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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Stock Market Reaction to Terror: Empirical Evidence |
| Organization Unit | |
| Authors |
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| Supervisors |
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| Language |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 47 |
| Date | 2018 |
| Zusammenfassung | This paper investigates the impact of 71 national and transnational terror attacks on the stock markets of the G7 member countries as well as Switzerland. After a brief intro-duction Section 2 gives an overview of the existing literature. There has been conducted a lot of research examining the various repercussions of terrorism in areas such as psychol-ogy, economic growth, and security, to name just a few. However, financial markets have not received as much attention. This paper adds to the existing literature by taking a more general approach, analyzing how stock markets react to terror in general. Thereby the focus is set on a broad catalog of incidents and not on a single isolated attack. Section 3 discusses the nature of terrorism risk and the di˙erent ways to cope with it. According to ?, it can be distinguished between three types of costs when it comes to a terror attack: the short-term direct costs which are a consequence of the immedi-ate aftermath, the medium-term confidence costs harming consumption and investment decisions, and the long-term growth costs materializing due to higher transaction costs. The economy can cope with these costs on two di˙erent levels. On the one hand flexible and instantaneous government intervention as well as regulation are crucial to restore confidence among investors and consumers. Possible instruments that might be used by the authorities are fiscal stimulus or expansive monetary policy (?). On the other hand there are investors who cope with terrorism risk on their own way by hedging themselves against it using various portfolio diversification strategies (?). A description of the data used in this paper is given in Section 4. The stock market data as well as the data on terror attacks span from 1999 to 2016. During this time period harmful terrorist activity surged to new heights. The number of attacks (8’083) and the amount of people harmed (66’655) peaked in 2014 (?). Possible catalysts behind this negative trend are the launch of the "war on terror" after the devastating attacks around 9/11 (?) and the spread of the Internet in the 21st century making it easier for the terrorists to organize attacks and to recruit new fighters (?). The methodology used in this paper is described in Section 5. An event study approach along the lines of ? was applied, using the mean adjusted return method to determine the normal returns. The results are thus published in Section 6. When combining all indices, they show a strongly significant negative abnormal return on the day of an event of -21 basis points. When breaking the analysis down on the individual index, the significance partly fades away. Still significant negative impacts experience the German DAX, the French CAC, and the MSCI Italy at the 5% level, as well as the American S&P 500 at the 10% level. Nevertheless, the other indices examined showed, even though not significant, a negative relationship too. The realized abnormal return seems to be strongly influenced by the severity of the attack which was measured by the number of people killed or injured. This e˙ect remains valid, even when looking at every index separately. However, when a remarkable outlier, marked by the events of 9/11, is excluded, the results lose significance. Furthermore, the stock markets do not seem to pay much attention to the geographical position of an attack. Whether the attack happens on the other side of the globe or in a neighboring country, financial markets do not seem to care. When shifting the focus from the event day only to a whole week of trading after an attack, the results show that on average the stock markets analyzed recover in that time frame. The findings in my thesis do not alter significantly when expanding the estimation window for the normal returns from 20 days to 250 days. |
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