What are the effects of the recent terror attacks carried out by Islamic State
on the stock market of France?
By Jip Mukanay
Statement of Originality
This document is written by Student Jip Mukanay who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is 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 of the work, not for the contents.
Introduction
Over the last few years, a growing number of academics have devoted their research to the effects of terror attacks on the economy. This is due to the frequent occurrence of these types of attacks in the last years. Terror attacks are important and unforeseen events that severely disrupt the daily routine of life, due to the victims and economic costs they cause (Kollias, Papadamou & Stagiannis, 2011). From 2014, France has been the victim of several attacks carried out by terrorist group Islamic State (IS). The country had to deal with more than ten attacks in less than two years (CNN, 2016). Table 1 shows the attacks that recently took place in France.
Date France -incidents
Perpetrator City Target Fatalities Injuries
07/01/2015 adherent of IS Paris Two attackers opened fire on journalists of Charlie Hebdo, a
satirical magazine
12 12
07/01/2015 adherent of IS Paris An assailant shot a jogger in a park 0 1
08/01/2015 adherent of IS Paris An assailant shot and killed a police officer and injured a city
employee
1 1
09/01/2015 adherent of IS Dammartin en
Goele
The attackers took one employee hostage during an eight-hour standoff with elite forces
2 2
09/01/2015 adherent of IS Paris A gunman took 19 employees and customers of a kosher
supermarket hostage
5 3
03/02/2015 adherent of IS Nice An assailant attacked soldiers guarding a Jewish cultural center
with a knife
0 2
26/06/2015 adherent of IS
Saint-Quentin-Fallavier
Anassailant beheaded a supervisor and hung his head from a fence
1 2
21/08/2015 adherent of IS Arras An assailant attempted to attack passengers on a Thalys high-speed train
0 3
13/11/2015 IS Saint Denis 3 suicide bombers detonated explosives-laden vests near the
Stade de France, where a soccer match was taking place
4 0
13/11/2015 IS Paris Assailants opened fire on the restaurants Le Carillon and Le Petit
Cambodge
15 10
13/11/2015 IS Paris Assailants opened fire on Cafe Bonne Biere 5 8
13/11/2015 IS Paris Assailants opened fire on the terrace of La Belle Equipe bar 19 9
13/11/2015 IS Paris A suicide bomber detonated an explosives-laden vest at Comptoir
Voltaire restaurant
1 1
13/11/2015 IS Paris 3 suicide bombers with explosives-laden vests opened fire on
Bataclan concert hall. Additionally, 20 civilians were taken hostage for at least 2 hours
92 101
13/06/2016 adherent of IS Magnanville An assailant killed a police commissioner and his wife 3 0
14/07/2016 IS Nice An armed assailant drove his truck into a crowd of people who
were celebrating a national holiday
87 434
Source: Global Terrorism Database
According to Laskar (2014), IS is now located in Afghanistan, Algeria, Egypt, Libya, Nigeria, North Caucasus, Pakistan, Yemen and Saudi Arabia. It is argued that they trained more than 400 fighters to attack Europe. We can therefore assume that more attacks on European grounds are coming (Hinnant & Dodds, 2016). The attacks are used to manipulate the economy of these countries (Welby, 2015). In this light, it would be useful to know if they are actually succeeding in this goal. That is why in this paper I will look into this, by answering the following research question: What are the effects of the recent terror attacks carried out by Islamic State on the stock market of France? For the analysis, the daily stock prices of Euronext Paris are used. The data is drawn from the Yahoo Finance database. I will investigate the impact of the attacks on the general index, and the indices of the nine biggest sectors of France: basic materials, consumer goods, consumer service, financials, Table 1
* * health care, industrials, oil and gas, technology and utilities. With event study methodology I calculate the abnormal returns (ARs). The possibility exists that the effects of terror attacks on the stock market happen several days after the actual attack took place. This is probably because even the authorities sometimes do not know what exactly happened, and it also takes time before the media is present (Kaplanski & Levy, 2010). Therefore, I will also look at the cumulative abnormal return (CAR). Event-windows of six and twelve days are used, because the biggest deviation was observed in these time frames. Subsequently, there will be looked at the contribution of the terror attacks on the abnormal returns, by applying regression. In addition, the GARCH method is used to test the effects of attacks on the volatility of stocks.
No research has been done yet on the effect of the above mentioned events, even though the event windows show significant abnormal returns, as can be seen in Table 2-11. A t-test was used to determine the significance of the ARs and CARs.
Table 2
* * * * * . * * * Table 4 Table 5 Table 6
Table 7
Table 8
Table 10
Table 11
The regression results show that terror attacks can affect the return on stocks for some sectors in France. This effect can be both positive and negative. The occurrence of a terror attack can also influence the stock volatility for certain sectors. This relation is strictly negative. A contribution to the existing literature is made, by estimating the effect of the recent terror attacks on stock returns for the French market. This field has not been touched yet.
The structure of this paper will be as follows: the subsequent section contains a review of the existing literature on terrorism and financial markets. Subsequently, the research method will be described. The section following the research method will contain the results and the final section provides the concluding remarks on the implication of these results
Literature review
In this literature review the findings on the effects of terror attacks on the financial market will be discussed together with background information. The occurrence of terror attacks triggers negative sentiment, which should be reflected in falling stock prices according to theory. But, in practice it turns out that different attacks have different impacts.
Behavioral economic studies have found that whenever investors experience negative sentiment, this will affect the investment decisions and therefore influence the stock market (Kaplanski & Levy, 2010). Terror attacks are high-impact events, that can influence the sentiment of investors. Their beliefs about future cash flows and investment risks are being compromised by the unpleasant events, and push the investment climate to a more irrational direction (Baker & Wurgler, 2007). According to Lerner et al. (2003), this is because nobody knows what to expect next. Everybody lives in uncertainty and will behave accordingly, e.g. not rational. Fischer et al. (2006) also examined the emotions of people with respect to terrorism. After the terror attack on the World Trade Center in New York on September 11th 2001 (9/11), 50 till 70 percent of the people felt depressed. They also felt less safe, since anyone could be the next victim. These feelings can have an effect on the investment decisions in bonds and stocks, right after an attack takes place (Solomon, Greenberg, & Pyszczynski, 1991). Financial markets behave according to what investors believe will happen in the future. Thus, markets will go up if investors have a positive outlook on the future and vice versa. Terror attacks are being labeled as negative, because of the fact that they hurt innocent people. They therefore should have a negative impact on financial markets (Chen and Siems, 2004).
Multiple researchers have looked into the actual effects of terror attacks on financial markets. For example, Johnston and Nedelescu (2006) investigate the attacks of 9/11 and the attacks of Madrid, Spain, on March 2004. They found that the attacks in Madrid mostly had a regional effect, whereas the attacks of 9/11 had a large international impact on financial markets. They note that this can be explained by the timing of the two events. While the economy was booming in Madrid, there was a recession in the US. Another reason could be that the perpetrators aimed at different targets. In New York the financial center was hit, whereas the attacks in Madrid hit the transportation sector. What also became clear in the paper of Johnston and Nedelescu (2006), is that financial markets are not only disrupted by property damage, but also by market volatility and uncertainty. Abadie and Gardeazabal (2008) report that through increased uncertainty, terrorism reduces the expected return to investment. This may cause significant movements of capital across countries.
Eldor and Melnick (2004) compared the impact of events like 9/11 and the Madrid bombings, with the frequent and continuous terrorist incidents that Israel faces. They found that the foreign
exchange market was affected, but this was not the case for the Tel Aviv Stock Exchange. Barros and Gil-Alana (2008) examined the continuous terror attacks carried out by ETA terrorists on the Basque country stock market. They found an adverse effect through negative returns. A reduction in the terrorist violence would increase the stock returns of the Basque country.
Furthermore, there are researchers who have conducted similar research as this paper, but focused on different attacks. For example, Chen and Siems (2004), investigated the magnitude of the effects of 9/11 on global and US capital markets, using event study methodology. They found a significant impact, which was not unique compared to other economic or political shocks. Drakos (2004) also examined the 9/11 effects on financial markets, but solely focused on shares of the airline industry. His findings also indicate a significant impact. The changed risk perceptions by consumers led to lower demand for air travel and higher insurance premia. This resulted in falling stock prices. Kollias,
Papadamou and Stagiannis (2011) investigated the effects of the bomb attacks in Madrid and London (July 2005). Using event study methodology and GARCH family models, they found significant
negative abnormal returns across the majority of sectors in the Spanish markets. This was not the case for London. London needed significantly less days to rebound compared to the Spanish markets. Overall, their findings point to a transitory impact on return and volatility. Essaddam and Karagianis (2014) also looked into the effects of terror attacks on volatility. They came to the conclusion that terror attacks have a major influence on the volatility of the stocks of American companies. Targeted firms became more volatile after the attacks. Nguyen and Enomoto (2009) came to the same
conclusion, only for stock markets in Pakistan and Iran.
Given the previous findings, one would expect that the terror attacks under examination affect the French stock market.
Research method
This section provides an explanation of the derivation of the (cumulative) abnormal returns and the corresponding regression model. Furthermore, the GARCH and VIF models are exemplified.
For the analysis that will follow, daily prices of the major stock exchange of France (Euronext Paris) are used. The data is drawn from the Yahoo Finance database. It ranges from 06/01/2014 (one year before the first attack happened in France) till 26/07/2016 and includes 636 trading days.
The impact of the attacks is investigated on the general index (CAC 40) and on the nine biggest sector indices of France (CAC Basic Materials, CAC Consumer Goods, CAC Consumer Service, CAC Financials, CAC Health Care, CAC Industrials, CAC Oil&Gas, CAC Technology and CAC Utilities).
With event study methodology, I calculated the abnormal returns. An abnormal return ( ) is the difference between the firm’s actual return and the predicted return on a specific date (Kollias et al., 2011). This methodology often has been used in relevant literature to assess the impact of various events (Chen & Siems, 2004). With the market model, normal behavior is determined:
Where is return of a stock at a certain day (t), is the intercept, describes the relationship between the returns of the index with the returns of the market and is the return of the market
(S&P 500 is used as a market proxy). With an estimation period of 06-01-2014 till 06-01-2015, and were calculated.
There will also be looked at the cumulative abnormal return (CAR). The CAR is a measure of total abnormal returns. For this analysis I used a six- and twelve day event-window because in these time frames the biggest deviation was observed.
where is the event day and is either five or eleven.
To test whether these abnormal returns are significantly different from zero, a simple t-test is used.
To see if the observed abnormal returns can be contributed to terrorism, a regression will be
performed. A similar methodology as the one used in the research done by Kamstra, Kramer and Levi (2003), and Kaplanski and Levy (2010) will be applied. The impact of different variables will be tested on the AR and CARs. With the following model I seek to find the explanation for the abnormal returns
Where is the intercept of the regression and is the rate of return i days before the actual
attack happened. By including these historical returns, all the significant serial correlations are accounted for according to Kaplanski and Levy (2010). (i=1…4) is a dummy variable which
represents which day of the week it is: Monday, Tuesday, Wednesday or Thursday respectively. This variable is included in the regression, because of the weekend effect that was found by French (1980). Friday is the omitted variable. is a dummy variable for the first five days of a new taxation year. This variable is included because the first five days of the new taxation year can affect the stock market (Kaplanski & Levy, 2010). Country characteristics like GDP , the inflation rate and unemployment rate are taken into account because they also can have an influence on the results (Kaplanski & Levy, 2010). The data for GDP and the unemployment rate are quarterly and for the inflation rate monthly. They are drawn from the Federal Reserve Economic Data (FRED) dataset. Apart from this, three more variables are used to see what the effects of terror attacks are on the French stock market. is a dummy variable which takes the value of 1 whenever an assessment takes place. is the number of people that have died as a result of the attack (fatalities) and is
the number of people that were wounded because of the attack (injuries). is the error term. The last two variables test what the effects of larger attacks are.
The tested hypothesis will be:
Furthermore, the GARCH modeling technique is used to test the volatility of the stocks. It is a statistical model in which the variance of the dependent variable is analyzed with respect to its time dependency and substantive explanatory variables (Bollersley, 1986), and extends the ARCH
framework introduced by Engle (1982). In this paper the EGARCH(1,1) is used, because it does not impose symmetry on the conditional variance structure (Nelson, 1991).
The following equation is used:
where is the error term with conditional mean zero and is the conditional variance. The dummy variable , which takes the value of 1 on the event day, is included to see if there is any possible effect of an attack on volatility.
The tested hypothesis will be:
And finally, a VIF test for multicollinearity will be conducted with the following formula:
where S is the standard deviation, n the sample size and is the standard error of the slope coefficient b.
Results
In this section, the results are analyzed. There are no signs of multicollinearity. The terror-related variables are significant for some sectors and the event day can have a significant negative effect on the volatility.
The highest VIF-value for the tested variables is 1.56 and the mean VIF amounts to 1.20. Because all values are relatively close to 1, one can assume that there is no multicollinearity between the tested variables (Allison, 2012).
Table 12 shows the results of the abnormal returns regression. It is noticeable that terrorism has a statistically significant influence on ARs and CARs only in a few sectors. Furthermore, the coefficients are remarkably low.
Table 12
attack fatalities injuries GDP inflation Unemploy- ment
mon tue wed thur 5DB 4DB 3DB 2DB 1DB first
5 tax day GEN AR CAR6 CAR12 -0.003 -0.007 0.011 -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.000 -0.000*** -0.000*** -0.006* -0.021*** -0.039*** -0.004 -0.016* -0.026** -0.002 -0.002 -0.001 -0.001 0.000 0.001 0.001 -0.000 0.004 -0.000 -0.002 0.000 -0.081** -0.062 -0.259*** -0.026 -0.114 -0.231** -0.013 -0.146** -0.205** -0.029 -0.139* -0.174* -0.014 -0.071 -0.110 -0.003 0.002 0.018 BM AR CAR 6 CAR 12 -0.003 -0.016* 0.002 -0.000 -0.000 -0.001* 0.000 0.000* 0.000* -0.000 -0.000* -0.000** -0.007* -0.026*** -0.050*** -0.006 -0.025** -0.041*** -0.003 -0.002 -0.002 -0.002 0.001 -0.001 -0.001 -0.001 0.004 -0.001 -0.001 -0.001 -0.046 -0.011 -0.159 -0.000 -0.048 -0.114 -0.018 -0.094 -0.109 -0.021 -0.091 -0.069 -0.009 -0.006 -0.052 -0.025 -0.060 -0.016 CG AR CAR 6 CAR 12 -0.002 0.003 0.013 -0.000 -0.000 -0.001** 0.000 0.000 0.000** -0.000 -0.000*** -0.000*** -0.005 -0.015** -0.029*** -0.005 -0.020** -0.034*** -0.000 -0.002 -0.001 -0.001 -0.001 0.001 0.002 -0.001 0.003 -0.000 -0.002 -0.001 -0.067 -0.060 -0.205** 0.000 -0.104 -0.141 0.013 -0.085 -0.148 -0.082* -0.123 -0.128 -0.026 -0.008 -0.077 -0.001 -0.116 0.015 CS AR CAR 6 CAR 12 -0.002 0.004 0.017 -0.000 -0.000 -0.001** 0.000 0.000 0.000* -0.000 -0.000** -0.000*** -0.005 -0.017*** -0.036*** -0.001 -0.008 -0.010 -0.002 -0.003 -0.002 -0.002 -0.000 -0.000 -0.000 -0.001 0.003 -0.002 -0.002 -0.000 -0.095** -0.069 -0.253** -0.054 -0.134* -0.264*** 0.026* -0.123 -0.210** -0.078 -0.196** -0.209** 0.052 0.002 -0.031 0.009 -0.104 0.017 FIN AR CAR 6 CAR 12 -0.005 -0.015 0.001 -0.000 -0.000 -0.000 0.000 0.000 0.000** -0.000* -0.000*** -0.000*** -0.008** -0.033*** -0.062*** -0.003 -0.012 -0.020 -0.003 -0.001 0.001 -0.001 0.000 0.001 0.001 -0.002 0.004 -0.001 -0.003 -0.001 -0.111*** -0.035 -0.184* -0.029 -0.095 -0.158 -0.038 -0.179** -0.181* -0.055 -0.231*** -0.206** 0.035 0.000 -0.077 -0.000 -0.090 0.021 HC AR CAR 6 CAR 12 -0.005 -0.005 0.017 0.000 -0.000 -0.001 -0.000 0.000 0.000 -0.000* -0.000*** -0.000*** -0.003 -0.005 -0.010 -0.007 -0.025*** -0.047*** 0.002 -0.001 0.000 -0.001 -0.001 0.001 0.002 0.002 0.004 0.001 -0.000 0.002 -0.064 -0.057 -0.334*** -0.001 -0.104 -0.264** 0.019 -0.030 -0.191* -0.012 -0.049 -0.106 -0.019 0.004 -0.007 0.019 -0.064 0.016 IND AR CAR 6 CAR 12 -0.003 -0.003 0.010 0.000 0.000 -0.000 0.000 0.000 0.000 -0.000 -0.000** -0.000*** -0.007** -0.025*** -0.050*** -0.001 -0.005 -0.006 -0.003 -0.003 -0.002 -0.002 0.000 0.000 -0.000 -0.001 0.003 -0.002 -0.002 -0.001 -0.088** -0.084 -0.237** -0.054 -0.146* -0.266*** 0.005 -0.145* -0.238** -0.085** -0.219*** -0.252** 0.069 -0.001 -0.054 -0.003 -0.138 0.011 OG AR CAR 6 CAR 12 -0.001 -0.027** 0.007 -0.000 0.000 -0.000 0.000 0.000 -0.000 -0.000 -0.000 -0.000 -0.008** -0.035*** -0.060*** -0.002 -0.011 -0.006 -0.005* 0.000 -0.003 0.000 0.003 0.002 0.001 -0.002 0.004 -0.001 -0.003 0.000 -0.012 -0.097 -0.274*** -0.082* -0.128* -0.232** -0.047 -0.197*** -0.198** 0.016 -0.138* -0.227** -0.012 -0.003 -0.125 -0.023 -0.220** 0.002 TEC AR CAR 6 CAR 12 -0.001 -0.004 -0.004 -0.000 -0.000 -0.000 0.000 0.000 0.000** -0.000* -0.000*** -0.000*** -0.003 -0.010** -0.026*** -0.004 -0.019** -0.036*** 0.000 -0.001 0.000 0.001 -0.001 0.001 0.000 -0.002 0.002 0.001 -0.001 -0.001 -0.068 -0.052 -0.204** -0.010 -0.069 -0.111 -0.026 -0.114 -0.140 -0.080* -0.176** -0.134 0.009 -0.001 -0.103 0.008 -0.152 0.022 UTI AR CAR 6 CAR 12 -0.007 -0.016 0.002 0.000 0.000 -0.000 0.000 0.000 0.000 -0.000 -0.000*** -0.000** -0.002 -0.007 -0.005 -0.008 -0.035*** -0.058*** -0.005** -0.003 -0.001 -0.002 0.002 0.002 -0.000 -0.000 0.006 -0.002 -0.003 0.001 -0.064 -0.021 -0.156 -0.089** -0.129 -0.139 -0.039 -0.254*** -0.108 -0.036 -0.228*** -0.133 0.014 -0.008 -0.153* 0.004 -0.127 0.010 Table 12 shows the effects of different (control) variables (respectively: Attack, Fatalities, Injuries, GDP, Inflation rate,
Unemployment rate, Monday, Tuesday, Wednesday, Thursday, Rate of Return 5/4/3/2/1 Day(s) Before and the First 5 Days of a New Taxation year) on the AR, 6-day CAR and 12-day CAR, for ten different sectors (respectively: General, Basic Materials, Consumer Goods, Consumer Service, Financials, Health Care, Industrials, Oil&Gas, Technology and Utilities). One, two, and three asterisks (*) indicate a significance level of 10%, 5% , and 1% respectively.
The effects of terror attacks are relatively small. When looking at the coefficients for Fatalities and Injuries, they are all close to zero. The coefficients of the variable Attack are bigger, but still small. Attack only has a statistically significant effect on the 6-day CAR in the sectors of Basic Materials (-0.016) and Oil&Gas (-0.027). In both cases the effect is negative and only significant in the 6-day
Table 13
event window. The variable Attack has no significant influence on the AR and CARs of the General index and the sector indices of Consumer Goods, Consumer Services, Financials, Health Care, Industrials, Technology and Utilities.
The coefficients of the variable Fatalities are significant in three cases. Again, in the Basic Materials sector it has a statistically significant negative effect of -0.001 on the 12-day CAR. The number of fatalities has the same effect in the sectors of both Consumer Goods and Consumer Services. In these cases the significant effect is also -0.001 and related to the 12-day CAR. The number of fatalities does not influence the following sector indices: General, Financials, Health Care, Industrials, Oil&Gas, Technology and Utilities.
Injuries is the terrorism-related variable with significant effects in the most sectors, when comparing to Attack and Fatalities. The coefficients of Injuries show statistically significant numbers in the cases of Basic Materials, Consumer Goods, Consumer Services, Financials and Technology. The number of injured people has a statistically significant effect on the 6-day CAR and 12-day CAR. In all the other cases, only the 12-day CAR is affected. The coefficient is the same for all sectors, namely 0.000. The number of injuries has a positive effect, but of an extremely small size.
The General index and the sector indices of Health Care, Industrials and Utilities are not affected by any of the terror attack characteristics. The terrorism-related variables also did not have an influence on the regular abnormal returns in any sector. The effects only became significant in a six or twelve day event-window.
Furthermore, it can be seen that control variables like GDP, Inflation and Unemployment have a serious impact on the results.
The results from the GARCH model with a dummy variable (Attack) are reported in table 13. The long term volatility in all sectors is positive and statistically significant. The Oil&Gas sector has the highest long term volatility (0.013).
The effect of previous day’s return is in all cases insignificant. The effect of previous day’s conditional variance on today’s conditional variance (volatility) is in all cases significant. The coefficients for all sectors are quite similar.
And finally, the effects of the Attack dummy variable is significant in the case of the General index and the Consumer Goods, Health Care and Oil&Gas indices. Remarkable is that all the significant coefficients have a negative sign. This means that whenever an attack occurs, the volatility will go down by -0.013, -0.012, -0.014 or -0.019 respectively.
ω
α
β
λ
GEN
0.008***
2.168
0.958***
-0.013*
BM
0.009***
1.731
0.957***
-0.005
CG
0.006***
2.611
0.965***
-0.012**
CS
0.006***
0.317
0.967***
-0.006
FIN
0.008*** -1.068
0.964***
-0.008
HC
0.009***
0.214
0.956***
-0.014**
IND
0.007*** -1.082
0.963***
-0.002
OG
0.013*** 1.067
0.952***
-0.019**
TEC
0.008*** 0.655
0.954***
-0.006
UTI
0.008*** -1.206
0.963***
-0.005
Conclusion
The following section will conclude this paper in which I seek to find an answer to the following question: What are the effects of the recent terror attacks carried out by Islamic State on the stock market of France? It can be concluded that terror attacks have an impact on the stock returns of different sectors in France. This is noticeable several days after the event day. Furthermore, the significant negative effect of terror attacks on volatility is remarkable. Further research is needed. Overall, terror attacks do have a significant effect on the stock market of some sectors in France. The event day of an attack has a significant negative effect on the stock indices of Basic Materials and Oil&Gas. These effects will show within six days after the event. The number of fatalities has a significant negative effect on the stock indices of Basic Materials, Consumer Goods and Consumer Services. These effects will show within twelve days after the attack occurs. And finally, the number of injured people has a significant effect on the stock indices of Basic Materials, Consumer Goods, Consumer Services, Financials and Technology. In this case all the coefficients are positive, but almost equal to zero. The effects will show within a six or twelve day event-window.
Remarkable is that the sector of Basic Materials is affected by all terror-related variables (Attack, Fatalities and Injuries). On the other hand, the General index and the indices of Health Care, Industrials and Utilities, are not affected by any of the terror-related variables.
Another striking detail is that the results only showed significant effects on the cumulative abnormal returns, and not on the event-day abnormal return. This means that after a terror attack, the stock market needs some days to process this information. This is in line with the findings of Kaplanski & Levy (2010). In their paper they explain that often even the authorities do not know what exactly has happened and that it also takes time before the media is present at the site where the attack took place.
When looking at the results of the GARCH-test, we find a significant negative effect of the event day a terror attack on the indices of Consumer Goods, Health Care and Oil&Gas, and the General index. It is striking that all coefficients are negative, since this implies a lower volatility when a terror attack occurs. This is not in line with Kollias et al. (2011), who have found a positive effect of terrorism on volatility. Also remarkable, is the fact that the abnormal returns of Health Care are unaffected by terrorism, but the volatility of the same sector is affected.
In the future, one could look into the relation of Basic Materials and terror attacks. The results showed an impact of the event day, number of fatalities and number of injuries on the cumulative abnormal returns of this sector. This was only the case for this sector. It is unknown why, since they do not seem directly related.
An interesting topic for further research would also be to see what the effects of these attacks are, in countries other than France. In this way, one can examine whether the effects are regional or of global nature(like 9/11).
Another topic for future research would be to have a deeper look into the negative effect of terror attacks on the volatility in some sectors of France. It would be interesting to try to explain the fact that these important and unforeseen events disrupt daily life, but simultaneously contribute to a lower volatility. To investigate this is beyond the scope of this paper.
And finally, it would be interesting to see what caused the substantially high abnormal returns after 26/06/2015 in every sector of France.
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