Stock Price Reaction in Stressful Circumstances:
An International Comparison*
M. Ameziane Lasfer
City University of London

Arie Melnik
Haifa University

Dylan Thomas
City University of London

Abstract
The objective of this paper is to document stock price behaviour in the period
following sharp price changes. In particular we focus on price behaviour using
daily market indexes from 40 stock exchanges over the period 1989 to 1997. Our
results are not consistent with the over-reaction hypothesis. We find positive(negative) abnormal price performance in the short-term windows (up to 10 days)
following positive (negative) price shocks. Our analysis also highlights some
important differences across markets classified as either developed or emerging.
While the price shock occurrences are evenly distributed between our two groups
of markets, we show that the post-shock abnormal performances are significantly
larger for our sample of emerging markets. We find that the incidence of priceshocks in both developed and emerging markets are not year- or monthdependent.

*
We would like to thank Manolis Liodakis for excellent research assistance. The usual disclaimer
applies.

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1

M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

Introduction

Sharp changes in stock prices were experienced by many stock markets in thepast few years. Such changes should generate a renewed interest in the pricebehaviour of security markets under “extreme circumstances.” This paper describes the price behaviour following large single day stock price changes.
Stock price movements are impacted by macroeconomic policy measures.1 At
the same time they require regulatory monitoring in order to maintain stability inthe financial sector. The market crash of 1987 caused a variety of regulatorychanges such as circuit breakers, daily price limits, improved settlement procedures and increased market making capacities.2 These regulatory changes are
geared to assist “markets under stress.” Therefore the price behaviour underunusual circumstances is of particular interest.
Price behaviour under unusual circumstances has also been investigated aspart of the “overreaction hypothesis.” Earlier research on overreaction focused on
US markets. US markets have been investigated by Brown, Harlow and Tinic(1988), Atkins and Dyl (1990), Park (1995) and many others.3 It is interesting to
examine if similar results hold for other exchanges and in particular in markets
that are substantially less liquid than the US market.
The literature on overreaction is still inconclusive as to the underlying reasonsfor changes that follow large price changes. Further empirical research in different markets can shed more light on the issue. The importance of further investigation is re-emphasized following recent drastic price changes in various Asianstock markets.
The purpose of this paper is to contribute to the overreaction literature using
daily returns and a large number of national stock market indexes from emerging
and developed markets. Using various short-term event windows we examine thedaily price reaction to large price changes. Large price changes are defined asrealized daily returns which are two standard deviations away from the average
return. We compare the price reaction patterns of developed markets to those of1
In this paper we do not explore the cause of a large single day change in stock prices. There is,of course, a large volume of literature that links stock price changes to economic and monetaryfactors. See, for example, Schwert (1990), Fama and French (1989), Fama (1990) and Jensen,
Mercer and Johnson (1996).
2
Many securities exchanges regulate asset price movements as a protective measure. Different
exchanges impose different regulatory limits on drastic price changes. In the Tokyo Stock Exchange(TSE), for example, each stock has a daily price limit that is set at a fixed yen amount based on theprevious day’s closing price. Circuit breakers are used on the New York Stock Exchange (NYSE).
These are the two main systems that are used to restrict drastic price movements. The NYSE
“breakers” resemble the TSE price limits with two differences. First, circuit breakers are based on amarket index rather than on individual securities. Second, circuit breakers trigger trading halts while
price limits allow transactions to continue as long as they are within the limits. For a recent review
of price limits see Kim and Rhee (1997).
3
There are only a few studies that covered daily price distributions and overreaction in non-US
markets. Good examples are the works of Alonzo and Gonzalo (1990) about the Spanish stock
market and Da-Costa (1994) about the market in Brazil.

Stock price reaction in stressful circumstances

243

emerging markets.
The plan of the paper is as follows. In the next section we review the literature
on overreaction. In Section 3 we provide some background information about themarkets that we investigate. Section 4 describes the methodology and Section 5contains the main empirical results. Section 6 describes some temporal effects andSection 7 extends them. Some concluding remarks appear in Section 8.

2

Review of the literature

One of the most widely documented stock market phenomena is the overreaction
hypothesis. DeBondt and Thaler (1985, 1987) argued that investors tend to overreact to extreme price changes due to the human tendency to overweigh current
information and underweigh prior data. If stock prices tend to “overshoot” theirtarget levels after a large shock, the price reversal (following large price change“events”) will reflect a movement back to equilibrium. Price reversals, following
large unexpected changes, would thus be the expected result of bringing prices
back to their fundamental values. This finding, which challenges the notion ofmarket efficiency and is referred to in the literature as stock market overreaction,
has generated a number of studies focusing on share price returns following large
price changes.
Howe (1988), Brown and Harlow (1988), Atkins and Dyl (1990), and Bremer
and Sweeney (1991) all document evidence which is broadly supportive of theinvestor overreaction hypothesis. In general these studies have tested for subsequent price reversal patterns in the short term (ranging from one day to a full year
using daily/weekly data). Qualified support is also provided by Park (1995) on
short term price movements (up to 20 days) following large price changes. Park
finds that apparent price reversals on the first day following a price shock disappear when mid-prices are used instead of closing transaction prices which areaffected by the bid-ask bounce. Moreover, while the short-run pattern of abnormal
returns following large price changes does suggest overreaction, the magnitude of
the returns is not sufficient to exploit economically. Overall, the sensitivity ofsome of these findings suggests some fragility in the definition and measurementof large price changes and in economic significance of the overreaction.
The pattern of price reversals cannot, however, be unequivocally attributed toinvestor overreaction. Other researchers, maintaining the underlying assumptionof market efficiency, attribute the results of DeBondt and Thaler to factors otherthan overreaction. Chan (1988) reports small positive, “probably economicallyinsignificant” risk-adjusted returns from an arbitrage portfolio of winners andlosers after allowing for the differential risk characteristics of the two groups.
More recently, Richards (1996 and 1997), using monthly index returns from 16
countries, shows that this apparent anomaly of winner-loser reversal is not a small
market phenomenon and that loser countries are not more risky than winnercountries. Jones (1987) relates the overreaction results to the January effects

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M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

while Zarowin (1989, 1990) extends the price reversals following large pricechanges to differences in firm size.4
Another strand of the academic literature has focused on momentum strategies.
Momentum strategies also assume that present stock returns are influenced bypast performance. However, present performance is positively correlated withearlier returns. Jegadeesh and Titman (1993) show that stocks with high returns
over a given time period (of 3 to 12 months) continue to outperform the stocks offirms with lower past returns during the same period. Chan, Jegadeesh and
Lakonishok (1996) also document the existence of medium-term continuation. In
fact they argue that it can be partly explained by investors’ underreaction to new
information. More in line with the present research, a recent study by
Rouwenhorst (1998) found that developed international equity markets exhibit a
medium-term return continuation.
Rouwenhorst (1998) focuses on individual stocks using a sample of 2,190
stocks from 12 European countries. He finds that internationally diversified
portfolios that acquire past medium-term “winners” and sell past medium-term“losers” outperform other possible portfolios. This outperformance cannot be
attributed to conventional measures of risk. Our research is different in that, like
Richards (1996), we study return patterns across markets at the country stock
market index level.

3

Background information

We analyze a sample of 40 stock markets for the period of January 1, 1989 to
December 31, 1997. The markets contain a wide spectrum of size, trading methods,
government regulations and foreign participation. The markets can be split into two
broad categories: advanced financial markets and emerging markets, particularly in
Asia. The individual markets are categorized as either developed financial markets
or emerging markets using the Financial Times and Morgan Stanley classification.
In terms of size, the sample includes the largest and most liquid stock markets.
The top ten stock markets in terms of market value are all included. These are theUS, Japan, UK, Germany, France, Canada, Switzerland, Hong Kong, Netherlands
and Australia. At the other end we have some of the lesser liquid markets, such as
Turkey, Greece, Pakistan, Jordan and Sri Lanka.
Our focus of attention is the stock index in each country (rather than individual shares). Table 1 provides summary statistics about the various indices. It confirms the known fact that there is a wide variation among financial markets.5 The
nine years that we cover (January 1, 1989 to December 31, 1997) include 2,331
trading days in most markets. We focus, of course, on daily returns. These are the4
Brown and Harlow (1986) show that even when the data for January are eliminated and thepost-event betas are used, the overreaction still persists. DeBondt and Thaler (1987) show that thesize effect is a complement and not a substitute for the overreaction results.
5
The risks of international investments have been analysed extensively in the literature. See Erb,
Harvey and Viskanta (1995, 1996), Ferson and Harvey (1991, 1994), and Harvey (1995).

Stock price reaction in stressful circumstances

245

returns of buying, in each country, a wide stockmarket index at the beginning of
the day and selling it at the day’s end. The mean daily return for each stock
market is shown in Table 1. This measure is subject to significant variations as
indicated by the relevant standard deviation.
The extreme values show that major price shocks can and do occur in eachmarket. Negative and positive shocks in a single day should be compared with themean daily return. Even on a non-comparative basis in many markets close to
10% price changes in a single day cannot be ruled out.6

6
Of course, extreme price shocks are not an everyday occurrence. They are fairly rare. Even in
the turbulent period that we investigate we find major advances (or declines) in only about 1.5% of
the cases.

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M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

Table 1: Summary statistics of daily returns—Jan. 1989 - Dec. 31, 1997
# DaysAustralia
Austria
Belgium
CanadaDenmark
FinlandFranceGermany
Hong Kong
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
NorwaySingapore
Spain
SwedenSwitzerland
UK
USA

2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331

Bangladesh
Chile
GreeceIndia
Indonesia
IsraelJordan
Malaysia
MexicoPakistan
Philippines
Portugal
South Africa
South Korea
Sri Lanka
TaiwanThailandTurkey

2070
2331
2058
2331
2331
2240
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331
2331

Mean
Standard
Minimum
Daily Return Deviation
Value
Panel A: Developed Markets
0.027
0.809
-8.0960.046
1.282-8.9170.030
0.649-5.1460.031
0.574-6.1740.040
0.649-6.2500.034
1.120-7.7370.033
1.103-7.2940.056
1.135-12.8120.076
1.595-21.7450.050
0.856-8.3870.025
1.252-12.378-0.013
1.168-6.9650.044
0.727-8.1790.060
0.790-5.6540.027
1.031-12.158
0.063
1.078-10.700
0.028
1.004-10.2500.040
0.997-8.2510.053
1.073-7.4650.067
0.979-10.517
0.044
0.715-4.0620.054
0.705-6.568Panel B: Emerging Markets
0.047
2.151
-29.280
0.106
0.890-4.3060.068
1.681-7.8930.076
1.582-9.7620.025
1.489-13.1260.101
1.485-11.300
0.035
0.732-9.2740.036
1.330-11.5130.149
1.563-13.3370.055
1.187-8.7400.049
1.577-9.2850.032
0.765-7.2270.053
0.964-11.176-0.023
1.465-10.953
0.065
0.967-6.1280.044
2.147-9.7770.014
1.695-10.9630.326
2.755-10.976

Sources: Authors’ calculations.

Maximum
Value
6.254
7.911
6.881
2.007
4.667
7.973
7.045
7.599
18.8245.935
14.97011.050
8.075
5.245
9.660
9.346
7.432
6.569
8.784
5.817
5.863
4.199
38.5965.305
14.74018.10638.2328.199
9.080
12.374
11.690
7.382
10.212
6.828
6.925
8.681
9.040
13.696
10.60313.855

Stock price reaction in stressful circumstances

4

247

Methodology and data

We collected daily data for 40 developed and emerging market indices for theperiod January 1, 1989 to December 31, 1997. For each market we use a broadindex that provides data, for most countries, on 2,331 trading days. The individual
markets are categorised as either advanced financial markets or emerging markets
using the Financial Times and Morgan Stanley classification.
In the existing literature there is no uniform definition of ‘large’ price changes.
Various researchers use different definitions. For example, Howe (1986) defines‘large’ price changes as those weekly price changes exceeding 50 percent. Brown,
Harlow and Tinic (1988) and Park (1995) define ‘large’ changes with reference tothe market model residuals. Atkins and Dyl (1990) choose those stocks with thelargest one-day price change in a 300-day window. Bremer and Sweeney (1991)
base their results on the stocks that recorded a daily price decrease of at least 10
percent.
We define a positive price shock as one where the return on a particular day
exceeds, by two standard deviations, the average market daily return. The average
return is computed over the previous 50-day trading period ending 10 days before
the day on which the positive price shock is recorded. The standard deviation is
also computed over the same 50-day period. Similarly, we define negative shocks
as occurring when the daily price return lies two standard deviations below theaverage of the previous 50 trading days. This method allows us to consider, foreach market and for each day in our sample period, changes in risk characteristics.
Table 2 provides the classification of these markets, the indexes used, the numberof positive and negative shock, and the average size of the price shocks.
In a number of instances, successive price shocks, as defined above, followed
within less than 10 days of each other. These cases obviously cause confounding
results when tracking the post-shock abnormal returns for a particular market. To
avoid any confounding effects, we eliminate for each individual market, those“double” price shocks occurring with 10 trading days of the previous shock. This
group of events constitutes our “Sample” data.7
The first two columns of Table 2 report the country and the name of the index.
We have included 22 countries in the developed markets sample and 18 countries
in the emerging countries sample. When we use non-overlapping events we find
32 positive and 39 negative shocks per developed country on average. The corresponding figures for the emerging markets are 36 and 34, respectively. Thus, there
are more positive shocks per market in the emerging markets and more negative
shocks per market in the developed markets.

7
The cases of double price shocks should be analyzed separately by treating the second andthird shocks as “after shocks.” In this study we treat each shock as an individual event and so do not
analyze cases of multiple shocks that occur in succession.

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M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

Table 2: Description of average shocks
Country

Index

Positive Shocks
Negative Shocks
N
ReturnN
ReturnPanel A. Sample and Characteristics of Shocks in Developed Markets
Australia
AUSTALL
33
1.87
39
-1.93
Austria
ATXINDX
32
3.37
47
-2.68
Belgium
BRUSINDX
28
1.44
52
-1.56
CanadaTTOCOMP
30
1.16
43
-1.25
Denmark
CHAGENI
30
1.47
44
-1.69
Finland
HEXINDX
33
2.78
41
-2.27
France
FRCAC40
31
2.38
40
-2.41
Germany
DAX
25
2.35
41
-2.78
Hong KongHNGKNGI
38
2.93
40
-3.74
Ireland
GJCIREQ
39
2.11
31
-2.51
Italy
ITMBGEN
30
3.10
29
-2.99
Japan
JAPA500
29
3.08
38
-1.72
Luxembourg
LXLUX13
39
1.88
37
-1.72
Netherlands
CBSKGEN
30
1.83
40
-1.86
New Zealand
TOTMKNZ
37
2.42
34
-2.54
NorwayOSEINDX
35
2.37
31
-2.69
Singapore
SNGPORI
40
2.12
37
-2.21
Spain
MADRIDI
35
2.09
37
-2.54
Sweden
AFFWALL
30
2.75
38
-2.18
Switzerland
SWISSMI
29
2.21
38
-2.52
UK
FTALLSH
22
1.82
39
-1.66
US
NYSEALL
31
1.52
42
-1.66
Panel B. Sample and Characteristics of Shocks in Emerging Markets
Bangladesh
BDTALSH
38
3.88
28
-4.97
Chile
IGPAGEN
37
1.89
32
-2.03
GreeceGRAGENL
34
4.13
22
-3.31
India
IBOMBSE
34
4.02
38
-3.25
Indonesia
JAKCOMP
35
2.49
35
-2.14
IsraelISTGENS
23
4.11
40
-3.61
JordanAMMANFM
52
1.83
25
-1.80
Malaysia
KLPCOMP
40
3.32
30
-2.48
MexicoMEXIPCI
34
3.78
29
-3.61
Pakistan
PKSE100
40
2.81
33
-2.86
Philippines
MANCOMP
35
3.86
39
-3.82
Portugal
LISBBTA
36
1.77
27
-1.83
S. Africa
JSEOVER
33
2.32
40
-2.02
S. Korea
KORCOMP
42
3.11
33
-3.43
Sri Lanka
SRALLSH
36
1.99
36
-2.04
TaiwanTAIWGHT
31
4.43
41
-4.24
ThailandBNGKBKC
40
3.66
33
-3.65
TurkeyTOTMKTK
34
6.38
33
-5.92
The table reports the sample of markets included in the analysis. Each country contains 2,331
observations except for Bangladesh with 2,070, Israel with 2,240 and Greece with 2,058.
Sources: Authors’ calculations.

Stock price reaction in stressful circumstances249

The magnitude of the shocks is, however, substantially larger in the emerging
markets compared to developed markets. The average positive shocks range
between 1.16 percent (Canada) to 3.37 percent (Austria) in the developed markets.
In contrast, in the emerging markets, the lowest positive return is 1.83 percent(Jordan) and the maximum is 6.38 percent (Turkey). Similarly, the negative
shocks range between -1.25 percent (Canada) to -3.74 percent (Hong Kong) for
the developed markets but between -1.80 (Jordan) to -5.92 (Turkey) percent for
the emerging markets. When we use all data over the whole sample period, we
find that Canada (Jordan) has the lowest standard deviation while Hong Kong
(Turkey) has the highest standard deviation amongst the developed (emerging)markets.
After computing the price shocks, we calculate the post-shocks abnormal
returns, ARit, as follows:
ARit = Rit - E(Rit)where Rit is the daily return on the index i and E(Rit) is the average return of a 50day window ending 9 days prior to the price shock. The 9-day gap allows us toabstract from any possible unusual price lead-up immediately prior to the priceshock.8 These abnormal returns are accumulated over 1, 3, 5, and 10 days following the price shock by adding them up over these periods.9

5Main empirical results

Table 3 provides a summary of the post-price shock cumulative abnormal returns.
Panel A shows the post-positive shocks for the non-overlapping cases. There are
706 such events. The post-shock cumulative abnormal returns are generally all
positive and significant. In the case of developed markets the CARs increase from0.29 percent in the day that follows the positive shock to 0.92 percent in thefollowing 10 days.
The cumulative abnormal returns experienced by investors in developed
markets following positive price shocks are similar in pattern to those obtained inemerging markets. As noted earlier in emerging markets, the positive shocks arelarger than those in the developed markets. The post-shock CARs are also larger
in emerging markets. They increase from 0.65 percent on the first day after theshock to 1.95 percent after 10 days. Figure 1 displays the CAR values following
positive price shocks for both developed and emerging markets using our sample.

8

We tried other gaps without much impact on the results.
This general approach is the one used by Brown and Warner (1980, 1985) and by mostresearchers in the field.
9250

M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

Table 3: Post-price shock cumulative abnormal returns
The table reports the cumulative abnormal returns following positive and negative shocks in bothdeveloped and emerging markets.
N

Mean
CAR 1
CAR 3
CAR 5
CAR 10Shock
Panel A: Post-Positive Shocks
Developed
706
2.24
0.290***
0.515***
0.697***
0.916***(44.68)(6.29)(7.41)(7.31)(6.46)
Emerging
654
3.23
0.651***
1.214***
1.481***
1.951***(34.90)(9.14)(9.42)(8.52)(7.62)
Panel B: Post-Negative Shocks
Developed
847-2.24-0.12***-0.192***-0.227***-0.526***(-41.46)(-2.26)(-2.26)(-2.29)(-4.03)
Emerging
591-3.20-0.31***-0.213-0.398***-0.872***(-34.65)(-3.52)(-1.31)(-1.99)(-3.19)Panel C: t-statistics in Differences in Means between Developed and Emerging Markets
Positive Shocks

9.38

4.26***

4.77***

3.95***

Negative Shocks
8.98
1.90
0.11
0.76
Panel D: t-statistics in Differences in Means between CARs
CARs
Positive
Negative
CAR1
CAR3
CAR5
CAR1
CAR3
Developed Markets
CAR3
2.71***-0.76
CAR5
3.85***
1.54
-0.99
-0.27
CAR10
4.20***
2.54***
1.29-2.96***-2.14**
Emerging Markets
CAR3
3.83***-0.52
CAR5
4.42***
1.23
-0.40
-0.72
CAR10
4.89***
2.57***
1.52-1.96***-2.07***

3.53***
1.14

CAR5-1.82*-1.40***, **, * significant at 0.01, 0.05, and 0.1 levels, respectively.

Panel B in Table 3 contains CAR values following negative shocks. There are
847 such cases in the developed markets. The cumulative abnormal returns are
significant. They decrease from -0.12 percent in the day immediately following
the price shock to -0.53 percent in the subsequent 10 days. In emerging markets
there are 591 events of drastic price shocks. With the exception of the 3 day
CARs all the post-negative shock abnormal returns are negative and statisticallysignificant. Figure 2 depicts the post-negative shock CARs in both developed andemerging markets.
The decline in returns seems to be larger than the comparable decline observed in the developed markets. Panel C (in Table 3), however, reports that thedifferences in cumulative abnormal returns following a negative shock are notStock price reaction in stressful circumstances251

significant. In contrast, the differences in the average size of CARs following
positive shocks are all significant. It appears that price increases in the emerging
markets are significantly larger and that investors in these markets earn substantially higher returns after their markets experience a large positive price shock.
Panel D, Table 3 reports the t-statistics of the differences in means between
the different CARs. In both the developed and the emerging markets, the CARs
following positive shocks exhibit a similar pattern. The increases in abnormal
returns following the first day cumulative abnormal returns are all positive andstatistically significant. This suggests that prices continue to adjust in the same
direction. Similarly, the increase in the abnormal returns from day 3 to day 10 isstatistically significant. However, the increase in CARs from the 3rd to the 5th
day is not significant. In contrast, the differences in cumulative abnormal returns
following negative shocks are not always statistically significant. The pattern ofsignificance suggests that the changes in abnormal return continue to be negative.
However, the differences in the CARs are only significant for day 10 relative tothe other CARs. In particular, we observe that most of the decline occurs between
day 5 and day 10.

Figure 1: Post-positive shock cumulative abnormal returns (sample)

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M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

Figure 2: Post-negative shock cumulative abnormal returns (sample)

6

Temporal effects

To investigate the possibility that our results are driven by price jumps in a particular year of our sample period, we split our data into each individual calendar
year in our sample period. The full results are reported in Table 4. The first row ofeach panel reports the number of annual cases. We note that the positive and thenegative price shocks are not confined to a particular year in the sample period.
The lowest number of shocks appears to have happened in 1989 and the largest
number is in 1993. However, the differences across the years are not substantiallylarge to affect our results. The results are qualitatively similar to those obtainedusing the full sample.

Table 4: Annual distribution of positive and negative shocks and post-shock cumulative abnormal returns
1989
51 (7.2)
1.716
0.485
0.752
1.123
1.085

Number (%)
Average
CAR 1
CAR 3
CAR 5
CAR 1045 (6.9)
2.935
0.912
1.126
0.984
1.806

Number (%)
Average
CAR 1
CAR 3
CAR 5
CAR 1069 (8.2)-3.024-0.2420.233
0.376-1.337

Number (%)
Average
CAR 1
CAR 3
CAR 5
CAR 1048 (8.1)-3.048-0.233-0.389-0.287-0.539

1991
1992
1993
1994
1995
Panel A: Positive Shocks - Developed Markets Sample
76 (10.8)86 (12.2)
81 (11.5)
103 (14.6) 66 (9.4)
90 (12.8)
2.582
2.668
2.663
2.177
2.245
1.866
0.782
0.161
0.281
0.397
0.291
0.114
0.994
0.442
0.553
0.542
0.761
0.499
1.244
0.742
1.076
0.633
0.741
0.549
1.351
1.908
1.229
0.561
0.759
0.854
Panel B: Positive Shocks - Emerging Markets Sample
68 (10.4)80 (12.2)
77 (11.8)
94 (14.4)
63 (9.6)
73 (11.2)
3.873
3.551
2.883
2.932
3.138
2.844
1.174
0.525
0.450
0.388
0.502
0.470
2.511
0.831
0.804
1.128
1.079
1.329
3.188
1.122
1.112
1.288
1.230
1.598
2.789
2.146
2.191
1.888
1.357
2.158
Panel C: Negative Shocks - Developed Markets Sample
111 (13.1) 86 (10.2)
84 (9.9)95 (11.2)
102 (12.0) 85 (10.0)-2.357-2.782-2.122-1.905-2.186-1.789-0.5580.251-0.232-0.2600.062-0.120-1.0690.977-0.125-0.1600.179-0.007-0.6600.764-0.189-0.431-0.1270.072-1.7820.738-0.250-0.565-0.282
0.344
Panel D: Negative Shocks - Emerging Markets Sample
74 (12.5)
54 (9.1)
60 (10.2)
58 (9.8)
72 (12.2)
72 (12.2)-3.266-3.452-2.780-2.622-3.334-2.864-1.064-0.129-0.183-0.090-0.514-0.451-1.506-0.249-0.2400.052-0.9580.386-1.606-0.737-0.498-0.065-1.2180.339-1.917-1.684-1.578-0.128-2.602
0.324

1996

1997

80 (11.3)
1.956
0.003
0.138
0.151
0.275

73 (10.3)
2.139
0.179
0.068
0.182
0.254

86 (13.2)
3.323
0.591
1.023
1.368
1.173

68 (10.4)
3.596
1.098
1.254
1.458
2.106

100 (11.8)-1.7400.063-0.100-0.436-0.489115 (13.6)-2.406-0.287-1.092-0.899-0.83564 (10.8)-3.147-0.0720.450
0.302-0.12589 (15.1)-3.912
0.049
0.457
0.168
0.193

Stock price reaction in stressful circumstances

Number (%)
Average
CAR 1
CAR 3
CAR 5
CAR 10

1990

253

254

M. Ameziane Lasfer, Arie Melnik, and Dylan Thomas

In the analysis of the post-shock cumulative abnormal returns, we find significant annual differences across the two types of market throughout the sample
period. The cumulative abnormal returns reached their peak in 1990 and 1991.
However, throughout the sample period, the post-shock abnormal returns havedecreased substantially. In 1996 and 1997 they decreased to their lowest level. In
contrast, in the emerging markets, the post-positive shock cumulative abnormal
returns are relatively constant throughout the sample period. Again, the levels in1990 are, however, larger than the other years.
In the case of stock market behavior that follows negative shocks, we observea slightly different pattern. In the case of developed markets in 1991 (Panel C in
Table 4) we observe a pattern of reversal. This is the only year where the cumulative abnormal return (following the shocks) appears to provide some support forthe price reversal hypothesis as negative shocks are followed by positive returns.
Panel D of Table 4 displays the annual distribution of the average post-negative
shocks in the emerging markets. In the 1989-1994 period almost all the negative
shocks are followed by substantial cumulative returns that go in the same direction. The largest 10-day cumulative abnormal returns occurred in 1994 when theyreached -2.6 percent. In the last three years—1995-1997—of our sample period,
the negative shocks are followed by positive returns, thus supporting the overreaction hypothesis.
Previous studies show that price reversals may be driven by the month of theyear (e.g. Jones (1987)). In particular, if all the abnormal price changes occur inJanuary, the results will be driven by the so-called January effect. The monthly
distribution of the shocks using all cases is reported in Table 5. In Table 5 wereport the full results. Table 5 shows that, in both markets, the positive and thenegative shocks are distributed evenly across the months of the year. The cumulative abnormal returns are, again, not month of the year dependent. The postpositive (negative) cumulative abnormal returns are always larger (more negative)in emerging markets relative to developed markets.

Table 5: Monthly distribution of post-positive and post-negative shock cumulative abnormal returns

Number (%)

Average
CAR 1
CAR 3
CAR 5
CAR 10

Average
CAR 1
CAR 3
CAR 5
CAR 10
Number (%)

Average
CAR 1
CAR 3
CAR 5
CAR 10
Number (%)

Average
CAR 1
CAR 3
CAR 5
CAR 10

Feb.

March

91 (12.9)
2.805
0.101
0.113
0.240
1.078

37 (5.2)
2.355
0.142
0.238
0.805
1.360

37 (5.2)
2.395
0.084
0.424
0.606
0.694

65 (9.9)
3.182
0.795
1.466
2.171
3.558

52 (8.0)
3.445
0.803
1.087
1.208
1.737

34 (5.2)
3.549
0.618
1.242
2.018
1.964

67 (7.9)-2.224-0.096
0.015
0.017
0.597

63 (7.4)-1.917-0.037-0.649-1.181-1.71670 (8.3)-2.136-0.257-0.654-0.701-0.96362 (10.5)-3.108-0.662-0.605-0.858-0.80844 (7.4)-3.617-0.832-1.083-1.510-3.62343 (7.2)-3.600
0.098
0.310
0.713
-0.313

April
May
June
July
Aug.
Panel A: Positive Shocks - Developed Markets
55 (7.8)
60 (8.5)
33 (4.7)
83 (11.8) 63 (8.9)
3.195
1.851
1.832
1.725
2.177
0.321
0.354
0.348
0.354
0.534
1.058
0.881
0.457
0.597
0.768
1.153
1.213
0.505
0.637
0.788
1.814
1.537
0.539
0.747
0.834
Panel B: Positive Shocks - Emerging Markets
43 (6.6)
62 (9.5)
49 (7.5)
59 (9.0)59 (9.0)
3.057
2.686
3.557
2.972
2.667
-0.065
0.843
0.740
0.586
0.805
0.195
1.566
1.526
1.422
1.275
0.477
2.050
2.490
1.401
1.017
1.222
2.523
3.174
1.168
1.264
Panel C: Negative Shocks - Developed Markets
60 (7.1)
53 (6.3)
83 (9.8)
76 (9.0)111 (13.1)-2.080-1.907-1.945-1.697-2.930-0.248-0.061-0.192-0.349-0.3250.052-0.1970.003-0.3110.026
0.496-0.338-0.226-0.891
0.169
0.624-0.286-0.231-1.922-0.442
Panel D: Negative Shocks - Emerging Markets
47 (8.0)37 (6.3)
56 (9.5)
45 (7.6)
65 (11.0)-3.304-3.483-2.393-2.401-3.3520.178-0.0920.131-0.650-0.9810.341
0.237-0.429-0.638-0.837-0.2020.160-0.791-0.778-0.722-1.5520.211-1.311-1.779-1.385

Sept.

Oct.

Nov.

Dec.

41 (5.8)
2.880-0.150-0.0960.263-0.29365 (9.2)
2.418
0.418
0.627
0.595
0.523

56 (7.9)
2.387
0.430
0.321
0.561
0.294

85 (12.0)
1.972
0.334
0.586
0.959
1.295

48 (7.3)
3.333
0.854
1.135
1.256
1.190

60 (9.2)
3.708
0.866
1.502
1.239
0.672

62 (9.5)
3.033
0.424
0.990
0.965
2.261

61 (9.3)
3.095
0.324
0.783
1.305
2.183

70 (8.3)-1.810-0.160-0.580-0.692-1.77797 (10.7)-3.1880.303-0.2520.240-0.26654 (6.4)-1.986-0.170-0.122-0.359-0.42549 (5.8)-2.235
0.358
0.426
0.649
1.113

44 (7.4)-2.841-0.212-0.171-0.563-0.42254 (9.1)-3.587-0.119-0.0290.312-0.56960 (10.2)-3.723-0.406
0.3443-0.167-0.14434 (5.8)-2.833
0.335
0.500
0.047
2.174

Stock price reaction in stressful circumstances

Number (%)

Jan.

255

256

7

M. Ameziane Lasfer, Arie Melnik, and Dylan ThomasMarket reaction and shock level

Table 6 reports the results of the regressions of the various cumulative abnormal
returns against the level of the shock for each market using only the nonoverlapping cases. The results reported in Panel A indicate that, for both developed and emerging markets, the cumulative abnormal returns are not always
strongly related to the levels (or size) of the shocks. Although the coefficients are
positive, suggesting that positive abnormal returns follow positive shocks, theirlevel of significance is not always high. For the developed markets, the highsignificance is limited to the 5 and 10-day CARs. For the emerging markets, only
the first day abnormal returns are significantly related to the levels of the shocks.

Table 6: Regressions
Panel A: Positive Shocks
Developed Markets n = 706

1 Day CAR
3 Day CAR
5 Day CAR
10 Day CAR
1 Day CAR
3 Day CAR
5 Day CAR
10 Day CAR

Regression Coefficientt-stat

0.022
0.016
0.178
0.308
Emerging Markets n = 654
0.067
0.026
0.082
0.064

0.635
0.298
2.348
2.676
1.977
0.424
0.986
0.521

Panel B: Negative Shocks

1 Day CAR
3 Day CAR
5 Day CAR
10 Day CAR
1 Day CAR
3 Day CAR
5 Day CAR
10 Day CAR

Developed Markets n = 847-0.259-0.513-0.757-0.627Emerging Markets n = 591-0.106-0.342-0.505-0.503-8.187-9.982-13.153-7.793-2.395-4.463-5.178-3.649

Stock price reaction in stressful circumstances

257

In contrast, the results reported in Panel B suggested that all the post-event
cumulative abnormal returns are significantly related to the level of shocks. Thatis, larger single day shocks are followed by further price declines in the following
five trading days.

8

Conclusions

In this paper, we test the over-reaction hypothesis using daily data from a large
number of developed and emerging markets. In contrast to many previous studies,
our results do not support the price reversal hypothesis. We show that, for bothdeveloped and emerging markets, the post-positive shocks are followed by subsequent large positive abnormal returns and the post-negative price shocks arefollowed by negative abnormal returns. As expected, we find strong differences
across the two types of market. The magnitude of the shocks is significantly large
in the emerging markets. In addition, the cumulative abnormal returns following
the positive shocks (but not the negative ones) are substantially larger in theemerging compared to the developed markets. We show that both types of shockand the cumulative abnormal returns are relatively evenly distributed across theyears of our sample period. In addition, we show that the results are not driven by
the month effect. In general our findings are inconsistent with DeBondt andThaler (1985). They are consistent with Cox and Peterson (1994) who also foundthat prices tend to decline after large negative shocks.
The puzzling price pattern following a major price shock may be explained bya rapid transmission of information. The sharp increase or decrease serves as asignal. The new information that caused a large advance or decline in the pricelevel leads to gradual after-shock in the same direction. Not all participants react
in the first day. This results in an apparent “trend chasing” process that continues
for a few more days.

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