds as a whole (Figlewski 1979,

Campbell and Kyle 1993). These broad classes of securities do not have substitute portfolios,

28

and therefore if for some reason they are mispriced, there is no riskless hedge for the

arbitrageur. An arbitrageur who thinks that stocks as a whole are overpriced cannot sell short

stocks and buy a substitute portfolio, since such a portfolio does not exist. The arbitrageur can

instead simply sell or reduce exposure to stocks in the hope of an above-market return, but this

arbitrage is no longer even approximately risk-less, especially since the average expected return

on stocks is high and positive (Siegel 1998). If the arbitrageur is risk-averse, his interest in such

arbitrage will be limited. With a finite risk-bearing capacity of arbitrageurs as a group, their

aggregate ability to bring prices of broad groups of securities into line is limited as well.

Even when individual securities have better substitutes than does the market as a whole,

fundamental risk remains a significant deterrent to arbitrage. First, such substitutes may not be

perfect, even for individual stocks. An arbitrageur taking bets on relative price movements then

bears idiosyncratic risk that the news about the securities he is short will be surprisingly good,

or the news about the securities he is long will be surprisingly bad. Suppose, for example, that

the arbitrageur is convinced that the shares of Ford are expensive relative to those of General

Motors and Chrysler. If he sells short Ford and loads up on some combination of GM and

Chrysler, he may be able to lay off the general risk of the automobile industry, but he remains

exposed to the possibility that Ford does surprisingly well and GM or Chrysler do surprisingly

poorly, leading to arbitrage losses. With imperfect substitutes, arbitrage becomes risky. Such

trading is commonly referred to as 'risk arbitrage,' because it focuses on the statistical

likelihood, as opposed to the certainty, of convergence of relative prices.

There is a further important source of risk for an arbitrageur, which he faces even when

securities do have perfect substitutes. This risk comes from the unpredictability of the future

29

resale price or, put differently, from the possibility that mispricing becomes worse before it

disappears. Even with two securities that are fundamentally identical, the expensive security

may become even more expensive and the cheap security may become even cheaper. Even if

the prices of the two securities ultimately converge with probability one, the trade may lead to

temporary losses for an arbitrageur. If the arbitrageur can maintain his positions through such

losses, he can still count on a positive return from his trade. But sometimes he cannot maintain

his position through the losses. In the cases where arbitrageurs need to worry about financing

and maintaining their position when price divergence can become worse before it gets better,

arbitrage is again limited. This type of risk, which De Long et al. (1990s) dubbed 'noise trader

risk,' shows that even an arbitrage that looks nearly perfect from the outside is in reality quite

risky and therefore likely to be limited. As a consequence, the arbitrage-based theoretical case

for efficient markets is limited as well—even for securities that do have fundamentally close

substitutes.

An example may help illustrate the idea of risky and limited arbitrage. Consider the case of

American stocks, particularly the large capitalization stocks, in the late 1990s. At the end of

1998, large American corporations were trading at some of their historically highest market

values relative to most measures of their profitability. For example, the ratio of the market value

of the S&P 500 basket of stocks to the aggregate earnings of the underlying companies stood at

around 32, compared to the post-war average of 15. Both distinguished financial economists,

such as Campbell and Shiller (1998) and leading policy makers such as Federal Reserve

Chairman Alan Greenspan, called attention to these possibly excessive valuations of large

capitalization American stocks as early as 1996. But their warnings are contradicted by the

30

assessments of the new financial gurus, such as Abby Joseph Cohen of Goldman Sachs, who

argue that large American companies are operating in a new world of faster growth and lower

risk and hence rationally warrant higher valuations. In 1929, Irving Fisher similarly argued that

'stock prices have reached a new and higher plateau,' just before the market tanked and the

economy plunged into the Great Depression.

But what is an arbitrageur to do? If he sold short the S&P 500 Index at the beginning of 1998,

when the price earnings multiple on the Index was at an already high level of 24, he would have

suffered a loss of 28.6 percent by year end. In fact, if he sold short early on when the experts got

worried, at the beginning of 1997, he would have lost 33.4 percent that year before losing

another 28.6 percent the next. If he followed a more sophisticated strategy of selling short the

S&P 500 at the beginning of 1998 and buying the Russell 2000 Index of smaller companies as a

hedge on the theory that their valuations by historical standards were not nearly as extreme, he

would have lost 30.8 percent by the end of the year. Because the S&P 500 Index does not have

good substitutes and relative prices of imperfect substitutes can move even further out of line,

arbitrage of the Index is extremely risky. An arbitrageur who tried to exploit this apparent

mispricing is unlikely still to be in business. Not surprisingly, very few arbitrageurs or even

speculators have put on such trades. In the meantime, the puzzle of the overvaluation of large

stocks as well as the market as a whole has only deepened.

Once it is recognized that arbitrage is risky, Friedman's selection arguments become

questionable. When both noise traders and arbitrageurs are bearing risk, the expected returns of

the different types depend on the amount of risk they bear and on the compensation for the risk

that the market offers. Moreover, even if the average returns of the arbitrageurs exceed those of

31

the noise traders, the former are not necessarily the ones more likely to get rich, and the latter

impoverished, in the long run. Consider two illustrations. First, if the misjudgments of the noise

traders cause them to take on more risk, and if risk taking is on average rewarded with higher

average returns, then the noise traders may earn even higher average returns despite their

portfolio selection errors. Second, there is an 'optimal' amount of risk taking for long-run

survival. A risk-neutral investor, for example, may earn very high expected returns, but end up

bankrupt with near certainty (Merton and Samuelson 1974). Some types of noise traders may

have as good as or better chances of maintaining their wealth above a certain level in the long

run as the arbitrageurs, simply because they bear the superior amount of risk from the

perspective of survival. The point here is not to make descriptive statements, but rather to point

out that the theoretical case for the irrelevance of irrationality for financial markets is far from

watertight.

The bottom line is that the theory by itself does not inevitably lead a researcher to a

presumption of market efficiency. At the very least, theory leaves a researcher with an open

mind on the crucial issues.

Empirical challenges to the (EMH):Chronologically, the empirical challenges to the EMH

have preceded the theoretical ones. An early and historically important challenge is Shiller's

(1981) work on stock market volatility, which showed that stock market prices are far more

volatile than could be justified by a simple model in which these prices are equal to the

expected net present value of future dividends. Shiller computed this net present value using a

constant discount rate and some specific assumptions about the dividend process, and his work

became a target of objections that he misspecified the fundamental value (e.g., Merton 1987).

32

Nonetheless, Shiller's work has pointed the way to a whole new area of research.

Consider first the weak form EMH: the proposition that an investor cannot make excess profits

using past price information. De Bondt and Thaler (1985) compare the performance of two

groups of companies: extreme losers and extreme winners. For each year since 1933, they form

portfolios of the best and the worst performing stocks over the previous three years. They then

compute the returns on these portfolios over the five years following portfolio formation. The

results on the average performance of these loser and winner portfolios, presented in Figure 1,

point to extremely high post-formation returns of extreme losers and relatively poor returns of

extreme winners. This difference in returns is not explained by the greater riskiness of the

extreme losers, at least using standard risk adjustments such as the Capital Asset Pricing Model.

An alternative explanation of this evidence, advanced by De Bondt and Thaler, is that stock

prices overreact: the extreme losers have become too cheap and bounce back, on average, over

the post-formation period, whereas the extreme winners have become too expensive and earn

lower subsequent returns.

33

Figure 3: Cumulative average residuals for winner and loser portfolios of 35 stocks (1-36

months into the test period). Source: De Bondt and Thaler (1985).

This explanation fits well with psychological theory: the extreme losers are typically companies

with several years of poor news, which investors are likely to extrapolate into the future, thereby

undervaluing these firms, and the extreme winners are typically companies with several years of

good news, inviting overvaluation.

Subsequent to De Bondt and Thaler's findings, researchers have identified more ways to

successfully predict security returns, particularly those of stocks, based on past returns. Among

these findings, perhaps the most important is that of momentum (Jegadeesh and Titman 1993),

which shows that movements in individual stock prices over the period of six to twelve months

tend to predict future movements in the same direction. That is, unlike the long-term trends

identified by De Bondt and Thaler, which tend to reverse themselves, relatively short-term

trends continue, but until now suffice it to say that even the weak form efficient markets

34

hypothesis has faced significant empirical challenges in recent years. Even Fama (1991) admits

that stock returns are predictable from past returns and that this represents a departure from the

conclusions reached in the earlier studies.

The semi-strong form efficient markets hypothesis has not fared better. Perhaps the best known

deviation is that, historically, small stocks have earned higher returns than large stocks.

Between 1926 and 1996, for example, the compounded annual return on the largest decile of

the New York Stock Exchange stocks has been 9.84 percent, compared to 13.83 percent on the

smallest decile of stocks (Siegel 1998, p. 93). Moreover, the superior return to small stocks has

been concentrated in January of each year, when the portfolio of the smallest decile of stocks

outperformed that of the largest decile by an average of 4.8 percent. There is no evidence that,

using standard measures of risk, small stocks are that much riskier in January. Since both a

company's size and the coming of the month of January is information known to the market, this

evidence points to excess returns based on stale information, in contrast to semi-strong form

market efficiency. More recent research uncovered other variables that predict future returns.

Suppose that an investor selects his portfolio using the ratio of the market value of a company's

equity to the accounting book value of its assets. The market to book ratio can be loosely

thought of as a measure of the cheapness of a stock. Companies with the highest market to book

ratios are relatively the most expensive 'growth' firms, whereas those with the lowest ratios are

relatively the cheapest 'value' firms. For this reason, investing in low market to book companies

is sometimes called value investing. Following De Bondt and Thaler's logic, the high market to

book ratios may reflect the excessive market optimism about the future profitability of

companies resulting from overre-action to past good news. Consistent with overreaction, De

35

Bondt and Thaler (1987), Fama and French (1992), and Lakonishok et al. (1994) find that,

historically, portfolios of companies with high market to book ratios have earned sharply lower

returns than those with low ratios. Moreover, high market to book portfolios appear to have

higher market risk than do low market to book portfolios, and perform particularly poorly in

extreme down markets and in recessions (Lakonishok et al. 1994).

The size and market to book evidence, on the face of it, presents a serious challenge to the

EMH, because stale information obviously helps predict returns, and the superior returns on

value strategies are not due to higher risk as conventionally measured. Yet this evidence again

has been subjected to a radical version of the Fama critique. Fama and French (1993, 1996)

ingeniously interrpret both a company's market capitalization and its market to book ratio as

measures of fundamental riskiness of a stock in a so-called three-factor model. According to this

model, stocks of smaller firms must earn higher average returns precisely because they are

fundamentally riskier as measured by their higher exposure to size and market to book 'factors.'

Conversely, large stocks earn lower returns because they are safer, and growth stocks with high

market to book ratios also earn lower average returns because they represent hedges against this

market to book risk.

It is not entirely obvious from the Fama and French analysis how either size or the market to

book ratio, whose economic interpretations are rather dubious in the first place, have emerged

as heretofore unnoticed but critical indicators of fundamental risk, more important than the

market risk itself. Fama and French speculate that perhaps the low size and market to book ratio

proxy for different aspects of the 'distress risk,' but up to now there has been no direct evidence

in support of this interpretation, and indeed Lakonishok et al. (1994) find no evidence of poor

36

performance of value strategies in extremely bad times. The fact that the small firm effect has

disappeared in the last 15 years, and before that was concentrated in January, also presents a

problem for the risk interpretation. And even Fama and French do not offer a risk interpretation

of the momentum evidence. Chapter 5 revisits some of this evidence and related controversies,

and offers a behavioral analysis.

Finally, what about the basic proposition that stock prices do not react to non-information? Here

again there has been much work, but three types of findings stand out. Perhaps the most salient

piece of evidence bearing on this prediction is the crash of 1987. On Monday, October 19, the

Dow Jones Industrial Average fell by 22.6 percent—the largest one day percentage drop in

history—without any apparent news. Although the event caused an aggressive search for the

news that may have caused it, no persuasive culprit could be identified. In fact, many sharp

moves in stock prices do not appear to accompany significant news. Cutler et al. (1991)

examine the 50 largest one day stock price movements in the United States after World War 2,

and find that many of them came on days of no major announcements. This evidence is, of

course, broadly consistent with Shiller's earlier finding of excess volatility of stock returns.

More than news seems to move stock prices.

Many of the results described here have been challenged on a variety of grounds, including data

snooping, trading costs, sample selection biases, and most centrally improper risk adjustment.

Nonetheless, it is difficult to deny that the thrust of this evidence is very different from what

researchers found in the 1960s and the 1970s, and is much less favorable to the EMH. An

interesting question is why have researchers failed to report much evidence challenging market

efficiency until 1980? One possible explanation is the professional dominance of the EMH

37

supporters, and the difficulty of publishing rejections of the EMH in academic journals. This

explanation is not entirely satisfactory, since there are many competing journals in finance and

economics aiming to publish novel findings. A more plausible, and scientifically more

satisfactory, account of the failure to find contradictory evidence is provided by Summers

(1986). Summers argues that many tests of market efficiency have low power in discriminating

against plausible forms of inefficiency. He illustrates this observation by showing that it is often

difficult to tell empirically whether some time series, such as the value of a stock index, follows

a random walk or alternatively a mean-reverting process that might come from a persistent fad.

It takes a lot of data, and perhaps a better theoretical idea of what to look for, before researchers

can find persuasive evidence. Whatever the reason why it took so long in practice, the

cumulative impact of both the theory and the evidence has been to undermine the hegemony of

the EMH and to create a new area of research—behavioral finance. This book describes some of

the theoretical and empirical foundations of this research.

Behavioral Finance : At the most general level, behavioral finance is the study of human

fallibility in competitive markets. It does not simply deal with an observation that some people

are stupid, confused, or biased. This observation is uncontroversial, although understanding the

precise nature of biases and confusions is an enormously difficult task. Behavioral finance goes

beyond this uncontroversial observation by placing the biased, the stupid, and the confused into

competitive financial markets, in which at least some arbitrageurs are fully rational. It then

examines what happens to prices and other dimensions of market performance when the

different types of investors trade with each other. The answer is that many interesting things do

happen. In particular, financial markets in most scenarios are not expected to be efficient.

38

Market efficiency only emerges as an extreme special case, unlikely to hold under plausible

circumstances.

As a study of human fallibility in competitive markets, behavioral finance theory rests on two

major foundations. The first is limited arbitrage. This set of arguments suggests that arbitrage in

real-world securities markets is far from perfect. Many securities do not have perfect or even

good substitutes, making arbitrage fundamentally risky and, even when good substitutes are

available, arbitrage remains risky and limited because prices do not converge to fundamental

values instantaneously. The fact that arbitrage is limited helps explain why prices do not

necessarily react to information by the right amount and why prices may react to non-

information expressed in uninformed changes in demand. Limited arbitrage thus explains why

markets may remain inefficient when perturbed by noise trader demands, but it does not tell us

much about the exact form that inefficiency might take. For that, we need the second foundation

of behavioral finance, namely investor sentiment: the theory of how real-world investors

actually form their beliefs and valuations, and more generally their demands for securities.

Combined with limited arbitrage, a theory of investor sentiment may help generate precise

predictions about the behavior of security prices and returns.

Both of these elements of behavioral theory are necessary. If arbitrage is unlimited, then

arbitrageurs accommodate the uninformed shifts in demand as well as make sure that news is

incorporated into prices quickly and correctly. Markets then remain efficient even when many

investors are irrational. Without investor sentiment, there are no disturbances to efficient prices

in the first place, and so prices do not deviate from efficiency. So a behavioral theory thus

requires both an irrational disturbance and limited arbitrage which does not counter it.

39

Hypotheses

This research tests the following six hypotheses:

The First Main Hypothesis:

H0 : there is no statistically significant relation between the financial markets variables, and the

weather related variables.

H1 : there is statistically significant relation between the financial markets variables, and the

weather related variables .

This main hypothesis can be divided into the following four hypotheses :

The First Hypothesis:

• H0:There is no statistically significant relation between the sunny days and the trading

volume on that days .

• H1: There is statistically Significant relation between the sunny days and the trading

volume on that days .

The Second One:

• H0:there is no statistically significant relation between the sunny days and market return

index on that days.

40

• H1: There is statistically Significant relation between the sunny days and market return

index on that days.

The Third One:

• H0: There is no statistically significant relation between the (Rainy and\or Cloudy) days

and the trading volume on that days .

• H1: There is statistically Significant relation between the (Rainy and\or Cloudy) days

and the trading volume on that days .

The Fourth One:

• H0: There is no statistically significant relation between the (Rainy and\or Cloudy) days

and the market return index on that days.

• H1: There is statistically Significant relation between the (Rainy and\or Cloudy) days

and the market return index on that days .

The Second main Hypothesis:

H0: There is no statistically significant relation between trading volume in Ramadan ,and the

average of annual trading volume in that year.

H1: There is statistically significant relation between trading volume in Ramadan ,and the

average of annual trading volume in that year.

The third main Hypothesis:

41

H0: There is statistically significant positive relationship between the market return index in

last 15-day window and the return of new 15-day window in Ramadan month.

H1: There is statistically significant negative relationship between the market return index in

last 15-day window and the return of new 15-day window in Ramadan month.

Methodology

Data and methodology that adopted in studying the effects of weather on stock markets consists

of two kinds of data from the Damascus Securities Exchange (DSE) database and Damascus /Al-

Maza Syrian State Meteorological Service database. The first one of these data sets includes

trading Volume and values of (DWX) for 14 months .The second data set consists of observed

(cloudy, sunny, rainy) days variables for Damascus city. Both data sets have 135 observations and

cover from April 2, 2009 to April 29, 2010.

This data set is collected from Damascus /Al-Maza Syrian State Meteorological Service database

and includes Cloudy/Rainy days variables, and also includes Sunny variables. The measure that

used to determine if the day is sunny or cloudy is an accurate device that determines the daily

sunny hours of the total sunshine's hours a day, so if the sunny hours equal to 50% or more of

sunshine's hours on a day with maximum sunshine hours on a day equal to 13.5 hours in summer

and about 11 hours in winter ,so we can consider that day as a sunny day and vice versa .In this

paper, we dealing with rainy and cloudy as the same, so the researcher divide the weather in two

categories ; (cloudy and/or rainy) and (sunny) .

42

In literature The Meteorological Service makes observation of the data three times in a day with

naked eyes and ranked it from zero to ten. Zero is indicated the lowest cloudiness, which it also

means the highest sunny light where ten, indicates the highest cloudiness.

In this study, It has used non-parametric method ; Kruskal and Wallis Test (H-test) . In literature,

researchers usually use regression method such as Dowling and Brain (2002). On the other hand,

there are also some researchers that use parametric and non-parametric methods such as Kramer

(1997) and Pardo (2002).

The Data and methodology that adopted in studying whether there are relation or not between

trading volume in Ramadan month and the average of annual trading volume consists of two kinds

of data from both Abu Dhabi stock exchange and Amman stock market. The first set of data

includes the annual trading volume for 4 years in each stock market from 2006 to 2009, and the

second set of data includes daily trading volume in Ramadan month (as a lunation) which expands

over two calendar months.

The number of Ramadan days is 30 days during the time series and about number of trading

session during this month in both stock market can be seen the table (1).

Trading session 2006 2007 2008 2009

Amman SE 20 21 21 20

Abu Dhabi SE 20 20 21 20

Table1 Number of trading session in Ramadan in both stock markets

Finally, Two kinds of data are used the relation between market return data (market Indexes) in

Ramadan month for Abu Dhabi stock exchange and Amman stock market and the timing of lunar

43

cycle’s calendar for 4 started from 2006 to 2009 . Table (2) determines the start and the end date

of Ramadan in Jordan and UAE over 4 years.

Ramada

n

2006 2007 2008 2009

Start End Start End Start End Start End

Jordan 24 Sep 19 Oct 13 Sep 11 Oct 1 Sep 30 Sep 23 Aug 17 Sep

UAE 24 Sep 19 Oct 13 Sep 10 Oct 1 Sep 30 Sep 23 Aug 17 Sep

Table 2 The Exact date of Ramadan in both Jordan and U.A.E .

As we can see ; Ramadan in our situation extends over 3 months (August , September and

October ) which means that makes the effects of many “anomalies” of the calendar effect less,

such as the January effect.

We divide Ramadan in two 15-day windows ( New-moon and Full- moon cycle ) as it illustrated

in figure (3).

New Moon Full Moon New Moon

The First 15-day Window The Second 15-day Window

Figure (3) Illustration of 15 days window

To determine the market return of each 15-day Window, we calculate the difference between the

value of index on the first day of window and the last day of window (which is the 11th trading

44

session for the first 15-day window and the last trading session of Ramadan for the second 15-day

window ) .

Results and Discussion

We chose non-parametric method in this study because we found that the distribution of our

sample isn't normal distribution, further more the independent variables of our study is nominal

variable (sunny, cloudy/rainy).We checked if data distribution was normal or not by using two

methods; the first one was graphical methods for describing quantitative data (trading volume),we

were constructing histogram as shown in the figure (4) and figure (5).

The second one was calculating skewness and kurtosis Coefficients with confidence level equal to

95, by using the data in table (4), we found that the significant level of skewness factor equal to

0.409 which is less than 1.452, which means that the distribution curve is not normal distributed

,also the significant level of kurtosis factor equal to 0.563 which is less than 0.81144 .

By using H-test (Kruskal -Wallis one way analysis of variance ), we were dealing with weather

variable as a grouping variable and trade volume as a test variable as it clear in table (11).after

analysis we found P-value as it clear in table (5) which is equal to 0.575 bigger than 0.05 , hence

we accept the Null-hypothesis , which means that there is no statistically significant relation

between the weather and the trading volume. We do H-test too but by using market return index

45

as a test variable and we also found P-value as it clear in table (6) which is equal to 0.704 bigger

than 0.05,which means that there is no statistically significant relation between the weather and

the market return index, hence we accept the Null-hypothesis .

But using H-test (Kruskal -Wallis Test) helps us to determine whether there are significant

relation or not between the grouping variable and test variables, without determine the strength of

this insignificant relations, may be that is the reason behind calculating Theta factor manually as it

illustrated in Appendix, and we found theta factor θ= 0.1578, which means that the strength of

relation between trading volume and weather conditions equal to 15,78% . also the strength of

relation between market return index and weather but just for (47) observations equal to 26.43% .

To test the assumption about the trading volume of Ramadan vs. arithmetic mean of annual

trading volume, we calculate the total trading volume of Ramadan for each year from 2006 to

2009 in both Abu Dhabi stock exchange and Amman stock market, as it illustrated in table (9) .

We found that two of eight observations refer to that trading Volume in month of Ramadan

exceeds the average of annual trading Volume in that year, and the maximum of this growing

equal to 10.6%, and six of eight observations refer that trading Volume in Ramadan less than the

average of annual trading Volume in that year, and the maximum of this Decreasing ratio equal to

-40.8 %. We test the correlation (two tail-test ) between trading volume in Ramadan and the

average of annual trading volume in that year. We found P-value as it clear in table (7) which is

equal to 0.0 less than 0.01,which means that there is statistically significant relation between

trading volume in Ramadan and the average of annual trading volume in that year, further more

Person correlation equals to 0.969,which means that the relationship in two directions between

trading volume in Ramadan and the average of annual trading volume in that year is positive and

strong hence we reject the Null-hypothesis , and accept the hypothesis that said ; there is

46

statistically significant relationship between the trading volume of Ramadan and the average of

Annual trading Volume in a certain year .

To test the Relation between the full moon cycle market return and the new moon cycle market

return in month of Ramadan , we calculate the return of first 15-day window and last 15-day

window for each year from 2006 to 2009 as it determined in table(10),the summary of this table is

clear in table(11).We test the correlation (One tail-test ) between the return in last 15-day window

and the return of new 15-day window in month of Ramadan. As it clear from table (8), P-value

equals to 0.044 less than 0.05, and Person correlation equals to -0.641 which means that there is

statistically significant negative relationship between the return in last 15-day window and the

return in first 15-day window in the month of Ramadan.

47

Conclusion

In this study it is being investigated if there relation between the weather related variables and the

Market variables, which is DWX index return and the trading volume in DSE .We found that

there is no statistically effective relation between( cloudy or/and rainy, sunny ) days and the

market return or trading volume , and we also test if there are relation between the trading volume

return in Ramadan vs. the average annual return in the same year, but with data from Abu Dhabi

and Amman securities markets ,and we found that there are statistically significant relationship

between the trading volume return in Ramadan and. the average annual return in the same year.

Finally we test if the lunar phase affects the market return index but in Ramadan month only by

dividing this month into two 15-day Window (New moon and full moon window) ,and we found

that there is statistically significant negative relationship between the return in last 15-day window

and the return in first 15-day window in the month of Ramadan.

Our research don't mention the amount of foreign investments in the Arab Countries financial

markets when we studied the effects of weather related variables effects or Ramadan effects, so it

will be useful to dealing with this matter in future.

48

It is fair to say that there are many limitations of the results of this research, such as the shortness

( Damascus securities exchange started in march 2009), so it will be better to study the effect of

the weather on stock market variables (index returns and trading volumes) over more longer

period than now.

Appendix

(Figures)

49

Figure (3) Describing the trading volume during 135 days (Observations)

Figure (4)Describing the trading volume in each of sunny and cloudy days

Note:

Throughout this research , the terms ‘emotions’, ‘moods’, ‘feelings’ and ‘affect’ are used interchangeably, as the distinction between these terms is not consistent in the psychology literature. The general distinction between the terms is that :

Are defined as lasting for a very short period of time and being directed at an object. Emotions :

Are defined as being longer lasting than emotions, not directed at anything in particular, and of a lower intensity than emotions.

Moods :

Are general terms used to describe either emotions or moods. Oatley & Jenkins (1996) give further information on these distinctions.

Feelings & Affect:

Resource: ( Feelings in investors decision-making ,2005,Blackwell Publishing Ltd.)

50

Calculating Thera factor : By using H-test (Kruskal -Wallis one way analysis of variance ),we found that there is no statistically significant relation between dependent and independent variables, but for more specific we calculate Thera factor to determine the strength of relation between the trading volume as dependent variables and trading volume or market index return as independent variables as the following: The TERA equation is:

Which; Chi-Square value derived from table 5.3 n refers to the number of weather observations. K refers to the Number of groups ;here we have sunny and rainy\cloudy group .

Which; Chi-Square value derived from table 6.2 n refers to the number of Market Return Index observations. K refers to the Number of groups ;here we have sunny and rainy\cloudy group .

= 15.75%

=26.43%

51

Appendix

Tables

Descriptive Statistics

N Mean Std. Deviation Minimum Maximum

Market Index Return 48 5.3735 6.12981 -7.31- 16.98

weather 135 1.2000 .40149 1.00 2.00

Table 4.1 Descriptive Statistic of Market return and weather Variables

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

trade

volume

135 419 89873 21521.60 18143.945 1.452 .209 2.437 .414

weather 135 1.00 2.00 1.2000 .40149 1.517 .209 .305 .414

Valid N

(listwise)

135

Table 4.2 skewness and kurtosis Coefficients with confidence level equal to 95

Kruskal-Wallis Test (1): Descriptive Statistics

N Mean Std. Deviation Minimum Maximum

Trade volume 135 21521.60 18143.945 419 89873

weather 135 1.2000 .40149 1.00 2.00

Table 5.1 Descriptive Statistic of Trading Volume and weather Variables

52

Table 5.2

Test Statisticsa,b

Trade volume

Chi-Square .315

df 1

Asymp. Sig. .575

a. Kruskal Wallis Test

b. Grouping Variable: weather Table 5.3

Kruskal-Wallis Test(2):

Ranks

weather N Mean Rank

Market Index Return sunny 37 24.92

rainy or\and cloudy 11 23.09

Total 48 Table 6.1

Test Statisticsa,b

Market Index

Return

Chi-Square .145

df 1

Asymp. Sig. .704

a. Kruskal Wallis Test

b. Grouping Variable: weather Table 6.2

Ranks

weather N Mean Rank

Trade volume sunny 108 68.94

rainy or\and cloudy 27 64.22

Total 135

53

Correlations

Trading Volume

in Ramadan

Annual Trading

Volume in

average

Trading Volume in Ramadan Pearson Correlation 1 .969**

Sig. (2-tailed) .000

N 8 8

Annual Trading Volume in

average

Pearson Correlation .969** 1

Sig. (2-tailed) .000

N 8 8

**. Correlation is significant at the 0.01 level (2-tailed). Table 7

Correlations

The Last 15- day

window Market

rate of return

The First 15- day

window Market

rate of return

The Last 15- day window

Market rate of return

Pearson Correlation 1 -.641-*

Sig. (1-tailed) .044

N 8 8

The First 15- day window

Market rate of return

Pearson Correlation -.641-* 1

Sig. (1-tailed) .044

N 8 8

*. Correlation is significant at the 0.05 level (1-tailed). Table 8

54

Annual Trading Volume of Abu Dhabi Securities Exchange Year Annual Trading Volume Annual Trading in Average

٣٣٣,٦٩٥,٦١٠ ٤,٠٠٤,٣٤٧,٣٢١ ٢٠٠٦

٣٤١,٦٣٤,٢٨٢ ٤,٠٩٩,٦١١,٣٨٩ ٢٠٠٧

٤٢٨,٥٣٧,٧٢١ ٥,١٤٢,٤٥٢,٦٦١ ٢٠٠٨

٤٧٨,٨٥٧,٣٠٣ ٥,٧٤٦,٢٨٧,٦٤٦ ٢٠٠٩

Annual Trading Volume of Amman Securities Exchange Year Annual Trading Volume Annual Trading in Average

٩٤١,٣٤١,٢٢١ ١١,٢٩٦,٠٩٤,٦٥٦ ٢٠٠٦

٤,٣٣٨,٨٥٤,٥٢١ ٥٢,٠٦٦,٢٥٤,٢٥٧ ٢٠٠٧

٤,١٦٥,٣٢٥,١١٣ ٤٩,٩٨٣,٩٠١,٣٥٣ ٢٠٠٨

٣,١٣٤,٦٥٥,٨٦٠ ٣٧,٦١٥,٨٧٠,٣٢٣ ٢٠٠٩

Table ( 9.1) Annual trading Volume in average for both Amman and Abu Dhabi Securities

Exchange

55

Trading Volume Summary of Amman Securities Exchange Difference Annual Trading in average Ramadan trading Volume

+ 2% 333,695,610 340,241,080 2006

-14.4% 341,634,282 292,342,276 2007

- 28% 428,537,721 308,299,363 2008

+10.6% 468,857,303 529,727,615 2009

Table (9.2) Ramadan trading volume vs. Average for Amman stock market.

Trading Volume Summary of Abu Dhabi Securities Exchange Difference Annual Trading in average Ramadan trading Volume

- 40.8% 941,341,221.4 556,945,145 2006

-12.7% 4,338,854,521 3,788,522,694 2007

- 39% 4,165,325,113 2,533,624,121 2008

-11.8% 3,134,655,860 2,766,514,062 2009

Table (9.3) Ramadan trading volume vs. Average for Abu Dhabi stock market.

56

Trading Volume Summary of Amman Securities Exchange

Month of Ramadan\ 2006 \ Month of Ramadan\ 2007 \

No. Date Trading Volume No. Date Trading Volume

1 ١٣,١٠٧,٤٨٧ ٢٠٠٧\٩\١٣ ١ ١٧,٨٢٠,٣٣٥ ٢٠٠٦\٩\٢٤

١١,٥٥٧,٥٣٧ ٢٠٠٧\٩\١٦ ٢ ٢١,٠٩٦,٧٣٨ ٢٠٠٦\٩\٢٥ ٢

١٥,٧٣٩,١٦٧ ٢٠٠٧٩\\١٧ ٣ ١٤,١٦٦,٢١٠ ٢٠٠٦\٩\٢٦ ٣

١٤,٨١٥,٤٧٥ ٢٠٠٧\٩\١٨ ٤ ١٥,٧٤٢,١٦٦ ٢٠٠٦\٩\٢٧ ٤

١٢,٩٩٢,٥٦٠ ٢٠٠٧\٩\١٩ ٥ ٢١,٠٨٥,١١٣ ٢٠٠٦\٩\٢٨ ٥

١٤,٢٧٧,٧٢٣ ٢٠٠٧\٩\٢٠ ٦ ١٥,٣٨٣,٦٠٩ ٢٠٠٦\١٠\١ ٦

١٣,٦٢٣,٠٠٨ ٢٠٠٧\٩\٢٣ ٧ ١٥,٣٦٢,٠٩٤ ٢٠٠٦\١٠\٢ ٧

١١,٨١٦,٥٦٤ ٢٠٠٧\٩\٢٤ ٨ ٢١,٥٨٧,٩٧٣ ٢٠٠٦\١٠\٣ ٨

١٧,٠٤٥,٧٦٤ ٢٠٠٧\٩\٢٥ ٩ ١٩,٥٣٢,٩٨١ ٢٠٠٦\١٠\٤ ٩

١٥,٦٢٦,٤٧٨ ٢٠٠٧\٩\٢٦ ١٠ ١٨,٠٨٥,١٢٦ ٢٠٠٦\١٠\٥ ١٠

١١,٢٩٩,٥١٠ ٢٠٠٧\٩\٢٧ ١١ ١٥,٣٩٤,٦٤٧ ٢٠٠٦\١٠\٨ ١١

١٢,٠٤٧,٩٧٨ ٢٠٠٧\٩\٣٠ ١٢ ١٥,٢٣٢,٦٥١ ٢٠٠٦\١٠\٩ ١٢

٩,١١٣,١٨٦ ٢٠٠٧١٠\\١ ١٣ ١٨,٠٦٨,٨٩٤ ٢٠٠٦\١٠\١٠ ١٣

١٢,٤٣٦,٩٧٩ ٢٠٠٧\١٠\٢ ١٤ ١٤,٦٨١,٨١٣ ٢٠٠٦\١٠\١١ ١٤

١٦,٦٦٦,٣٥٠ ٢٠٠٧\١٠\٣ ١٥ ١٦,٣٦٤,٨٨٧ ٢٠٠٦\١٠\١٢ ١٥

١٥,٣٨٩,٦٠٨ ٢٠٠٧\١٠\٤ ١٦ ١٤,٥٦١,١٥٧ ٢٠٠٦\١٠\١٥ ١٦

١٦,٥٨٣,١٤١ ٢٠٠٧\١٠\٧ ١٧ ١١,٨٤٣,٦٨٨ ٢٠٠٦\١٠\١٦ ١٧

١٢,٦٥٣,٤٣١ ٢٠٠٧\١٠\٨ ١٨ ١٤,٢٣٠,١٠٦ ٢٠٠٦\١٠\١٧ ١٨

١١,٨٤٦,٤١٢ ٢٠٠٧\١٠\٩ ١٩ ١٩,٣٨٩,٥٠٧ ٢٠٠٦\١٠\١٨ ١٩

١٤,٢٧٢,٩١٧ ٢٠٠٧ \١٠\١٠ ٢٠ ٢٠,٦١١,٣٨٥ ٢٠٠٦\١٠\١٩ ٢٠

-- ----- ------- ١٩,٩٣١,٠٠١ ٢٠٠٧\١٠\١١ ٢١

Total 340,241,080 Total ٢٩٢,٣٤٢,٢٧٦

Table (9.4) Ramadan Trading Volume for Amman Securities Exchange during 2006 and 2007

57

Trading Volume Summary of Amman Securities Exchange

Month of Ramadan \2008\ Month of Ramadan \2009\

No. Date Trading Volume No. Date Trading Volume

1 ١٢,٢٦٥,٤٢٨ ٢٠٠٩\٨\٢٣ ١ ١٣,١٢٩,١٨٨ ٢٠٠٨\٩\١

١٠,٩٢٥,٥٨١ ٢٠٠٩\٨\٢٤ ٢ ١٥,٥٢٧,٧١٧ ٢٠٠٨\٩\٢ ٢

١٣,٦٠٠,٥٦٨ ٢٠٠٩\٨\٢٥ ٣ ١٦,٤٥٢,٩١٦ ٢٠٠٨\٩\٣ ٣

١٩,٧٤٠,٨٩٤ ٢٠٠٩\٨\٢٦ ٤ ١٥,٩٥٣,٨٥٩ ٢٠٠٨\٩\٤ ٤

٢٢,٧٥٤,٣٢٠ ٢٠٠٩\٨\٢٧ ٥ ١٣,٤٠١,٩٤١ ٢٠٠٨\٩\٧ ٥

٢٢,٨٠٨,١٩٠ ٢٠٠٩\٨\٣٠ ٦ ٩,٠٥١,٠٥٧ ٢٠٠٨\٩\٨ ٦

١٩,٦٢٢,٦١٥ ٢٠٠٩\٨\٣١ ٧ ١٧,٧٧٧,٤٣١ ٢٠٠٨\٩\٩ ٧

٢٢,٤٦١,٩٨١ ٢٠٠٩\٩\١ ٨ ١٥,٣٥٩,٩٣٧ ٢٠٠٨\٩\١٠ ٨

٣٣,٠٦٩,٥٠٨ ٢٠٠٩\٩\٢ ٩ ١٥,٣١٥,٣٤٩ ٢٠٠٨\٩\١١ ٩

٣١,٧٤٠,٩٦٧ ٢٠٠٩\٩\٣ ١٠ ٩,٣٦٣,٠١٣ ٢٠٠٨\٩\١٤ ١٠

٢٩,١٠٦,٦٠٨ ٢٠٠٩\٩\٦ ١١ ١٤,٤٦٢,٨٩٧ ٢٠٠٨\٩\١٥ ١١

٤٦,٢٠١,١٦٩ ٢٠٠٩\٩\٧ ١٢ ١٥,١٠٦,٢٨٢ ٢٠٠٨\٩\١٦ ١٢

٤١,٠٦٨,٢١٤ ٢٠٠٩\٩\٨ ١٣ ٢٠,١١٢,٥٠٧ ٢٠٠٨\٩\١٧ ١٣

٢٢,٧٣٤,٩٦٧ ٢٠٠٩\٩\٩ ١٤ ١٥,٧١١,٨٦٢ ٢٠٠٨\٩\١٨ ١٤

٣٥,٧٠١,٢٠٤ ٢٠٠٩\٩\١٠ ١٥ ١٧,٠٦٦,٣٢٨ ٢٠٠٨\٩\٢١ ١٥

٣١,٣٧٤,٨٩٨ ٢٠٠٩\٩\١٣ ١٦ ١٢,٧٢٢,١٠٠ ٢٠٠٨\٩\٢٢ ١٦

٣٧,٤١٥,٧٩٨ ٢٠٠٩\٩\١٤ ١٧ ٢١,٩١٢,٠١٥ ٢٠٠٨\٩\٢٣ ١٧

٢٦,٤٩٢,٣٦٨ ٢٠٠٩\٩\١٥ ١٨ ١١,٩٥٢,٤٨٥ ٢٠٠٨\٩\٢٤ ١٨

٢٣,٤٦٥,٨٣٨ ٢٠٠٩\٩\١٦ ١٩ ١٠,١٩٨,٩٦٠ ٢٠٠٨\٩\٢٥ ١٩

٢٧,١٧٦,٤٩٩ ٢٠٠٩\٩\١٧ ٢٠ ١٠,٣٠٩,٦٥١ ٢٠٠٨\٩\٢٨ ٢٠

١٧,٤١١,٨٦٨ ٢٠٠٨\٩\٢٩ ٢١ -- ------- --------

Total 308,299,363 Total 529,727,615

Table (9.5) Ramadan Trading Volume for Amman Securities Exchange during 2006 and 2007

58

Trading Volume Summary of Abu Dhabi Securities Exchange Month of Ramadan \ 2006 \ Month of Ramadan \ 2007 \

No. Date Trading Volume No. Date Trading Volume

1 ٢٨,٢١٩,٨٢٦ ٢٠٠٧\٩\١٣ ١ ١٠,٧٩٣,٧٤٢ ٢٠٠٦\٩\٢٤

٢٩,٨٥٥,٠٢٤ ٢٠٠٧\٩\١٦ ٢ ٢١,٧٢٨,٠٧٩ ٢٠٠٦\٩\٢٥ ٢

٢٩,٧٨٧,٣٧٧ ٢٠٠٧٩\\١٧ ٣ ١٩,٢٤٩,٩١١ ٢٠٠٦\٩\٢٦ ٣

٣٣,٤٥١,٣٨٠ ٢٠٠٧\٩\١٨ ٤ ٣٣,٣٦٨,٤٧٥ ٢٠٠٦\٩\٢٧ ٤

٦٢,٢٣١,٩٢٤ ٢٠٠٧\٩\١٩ ٥ ١٩,٨٠٠,٧٥٧ ٢٠٠٦\٩\٢٨ ٥

٩٣,١٥٩,٨٥٤ ٢٠٠٧\٩\٢٠ ٦ ١٧,٧٧٧,٢٦٣ ٢٠٠٦\١٠\١ ٦

٨١,٦٤١,٧٠٩ ٢٠٠٧\٩\٢٣ ٧ ٣٣,٤١٨,٠٦٧ ٢٠٠٦\١٠\٢ ٧

٦٦,٧٢٨,٤٠٤ ٢٠٠٧\٩\٢٤ ٨ ١٤٣,٨٢٦,٨٢٧ ٢٠٠٦\١٠\٣ ٨

٩٣,٤٥٣,٠٢٠ ٢٠٠٧\٩\٢٥ ٩ ٤١,٨١٧,٤٤١ ٢٠٠٦\١٠\٤ ٩

٢٨٩,٦٩٥,٨٠٧ ٢٠٠٧\٩\٢٦ ١٠ ١٦,٨٨٥,٩٦٩ ٢٠٠٦\١٠\٥ ١٠

١٨٩,٤٥١,٠٩٧ ٢٠٠٧\٩\٢٧ ١١ ١٤,١٦٨,٠٩٤ ٢٠٠٦\١٠\٨ ١١

١٩٠,٤٩٠,١٩٣ ٢٠٠٧\٩\٣٠ ١٢ ١٥,٣١٠,٧٦٧ ٢٠٠٦\١٠\٩ ١٢

٣٦١,٧٦٠,٤٢٢ ٢٠٠٧١٠\\١ ١٣ ٢٤,١٨٠,٧٣٨ ٢٠٠٦\١٠\١٠ ١٣

٣٩٢,٨٢٧,٩٦٦ ٢٠٠٧\١٠\٢ ١٤ ٣٨,٥٧٥,٤٩٦ ٢٠٠٦\١٠\١١ ١٤

٢٠٣,٩٨٩,٩٩٤ ٢٠٠٧\١٠\٣ ١٥ ١٦,٨٣٥,٣٤٤ ٢٠٠٦\١٠\١٢ ١٥

١٠٧,٩٦٠,٨١١ ٢٠٠٧\١٠\٤ ١٦ ٢٢,٦٠١,٦٠٩ ٢٠٠٦\١٠\١٥ ١٦

٢٠٤,٠٧٨,٦٣٨ ٢٠٠٧\١٠\٧ ١٧ ٢٠,١٧٨,٢١٥ ٢٠٠٦\١٠\١٦ ١٧

١٤١,٩٦٧,٥١٠ ٢٠٠٧\١٠\٨ ١٨ ١٤,٨٦٨,٩٨٠ ٢٠٠٦\١٠\١٧ ١٨

٦٠٩,٧٣٨,٢١٩ ٢٠٠٧\١٠\٩ ١٩ ١١,٩٩٤,٦١٦ ٢٠٠٦\١٠\١٨ ١٩

٥٧٨,٠٣٣,٥١٩ ٢٠٠٧\١٠\١٠ ٢٠ ١٩,٥٦٤,٧٥٥ ٢٠٠٦\١٠\١٩ ٢٠

Total ٥٥٦,٩٤٥,١٤٥ Total ٣,٧٨٨,٥٢٢,٦٩٤

Table (9.6) Ramadan Trading Volume for Abu Dhabi Securities Exchange during 2006 and 2007

59

Trading Volume summery of Abu Dhabi Securities Exchange Month of Ramadan\ 2008 \ Month of Ramadan\ 2009 \

No. Date Trading Volume No. Date Trading Volume

1 ٢٥٧,١٨٠,٢٣٤ ٢٠٠٩\٨\٢٣ ١ ٤٣,٣٥٥,٨٤٨ ٢٠٠٨\٩\١

٢٣٢,٤٥٩,٠١٠ ٢٠٠٩\٨\٢٤ ٢ ٩١,٣٠٧,٢٦٨ ٢٠٠٨\٩\٢ ٢

٢٠١,٩٦٧,٢٧٥ ٢٠٠٩\٨\٢٥ ٣ ٧٦,١٩٧,٠٣٩ ٢٠٠٨\٩\٣ ٣

٣٢٣,٨١٩,٨٣١ ٢٠٠٩\٨\٢٦ ٤ ٦١,٠٨٥,٤٦٥ ٢٠٠٨\٩\٤ ٤

٣١٨,٢٦٢,٤٥٧ ٢٠٠٩\٨\٢٧ ٥ ٩٧,٣٦٠,٧٣٩ ٢٠٠٨\٩\٧ ٥

٢٤٠,٨٢٢,٦٧٢ ٢٠٠٩\٨\٣٠ ٦ ٩٠,٠٩٧,١٩٠ ٢٠٠٨\٩\٨ ٦

٢٤٣,٠٦٧,٤١٣ ٢٠٠٩\٨\٣١ ٧ ٧٦,٨٢٧,٢٥٨ ٢٠٠٨\٩\٩ ٧

٣٧٣,٧٧٠,٥٥٦ ٢٠٠٩\٩\١ ٨ ١٢٠,٥١٩,٤٨٠ ٢٠٠٨\٩\١٠ ٨

٢٦٢,٥٦٨,٧٥٥ ٢٠٠٩\٩\٢ ٩ ٩٩,٨١٤,٢٥١ ٢٠٠٨\٩\١١ ٩

٢٤٣,٩٩٥,٩١٦ ٢٠٠٩\٩\٣ ١٠ ١٠٢,٠٤٣,٣٣٨ ٢٠٠٨\٩\١٤ ١٠

٤٤٩,٤٤٤,٨٤٥ ٢٠٠٩\٩\٦ ١١ ١٧٤,٩٠٥,٩٨٦ ٢٠٠٨\٩\١٥ ١١

٣٨٦,٠٧٣,٢٨٣ ٢٠٠٩\٩\٧ ١٢ ١٨٤,٦٣٧,٠٨٧ ٢٠٠٨\٩\١٦ ١٢

٢٩٥,١٢٨,٤٤٦ ٢٠٠٩\٩\٨ ١٣ ١٠٩٢٦٨,٢٠٤ ٢٠٠٨\٩\١٧ ١٣

٢٦٤,٠٥٣,١٩٢ ٢٠٠٩\٩\٩ ١٤ ٩٢,٠٠١,٤٣٨ ٢٠٠٨\٩\١٨ ١٤

٤٦٥,٩٨٣,١٩٤ ٢٠٠٩\٩\١٠ ١٥ ٢٠٢,٤٥٥,٠٢٦ ٢٠٠٨\٩\٢١ ١٥

٢٢٢,٦٨٨,٩٨٥ ٢٠٠٩\٩\١٣ ١٦ ٢٥٨,٥٢٨,٧٢٢ ٢٠٠٨\٩\٢٢ ١٦

٢٠٠,١٦٢,٤١٥ ٢٠٠٩\٩\١٤ ١٧ ١٠٤,٩٩٠,٨٣٨ ٢٠٠٨\٩\٢٣ ١٧

٢٠٦,٧٧٦,٣٤٧ ٢٠٠٩\٩\١٥ ١٨ ٨٧,٠٢٦,٨٠٠ ٢٠٠٨\٩\٢٤ ١٨

٣٩٣,٩٦٦,٣١٥ ٢٠٠٩\٩\١٦ ١٩ ٢١٦,٠٣٤,٦٢٦ ٢٠٠٨\٩\٢٥ ١٩

٧٦٣,٢٣٨,٤٤٣ ٢٠٠٩\٩\١٧ ٢٠ ١٣٦,١٨١,٧٣٠ ٢٠٠٨\٩\٢٨ ٢٠

١٠٨,٩٨٥,٧٨٨ ٢٠٠٨\٩\٢٩ ٢١ -- ------- -------

Total ٢,٥٣٣,٦٢٤,١٢١ Total ٢,٧٦٦,٥١٤,٠٦٢

60

Table (9.7) Ramadan Trading Volume for Abu Dhabi Securities Exchange during 2008 and 2009

Summary of The 15 window market index return The Last 15-day window The First 15-day Window Year

-28 Point -50 Point 2006

+271 Point +107 Point 2006

+402 Point -504 Point 2007

+165 Point +214 Point 2007

-24 Point +115 Point 2008

+566 Point +147 Point 2008

+858 Point -579 Point 2009

+7 Point +184 Point 2009

Table (10.1) summary of first and last 15-day window of market return for both markets

61

Market Index Return of Abu Dhabi Securities Exchange

The First 15-day Window Ramadan (2006) Market Index Return(Point) Market Index NO. Date of trading session

None 3,533 1 24\09\2006

-24 3,509 2 25\09\2006

+35 3,544 3 26\09\2006

+15 3,559 4 27\09\2006

-15 3,544 5 28\09\2006

-5 3,539 6 1\10\2006

+1 3,540 7 2\10\2006

-3 3,537 8 3\10\2006

-23 3,514 9 4\10\2006

-14 3,500 10 5\10\2006

-17 3,483 11 8\10\2006

-50 The First 15-day Window \ RAM 06

Table 10.2 the First 15-day Window market return in Ramadan in 2006

62

Market Index Return of Abu Dhabi Securities Exchange

The Second 15-day Window Ramadan (2006) Market Index Return(Point) Market Index NO. Date of trading session

0 3,483 12 9\10\2006

+6 3,489 13 10\10\2006

+12 3,501 14 11\10\2006

+10 3,511 15 12\10\2006

+9 3,520 16 15\10\2006

-35 3,485 17 16\10\2006

-20 3,465 18 17\10\2006

+15 3,480 19 18\10\2006

-25 3,455.55 20 19\10\2006

-28 The Second 15-day Window \ RAM 06

Table 10.3 the Second 15-day Window market return in Ramadan in 2006

63

Market Index Return of Abu Dhabi Securities Exchange

The First 15-day Window Ramadan (2007) Market Index Return(Point) Market Index NO. Date of trading session

None 3,414 1 13\9\2007

-1 3,413 2 16\9\2007

4 3,417 3 17\9\2007

12 3,429 4 18\9\2007

14 3,443 5 19\9\2007

16 3,459 6 20\9\2007

-1 3,458 7 23\9\2007

18 3,467 8 24\9\2007

17 3,493 9 25\9\2007

34 3,527 10 26\9\2007

-6 3,521 11 27\9\2007

+107 The First 15-day Window \ RAM 07

Table 10.4 the First 15-day Window market return in Ramadan in 2007

64

Market Index Return of Abu Dhabi Securities Exchange

The Second 15-day Window Ramadan (2007) Market Index Return(Point) Market Index NO. Date of trading session

+36 3,557 12 30\9\2007

+55 3,612 13 1\10\2007

+44 3,656 14 2\10\2007

+4 3,660 15 3\10\2007

+17 3,657 16 4\10\2007

+39 3,696 17 7\10\2007

-2 3,694 18 8\10\2007

+40 3,734 19 9\10\2007

+38 3,772 20 10\10\2007

+271 The Second 15-day Window \ RAM 07

Table 10.5 the Second 15-day Window market return in Ramadan in 2007

65

Market Index Return of Abu Dhabi Securities Exchange

The First 15-day Window Ramadan (2008) Market Index Return(Point) Market Index NO. Date of trading session

None 4,358 1 1\9\2008

-8 4,350 2 2\9\2008

-6 4,344 3 3\9\2008

-33 4,311 4 4\9\2008

-162 4,149 5 7\9\2008

+19 4,168 6 8\9\2008

+14 4,182 7 9\9\2008

+7 4,089 8 10\9\2008

-55 4,043 9 11\9\2008

-109 3,925 10 14\9\2008

-171 3,754 11 15\9\2008

-504 The First 15-day Window \ RAM 08

Table 10.6 the First 15-day Window market return in Ramadan in 2008

66

Market Index Return of Abu Dhabi Securities Exchange

The Second 15-day Window Ramadan (2008) Market Index Return(Point) Market Index NO. Date of trading session

+16 3,770 12 16\9\2008

+61 3,831 13 17\9\2008

+99 3,730 14 18\9\2008

+219 3,949 15 21\9\2008

+65 4,014 16 22\9\2008

-114 3,900 17 23\9\2008

-89 3,811 18 24\9\2008

+25 3,836 19 25\9\2008

+103 3,939 20 28\9\2008

+17 3,956 21 29\9\2008

+ 402 The Second 15-day Window \ RAM 08

Table 10.7 the Second 15-day Window market return in Ramadan in 2008

67

Market Index Return of Abu Dhabi Securities Exchange

The First 15-day Window Ramadan (2009) Market Index Return(Point) Market Index NO. Date of trading session

None 2,825 1 23\8\2009

+4 2,829 2 24\8\2009

-6 2,823 3 25\8\2009

+32 2,855.99 4 26\8\2009

0 2,855.93 5 27\8\2009

+27 2,882 6 30\8\2009

+15 2,897 7 31\8\2009

+15 2,912 8 1\9\2009

+53 2,859 9 2\9\2009

+29 2,888 10 3\9\2009

+45 2,933 11 6\9\2009

+ 214 The First 15-day Window \ RAM 09

Table 10.8 the First 15-day Window market return in Ramadan in 2009

68

Market Index Return of Abu Dhabi Securities Exchange

The Second 15-day Window Ramadan (2009)

Market Index Return(Point) Market Index NO. Date of trading session

+23 2,956 12 7\9\2009

+21 2,977 13 8\9\2009

+41 3,018 14 9\9\2009

+39 3,059 15 10\9\2009

-41 3,018 16 13\9\2009

-24 2,994 17 14\9\2009

+7 3,001 18 15\9\2009

+50 3,051 19 16\9\2009

+49 3,100 20 17\9\2009

+165 The Second 15-day Window \ RAM 09

Table 10.9 the Second 15-day Window market return in Ramadan in 2009

69

Market Index Return of Amman Securities Exchange

The First 15-day Window Ramadan (2006) Market Index Return(Point) Market Index NO. Date of trading session

None 6,145 1 24\09\2006

+11 6,156 2 25\09\2006

+39 6,095 3 26\09\2006

+11 6,106 4 27\09\2006

-22 6,084 5 28\09\2006

+28 6,112 6 1\10\2006

-13 6,099 7 2\10\2006

+45 6,144 8 3\10\2006

+35 6,179 9 4\10\2006

-59 6,120 10 5\10\2006

+40 6,160 11 8\10\2006

+115 The First 15-day Window \ RAM 06

Table 10.10 the First 15-day Window market return in Ramadan in 2006

70

Market Index Return of Amman Securities Exchange

The Second 15-day Window Ramadan (2006) Market Index Return(Point) Market Index NO. Date of trading session

-2 6,158 12 9\10\2006

-31 6,129 13 10\10\2006

-62 6,067.36 14 11\10\2006

+69 6,136 15 12\10\2006

-69 6,067.56 16 15\10\2006

-40 6,027 17 16\10\2006

+50 6,077 18 17\10\2006

+69 6,146 19 18\10\2006

-8 6,138 20 19\10\2006

-24 The Second 15-day Window \ RAM 06

Table 10.11 the Second 15-day Window market return in Ramadan in 2006

71

Market Index Return of Amman Securities Exchange

The First 15-day Window Ramadan (2007) Market Index Return(Point) Market Index NO. Date of trading session

None 5,580 1 13\9\2007

+23 5,603 2 16\9\2007

+27 5,630 3 17\9\2007

-15 5,615 4 18\9\2007

-30 5,585 5 19\9\2007

+95 5,680 6 20\9\2007

+3 5,683 7 23\9\2007

+8 5,691 8 24\9\2007

+18 5,709 9 25\9\2007

+21 5,730 10 26\9\2007

-3 5,727.33 11 27\9\2007

+147 The First 15-day Window \ RAM 07

Table 10.12 the First 15-day Window market return in Ramadan in 2007

72

Market Index Return of Amman Securities Exchange

The Second 15-day Window Ramadan (2007)

Market Index Return(Point) Market Index NO. Date of trading session

0 5,727.60 12 30\9\2007

-4 5,723 13 1\10\2007

+82 5,805 14 2\10\2007

+36 5,841 15 3\10\2007

+155 5,996 16 4\10\2007

+10 6,106 17 7\10\2007

+105 6,211 18 8\10\2007

+28 6,239 19 9\10\2007

+78 6,317 20 10\10\2007

+76 6,393 21 11\10\2007

+566 The Second 15-day Window \ RAM 07

Table 10.13 the Second 15-day Window market return in Ramadan in 2007

73

Market Index Return of Amman Securities Exchange

The First 15-day Window Ramadan (2008) Market Index Return(Point) Market Index NO. Date of trading session

None 9,727 1 1\9\2008

+10 9,737 2 2\9\2008

+36 9,573 3 3\9\2008

+35 9,608 4 4\9\2008

-230 9,378 5 7\9\2008

152 9,226 6 8\9\2008

103 9,123 7 9\9\2008

-209 8,914 8 10\9\2008

-121 8,793 9 11\9\2008

-168 8,625 10 14\9\2008

-187 8,438 11 15\9\2008

-579 The First 15-day Window \ RAM 08

Table 10.14 the First 15-day Window market return in Ramadan in 2008

74

Market Index Return of Amman Securities Exchange

The Second 15-day Window Ramadan (2008) Market Index Return(Point) Market Index NO. Date of trading session

+225 8,213 12 16\9\2008

+309 8,522 13 17\9\2008

-198 8,324 14 18\9\2008

+296 8,620 15 21\9\2008

+62 8,682 16 22\9\2008

+35 8,717 17 23\9\2008

+52 8,769 18 24\9\2008

-100 8,669 19 25\9\2008

+115 8,784 20 28\9\2008

+62 8,846 21 29\9\2008

+858 The Second 15-day Window \ RAM 08

Table 10.15 the Second 15-day Window market return in Ramadan in 2008

75

Market Index Return of Amman Securities Exchange

The First 15-day Window Ramadan (2009)

Market Index Return(Point) Market Index NO. Date of trading session

None 5,549 1 23\8\2009

-52 5,497.47 2 24\8\2009

0 5,497.19 3 25\8\2009

57 5,554 4 26\8\2009

26 5,580 5 27\8\2009

119 5,699 6 30\8\2009

50 5,749 7 31\8\2009

-34 5,715 8 1\9\2009

+3 5,718 9 2\9\2009

+4 5,722 10 3\9\2009

+11 5,733 11 6\9\2009

+184 The First 15-day Window \ RAM 09

Table 10.16 the First 15-day Window market return in Ramadan in 2009

76

Market Index Return of Amman Securities Exchange

The Second 15-day Window Ramadan (2009) Market Index Return(Point) Market Index NO. Date of trading session

11 5744 12 7\9\2009

10 5754 13 8\9\2009

-46 5698 14 9\9\2009

26 5724 15 10\9\2009

-22 5702 16 13\9\2009

-49 5653 17 14\9\2009

34 5687 18 15\9\2009

-5 5682 19 16\9\2009

48 5730 20 17\9\2009

+7 The Second 15-day Window \ RAM 09

Table 10.17 the Second 15-day Window market return in Ramadan in 2009

77

Trading Volume of Damascus Securities Exchange 2009 NO. Date of Session Trading Volume Weather ٢٠٠٩\٤\٢ ١ 668 Cloudy ٢٠٠٩\٤\٦ ٢ 419 Sunny ٢٠٠٩\٤\٩ ٣ 2595 Sunny ٢٠٠٩\٤\١٣ ٤ 1020 Sunny ٢٠٠٩\٤\١٦ ٥ 3542 Cloudy\Rainy ٢٠٠٩\٤\٢٠ ٦ 1550 Sunny ٢٠٠٩\٤\٢٣ ٧ 1414 Cloudy ٢٠٠٩\٤\٢٧ ٨ 2520 Sunny ٢٠٠٩\٤\٣٠ ٩ 1100 Sunny ٢٠٠٩\٥\٤ ١٠ 1629 Cloudy ٢٠٠٩\٥\٧ ١١ 552 Sunny ٢٠٠٩\٥\١١ ١٢ 1095 Sunny ٢٠٠٩\٥\١٤ ١٣ 11019 Rainy ٢٠٠٩\٥\١٨ ١٤ 1229 Sunny ٢٠٠٩\٥\٢١ ١٥ 7220 Sunny ٢٠٠٩\٥\٢٥ ١٦ 2065 Sunny ٢٠٠٩\٥\٢٨ ١٧ 4750 Sunny ٢٠٠٩\٦\١ ١٨ 1464 Sunny ٢٠٠٩\٦\٤ ١٩ 16888 Sunny ٢٠٠٩\٦\٨ ٢٠ 1715 Sunny ٢٠٠٩\٦\١١ ٢١ 673 Sunny ٢٠٠٩\٦\١٥ ٢٢ 2048 Sunny ٢٠٠٩\٦\١٨ ٢٣ 7381 Sunny ٢٠٠٩\٦\٢٢ ٢٤ 10119 Sunny ٢٠٠٩\٦\٢٥ ٢٥ 25968 Sunny ٢٠٠٩\٦\٢٩ ٢٦ 32785 Sunny ٢٠٠٩\٧\٢ ٢٧ 39799 Sunny ٢٠٠٩\٧\٦ ٢٨ 23630 Sunny ٢٠٠٩\٧\٩ ٢٩ 26983 Sunny ٢٠٠٩\٧\١٣ ٣٠ 20437 Sunny ٢٠٠٩\٧\١٦ ٣١ 15481 Sunny ٢٠٠٩\٧\٢١ ٣٢ 24665 Sunny ٢٠٠٩\٧\٢٣ ٣٣ 13788 Sunny ٢٠٠٩\٧\٢٧ ٣٤ 6461 Sunny

78

٢٠٠٩\٧\٣٠ ٣٥ 19164 Sunny ٢٠٠٩\٨\٣ ٣٦ 10772 Sunny ٢٠٠٩\٨\٦ ٣٧ 27109 Sunny ٢٠٠٩\٨\١٠ ٣٨ 27630 Sunny

Table (11.1) Trading Volume of Damascus Securities Exchange 2009

Trading Volume of Damascus Securities Exchange 2009 NO. Date of Session Trading Volume Weather ٢٠٠٩\٨\١٣ ٣٩ 36661 Sunny ٢٠٠٩\٨\١٧ ٤٠ 48437 Sunny ٢٠٠٩\٨\٢٠ ٤١ 14391 Sunny ٢٠٠٩\٨\٢٤ ٤٢ 27412 Sunny ٢٠٠٩\٨\٢٧ ٤٣ 19561 Sunny ٢٠٠٩\٨\٣١ ٤٤ 9960 Sunny ٢٠٠٩\٩\٣ ٤٥ 8367 Sunny ٢٠٠٩\٩\٧ ٤٦ 3990 Sunny ٢٠٠٩\٩\١٠ ٤٧ 10034 Sunny ٢٠٠٩\٩\١٤ ٤٨ 15536 Sunny ٢٠٠٩\٩\١٧ ٤٩ 14732 Sunny ٢٠٠٩\٩\٢٨ ٥٠ 3702 Sunny ٢٠٠٩\٩\٢٩ ٥١ 4257 Sunny ٢٠٠٩\١٠\١ ٥٢ 6830 Sunny ٢٠٠٩\١٠\٥ ٥٣ 7810 Sunny ٢٠٠٩\١٠\٨ ٥٤ 19864 Sunny ٢٠٠٩\١٠\١٢ ٥٥ 23268 Sunny ٢٠٠٩\١٠\١٣ ٥٦ 34770 Sunny ٢٠٠٩\١٠\١٥ ٥٧ 30311 Cloudy ٢٠٠٩\١٠\١٩ ٥٨ 12552 Sunny ٢٠٠٩\١٠\٢٠ ٥٩ 20022 Sunny ٢٠٠٩\١٠\٢٢ ٦٠ 26918 Sunny ٢٠٠٩\١٠\٢٦ ٦١ 67740 Sunny ٢٠٠٩\١٠\٢٧ ٦٢ 21215 Sunny ٢٠٠٩\١٠\٢٩ ٦٣ 33780 Rainy \ Cloudy ٢٠٠٩\١١\٢ ٦٤ 30528 Rainy\ Cloudy ٢٠٠٩\١١\٣ ٦٥ 67475 Sunny ٢٠٠٩\١١\٥ ٦٦ 65590 Sunny ٢٠٠٩\١١\٩ ٦٧ 39585 Sunny ٢٠٠٩\١١\١٠ ٦٨ 52409 Cloudy ٢٠٠٩\١١\١٢ ٦٩ 81934 Sunny ٢٠٠٩\١١\١٦ ٧٠ 48797 Cloudy ٢٠٠٩\١١\١٧ ٧١ 39812 Sunny

79

٢٠٠٩\١١\١٩ ٧٢ 38669 Sunny ٢٠٠٩\١١\٢٣ ٧٣ 18573 Sunny ٢٠٠٩\١١\٢٤ ٧٤ 22437 Sunny ٢٠٠٩\١١\٢٦ ٧٥ 3511 Cloudy ٢٠٠٩\١٢\٣ ٧٦ 84389 Sunny

Table (11.2) Trading Volume of Damascus Securities Exchange 2009

Trading Volume and Market Index Return of DSE (2009) NO. Date of Session Trading Volume Weather ٢٠٠٩\١٢\٧ ٧٧ 18857 Cloudy ٢٠٠٩\١٢\٨ ٧٨ 18208 Sunny ٢٠٠٩\١٢\١٠ ٧٩ 11029 Cloudy ٢٠٠٩\١٢\١٤ ٨٠ 21519 Cloudy ٢٠٠٩\١٢\١٥ ٨١ 8472 Cloudy ٢٠٠٩\١٢\٢١ ٨٢ 13208 Cloudy ٢٠٠٩\١٢\٢٢ ٨٣ 7776 Sunny ٢٠٠٩\١٢\٢٤ ٨٤ 10792 Sunny ٢٠٠٩\١٢\٢٨ ٨٥ 14251 Sunny ٢٠٠٩\١٢\٢٩ ٨٦ 23848 Sunny ٢٠٠٩\١٢\٣١ ٨٧ 38721 Sunny

Trading Volume and Market Index Return of DSE (2010) NO. Date of Session Index Return Trading Volume Weather ٢٠١٠\١\٤ ٨٨ 2.42 11985 Sunny ٢٠١٠\١\٥ ٨٩ 8.59 11957 Sunny ٢٠١٠\١\٧ ٩٠ 4.89 34906 Sunny ٢٠١٠\١\١١ ٩١ 10.31 11372 Sunny ٢٠١٠\١\١٢ ٩٢ 3.2 8578 Cloudy ٢٠١٠\١\١٤ ٩٣ 4.9 13556 Cloudy ٢٠١٠\١\١٨ ٩٤ 5.3 15037 Rainy \ Cloudy ٢٠١٠\١\١٩ ٩٥ 8.21 46615 Cloudy ٢٠١٠\١\٢١ ٩٦ 5.59 24726 Cloudy ٢٠١٠\١\٢٥ ٩٧ 8.04 22792 Cloudy ٢٠١٠\١\٢٦ ٩٨ 12.16 44241 Sunny ٢٠١٠\١\٢٨ ٩٩ 10.34 23829 Sunny ٢٠١٠\٢\١ ١٠٠ 7.36 29008 Sunny ٢٠١٠\٢\٢ ١٠١ 7.4 15351 Sunny ٢٠١٠\٢\٤ ١٠٢ 11.59 33514 Sunny ٢٠١٠\٢\٨ ١٠٣ 13.52 89873 Cloudy ٢٠١٠\٢\٩ ١٠٤ -0.51 12556 Cloudy ٢٠١٠\٢\١١ ١٠٥ -5.09 58732 Sunny ٢٠١٠\٢\١٥ ١٠٦ 0.66 23240 Cloudy

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٢٠١٠\٢\١٦ ١٠٧ -0.16 15168 Sunny ٢٠١٠\٢\١٨ ١٠٨ 6.59 22588 Sunny ٢٠١٠\٢\٢٢ ١٠٩ 3.76 9299 Sunny ٢٠١٠\٢\٢٣ ١١٠ -2.23 27460 Sunny ٢٠١٠\٣\١ ١١١ -4.46 21101 Sunny ٢٠١٠\٣\٢ ١١٢ -7.31 14348 Sunny

Table (11.3) Trading Volume of Damascus Securities Exchange 2009 & 2010

Trading Volume and Market Index Return of DSE (2010) NO. Date of Session Index Return Trading Volume The Weather ٢٠١٠\٣\٤ ١١٣ -6.88 10549 Sunny ٢٠١٠\٣\٩ ١١٤ -2.95 3168 Cloudy ٢٠١٠\٣\١١ ١١٥ 4.5 15432 Sunny ٢٠١٠\٣\١٥ ١١٦ 13.9 25853 Sunny ٢٠١٠\٣\١٦ ١١٧ 9.22 10378 Sunny ٢٠١٠\٣\١٨ ١١٨ -1.35 12495 Sunny ٢٠١٠\٣\٢٢ ١١٩ 1.19 15018 Sunny ٢٠١٠\٣\٢٣ ١٢٠ -2.03 9389 Sunny ٢٠١٠\٣\٢٥ ١٢١ 9.25 10142 Cloudy ٢٠١٠\٣\٢٩ ١٢٢ 1.26 26709 Sunny ٢٠١٠\٣\٣٠ ١٢٣ 3.87 14499 Sunny ٢٠١٠\٤\١ ١٢٤ 4.24 20923 Sunny ٢٠١٠\٤\٦ ١٢٥ -2.35 51480 Sunny ٢٠١٠\٤\٨ ١٢٦ 1.86 13656 Sunny ٢٠١٠\٤\١٢ ١٢٧ -1.87 39267 Sunny ٢٠١٠\٤\١٣ ١٢٨ 13.49 35063 Sunny ٢٠١٠\٤\١٥ ١٢٩ 16.98 27287 Sunny ٢٠١٠\٤\١٩ ١٣٠ 9.46 46014 Sunny ٢٠١٠\٤\٢٠ ١٣١ 11.05 28725 Sunny ٢٠١٠\٤\٢٢ ١٣٢ 15.06 59460 Sunny ٢٠١٠\٤\٢٦ ١٣٣ 8.97 22342 Sunny ٢٠١٠\٤\٢٧ ١٣٤ 14.49 40455 Sunny ٢٠١٠\٤\٢٩ ١٣٥ 10.52 23423 Sunny

Table (11.4) Trading Volume of Damascus Securities Exchange 2010

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