Changes in Equity Returns and Volatility across Different Australian Industries Following the Recent Terrorist Attacks. Introduction Data & Methods Empirical.

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Changes in Equity Returns and Volatility across Different Australian Industries Following the Recent Terrorist Attacks. Introduction Data & Methods Empirical Findings Conclusion Dr. Vikash Ramiah Cam Marie-Anne Michael Calabro David Maher Shahab Ghafouri School of Economics, Finance, & Marketing Acknowledgements: The authors are grateful to the assistance of Richard Heaney and George Tawadros. Co-Authors Research Assistants Jason Lont Rebecca Vassil Christian Kypreos

Reference Ramiah, Vikash, Micahel Calabro, David Maher, and Shahab Ghafouri, The Short Term Impact of Terrorism. Is Australia an Investment Heaven?, 14th Global Finance Conference 2007, Melbourne, Australia. Ramiah, Vikash, Marie-Anne Cam, Micahel Calabro, David Maher, and Shahab Ghafouri, Changes in Equity Returns and Volatility across Different Australian Industries Following the Recent Terrorist Attacks, Pacific-Basin Finance Journal, Vol 18, Issue 1, pg 64-76, January 2010.

Introduction CAM (2006) Sept 11, Bali & Madrid Bombing 135 Industry Equity Indexes in the US Sept 11 had the Most Influence on the US market Negative: Airline, Hotel, and Leisure Positive: Water, Defence, and telecom We look at Sept11, Bali, Madrid, London & India 13 Australian Equity Indexes

Financial Markets & Terror Risk  We do not assume that investors necessarily react negatively to terrorist attacks.  Equity holders tend to respond negatively to such events only when they perceive an increase in the expected costs of terrorist activities.  We argue that market players may well not react if they do not perceive that the attack has an impact on expected returns.  Through substitution effects, investors may move their investments to neighbouring countries and this can result into a positive externality for other financial markets.  We believe that markets can respond differently to the different attacks and that the variation in risk and return varies significantly across different sectors within an economy.

Australia: The Ideal Testing Ground Australia’s strong ties with the United States and the war on terrorism attract terrorist activity Australia’s geographic isolation may project the image of an investment haven

Literature Review Chen and Siems (2004) Using Event Studies, they assess the effects of terrorism on global capital markets. They showed that the major Australian Market index was negatively affected by September 11 Event Day AR6-Day CAR11-Day CAR -4.19% -6.81% -8.60% Cam (2006) Showed an industry effect in the US. Following September 11, the airline, hotel and leisure industries recorded strong negative abnormal returns water, defence and telecom industries showed strong positive abnormal returns. Hence the need for an industry effect Analysis is justified for Australia

Literature Review Worthington and Valadkhani (2005) Use time series analysis of ten industries in Australia and concluded that the Financial Sector was the only sector affected negatively by September. In their model specification, they included other factors like Sydney hail storm, Canberra bushfire, Victorian gas supply crisis, HIH collapse, September 11 and Bali bombing. This research looks at the long term impacts of any of the above disasters.

Introduction: Innovation To this Date there is NO current study on the short term impact of Recent Terrorist Attacks on the Australian Industries Chen and Siems (2004) showed that the Australian Equity Market as a whole was negatively affected. Worthington and Valadkhani (2005) shows that only the Financial Industry was. A direct comparison between these two research is not reasonable as the horizons are different One objective of this research is to resolve the above conflicting results

Introduction: Findings By observing the industry effects in Australia, we can determine how Australian investors’ reacted to the recent major terrorist attacks. Our results are consistent with the prior literature, in that September 11 had a negative impact on the Australian Market. Further, over our sample period we find that, the market as a whole is fairly insensitive to the major terrorist attacks past September Our contribution to this debate is that while the major terrorist attacks following September 11 did not affect the Australian equity market as a whole certain industries were affected.

Data & Methods Data Datastream Daily data: August 1999 – August Stocks in our Sample We construct industry portfolios based on the Global Industry Classification Standards (GICS). One of the practical issues that we face in this process is the small number of firms within some industry sectors and thus we used a modified version of the GICS. Firms with price sensitivity information (15 days either side of event day) were excluded from industry indexes Total Sample of 503 firms in 13 industries

Results – Descriptive Statistics Descriptive Statistics of daily Returns, for sectors in Australia from August 1999 to August 2006.

Events September 11, ,801 people killed Madrid, March people killed London, June people killed Mumbai, June people killed Bali, October people killed

EVENT DATES Event Day Manipulation The US & Australian stock markets operate in different time zones. Methodologies are therefore applied to the date at which the impact was felt on the Australian stock market. EventEvent DateDate of Aust. Market Impact September 11, /9/200112/9/2001 Bali, October /10/200214/10/2002 Madrid, March /3/200412/3/2004 London, June /6/20068/6/2006 Mumbai, June /6/

Abnormal Return Calculation Where DRi is the daily return for stock i, SRIit is the stock return index for stock i at time t. Ex-post abnormal returns are estimated following Cam (2006) & Brown & Warner (1985). Where The abnormal return for industry i (ARIt) at time t is then obtained by averaging the abnormal return of each firm within the industry. Note that we exclude all firms with firm specific information around the event date

Parametric Tests The parametric tests assume that industry abnormal returns are normally distributed. Thus the standard statistic for the abnormal return is given by By cumulating the periodic abnormal return for each industry over five days, we obtain the five day cumulative abnormal return (CAR5 It ).

Non-Parametric Tests 1.The literature on AR distributions show that they are not normally distributed. 2.AR distributions tend to exhibit fat tails and positive skewness. 3.Under these circumstances parametric tests can be misleading in that we will reject too often when testing for positive abnormal performance and too seldom when testing for negative abnormal returns. 4.As a result we turn to a more appropriate test, the Corrado (1989) rank test. This is a non-parametric test and is a more powerful at detecting the false null hypothesis of no abnormal returns.

Non-Parametric Tests 1. We transform each firm’s abnormal returns (ARit) into ranks (Ki) over the combined period (Ti) of 260 days. The period is broken up into the 244 days prior to the event, the event day and 15 days after the event. 2. The ranks in the event period for each firm are then compared with the expected average rank Where

Regression Analysis

The inclusion of an additive dummy variable in the above equation results in near singular matrix, and as a result we estimate a separate equation to test if the intercept was affected by the attacks. All the other variables are defined as per the first econometric model. We gathered the returns for each industry 244 days prior to the event and days after the event. Standard tests and residual diagnostics revealed no major concerns with these econometric models. We also test if these dummy variables were redundant in the above equations using the Wald test for restrictions.

20 Regression Analysis where is the industry i’s return at time t is the risk free return at time t is the return on the market at time t SDis a structural dummy variable is the CAPM beta is the coefficient of the dummy variable is the intercept of the regression equation is the change of the intercept.

Results – Abnormal Returns Abnormal Returns on Australian Industry Indices Following Five Terrorist Attacks

Results – CAR-5 Cumulative Abnormal Returns on Australian Industry Indices Following Five Terrorist Attacks

Results – AR &CAR-5

Results – Non-Parametric The Impact of Five Terrorist Attacks on Australian Industry Indices- Non- Parametric Results

Results – Regression Analysis Short Term

Results – Regression Analysis Short term Continued

Results – Regression Analysis Long Term

Conclusion Terrorist attacks like September 11, Madrid and London negatively affected some industries in Australia while the Bali bombing had a positive influence on two sectors. The Mumbai attack on the other hand, did not have any short term impact on the ASX. September 11 had the most impact with five industries (Capital goods, defence, water, Utilities and materials) with negative abnormal return on the day after the event.

Conclusion This study also provides evidence of an increase in systematic risk in three sectors namely Capital goods, Defence and Water. Using the Bali Bombing evidence, we argue that terrorist attacks do not always nurture negative sentiment but can also be good for the neighbouring country out of substitution effect. The Mumbai evidence can be use to demonstrate that some capital markets can be insensitive to terrorist attacks, and thus investment heavens do exist even just after an attack.

Conclusion The Australian Equity Market is a relative safe haven from Terrorist Risk with the exception of major attacks that occur in the US or on domestic soil.