Market Microstructure Changes and Time to Equilibrium (TTE)? Evidence Bursa Malaysia

International Research Journal of Finance and Economics
ISSN 1450-2887 Issue 6 (2006)
© EuroJournals Publishing, Inc. 2006
http://www.eurojournals.com/finance.htm

Market Microstructure Changes and Time to Equilibrium
(TTE)? Evidence Bursa Malaysia


Jothee Sinnakkannu
Senior Lecturer of Finance, Inti College Malaysia

Annuar Md Nassir
Professor and Dean, FEP UPM


Abstract

The central idea of market microstructure changes is to gain market efficiency.
Since 1990, Bursa Malaysia has made various changes to the market microstructure,
including the introduction of computerized trading, a central depository system, and
efficient clearing and settlement systems. This study determines whether market
microstructure changes affect market efficiency using the time to equilibrium (TTE)
estimator as the main investigative tool. Theobald and Yallup’s (2002, 2004) Auto
covariance ratios and the ARMA processes provide the framework for the analysis of this
study. The response of the time to equilibrium coefficients for this study was determined
for individual sample stocks, based on all information announcements made at specific
dates from 1990 to 2001. The findings of this study provide some evidence that the market
microstructure changes implemented by the Bursa Malaysia reduced the time to
equilibrium for all information from 14 days in 1993 to nine days in 2001. The findings
presented in this study also reveal that the prices of stocks traded in Bursa Malaysia
generally under-react for the first nine days from the day the announcement is made
publicly available.


Key words: Market Microstructure, Time to equilibrium, Over-reaction, Under-reaction,
JEL Classification: G14

Introduction
Market microstructure deals with the financial intermediation in the process of trading a financial asset,
such as a stock or a bond. In a trading market, assets are not transformed (as they are, for example, by
banks that transform deposits into loans) but are simply transferred from one investor to another. The
financial intermediation service provided by a market, first described by Demsetz (1968) is immediacy.
An investor who wishes to trade immediately is a demander of immediacy and one does it by placing a
market order to trade at the best available price; the bid price if selling or the ask price if buying.
Suppliers of immediacy establish the bid and ask prices. Depending on the market design, suppliers of
immediacy may be professional dealers that quote bid and ask prices or investors that place limit
orders, or some combination.
Studies on the subject of market microstructure include Stoll (1999) and Madhavan (2000).
Coughenour and Shastri (1999) provide a detailed summary of empirical studies regarding the
estimation of the components of the bid-ask spread, order flow properties, the Nasdaq controversy, and
linkages between option and stock markets. Keim and Madhavan (1998) survey the literature on

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32
trading costs, with a focus on institutional trades. Lyons (1999) examines the market microstructure of
foreign exchange markets. Meanwhile, the development of market microstructure as a subject has
coincided with a period of establishment of new stock markets and revitalization of existing markets in
many developing and transitional economies. The revitalization of these “emerging” stock markets is
typically characterized by institutional reforms, including modernization of the trading and information
systems, expanding stock market membership, revamping the regulatory framework, and opening
access to foreign capital. The reforms are aimed at improving stock market performance by increasing
liquidity and transparency, enhancing efficiency, and reducing volatility and trading costs. The wider
goal is to promote the development of local capital markets and facilitate access to long-term capital.
The main issue in stock markets is whether and how market microstructure changes can create a
positive value in terms of liquidity, efficiency, and volatility. Previous studies on more established
markets, which have implemented changes in trading systems, have reported a positive impact,
creating gains in market efficiency, increased liquidity and lower volatility. These include studies in
Milan (Amihud, Mendelson and Murgia, 1990), Tokyo (Amihud and Mendelson, 1991) and Tel Aviv
(Amihud, Mendelson and Lauterbach, 1997). Blennerhassett and Bowman (1998) reported a fall in
transactions costs on the New Zealand stock exchange following the move from open outcry to screen
trading, and Majnoni and Massa (2001) report broadly positive results after implementing market
microstructure changes in terms of trading regulations and transaction cost introduced by the Italian
Stock Exchange. There are fewer such studies in emerging markets, and their results are more mixed.
Some suggest that the entry of foreign investors is an important factor than internal market reform
(although the former may be predicated on the latter), and that this is followed by increased liquidity
and enhanced market efficiency, with market volatility either remaining unchanged or declining
(Richards, 1996; Kim and Singal, 2000; Ngugi, Murinde, and Green, 2002a, 2002b). However, Chang,
Hsu, Huang and Rhee (1999) found no change in liquidity or in the efficiency of the price discovery
process, while volatility increased, following the introduction of a continuous auction system in Taipei.
However, the central idea of market microstructure change is to gain market efficiency. Stock
markets are constantly thriving to implement changes in market microstructure, which is most
obviously driven by the rapid structural, technological, and regulatory changes affecting the securities
industry worldwide. The causes of these structural shifts are complex. They include the substantial
increase in trading volume, competition between exchanges within the same country and regions, the
introduction of Electronic Communications Networks (ECNs), changes in the regulatory environment,
technological innovations, the growth of the Internet usage, and the proliferation of new financial
instruments. When the relevant market microstructures are changed in tandem with the said changes
the market is expected to gain efficiency.


Objectives of Study
The main objective of this paper is to determine whether market microstructure changes affect the
stock price’s Time to Equilibrium (TTE), thus to measure the relative changes in the efficiency of
prices of the stocks listed in the main board of Bursa Malaysia in terms of time to equilibrium (TTE)
coefficient as the changes in the market microstructure were implemented. The time to equilibrium is
measured for every two years starting 1990 to 2001, after some key market microstructure changes
were implemented. This is to ensure that the aggregate effect of the market microstructure changes is
well established by each time the time to equilibrium coefficient is measured.

Time to Equilibrium as another Dimension of Market Efficiency
The study and analysis of how share prices of listed companies react and adjust to publicly available
information in particular, has long been a focus of attention in the finance literature. Most of the early
studies confined its attention to the direction and magnitude of the price movement around the
announcement time. These studies gathered many evidence on the share price reactions or impacts and
the wealth effects of different types of price sensitive announcement events.

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International Research Journal of Finance and Economics - Issue 6 (2006)
Besides the direction and magnitude, the time to equilibrium (TTE) is another dimension of
market efficiency that received less attention from the researchers all this while. The Time to
equilibrium or also known as “the speed of price adjustment” to new information, measures how fast in
terms of time lapse over which information is incorporated as price changes, thus enabling researchers
to typecast a market with the quickness to incorporate the full information content into the price
discovery.

Figure 1: Dimensions of Market Efficiency


Therefore the time to equilibrium can be identified as another important measure to determine
the market efficiency. The Time to equilibrium of stock price can be linked directly to market
efficiency in terms of the quickness of information inducement into price discovery. In an efficient
market, observed stock price should instantaneously incorporate new information released into its price
in order to reflect the change that occurred to the equilibrium price of the stock. Such a reflection or
adjustment should be instantaneous if the process is considered to be strongly efficient (Fama, 1991).
Currently, even in the developed stock markets there are limited numbers of studies that have
documented the time to equilibrium either in terms of intra-day hours or inter-days. Although the
measurement of the Time to equilibrium was attempted by several studies, most of them have not
developed a right coefficient of the time to equilibrium.
This paper aims to improve and extend other market efficiency research in general and
emerging market in particular such as Bursa Malaysia, which is a fast growing security market in the
Asian region by using the time to equilibrium methodology as the principle investigative tool.
As the time to equilibrium coefficients are determined for the sample stocks listed in Bursa
Malaysia, the said estimator also provide direct measure of the degrees of price over and under-
reactions in this stock market. The empirical documentation of overreactions (DeBondt and Thaler,
1985, 1987), under-reactions (Michaely et al., 1995; Bernard and Thomas, 1989) have identified two
families of pervasive regularities: under-reaction and over-reaction.


The Malaysian Stock Market: Structure, Historical Performance and Changes in
Market Microstructure
Bursa Malaysia (formerly known as Kuala Lumpur Stock Exchange: KLSE) is the premier secondary
stock market in Malaysia, which is classified to be one of the 16 emerging stock markets that exist in
the Asia Pacific region. The Bursa Malaysia (KLSE) was established in 1930, when the Singapore
Stockbrokers’ Association was formed. In July 1973, the Kuala Lumpur Stock Exchange Berhad was
incorporated under the Companies Act of 1965. The Malaysian stock market has undergone a robust
development since the late 1980s. With the proliferation of privatization projects and the equity boom
in 1993, market capitalization exceeded the Singapore Stock Exchange by mid-1990s.
Since then, Bursa Malaysia has taken various measures in changing the market microstructure,
including the introduction of computerized trading, a central depository, and efficient clearing and
settlement systems, to develop market infrastructure in the effort to gain market efficiency.

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International Research Journal of Finance and Economics - Issue 6 (2006)
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Some of the key changes in the market microstructure of Bursa Malaysia are that the fist major
market microstructure change was the implementation of the fully computerized screen based trading
system in November 1992. The Implementation of SCORE (System On Computerized Order Routing
and Execution) eliminated the open outcry system and hence, the use of a trading floor at the
Exchange’s premises. At the same epoch, the launching of Central Depository System (CDS) account
opening in November 1992 marked a major milestone for Bursa Malaysia and expected to result in a
more efficient settlement and clearing system. The third major market microstructure change was the
establishment of the Securities Commission (SC) which monitors and implements regulatory changes
to enhance the investor confidence and meet the requirements by statute.
The implementation of Broker Front End System, or WinSCORE, in 1994, was another key
change in the market microstructure of the Bursa Malaysia. A system of graduated commissions was
implemented also in 1995 to increase Malaysia’s competitiveness in regard to transaction costs.
The year 1996 recorded two other eventful market microstructure changes. The first is the
Ministry of Finance approving the listing of foreign companies on the Bursa Malaysia and also marked
the successful completion of prescribing the ordinary shares of all listed companies, (except Amanah
Harta PNB) under the Central Depository System (CDS).
In 1997, Bursa Malaysia was granted the status of an "approved foreign stock exchange" by the
Australian Securities Commission on 2 July. Bursa Malaysia launched the KLSE-RIIAM Information
System, which is a unique, comprehensive and consolidated Malaysian securities industry information
system on 15th August 1997. In line with other developed stock exchanges, Bursa Malaysia
implemented the T + 5 Rolling Settlement System on 18 August 1997. This system applies to all
instruments listed on the Exchange. The MESDAQ was launched on 6th October 1997. Effective 15th
December, KLSE began trading at 9.00 a.m. Previously trading starts at 9.30 a.m. The 12.30 p.m. noon
close and 5.00 p.m. end of day close remain same as before.
The year 1998 was the year during Asian Currency Crisis, which has brought serious regressive
implications to the Malaysian economy and its capital market. However, the Bursa Malaysia’s
activities on the market microstructure changes did go on to enhance investor confidence and further
regularize its trading and settlement system.
Effective 1st September, 1998 Bursa Malaysia instituted incisive new measures to further
enhance transparency in the stock market with changes in the rules, regulations and procedures of the
Exchange. Effective from 16th September, trading of Malaysia securities on the Stock Exchange of
Singapore's Central Limit Order Book International OTC market ceased.
In 1999, on 17th April, Bursa Malaysia launched a new index called the KLSE Syariah Index, to
expand participation in the stock market from local and foreign investors who are keen to invest in
securities approved by the Islamic principles of Syariah. In May 1999 Bursa Malaysia became an
affiliate member of the International Organization of Securities Commissions (IOSCO). The
membership will serve as another platform for Bursa Malaysia to learn from other members, and also
present its views and opinions in various areas of securities regulations.
On 12th May, 2000 Bursa Malaysia launched the Technology sector and a corresponding
technology index to track technology stock investments. On 7th June, 2000 the Borneo Securities was
admitted as a member company of the Bursa Malaysia and effective from 1st September, the new
brokerage rates were implemented.
The T+3 settlement cycle was implemented on 20th December 2000 in place of the previous
T+5 Rolling Settlement System. The transition from T+5 to T+3, which shortens the delivery and
settlement period for securities transactions from five (5) market days to three (3) market days.
In 2001, Bursa Malaysia issued its revamped Listing Requirements on 22nd January, a move
widely seen as a major effort to further strengthen the capital market and securities industry in
Malaysia. The Securities Commission launched the Capital Market Masterplan (CMP), which details
recommendation in the strategic positioning and future direction of the Malaysian capital market, on 22
February 2001. Malaysia Derivatives Exchange (MDEX), an integrated derivatives exchange was
launched on 11th June 2001. MDEX will offer a wide range of derivatives products and services

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including Bursa Malaysia Composite Index Futures and Options, Crude Palm Oil Futures and Kuala
Lumpur Inter-Bank Offered Rate Futures.
The inaugural listing on Labuan International Financial Exchange (LFX) took place on 20th
June, with the secondary listing of USD250 million bonds by 1st Silicon (Labuan) Inc.
The various key market microstructure changes in the observed two years between 2000 and
2001 were very critical to enhance transparency, trading efficiency and transaction costs of not only
individual investors bit also for institutional investors. These changes were aimed to strengthen the
stock market in terms of investor confidence and efficient price discovery at the post Asian Currency
Crisis.


Evidence that Stock Market Microstructure affects Time to Equilibrium
Masulis and Shivakumar (2001) studied how different stock markets with different market
microstructure affect the speed with which new information is incorporated into prices. Using
transactions data, they compared the price reaction speeds on NASDAQ with those on the New York
Stock Exchange (NYSE) and American Stock Exchange (AMEX). These markets are governed by
separate rules and employ distinctly different organizational structures. Thus, they offered the
opportunity to explore how these structural differences affect the speed of price reaction to news. Their
results also suggest that differences in market microstructure can significantly accelerate or retard the
incorporation of news into market prices.
A number of prior studies examine the impact of alternative market structures on stock return
volatility (e.g., Amihud and Mendelson (1987), Stoll and Whaley (1990), Masulis and Ng (1995)).
Masulis and Shivakumar (2001), extend this comparative analysis by examining price adjustment
speeds across markets to a common type of information event, an announcement of seasoned equity
offering (SEO) Studies by Jennings and Stark (1985) and Woodruff and Senchack (1988) document a
positive correlation between price reaction speed to earnings announcements and the size of the
earnings price reaction. Mech (1993) reports that larger announcement effects are more rapidly
incorporated into stock returns.


Methodology, Research Design, and Data
The Amihud and Mendelson’s (1987), Damodaran’s (1993) partial adjustment with noise model and
Theobald and Yallup’s( 2000,2004 ) ratios of auto covariance and the ARMA processes provides the
framework for the analysis of this study.

The Auto-covariance Ratio estimator
The partial adjustment with noise model (Amihud and Mendelson, 1987) specifies stochastic processes
for logarithmic observed price series and intrinsic value series. Observed prices are assumed, for
example, to incompletely adjust towards their equilibrium or fundamental values and the extent of
adjustment is being reflected in the Time to equilibrium coefficient. The equilibrium value is assumed
to follow a random walk process, i.e. equilibrium values fully (efficiently) adjust to information
shocks. The observed price and intrinsic value series are specified as:
∆ (
P t ) = π{(V ( t ) − (
P t − 1)} + u( t ) (1)
V
∆ ( t ) = µ + (
e t ) (2)
where P(t) is the observed price at time t, ∆P(t) is the change in the actual (or observed) price (with
∆the change operator), expressed in natural logarithms, π, the time to equilibrium coefficient ( assumed
stationary), which will be within the range of [0,2] for non-explosive processes, V(t) is the intrinsic
value at time t and u(t) is the noise due to the valuation and interpretational errors. u(t) has been
interpreted as white noise with zero mean and finite variance σ2. Based on equation (2), the ∆V(t) is the
change in logarithmic intrinsic values, µthe mean of the intrinsic value random walk process and e(t)

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the innovations in logarithmic intrinsic values which will be serially uncorrelated in efficient markets.
The Time to equilibrium coefficient, π; will equal one when prices have fully adjusted to a new
equilibrium while it will be greater (less) than unity where over (under) reactions occur.
Abstracting from noise /spread effects, it would appear to be intuitively plausible that under or
overreactions would induce autocorrelations into return series. That is, when prices under-react, with a
consequent sluggish adjustment to information, positive autocorrelations would occur (Barberis et al.
(1998) demonstrate how a ‘‘conservatism bias’’ will lead to investors under-reacting). The auto-
covariance for lags one and two can be derived as:
π
= Cov[ (
R t ), (
R t − 1 ) =
[(1 − π )var{ (
e t )} − var{ u( t )}]
Lag one
2 − π

(3a)
( Amihud and Mendelson (1987)),
π(1 − π )
= Co {
v
(
R t ), (
R t − 2 )} =
[(1 − π )var{ (
e t ) − var{ u( t )}]
and lag two
2 − π
(3b)
assuming that the innovation and noise processes {u(t), e(t)} are stationary stochastic processes and
that cross-covariance between these two processes are zero at all lags, with cov, the covariance
operator, and var, the variance operator.
Taking ratios,
Co {
v
(
R t ), (
R t − 2 )}
1 − π = Co {v (Rt ), (R 1
− )}
{
Cov
(
R t ), (
R t − 2 )}
Where byπ =
− 1 (4)
{
Cov
(
R t ), (
R t − 1)}
In taking the ratio 3(a) / 3(b), the common spread impacts due to bid-ask price bounces cancels
out in the numerator and denominator; that is the spread effect reflected by var{u(t)} cancels out via
the common [(1-π)var{e(t)} – var {u(t)}] terms in equations 3(a) and 3(b). The π measures the price
equilibrium for any given differencing day.
Delays in Time to equilibrium may also arise due to thin trading effects rather than a sluggish
adjustment towards new equilibrium values. These two effects are distinct phenomena. Their differing
impacts within a stochastic process specification are demonstrated in Theobald and Yallup (2000).
That is, autocorrelations can be induced by non-trading (see Miller et al., 1994; Scholes and Williams,
1977); however, if trade to trade prices are used, the autocorrelations will vanish in an efficient market
corresponding to the full adjustment of these prices in efficient market settings. The estimator provided
by Eq. (4) will be inconsistent in the presence of such trading effects; however, in this case, a
consistent estimator of 1 - π will be given by
{
Cov
(
R m,t ), (
R m,t − 2 − q )}
1 − π =
{
Cov
(
R m,t ), (
R m,t − 1 − q )} (5)
where R(m,t) is the observed returns variable subject to thin trading and q is the longest lag in ‘‘true’’
returns that impacts upon R(m,t). That is, for example, with the consecutive trades assumption (Scholes
and Williams, 1977; Miller et al., 1994), a consistent estimator of 1 - π is provided by the ratio of the
lag three-sample auto-covariance to the lag two sample auto-covariance

The ARMA estimator
The estimator at equation (1) can be related to a linear stochastic process. If R (t) is second-order
stationary then the estimator at equation (1) will equal the ratio of the lag two to lag one-
autocorrelation coefficients. If R(t) is modeled as an Autoregressive AR(1) process, the coefficient on
the AR(1) term will equal the ratio of the lag two and lag one autocorrelation coefficients. Since the
ratio of the lag three to lag two autocorrelations will also be equal to the coefficient on the AR(1) term,
this estimator will also be appropriate in the presence of thin trading.

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An alternative time series estimator may be derived by noting that equation (1) may be re-
expressed, after first differencing and rearranging, as
(
R t ) = ( 1 − π ) (
R t − 1) + ∆
π V( t ) + ∆u( t ) (6)
and by substituting for ∆V(t) from equation (10), equation (14) becomes
(
R t ) = πµ + ( 1 − π ) (
R t − 1 ) + π (
e t ) + u( t ) − u( t − 1) (7)
Again, then, within this modeling structure, the autocorrelations induced by under/
overreactions are reflected as an ARMA(1,1) process. Effectively, the price adjustment effects manifest
themselves within the AR(1) coefficient which will provide estimates of the speed of adjustment
coefficient. When adjustment is ‘‘full’’ (i.e. π = 1), the process will be an MA (1) process; that is,
‘‘noise’’, such as bid-ask prices bounces, drives the return process. The autoregressive component will
be stationary provided that ⏐1 - π⏐ <1, i.e. 0 < π< 2, which corresponds to the conditions imposed by
Amihud and Mendelson (1987) to ensure that prices were finite when developing their model. When
non-synchronicities are present Eq. (7) modifies to
q
(
R m,t ) = πµ + (1 − π ) (
R m,t − 1 ) + ∑
i
(
w i )L {π (
e t − i ) − u( t − 1− i )} + ( 1 −( 1 −π )L r
) ( t )
i=0

(8)
where Li is the lag operator for i steps back. That is effectively an ARMA (1, q+1) process. Again,
then, the autoregressive coefficient provides an estimator for (1 - π); the moving average component,
which captures the thin trading effects, is now of a higher order. For the case of consecutive trades
considered previously, the appropriate process is an ARMA (1,2) process. [An ARMA (p,q) approach
to modeling non-synchronicities only was also used in Stoll and Whaley (1990) and Miller,
Muthuswamy and Whaley (1994)].
Since, as previously discussed, both estimators may be related directly to the lag one
autocorrelation coefficient, the most general insights into the behavior of the estimators with
intervalling can be generated by an analysis of the intervalling properties of the autocorrelation
coefficient.
Equation (8) is of a similar nature to the ARMA (1,1) model used in Amihud and Mendelson
(1987), although the model was used in a different context (to test weak form efficiency) and they did
not indicate that the AR(1) coefficient provided an estimate of 1-π. Note that the ARMA(1,1) process
is under-specified in the presence of non-synchronicities. Their results, for a sample of 30 stocks,
indicated that at the opening on the NYSE stocks significantly overreacted, on average, while using
closing prices the reaction was unbiased, i.e. full. Roll (1994) used the AR(1) coefficient to estimate
speed of adjustment factors for Indonesian stocks. Hasbrouck and Ho (1987) developed an ARMA
(2,2) return generating process for transaction returns from which an estimate of the speed of
adjustment factor could be generated; they did not, however, estimate speeds of adjustment.
The observed returns were not adjusted for transactions costs as these effects, will be subsumed
in the noise term of the partial adjustment process (Barber et al., 2001). The observed returns expressed
in natural logarithms were combined over the years of observation for each relevant company and the
covariance of R(t) and R(t-2) were obtained for lag two covariance than the process was repeated to
obtain the covariance of R(t) and R(t-1) which is lag one covariance for all the 30 days time frame. The
lag two-covariance output was divided by the lag one covariance output to obtain the (1 - π) figures,
which later subtracted from 1 to derive the just πas the coefficient for the speed of price adjustment.

Research Design
From an efficient markets perspective, an important question is whether the Time to equilibrium of
observed market prices to full information prices is complete (i.e. π = 1) and, if not so, how rapidly the
equilibrium process becomes complete with an increasing differencing interval. The investigations of
over and under reactions are conducted simultaneously in this research in the spirit of Fama (1998),
who argued that in an efficient market where expected abnormal returns are zero, anomalies such as

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38
under and overreactions would be split on a random basis. Accordingly, the statistical significance of
the cross-sectional average time to equilibrium is investigated.
As per the objective of this study, the time to equilibrium is estimated for the market
microstructure changes in effort to determine the effects of such changes. As the Malaysian stock
market experienced several key market microstructure changes from the year of 1990 onwards, the
study measures the Time to equilibrium based on all announcements at the end of 1993, 1995, 1997,
1999 and 2001. The time to equilibrium coefficients were estimated at the end of the above said years
mainly to allow the key market microstructure change to be implemented and operationalized in the
market for a reasonable period of time.
However, in this study the response of The Time to equilibrium coefficients were determined
for individual sample stocks, based on all information announcements. The all information
announcements is a combinations of firm specific and market wide announcements which were made
at specific dates between 1990 to 2001 by individual companies which are grouped in their sectors as
listed in the main board of Bursa Malaysia. This study used five firm specific announcements based on
the post-announcement stock price reactions. The five announcements are: (i) Final Dividend
Announcement, (ii) The Rights Issues Announcement, (iii) Bonus Issues Announcement, (iv) Chief
Executive Officers’ Changes, (v) Employee Share Option Scheme (ESOS) Announcement. The
Market-wide Announcements are: (i) The National Annual Budget Announcement (ii) The National
General Elections Announcement. (iii) The Appointment of Deputy Prime Minister (DPM)
Announcement (iv) The National Capital and Exchange Control Announcements.

The Empirical Tests
The empirical tests in this study will be conducted in two distinctive Time to equilibrium coefficient
methods. Firstly, using the auto covariance ratio estimator (equation (4), without adjusting for thin
trading), the Time to equilibrium is determined in terms of the cross-sectional mean of the Time to
equilibrium coefficient π, and standard deviation of the adjustment coefficient π for the differencing
period of + 30 days from the day of the information being publicly available. The investigations of
over and under reactions are conducted simultaneously to determine the reactions of market
participants to various different announcement types in different sectors.
Secondly, the earlier tests (the auto-covariance ratio without adjustment for thin trading) are
determined again using the ARMA processes. Theobald and Yallup (2002 ,2004) used ARMA (1,2)
based on equation (7) to determine the speeds of price adjustment besides the Auto covariance Ratios.
They found that the Auto-covariance Ratio and ARMA estimators were broadly very similar in terms
of both biases and mean square errors. With high noise to innovation ratios the ARMA (1,1) based
estimator tended to perform the strongest, both in terms of the bias and mean square error criteria. In
this study the ARMA (1,2) estimator is used for its properties of suitability to an emerging market
microstructure. The ARMA (1,2) which is of a higher order of MA, that is effectively an ARMA (1,
q+1) process where autoregressive coefficient provides an estimator for (1-π) the moving average
component, which captures the thin trading effects, is now of a higher order.


Data
The data sets used in this study are the daily closing prices of stocks from the Security Clearing
Automated Network Services (SCANS) issued by the Bursa Malaysia. The sample comprises all
companies that are continuously listed in Bursa Malaysia’s main board for a period of 11 years from
January 1990 to December 2001. There are four categories of data collected for this study, the Daily
closing stock prices of the test samples from SCANS, Bursa Malaysia Daily, The Announcement Date
of the involved corporate announcements based on the e date when the announcement is filed at the
Bursa Malaysia and recorded in the daily diary is taken as the appropriate date of announcement.
Announcement dates of market-wide announcements are obtained from local Daily Newspapers such

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as News Straits Times and the Star as well as in the web sites of the Ministry of Finance for National
Budget Announcements and Capital and Currency Control and web site of Prime Ministers Department
for the announcements of the Appointment of Deputy Prime Minister and announcements of the
National General Elections. Key market microstructure changes implementations dates are obtained
from the Bursa Malaysia media center archives, Bursa Malaysia web site’s history records at
www.bursamalaysia.com and the press release in main local newspapers.
The sample companies for this study were selected subject to the criteria that only pure
corporate announcements, uncontaminated by other events during the test window period over 0 and +
30 days around the announcement date were finally selected to ensure that the results are not
contaminated by other confounding information effects. Sample with two or more over lapping
announcements within the window period of the said 30 days were dropped off. It is valid that the
stock price movements are subjected to many other determinants. In this study any other events other
then the analyzed corporate or corporate related events are considered to be noise. The companies
should have recorded trade prices 70 percent of the time. The company must be Malaysian domiciled.

The Empirical Findings
Based on the empirical findings in the previous studies, this study uses the Auto covariance ratio
estimator and the ARMA (1,2) estimator to determine the Time to equilibrium coefficients on the
effects of the market microstructure changes. The number of days taken for each category of
announcement are determined through the Time to equilibrium coefficient, π. The day on which the π
= 1 (statistically significantly not different from one at 0.05 significance level) the price in terms of
daily returns is considered to have fully adjusted or achieved a new information induced equilibrium
price. Together with the number of days taken by the share prices to fully adjust to new information,
the degrees of over or under-reaction of the prices were also investigated and reported.
According to the World Bank statistics in 1998, there are about 67 emerging markets all
together, which accounts for 20 percents of the world capitalization. The Bursa Malaysia is probably
an example of a market among the more experienced, and institutionally (disclosure standards, investor
protection, accounting standards, etc.) experienced then other emerging markets. Therefore the findings
in this study is highly unlikely to be generalized to all emerging markets until further research of more
markets are attempted.

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International Research Journal of Finance and Economics - Issue 6 (2006)
40
Table 1: Key Market Microstructure Changes to Corresponding Years and it’s time to equilibrium.

Years
Key Market Microstructure Changes
Days to Equilibrium Auto
Days to Equilibrium
Covariance Ratio
Arma (1,2)
1990to 1993
• Implementation of SCORE

• Implementation of CDS

• Establishment of Securities Commission
15 days Under-reaction
14 days Under-reaction

• Introduction of FDSS

• Increase of 90 minutes in trading time
1994 to 1995 • Implementation of WinSCORE

• System of Graduated Commission
13 days Under-reaction
13 days Under-reaction

• Trading in smaller board lots of 200
shares
1996 to 1997 • Approval for listing foreign companies

• Approved foreign stock exchange of
Australian Securities Commission

• Increase in paid up capital requirement
12 days Under-reaction
10 days Under-reaction

• Implementation of T+5 rolling settlement

• Launch of MESDAQ

• Increase of 30 minus in trading time
1998 to 1999 • Institution of incisive measures

• Allocation of ISIN

• Launching of Syariah Index
11 days Under-reaction
9 days Under-reaction

• Affiliate member of IOSCO

• Introduction of ISS
2000 to 2001 • MoU to consolidate COMMEX

• Launch of Technology sector

• New brokerage rates
11 days Under-reaction
9 days Under-reaction

• Implementation of T+3 Settlement

• Revamped listing requirement

• Launch of Capital Market master plan

The findings of this study reveal that the stock market microstructure changes over a period of
time does have a positive effect on the Time to equilibrium. It is evident that the market microstructure
changes implemented by the Bursa Malaysia reduced the Time to equilibrium, which is determined
here by the Time to equilibrium coefficients through the two main estimators, that is the Auto
covariance ratio and the ARMA (1,2) estimator. The major market microstructure changes
implemented by Bursa Malaysia since the early 1990 to 2001 have improved the market efficiency in
terms of the number of days taken to reflect all information into is prices.

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