1 The effect of air crashes on plane manufacturers value.

1 The effect of air crashes on plane manufacturers value.

Abstract.

The effect of air crashes on the airline industry is widely discussed in previous literature. The effect on the manufacturer of the plane is a less commonly discussed topic and this paper will examine this. There is literature available that discusses the effect of air crashes on the manufacturer of the plane; however, this literature is outdated and was based on small samples. This paper uses two models to test the hypothesis that plane manufacturers are negatively affected in their value because of air crashes. Those models are the capital asset pricing model and the market model. Also there will be tested if there is a difference in effect for crashes before the year 2000 and after the year 2000. This study finds that air crashes have no significant effect on the returns of aircraft manufacturers when using the entire sample. The average abnormal returns are not significant for both models. Moreover, when comparing the subsamples with the two different models the test results do not show any difference. The value of plane manufacturers is not negatively affected by plane crashes, regardless of when they happened.

Bachelor thesis for E&BE, BE

Jochem Loonstra, S2543427

Rijksuniversiteit Groningen

2 1. Introduction

Ever since the beginning of aviation in human history there have been accidents. Accidents involving aircrafts often create horrible scenes because they frequently lead to loss of human life. A loss of human life is not something that cannot be accounted for or compensated.

Airline companies suffer, as described by Davidson, Chandy, and Cross(1987), in at least three ways. Firstly, the damage to the aircraft itself, mostly beyond repair. Secondly, the compensation for physical injury or death to the passenger. Lastly (and perhaps the most important one) is the loss of goodwill, often resulting in lower demands for the industry or switching to different airlines. That the airline suffers major losses due to an aviation disaster is a commonly known fact. Whether the manufacturers of the involved planes also suffer from a decrease in value will be tested in this paper.

Aviation is an industry that is sensitive to political and economic changes. Therefore, a change in the value of companies involved in air crashes is something that can be expected.

The hypothesis that the value of the involved airlines decrease after air crashes has also been shown by research. Chance and Ferris(1987) proved that there is a decrease in stock value of the carrier right after a crash. On average, the manufacturer of the airplane has less harm of a crash, this is because there are less substitutes for them. A consumer can easily switch between airlines, but plane manufacturers are less sensitive to switching as there are less options, and often there are contracts involved. Commercial aviation has only been around since 1914 and people were more sceptical towards flying in the early days. The aviation industry has developed a lot in the past decades and has become more widely available. Scepticism has decreased and rules for safety have increased. Therefore, we will also test if investors reacted stronger to aviation disaster before the year 2000 than after the year 2000.

In this paper the effect of air crashes on the returns of plane manufacturers will be tested,

by the use of event study methodology. The hypothesis which will be tested, is that the

value of plane manufacturers is negatively affected because of plane crashes. The second

3 hypothesis that will be tested is the one that crashes that occurred before the year 2000 have more negative effect than crashes after the year 2000.

2.1 Literature review

Literature on the effect of air crashes on the value of companies involved is easily available.

Three papers are very relevant to my paper because of their similarity in topic and use of event study methodology. These papers are are Chance and Ferris(1987), Davidson, Chandy, and Cross(1987) and Walker, Thiengtham, and Yi Lin(2005).

Chance and Ferris(1987) examined the effect of air crashes on the value of the two companies that are most involved in an air crash; the manufacturer of the plane and the carrier. Their samples consist of crashes that occurred in the period 1962-1987, a period in which aviation was still developing on a large scale. Their main findings is that the stock market immediately reacts to an air crash by a significant negative abnormal returns of 1.2%

on average. This effect does not continue after the day of the crash suggesting that the stock market only reacts on the economic impact on one trading day. They do not find significant effects on the value of the plane manufacturer after a crash. As an explanation for this they argue that investors apparently see aviation disaster as a carrier specific event, that has no financial relevance for the manufacturer.

Davidson, Chandy, and Cross(1987) also examined how the value of airlines is affected by air crashes. They also use event study methodology and have a sample of 57 crashes. They also examine whether the most severe crashes in their sample have a more significant effect than the less severe ones. They find that, when using all crashes in their sample, the value of the airliners decreases by 0.785% on average. They explicitly mention that the negative effect is reversed within 5 trading days after the crash.

Walker et al.(2005) also examine the impact of plane crashes on the value airline and

manufacturer. They also test the effect of different causes and severity of the crash. They

find that investors react to a crash by a decrease in value of 2.8% and 0.8% respectively for

the carrier and the manufacturer. Moreover, they find that crashes which are caused by acts

of terrorism have a more significant effect and this is the same for crashes with a higher

4 number of fatalities. This counts both for manufacturers and carriers, so they do find a negative effect for manufacturers, in contradiction to the findings of Chance and Ferris (1987).

The effect of plane crashes on the carrier has been tested many times, and all literature agrees on the fact that it has a negative effect. When it comes to manufacturers of the planes, the literature is contradictory. Chance and Ferris(1987) use old data and have a small sample, they find no effect. Walker et al.(2009) on the other hand use a large sample that is up to date; however, they have included 9/11, which is more than a firm-specific event and they do find an effect. By using a large sample and leaving out 9/11 this paper hopes to give a decisive answer to the question whether plane manufacturers are also significantly affected by plane crashes. Thereby, the test to see if there is a more negative effect for crashes before than after the year 2000 will be conducted.

2.2 Hypothesis.

This paper will test two hypotheses.

Hypothesis 1: the value of plane manufacturers is negatively affected by air crashes.

Hypothesis 2: crashes that occurred before the year 2000 have more negative impact on the manufacturers value than crashes that occurred after the year 2000.

3. Data and methodology 3.1 Sample

The sample used for this study consists of 120 events, that occurred between 1963 and 2015. For this study it was necessary to have crashes from a wide range of time to make sure the effects for the entire sample are not time related. The first event used in this study was in 1963, one year after the Boeing went public for the first time. The sample was collected by working down a list of most severe crashes, provided by the aviation

network(https://aviation-safety.net/), an online database, that is updated every week and contains descriptions from over 15,800 aviation accidents and incidents since 1921.

The sample contains three different plane manufacturers, Boeing, Airbus and Lockheed.

Appendix 1 shows the event number, date, airline, manufacturer and number of fatalities of

the selected crashes. The subsample of crashes that happened before 2000 contains 82

5 events and the one subsample of crashes after 2000 has 38 events in total. The average number of fatalities was 131 and the average date of occurrence was 28-4-1992. The data on historical stock prices of the plane manufacturers was obtained from Yahoo Finance.

3.2 Methodology

This study uses event study methodology using two different models. These models are the constant mean return model and the market model.

According to MacKinlay (1997) the constant mean return model is the simplest model and uses an estimation window with the stocks on returns to obtain an expected return for the event window. Although it is a very simple model Brown and Warner(1980,1985) find that it often creates results similar to more complex models.

The market model works in a manner that it relates the return of any given security to the performance of the market portfolio. The models linear specification comes forth out of the assumed joint normality of the returns of the security and the market. In case the day of the crash was not a trading day, the first following trading day was used as the event day.

Every event study, no matter what model, uses abnormal returns to measure the economic impact of an event on the company’s value.

The return function used in this study is:

R

= LN(P

) – LN(P

) (1)

In this formula R is the return on day t, given as a function of the adjusted closing price of stock i, on time t and t-1. The adjusted closing price is the stock price adjusted for dividends and stock splits. This function makes use of natural logarithm because it reduces problems of non-normality.

The formula for the abnormal returns looks as follows:

AR

= R

– E(R

) (2)

In this formula R is the actual return of security i on time t and E(R

) is the expected return

of security i on time t.

6 This study uses two different methods to obtain expected returns, the mean return of an estimation period and the market model which runs a regression using the market returns as the independent variable, and the actual returns as the explanatory variables.

Expected return in the constant mean return model is the mean return of an estimation period. This study uses a estimation period of 200 days.

The expected return function looks like this:

E(R

) = (1/200) * ∑

𝑅𝑖, 𝑡 + e (3)

In this formula E(Ri,t) is the expected return from stock i, on time t, R

is the actual return of security i on time t, and e is an error term. The 200 represents the 200 days of the

estimation window which ends 11 days before the event date.

The market model, on the other hand, uses a market portfolio to derive expected returns.

This study uses the S&P500 returns as a benchmark for the returns of the company. Hereby a regression was run using the S&P500 returns as the independent variable and the returns of the companies used in this study as the explanatory variable. The expected returns formula of the market model looks as follow:

E(R

) = α + β R

+ e (4)

In this formula E(Ri, t) is the expected return of security i, on time t. Furthermore α is the intercept obtained from the regression, β is the slope and e the error term. R is the return of the benchmark which is used, in this case the S&P500, on time t.

To test the average abnormal returns per day on normal distribution a Jarquebera test was used with the following formula:

𝐽𝑎𝑟𝑞𝑢𝑒𝑏𝑒𝑟𝑎 =

6

+ (𝑆

+

4

(𝐶 − 3)

) (5)

In this test N is the sample size, k the number of regressors, S the skewness of the

distribution and C the kurtosis. The Jarquebera test tests the null-hypothesis of normal

distribution.

7 To observe whether the market reacts to a plane crash we are going to test if the average abnormal return of a day is different from zero. The average abnormal return on time t is given by this formula:

AAR

= 1/N * ∑

ARi, t (6)

Here, N is the number of observations used and AR

is the abnormal return of security i on time t.

There is a good possibility that the stock market does not react significantly on the day itself but over the window of several days. Therefore this study also tests if the cumulative

average abnormal returns deviate from zero. The formula of the Cumulative average abnormal returns is given by:

CAAR

= ∑ 𝐴𝐴𝑅𝑡

(7)

In this formula the CAAR is given as the sum of the average abnormal returns in the period that starts at t1 and ends at t2.

Now, to test if the average abnormal returns for a specific day deviates from zero we need to calculate the variance of the average abnormal returns. The formula for this variance is given by:

VAR(AAR

) = ∑

((𝐴𝐴𝑅𝑡 − 𝑀𝐴𝐴𝑅)

/(𝑥 − 1)) (8)

In this formula AAR

is the average abnormal return on day t, MAAR is the mean of the average abnormal returns per day and x the number of days used in the calculations.

Assuming normal distribution a t-test can be used to test whether the average abnormal returns deviate from zero.

t = AAR

/ (VAR(AAR

))

(9)

We expect the abnormal returns to be negative, so a one-sided test is used. The degrees of

freedom for this test is N-1, so 119 here. The null-hypothesis of AAR

being equal to zero can

be rejected if the t-value exceeds the critical values, in this case 1.984 for 95% confidence

level and 2.626 for 99% confidence level.

8 Now, for testing if the cumulative average abnormal returns deviates from zero we first need to obtain the variance of the CAAR. The formula for the variance of the CAAR is:

VAR(CAAR(t

,t

)) = ∑

𝑉𝐴𝑅 (AAR

) (10)

In this formula the variance of the cumulative average abnormal returns from t

to t

is obtained by adding up the variances of the average abnormal returns from t

to t

.

To test if the CAAR(t

,t

) is different from zero, assuming normal distribution, we use the following formula:

t = CAAR(t

,t

) / VAR(CAAR(t

,t

))

(11)

All formulas to test the statistical significance so far have assumed normal distribution.

Also, we test the hypothesis that crashes that happened before 2000 have a more negative effect on the manufacturers value than crashes that happened after 2000. We test the difference in means using the following formula:

t = MAAR

– MAAR

/ ((VAR^0.5(AAR

) * √

𝑁1

+

𝑁2

) (12)

Here MAAR

is the mean of the average abnormal returns per day of all crashes after 2000, MAAR

is the mean of all average abnormal returns for crashes after 2000 and N

and N

are the number of observations used to compute the average. The Average abnormal returns here are for all crashes and all days in the event window. The test uses df = N

+ N

– 2 degrees of freedom. The VAR^0.5(AAR

) term is the standard deviation of the entire sample for the average abnormal returns.

We also used a non-parametric Corado-test to examine if the average abnormal returns on the event-day deviates from 0. The Corado test looks as follows:

Θ

= (1 / N) *

𝐿2+1 2 ) 𝑁

𝑖=1

𝑆(𝑘)

(13)

In this test N is the total number of events, K

is the rank event i on day 0, L2 is the number

of days used in the test and S(k) is the standard deviation given by the following formula:

9 S(k) = √

∑

(

𝑁

∑ (𝐾𝑖, 𝑡 −

2

𝑁𝑖=1

))^2 (14)

Again, L

is the number of days used in the test, K

is the rank of the return from stock i on time t and N is the total number of events.

The Mann-Whitney u-test is a rank-test that can be used to compare both subsamples used.

First, a U value is computed for both subsamples using this formula:

U

= R

– (N

(N

+ 1)/2) (15)

In this formula R

is the sum of ranks of subsample i, and N

is the number of observations in the subsample, in this case 31 days.

For larger samples, U is normally distributed and the test statistic is:

Z = (U - ɱ

) / Ϭ

(16)

Here ɱ

is given by:

ɱ

= (N

N

)/2 (17) And Ϭ

is given by:

Ϭ

= √

12

(18) N

and N

are the number of observations used in both subsamples.

4 Results

The average expected returns were 0.0072% and 0.00022% respectively for the constant mean return model and the market model. Apendix 2 present the descriptive statistics from the average abnormal returns per day of the estimation window. The first thing to mention are the jarquebera values of 36.17 and 36.19 were the critival value is 9.21, so the average abnormal returns in the estimation window are not normally distributed.

The average abnormal returns per day and their belonging t-values of the constant mean

return model and the market model can be found in appendix 3 and 4 respectively. When

considering the total sample, only the market model shows one significant abnormal return,

10 which is on day -4, no clear effect of the event. The value of plane manufacturers is not decreasing as the result of a crash. The t-test used to compare the means of the average abnormal returns from both samples gave a t-statistic of 1.256 for the constant mean return model and -0.074 for the market model. As the critical value is 1.697, the conclusion that there is no difference between the two subsamples can be drawn, based on the parametric test.

The Corrado-rank test gave test statistics of 0.794 and 0.853 for the constant mean return and market model respectively. In this case the critical value was 1.962(for a 95% confidence level). Thus the conclusion can be drawn that both models do not find any significant

abnormal returns on the event day. The Mann-Whitney u-test used to test the difference between the two subsamples gave z-values of -1.316 and -0.605. These z-scores led to p- values of 0.0885 for the constant mean return model and 0.2578 for the market model, both too high to reject the null-hypothesis that both samples are equal.

No significant values have been found. The return of plane manufacturers is not negatively influenced by crashes, regardless of when they happened.

5 Conclusion

The results of this paper are very clear, plane manufacturers are not affected in their value as a result of plane crashes. After examining the effect of plane crashes on the stock returns of three different manufacturers using two different models, we can conclude that there are no significant effects. As mentioned before, Walker et al.(2009) do find a negative effect on the value of manufacturers. However, this is probably because they included 9/11 in their test, which has a high negative effect on the average abnormal returns. This study shows that the airplane manufacturer does not suffer economic downturn as the result of a plane crash, at least not in the short term.

Also the hypothesis that crashes that occurred before 2000 have more negative impact than

the ones after can be rejected. We find no difference in effect when separating crashes that

occurred before 2000 and after 2000. Apparently investors have never felt that

11 manufacturers of airplanes face economic consequences due to plane crashes, at least the stock market does not react in the short term.

As a conclusion we can say that investors are confident in the financial performance of a

plane manufacturer and that it will not be affected by a plane crash. This might be because

there are only a few manufacturers that an commercial airline can switch to, in case they

lose faith in their current supplier. Also the cause of an air crash is almost never relatable to

the manufacturer. A major weakness of this study is that the estimation windows contains

other events, so this has an effect on the validity of the results. Future research should leave

other events out of the estimation window and test to see if in case plane malfunction

caused a crash the returns do decrease.

12 6 References

Brown, S.J., and Warner, J.B. (1980). Measuring Security Price Performance. Journal of Financial Economics, 8, 205-258. North-Holland Publishing Company.

Brown, S.J., and Warner, J.B. (1985). Using daily stock returns: The case of event studies.

Journal of Financial Economics, 14, 3-31, North-Holland Publishing Company.

Chance, D.M., and Ferris, S.P. (1987). The effect of aviation disasters on the air transport industry: A financial market perspective. Journal of Transport Economics and Policy, 21, 151- 165.

Davidson ΙΙΙ, W.N., Chandy, P.R., and Cross, M. (1987). Large losses, risk management and stock returns in the airline industry. The Journal of Risk and Insurance, 54, 162-172.

Lin, M.Y., Thiengtham, D.J., and Walker, T.J. (2005). On the performance of airlines and airplane manufacturers following aviation disasters. Canadian Journal of Administrative Sciences, 22, (1), 21-34.

MacKinlay, A.C (1997). Event Studies in economics and finance. Journal of Economic

Literature, 35, 13-39.

13 7 Appendix

Apendix 1. The sample.

Event no. Date. Airline. Manufacturer. Fatalities.

1 27-3-1977 KLM Boeing 583

2 12-11-1996 Saudi Arabian flight Boeing 349

3 1-6-2009 Air France Airbus 228

4 6-8-1997 Korean Air Boeing 228

5 31-10-1999 Egypt Air Boeing 217

6 6-2-1996 Birgenair Boeing 189

7 17-7-2007 TAM Airbus 199

8 28-12-2014 Indonesian Air Airbus 166

9 20-12-1995 American airlines Boeing 158

10 22-5-2010 Air India Boeing 156

11 19-9-1976 Turkish Airlines Boeing 152

12 30-6-2009 Yemenia airlines Airbus 152

13 24-3-2015 Germanwings Airbus 150

14 19-2-1985 Iberia Boeing 148

15 7-11-1996 ADC-Airlines Boeing 144

16 8-2-1989 Indepent Air Boeing 144

17 18-12-1995 TSA Lockheed 143

18 25-12-2003 UTAGE Boeing 141

19 24-11-1992 China southern airlines Boeing 141

20 8-6-1982 VASP Boeing 137

21 19-5-1993 SAM Colombia Boeing 132

22 19-4-2000 Air Philipines Boeing 131

23 21-10-1989 Tan-Sahsa Boeing 131

24 8-11-1983 TAOG Angola Air Boeing 130

25 9-7-2006 Sibir Airlines Airbus 125

26 20-4-1968 South african airlines Boeing 123

27 14-8-2005 Helios Boeing 121

28 11-7-1983 TAME Ecuadorr Boeing 119

29 11-2-1978 Pacific Western airlines Boeing 42

30 23-8-2005 Korean Air Boeing 40

31 25-7-2000 Air France Boeing 113

32 24-1-1966 Air India Boeing 117

33 3-2-2005 Phoenix Aviation Boeing 104

14

34 3-5-2006 Armavia Boeing 113

35 22-4-1974 Pan American Boeing 107

36 12-5-2010 Afriqiyah airways Airbus 103

37 1-1-2007 ADAM air Boeing 102

38 20-5-2009 Indonesian Airforce Lockheed 99

39 29-10-2009 ADC-Airlines Boeing 96

40 28-1-2002 TAME Ecuadorr Boeing 94

41 31-8-1988 Delta airlines Boeing 14

42 31-10-2000 Singapore Airlines Boeing 83

43 25-2-2009 Turkish Airlines Boeing 9

44 12-8-1985 Japan Airlines Boeing 520

45 17-7-1996 TWA Boeing 230

46 25-2-2002 China Airlines Boeing 225

47 26-5-1991 Lauda Boeing 223

48 31-3-1986 Mexicana Boeing 167

49 25-9-1978 Pacific Southwest airlines Boeing 135

50 8-9-1994 US Air Boeing 132

51 21-1-1980 Iran Air Boeing 128

52 11-7-1973 Varig Boeing 123

53 2-4-1986 TWA Boeing 4

54 27-11-1989 Avianca Boeing 107

55 24-1-1966 Air India Boeing 117

56 8-7-2003 Sudan Airways Boeing 117

57 22-8-1981 Far eastern air Boeing 110

58 19-12-1997 Silk air Boeing 104

59 6-3-2003 Air Algerie Boeing 102

60 20-11-1974 Lufthansa Boeing 59

61 24-2-1989 United airlines Boeing 9

62 3-3-1991 United airlines Boeing 25

63 23-6-1985 Air India Boeing 329

64 17-7-2014 Malaysia Airlines Boeing 298

65 21-12-1988 Pan American Boeing 270

66 1-9-1983 Korean Air Boeing 269

67 22-12-1992 Lybian arab air Boeing 159

68 2-10-1990 Xiamen air/China Southwest/China southern Boeing 128

69 23-11-1996 Ethiopian Airline Boeing 125

70 5-5-2007 Kenya Airways Boeing 114

15

71 28-11-1987 South african airlines Boeing 159

72 23-9-1983 Gulf air Boeing 112

73 21-2-1973 Lybian air Boeing 108

74 29-11-1987 Korean Air Boeing 115

75 4-12-1977 Malaysia Airline systems Boeing 100

76 3-8-1975 JY-AEE Boeing 188

77 27-11-1983 Avianca Boeing 181

78 28-7-2010 Airblue Airbus 152

79 25-5-1980 Dan-Air Boeing 146

80 9-7-1982 Pan American Boeing 145

81 17-3-1988 Avianca Boeing 143

82 19-11-1977 TAP-Portugal Boeing 131

83 20-4-2012 Bhoja Air Boeing 127

84 29-2-1996 Faucett Boeing 123

85 24-6-1975 Eastern airlines Boeing 113

86 30-1-1974 Pan American Boeing 97

87 20-4-1998 Air France Boeing 53

88 3-5-1986 Air Lanka Lockheed 21

89 29-11-2006 GOL Boeing 154

90 19-10-1988 Indian airlines Boeing 133

91 1-2-1991 US Air Boeing 23

92 8-3-2014 Malaysia Airlines Boeing 239

93 31-10-2015 Metrojet Airbus 224

94 26-11-1979 PIA Boeing 154

95 3-1-2004 Flash airlines Boeing 148

96 22-10-2003 Bellview Airlines Boeing 117

97 4-9-1971 Alaska airlines Boeing 111

98 3-2-1998 KAM air Boeing 104

99 1-12-1974 TWA Boeing 92

100 25-1-2010 Ethiopian Airlines Boeing 90

101 14-9-2008 Aeroflot Boeing 88

102 8-9-1974 TWA Boeing 88

103 31-8-1987 Thai airways Boeing 83

104 1-1-1976 Middle east airlines Boeing 81

105 8-12-1963 Pan American Boeing 81

106 4-6-1969 Mexicana Boeing 79

107 13-1-1982 Air florida Boeing 78

16

108 22-7-1973 Pan American Boeing 78

109 9-1-2011 Iran Air Boeing 77

110 15-9-1974 Air Vietnam Boeing 75

111 2-7-2011 Hewa Bora Boeing 74

112 25-1-1990 Avianca Boeing 73

113 3-12-1995 Cameroon Air Boeing 71

114 25-12-1976 Egypt Air Boeing 71

115 2-10-1996 AeroPeru Boeing 70

116 21-1-1985 Galaxy air Lockheed 70

117 26-7-1993 Asiana airlines Boeing 68

118 24-8-2008 Itek Air Boeing 65

119 31-8-1999 Lapa Boeing 65

120 9-8-1995 Aviateca Boeing 65

17 Apendix 2. The Descriptive statistics of the average abnormal returns per day of both models.

Descriptive statistics CMRM

Mean -0.00001

Median 0.00006

Maximum -0.00554

Minimum 0.00417

Standard Dev. 0.00194

Kurtosis -0.11226

Skewness -0.45923

Jarquebera 36.17084

Descriptive statistics MM

Mean 0.00047

Median 0.00052

Maximum -0.00460

Minimum 0.00444

Standard Dev. 0.00186

Kurtosis -0.47306

Skewness -0.09352

Jarquebera 36.19095

18 Apendix 3. The average abnormal returns for the entire sample and both subsamples.

Constant Mean Return Model

Average abnormal returns per day and their t-values.

For the total sample(T), crashes before 2000(B) and after 2000(A).

Days: AAR(T): t-value: AAR(B): t-value: AAR(A): t-value:

20 0.00208 1.084 0.00012 0.063 0.0043 2.232*

19 -0.00034 -0.179 -0.00148 -0.776 0.0035 1.847

18 -0.00014 -0.075 0.00072 0.379 0.0002 0.128

17 -0.00244 -1.276 -0.00293 -1.532 -0.0002 -0.115

16 0.00107 0.557 0.00114 0.596 0.0011 0.551

15 -0.00135 -0.707 0.00020 0.102 -0.0045 -2.330*

14 -0.00173 -0.903 -0.00206 -1.076 0.0006 0.300

13 0.00017 0.089 -0.00011 -0.058 0.0016 0.862

12 -0.00101 -0.530 -0.00278 -1.452 0.0029 1.541

11 -0.00072 -0.378 -0.00238 -1.246 0.0029 1.526

10 -0.00075 -0.393 -0.00007 -0.038 -0.0024 -1.273

9 0.00054 0.283 0.00191 0.999 -0.0023 -1.202

8 -0.00081 -0.425 -0.00282 -1.472 0.0026 1.373

7 0.00065 0.338 0.00142 0.741 -0.0010 -0.511

6 -0.00059 -0.311 0.00020 0.102 -0.0030 -1.553

5 0.00205 1.073 0.00166 0.869 0.0042 2.186

4 0.00120 0.625 -0.00057 -0.297 0.0056 2.903**

3 -0.00169 -0.882 -0.00224 -1.169 -0.0019 -0.980

2 0.00010 0.051 0.00056 0.291 -0.0020 -1.043

1 0.00094 0.491 -0.00113 -0.590 0.0033 1.740

0 0.00230 1.202 0.00095 0.497 0.0032 1.688

-1 0.00322 1.685 0.00309 1.612 0.0024 1.246

-2 -0.00147 -0.768 0.00024 0.123 -0.0035 -1.830

-3 -0.00253 -1.322 -0.00334 -1.747 -0.0018 -0.966

-4 0.00361 1.885 0.00400 2.088 0.0022 1.148

-5 0.00022 0.113 -0.00055 -0.287 0.0027 1.391

-6 -0.00261 -1.365 -0.00052 -0.273 -0.0077 -4.044**

-7 0.00175 0.915 0.00189 0.988 0.0024 1.256

-8 0.00019 0.101 0.00060 0.311 -0.0003 -0.173

-9 0.00132 0.692 0.00036 0.191 0.0030 1.581

-10 0.00220 1.149 0.00348 1.817 0.0006 0.320

* is significant at 95% confidence, ** is significant at 99% confidence

19 Appendix 4. The average abnormal returns for the entire sample and both subsamples, market model.

Market model

Average abnormal returns per day and their t-values.

For the total sample(T), crashes before 2000(B) and after 2000(A).

Days: AAR(T): t-value: AAR(B): t-value: AAR(A): t-value:

20 0.00316 1.695 0.00223 1.194 0.0043 2.329*

19 0.00044 0.236 -0.00072 -0.385 0.0042 2.233*

18 0.00060 0.323 0.00198 1.059 0.0005 0.246

17 -0.00159 -0.854 -0.00197 -1.058 0.0001 0.077

16 0.00159 0.853 0.00160 0.858 0.0020 1.058

15 -0.00034 -0.183 0.00123 0.661 -0.0039 -2.079*

14 -0.00146 -0.781 -0.00154 -0.826 -0.0001 -0.037

13 0.00051 0.274 0.00055 0.294 0.0015 0.804

12 -0.00028 -0.152 -0.00176 -0.945 0.0028 1.518

11 -0.00054 -0.288 -0.00178 -0.955 0.0024 1.290

10 -0.00006 -0.033 0.00071 0.381 -0.0022 -1.171

9 0.00063 0.340 0.00263 1.409 -0.0033 -1.756

8 -0.00065 -0.347 -0.00236 -1.267 0.0022 1.179

7 0.00085 0.458 0.00206 1.106 -0.0015 -0.819

6 0.00038 0.205 0.00115 0.615 -0.0017 -0.920

5 0.00246 1.319 0.00266 1.425 0.0031 1.640

4 0.00141 0.759 0.00012 0.067 0.0040 2.133*

3 -0.00106 -0.570 -0.00175 -0.941 -0.0011 -0.585

2 0.00100 0.534 0.00115 0.616 -0.0005 -0.249

1 0.00193 1.032 -0.00021 -0.113 0.0043 2.291*

0 0.00265 1.422 0.00179 0.959 0.0032 1.720

-1 0.00295 1.584 0.00344 1.847 0.0007 0.358

-2 -0.00033 -0.179 0.00112 0.601 -0.0020 -1.048

-3 -0.00176 -0.943 -0.00251 -1.345 -0.0013 -0.694

-4 0.00451 2.417* 0.00494 2.648** 0.0034 1.831

-5 0.00113 0.604 0.00054 0.291 0.0028 1.519

-6 -0.00165 -0.887 0.00015 0.080 -0.0059 -3.158**

-7 0.00237 1.270 0.00322 1.729 0.0017 0.898

-8 0.00063 0.339 0.00125 0.673 -0.0004 -0.231

-9 0.00178 0.954 0.00160 0.858 0.0024 1.306

-10 0.00263 1.408 0.00329 1.766 0.0019 1.043

* is significant at 95% confidence, ** is significant at 99% confidence

20 Apendix 5. The cumulative average abnormal returns for the entire sample and both subsamples, constant mean return model.

Cumulative average abnormal returns (constant mean return model):

Days: Total sample

T-value Before 2000

T-value After 2000

T-value

-10 to -1

0.00590 0.30384 0.00923 0.47497 -0.00013 -0.00694

0 to 1

0.00324 0.83395 -0.00018 -0.04583 0.00656 1.68785

0 to 2

0.00334 0.57257 0.00038 0.065092 0.00456 0.78270

0 to 5

0.00490 0.42028 -0.00076 -0.06541 0.01243 1.06582

Apendix 6. The cumulative average abnormal returns for the entire sample and both subsamples, market model.

Cumulative average abnormal returns (Market model):

Days: CAAR Entire Sample

T-value CAAR Before 2000

T-value CAAR After 2000

T-value

t/m -10 -1

0.01224 0.65904 0.01706 0.914862 0.00340 0.18234

0 to 1 0.00458 1.23183 0.00158 0.422776 0.00701 1.88072

0 to 2 0.00557 0.99978 0.00273 0.487268 0.00701 1.25381

0 to 5 0.00838 0.75212 0.00375 0.335404 0.01296 1.15825

21 Appendix 7. Results of the Whitney-Mann u test

The constant mean return model:

The market model:

Sum of ranks, after 2000

1070 1004

Sum of ranks, before 2000

883 949

U after 2000 387 437.5

U befor 2000 574 492.5

Z statistic -1.312 -0.605