The Effect of Air Crashes on Plane Manufacturers Value. Abstract. The

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available that discusses the effect of air crashes on the manufacturer of the plane; however, this literature is outdated and was based on small samples.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

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

2 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

3 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

4 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:

Ri,t= LN(Pi,t) – LN(Pi,t-1) (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:

ARi,t = Ri,t – E(Ri,t) (2)

In this formula R is the actual return of security i on time t and E(Ri,t) is the expected return of security i on time t.

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: 200 E(Ri,t) = (1/200) * ∑푡,푖=1 푅푖, 푡 + e (3)

In this formula E(Ri,t) is the expected return from stock i, on time t, Ri,t 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(Ri,t) = α + β Ri,t + 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: 푁−푘 1 퐽푎푟푞푢푒푏푒푟푎 = + (푆2 + (퐶 − 3)2) (5) 6 4

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.

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: 120 AARt = 1/N * ∑푖=1 ARi, t (6)

Here, N is the number of observations used and ARi,t 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: 푡1 CAAR(t1 , t2) = ∑푡2 퐴퐴푅푡 (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: −11 2 VAR(AARt) = ∑푡=−210((퐴퐴푅푡 − 푀퐴퐴푅) /(푥 − 1)) (8)

In this formula AARt 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.

^0.5 t = AARt / (VAR(AARt)) (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 AARt 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.

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:

푡1 VAR(CAAR(t1 ,t2 )) = ∑푡=푡2 푉퐴푅(AARt) (10)

In this formula the variance of the cumulative average abnormal returns from t1 to t2 is obtained by adding up the variances of the average abnormal returns from t1 to t2.

To test if the CAAR(t1,t2) is different from zero, assuming normal distribution, we use the following formula:

^0.5 t = CAAR(t1,t2) / VAR(CAAR(t1 ,t2 )) (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:

1 1 t = MAARa – MAARb / ((VAR^0.5(AARt) * √ + ) (12) 푁1 푁2

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

MAARb is the mean of all average abnormal returns for crashes after 2000 and N1 and N2 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 = N1 + N2 – 2 degrees of freedom. The VAR^0.5(AARt) 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:

퐿2+1 ∑푁 (퐾푖,0− ) 푖=1 2 Θ3 = (1 / N) * (13) 푆(푘)

In this test N is the total number of events, Ki,0 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:

1 1 퐿2+1 S(k) = √ ∑푡2 ( ∑푁 (퐾푖, 푡 − ))^2 (14) 퐿2 푡=푡1+1 푁 푖=1 2

Again, L2 is the number of days used in the test, Ki,t 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:

Ui = Ri – (Ni(Ni + 1)/2) (15)

In this formula Ri is the sum of ranks of subsample i, and Ni 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 - ɱU) / Ϭu (16)

Here ɱu is given by:

ɱu = (N1N2)/2 (17)

And Ϭu is given by:

푁1푁2(푁1+푁2+1) Ϭu = √ (18) 12

N1 and N2 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,

9 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

10 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.

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.

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

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

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

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

Apendix 2. The Descriptive statistics of the average abnormal returns per day of both models. Descriptive statistics CMRM Descriptive statistics MM Mean -0.00001 Mean 0.00047 Median 0.00006 Median 0.00052 Maximum -0.00554 Maximum -0.00460 Minimum 0.00417 Minimum 0.00444 Standard Dev. 0.00194 Standard Dev. 0.00186 Kurtosis -0.11226 Kurtosis -0.47306 Skewness -0.45923 Skewness -0.09352 Jarquebera 36.17084 Jarquebera 36.19095

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

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

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 T-value Before T-value After T-value sample 2000 2000 -10 0.00590 0.30384 0.00923 0.47497 -0.00013 -0.00694 to -1 0 to 0.00324 0.83395 -0.00018 -0.04583 0.00656 1.68785 1 0 to 0.00334 0.57257 0.00038 0.065092 0.00456 0.78270 2 0 to 0.00490 0.42028 -0.00076 -0.06541 0.01243 1.06582 5

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 T-value CAAR T-value CAAR T-value Sample Before After 2000 2000 t/m -10 0.01224 0.65904 0.01706 0.914862 0.00340 0.18234 -1 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

Appendix 7. Results of the Whitney-Mann u test The constant mean The market model: return model: Sum of ranks, after 1070 1004 2000 Sum of ranks, before 883 949 2000 U after 2000 387 437.5 U befor 2000 574 492.5 Z statistic -1.312 -0.605