1

Electronic Trading and Market Efficiency in an Emerging Market:

The Case of the Jordanian Capital Market

Aktham Maghyereh

&

Ghassan Omet

Faculty of Economics & Administrative Sciences

The Hashemite University

Abstract

Realizing the benefits of stock markets in real economies, the Jordanian stock exchange (Amman Securities Market, ASM) was established in 1978. After more than two decades, the manual trading system of the market was replaced by a computerized trading mechanism on 16 June 2000. The primary objective of the new system is to offer investors more protection and transparency. This paper examines the efficiency of the Jordanian capital around the date of its automation. Based on a multi-factor model with time varying coefficients and the GARCH-M model, the results show that the move to the electronic trading system has had no impact on the pricing efficiency of the Jordanian capital market. Based on this empirical finding, a number of practical recommendations that should improve the market’s efficiency are recommended.

Correspondence to:

Aktham Maghyereh Department of Banking & Finance Faculty of Economics & Administrative Sciences The Hashemite University Zarka Jordan

E-Mail: maghyreh@hu.edu.jo

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I. Introduction

The last decade witnessed a tremendous interest in the link between financial development

and economic growth. Following the early research by Gurley and Shaw (1955), Goldsmith

(1969), McKinnon (1973), and Shaw (1973), many models emphasized the role of well-

functioning financial intermediaries and markets in ameliorating information and transaction

costs and thereby in fostering a more efficient allocation of scarce economic resources

(Bencivenga and Smith, 1991; King and Levine, 1993a; Bencivenga et al., 1995). In

addition, some theories provide various predictions about the relative importance of banks

and stock markets in the performance of economies. (Stiglitz, 1985; Boyd and Prescott, 1986;

Bhide, 1993). Finally, the importance of both banks and markets in economic growth has

been analyzed (Levine, 1997; Boyd and Smith, 1998; Huybens and Smith, 1999; and

Demirguc-Kunt and Levine, 2001).

The burgeoning empirical work, which examines the importance of banks in economic

growth, is provided by King and Levine (1993a,b). Based on a measure of bank development

(total liquid liabilities of financial intermediaries divided by Gross Domestic Product) and

other control variables, they show that this measure explains economic growth in a sample of

about 80 countries. Moreover, using instrumental variable procedures and credit to the

private sector as a proxy measure of bank development, Levine (1998, 1999) and Levine et

al. (2000) confirm this finding. Finally, Watchel and Rousseau (1995) and Rousseau (1998)

use time-series data to confirm the positive impact of financial intermediary development on

economic growth.

More recently, a number of empirical papers considered the impact of both bank and stock

market development on economic growth. These include, among others, Atje and Jovanovic

3

(1993), Jappelli and Pagano (1994), Harris (1997), Levine and Zervos (1998), Rousseau and

Wachtel (2000), Levine (2001), Bekaert et al. (2001) and Beck and Levine (2002). This

empirical literature supports the hypothesis that there is a relationship between stock markets

and banks and economic growth.

Given the importance of stock markets in providing listed companies with long term finance,

promoting the role of the private sector in stimulating growth (Khambata, 2000), enhancing

the international risk process and improving the resource allocation process (Kim and Singal,

2000), prominent financial economists have developed a number of concepts that are known

to be essential prerequisites for fulfilling their economic roles. These concepts include

pricing efficiency. A stock market is said to be efficient (pricing) if current securities prices

reflect all available information (Fama, 1965, 1970, 1991, 1998). This efficiency is an

essential prerequisite in stock markets for fulfilling their primary role; the allocation of

scarce capital resources. For example, stock prices (in an efficient market) provide investors

with a good measure of firms’ performance and their values. In other words, an efficient

market can discipline managers and consequently improve the process of capital allocation.

The Amman Securities Market (ASM) was established in 1978. Since its formation, the

market has experienced some growth in a number of aspects. In 1978, for example, the total

number of listed companies was 66. By the end of 2002, this number increased to 161.

Moreover, the fact that the market capitalization of all listed companies as a proportion of

Gross Domestic Product (GDP) is equal to about 76% (2002) is an indication of the relative

importance of the market. However, similar to many emerging markets, the ASM suffers

from a number of weaknesses. In 2002, for example, 10 companies only accounted for about

70% of the total market in terms of market capitalization and trading volume. In other words,

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the market is concentrated in terms of market capitalization and trading volume and most

listed shares are thinly traded on the secondary market.

Given the importance of the ASM in the national economy, the Jordanian capital market has

seen the introduction of a number of major changes. At the forefront of these changes are the

June 2000 implementation of the Electronic Trading System (ETS) and the elimination of the

traditional trading floor. This event can be considered as a qualitative leap because it means

more transparency and safety for traders and investors. The system ensures a fair and orderly

entrance of all buying and selling orders into the computer and an accurate matching of

supply and demand in the determination of securities prices. However, it must be noted that

the market-making mechanism of the market has not changed. In other words, the manual

trading mechanism with which the market started has simply been replaced by an electronic

system.

In this paper, we examine how the behavior of stock prices on the ASM has changed (if any)

in the wake of the movement towards automation. Indeed, several studies, such as Domowitz

(1990, 2001) and Naidu and Rozeff (1994), discussed the effects of automation on the pricing

efficiency of securities markets. Advocates of automation suggest that the execution process

of trades becomes faster and less costly. Moreover, traders have access to broader

information including bid and ask prices, and trading activities occur at lower costs due to the

existence of a limit order book. Such a system (computerized) is expected to attract more

investors, increase trading volume and liquidity and improve the price discovery process.

Critics of automation, on the other hand, argue that electronic trading could lead to less

efficient prices since any judgmental aspects of trade execution is lost with automation and

this could be particularly important in times of fast market movements. Furthermore, it can

5

be argued that the pricing efficiency of stock markets depends on the competition between

traders (investors) and their analysis of the impact of information on security prices. Indeed,

this is why stock-broking companies are expected to analyze the impact of information on

security prices and “advice” their customers (investors) to act accordingly. In other words,

automation in itself is not expected to improve pricing efficiency without a concomitant

improvement in the quality and speed of financial analysis of listed companies.

A number of researchers investigated the impact of electronic trading on market efficiency in

developed countries (see, Freund and Pagano, 2000; Taylor et al., 2000; and Anderson and

Vahid, 2001). In their paper Freund and Pagano (2000) examined pricing efficiency by using

the re-scaled range analysis before and after automation on the New York Stock Exchange

(NYSE) and the Tel Aviv Stock Exchange (TSE). The reported results indicated that while

automation led to an improvement in the pricing efficiency of the TSE relative to the NYSE,

the non-random patterns in stock returns did not show any significant change after

automation. These results led them to conclude that automation did not affect the pricing

efficiency of the TSE. Similarly, other empirical studies (Taylor et al., 2000 and Anderson

and Vahid, 2001) investigated the impact of electronic trading on the pricing efficiency of the

London and Australian stock exchanges by using smooth transition error-correction models.

These studies focused on the arbitrage between the spot and future markets of stock indices

and reported a significant decrease in transaction costs by arbitrageurs. Based on this fining,

they concluded that these markets have become more efficient under electronic trading.

Similarly, Naidu and Rozeff (1994) examined the behavior of stock prices on the Singapore

Stock Exchange and reported a reduction in autocorrelations. As far as stock markets in

developing countries are concerned, however, extant tests of market efficiency are, to our

knowledge, limited to the work of Naidu and Rozeff (1994).

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In addition to the above, a growing number of papers investigated the pricing efficiency of

emerging markets. For example, evidence of efficiency is found in the Greek market (Panas,

1990), the Kuwaiti market (Butler and Malaikah, 1992) and the Nairobi market (Dickinson

and Muragu, 1994). Similar studies of the Kuala Lumpur stock market (Barnes, 1986; and

Berry et al., 1997), Istanbul stock market (Zychowicz et al., 1995) and the Sri Lankan stock

market (Abeysekera, 2001) suggested that they are not efficient. As far as the Jordanian

capital market is concerned, El-Erian and Kumar (1995) and Omet at el. (2002) stated that

their results indicate significant departures from the efficient market hypothesis.

This paper builds on the motivation of the above theoretical and empirical studies and

investigates whether the 16 June 2000 automation of the trading system of the ASM has

caused stock prices to behave in a more efficient manner. The empirical implication of this

study is important for the ASM and other emerging markets which have similar

microstructure and characteristics and have plans to automize their trading systems.

The analysis combines a multi-factor model with time-varying coefficients and the GARCH-

M model. This approach allows us to show how market efficiency changes over time. The

empirical finding of the paper shows that the first-order autocorrelation in the ASE index has

not changed after automation and this finding suggests that the new trading mechanism

(electronic) has not improved the pricing efficiency of the market.

The rest of the paper is organized as follows. In the following section, some basic

information about the ASM is provided. In section III, the methodological framework is

discussed. In section IV, we present and discuss the results. Finally, section V summarizes

and concludes the paper.

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II. Arab Stock Exchanges: The Jordanian Case

Realizing the importance of securities markets, the ASM was established in 1978. In this

market, any investor who wants to trade in a security must do so through the agency of a

stockbroker. The trading mechanism is continuous and strict price and time priority rules are

followed. For example, for any two or more buy (sell) orders, the one with the highest

(lowest) price has the execution priority. Similarly, if two or more orders of the same type

have similar prices, the order that is noted on the trading board first, has the execution

priority.

In Table 1, we report the size of all Arab stock exchanges in terms of the total number of

listed companies, market capitalization and liquidity. The ASM ranks sixth in terms of

market capitalization. However, when judged by the ratio of market capitalization to GDP,

the mean proportions of 75 percent (2002) signifies the importance of the market in the

Jordanian economy.

The performance of the ASM is less impressive if we consider its trading activities on the

secondary market. In common with other Arab markets, for example, ten listed companies on

the ASM account for more than 50% of the total trading volume. Moreover, if we consider

the fact that the largest ten companies only account for at least 50% of the capitalization of

the whole market, we can state that the ASM is a highly concentrated market in terms of both

market value of listed companies and trading volume. In 2001, for example, only 10

companies accounted for about 61.3% of the total market trading volume and the market

value of these companies’ shares accounted for about 70% of the capitalization of all listed

companies.

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As it stands, the trading mechanism in ASE suffers from one major weakness; lack of

immediacy. If, for example, there is an imbalance between buy and sell orders during a

trading day, successive buy (sell) orders may well get noted on the trading board without

counter sell (buy) orders arriving at the market. Furthermore, any imbalance between buy

and sell orders would cause the price of a stock to change suddenly (and by a large

percentage) from one transaction to the next. This is due to the absence of somebody (dealer)

who stands ready and willing to buy a stock at the bid and sell a stock at the ask. Indeed

Cohen et al. (1983) analyzed the impact of the specialist on the standard deviation of daily

price changes. In their simulation study, they showed that the presence of specialists reduces

the standard deviation of daily transaction prices from an average of 1.44% to about 0.89%.

In other words, the behavior of price changes on ASM would be more continuous if there

were specialists operating in the market. Moreover, investors would be assured of getting

their orders executed immediately when the submit market orders. This is perhaps why the

trading volume in the shares of only 10 companies accounts for more than 60 percent of the

trading volume in the shares of all listed companies.

III. The Methodological Framework

In the financial literature, the question of market efficiency is most often examined by

investigating whether prices in stock markets display patterns. For a market to be efficient,

such patterns should not be present and prices should follow a random walk process, or at

least be a martingale.

Most of the literature about emerging markets that investigated the weak form efficiency has

concentrated on testing whether there exist predictable patterns in prices. That is,

( )1,...,11

Ttrrttt

=++= − εβα

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where, tr and

1−tr are the stock returns at time t and t-1 respectively, and t

ε is a random error.

For a market to be efficient, the value of β should be insignificantly different from zero and

the error term should be white noise (independent and normal distribution, ),0(~2σε IND

t).

Since asset prices typically display heteroskedasticity in high frequency data, the use of the

Ordinary Least Method (OLS) may be inefficient in estimating the coefficients of equation

(1). As a consequence, the more recent empirical literature uses the generalized

autoregressive conditional heteroskedasticity (GARCH) approach of Bollerslev (1986) to

investigate market efficiency. Specifically, the GARCH framework, which is known as the

GARCH-in-mean (or GARCH(p,q)-M), allows for mean returns to be specified as a linear

function of time-varying conditional second moments. As a result, this framework uses the

conditional variability of returns as a measure of time-varying risk, and captures the

independence between the expected returns and changing volatility of asset holdings

postulated by portfolio theory.

The general GARCH(p,q)-M model for stock returns at time t (rt) may be represented by the

following equations:

)3(

)2(),0(~/,

1

2

1

11

jt

q

j

jit

p

i

it

ttttttt

hh

hNhrr

−=

−=

−−

∑∑ ++=

+++=

δεγω

ψεεϕβα

where t

ε is a zero mean, serially uncorrelated error term with a normal distribution

conditional on past information and th is the conditional variance of the error term.

10

In other words, the GARCH model allows for stock returns (rt) to be determined by past

stock returns (rt-1) and by their own conditional variance (ht) with a general parameterization

of heteroskedasticity which encompasses simpler specifications as special cases. The

conditional variance, in fact, may vary over time as a result of the linear dependence on the

behavior of past squared innovation 2

1−tε , …, 2

qt−ε (with volatility clustering effects up to q

periods indicated by nonzero δ parameter) and as a result of own temporal persistence (with

serial correlation up to p periods indicated by nonzero γ parameter). The squared

innovation term implies that volatility shocks are likely to continue to be large (given that

they are in the past) and therefore capture the observed tendency for volatility to cluster over

time1. Within this framework, the 1−tr term provides a channel to examine ASM’s efficiency.

Thus, for a market to be efficient, β in equation (2) should be insignificantly different from

zero.

Relative to the above, there is one issue that needs to be considered when one uses the

GARCH method in testing market efficiency, especially in emerging markets. The GARCH

approach merely looks at the long-run characteristics of the market and completely ignores

any changes in market efficiency due to particular events (automation in our case). To test for

this important issue, we need a tool that allows us to detect changes (if any) in efficiency

over time. To attain this requirement, and following Zalewska-Mintura and Hall (1999) and

Rockinger and Urga (2000), we can modify equations 2 and 3 by adding a time subscript to

1 Engle and Bolleslev (1986) show that the persistence of shocks to volatility depends on the sum of the δγ + .

Values of the sum lower than unity imply a tendency for the volatility response to decay over time, at a slower

rate the closer the sum is to unity. In contrasts, values of the sum equal (or greater) than unity imply indefinite (or increasing) volatility persistence to shocks over time. For a review of GARCH modeling in finance see Bollerslev

et al. (1992).

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the β coefficients and defining a set of p equations describing the behavior of the β

coefficients themselves. That is,

( )6),0(~,

)5(

)4(),0(~/,

2

1

1

2

1

11

itttt

jt

q

j

jit

p

i

it

ttttttttt

N

hh

hNhrr

συυββ

δεγω

ψεεϕβα

+=

++=

+++=

−

−=

−=

−−

∑∑

where (6) describes the process of determining the coefficient vector (t

β ). Hence, thet

β ’s

are determined by the level of the previous period’s (1−tβ ) and the innovation term (

tυ ). We

use the standard Kalman Filter procedure to estimate this model with equation 4 being the

measurement equation, and the set of state equations defined by expressions 5 and 6. This

estimation procedure allows us to comment on the efficiency of the market over the sample

period by testing the significance of β in equation 4 as well as providing us with a time

series of β ’s to detect any changes in efficiency over time.

To establish the model specification of a GARCH-M(p,q) process, the BIC (Schwarz, 1978)

is employed to determine the p and q. The BIC criterion is computed as follows:

TTqpqpBIC ln.)(ln),( 12 −++= σ

where 2σ is the estimated error variance and T is the number of time periods employed.

A small value of the criterion is preferred. The criterion reward good fits as represented by

small 2lnσ and uses the term TTqp ln)( 1−+ to penalize good fits that is got by means of

excessively rich parameterizations. The criterion is conservative in that it selects sparser

parameterization than Akaike information criterion (Akaike, 1969) (AIC), which uses the

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penalty term 1).(2 −+ Tqp instead of TTqp ln)( 1−+ . The BIC is also conservative in the

sense it is at the high end of the permissible range of penalty terms in certain model selection

settings. An iterative procedure is also used based upon the method of Berndt-Hall-Hall-

Hausman (BHHH) to maximize the log-likelihood function.

IV. The Data

To investigate whether automation of the ASM has indeed caused stock prices to behave

more efficiently, the daily closing price index for the period from 1st January 1999 to 30th

August 2002 is used. This daily index is the official indicator of the exchange. It includes

stocks of the largest and most liquid Jordanian companies. During the period under

investigation, the number of stocks included in the index did not change and equal to 60.

These stocks accounted for more than 90% of the capitalization of the whole market. In other

words, the index is very comprehensive and indicates the return patterns of the market. The

ASE price index is published on-line on the ASM web server at www.ase.jo, where a detailed

description of the methodology of the index calculation can be found. The daily frequency of

the price index during the period under investigation is reported in Figure 1.

The daily index returns (rt) are calculated using first differences of the logarithmic price

index, i.e. )()(1−−=

tttPLnPLnr where P is the price index in days t and t-1 respectively.

After excluding non-trading days, the daily time series consists of 911 observations. The

returns series is plotted in Figure 2.

Table 2 summarizes the basic statistical characteristics of the daily returns. The series

indicates significantly flatter tails than does the stationary normal distribution. The

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coefficient of skewness indicates that the series typically has asymmetric distributions

skewed to the right. In addition, the returns series displays excess kurtosis. In this case, the

null hypothesis of coefficient conforming to the normal value of three is rejected. Thus, the

returns series is leptokurtic. That is, its distribution has thicker (flatter) tails than the normal

distribution. The hypothesis of normality is rejected by the bi-variate Jarque-Bera test and

this confirms the results of either skewness or kurtosis. The Phillips-Perron (PP) and the

Augmented Dickey Fuller (ADF) unit root tests strongly reject the hypothesis of non-

stationarity. This indicates that the returns series displays a degree of time dependence. From

the Ljung-Box test statistics for twelfth-order serial correlation for the levels and squares, we

find significant serial correlations and this strongly suggests the presence of time-varying

volatility. Furthermore, the ARCH test statistics report highly significant changing volatility.

This is the so-called stylized fact of volatility clustering in the returns series. In addition, the

RESET (Ramsey, 1969) test statistic suggests the presence of non-linearity in the series.

Based on the above, the ASM stock returns are characterized by positive skewness, excess

kurtosis and deviation from normality and these findings are consistent with the findings

from other emerging markets2. The results also display a degree of volatility clustering and

non-linear dependence in returns. Following Diebond (1986), these characteristics (high

kurtosis and variance clustering) suggest that the GARCH specification provides a good

approximation for capturing the time-series characteristics of the daily returns in the

Jordanian stock market.

2 Bekaert et al. (1998) pointed out that 17 out of the 20 examined emerging markets (the sample included ASM) had positive skewness and 19 markets had excess kurtosis. In other words, normality was rejected in most

markets.

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V. The Empirical Results

Table 3 reports the computed AIC, BIC and the log likelihood functions for five GARCH-M

model specifications. The reported distribution characteristics of the residuals in Table 4,

suggest that the GARCH-M-(1,1) specification successfully captures the underlying

characteristics of the data.

The numbers for skewness and kurtosis are reduced relative to the same numbers for the raw

data series. Moreover, the ARCH(6) test statistic rejects strongly conditional

heteroskedasticity. The Ljung-Box statistics tests reject the presence of residuals’ serial

correlation. In addition, the RESET test statistic accepts linearity in the mean. In sum, the

model specification tests statistics (diagnostic test statistics) suggest that the GARCH-M-

(1,1) model capture the dynamics of the market appropriately.

Our estimation results of equations 4, 5 and 6 are reported in Table 5. As can be observed,

the estimated variance equation shows that all coefficients are statistically significant and this

indicates the presence of GARCH effects. In other words, the significant γ and δ

parameters support the hypothesis that the conditional variance (volatility) changes over time

as a result of volatility clustering (significant γ ) and as a result of temporal dependence

(significant δ ). The sum of the conditional variance parameters ( 85.0=+δγ ) being less than

unity, is consistent with a stable conditional volatility process. This result indicates the

tendency for volatility responses to shocks to display a short memory. The implied duration

of a shock to volatility is estimated to be less than half-life of 7.35 days3. Finally, the γ is

found to be less than the δ parameter. The implication of this finding is that market agents

3 The half-life of volatility shock is estimated as )(ln/)5.0(ln δγ + and represents the time it takes for half the

shock to volatility to disappear.

15

appear to have a relatively short memory with distant news on returns having a less impact

on the volatility process than more recent information.

There is evidence of significance in the GARCH-in-mean of return series (the t value of ϕ is

3.721). This suggests a positive risk premium on stock prices. The important implication of

this finding is that Jordanian investors may have predominantly risk-averse characteristics

with ex ante total risk exhibiting a positive correlation with the expected returns. In other

words, this result is consistent with the basic postulates of modern portfolio theory, and

indicates that, on average, investors are compensated with higher returns for bearing risk.

The parameter associated with the autoregressive term of returns is found to be significantly

different from zero (the t value of t

β is 4.212). The value of this parameter, which is equal to

0.16, suggests that past values of returns can be used to predict at least part of current returns,

or any information contained in past price movements is not fully known and / or exploited

by profit-maximizing investors. In this sense, we argue that the ASM is not even weak-form

efficient.

It is interesting to look at the evolution of t

β . All of them are time-varying being i

ν different

from zero. Thus, the results may be evaluated using the graphs of the coefficients. Figure 3

reports 2�t

β standard errors of the autocorrelation coefficients of the model. We find a

very clear pattern showing strong and significant signs of inefficiency throughout the entire

period under investigation and this provides some further evidence that the ASM is not

efficient. However, the Figure shows that even the autocorrelation coefficients remained

significant throughout the sample period and any structural shift towards zero in the

autocorrelation coefficients was not forthcoming around the date of automation (16 July

16

2000). In particular, it appears that the autocorrelation coefficients settled on a constant path

with no significant changes around the automation date. This finding provides some further

evidence that the transfer of stocks to an automated trading mechanism has had no significant

impact on market efficiency.

To further test whether the move to automation has not improved the efficiency of the

market, we plot (Figures 4 and 5) the recursive residuals and the cumulative sum of the

recursive residuals (CUSUM), respectively4. As can be seen, the absence of any large

forecasting errors and parameter instability around the automation date confirm the above

conclusion in that the move towards automation has had no significant impact (positive) on

the efficiency of the market. Thus, our results support the argument that the pricing

efficiency of stock markets depends on the competition between traders (investors) and their

analysis of the impact of information on security prices. If these do not change, as is the case

in Jordan, the efficiency of the market is not expected to change (improve).

VI. A Summary and Conclusions

The last decade witnessed a tremendous interest in the link between financial development

and economic growth. Many models emphasized the role of well-functioning financial

intermediaries and markets in ameliorating information and transaction costs and thereby in

fostering the efficient allocation of scarce economic resources. Moreover, a number of

empirical papers considered the impact of bank and stock market development on economic

growth. These include, among others, Atje and Jovanovic (1993), Jappelli and Pagano

(1994), Harris (1997), Levine and Zervos (1998), Rousseau and Wachtel (2000), Levine

(2001), Bekaert et al. (2001) and Beck and Levine (2002). This empirical literature supports

4 For more detailed information about these stability tests see, for example, Green (2003, Ch.7).

17

the hypothesis that there is a relationship between both stock markets and banks and

economic growth.

In the context of stock markets, prominent financial economists have developed a number of

concepts that are known to be essential prerequisites for fulfilling their economic roles. These

concepts include pricing efficiency. A stock market is said to be efficient (pricing) if current

securities prices reflect all available information (Fama, 1965, 1970, 1991, 1998). This level

of efficiency is an essential prerequisite in stock markets for fulfilling their primary role; the

allocation of scarce capital resources. For example, stock prices (in an efficient market)

provide investors with a good measure of firms’ performance and their values. In other

words, an efficient market can discipline managers and consequently improve the process of

capital allocation.

On 16 June 2000, the Jordanian stock authorities adopted an automated trading mechanism to

improve the microstructure of the ASM and to offer investors more protection and

transparency. In this paper, we examined whether the ASM has moved towards greater

efficiency following its major micro-structural change. Following Zalewska-Mitura and Hall

(1999), we extend the conventional test for autocorrelation of returns by combining a multi-

factor model with time-varying coefficients and the GARCH-M model to investigate this

issue. Unlike the previous literature that investigated this issue by using the classical dummy

variable or sub-sample techniques, our approach allows us to show how market efficiency

changes overtime.

The main findings of our paper include the followings. First, the Jordanian stock market is

not efficient in that its historical returns can be used to predict future returns. Second, the

observed autocorrelation coefficients did not reflect any move towards zero after the

18

automation date. In other words, the results indicate that the ASM has not been affected by

automation. This empirical finding does not support the argument that the move to

automation coincides with an improvement in the price discovery process and contradicts

previous studies (Naidu and Rozeff, 1994, Taylor et al., 2000 and Andersen and Vahid,

2001) which concluded that automation coincided with an improvement in the pricing

efficiency of the stock exchanges in Singapore, London and Australia.

Based on the empirical findings of this paper, it is recommended that the ASM must be

examined in terms of the factors that make markets price their listed securities in an efficient

manner. In other words, automation in itself is not expected to improve pricing efficiency

without a concomitant improvement in the quality and speed of financial analysis

(professional portfolio management) of listed companies. As stated above, the pricing

efficiency of stock markets depends on the competition between traders (investors) and their

analysis of the impact of information on security prices. Indeed, this is why stock-broking

companies (and fund management companies) are expected to analyze the impact of

information on security prices and “advice” their customers (investors) to act accordingly.

Given the importance of pricing efficiency, and the use of modern portfolio tools and

techniques by investors in making markets more efficient, the international literature contains

a number of surveys whose aim is to describe the current practice of portfolio management.

These studies include Badrinath et al. (1989), Brocket et al. (1997), Freeman and Bartels

(2000), Arnswald, (2001) and others. While it is not the objective of this paper to review the

findings of the literature, it is useful to note that the results of these works indicate that

portfolio management concepts, techniques and theories are being used, in varying degrees,

by portfolio managers in the Western World. Hence, it would be useful to carry out a

19

comprehensive survey that describes the current practice of modern investment (portfolio)

techniques in the Jordanian capital market. Once we know what licensed fund managers and

stock-broking companies in Jordan do in terms of research and analysis, some remedial

measures to improve the pricing efficiency of the ASM can be provided.

20

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Table 1

Stock Markets in the Arab World

Stock Market No. of Listed

Companies

Market Capitalization

(million Dollars)

Market Capitalization to

GDP

Bahrain 41 6624.35 0.83

Egypt 1071 30791.26 0.32

Jordan 163 4943.16 0.59

Kuwait 86 19847.98 0.53

Lebanon 13 1582.50 0.10

Morocco 54 10875.84 0.33

Oman 131 3518.13 0.18

Saudi Arabia 75 67166.04 0.39

Tunisia 44 2809.12 0.14

Total 1678 148158.37 0.36

Source: Arab Monetary Fund (Various Quarterly Reports).

Table 2

Basic statistical of data

Mean Std. Dev. Kurtosis Skewness Normality )12(Q )12(2Q ADF PP ARCH(6) RESET(12;6)

0.00005 0.00719 7.519 0.212 782.208* 43.402* 200.04* -13.623* -25.802* 31.363* 11.3*

Note: significant at * 1%. ADF and PP are Augmented Dickey Fuller and Phillips-Perron tests for a unit root, respectively.

Normality represents the Jarque-Bera (1980) normality test, which follows a chi-squared distribution, with two degrees of

freedom. )12()12( 2QandQ represent the Box-Pierce-Ljung Portmanteau tests for autocorrelation in levels and

squares. ARCH (6) is a test for conditional heterocedasticity in returns RESET (12,6) is a test for linearity in mean equation: 12 is

number of lags and 6 is the number of moments that is chosen in implementation of the test statistic..

Table 3

Model selection (AIC and BIC Criteria’s and likelihood function values for

GARCH(p,q) (1,1)* (1,2) (2,1) (0,2) (2,2)

AIC -7.236 -7.231 -7.231 -7.226 -7.226

BIC -7.210 -7.199 -7.199 -7.193 -7.189

Likelihood 3297.614 3296.188 3296.188 3293.637 3294.811

Note: * preferred model.

26

Table 4

Summary characteristics from a GARCH-M (1,1) specification

Kurtosis Skewness Normality )12(Q )12(2Q ARCH(6) RESET(12;6)

4.019 0.163 107.788* 14.380 16.372 0.286 1.046

Note: significant at * 1%.

Table 5

Kalman Filter estimates of GARCH-M (1,1) model

),0(~,

),0(~/,

2

1

1

2

1

11

itttt

ttt

ttttttttt

N

hh

hNhrr

συυββδεγω

ψεεϕβα

+=

++=

+++=

−

−−

−−

α β ϕ ϖ γ δ

-0.0005

(-0.7297)

0.1653*

(4.2122)

10.0799*

(3.7217

0.00002**

(2.1983)

0.1396*

(4.0762)

0.7206*

(19.852)

Note: Figures in parenthesis under the coefficient estimates are “t” statistics. “*”, “**” and “***” significant at the 1% , and

5%, respectively.

27

120

140

160

180

200

1/05/99 10/12/99 7/18/00 4/24/01 1/29/02

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

1/05/99 10/12/99 7/18/00 4/24/01 1/29/02

Figure 2: Daily Stock Returns of Amman Stock Exchange

Figure 1: Daily Stock Price Index of Amman Stock Exchange

28

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

2/02/99 11/09/99 8/15/00 5/22/01 2/26/02

tβ ± 2SE

Figure 3: Time-Varying Parameters

-0.04

-0.02

0.00

0.02

0.04

0.06

1/08/99 10/15/99 7/21/00 4/27/01 2/01/02

Recursive Residuals ± 2 S.E.

Figure 4: Recursive Residuals

29

Figure 5: CUSUM Test

-100

-50

0

50

100

7/01/99 14/10/99 20/07/00 26/04/01 31/01/02

CUSUM 5% Significance