The efficiency of trading halts: Emerging market evidence


The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 125

THE EFFICIENCY OF TRADING HALTS: 
EMERGING MARKET EVIDENCE

Obiyathulla I. Bacha, Mohamed Eskandar S. A. 
Rashid and Roslily Ramlee
Internacional Islamic University Malaysia

Abstract

This paper reports new findings on the price effect from trading halts - both voluntary 
and mandatory - over 2000-04 in an emerging share market, Malaysia.  Based on 
our overall sample, trading halts lead to positive price reaction, increased volume, 
and increased volatility.  We found evidence of information leakage resulting in a 
significant difference between voluntary and mandatory halts as well as the type of 
news released during halts to warrant such an impact.  The duration of the halt has 
an isolated impact and is largely inconsequential.  The frequency of halts does not 
seem to matter.

Keywords: Trading Halts, Price, Efficiency, and Malaysia
JEL classification: G14

1.  Introduction

The efficacy of trading halts (or trading suspensions) in overcoming informational 
asymmetries remains a controversy.  Being that it is a temporary suspension of trading 
in a stock, a trading halt is essentially a signal by the exchange that disequilibrium 
exists or is expected to exist.  That disequilibrium may be the result of an order 
imbalance or more likely due to pending news.  The halt therefore is a “time-out” for 
the adjustment of prices during real or perceived, temporary disequilibria.  Though 
numerous studies have been undertaken in various markets, there appear to be no 
consensus on the usefulness of trading halts/suspensions.  The objective of a trading 
halt, whether voluntary (one initiated by the firm) or mandatory (one imposed by 
regulators), is to always ensure a fair access to information and price formation.  
Therein lies the debate of whether that objective is achieved or not.  Supporters of 
trading halts propose that calling a halt to trading just prior to the voluntary or forced 
release of critical information enhances price formation/discovery by ensuring 
equitable dissemination and assimilation.

Often denoted as the Price Efficiency Hypothesis of Trading Halts1, the 
rationale is that since a trading suspension gives time for investors to better digest 
the impact of news, price dispersions should be smaller, implying lower volatility 

1 See Chen H, Chen H, and Valerios, N(2003).

IJBF



126  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

and a higher degree of price efficiency upon reopening.  Opponents of trading halts 
using the “learning through trading models” would argue that since a halt prevents 
trading during the suspension period, excess volatility is not necessarily being 
avoided, but rather, merely postponed.  Based on the logic of “learning through 
trading models”, reopening prices would be noisy.  This implies reduced pricing 
efficiency and increased volatility.

Just as the arguments have been diametric, empirical evidence thus far have 
been mixed.  Most empirical work on firm-specific trading halts had focused on the 
three key variables most likely to be affected by a halt:  
• excess returns
• price/returns volatility
• trading volume.  

While the behavioral findings of these three variables, post-halt, provide a 
mixed picture, studies that had refined the analysis had produced interesting results.  
Indeed, the mixed evidence begs for refinement.  It appears that factors driving the 
halt may be just as important as the halt itself in explaining the post-halt behavior 
of the three variables.  

Our examination of existing literature points to at least four parameters 
associated with a trading halt that could throw additional light on the evidence.  
Firstly, we will examine the reason for the halt/suspension on whether it was 
voluntarily or mandatorily imposed.  Secondly, we will take a look at the type of 
news or information released during the halt, whether it constitutes good, bad, or 
neutral news.  Thirdly, we will also take into account the duration of the halt.  Lastly, 
the fourth parameter would be the frequency of the stock in question, whether the 
halt constitutes a first (single) halt or one of several- (multiple) halts.  While earlier 
studies had focused on the impact of the halt/suspension alone, later studies had 
brought to bear at least one or more of the above parameters.

1.1    Motivation 
Trading halts by definition are ‘disruptive’ events that are almost always associated 
with events that have the potential to cause abrupt or extreme moves.  While market-
wide, trading halts are indeed rare and unpredictable events2, whereas firm specific 
trading halts are not.  Indeed, as emerging markets have expanded their market 
capitalization, firm specific halts have been more frequent.  There have been more 
than 300 trading suspensions over the last five-years at Bursa Malaysia.  Despite the 
fact that trading halts appear to be a fairly common occurrence, we are unaware of 
any systematic studies of the phenomenon in Malaysia.  This absence provides the 
motivation and justification for this study.

As is the case with other markets, trading halts or temporary suspensions of 
listed stocks in Bursa Malaysia may be voluntary or mandatorily imposed.  The 
administrative framework for temporary suspensions is outlined in Chapter 16 of 
Bursa Malaysia’s Listing Guidelines.  While a listed issuer through a request to 
the exchange initiates voluntary suspensions, it remains the Exchange’s discretion 
to grant such a suspension.  Mandatory suspensions can be initiated either by the 

2  Such as the trading halt on the NYSE following the September 11 attacks.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 127

Exchange or the Securities Commission (SC).  The SC can notify the exchange 
to suspend a stock from trading if it deems the issuer to be in breach or failure in 
compliance with the Securities Industry Act 1983, the Securities Commission Act 
1993, or when the Commission feels that “it is necessary or expedient in the public 
interest or where necessary for the protection of investors”.  

Section 16.02 of the Listing Guideline provides the Exchange the right to 
suspend at any time the trading of any listed security if the issuing company is 
undergoing a substantial corporate exercise, capital restructuring, stock conversion, 
or exercise in the event of any breach of the listing requirements.

Additionally; the exchange may also mandatorily suspend trading of a stock if 
in its opinion, “it is necessary or expedient in the interest of maintaining an orderly 
and fair market in securities traded on the Exchange”.  As is evident, both voluntary 
and mandatory suspensions can be the result of a wide range of reasons.  While 
authorities obviously have broad powers to halt trading, companies as well can 
request the voluntary suspension of their stock for any number of reasons.

In this paper, we examine a total of 291 firm-specific trading halts that occurred 
on Bursa Malaysia over the five-year period from 2000 to 2004.  The paper is 
divided into four sections.  Section 2 provides a review of the literature, where as 
Section 3 lays out our research questions, describing the data, research, design, and 
methodology implemented.  Section 4 presents the analysis of our results, while 
Section 5 concludes the paper with our evaluation of the efficacy of trading halts in 
the Malaysian context, along with providing implications for policy.

2.    Literature Review

Much of the research interest in trading halts appears to have originated from the 
seminal work of Hopewell and Schwartz (1978).  In that study of several hundred 
firm specific halts on the NYSE, they had reported three key findings:
1. There exists a permanent price adjustment in response to new information 

released
2. There exists an anticipatory price behavior consistent with insider trading and 

information leakage.  
3. Post-suspension price behavior presents little, if any opportunity for systematic 

trading profits.
Additionally, they had also reported that suspensions of longer duration 

typically result in price adjustments of greater magnitude.  Most other U.S. based 
studies on trading halts have also examined their impact on trading volume and 
volatility.  At least three such studies document significant increases in both volume 
and volatility, post-halt.  Lee, Ready, & Seguin (1994) had compared the impact of 
trading halts versus ‘pseudo-halts’, which are non-halt control periods on volume 
and volatility.  They had concluded that rather than reducing both volume and 
volatility, the number of trading halts had increased.  For the first, full day, post-halt, 
they had found trading volume to be 230% higher with volatility between 50 - 115% 
higher compared to pseudo-halts.  Further, the persistently high volume for days +2 
and +3 does not seem to fit the “learning through trading model”.  



128  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

These results are consistent with the earlier findings of Ferris, Kumar, and 
Wolfe (1992), who had examined the impact of mandatory SEC ordered trading 
halts.  They had found that volume and volatility tended to be higher than normal 
in the pre-suspension period with the trend continuing in the immediate post-
suspension period.  They had also reported a permanent devaluation of stocks during 
the suspension with the extent of the devaluation dependent on the announced reason 
for the halt.  Examining trading halts on the NASDAQ, Christie, Corwin, and Harris 
(2002) had reported significantly higher volatility, volume, and bid-ask spreads in 
the period following halts.  Comparing their results with those based on the NYSE 
and others, they had found trading halts to have important effects, independent of 
the market structure and the specific halt mechanism used.

Yet, other U.S. studies (while reporting similar findings with regards to price, 
volatility, and volume) have examined additional parameters.  In addition to the 
argument that suspensions are almost always ‘bad news’,3 Howe and Schlarbaum 
(1986) had shown the existence of a correlation between the length of suspension 
(measured in trading days) and cumulative, abnormal returns.  Longer suspensions 
coincide with bigger, negative residuals.  Chen, Chen, and Valerios (2003) had 
examined the intraday data for 1992 of NYSE stocks.  Their findings had showed 
that the type and significance of news determines the benefit from the trading halt.  
They had argued that halts can be beneficial in light of significant news coming to 
fruition.  However, when a halt is called, pending a news release of little significance, 
the halt actually injects more noise into prices, undermining price discovery.

A number of papers have examined trading halts in other countries.  In 
analyzing stock specific suspensions on the London Stock Exchange, Kabir (1994) 
had pointed towards anticipatory behavior, pre-halt, arguing that the presence of 
significantly positive abnormal returns up to a month following the reinstatement of 
trading had implied one of two things: 
• the complete impact of new information release takes place gradually
• not all-relevant information is disclosed during the halt.  

Wu (1998) had also examined the issue of information dissemination during 
halts for the Hong Kong market.  He had found that mandatory suspension shows 
more effectiveness in disseminating information than voluntary suspensions.  His 
findings about price reaction, volatility, and volume are largely in sync with that of 
U.S. studies.  Tan and Yeo (2003) had studied the impact surrounding the type of news 
on voluntary suspensions in Singapore.  Grouping firm-initiated suspensions into 
‘favorable’ and ‘unfavorable’ news, they had found that while the first group showed 
significant, positive, abnormal returns around the event date, the latter group had 
suffered a prolonged decline.  They had also pointed out that the much higher post-
suspension volatility of returns implies the rationale behind voluntary suspensions 
to be a release of price sensitive info rather than to curb existing volatility.

At least one study of the Italian market (Borsa Italiana) and another of the 
Portuguese market had produced results in conformity with findings elsewhere that 

3  They had found that 80% of suspended securities have suffered a substantial 
devaluation.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 129

volume and volatility are higher, post-halt.  Generalizing across studies conducted in 
different markets, the broad conformity of results implies that market microstructures 
do not matter.  What appears to matter is the type of information released, duration 
of the halt, and whether they are voluntarily or mandatorily imposed.

3.    Methodology and Data

3.1   Research Design
In line with previous works cited in Section 2, we begin by examining the variables 
commonly impacted by trading halts (i.e., stock price reaction, volatility of returns, 
and trading volume).  Since the leakage of information has a direct impact on the 
efficacy of halts, we also explore for the evidence of such a leakage.  In addition 
to these four factors, as the first study on trading halts in the Malaysian context, 
we seek to add to the comprehensiveness of the study by including four, related 
dimensions:
1. type of halt (whether voluntary or mandatory)
2. category of news released (whether good, bad, or neutral)
3. duration of halt
4. frequency (whether the halt constitutes a single halt for the stock or is one of 

several).  
Previous studies have included one or more of these four dimensions when 

examining price, volume, and volatility.  Results have shown that these dimensions 
could have a material effect on the overall effectiveness of trading halts.  Thus, we 
seek to examine each of the four standard variables of price reaction, volatility, 
volume, and information leakage within each of the four dimensions.  For example, 
we seek to determine whether or not the price reaction, volatility, volume, and extent 
of information leakage are any different for voluntary versus mandatory halts, or 
when the news released is of a different type.  Given this objective, we frame the 
following 5 broad research questions: 
1. What is the overall impact of trading halts on stock returns, volatility, and 

volume? 
2. How different are these results when the halt is voluntary as opposed to 

mandatory?
3. What difference if any, does the type of news released make?
4. Does the duration of the halt/suspension have any influence?
5. Are the results of single halts any different from those of multiple halts?  

Differentiating between voluntary and mandatory halts is fairly straightforward.  
Where the issuing firm had requested a halt, it is the former; where either Bursa 
Malaysia or the SC imposed the halt, it is a mandatory halt.  

Unlike Ferris et. al., (1992) or Tan and Yeo (2003), who had used the positive/
negative daily abnormal returns prior to a halt to classify their sample in the 
‘good’ or ‘bad’ news category, we examine the news released during the halt4.  A 
judgment is then made on the category of news.  Most were fairly straightforward.  

4  Or news released just after the announcement of the halt.



130  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

Announcements reporting increased earnings, profits, higher dividends, the winning 
of new contracts, etc., were categorized as ‘good’ news.  The opposite would 
constitute ‘bad’ news.  Where the news was deemed to be neither, for example, 
unchanged earnings, etc., or where we were unsure, we classified it in the ‘neutral’ 
news category.  During the duration of the halt, we had three classifications.  A halt 
is classified to be:
1. ‘short’ if the halt is for one trading day5

2. ‘medium’ if the halt has a duration between 2 to 5 trading days.
3. ‘long’  if the halt has a duration longer than 5 trading days.

Finally, in determining whether a halt belongs to the single or multiple 
suspension category, we used a one-year cut-off.  If a stock was suspended more than 
once within a one-year period, we classified it in the multiple suspension category.

Our dataset consists of 291 trading halts that occurred on Bursa Malaysia over 
the five-year period from 2000 to 2004.  Of the 291 total trading halts, the issuing 
firms had voluntarily requested 263 halts, whereas 28 trading halts were mandatory 
halts imposed by either Bursa Malaysia or the SC.  The basis of our analysis is the 
daily closing price data of each sample for a 120-trading-day period around the 
announcement date of the halt.  That is 60 days immediately prior to the announcement 
date and 60 days following the resumption of trading. (All daily price data were 
sourced from Bloomberg.) Others, such as information on announcements and firm 
specific information, were from Bursa Malaysia publications and newspapers.

3.2   Methodology
In line with almost all-previous work in this area, we used an event-study framework.  
Thus, in examining the price reaction to an announcement of a trading halt, we had 
computed the Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) for 
the two 60-day period windows for each firm.  Where necessary, we also examined 
the smaller windows within the 60-day windows.  The daily CAR is computed as:

                                                (1)

where, the Daily Abnormal Return on day t for stock i is determined as:

                                                                                                           
AR

i,t 
= AR

i,t
 - R

i,t
                                                                                                       (2)

The Abnormal Return AR
i,t
 is the difference of day t’s actual return of R

i,t
  less the 

expected return of R
i,t
 where:

                                                                                                                                (3)

RM equals the returns of the KLSE CI (Kuala Lumpur Stock Exchange Composite 
Index).  

5  The shortest duration of halts in Malaysia is one day.

ˆ

ˆ

∑
=

==
t

t
titi NIiARCAR

1
,, .,.........

tiiti RMBR
^^

,

^
+= α



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 131

Beta was estimated using daily stock and market returns for the 60-day period: -61 
to -120 pre-halt announcement.

Next, we computed the daily mean Abnormal Return (that is, the average abnormal 
return across all samples on day t). 

The daily mean abnormal return (MAR) is:

                                                                                                                                (4)

and Variance

                                                                                                                                (5)

In addition to computing the daily CAR
s
 for each of our sample companies, we 

computed the mean overall CAR
s
 for all sample companies for each window period.  

The Mean Cumulative Abnormal Return (MCAR) is determined as:

                                                                                                           (6)

In determining the daily returns volatility for each window period, we first 
determined the volatility of returns across all sample firms by day (t).  This is 
computed as:

Where τ
it
 is the % return for firm i, on day t, CP

it
 is the closing price for stock of firm 

i on day t. 

                                                                                                (7)

For trading volume, we used the absolute number of stocks traded for each sample 
firm on day t.

                                                                                                               (8)

Where TV
t
 is the mean trading volume across all sample firms on day t, tv

i,t
 is the 

trading volume of stock for firm i on day t.

Tt
N

AR

MAR

N

ti
ti

t ...........,1
,

==
∑

=

Tt
N

ARVAR

MARVAR

N

ti
ti

t ...........,1

)(

)(
2

,

==
∑

=

Tt
N

CAR

N

CAR

MCAR

N

i
ti

N

ti
ti

t ...........,1

)(

2

1
,,

==
∑∑

==

( )[ ] 100Re 1,1,,, xCPCPCP titititi −−−=τ

( )
( )
2

,Re

Re
N

tVar

tVar

N

ti
ti

t

∑
=

=

NtvTV
N

ti
tit ⎟
⎟
⎠

⎞
⎜
⎜
⎝

⎛
= ∑

=
,



132  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

While CARs and Abnormal Returns are used to determine the impact of trading 
halts on price behavior, we examined the impact on returns volatility and trading 
volume.  Firstly, we employed the standard means test using the t-statistics and the 
non-parametric Wilcoxon signed rank test (the latter test assumes the distribution 
is unknown or non-normal).  Additionally, the Wilcoxon test uses the median to 
avoid test misspecifications that may arise from asymmetry in cross-sectional 
tabulations and non normal distribution.  In comparing volatility, pre- and post-
halt, we compared the variance between the samples using the F-test instead of the 
t-test.   Previous studies have shown that the results drawn from the event study 
can be sensitive to sample size and estimation period6.  To avoid any bias that may 
arise from the selection of a pre- and post-event period, we examined the results 
by varying the window periods and by dividing the sample into those additional 
dimensions mentioned above (categorization by type of halt, type of news, duration, 
etc.).

3.3   Testable Hypotheses
A number of testable hypotheses about the impact of halts on price, volatility, and 
volume may be inferred from previous literature.
(i) Price Effect: a trading halt can either enhance  price discovery by providing 

participants  a “time-out” to assimilate impending new information as its 
proponents argue, or  interfere with price discovery as its opponents point out.  
Either way, more important  than the halt is the information released during 
the  halt.  A company calls for a  voluntary trading halt of its shares in 
order to release new information.  In the case  of a mandatorily required 
halt, even if no information is forthcoming immediately  following the halt, 
the mere fact that authorities had stopped trading in the stock is a  signal that 
there is a substantive problem.  Thus, in the absence of information leakage,  
the impact of trading halts on price behavior would be in one of the following 
three forms:
1. if information released is insignificant, there should be no abnormal 

returns on trading resumption.
2. if the information released is significant, a price reaction should occur 

(the kind of reaction being dependent on the type of news).
3. if the market had anticipated the information released during the halt, then 

we should expect ‘price continuation’; i.e. prices continue to move in the 
same direction.  On the other hand, if the news released was unanticipated, 
then a price reversal could be the case.

 Thus, with these three hypotheses, we test the following:
1. whether trading halts are significant events where price behavior is 

concerned 
2. whether post-halt price behavior is dependent on the type of news 
3. whether information released during halts merely reinforce anticipations 

or otherwise.

6  Coutts, Mills, and Roberts (1994), cited in Wu (1998)



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 133

(ii) Effect on Returns Volatility: if trading halts require a time-out for participants to 
properly evaluate information during real or perceived market disequilibrium, 
then one should expect lower returns volatility, post-halt.  Alternatively, going 
by the logic of “learning through trading models”, the absence of transactions 
should exacerbate the uncertainty/disequilibria, thereby causing an increase in 
returns volatility, post-halt.

(iii) Effect on Trading Volume:  there are two reasons why halts can be expected 
to have an impact on traded volume.  Firstly, the release of new information 
may require market participants to adjust their positions/exposure in the stock.  
Volume increases, post-halt, as the stock changes hands.  Secondly, if one 
assumes trading in normal times to be evenly distributed over time, then it 
can be expected that when trading resumes following a halt, volume ought to 
be higher in order to compensate for the disruption.  This also implies that the 
longer the trading halt, the greater the impact would be on volume.

4. Results and Analysis

Given the numerous permutations involved in our analysis, for ease of elucidation, 
we presented our results by the four variables of interest: 
• Price
• Returns Volatility
• Volume
• Information Leakage. 

Following an overall examination of the impact of trading halts on each of these 
variables, we then presented the results of our analysis examining each variable by 
the four dimensions: 
• type of halt
• type of news released
• duration of halt
• frequency.  

The breakdown of sample size within each of these subcategories is shown in 
Table 6.

Table 6:  Breakdown of Voluntary Halts Sample by Category

Note: The total number of Voluntary halts in our sample was 263.  The Mandatory halts 
which had a sample size of 28 firms was not subdivided by category since these are irrelevant.  
Mandatorily ordered halts are by definition ‘bad’ news, are of long duration and are only 
subjected to a single halt.

Type of news released Duration of halt Frequency of halt

Good news 120 Short 162 Single 106

Neutral news 120 Medium 92 Multiple 157

Bad news 23 Long 9

Total 263 Total 263 Total 263



134  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

4.1   Price Effect
Table 1 and Figure 1 show the results of our analysis of daily Cumulative Abnormal 
Returns (CARs) for the 120-day period surrounding the halt announcement.  Table 
1 shows the Mean CARs (MCARs) for 3 pairs of Window periods, 60 days pre/
post; 30 days pre/post; and 5 days pre/post announcement.  The results of the paired 
samples t-test and the non-parametric Wilcoxon Signed Ranks test for difference in 
means.  The probability levels are also shown.

Overall, for the full sample of 291 firm-specific halts, MCARs are higher for all 
3 windows in the post-halt period.  In other words, there is a positive price reaction 
once a halt is lifted.  There is a big jump in the MCAR for the +5 day window.  This 
is followed by a steady rise in the two subsequent window periods +30 and +60.  
Thus, prices are much higher 60 days after the resumption of trading relative to 
where they were 60 days before the announcement of halt.  Both statistical tests (t 
and Wilcoxon) showed post-halt MCARs to be significantly higher relative to their 
pre-halt windows at the 5% level.  Interestingly enough, when observing the MCARs 
for the 3 pre-halt periods, we saw a steady increase as we approached announcement 
date.  Pre-halt MCAR is at it’s maximum for the -5 day window.  This steady buildup 
in prices is also clearly evident in Figure 1 that plots daily CARs. 

Figure 1:  Daily CARs for Overall Sample; Voluntary and Mandatory Halts

4.2   Price Effect by Type of Halts
The subsequent two columns show the results when the overall sample is separated 
into Voluntary and Mandatory halts.  Recall that our sample constituted of 263 
voluntary and 28 mandatory halts.  Looking at the results for the voluntary halts, 
we see results very similar to the overall.  All 3 post-halt MCARs are higher and 
significant for the 60 and 30-day windows.  We also see the marked increase in 
MCAR just prior to the announcement.  The MCAR for the -5 day window is more 
than 5 times larger than that of the -60 day window.  The results for voluntary halts 
are very similar to that of the overall sample.  That should not be surprising given 
that the large majority (almost 90%) of our sample constituted of voluntary halts.

-5

0

5

10

15

20

25

30

35

-80 -60 -40 -20 0 20 40 60 80

DAYS

C
A
R
s

OVERALL

VOLUNTARY
MANDATORY



T
h
e In

tern
a
tio

n
a
l Jo

u
rn

a
l o

f B
a
n
kin

g
 a

n
d
 F

in
a
n
ce, 2007/08 V

ol. 5. N
um

ber 2: 2008: 125-148
 

135

Table 1:  Cumulative Abnormal Returns by Category

Window 
Period

Price Effect By News By Duration Frequency

Overall Voluntary Mandatory Good Neutral Bad Short Med Long Single Multiple

(-60 to -1) 1.0770 .9846 1.9456 2.3463 2.6350 -1.12029 2.0517 .3981 -12.4045 .9547 1.1184
(+1 to 
+60)

8.8800 8.0651 16.5343 14.8738 2.7264 .4066 10.5503 4.2919 2.3364 11.4132 7.2065

(-30 to -1) 2.0115 1.9196 2.8749 3.7162 .6580 -.8714 3.6891 .5252 -15.7090 1.7727 2.2686
(+1 to 
+30)

7.3775 6.815 12.6598 12.8591 2.7632 -3.5775 9.3678 3.6907 -6.6638 10.5240 5.4042

(-5 to -1) 4.8120 5.143 1.6954 6.1787 4.8091 1.4904 7.6503 2.771 -15.4385 5.7903 4.5031

(+1 to +5) 6.7212 6.253 11.112 10.4326 3.5251 -1.3127 9.1165 3.6264 -17.3855 9.3882 5.7221

T-Stat

-62.680
(000)

-50.949
(.000)

-16.762
(.000)

-81.080
(.000)

-9.231
(.000)

-2.544
(.014)

-43.114
(.000)

-13.947
(.000)

-7.275
(.000)

-50.665
(.000)

-42.387
(.000)

-25.200
(000)

-18.766
(.000)

-15.507
(.000)

-54.777
(.000)

-4.294
(.000)

4.708
(.000)

-16.533
(.000)

-7.130
(.000)

-5.405
(.000)

-25.960
(.000)

-10.523
(.000)

-4.187
(014)

-2.656
(.057)

-7.737
(.002)

-8.516
(.001)

2.626
(.058)

6.647
(.003)

-4.111
(.015)

-1.256
(.277)

.667
(.553)

-6.308
(.003)

-4.940
(.008)

Wilcoxon 
Z-Stat

-6.736 
(.000)

-6.736 
(.000)

-6.736
(.000)

-6.736
(.000)

-5.904
(.000)

-2.245
(.025)

-6.736
(.000)

-6.729
(.000)

-5.382
(.000)

-6.736
(.000)

-6.736
(.000)

-4.782
 (.000)

-4.782 
(.000)

-4.782
(.000)

-4.782
(.000)

-3.466
(.001)

-3.651
(.000)

-4.782
(.000)

-4.391
(.000)

-4.184
(.000)

-4.782
(.000)

-4.782
(.000)

-2.023
(.043)

-1.753
 (.080)

-2.023
(.043)

-2.023
(.043)

-1.753
(.080)

-2.023
(.043)

-2.023
(.043)

-1.214
(.225)

-.730
(.465)

-2.023
(.043)

-2.023
(.043)

The table shows the Mean CARs by window period for the different categories.  T-stat values shown are for paired sample test the prob. values are 
shown below in brackets.

 



136  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

Analysis of mandatory halts produced some very interesting results.  A first 
glance at Figure 1 shows two things.  Firstly, relative to voluntary halts, daily CARs 
in the post halt period is much higher.  Secondly, they are also much more volatile in 
both the pre- and post-halt period relative to the voluntary sample.  Going by MCAR 
numbers in Table 1, with the exception of the -5 day window, all MCARs are higher 
for mandatory halts relative to voluntary ones.  Paired tests that compare the means 
across the two sample (as shown in Table 2) confirm this.  

Not only are MCARs higher after the halt, but also, they are higher by several 
fold relative to pre-halt windows.  Daily CARs exceed 30% as at day +60 (Figure 
1).  These results are contrary to expectations.  Mandatory halts, being suspensions 
imposed by authorities, are a negative signal.  It implies a breach/wrong doing or 
some other inadequacy on the part of the issuing firm.  From this viewpoint, the huge 
positive CARs, post-halt, are indeed a contradiction.  However, when we consider 
the regulatory structure of mandatory halts and the survivor bias of our sample, the 
results are logical.

Malaysian authorities only initiate mandatory halts when there are serious 
inadequacies.  In the post-1998 Asian Financial crisis environment, most of these 
inadequacies have to do with financial distress.  Upon having initiated a trading 
halt, the Securities Commission would require the company to come up with a 
restructuring plan.  The restructuring plan is usually in the form of asset sales, debt 
restructuring, recapitalization, or some combination thereof.  Since trading in the 
stock will only be allowed to resume when the firm has come-up with a viable plan, 
inability to do so will mean continued suspension, followed by a possible delisting.  
A restructuring plan to be acceptable must obviously be one that will subsequently 
put the firm in a better financial position.  

Thus, the 28 mandatory halts by definition are those that had successfully 
`restructured’ and were subsequently allowed to resume trading.  This survivor 
bias and uniqueness of the Malaysian regulatory requirement explains the hugely 
positive CARs, post-halt.

4.3   Price Effect by Type of News
Figure 2 shows the plot of daily CARs for our sample of voluntary halts7, categorized 
by type of news.  The difference in price reaction is obvious.  The good news 
category shows a steady increase in prices all the way to day +60.  The bad news 
category shows a sharp, initial decline for about 20 trading days (one month) before 
stabilizing and reversing the course.  The neutral news category shows no distinct 
trend.  The statistical tests in Table 1, confirm this price behavior.  For the good news 
sample, all 3 post-halt windows have significantly higher MCARs.  Most of the 
positive CARs happen in the first 5 days following announcement.  For the neutral 
news category, though MCARs for 60 days are marginally higher, both the t and 

7 Mandatory halts were not categorized by news, duration, or frequency of  halt since these 
are irrelevant.  
   Mandatory halts are by definition ‘bad’ new; of long duration; and subject to a single 
halt.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 137

Wilcoxon tests show price performance in the 5 days following announcement to be 
no different from the 5 days immediately prior.  

Figure 2:  Daily CARs by Type of News

Thus, the price reaction in the neutral news category is largely muted.  The 
MCARs for bad news category is already in negative territory for the -60 and -
30 day windows.  However, the MCARs show a positive build-up just before 
announcement as seen from the -5 days window.  Of course, all these positive build-
ups are erased following the announcement.  The MCARs are sharply negatively for 
the +5 and +30 day windows.

The price reaction seen here is in line with expectations.  What is interesting to 
note is the build-up of prices just before announcement.  Though Table 1 shows this 
to be true for all 3 news categories, the build-up is most obvious in Figure 2 for the 
Good and Neutral news categories. 

4.4   Price Effect by Duration of Halts
We next examined whether the duration of the halt matters for a price reaction.  The 
results in Table 1 confirmed the relevance.  For short duration halts, MCARs for all 
3 windows are significantly higher, post-halt.  We saw two obvious differences for 
Medium term halts.  The MCARs, though higher, post-halt, are all lower relative to 
the ones we saw for short duration halts.  Secondly, both tests show MCARs to be no 
different between the -5 and +5 day windows, implying that there is no significant 
initial price reaction when trading resumes.  In sharp contrast to short and medium 
duration halts, long term halts show very different price behavior.  MCARs are 
already negative for all 3 windows even before the halt.  When trading resumes, 
initially, there is a sharp, negative reaction.  The stocks experience a MCARs of 
-17% within the first week.  This decline however abates with time.  Though the 
+5 and +30 day windows have negative MCARs, it is marginally positive for +60 

-10

-5

0

5

10

15

20

25

- 80 - 60 - 40 - 20 0 20 40 60 80

DAYS

C
A
R

GOOD

NEUTRAL

BAD



138  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

days.  Interestingly enough, these results are similar to that of Hopewell & Schwartz 
(1978) who had found price adjustments of greater magnitude for halts of longer 
duration.

Table 2:  Cross Comparison across Sample by Window Period

Category

Mean T-Stat
Wilcoxon Z-

StatBy Type of Halt 
Voluntary Vs. Mandatory

(-60 to -1) -.9610 -2.790 (007) -2.665 (.008)

(-1 to -30) -.95533 -1.624 (.115) -1.697 (.090)

(-1 to -5) 3.4483 12.128 (.000) -2.023 (.043)

(1 +60) -8.4692 -12.638 (.000) -6.729 (.000)

(1 + 30) -5.8446 -19.558 (.000) -4.782 (.000)

(1 to 5) -4.8587 -5.427 (.006) -2.023 (.043)

 By News Category 
Good News vs. Bad News

(-60 to -1) 3.4666 15.110 (.000) -6.736 (.000)

(-1 to -30) 4.5877 15.158  (.000) -4.782 (.000)

(-1 to -5) 4.6882 9.025 (.001) -2.023 (.043)

(1 +60) 14.4671 29.252 (.000) -6.736 (.000)

(1 + 30) 16.4367 35.555 (.000) -4.782 (.000)

(1 to 5) 11.7453 23.296 (.000) -2.023 (.043)

By Frequency of Halt
Single vs Multiple

(-60 to -1) -.1636 -.997 (.323) -1.097 (.273)

(-1 to -30) -.4958 -1.826 (.078) -2.005 (.045)

(-1 to -5) 1.2871 1.917 (.128) -2.023 (.043)

(1 +60) 4.2067 18.022 (.000) -6.736 (.000)

(1 + 30) 5.1198 19.793 (.000) -4.782 (.000)

(1 to 5) 3.6661 9.908 (.001) -2.023 (.043)

By Duration of Halt
Short vs Long

(+1 to +60) 8.6287 6.352 (.000) -5.212 (.000)

(+1 to +30) 16.5613 10.575 (.000) -4.782 (.000)

(+1 to +5) 27.5355 11.484 (.000) -2.023 (.043)
 
The table shows the Mean CARs by window period for the different categories.  T-stat values 
shown are for paired sample test the prob. values are shown below in brackets.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 139

The above results, together with those in Table 2 comparing MCARs across 
categories, imply a link between duration of halt and price behavior, post-halt.  
Generally, it appears from our results that shorter duration halts experience positive 
price reactions, post-halt, whereas long duration halts experience the opposite.  
These results are sensible when we consider the fact that all halts are disruptive, 
with longer term ones even more so.  Even if the halt was voluntarily requested, 
issuing companies will only want longer halts if they have more complex issues to 
solve.  Complicated problems will require a longer time-out.  The negative MCARs 
we saw for all three pre-halt windows would imply that may indeed be the case.  
Companies needing long-duration halts already experience problems; thus they 
need time to sort these out.

Our final analysis with regards to price effect was to see if the frequency of 
halts had any different price behavior.  The results in Table 1 show similar price 
reactions, post-halt.  When the MCARs are compared between the two categories 
(Table 2) for matched window periods, we see no difference, pre-halt, but when 
trading resumes, single halts outperform multiple halts.  This out-performance is 
significant at the 5% level by both the parametric and non-parametric tests.

4.5   Evidence of Information Leakage
Scrutinizing the MCARs in Table 1 shows an interesting feature.  For almost all 
categories of  analysis, we see a marked increase in MCARs for the -5 day window.  
Such a pattern is also clearly visible from the daily CARs plotted in Figures 1 & 
2.  This appears to be a tentative evidence of information leakage.  The presence 
of such leakage has been documented for several markets in the previous studies 
cited in Section 2.  To seek confirmation of such leakages for Bursa Malaysia, we 
examined the MCARs for any possible significant differences across two different 
window periods, 20 days before halt.  The first is the 10-day pre-halt window (-20 
to -11) and the final 10 days (-10 to -1).  Essentially, we want to see if there is a 
significant price change in the last 2 weeks leading to the halt, relative to the 2 weeks 
prior.  The results are shown in Table 3.  At the 5% level, both the parametric and 
non-parametric tests show consistent results.

For the overall sample of 291 companies, MCARs for the last 10 days was 
indeed significantly higher than the 10 previous days.  This initial evidence of 
leakage was reinforced when we examined the voluntary and mandatory sub-
segments.  Voluntary halts have MCARs almost 3 times higher in the final 10 days 
than the previous 10-day window.  Mandatory halts on the other hand, have MCARs 
more than 3 times lower in the last 10 days.  The differences are statically significant 
for both cases.

When the same 10-day windows were examined across the different type of 
news categories, MCARs for the latter window period was significantly higher in all 
cases, even for the bad news category.  What is interesting is that when we go from 
good to neutral to bad news categories, the MCARs steadily reduce.  Finally, both 
the single and multiple suspension categories had significantly higher MCARs for 
the last 10-day window8.

8 We did not evaluate the duration of the halt category since duration of halt cannot be 
known prior to or even at announcement.



140  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

Table 3:  Test for Evidence of Information Leakage

By Category of Halt
Mean T-Stat Wilcoxon Z-Stat

Overall – Voluntary & Mandatory

(-20 to -11) 1.6824
-6.333
(.000)

-2.803
(.005)

(-10 to -1) 3.8032

Voluntary –Suspension

(-20 to -11) 1.4912
-8.446
(.000)

-2.803
(.005)

(-10 to -1) 4.0933

Mandatory

(-20 to -11) 3.477
3.565
(.006)

-2.497
(.013)

(-10 to -1) 1.0783

Voluntary By News Category

Good News

(-20 to -11) 3.748
-4.179
(.002)

-2.803
(.005)

(-10 to -1) 5.259

Neutral

(-20 to -11) -.3488 -7.527
(.000)

-2.803
(.005)

(-10 to -1) 3.410

Bad

(-20 to -11) -.6825 -3.184
(-.011)

-2.599
(.009)

(-10 to -1) 1.574

By Frequency of Halt

Multiple

(-20 to -11) 2.6763 -4.802
(.001)

-2.803
(.005)

(-10 to -1) 3.6217

Single

(-20 to -11) .7280 -8.891
(.000)

-2.803
(.005)

(-10 to -1) 4.636

The table shows the Mean CARs by window period for the different categories.  T-stat values 
shown are for paired sample test the prob. values are shown below in brackets.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 141

Summarizing the results, with the exception of mandatory halts that saw a marked 
price decline immediately prior to announcement, all voluntary halts (regardless of 
subcategory) had experienced a significant price build-up just prior to halt.  There 
are two reasons why we believe insider trading is the cause of the sharp rise in 
prices.  Firstly, when we consider voluntary halts, even if the market can anticipate 
the release of good news, it is difficult for outsiders to know when a company will 
ask for a voluntary suspension.  Only those with inside information can tell the 
timing of a trading halt request.  This can explain the very significant build-up in 
MCARs/prices just prior to halt announcements that are then followed by the release 
of good news.  Secondly, going by the same logic, even if a firm’s financial distress 
is known, the timing of a mandatory halt is difficult to gauge for outsiders.  Yet, the 
fact that MCARs are significantly negative just before an official announcement of 
halt, it appears to be the work of those trading on privileged information.  While 
we believe our analysis provides sufficient evidence of information leakage, we 
are left questioning the unexplained, positive price build-up even for the bad news 
category9.

4.6   Effect on Trading Volume
Lee et. al., (1994), Ferris et. al. (1992), and Christie et. al. (2002) had all shown 
similar findings with regards to volume and volatility.  All three studies had shown a 
higher than normal volume and volatility in the pre-suspension period with the trend 
continuing in the immediate post-suspension period.  Where volume is concerned, 
our results appear to be very much inline with these U.S. based studies.  Figure 
3 shows the Mean Daily Volume for our overall sample and voluntary/mandatory 
categories for the 120-day period surrounding halt announcements.  There is a clear 
build-up in daily volume in the period immediately before the halt announcement.  
The uptrend continues in the period immediately following trading resumption.  In 
all three cases, the rise in traded volume is short lived.  It peaks at about 5 days 
after resumption before sliding steadily back to normal levels.  In fact, all the action 
appears concentrated between days -20 and +20.  This is identical to Ferris et. 
al. (1992) who had reported that volume levels returned to normal 20 days after 
suspension.

Results of our statistical tests for volume are shown in Table 4.  For the 
overall sample, mean volumes are higher for all 3 post-halt windows; however, 
the significance tests are mixed.  For the sample of 263 voluntary halts, volume is 
significantly higher (post-halt) in the 5 and 60-day windows.  This is in stark contrast 
to the sample of mandatory halts.  Volume, though higher for the +5 day window, 
is lower for both +30 and +60 day windows.  Both the t-test and Wilcoxon show 
a significantly lower volume for post 60 days relative to 60 days, pre-halt.  When 
we examined volume patterns by news category, though all three categories show 
higher volume for the +5 day window, in contrast to good and neutral news, the bad 
news category showed a significantly lower volume for the +60 day window.

9  MCARs for the bad news category is negative for the first 10 day window, but turns 
marginally positive for the final 10 days.



142  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

Figure 3:  Mean Daily Volume for Overall, Voluntary and Mandatory Halts

When examining volume by duration of halt, short duration halts had no 
different volume in all 3 post-halt windows.  Halts of medium duration showed a 
significantly higher volume in the +5 and +30 windows.  Both tests confirmed this 
at the 5% level.  This is interesting since it lends support to the argument that halts 
will lead to pent-up demand, and therefore, longer duration halts should see a bigger 
build-up in demand.  Indeed, this is true when we go from short to medium duration 
halts.  However, our results for long duration halts are not consistent with this 
argument.  Volume is in fact significantly lower for the +5 and +60 day windows.  
We believe this could be due to the same argument we had made in explaining 
the negative CARs for long duration halts.  Implying that a long duration halt was 
needed for a stock signifies more complicated problems; hence, the negative CARs 
and significantly lower trading volumes.

4.7   Impact on Volatility
Our analysis of the impact of halts on returns volatility showed interesting results.  
Recall that we measure volatility as the variance of daily returns.  Table 5 shows the 
results of our F-test and the non-parametric, Wilcoxon test on the variance of daily 
returns.  Figures 4 and 5 show the plot of daily returns over the 120-day period.  
Turning to Table 5, variance of returns for the overall sample is significantly higher 
for all 3 post-halt windows.  Post-halt volatility increases as the window period 
is lengthened.  This overall picture changes drastically when we decompose the 
sample by category.  

There is a marked contrast in the post-halt behavior of returns volatility between 
mandatory and voluntary halts.  The mandatory sample has a much higher volatility 
relative to the voluntary sample, even before halts.  This volatility increases very 
substantially after trading resumes for the mandatory sample.  As is obvious from 
the table, post-halt volatility is sharply higher in the +5 day window than abates, 

0

500 000

1000000

1500000

2000000

2500000

3000000

-80 -60 -40 -20 0 20 40 60 80

Days

V
o
lu
m
e OVERA LL

VOLUNTARY

MANDATORY



T
h
e In

tern
a
tio

n
a
l Jo

u
rn

a
l o

f B
a
n
kin

g
 a

n
d
 F

in
a
n
ce, 2007/08 V

ol. 5. N
um

ber 2: 2008: 125-148
 

143

Table 4:  Mean Daily Volume

Window 
Period

Price Effect By News By Duration Frequency

Overall Voluntary Mandatory Good Neutral Bad Short Med Long Single Multiple

(-60 to -1) 1286655.67 1356603.54 629645.35 822111.63 941522.34 1088635.79 854786.59 1023620.43 254520.92 689692.78 1187643.23

(+1 to 
+60)

2185269.80 2362928.92 516543.12 1997982.33 994148.79 770226.68 1644844.24 1141272.14 328407.22 774849.27 2399114.35

(-30 to -1) 1364379.15 1439878.43 655225.23 955564.17 900850.98 1082193.33 935325.93 985441.69 294677.40 684730.43 1289971.62

(+1 to 
+30)

2807778.98 3048612.53 545663.86 2685852.84 1115735.89 903458.55 2160076.39 1326152.05 273102.69 900419.21 3162613.77

(-5 to -1) 1492847.28 1557766.61 883069.28 953688.30 1236462.93 1250656.52 1038663.19 1292162.81 350597.77 1005471.20 1244040.27

(+1 to +5) 2141049.20 2232657.49 1280585.71 1376211.82 1770799.50 1219892.17 1376092.77 1966888.47 150451.11 1395489.80 1738650.14

T-Stat

-2.190
 (.032)

-1.830
 (.078)

-3.741 
(.020)

-2.216 
(.031)

-1.843 
(.076)

-4.142 
(.014)

3.397 
(.001)

1.218 
(.233)

-1.388 
(.238)

-1.609 
(.113)

-1.257
 (.219)

-3.440
(.026)

-.939 
(.351)

-3.202 
(.003)

-3.587 
(.023)

4.355
(.000)

1.758
(.089)

153
(.886)

-1.468
(.147)

-1.203
(.239)

-1.823
(.142)

-1.938
(.057)

-4.383
(.000)

-5.730
(.005)

-3.102
(.003)

.637
(.529)

3.392
(.027)

-1.955
(.055)

-3.222
(.003)

-5.284
(.006)

-1.453
(.151)

-1.185
(.246)

-2.063
(.108)

Wilcoxon 
Z-Stat

-1.708 
(.088)

-2.396
 (.017)

-2.023 
(.043)

-1.855
 (.064)

-2.581 
(.010)

-2.023 
(.043)

-3.401
(.001)

-2.252
(.024)

-1.214
(.225)

-4.667
(.000)

-3.116
(.002)

-2.023
(.043)

-.199
(.842)

-2.684
(.007)

-2.023
(.043)

-4.086
(.000)

-1.944
(.052)

-.135
(.813)

-1.701
(.089)

-2.417
(.016)

-1.214
(.225)

-1.362
(.173)

-3.363
(.001)

-2.023
(.043)

-2.739
(.006)

-.812
(.417)

-.2.023
(.043)

-2.010
(.044)

-2.787
(.005)

-2.023
(.043)

-1.494
(.135)

-2.910
(.004)

-1.753
(.080)

The table shows Mean Daily Volume by window period for the different categories.  T-stat values shown are for paired sample test the prob. values are 
shown below in brackets.



14
4 

 
T

h
e 

In
te

rn
a
ti

o
n
a
l 

Jo
u
rn

a
l 

o
f 

B
a
n
ki

n
g
 a

n
d
 F

in
a
n
ce

, 2
00

7/
08

 V
ol

. 5
. N

um
be

r 
2:

 2
00

8:
 1

25
-1

48 Table 5:  Volatility of Daily Returns

Window 
Period

Price Effect By News By Duration Frequency

Overall Voluntary Mandatory Good Neutral Bad Short Med Long Single Multiple

(-60 to -1) 2.554705 0.001254 26.5389 0.001138 0.001404 0.001767 0.00138 0.000976 0.001824 0.001354135 0.001106

(+1 to +60) 88.40587 0.001234 918.7779 0.000976 0.000917 0.001782 0.00102 0.000876 0.001212 0.001099934 0.000793

(-30 to -1) 3.472286 0.001357 36.07422 0.001295 0.00135 0.001897 0.00143 0.00106 0.003081 0.001350367 0.001368

(+1 to +30) 3.142628 0.000842 32.65298 0.000758 0.000844 0.001957 0.000958 0.000693 0.000256 0.00096044 0.000665

(-5 to -1) 4.707086 0.00212 48.90016 0.001632 0.002854 0.001365 0.002922 0.000976 0.000256 0.001648783 0.002818

(+1 to +5) 7.420658 0.00049 77.11724 0.000463 0.000455 0.000797 0.00052 0.000462 0.000243 0.000502735 0.000471

F-Stat

9.08E-05
(0)

1.225924
(0.041689)

0.34929
(0)

0.23357
(0)

2.938413
(8.18E-18)

169.7847
(3E-216)

6.34E-05
(0)

1.227714
(0.298876)

0.345276
(0.003712)

1.440614
(0.023763)

2.258433
(6.07E-06)

36.14764
(1.96E-60)

2.844534
(1.29E-08)

3.175171
(4.51E-10)

477.5973
(7.5E-126)

0.62466
(0.138705)

0.69845
(0.203276)

2.03542
(0.051991)

1.713403
(0.000349  )

1.938854
(1.61E-05)

217.5889
(2.9E-142)

2.628701
(3.2E-06)

11.01392
(1.77E-25)

5.811334
(1.29E-15)

1.849358
(0.201425)

1235.891
(1.49E-11)

0.92031
(0.454689)

1.941295716
(2.07403E05)

1.82645162
(9.64929E-05)

35.83203279
(2.45595E-78)

1.683927
(0.004039)

5.679062
(2.25E-17)

377.5843
(6.1E-106)

Wilcoxon 
Z-Stat

-1.558
(.119)

-3.650
(.000)

-5.200
(.000)

-2.307
(.021)

-3380
(.001)

-5.518
(.000)

-1.070
(.285)

-1.298
(1.94)

-.319
(.750)

-1.563
(.118)

-2.208
(.027)

-3.418
(.001)

-2.092
(.036)

-2.092
(.036)

-4.254
(.000)

-.274
(.784)

-.791
(.429)

-1.460
(.144)

-3.262
(.001)

-3.173
(.002)

-4.264
(.000)

-.343
(.732)

-.857
(.392)

-3.345
(.001)

-.296
(.767)

-2.073
(.038)

-1.007
(.314)

-1.049
(.294)

-2.102
(.036)

-4.501
(.000)

-2.463
(.014)

-2.703
(.007)

-3.166
(.002)

The tables shows average volatility of daily return by window period for the different categories.  T-stat values shown are for paired sample test the 
prob. values are shown below in brackets.



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 145

and is marginally lower for the +30 day window.  For the 60-day window, post-halt 
volatility is several times higher.  

While one would expect higher volatility as the sample period is lengthened, 
the increase in variance is very high for the 60-day windows. 10 In stark contrast to 
these results, the sample of voluntary halts showed a significantly lower volatility, 
post-halt.  Volatility falls substantially in the +5 day window before rising steadily 
as the window period lengthens.  In fact, as opposed to the mandatory sample, 
volatility is several fold lower for the 5 and 30-day windows, post-halt.

Figure 4, which shows a scaled plot of daily returns, captures this vast difference 
in volatility behavior between the two samples11.  When we tried to explore the 
reasons for the very high volatility of the mandatory sample, we came up with 4 
possible explanations.  Firstly, stocks of companies subjected to mandatory halts 
(being troubled companies to begin with), had been severely beaten down from their 
original IPO and par values.  Only about half of the sample was selling for above 
one ringgit, a typical par value.  Many selling below 50 sen were essentially “penny 
stocks”.  So, one key reason for the high volatility is the very low traded price.  
Small, absolute prices lead to large variance.  

Figure 4:  Volatility of Daily Returns (Scaled), Voluntary vs Mandatory

Secondly, mandatory halts experienced much longer suspensions.  The average 
length of trading suspension for our mandatory sample was 42 days.  Howe and 
Schlarboum (1986) had shown longer suspensions to coincide with bigger, negative 
residuals.  Thirdly, the low liquidity of these stocks might have been a factor, being 
that low liquidity tends to go hand-in-hand with high volatility.  Lastly, probably the 

10  This appears to be the instance where the parametric and non-parametric tests produced 
inconsistent results.
11  Daily returns of the mandatory sample were scaled by 10.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

-80 -60 -40 -20 0 20 40 60 80

Days

V
o
la
ti
li
ty VOLUNTARY

MANDATORY



146  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

most important reason for the very high post-halt volatility is information released 
during suspension.  As mentioned earlier, companies subjected to mandatory halts 
have to come up with viable restructuring plans before trading is allowed to be 
resumed.  We believe the announcement of these plans, which are extensive by 
nature, leads to an increased uncertainty, especially when initially implemented.  
Thus, the increased post-halt volatility.  

Our results of higher volatility for mandatory halts in the post suspension 
period are in-line with those findings of Ferris, et. al. (1992) who had examined SEC 
ordered trading halts.  There is also conformity with the results of Wu (1998) who 
had examined the mandatory halts in Hong Kong.  Further, as reported in Wu (1998), 
the largest change in value is typically on the 1st day of the resumption of trading.  
This too was consistent in our case.  Price variance on day 1 for our mandatory 
sample was 180%!  In fact, price reaction was highest on day 1 for most of our 
categories.  However, our findings of significantly lower volatility for voluntary 
halts are contradictory to Wu (1998), who had found volatility to be higher post-
suspension, even though they were lower than that of the mandatory sample.    

When the voluntary halts were categorized by type of news, the volatility 
results were in line with expectations.  The good news category showed significantly 
lower volatility for all 3 windows, post-halt.  Neutral news showed similar results.  
The bad news category on the other hand had a volatility that was no different post/
pre-halt.  Both tests showed post-halt volatility to be no different at the 5% level.  
In fact, variance was marginally higher for the 30 and 60-day windows, post-halt.  
Figure 5 plots changes in daily returns by news category.  The bad news category 
obviously has higher fluctuations relative to the other two categories and higher 
volatility, post-halt, relative to its pre-halt volatility (a result consistent with the 
findings of Tan and Yeo [2003]).

When volatility was examined by the duration of halt, both short and medium 
duration halts showed lower post-halt volatility.  For long duration halts, both tests 
showed no change in volatility.  Single/Multiple suspensions both displayed lower 
volatility, post-halt.

Figure 5:  Volatility of Daily Returns by Type of News

- 0.03

- 0.02

- 0.01

0

0.01

0.02

0.03

0.04

- 80 - 60 - 40 - 20 0 20 40 60 80

DAYS

V
O
L
A
T
IL
IT
Y

GOOD
NEUTRAL

BAD



The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148 147

5.    Conclusion

Most of our findings were broadly consistent and logical.  Trading halts are indeed 
significant events, with the type of halt (whether voluntary or mandatory) mattering 
tremendously.  The type of news released during the halt is the critical determinant 
of how price, volume, and volatility would behave, post-halt.  Also, the duration of 
halt and frequency, whether single or multiple, appears to be largely inconsequential, 
while anticipatory behavior/information leakages appear to go hand-in-hand with 
trading halts.

Our results support the price efficiency hypothesis of trading halts.  Mandatory 
halts and the ‘bad news’ category had shown an increased volatility and reduced 
trading volume, thereby lending support to the “learning through trading argument”.  
With the exception of these two subsets, our overall results are consistent with the 
argument that trading halts help disseminate information, thus enhancing the price 
discovery process.

Trading halts had resulted in a positive price reaction, increased volume, and 
volatility.  There was indeed a significant difference in the results of voluntary 
as opposed to mandatory halts.  The type of news released during the halt had a 
huge impact on all three variables of price, volume, and volatility, post-halt.  The 
duration of halt had an isolated impact and appears to be largely inconsequential.  
The frequency of halts, whether single or multiple, did not seem to matter either.

Comparing our results with that of previous studies, we found that for all three 
variables examined, there was broad conformity concerning our overall sample.  
However, when we refined the analysis by examining the sub categories, we found 
some interesting differences.  We found significantly positive CARs, post-halt, for 
our sample of mandatory halts, along with significantly lower volatility, post halt, 
for the voluntary sample.  

Author statement: Obiyathulla I. Bacha, Mohamed E. S. Abdul Rashid and Roslily 
Ramlee are staff members of the International Islamic University Malaysia. The 
authors acknowledge with thanks the financial support provided by the Research 
Centre at the IIUM. E-mail: obiya@iiu.edu.my

References

Bhattacharya, U., and M. Spiegel (1998). Anatomy of a Market Failure; NYSE 
Trading 

Suspensions (1974 – 1988), Journal of Business & Economic Statistics April, 216 
– 226.

Chen, H., H. Chen and N. Valerios (2003) The effects of trading halts on price 
discovery for NYSE stocks, Applied Economics 35:  91 – 97.

Christie, W.G., S.A. Corwin, and J.H. Harris, (2002). Nasdaq Trading Halts:  The 
Impact of Market Mechanisms on Prices, Trading Activity, and Execution 
Costs, The Journal of Finance LVII(3): 1443 – 1478.



148  The International Journal of Banking and Finance, 2007/08 Vol. 5. Number 2: 2008: 125-148

Duque, J., and A.R. Fazenda (2003). Evaluating market supervision through an 
overview of trading halts in the Portuguese stock market, Journal of Financial 
Regulation and Compliance  11(4): 349 – 376.

Ferris, S.P., R. Kumar and G.A. Wolfe (1992)  The Effect of SEC – Ordered 
Suspensions on Returns, Volatility and Trading Volume, The Financial 
Review 27(1): 1 – 34.

Hopewell, M.H., and A.L. Schwartz (1978). Temporary Trading Suspensions In 
Individual NYSE Securities, The Journal of Finance 33(5): 1355 – 1373.

Howe, J. S., and G.G. Schlarbaum (1986). SEC Trading Suspensions:  Empirical 
Evidence, Journal of Financial and Quantitative Analysis 21(3): 323 – 333.

Kabir, R., (1994). Share price behaviour around trading suspensions on the London 
Stock Exchange, Applied Financial Economics 24: 289 – 295.

Lee, M.C., M. J. Ready and P.J. Seguin (1994). Volume, Volatility, and New York 
Stock Exchange Trading Halts, The Journal of Finance XLIX(1): 183 – 
211.

McDonald, C.G., and D. Michayluk (2003). Suspicious trading halts, Journal of 
Multinational Financial Management 13: 251 – 263.

Tan, S.K., and W.Y. Yeo (2003). Voluntary Trading Suspension in Singapore, Applied 
Financial Economics 13: 517 – 523.

Wu, L., (1998). Market Reactions to the Hong Kong Trading Suspensions:  
Mandatory versus Voluntary, Journal of Business Finance & Accounting 
25(3 & 4): 419 – 437.


	International Journal of Banking and Finance
	3-1-2008

	The efficiency of trading halts: Emerging market evidence
	Obiyathulla I. Bacha
	Mohamed Eskandar S. A. Rashid
	Roslily Ramlee
	Recommended Citation