university of amsterdam, amsterdam business school msc

University of Amsterdam, Amsterdam Business School

MSc Business Economics, Finance track

Master Thesis

The impact of commercial air crashes on the aviation industry

Daniel Sliepenbeek

November 2017

dr. R. Almeida da Matta

This study examines the impact of commercial air crashes on the aviation

industry. The event study methodology is used to test the effect on the

crashed airline, competitive airlines and the manufacturer. On top of that it

differs the cause of crash, whether the crash is caused by the airline. The

event study shows significant results for the crashed airline, regardless of

what the cause of the crash is. However, no significant results are found for

the competitive airlines and manufacturers. So this study concludes that a

commercial air crash is an isolated event.

2

Statement of Originality

This document is written by Daniel Sliepenbeek who declares to take the full responsibility

for the contents of this document.

I declare that the text and the work presented in this document are original and that no

sources other than those mentioned in the text and its references have been used in

creating it.

The Faculty of Economics and Business is responsible solely for the supervision of

completion o the work, not for the contents.

3

Table of contents

1. Introduction ..................................................................................................................................................................... 4

2. Literature review .......................................................................................................................................................... 7

3. Methodology ................................................................................................................................................................ 13

4. Data ................................................................................................................................................................................. 18

5. Results ............................................................................................................................................................................ 23

6. Robustness checks .................................................................................................................................................... 28

7. Conclusion .................................................................................................................................................................... 31

8. References .................................................................................................................................................................... 33

4

1. Introduction

The year 2014 was for the airline company Malaysian Airlines a disastrous one. Within 131

days the airline lost two of their aircrafts. The first one, flight MH370, disappeared above the

Southern Indian Ocean and until today it is still not clear what happened. Investigators from

all over the world are still searching for the plane and recently one of the wings washed

ashore in Madagascar. The second aircraft, flight MH17, crashed above Ukrainian soil, where

a civil war takes place, very likely taken down by pro-Russian separatists. The two crashes

took the life of 537 innocent passengers and crewmembers. After the disappearance of flight

MH370 the value of the stock fell down 20 percent.1 On top of this, almost 200 members of

the cabin crew resigned after the MH17 crash a few months later.2 But only a few days after

the MH17 crash, the value of the stock revived and was already above its pre-MH17 level

and one month later it was at its pre-MH370 level.3 Despite of this resurrection of the stock

price, Malaysia Airlines ceased their operations in August 2015. But they restarted their

operations in September 2015 under a slightly adapted brand name Malaysia Airlines

Berhad.4 Unfortunately, Malaysia Airlines was not the only Malaysian airline that did not

have a very successful 2014. Another Malaysian airline, AirAsia, did not end the year 2014

very well. On the 28th of December they had a likewise disappearance as flight MH370. This

vanishing caused the greatest drop in the last three years of AirAsia’s shares.5 Another

recent crash of a commercial airline with a lot of fatalities is the crash of Flight 9525 of

1 http://www.nytimes.com/2014/05/16/business/international/flights-disappearance-knocks-malaysia-

airlines.html?_r=2 2 https://uk.finance.yahoo.com/news/malaysia-press-nearly-200-mas-003948762.html 3 http://www.ibtimes.com/airasias-share-price-tumbles-malaysia-airlines-recovery-becomes-beacon-hope-

1769330 4 https://en.wikipedia.org/wiki/Malaysia_Airlines 5 http://www.ibtimes.com/airasias-share-price-tumbles-malaysia-airlines-recovery-becomes-beacon-hope-

1769330

http://www.ibtimes.com/airasias-share-price-tumbles-malaysia-airlines-recovery-becomes-beacon-hope-1769330


5

Germanwings, a subsidiary of the German airline Lufthansa. On the 24th of March the co-

pilot of the flight crashed the airplane on purpose into the French Alps. The co-pilot, Andreas

Lubitz, was previously treated for suicidal tendencies and was declared unfit to work. On the

day of the crash the stock price took a steep decline of 5 percent. Two days after the crash

Lufthansa officially stated that the pilot crashed the airplane on purpose into the Alps,

resulting in another decline of 3 percent.6

All the above mentioned crashes are examples of recent tragedies involving globally

operating commercial airlines with a significant amount of fatalities. But the causes of these

crashes are significant different from each other and thereby also the question about the

guilt of the accident. In case of the MH17 disaster, Malaysia Airlines was not guilty on the

crash since airlines were still allowed to fly through the Ukrainian airspace where they were

taken down. But in case of the Germanwings crash, the pilot was declared unfit to fly in a

health check so the airline can be blamed for negligence. Does this difference explain the

fact that the stock price of Malaysia Airlines was above the pre-crash level in a few days after

the MH17 crash and back at the pre-MH370 level in a month and the opposite was the case

for Germanwings? So besides the question whether an airplane crash impacts its stock value,

also another question is very interesting: does the cause of the crash impact the way the

stock prices move?

Obviously not only the airline involved in the crash is impacted but also the airline industry.

The industry could be effected in two opposite ways. The first theory is that the stock prices

could increase because of the so-called switching effect. Basically, the consumers and

investors switch to other airlines, which are not involved in the crash. Resulting in an

6 http://www.ibtimes.com/germanwings-flight-9525-crash-sends-airline-stocks-tumbling-deutsche-lufthansa-

lha-1860718

6

increase in the share price of the competitors. Another way how a crash can impact the

competitors is due to the spillover effect (Ho et al., 2011).

This theory states that the crash negatively impacts the competitors because of the negative

exposure for the entire aviation industry. So this study will investigate not only the stock of

the involved airline but also the industry indices.

But as mentioned above, the cause of the crash could possibly explain the movement of the

stock so this study will also investigate the impact of the cause of the crash on top of the

impact on the airline itself and the industry. At last, not only the airlines will be impacted but

also the manufacturers of the aircrafts, so this will be investigated as well.

7

2. Literature review

First of all, the fundament of this study is the theory of efficient markets. Fama (1970) states

that in an efficient market stock prices reflect all the information available. When new

information is available in an efficient market, impacts this directly the stock price in the

market. In the past it has been shown that markets are rather efficient. Building upon this

theory, Brown and Warner (1980) developed a model for an event study, they state that an

event study provides a direct test of market efficiency. In case of an event study, a curtain

event takes place, which is unpredictable, and the market will show significant nonzero

abnormal returns when the market is efficient. In this study the event is the airplane crash, it

goes without saying that an airplane crash is unpredictable and at first sight the expectation

is that the impact of an airplane crash should be negative. In spite of the importance of the

airline industry in the contemporary world, the amount of papers written about the effect of

airplane crashes on the stock price is relatively low.

First, Chance and Ferris (1987) wrote about the effect of aviation disasters on the air

transport industry. They compute three different tests. First, the effect on the carrier itself,

expected to be negative. Second, the effect on carriers not involved, the air carrier industry.

In order to do so they derived an equally weighted index of all the carriers not involved. At

last they test the effect on the manufacturer of the crashed airplane. The sample of this

study had two important criteria: only US based airlines crashed on US soil and there had to

be at least ten fatalities. Their sample contained 49 crashes between 1962 and 1982. They

only find a significant effect on the day of the crash on the involved carrier. They conclude in

their paper: “Aviation disasters are viewed as isolated events that are financially significant

for the carrier only”. (Chance and Ferris, 1987)

8

Second, Kaplanski and Levy (2009) wrote also a paper about the impact of aviation disasters

but they studied the effect on a general stock market, the NYSE Composite Index, both

equally-weighted and value-weighted. They based their study on a behavioral economics

theory that states that negative sentiment, caused by bad mood or anxiety, affects

investments. Their sample contains all the crashes worldwide with at least 75 casualties

between 1950 and 2007. They conclude in their paper: “We find that aviation disasters are

followed by negative rates of return in the stock market accompanied by a reversal effect

two days later. As the transitory decline in the stock market is more than 60 times larger

than the direct economic loss” (Kaplanski and Levy, 2009). The papers of Kaplanski and Levy

(2009) and Chance and Ferris (1987) contradict each other because Chance and Ferris (1987)

state that a crash is an isolated event while Kaplanski and Ferris (2009) proof the opposite. A

possible explanation for this contradiction could be the chosen threshold in fatalities, where

Chance and Ferris (1987) and Kaplanski and Levy (2009) chose respectively ten and 75

fatalities.

Third, Bosch et al. (1998) examine the stock market reactions to aircraft crashes in the US,

they focus on the airlines not involved. Their sample contains 25 crashes and 250 airlines

which are not involved in the US in a timeframe between 1978 and 1996. They chose their

non-involved airlines based on route overlap. The paper uses a threshold of one fatality.

They conclude in their paper: “We find a positive relation between non-crash airline stock

reactions and the degree of overlap with the crash airline, supporting a switching effect

despite likely mitigating strategies by the crash airline” (Bosch et al., 1998). This paper also

contradicts the findings of Chance and Ferris (1987) while they use even a lower threshold of

fatalities and their samples are both US based.

9

Fourth, Davidson, Chandy and Cross (1987) did a likewise study, they investigated the impact

of a crash on the carrier. They divide the costs of a crash in three categories. First, the direct

costs of the loss of the airplane. Second, the costs of liability loss, such as the costs of the

property and of course the deaths. Against these two types of costs the carrier is enforced to

be insured against. But against the third type of costs a carrier cannot insure themselves, the

loss of goodwill of investors and passengers. They find a negative significant effect on the

event day but the reactions reverse in the days after the crash.

Borenstein and Zimmerman (1988) also investigated the impact of airplane crashes, they

used US based data of 67 crashes with at least one casualty. They conclude: “We find that an

airline’s shareholders suffer a significant wealth loss when the airline experiences a serious

accident” (Borenstein and Zimmerman, 1988). But they state that due to insurances the real

equity loss is relatively low. Then Ho et al. (2011), they examine the role of the degree of

fatalities in the impact of an aviation disaster. They only used US based data as well,

between 1950 and 2009. They conclude that the degree of fatalities has a positive relation

with the impact of the disaster, supporting the fact that chosen thresholds could impact the

study.

At last, Mitchell and Maloney (1989), this paper examines the brand-name effect of airline

crashes, the potential loss of consumer goodwill. They divide their sample into two groups: a

group where the accident is caused by the pilot and a group where the accident is not

caused by the pilot. They find that carriers experienced negative brand-name effect when

the crash is caused by the pilot. In case it was not the pilot’s cause, there was no reaction. An

important finding in their paper is that the probable cause of the crash is almost every crash

known on the day of the event.

10

As stated by Davidson, Chandy and Cross (1987) is the presence of insurances a really

important factor in airplane crashes. The presence of insurance could be considered

ambiguous. First, all airlines are insured for the first two types of costs: the costs of the loss

of the aircraft and the costs of the loss of lives and liabilities, for example their luggage. But

as stated before, airlines cannot insure their selves against the last type of costs, investment

withdrawals and the loss of goodwill. But very important is whether the insurance

companies pays out the same rate when the carrier is at fault or not. Mitchell and Maloney

(1989), who study the impact on the brand-name by differentiating whether it is the fault of

the pilot or not, state that there is no difference in paid out claims between the two

subsamples. So in this case, a possible impact difference could not be brought back to

payout ratios of insurance companies. But the crucial differences between this study and

the study of Mitchell and Maloney is first, that they study the pilot’s error and this study

investigates whether it is the airline’s fault and second, they study the effect on the brand-

name and this study the effect on the share value. Besides the effect whether the insurance

companies pay out yes or no. On top of that, the insurance companies also could change the

insurance premium after an accident. For example, insurance companies increased their

premiums after 2014, a bad year for the aviation industry with major crashes.7

Besides that a relatively small amount of papers has been written about this topic in such an

important industry in the contemporary world, there is no consensus in the above-

mentioned papers. Findings in the prior literature contradicts each other. Mitchell and

Maloney (1989) only found significant negative results when the crash of the aircraft was

due to a pilot’s fault. Where the other studies always found significant results on the day of

the crash. On top of that, Chance and Ferris (1987) conclude that an airplane crash is an

7 http://fd.nl/frontpage/economie-politiek/1086653/luchtvaart-maakt-zich-op-voor-duurdere-verzekeringen

11

isolated event and only has impact on the carrier itself while Bosch et al. (1998) find a

positive relation with non-crash airlines using a fatality threshold of one fatality where

Chance and Ferris (1998) use a threshold of ten fatalities. Also Kaplanski and Levy (2010) find

results against the theory of an isolated event.

Not only because of the above-mentioned contradictions and the relatively small amount of

papers written about the subject, this topic is a real contribution to the literature. Also

almost all the existing literature is only base on crashes in the US and their airlines. While the

aviation industry is very international and globally orientated. Therefore this study does not

take any location constraint into account and investigates all disasters of listed airlines with

more than 50 fatalities, this cut-off number is selected in order to achieve that the public

effect is significant. This leads to the following hypotheses being tested:

H1: There is no impact on the carrier of the crash

H2: There is no impact on the non-crash carrier

But the real contribution of this paper is that it differentiates between the cause of the

crash. Where Mitchell and Maloney just split the cause of the accident in pilot’s fault and not

pilot’s fault and test this differentiation on just the brand-name effect. This paper will

differentiate the cause in a different way. Because whether or not it is the cause of the pilot,

it does not investigate the role of the airline in the crash, the object that will be studied in

this paper. The stock price reactions of the airline could be different if the crash is for

example caused by bad maintenance of the airline or when it is caused by a fault of the

manufacturer, Mitchell and Maloney put these causes in the same group, not the pilot’s

fault. The hypothesis that can be derived from this test is:

12

H3: Different causes of crashes have no different effect on the impact on the stock price

At last, this paper will investigate the impact on the manufacturer. Only Chance and Ferris

(1987) did this research. They included the three biggest American manufacturers in their

time sample: Boeing, Lockheed and McDonnel-Douglas. They find no significant impact of

the crashes on the manufacturers while the expected impact is negative. This expected

negative impact is driven by the fact that also manufacturers have to pay a higher insurance

premium after a crash. On top of that, after a crash more costly safety standards are

expected and of course airlines can switch to competitive manufacturers. So this leads to

the last hypothesis to be tested:

H4: A crash has no significant impact on the manufacturer of the aircraft.

13

3. Methodology

To test the impact of the airplane crashes, the thesis uses the event study methodology of

Brown and Warner (1980, 1986) and MacKinlay (1997). An event study focuses on the

impact of a specific event on stock returns. The event study methodology is based on the

theory of market efficiency, Brown and Warner (1980): “Event studies provide a direct test

of market efficiency. Systematically nonzero abnormal returns which persist after a

particular type of event are inconsistent with the hypothesis that security prices adjust

quickly to fully reflect new information.” An event is an unexpected happening that possibly

has influence on the value of the involved firms. In this study the event is the airplane crash,

it goes without saying that an airplane crash is an unexpected happening. This methodology

tests the significance of average abnormal returns in a predetermined window around the

event. The methodology uses two kinds of windows: the estimation window and the event

window. The estimation window is used to determine the trend of the security and

determines the normal movement of the stock. The event window contains the days near

the event. Should these crashes have impact on the stock prices, then the average abnormal

returns in the event window are significant nonzero. The performance of the stock and the

“abnormality” in the event window are relative to the estimation window.

The determination of the estimation and the event window is dependent of a lot of factors.

First the estimation period, Brown and Warner (1980) state that the longer the estimation

period, the better the estimation of the model and the better the normal movement of the

stock is represented. But on the contrary, the longer the estimation period, the higher the

chance that other shocks or events could influence the stock. The estimation window used in

14

some previous papers are very divergent. Chance and Ferris (1987) use an estimation

window of (-20,0), Kaplanski and Levy (2009) use (-5,0) and Davidson et al. (1987) a window

of (-15,0). Since the longer the better this study makes use of a window of (-20,0). Because

the event is not predictable, the event window starts at the day of the event, day 0 (t=0).

Many event studies make use of a pre-event window because of possible leakage effects but

in this study leakage effects are not relevant. For the event window the theory also applies

that it should not be too long because otherwise other events could have impact. But the

event window also should not be too short because in some events it takes time for the

market to absorb all the information and in case of an airplane crash not all the information

is instantaneous available. The amount of days in the event window used in prior literature is

in this case very divergent as well. Mitchell and Maloney use an event window of ten days

(0,10), Chance and Ferris use 20 days (0,20), Borrenstein and Zimmerman (1988) use 14 days

(0,14) and Davidson, Chandy and Cross (1987) use a window of 30 days (0,30). Since the

above-mentioned papers look into crashes with at least one fatality and this investigates

crashes with at least 50 fatalities, an event window of ten trading days should be sufficient

to test the effect of the crash.

Brown and Warner (1980) describe several ways to compute the average abnormal returns

in the event window. In order to compute these abnormal returns a benchmark is needed,

the normal movement of the stock in the estimation period. The returns are considered to

be ‘abnormal’ related to this benchmark. Brown and Warner developed three models to

generate this benchmark, the expected returns of the stock: Mean Adjusted Returns, Market

Adjusted Returns and the Market and Risk Adjusted Returns. The three different models are

consistent with the Capital Asset Pricing Model under the assumption that the stock has a

constant systematic risk. This study will generate and make use of the returns based on the

15

three different models. The first step in generating the abnormal returns is in all three the

models the same, deriving the actual returns. The next formula is used to calculate the

actual returns.

(1) Ri,t = LN(Pi,t+1

Pi,t

)

Where R is the actual return of a stock or index i on time t, the logarithm is taken of the

change in price between time t and t+1. When the actual returns are derived, the abnormal

returns can be generated with the three above mentioned models. First the Mean Adjusted

Model, in this model the abnormal return can be calculated by subtracting the expected

returns from the observed returns.

(2) )( ,,, tititi RERAR

The expected returns are based on the mean of the returns of the stock in the estimation

window.

Second the Market Adjusted Model, this model makes the assumption that the stock

changes in the same way as the market. So the abnormal returns can be calculated by the

difference between the returns of the stock and the returns of the market.

(3)

Where 𝑅𝑚,𝑡 is the actual return of a specific market on the same time t as the observed

stocks. The last model is the Market and Risk model, this way of generating abnormal

returns is the most complicated one and has the most components. First the systematic risk,

tmtiti RRAR ,,,

16

measured in β and calculated by the covariance between the returns of the stock and the

returns of the market divided by the variance of the returns of the stock.

(4)

The second component of this model is measured in α, the volatility of the stock. The alpha

is calculated as follows.

(5) 𝛼𝑖 = 𝜇𝑖 − 𝛽𝑖 ∗ 𝜇𝑚

Where 𝜇𝑖 is the mean of the stock returns and 𝑢𝑚 the mean of the market returns. Then the

abnormal returns are derived the following way.

(6) 𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡 − ∝𝑖,𝑡− 𝛽𝑖 ∗ 𝑅𝑚

When the abnormal returns are derived, using one of the three models of Brown and

Warner (1980), the average abnormal returns (AAR) are tested whether they are significant.

The average abnormal returns are calculated as follows.

(7)

In order to test whether the AAR’s are significant Brown and Warner (1980) make use of

parametric and non-parametric tests. The parametric test used is the student t-test, it tests

whether the abnormal returns differ significantly from zero. But the student t-test has some

restrictive assumptions, for example the sample has to be normally distributed. To test

whether a sample is normally distributed the Jarque-Bera statistic is calculated, using the

next formula.

i

ti

iVar

Cov ,ˆ

AARi,t

ARi,ti1

N

N

17

(8) 𝐽𝐵 = 𝑁

6 [𝑠2 +

(𝐾−3)2

4]

Since not all the samples are normally distributed for this study a non-parametric test is

used, the Wilcoxon signed rank test, which tests whether the median differs significantly

from zero. At last, this study tests the significance of the difference in impact whether the

crash is caused by the airline itself or not. For this test a Student t-test is used.

18

4. Data

This study investigates the effect of airplane crashes on the carrier, the not-involved carriers

and the manufacturer. In order to do so, various datasets are needed. First, a fatality

threshold has to be derived. Prior literature uses many different thresholds but to test for a

significant effect, a crash also needs a significant impact. Kaplanski and Levy (2010) state:

“The casualties’ cut-off number has to be arbitrarily selected to include events whose effect

on public sentiment is large enough to be noticed”. So this study uses a cut-off number of 50

casualties. This number of casualties consists only out of people that were onboard of the

airplane, so the crew of the airline and the passengers. Of course, often a lot more victims

are involved in case of an airplane crash. For example the terrorist attacks of September

11th in 2001 on the World Trade Center in New York. The amount of casualties onboard of

the two airplanes, crew and passengers, counts 157 but the indirect number of casualties

was almost three thousand.8 But in order to consistently test the effect of an airplane crash

itself, only the onboard fatalities are taken into account.

Since 1950 there are 508 airplane accidents listed at the Aviation Safety Network with more

than 50 casualties.9 The Aviation Safety Network (ASN) is a service of the Flight Safety

Foundation and contains all the information on accidents and safety issues. Their

information is mostly based on official sources such as authorities. But this sample of 508

crashes contains all the aircraft crashes so first the events have to be filtered. Since this

study is an event study which investigates the impact on the stock returns, the sample can

8 https://en.wikipedia.org/wiki/Casualties_of_the_September_11_attacks 9 https://aviation-safety.net/

19

only contain listed and commercial airlines which has stock prices available. This filter

reduces the sample drastically.

The residual sample still contains some cases of collision. For example, on March 27th of

1977 two airplanes, a KLM aircraft and a Pan Am aircraft, had a collusion on the runway of

Tenerife airport.10 The total amount of fatalities combined was 583 but they were split up in

this sample, resulting in 335 fatalities on the Pan Am plane and 248 on the KLM plane.

Resulting in a final data sample of 43 airplane crashes of listed airlines with at least 50

fatalities. The next tables show some summary statistics of the events.

Table 1: Crashes per decade

Table 2: Crash-carriers per continent

1970-1980 9

Europe 9

1980-1990 6

Asia 15

1990-2000 10

North-America 16

2000-2010 14

Latin-America 1

2010-present 4

Africa 2

Total 43

Total 43

Based on the information from the Aviation Safety Network the causes of the crashes could

be derived. As stated, this study distinguishes two groups of crashes, airline’s fault and

miscellaneous. The category ‘airline’s fault’ is very broad taken: all the crashes due to errors

of the airline or their crewmembers. For instance, when an employee of the airline fails to

follow the right maintenance procedure and that leads to a crash, as happened to China

Airlines Flight 61111, it is classified as the fault of the airline or when one of the pilots makes

an error. An important theory in this part of the study is from Mitchel and Maloney (1989),

they state that the cause of the crash is almost every time on the day of the event common

10 https://en.wikipedia.org/wiki/Tenerife_airport_disaster 11 http://aviation-safety.net/database/record.php?id=20020525-0

20

knowledge, when this is not the case, the crash is classified as miscellaneous. But the

distribution of causes is not that straight forward. Of course, some crashes are rather

straightforward, like the Malaysia Airlines’ MH17 crash or the 9/11 terrorist attacks in New

York. But sometimes it is a combination of factors. For example the Air France flight 42212,

shortly after the takeoff they flew into a mountain resulting all onboard passengers were

killed. Due to bad weather the crew had very limited sight and could barely see something.

But the pilot in command reacted not the right way. So the crash is classified as the airline’s

fault because if the pilot had reacted properly the crash should not be occurred. Another

example is Singapore Airline’s flight 00613, the crew taxied to the wrong runway which was

not clear and during the takeoff they had a collision with mechanical equipment on the

runway. During the accident the weather was bad so the crew could not see the equipment

but if the crew did not taxi to the wrong runway it was not happened. So this crash is

classified as ‘airline’s fault’. The next tables summarize the causes of the crashes.

Table 3: General summary causes

Table 4: Specific summary of causes

Airline 23

Air Traffic Controller fault 1

Miscellaneous 20

Attacked using ground-based weapons 1

Total 43

Bombing 2

Crew 23

Hijacked 2

Other aircraft's fault 1

Technical 5

Unknown 2

Weather 6

Total 43

Since the events and the causes of the crashes are determined, the corresponding stock

prices have to be derived. Day 0 is the day that the crash occurred. When the disaster took

12 http://aviation-safety.net/database/record.php?id=19980420-0 13 http://aviation-safety.net/database/record.php?id=20001031-0

21

place after the closing of the stock market or during the weekend, the next trading day is

determined as day 0, the event day. As this study also calculates the market-adjusted and

the market and risk-adjusted abnormal returns of Brown and Warner (1980), the market

return has to be derived. As the market index, this study uses the MSCI World index14, a

global market index which contains 1.643 global stocks. Since this index is a common

benchmark for global and internationally operating firms and carriers are such firms, this

market benchmark is used. The index is market value-weighted. In order to calculate the

impact on not-involved carriers, industry indices are used. For example a Pan Am flight, the

North-American Airline Index is used and for Singapore Airlines the Asian Airline Index. To

prevent a possible bias, the crashed airline is excluded in this index. These indices are also

market value-weighted based. At last the stock prices of the manufacturers has to be

determined. Only the stock prices of three manufacturers are available: Airbus, Boeing and

Bombardier. All the daily stock prices and indices are gathered from Thomson Reuter

DataStream.

The next tables show the descriptive statistics in the estimation and event window. Where

the skewness and the kurtosis give some information about the shape of the dataset. The

skewness is equal to zero when the shape is symmetric. When the skewness is negative, the

data points are skewed to the left and vice versa. The kurtosis gives information about the

tailedness. The higher the kurtosis, the higher the peak of the dataset and the thinner the

tails, so the data is more focused around the mean. The lower the kurtosis the flatter the

dataset is. The Jarque-Bera is calculated to test for normal distribution, when the Jarque-

Bera statistic is larger than 5,99, a normal distribution cannot be assumed. This statistic is

important whether to use a parametric test or a non-parametric test.

14 https://en.wikipedia.org/wiki/MSCI_World

22

Table 5: Desriptive statistics in window (-20,10) for all events (N=43)

Table 6: Descriptive statistics in window (-20,10) for airline's fault (N=23)

Statistic Mean-adjusted Market-Adjusted Market and Risk-adjusted


Mean 5,462E-19 -0,001617485 -3,46945E-19

Mean -4,33681E-19 -0,000907327 -3,46945E-19

Variation 5,09808E-05 5,4728E-05 5,00408E-05

Variation 5,2275E-05 4,84965E-05 4,56554E-05

St. deviation 0,007140081 0,007397835 0,007073952

St. deviation 0,007230145 0,006963944 0,006756881

Median 0,000667112 -0,000994499 0,000494347

Median 0,001027944 0,000155217 0,000850866

Maximum 0,010790082 0,008510241 0,011644416

Maximum 0,013146824 0,011934908 0,013255202

Minimum -0,023648066 -0,026945148 -0,021731824

Minimum -0,016693007 -0,018112922 -0,017444542

Skewness -1,245689985 -1,245417462 -0,923600077

Skewness -0,55824877 -0,628923389 -0,819449048

Kurtosis 2,085463722 2,515558804 1,508910151

Kurtosis 0,121700421 0,24232839 1,26175414

Jarque-Bera 11,49545572 11,2210819 7,109307608

Jarque-Bera 5,944246316 6,241031997 6,165772933

Table 7: Descriptive statistics in window (-20,10) for miscellaneous (N=20)

Table 8: Descriptive statistics in window (-20,10) for non-crash carriers (N=41)



Mean -3,03577E-19 -0,002434167 0

Mean 9,27765E-06 -0,00037216 1,92943E-05

Variation 0,000180767 0,000172655 0,000156238

Variation 1,25218E-05 1,39466E-05 1,39065E-05

St. deviation 0,013444955 0,013139831 0,012499527

St. deviation 0,003538622 0,003734515 0,003729136

Median 0,002235722 0,000108646 0,001749979

Median 0,000301595 -0,000248617 0,000421711

Maximum 0,017537839 0,011092962 0,017305582

Maximum 0,005365352 0,005031936 0,006988932

Minimum -0,063758718 -0,063567807 -0,057440748

Minimum -0,014083009 -0,016219739 -0,014601303

Skewness -2,801374861 -2,67527164 -2,5738257

Skewness -1,522288381 -1,773896803 -1,410489345

Kurtosis 12,38530805 11,24786332 10,65292978

Kurtosis 5,42548557 7,287885741 5,140706937

Jarque-Bera 95,69615286 81,76293362 73,70943268

Jarque-Bera 19,2428453 30,78680049 16,31057602

Table 9: Descriptive statistics in window (-20,10) for manufacturers (N=32)


Mean 2,38524E-19 0,000429413 -5,96311E-20

Variation 2,0604E-05 2,03296E-05 1,74663E-05

St. deviation 0,004539159 0,004508839 0,004179268

Median -0,000207964 4,30472E-05 0,000292806

Maximum 0,008986523 0,008824802 0,008785182

Minimum -0,015308452 -0,017054956 -0,014766097

Skewness -0,628823423 -1,169177105 -0,716481275

Kurtosis 2,157858418 4,793699246 3,024233119

Jarque-Bera 3,151499024 11,23776271 3,679238529

23

5. Results

In this section the results of the Wilcoxon Signed Rank test for the three models of Brown

and Warner (1980) are discussed. As stated in the methodology section only this test is done

since the desciptive statistics show that all the de Jarque-Bera values are above the critical

value, so normal distribution cannot be assumed. The tables show the average abnormal

returns in the event window and whether these values are significant with different

significance levels.

Table 10. Test results Wilcoxon for all events (N=43)

Wilcoxon Signed Rank test

Mean Market M&R

AAR0 -0.0150*** -0.0165*** -0.0146*** AAR1 -0.0113 -0.0127 -0.0137 AAR2 -0.0100* -0.0116** -0.0087**

AAR3 0.0018 0.0000 0.0022 AAR4 -0.0236 -0.0269 -0.0217 AAR5 0.0066 0.0080 0.0067 AAR6 -0.0012 -0.0030 -0.0014 AAR7 -0.0016 -0.0014 0.0028 AAR8 0.0068 0.0065 0.0113** AAR9 0.0032 0.0003 -0.0002 AAR10 0.0009 -0.0007 0.0019

*= signficant at 10% level, **= significant at 5% level, ***= significant at 1% level

First the results of the tests of the impact on the involved airline. On the event day, day 0,

the Average Abnormal Returns are negative significant at 1% for all the methods. These

findings are in line with prior literature. Chance and Ferris (1987) and Davidson, Chandy and

Cross (1987) find also significance on the day on the event. But this study also finds

significant negative effect on day 2 where Davidson, Chandy and Cross (1987) finds a

reversed effect in the following days after the crash and Chance and Ferris (1987) do not find

any significance after day 0. A possible explanation for these differences is the cut-off

24

number of the amount of fatalities. Chance and Ferris and Davidson, Chandy and Cross both

use a cut-off number of 10 fatalities where this study uses a threshold of 50 fatalities. When

more people are involved in the crash the impact could be bigger.

Another explanation for the difference could be that the Chance and Ferris and Davidson,

Chandy and Cross only investigates crashes in the US, where this study investigates crashes

globally.

An important part of this study is the difference in impact between whether it is the fault of

the airline or not. The sample is divided in 23 crashes caused by the airline itself and 20

crashes with a miscellaneous cause. The three tables below show the significance tests of

respectively the airline’s fault crashes, miscellaneous crashes and the difference between

the two samples.

Table 11. Test results Wilcoxon for respectively airline's fault (N=23) and miscellaneous (N=20)



Mean Market M&R

Mean Market M&R

AAR0 -0.0158** -0.0171** -0.0174**

-0.0141** -0.0159** -0.0113

AAR1 -0.0135 -0.0128 -0.0166

-0.0087 -0.0125 -0.0103

AAR2 -0.0108 -0.0095 -0.0056

-0.0091 -0.0141** -0.0122**

AAR3 -0.0046 -0.0077 -0.0037

0.0092 0.0089 0.0091

AAR4 0.0112 0.0049 0.0093

-0.0638** -0.0636*** -0.0574**

AAR5 0.0035 0.0058 0.0036

0.0101 0.0106 0.0103

AAR6 0.0030 0.0017 0.0007

-0.0060 -0.0084* -0.0038

AAR7 -0.0006 -0.0011 -0.0008

-0.0028 -0.0019 0.0070

AAR8 0.0044 0.0045 0.0060

0.0094 0.0088 0.0173

AAR9 -0.0090 -0.0091* -0.0061

0.0173*** 0.0111 0.0065

AAR10 -0.0057 -0.0043 -0.0013 0.0086 0.0034 0.0055


25

Table 12. Student T-test for difference between fault

Student T-test

Mean Market M&R

AAR0 -0.0017 -0.0013 -0.0061 AAR1 -0.0048 -0.0004 -0.0063 AAR2 -0.0018 0.0046 0.0066 AAR3 -0.0139 -0.0166 -0.0129 AAR4 0.0750 0.0685 0.0668 AAR5 -0.0065 -0.0049 -0.0068 AAR6 0.0090 0.0100 0.0045 AAR7 0.0022 0.0008 -0.0077 AAR8 -0.0050 -0.0043 -0.0113 AAR9 -0.0263 -0.0202 -0.0126 AAR10 -0.0143 -0.0078 -0.0068

When the crash is the airline’s fault, day 0 is significant at 5% for the three models. At a

miscellaneous cause the results show way more significant values but table 12 shows that

there is no significant difference between the two subsamples. There is only one paper in

the literature that studies the difference in cause, namely Mitchell and Maloney (1989). But

this study is not comparable with the one of Mitchell and Maloney (1989) because of two

major differences. First, they divide their events in two different categories, namely pilot’s

error and not-pilot’s error. Second, they investigate the impact on the brand-name. But they

find a difference in impact between when it is the pilot’s fault or not.

A possible explanation why there is no significant difference in cause could be that this study

uses a cut-off number of 50 fatalities. This number of victims is relatively high so the impact

of the crash is also bigger. The effect of a crash with a minimum of 50 casualties has always

impact whatever the cause might be. So that could be reason why there is no significant

difference.

26

The next table shows the results of the Wilcoxon test for the airlines not involved. The

sample contains 41 events because for two events of African airlines there were no industry

indices available.

Table 13. Test results Wilcoxon for non-crash carriers


Mean Market M&R

AAR0 -0,0012 -0,0013 -0,0016

AAR1 -0,0043 -0,0043 -0,0039

AAR2 0,0005 0,0002 0,0012

AAR3 0,0008 -0,0002 0,0007

AAR4 -0,0141 -0,0162 -0,0146

AAR5 -0,0016 0,0007 0,0003

AAR6 -0,0029 -0,0034 -0,0029

AAR7 0,0007 0,0018 0,0039

AAR8 0,0019 0,0026 0,0054

AAR9 0,0024 0,0009 -0,0015

AAR10 0,0033* 0,0032 0,0017


The table only shows significance at 10% at day 10. These results are in line with the paper of

Chance and Ferris (1987), they state that an airplane crash is an isolated event. But the

results contradict the findings of Bosch et al. (1998) who find a significant impact on the non-

crash airlines. The difference between the results of these papers can be the way in which

the non-crash airlines are defined. The non-crash airlines in the sample of Bosch et al. (1998)

are based on the route overlap while this study uses the industry index.

The last test that is executed is to test the impact on the manufacturers. The results show a

5% significance at day 8 and a 10% significance for the Mean Adjusted method at day 6.

Which is again in line with the results of Chance and Ferris (1987), who state that a crash is

an isolated event.

27

Table 14. Test results Wilcoxon for manufacturers


Mean Market M&R

AAR0 -0,0063 -0,0042 -0,0057 AAR1 0,0052 0,0067 0,0038 AAR2 -0,0012 -0,0006 -0,0012 AAR3 -0,0009 -0,0013 -0,0009 AAR4 -0,0153 -0,0171 -0,0148 AAR5 -0,0047 -0,0010 -0,0039 AAR6 0.0052* -0,0043 -0,0048 AAR7 -0,0025 -0,0003 -0,0006 AAR8 0,0088** 0,0088** 0,0088** AAR9 0,0018 0,0003 -0,0011 AAR10 -0,0002 0,0016 0,0012


28

6. Robustness checks

Brown and Warner (1980) use, as discussed in the methodology part, two different tests to

compute their test results, a parametric and a non-parametric test, respectively the Student

t-test and the Wilcoxon test. As the student t-test has some restrictive assumptions, for

example the sample has to be normally distributed otherwise the result could be biased, the

Jarque-Bera statistic is calculated. Since all the Jarque-Bera calculations are above the critical

value of 5,99, normal distribution cannot be assumed, so in this study the non-parametric

Wilcoxon signed rank test is computed. But in order to critically assess the results of the

Wilcoxon test, the Student T-test will be computed in this part of the research. The student

T-test tests whether the abnormal returns differ significantly from zero.

In this study the industry and market indices are market value-weighted. In this section

these indices will be calculated as equally-weighted indices, to offset a possible impact of the

size of companies

Below the average abnormal returns tested with the Student T-test and the indices

calculated equally-weighted. The tables above show the average abnormal returns tested by

the Student T-test. On average the results are the same as the results of the Wilcoxon test.

The results from the tests on the impact on the carrier itself only differ on day 2 and 3 on the

Market and Risk Adjusted Returns. In the tests with the split in the cause of the crash the

only difference is that the Student T-test tests no significant abnormal returns on day 4 while

the Wilcoxon test does test significant returns four days after the event. For the tests on the

manufacturers and the non-crash carriers there is also no significant difference. So the

29

conclusion can be derived that the results of Wilcoxon singed ranked are robust since they

do not differ significant from the Student T-test.

Table 15. Test results Student t-test for all events (N=43)

Student T-test

Mean Market M&R

AAR0 -0.0150*** -0.0165*** -0.0146*** AAR1 -0.0113 -0.0127 -0.0137*

AAR2 -0.0100* -0.0116* -0.0087 AAR3 0.0018 0.0000 0.0022 AAR4 -0.0236 -0.0269 -0.0217

AAR5 0.0066 0.0080 0.0067 AAR6 -0.0012 -0.0030 -0.0014 AAR7 -0.0016 -0.0014 0.0028 AAR8 0.0068 0.0065 0.0113** AAR9 0.0032 0.0003 -0.0002 AAR10 0.0009 -0.0007 0.0019


Table 16. Test results Student t-test for respectively airline's fault (N=23) and miscellaneous (N=20)

Student T-test

Student T-test

Mean Market M&R

Mean Market M&R

AAR0 -0.0158** -0.0171** -0.0174**

-0.0141** -0.0159*** -0.0113

AAR1 -0.0135 -0.0128 -0.0166

-0.0087 -0.0125 -0.0103

AAR2 -0.0108 -0.0095 -0.0056

-0.0091 -0.0141** -0.0122*

AAR3 -0.0046 -0.0077 -0.0037

0.0092 0.0089 0.0091

AAR4 0.0112 0.0049 0.0093

-0.0638 -0.0636 -0.0574

AAR5 0.0035 0.0058 0.0036

0.0101 0.0106 0.0103

AAR6 0.0030 0.0017 0.0007

-0.0060 -0.0084* -0.0038

AAR7 -0.0006 -0.0011 -0.0008

-0.0028 -0.0019 0.0070

AAR8 0.0044 0.0045 0.0060

0.0094 0.0088 0.0173**

AAR9 -0.0090 -0.0091* -0.0061

0.0173** 0.0111 0.0065

AAR10 -0.0057 -0.0043 -0.0013 0.0086 0.0034 0.0055


30

Table 17. Test results Student T-test for respectivily non-crash carriers and manufacturers

Student T-test

Student T-test

Mean Market M&R

Mean Market M&R

AAR0 -0,0012 -0,0013 -0,0016

-0,0063 -0,0042 -0,0057 AAR1 -0,0043 -0,0043 -0,0039

0,0052 0,0067 0,0038

AAR2 0,0005 0,0002 0,0012

-0,0012 -0,0006 -0,0012 AAR3 0,0008 -0,0002 0,0007

-0,0009 -0,0013 -0,0009

AAR4 -0,0141 -0,0162 -0,0146

-0,0153 -0,0171 -0,0148 AAR5 -0,0016 0,0007 0,0003

-0,0047 -0,0010 -0,0039

AAR6 -0,0029 -0,0034 -0,0029

0.0052* -0,0043 -0,0048 AAR7 0,0007 0,0018 0,0039*

-0,0025 -0,0003 -0,0006

AAR8 0,0019 0,0026 0,0054

0,0088** 0,0088** 0,0088** AAR9 0,0024 0,0009 -0,0015

0,0018 0,0003 -0,0011

AAR10 0,0033 0,0032 0,0017 -0,0002 0,0016 0,0012


31

7. Conclusion

In this study the relation is described between airplane crashes and listed avation

companies: airlines and manufacturers. On top of that it differentiates for the cause of the

crash whether it is the fault of the airline or not. First of all, it is clear that an airplane crash is

a terrible event whatever the impact on the listed companies might be. Of course the impact

of the loss of the lives eliminates every dollar lost due to the accident. Nevertheless aviation

studies are crucial since the business is so important in the comtemporary world, among

other things it facilitates globalization and economic growth.

The impact on the involved airlines is tested significant negative on the day of the event and

day 2. These findings are in line with prior literature. Referring to the three types of costs

that Davidson, Chandi and Cross (1987) distinguish, this impact is driven by the third type of

cost, the loss of goodwill of investors and passengers since airlines are mandatory insured

against the first to types of costs.

Regarding the differentation of the cause of the crash there are no significant differences.

This result contradicts the finding of Mitchell and Maloney which states a significant

difference in brand-name effect. A possible explanation is that Mitchell and Maloney (1989)

use a threshold of one fatality and this study uses a threshold of 50 fatalities. But they also

state that the pay out from the insurance companies is the same, which supports the

insignificant difference in this paper. Nevertheless the third type of cost, the goodwill of

investors and passengers, could drive siginificant differences. However this study finds that

investors and passengers are not influenced by the cause of the crash, in both subsamples

the crash has negative impact on the stock price. As behavioral economists Kaplanski and

32

Levy (2009) state in their paper that investors are impacted by negative sentiment caused by

bad mood and anxiety, obviously both subsamples cause these moods.

But this negative sentiment does not impact the investors and passengers of the airlines not

involved in the crash. The outcome of the event study on the industry indices does not show

any significant abnormal returns. So an airplane crash can be seen as an isolated event and

investors do not assess an airplane crash as an industry issue. This is in line with the paper of

Chance and Ferris (1987) and the opposite of Bosch et al. (1998) who find results supporting

the switching effect and Kaplanski and Levy (2009) who test a negative impact on the NYSE.

Furthermore, the tests on the impact of the manufacturer are also insignificant. Only three

manufacturers are included in the dataset but next to these companies the market for

manufacturers is not big, Boeing and Airbus are by far the biggest players. So an explanation

can be that the market model of the manufacturers is not efficient.

So based on the event study methodology an airplane crash is an isolated event. The crash

only impacts the airline involved in the crash and the industry and the manufacturers are not

impacted. An interesting next step is to look at it from a behavioral economics view, in order

to get a better understanding about rationale of the investor.

33

8. References

Borenstein, Severin, and Martin B. Zimmerman. "Market incentives for safe commercial airline operation." The American Economic Review (1988): 913-935.

Bosch, Jean-Claude, E. Woodrow Eckard, and Vijay Singal. "Competitive Impact of Air Crashes: Stock Market Evidence, The." JL & Econ. 41 (1998): 503.

Brown, Stephen J., and Jerold B. Warner. "Measuring security price performance." Journal of financial economics 8.3 (1980): 205-258.

Brown, Stephen J., and Jerold B. Warner. "Using daily stock returns: The case of event studies." Journal of financial economics 14.1 (1985): 3-31.

Chance, Don M., and Stephen P. Ferris. "The effect of aviation disasters on the air transport industry: a financial market perspective." Journal of Transport Economics and Policy (1987): 151-165.

Davidson, Wallace N., P. R. Chandy, and Mark Cross. "Large losses, risk management and stock returns in the airline industry." The Journal of Risk and Insurance 54.1 (1987): 162-172.

Malkiel, Burton G., and Eugene F. Fama. "Efficient capital markets: A review of theory and empirical work." The journal of Finance 25.2 (1970): 383-417.

Ho, Jerry C., Mei Qiu, and Xiaojun Tang. "The Catalyst in the Air Crash-Stock Market Performance Relationship: The Aviation Disaster Fatality." Kaplanski, Guy, and Haim Levy. "Sentiment and stock prices: The case of aviation disasters." Journal of Financial Economics 95.2 (2010): 174-201. MacKinlay, A. Craig. "Event studies in economics and finance." Journal of economic literature 35.1 (1997): 13-39.

Mitchell, Mark L., and Michael T. Maloney. "Crisis in the cockpit? The role of market forces in promoting air travel safety." The Journal of Law & Economics 32.2 (1989): 329-355.

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http://www.nytimes.com/2014/05/16/business/international/flights-disappearance-knocks-malaysia-airlines.html?_r=2

https://uk.finance.yahoo.com/news/malaysia-press-nearly-200-mas-003948762.html

http://www.ibtimes.com/airasias-share-price-tumbles-malaysia-airlines-recovery-becomes-beacon-hope-1769330 https://en.wikipedia.org/wiki/Malaysia_Airlines http://www.ibtimes.com/airasias-share-price-tumbles-malaysia-airlines-recovery-becomes-beacon-hope-1769330

http://www.ibtimes.com/germanwings-flight-9525-crash-sends-airline-stocks-tumbling-deutsche-lufthansa-lha-1860718

http://fd.nl/frontpage/economie-politiek/1086653/luchtvaart-maakt-zich-op-voor-duurdere-verzekeringen

https://en.wikipedia.org/wiki/Casualties_of_the_September_11_attacks

https://aviation-safety.net/

https://en.wikipedia.org/wiki/Tenerife_airport_disaster

http://aviation-safety.net/database/record.php?id=20020525-0 http://aviation-safety.net/database/record.php?id=19980420-0

http://aviation-safety.net/database/record.php?id=20001031-0

https://en.wikipedia.org/wiki/MSCI_World



https://uk.finance.yahoo.com/news/malaysia-press-nearly-200-mas-003948762.html



https://en.wikipedia.org/wiki/Malaysia_Airlines





http://fd.nl/frontpage/economie-politiek/1086653/luchtvaart-maakt-zich-op-voor-duurdere-verzekeringen

https://en.wikipedia.org/wiki/Casualties_of_the_September_11_attacks

https://aviation-safety.net/

https://en.wikipedia.org/wiki/Tenerife_airport_disaster




https://en.wikipedia.org/wiki/MSCI_World

university of amsterdam, amsterdam business school msc

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