Master Thesis

Investment Analysis

“The BP-, a crisis for BP or for the whole industry?”

Author: A.H. Meeuwissen

Anr: 205668

Nr: 06-83698417

Department of finance Faculty: School of Economics and Management Supervisor: Dr. Paul Sengmüller Second review by: to be announced Graduation Date: 30-08-2012 Word count: 13,620 words

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Abstract This study contains 46 oil spills of public firms between 1978 and 2010. The reaction of the stock market is examined when an oil spill occurs. The cumulative abnormal return for responsible companies is significantly negative for 32 days after the announcement, with, on average, the most negative cumulative abnormal return of -3,75%. The cumulative average abnormal returns for competitors stay significantly positive up to an event window of 120 days after the announcement, reaching the highest point after 120 days with a cumulative return of 4,1%. Both effects are significant immediately after the announcement of an oil spill. With cross-sectional regressions, this study finds that the size of the oil spill, measured as either the estimated loss of oil and the number of hits in the media ten days after the spill, has an influence on the outcome of the economic effect of both responsible companies and their competitors. A higher size of the oil spill results in worse economic results for responsible companies, and in better economic results for competitors for at least ten days after the spill.

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Table of Contents

CHAPTER 1: INTRODUCTION ...... 1

1.1 SUBJECT AND RELEVANCE OF THE RESEARCH ...... 1

1.2 STRUCTURE OF THE RESEARCH ...... 2

CHAPTER 2: OVERVIEW BP OIL CRISIS ...... 3

2.1 TIMELINE OF THE DEEPWATER HORIZON OIL SPILL ...... 3 2.1.1 Timeline of the oil spill ...... 3 2.1.2 Political actions because of the oil spill ...... 5

2.2 EFFECT OF AN UNNATURAL ON COMPANIES ...... 7

2.3 PARTIAL CONCLUSION ...... 9

CHAPTER 3: HYPOTHESES ...... 11

CHAPTER 4: DATA AND METHODOLOGY ...... 13

4.1 DATA RESOURCES AND SAMPLE SELECTION ...... 13

4.2 THE SAMPLE ...... 14

4.3 METHODOLOGY ...... 17

CHAPTER 5: EMPIRICAL RESULTS ...... 21

5.1 ANNOUNCEMENT RETURNS FOR RESPONSIBLE COMPANIES COMPARED TO THE MARKET ...... 21

5.2 COMPARISON BETWEEN RESPONSIBLE COMPANIES AND THEIR COMPETITORS ...... 23 5.2.1 Announcement returns for competitors compared to the market ...... 24 5.2.2 The difference between responsible companies and the competitors ...... 26

5.3 THE IMPACT OF THE TWO SIZE VARIABLES ON THE ABNORMAL RETURN ...... 27 5.3.1 Size as the extent of media coverage ...... 28 5.3.2 Size as the amount of oil spilled ...... 32 5.3.3 Partial conclusion ...... 34

CHAPTER 6: CONCLUSION ...... 37

6.1 CONCLUSIONS FOR THE THREE HYPOTHESES ...... 37

6.2 THE FINAL ANSWER TO THE RESEARCH QUESTION ...... 39

6.3 CONCLUDING REMARKS ...... 40

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REFERENCES ...... 41

ARTICLES ...... 41

WEBSITES ...... 42

APPENDICES ...... 43

APPENDIX 1: SPILL CHARACTERISTICS ...... 43

APPENDIX 2: OVERVIEW VARIABLES ...... 45

APPENDIX 3: ANALYSIS RESULTS ...... 47

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Chapter 1: Introduction This chapter will explain the subject and the relevance of this research. This will result in the research question for this thesis. Furthermore, the structure of this thesis is presented in the second paragraph.

1.1 Subject and relevance of the research On the 20 th of April, a gas release created an explosion on the Deepwater Horizon oil rig in the Macondo exploration well in the Gulf of . The fire kept burning for 36 hours before the rig sank. After this, hydrocarbons leaked into the and this lasted for 87 days. Only then, the well was closed and sealed. This led to the death of eleven people, and 17 others were injured. Estimated is that almost 4.9 million (780,000 m 3) barrels of oil were lost in this spill. 1

Until the moment of today, BP is still working on restoring the environment and the economy. This confirms the large scope of this disaster. Obviously, such a gas release does not have a good influence on the image of BP. Less clear is what the exact magnitude of the losses are, and what is has done for the total oil industry. A quick look at the share price of BP can already give some indications. On the 20 th of April, the share price ended on 655,4 pence on the London Stock Exchange. The lowest point that year was reached on June 29, when the share price was down to 302,9 pence. This is less than halve of the end share price on April 20. Obviously, this is indeed an indication that the oil spill has had a huge impact on BP. It could be that the competitors of BP had an advantage of this, and therefore improved in that period. In other words, have the competitors of BP done better after the 20 th of April, or did the oil spill have a negative influence on the total oil industry?

This raises the question if this can also be stated for other oil spills. What are the economic consequences for either the responsible company and the their competitors? This leads to the following research question for this thesis:

Does an oil spill have a negative economic effect on either the company responsible for the spill or on their competitors, and does the size of the oil spill affect the outcome?

1 Source: http://en.wikipedia.org/wiki/Deepwater_Horizon_oil_spill | P a g e 1

Existing literature about is mostly focusing on natural disasters (Worthington and Valadkhani, 2005), while this study focuses on oil spills, where the cause and responsibility lays with companies. In other words, this study focuses on an analysis of one kind of an industrial disaster. This already gives a good contribution to the existing literature. Moreover, most existing literature concentrates on single events (Worthington and Valadkhani, 2005), while this study will analyze different oil spills. The latter part of the research question also gives a fine contribution to the existing literature. This study will examine two different kinds of size, and compares the impact of the different proxies. The first kind of size is the size of the spill itself, or even better: the assumption of the oil lost because of the spill around the day of the start of the spill. The second kind of size is the magnitude as in the attention it gets in the media. If a spill gets a lot of attention in the media, it could have a bigger impact on a company compared to a spill with almost no attention at all.

1.2 Structure of the research In the second chapter of this thesis there will be a short overview about the most important happenings during the BP oil spill, which will result in a timeline. Furthermore, a theoretical overview will be given about what the current literature states about the effects of an unnatural disaster on companies. In the third chapter I will present the hypotheses with explanations. The fourth part will be devoted to describing the sample. The methodology is also mentioned in chapter 4. After this, the results of the empirical tests are shown in chapter 5. This will include the abnormal returns on important event dates for both BP as their competitors, and a total effect for the oil industry may become clear. Furthermore, it will be determined if the size proxies have an influence on the abnormal return. The last chapter, chapter 6, contains the conclusions and further recommendations.

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Chapter 2: Overview BP oil crisis First, this chapter gives an overview about when the most important happenings were of the BP oil crisis. The most important date will be used in the empirical tests later on. Second, there will be a short overview of the current literature describing what a crisis does for a company, or for the industry they are in.

2.1 Timeline of the Deepwater horizon oil spill BP is one of the six so called ‘super-majors’ in the industry. These companies are the six largest non state-owned energy companies (Tansuchat et al, 2009). Besides BP, the companies are Mobil, , , ConocoPhilips, and Total S.A. All these companies are vertically integrated, which means that the company is involved in every stage of the product, from the production upwards to the selling of the product.

The Deepwater horizon spill started with an explosion on the 20 th of April, but after this date many other important events occurred, which also could have an impact on BP and their competitors. Therefore, a timeline is created for a good overview of the accident. 2 To keep the overview clear, the timeline is split up in two sections. The first section will give a timeline of the spill itself. This also included the actions to stop the spill. The second shows the timeline of the actions taken around the spill. This is mainly political pressure during and after the spill.

2.1.1 Timeline of the oil spill As stated before, the spill started on the 20 th of April with an explosion on the Deepwater horizon platform at the Macondo exploration well. The explosion killed eleven people. The rig, licensed by BP, sank two days later. At that time, the first reports came of the existence of an oil slick. Soon after that, it was found that oil is indeed leaking from the well. The first estimation of the magnitude of the leak by the US coast guard is around 1,000 barrels of oil a day.

2 Most of this information comes from different newspapers, like the Financial Times or . | P a g e 3

On the 28 th of April, after the discovery of a third leak, the estimated lost oil increased to 5,000 barrels of oil per day. The same day, the first attempt of BP to stop the leak failed. The attempt was to repair the preventer, but it was unsuccessful.

Obviously, BP also made more attempts to stop the leaking of oil. The first attempt was with remotely controlled underwater robots in order to close the blowout preventer valve on the head of the well. However, this attempt failed. In the meanwhile, a relief well is being drilled in order to stop the flow of oil.

The 5 th of May had a fine start, since one of the three leaks was shut off with a valve. Another attempt started on the 7 th of May, when a dome was placed over the largest leak in order to prevent the lost oil going into the US Gulf. However, because of methane was freezing on the dome, this effort was no good. Therefore, the amount of oil coming out the well was still not reduced. Therefore, BP announced a new plan on the 10 th of May with the nickname ‘junkshot’. This strategy applies a containment vessel to the leak, pushing mud down the tube to congest it. This so called ‘top-kill procedure’ started on the 26 th of May, but on the same day already, it turned out that this strategy fails to stop the oil flow. On the 29 th of May, BP declares that ‘top kill’ is abandoned. In the meanwhile, researcher Steve Werely believes the leak is 70,000 barrels of oil per day. Other experts estimate the leak between 20,000 and 100,000 barrels per day.

The next step for capturing the leak was done on the 4 th of June. BP placed a steel cap on the broken pipe, after having cut off a part of the pipe, to capture the oil. Finally, an action showed signs of success. On the 6 th of June, BP stated that they were capturing 10,000 barrels of oil per day. However, this action does not close the leak. In an attempt to stop the leak, BP placed a new cap over the leak. Once the new cap was installed, BP tested raising the internal pressure in the well to prepare for the sealing of the well. On July the 15 th , the flow of oil into the Gulf stopped. This was for the first time since 87 days. However, this would not be a permanent sealing of the well. This news finally gave a positive reaction to the share price, although this was not the only reason for the rise. In a need for cash to pay for the costs of the spill, the speculation comes that BP is looking for investors. BP also sold a part of its assets.

To finally seal the well, BP used a ‘static kill’, which is actually quite similar to the ‘top kill’ procedure, since it pumps drilling mud and cement into the well. On the fifth of August, BP pumped cement into the well, which seals the leak permanently. To make the well

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‘effectively dead’, the well had to be sealed at the bottom. This is done through the relief well, which was build since the beginning of May. The US government states that the well is ‘effectively dead’ on the 19 th of September. On the 23 rd of September, scientists estimate that 4.4 million barrels of oil were lost in the Gulf during last three months of leaking, and the estimated clean-up costs already neared 10 billion dollars.

2.1.2 Political actions because of the oil spill The US government has put pressure on BP since the beginning of the spill. The most important events will be mentioned in this paragraph.

On the 29 th of April, president Obama gave a first talk about the spill, and he announced that BP is responsible for the cleanup. The next day, BP said it would take full responsibility for the oil spill. This included paying all cleanup costs with additional claims. Furthermore, that day new drilling was banned in the Gulf of Mexico. And even on top of this, areas affected by the spill were closed for fishing for a period of at least 10 days on the second of May. Eventually, this no-fishing policy was extended to the 17 th of May, and the no-fishing zone captured 19% of the Gulf waters.

Three oil companies were involved in the drilling in the Deepwater horizon, namely BP, Transocean, and Halliburton. 3 All three companies had to testify before congress on the 11 th of May, and all three companies were putting the blame of both the spill and the incident to each other. In the hearing, all companies were told they ignored safety warnings, but BP still was seen as the most responsible party. Obama stayed active after this congress. On May 17, he announces the appointment of an independent commission to investigate the oil spill. This became final on the 22 nd of May. Furthermore, on the 27 th of May a suspension of all new drilling below 500 feet is announced.

Meanwhile, lawsuits kept coming in from people or companies who had taken damage from the spill. To put pressure on BP, the government stated that legal actions would be used to make BP decide to suspend or lower dividend payments. On the 16 th of June, Obama and the top management BP met. In this meeting, BP agreed to put aside twenty billion US dollars for the compensation of victims of the spill. They also agreed that this amount would not be the maximum, but BP would pay for costs and claims if this exceeded the twenty billion dollars. Furthermore, BP stated it would not pay dividends in the rest of 2010. For some image

3 BP leased the Deepwater Horizon rig of Transocean, and Halliburton cemented the well for BP. | P a g e 5

recovery, BP also officially apologized that day. However, this and a testify the next day of BP’s chief executive officer, Tony Hayward, did not improve the image of BP.

On the 27 th of July, BP announced that Tony Hayward would resign on the first of October. Bob Dudley, an experienced BP executive, would replace him. Later that week, BP offered a one-off lump sum payment to all claimants who could sue the company. This was an effort to temper the amount of claims coming in.

On the 12 th of October, the postponement on new drilling below 500 feet ends, which was good news for companies interested in offshore drilling. The US government did not have any more restrictions for the oil industry. This also ended the primary period of the oil spill, although the clean-up, claims, and lawsuits are not done.

Figure 1: BP’s share price development during the Deepwater Horizon oil spill. This figure shows the share price development of BP from the first of April until the end of October. The share price is shown in pence. The red dot indicates the 20 th of April, when the spill started with an explosion on the Deepwater horizon platform at the Macondo exploration well. The graph is retrieved from Yahoo Finance. Obviously, this spill had quite an impact on the share price of BP. On the 20 th of April, the share price ended on 655,4 pence on the London Stock Exchange. The lowest point that year was reached on June 29, when the share price was down to 302,9 pence 4. This is less than halve of the end share price on April 20. By the end October, the share price somewhat

4 A tropical storm probably also partially influenced this number, since it also caused delay to both the cleanup and the stopping of the spill. | P a g e 6

recovered, being around 425 pence. The development of the share price of BP between the first of April and the end of October can be seen in figure 1.

2.2 Effect of an unnatural disaster on companies Already quite some literature exists about natural, unnatural or terrorist disasters. As Worthington and Valadkhani (2005) state: “Through the disruption of production and consumption, the interruption of domestic and international trade, the destruction of infrastructure, buildings and vehicles, and the creation of uncertainty in the minds of investors, producers and consumers alike, disasters and catastrophes, whether of human or natural origin, have the clear and demonstrated potential to adversely affect financial activity and wellbeing.” The focus in this study is about unnatural disasters. Therefore, some noticeable studies will be discussed in this section, with the focus on results, which could well apply in this study.

There have been some case studies regarding the in 1989. This might give some useful comparable insights for a number of reasons. Obviously, it is also an oil spill, but it also came in the media quickly, and it had an impact on the US energy market and the environment (Herbst, Marshall and Wingender, 1996).

Herbst, Marshall and Wingender (1996) researched both the share price and the volatility of Exxon and their competitors during the oil spill in 1989. The share price of Exxon dropped sharply right after the spill with a negative cumulative abnormal return of 8,89% over the first 16 days after the spill. The long-term effect was negligible, which was explained by a quick and accurate reaction of the market of the losses of the spill. The emotional factor coming from the media seems not to have any influence. The cumulative abnormal returns of the competitors of Exxon were not significantly different from zero after the oil spill. The volatility of the share price was also not significantly different after the spill for Exxon and their competitors who were active in the same area. However, competitors who were not active in the region of the spill had a decline in their volatility.

White (1996) still studies the Exxon Valdez oil spill, but already has some results. The shareholders of Exxon have a value loss on their equity of 19% for the 120 days directly after the event. Competitors did not have significant abnormal returns in that period, with the exception of competitors who are seen as ‘green’, meaning firms who have a reputation for

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environmental responsibility. These competitors experienced positive and significant abnormal returns after the event.

The final case study mentioned is the study of Patten and Nance (1998), who find positive abnormal returns for the competitors after the Exxon Valdez oil spill for 15 trading days. The abnormal return of Exxon was negative. Patten and Nance state that the positive reaction of competitors is probably caused by the significant rise in petroleum product prices. Because previous event studies about the impact of industrial disasters found negative reactions for competitors, the authors tried to find what caused the impact of the abnormal returns of the competitors. In this analysis they found that both being active in the same area and firm size is negatively related to the abnormal return of competitors. Furthermore, extensive environmental disclosures before the spill had a positive relation.

A case study does bring a good starting point, but it is hard to generalize the outcome of a single accident or event. Therefore, also some studies with larger samples are taken into account. The first study of interest comes from Capelle-Blancard and Laguna (2010), who examined the reaction of the stock market to industrial disasters in the period of 1990 until 2005. Their sample consists of 64 explosions in chemical plants and refineries over the entire world. 32% of these explosions were caused by firms who are active in the oil sector. The share price of the firms in their sample dropped 1,3% on average over the two days after the disaster. This loss is significantly related to the seriousness of the accident 5.

Another study of Capelle-Blancard and Laguna (2006) examines 76 worldwide , which occurred in petrochemical plants between 1987 and 2005. In their sample, shareholders experience cumulative abnormal returns of -0,86% on the day of the disaster, and of -1.15% in the first 5 days after the event. 73% of all firms have negative abnormal returns. They also confirm that the stock market reacts almost immediately after the release of new information, which implies that financial market is efficient.

Klassen and McLaughin (1996) examined 18 accidents, including oil spills, in 1989 and 1990 caused by public companies listed on either the NYSE or the AMEX. In an event window of (-1,+1) the abnormal return equals –1.5%, with an average loss of 390 million dollars in value.

5 In the study of Capelle-Blancard, G. & Laguna, M.A. (2010) the seriousness of the accident is measured by the number of casualties and by chemical pollution. | P a g e 8

All these investigations give some indications regarding industrial disasters, which could help in the analyses of this study. First of all, in all studies the company that causes the disaster experiences a negative abnormal returns almost always immediately after the event. However, the abnormal returns for competitors are not that clear. It could be positive because of for example the increase in the oil price or a good reputation on environmental responsibility. But the firm size and being active in the same area can make it negative. Another important feature is that the market almost immediately responds to an industrial disaster, implying the efficient market hypothesis holds.

2.3 Partial conclusion The first section of this chapter showed a timeline of the BP oil spill, including the share price of BP during this time. As is also done in other studies, for example the studies mentioned in the second section, it is important to compare this with the market, to be certain that the drop in share price is caused by the accident, and not by other market effects. The results of the articles in the second section make it likely that it is at least partially caused by the spill, and this can be seen almost immediately in the stock market.

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Chapter 3: Hypotheses In this chapter, the hypotheses of this research are given and explained. This will be the foundation for the rest of this paper. Together with the data selection and the methodology, described in chapter four, these hypotheses will determine which empirical tests will be conducted.

As stated in chapter two, earlier research shows that when a company is responsible for an unnatural disaster, this has a negative economic effect on them. When the unnatural disaster is the oil spill, the same is expected to hold. Furthermore, the literature finds that the efficient market hypothesis holds for these type of events, which means that news about a disaster will be absorbed almost immediately in the stock price during the first days after the disaster (Capelle-Blancard and Laguna, 2006). This leads to the first hypothesis:

Hypothesis 1: When a company is responsible for an oil spill, it has a negative economic effect on them.

An event like an oil spill could have an impact for other firms too. Former literature has inconclusive results about the competitors of the company, which is responsible for a disaster. This makes it even more important to investigate what is the case in this study, where various oil spills are investigated. Therefore, the second hypothesis of this study is:

Hypothesis 2: When an oil spill occurs, the competitors of the responsible company profit economically from this.

The magnitude of the spill is one the variables of explicit interest in this thesis. Therefore, hypothesis 3 is created for this variable. The magnitude in this hypothesis is not only the magnitude of the spill itself, in terms of loss of oil, but also the magnitude as in the attention it gets in the media. If a spill gets a lot of attention in the media, it will have a bigger impact on a company compared to a spill with almost no attention at all. Capelle-Blancard & Laguna (2006) already found that the extent of media coverage has a negative relation with the abnormal return in their sample of 76 petrochemical accidents. The magnitude of the spill may be even less important than the magnitude of the media attention.

The first kind of size is the size of the spill itself, or even better: the assumption of the oil lost because of the spill around the day of the start of the spill. As White & Molloy (2003) state, it

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is a general agreement that the amount of oil spilled has an impact on the costs and thus on the economic impact of the company. It may be necessary to compare this with the size of the company, since the relative (expected) cost of the spill could give a better result (Capelle- Blancard and Laguna, 2008). The second kind of size is the magnitude as in the attention it gets in the media. Both size-variables will be used to determine if size has an influence on the economic impact of both the responsible company as their competitors. Furthermore, it can also be examined with type of size has the biggest economic impact.

Hypothesis 3: The magnitude of the spill has an influence on the economic impact of both the responsible company as their competitors.

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Chapter 4: Data and methodology In this chapter, the first section will describe where the data are found, and which requirements an oil spill must have to be included in the sample. The second section will show some descriptive statistics of the sample and it will explain which variables will be used in the analysis. The last section will explain the methodology of this study.

4.1 Data resources and sample selection In order to test all the hypotheses, the dataset must be constructed first. The sample should consist of enough oil spills to get a clear picture of the consequences of such a spill. It is important to determine what type of oil spills are examined in this research. Each spill must be caused in an unnatural way, with the owner, of for example the oil platform, as the responsible company.

For finding such oil spills, LexisNexis, which is one of the available databases at the University of Tilburg, is used as the source. This source has a database, which consists of information coming from journals, newspapers, etcetera. This source is also used by Capelle- Blancard and Laguna (2006). Furthermore, they state that the usage of print media allowed them to better identify the responsible companies, because authorities in almost every country don’t disclosure this. The study of Capelle-Blancard and Laguna (2006) is followed closely in the sample search.

The search in this study is based on the keyword “oil spill”, while the Financial Times, the Times and the Wallstreet Journal are used as sources. Furthermore, the industry ‘Oil and gas’ is selected. LexisNexis covers news from 1980 until now. News after 2010 is excluded from our sample. This selection gave almost 3000 hits to examine, which resulted in 104 spills of interest. 6 The search was based on the titles of each hit, examining possible spills in the news. Additional information on the spill, for example the responsible company, is found on information websites like Wikipedia. When the responsible company is not known almost directly after the announcement of the spill, the spill will be deleted.

Besides information of LexisNexis, also DataStream is used in order to find some variables for the analysis, for example the stock prices. One of the requirements of being included in

6 Spills that occurred because of the Gulf war or other wars or hurricanes are excluded from the sample. | P a g e 13

the sample therefore is that the responsible companies must be listed in the DataStream- database. This means that the responsible company must be publicly listed. 7

The search ultimately results in a sample of 46 oil spills. Compared to other studies of industrial disaster this sample is rather normal. Capelle-Blancard and Laguna (2010) had a sample of 64 accidents in the chemical industry in the period of 1990 until 2005, while Capelle-Blancard and Laguna (2006) found 76 accidents in their sample of industrial accidents for 1987 until 2005. This study focuses on oil spills only, which is only one kind of a chemical disaster, and only one kind of an industrial accident. This makes is plausible that the sample of this study is somewhat smaller than those samples. For further research, the number of hits in LexisNexis in ten days after the spill is noted. This number of hits will be used later on in the analyses. The list of oil spills with some characteristics is presented in Table 11 in appendix one.

4.2 The sample This section looks at the important variables used in this study. First, the way of measurement is explained, and the importance of each variable will be described. Then some descriptive statistics of the variables are shown.

Table 12 in appendix two gives an overview of the variables of interest in this study. For each variable, the way it is measured is given, just like the importance of the variable. Furthermore, the prediction of the (impact on the dependent) variable is given.

Next, some overview statistics for the variables of interest are given. The descriptive statistics like the mean, standard deviation, minimums and maximums are reported in table 13 of appendix two. The most important descriptive statistics are shown in table 1. The first variable shown is the cumulative equally weighted return, which is also the dependent variable in the cross-sectional regressions. The variable is shown for multiple event windows. The mean equally weighted (cumulative) abnormal return of the sample of responsible companies is -0.91% after the first day. This number suggests that responsible companies perform worse than the market after an oil spill. The median values are negative in each event window, suggesting that at least half of the sample performs worse than the market for a longer period. Furthermore, the volatility is quite high, which indicates that large differences exist between each spill. This makes it important to examine the determinants of the

7 DataStream covers more than 75% of the publicly traded companies in the world. | P a g e 14

abnormal return later on. The second variable in table 1 is the abnormal dollar return, which is also shown for multiple event windows. This variable gives more weight to companies with a higher market value. This variable almost equals -74 million US dollars after one day. As can be seen with the quartiles, some companies seem to take the average down substantially.

Taken all together, the numbers give an indication of a negative impact on responsible companies when a spill occurs, but also that large differences exist between the spills. This makes it even more important to analyze the determinants of the abnormal returns of responsible companies.

Table 1: Overview statistics for the variables of interest for determining the economic effect The descriptive statistics like the mean, standard deviation, minimums and maximums are reported. CAR (0,+t) shows the cumulative abnormal return of the event period, measured with the market model. ANPV gives the abnormal dollar return in millions, computed as CAR (0,+t) times the average equity market value of the competitors, one month prior to the announcement.

Table 2 shows the number of oil spills each year, sorted by the extent that it came on the media in the first ten days after the spill. In this table, three or more articles about the spill on LexisNexis is considered as large media coverage. This table shows that roughly 35% of the spills have come in the media extensively, and that the number of oil spills during the years stayed relatively stable. Furthermore, this table shows that in the later years of this sample,

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spills have come in the media more times, compared to the earlier years. This could be due to the increasing speed of the media. 3 spills have by far the most hits in the media in ten days after the spill. These are the Exxon Valdez oil spill in 1989, the oil spill in 1993, and the Deepwater Horizon spill of 2010. These spills had 36, 24, and 32 hits respectively.

Table 2: Sample distribution by announcement year, and media coverage The sample includes all oil spills mentioned in LexisNexis while the Financial Times, and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. Three or more articles about the spill on LexisNexis is considered as large media coverage.

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Table 3 shows the number of spills per country, thus stating in which country the spill occurred. The most spills in the sample occur in the . This may be somewhat biased, since our news sources may have more information in this country.

Table 3: Sample distribution by country of spill The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database.

4.3 Methodology For the first two hypotheses the abnormal returns have to be calculated with an event study. These event studies will be executed with STATA, a data-analyzing program. Campbell, Cowan & Salotti (2010) give an overview about how to conduct a multi-country event study. They state that both “market-adjusted and market models with local market indices, without conversion to a common currency, work well.” Following Capelle-Blancard and Laguna (2006), the market model is taken as the benchmark-model. Furthermore, the same windows are obtained to estimate the abnormal returns. This gives multiple event windows, differing from [-0,+1], up to [-0,+120], and an estimation window of 180 days, namely [-190,-10]. Country benchmark indices are retrieved from DataStream, and each company gets a country code, so each company is connected to the right country.

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Market model abnormal returns are:

(1)

Where R it is the return of share i on day t, α and β are OLS estimates of regression coefficients and R mt is the local value-weighted market index return. The “DataStream Global level one index” offers no equally weighted returns (Campbell, Cowan & Salotti, 2010). This however should not be a problem since Brown & Warner (1980), who have written important literature about event study methodology, conclude that value-weighted and equally weighted returns give similar results. Therefore, after these value-weighted returns are retrieved, the abnormal returns for the whole sample (or sub-group of sample) are calculated with equally weighted abnormal returns, unless stated otherwise.

The cumulative abnormal return for a company i over the event window is:

(2)

where (T 1, T 2) is the corresponding event window.

To test whether the cumulative abnormal return is significantly different from zero, a one- sample t-test will be performed. However, just the cumulative abnormal returns of responsible companies and their competitors do not give a complete answer to hypothesis 1 and 2. In order to fully find an answer, the economic impact of a return will also be considered. Therefore, the abnormal returns in absolute numbers, the abnormal dollar returns, are considered. This will we done through multiplying each return by the total market value of equity before the spill announcement. 8 The six ‘super-majors’ in the petroleum industry, as mentioned in chapter two, are considered as the competitors of the responsible company. Obviously, when one of these companies is the responsible company, this company will be deleted in the competitor sample. Furthermore, when they were responsible for a spill is either the estimation or the event period, they are deleted as competitors too. Else, the results could be biased.

For testing the difference between the competitors and the responsible companies the cumulative average abnormal return of the competitors will be calculated.

8 I use the market value of month prior to the announcement to be as certain as possible that the values are not affected by rumors of a spill. | P a g e 18

The cumulative average abnormal return for the competitors i over the event window is:

(3 ) where (T 1, T 2) is the corresponding event window.

Per spill, this return will be compared with the abnormal return of the responsible company. Of course, the significance of the differences between responsible companies and their competitors must be tested. Campbell, Cowan & Salotti (2010) investigated which tests will deliver the best results with multi-country event studies. They showed that for differences with mean values a two-independent sample t-test gave the most accurate outcomes.

For hypothesis 3, a cross-sectional regression analysis will be made, with the cumulative (average) abnormal return as dependent variable. This should give further information regarding the size variables, independent of other characteristics. This can determine which variables influence the outcome of the abnormal returns.

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Chapter 5: Empirical Results This chapter presents the results of the empirical analysis, as explained in the methodology section in chapter four. First, the announcement returns for companies, accounted as the responsible company, are calculated in section 1, and with these numbers the presence of an economic impact will be determined. In section 2, the announcement returns of the competitors are examined. These announcement returns will be compared with the returns of the responsible companies. In the third section, a cross-sectional regression will be used to determine if the two size variables have an effect the abnormal returns of responsible companies, irrespective of other characteristics.

5.1 Announcement returns for responsible companies compared to the market This section will give the results of the announcement return for the responsible companies. Table 14 in appendix three shows the cumulative abnormal returns for the responsible companies up to 120 days after the first announcement on the spill. From these results, some conclusions can be derived. First of all, the abnormal return stays significantly negative up to an event window of 32 days after the announcement. 9 The most negative cumulative abnormal return is reached after 26 days, where the cumulative abnormal return is -3,75%. It takes up to 76 days when a positive, but not significant, cumulative abnormal return is given. Furthermore, the cumulative abnormal return has not been significantly positive for one time in 120 days. Of the 46 responsible companies, on average, more than 28 of them had a negative cumulative abnormal return on a particular day, varying from 1 day after the announcement, up to 35 days. This is roughly 61% of the sample.

Table 4 below gives the most important results of the cumulative abnormal returns. Furthermore, the abnormal dollar return is mentioned as well. With an event window of 3 days, shareholders experience a significant negative abnormal return of -1,15%. This suggests that shareholders of the responsible company experience a negative reaction immediately. A stated above, this significant negative reaction stays for a month, with the most negative abnormal return being -3,75%. After this period, the cumulative abnormal returns are no longer significant, which could show that they are moving alongside the market. The abnormal dollar return is not significant in the first days, but only becomes significant after

9 Exceptions are the event dates [0,+4], [0,+5], and [0,+29]. However, these cumulative abnormal returns are almost significant with a 90% confidence interval. | P a g e 21

19 days. On the 20 th day after the announcement, a significant loss of more than one billion US dollars is found. After a month, the significance of the loss is no longer present, although the numbers stay very negative.

Table 4: Announcement abnormal returns and dollar abnormal returns of responsible companies The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. CAR (0,+t) shows the cumulative abnormal return of the event period, measured with the market model. ANPV gives the abnormal dollar return in millions, computed as CAR (0,+t) times the equity market value of the company, one month prior to the announcement.

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When the numbers of this study are compared with other studies some similarities and differences exist. First of all, the mid long-term effect is not significant. This was also the case in the study of Herbst et al. (1996), and Patten and Nance (1998) for the Exxon Valdez oil spill. However, White (1996) also studies the Exxon Valdez oil spill, with as a result a value loss on the equity of the shareholders of Exxon of 19% for the 120 days directly after the event. In this study, no significant numbers are retrieved after 32 days.

In this study, the abnormal return is -1,15% over the three days after the announcement. Capelle-Blancard and Laguna (2010), who examined the reaction of the stock market to industrial disasters in the period of 1990 until 2005, found that the share price dropped 1.3% on average over the two days after the disaster. Another study of Capelle-Blancard and Laguna (2006), about accidents that occurred in petrochemical plants between 1987 and 2005, showed cumulative abnormal returns of –0,86% on the day of the disaster, and of –1.15% in the first 5 days after the event. 73% of all firms have negative abnormal returns, while this is 61% in this study. They also confirm that the stock market reacts almost immediately after the release of new information, which implies that financial market is efficient. Klassen and McLaughin (1996) found that the abnormal return equals –1.5%, with an average loss of 390 million dollars in value in their sample of 18 accidents in 1989 and 1990. This was in an event window of (-1,+1).

To conclude, this study finds that shareholders of responsible companies experience negative abnormal returns immediately after the announcement, with a return -1,15% in three days after the announcement. This number is fairly consistent with earlier studies. The negative impact remains for a month. After this period, the sample shows no different reactions than the market itself. This also implies an efficient stock market. The same holds for the average abnormal return, although this only becomes significant after 19 days, with on abnormal loss of 1 billion US dollars after twenty days after the announcement. After a month, this number is also no longer significant.

5.2 Comparison between responsible companies and their competitors The first part of this section shows the results of the analysis of the abnormal returns of the competitors. The second part compares these results with the results of the responsible companies.

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5.2.1 Announcement returns for competitors compared to the market Table 14 in appendix three shows the cumulative abnormal returns for the competitors up to 120 days after the first announcement on the spill. From these results, some interesting conclusions follow. First, the abnormal return stays significantly positive up to an event window of 120 days after the announcement. 10 Remarkable is the fact that the cumulative abnormal return seems to keep rising, reaching the highest point after 120 days with a cumulative abnormal return of 4,1%. This suggests that after an oil spill, the competitors seem to have an advantage compared to the total market, which sustains for a longer period. Of the sample of 46 oil spills, on average, approximately 18 times the competing companies had a negative cumulative average abnormal return on a particular day, varying from 1 day after the announcement, up to 35 days. This is roughly 40% of the sample. On the 120 th day after the announcement, only 14 times the competing companies had a negative average abnormal return, which is 31% of the sample.

Table 5 below gives the most important results regarding competitors. As stated above the positive cumulative average abnormal returns stays significant up to 120 days, with the highest after 120 days, with an abnormal return of 4,1%. Furthermore, the shareholders of the competing companies feel a positive effect immediately one day after the announcement, with a significant abnormal return of 0,36%. That day, the shareholders have a significant abnormal dollar return of more than 275 US million dollars. However, the abnormal dollar return is no longer significant after this day.

The results of this study are not completely in line with some earlier case studies on the Exxon Valdez spill. Herbst et al. (1996) did not find significantly different abnormal returns for competitors after the spill. They did find a decline in volatility of competitors not active in the same region. White (1996) also found that competitors did not have significant abnormal returns for the 120 days after the event, with the exception of competitors who are seen as ‘green’, meaning firms who have a reputation for environmental responsibility. However, Patten and Nance (1998) did find positive abnormal returns for the competitors after the Exxon Valdez oil spill for 15 trading days. The reason they give for this positive reaction is the rise in oil prices. Other reasons they find are extensive environmental disclosures before

10 Exceptions are found in some periods of event dates, namely [0,+7], the event internals between [0,+12] and [0,+16], [0,+31], [0,+34], [0,+35], the event intervals between [0,+39] and [0,+42], and the event intervals between [0,+44] and [0,+55]. However, these cumulative abnormal returns are almost significant with a 90% confidence interval. | P a g e 24

the spill, not being active in the same area, and firm size is negatively related to the abnormal return of competitors. In future regressions, this information is useful to determine where the positive abnormal returns for competitors come from.

Table 5: Average announcement abnormal returns and dollar abnormal returns of competitors The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. CAAR (0,+t) shows the average cumulative abnormal return of the event period, measured with the market model. ANPV gives the abnormal dollar return in millions, computed as CAAR (0,+t) times the average equity market value of the competitors, one month prior to the announcement.

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5.2.2 The difference between responsible companies and the competitors This paragraph compares the returns of responsible companies and their competitors with each other. The results of the earlier analyses indicate the competitors perform better than the responsible companies, since responsible companies perform worse than the market, while competitors perform better than the market. However, to be completely certain, the results are also compared with each other. Table 6 gives the results of the two-independent sample t-test. The cumulative abnormal returns are significantly higher for competitors in the first fifty days after the announcement, with the greatest difference of almost 5%. After this period, the differences are no longer significant. However, when the abnormal dollar return is analyzed, only significance is found on event windows of one day, two days, and ten days. After ten days, the shareholders of competitors gained almost 800 US million dollars more than the shareholders of the responsible company. But when the event window becomes larger, the average abnormal dollar return becomes significant once more. After 120 days, the difference is 1.2 US billion dollars.

Taken all together, it can be stated that the responsible company of an oil spill performs worse than the market within the first month after the announcement of the spill. Average abnormal returns are also no longer significant after a month, but this variable also only becomes significant after 19 days, with on abnormal loss of 1 billion US dollars after twenty days after the announcement. Competitors have a better cumulative average abnormal return than the market for at least 120 days after the announcement, with an abnormal return of 4,1% after 120 days. Furthermore, the shareholders of the competing companies feel a positive effect immediately one day after the announcement, with a significant abnormal return of 0,36%. That day, the shareholders have a significant abnormal dollar return of more than 275 US million dollars. However, the abnormal dollar return is no longer significant after this day. When the differences between the responsible companies and their competitors are analyzed, the cumulative abnormal returns are significantly higher for competitors in the first fifty days after the announcement, with the greatest difference of almost 5%. The difference of the abnormal dollar return is significantly different in the first days after the announcement, but also after a longer period. This could mean that competitors gain a permanent advantage after a spill of another country.

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Table 6: Difference in announcement abnormal returns and dollar abnormal returns between responsible companies and their competitors The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. CAR (0,+t) (Caar (0,t) ) shows the (average) cumulative abnormal return of the event period, measured with the market model. ANPV gives the abnormal dollar return in millions, computed as CAR (0,+t) (CAAR (0,+t) ) times the average equity market value of the competitors, one month prior to the announcement. The difference tests are based on two-independent sample t-tests.

5.3 The impact of the two size variables on the abnormal return The results of the previous sections raise the question what the source is of the abnormal returns and abnormal dollar returns of both the responsible companies as the competitors. This will be analyzed in this section for different event windows, namely [0,+1], [0,+5], [0,+10], [0,+35], and [0,+120]. The first paragraph analyzes the abnormal returns with the

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extent of media coverage as proxy of the size of the spill. The second part gives the results when size as the amount of oil spilled is used as size variable. This will be done with cross- sectional regressions. Some control variables are considered each regression. The first one is the logarithm of the market value of the firm one month before the announcement of the spill. As Capelle-Blancard and Laguna (2010) state, “the effect of an equally sized accident on the stock market value should be lower for big firms.” Other reasons that they give for a possible bigger impact for a small firm are less liquid shares, and the lack of opportunities to transfer the production to other plants to meet the (contractual) demand. The source of the spill, as described in table 12 in appendix two, is also considered as a possible variable. Finally, the change in the oil price is also taken into consideration.

5.3.1 Size as the extent of media coverage In this section, the size of the spill is measured with the extent of media coverage. The abnormal media coverage is important for the measurement of the visibility of the firm in the media at the moment of announcement of the spill (Capelle-Blanchard and Laguna, 2010). This can also be of importance because it can increase information about the disaster, and it can proxy for other important news at the time of the spill. This could influence the effect of the announcement of the spill (Capelle-Blanchard and Laguna, 2010). The source of the spill- variables are only included in the short-term event windows. Table 7 shows the results of the cross-sectional regression for responsible companies. The results will be discussed now. In the [0,+1] event window, a higher visibility of the responsible company before the announcement has a negative influence on the abnormal return. This could suggest that information goes to investors quickly, which means that abnormal returns should be influenced almost directly after an announcement. In both the [0,+1] and [0,+10] event window, the source of the spill has an impact on the abnormal return. Also, in the [0,+5] event window, the size of the responsible company has a positive influence on the abnormal return. This suggests that the relative size of the spill is important. Furthermore, the number of hits in the first ten days after the announcement has a negative impact on the cumulative abnormal return, of 0,21 percentage points in the [0,+10] event window. This variable is also very significant in the [0,+5] event window, when the source-variables are not included. The regressions with event windows of [0,+35] and [0,+120] offers no significant outcomes. The adjusted R-squared is the highest when the event window [0,+1] is used.

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Table 7: Cross-sectional regression for abnormal returns of responsible companies with media coverage as the size variable The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. The dependent variable is the cumulative abnormal return measured with the market model in different event windows. Log market value is the logarithm of the responsible firm's market value 1 month prior to the announcement. The source of the spill is found in the newspapers, or on other websites. The source can be a pipeline, a tanker, or a platform. Media coverage is the number of hits on LexisNexis in the first ten days after the first announcement of the news on the spill. Abnormal media coverage is the ratio between the number of hits for the responsible firm in the twenty days before the spill announcement and the total number of hits in the previous year. Change in oil price is the percentage change of the oil price in either the first month before, first month after, or in the first four months after the announcement. Significance is tested based on White-adjusted standard errors. P-values are reported in brackets.

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Table 8 gives the results of the regression for the competitors. With an event window of [0,+1], the only significant results come from the source of the spill and the change in oil price. If the source is a pipeline, competitors experience a more positive impact on their abnormal return. Furthermore, if the oil price increased in the month before the announcement, competitors also experience better returns. In the [0,+5] event window, another outcome occurs. The larger the responsible company, the more negative the cumulative abnormal return of the competitors are. Together with the positive and significant intercept, this makes sense, since the relative size of the spill decreases as the size of the responsible company rises. The same holds in the 10-day event window. However, the event window of [0,+10] also gives a rather strange result. The more hits about the spill ten days after the announcement, the more negative the cumulative abnormal return of competitors becomes. This may suggest that more news about spill creates a negative view about the entire oil industry. Just as in the regression with the [0,+1] event window, an increase in the oil price in the month before the announcement, gives better returns for competitors. The event window [0,+35] gives even more significant results, also having a very high adjusted R-squared. Just as earlier, the larger the responsible company is, the more negative the cumulative abnormal returns become. Furthermore, if the responsible company was more present in the media before the spill, the cumulative abnormal return is larger. This is actually surprising, since it could be expected that this variable had more impact in the short-term event windows. Finally, the higher the change in the oil price in the event window, the higher the abnormal return of competitors becomes. Thus, fluctuations in the oil price have a positive influence after an oil spill of a competing company. This mostly will be price increases. The event window of [0,+120] approximately gives the same results as in the event window of [0,+35].

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Table 8: Cross-sectional regression for abnormal returns of the competitors with media coverage as the size variable The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. The dependent variable is the cumulative average abnormal return measured with the market model in different event windows. Log market value is the logarithm of the responsible firm's market value 1 month prior to the announcement. The source of the spill is found in the newspapers, or on other websites. The source can be a pipeline, a tanker, or a platform. Media coverage is the number of hits on LexisNexis in the first ten days after the first announcement of the news on the spill. Abnormal media coverage is the ratio between the number of hits for the responsible firm in the twenty days before the spill announcement and the total number of hits in the previous year. Change in oil price is the percentage change of the oil price in either first month before, first month after, or in the first four months after the announcement. Significance is tested based on White-adjusted standard errors. P-values are reported in brackets.

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5.3.2 Size as the amount of oil spilled In this paragraph, the estimated amount of oil spilled in US is added as the size variable. Since the logarithm of the market value before the spill is included, the relative size of the spill is taken into consideration. 11 Table 9 gives the results of the responsible companies. First of all, the event windows of [0,+1] and [0,+120] gives no significant results. In the event windows of [0,+5] and [0,+10], the source of the spill if of importance. When the source of the spill is a platform, worse abnormal returns occur compared to spills with tankers or pipelines as the source. Spills with a pipeline as the source gives better returns than spills with a tanker as the source of the spill. The five day event window also shows a significant importance of the size of the responsible company. The size of the responsible company has a significant positive effect on the abnormal return. This confirms that the relative size of the spill is of importance for the abnormal return. In the [0,+10] event window, a larger estimated loss of oil spilled, results in a significant worse abnormal return. The coefficient may seem somewhat low, but estimates losses are large numbers. For example, the coefficient of -0,0003 times the average estimated loss of almost 9 US million gallons results in a decline of 0,27 percentage points in the cumulative abnormal return. However, the size of the responsible company is not significant, although it does come close to significance. When the source variables are left out in the regression however, this variable is significant. Finally, the event window if [0,+35] is taken into consideration. The estimated loss of oil is the only significant variable, but the coefficient is the most negative of all event windows. The size of the responsible company is not significant. Furthermore, the change in the oil price during the event windows, and one month before the spill, is not significant one time. The adjusted R-squared is the highest in the [0,+10] event window.

11 The source variable Platform is deleted in this regression automatically, since of only one platform-spill, the estimated loss of oil was known. | P a g e 32

Table 9: Cross-sectional regression for abnormal returns of responsible companies with estimated loss of oil as the size variable The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. The dependent variable is the cumulative abnormal return measured with the market model in different event windows. Log market value is the logarithm of the responsible firm's market value 1 month prior to the announcement. The source of the spill is found in the newspapers, or on other websites. The source can be a pipeline, a tanker, or a platform. The estimated oil spilled is the estimated size of the spill around the announcement day in US million gallons. Change in oil price is the percentage change of the oil price in either first month before, first month after, or in the first four months after the announcement. Significance is tested based on White-adjusted standard errors. P-values are reported in brackets.

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Table 10 gives the results of the competitors. In the [0,+1] event window, only one variable is significant. The higher the percentage change in the oil price one month before the spill, the better the abnormal returns become. The estimated loss of oil is not significant. It could be that the information about the impact of the spill is not directly available, since this variable also was not significant for the responsible companies. In the [0,+5] and [0,+10] event windows, the source of the spill is of importance. When the pipeline is the source of the spill, better abnormal returns occur for competitors. In the [0,+5] event window, a higher estimated loss of oil results in more positive abnormal returns for competitors. Furthermore, a higher market value of the responsible company, results in worse abnormal returns. This indicates that relative estimated size of the spill is also of importance for the competitors. In the [0,+10] event window, a higher estimated loss of oil also results in a higher abnormal return for competitors if the oil price variable is not included. The market value of responsible companies is significant is significant in the [0,+10], [0,+35], and [0,+120] event windows. But in the window of [0,+35], the estimated loss of oil is not significant. Furthermore, the change in the price in the event window is significant, and results in higher abnormal returns for competitors. This is also true for the [0,+120] event window. Furthermore, a higher estimated loss of oil gives higher abnormal returns, while a market value of responsible companies gives lower returns. The regression with the event window [0,+35] has a high adjusted R-squared, with the highest number of more than 0,18.

5.3.3 Partial conclusion With the results of section 5.3.1 and 5.3.2, some conclusions can be derived. First, the source of the spill is of influence on the abnormal return for both responsible companies as their competitors. When a pipeline is the source of the spill, responsible companies experience better abnormal returns in the first ten days after the spill. The same holds for competitors. With the [0,+1] event window, no size variables show significant results. The only exception is for responsible companies, which have worse returns when they were more present in the media before the spill. This could suggest that information goes to investors quickly, which means that abnormal returns should be influenced almost directly after an announcement. Furthermore, the higher the oil price changes were in the month before the announcement, the better the abnormal return of competitors was. Next, the [0,+5] and [0,+10] event windows are considered. The media coverage has a significant negative impact on the abnormal returns

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Table 10: Cross-sectional regression for abnormal returns of the competitors with estimated loss of oil as the size variable The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. The dependent variable is the cumulative average abnormal return measured with the market model in different event windows. Log market value is the logarithm of the responsible firm's market value 1 month prior to the announcement. The source of the spill is found in the newspapers, or on other websites. The source can be a pipeline, a tanker, or a platform. The estimated oil spilled is the estimated size of the spill around the announcement day in US million gallons. Change in oil price is the percentage change of the oil price in either first month before, first month after, or in the first four months after the announcement. Significance is tested based on White- adjusted standard errors. P-values are reported in brackets.

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of responsible companies. Strangely enough, it as well has a negative impact on the abnormal return of competitors in one regression. This may suggest that more news about a spill creates a negative view about the entire oil industry. The other size variable taken into account, the estimated oil spilled, has a significant negative effect on responsible companies, and a significant positive effect on the competitors. Furthermore, the size of the responsible company has a positive influence on the abnormal return, while this is negative for the competitors, although the numbers are not significant each regression. This indicates that the relative size of the spill is of importance for both the responsible companies and their competitors. Next, the more longer-term event windows are considered. When the results of the responsible companies are analyzed, it becomes clear that not much significant variables are found. This is not strange, since no significant abnormal returns were found for responsible companies in the event windows of [0,+35] and [0,+120]. This makes the results somewhat less relevant for this category. The result that is significant, is the negative impact of the estimated loss of oil on abnormal returns in the [0,+35] window. The abnormal returns of the competitors are significant in the longer-term event windows, which make the results more important. In the [0,+35] and [0,+120] windows, the size of the responsible company is significant, and negatively related to the abnormal return. If the responsible company was more present in the media before the spill, better abnormal returns are found for competitors. This result was not expected, since it could be expected that this variable had impact especially in the short-term event windows. Furthermore, in the largest event window the estimated loss of oil once again has a significant and positive impact on the returns of competitors. Finally, the percentage change in the oil price in the event window had a very significant and positive influence on the return of competitors. Taken all together, both size variables have explanatory power about the abnormal return of both responsible companies and their competitors. This is not surprising, since these variables are correlated. 12 Furthermore, the adjusted R-squared is also not very different. For responsible companies, the adjusted R-squared is slightly larger when the estimated loss of oil is considered, while this number is larger for competitors when the media coverage is considered.

12 The correlation of the number of hits ten days after the spill and the estimated loss of oil is equal to 0,63. | P a g e 36

Chapter 6: Conclusion In this chapter I will make conclusions about the three hypotheses, as described in chapter three. This will be done in the first section. With these conclusions I can answer the research question, as mentioned in the first chapter. After this, I will give some concluding remarks, also regarding future research possibilities.

6.1 Conclusions for the three hypotheses Now, I will draw conclusions for the hypotheses of this thesis. These conclusions are based on the empirical results of chapter five.

Hypothesis 1: When a company is responsible for an oil spill, it has a negative economic effect on them.

This research tried to find the economic consequences of the announcement of an oil spill for the responsible companies of that spill. Shareholders of responsible companies experience significantly negative abnormal returns up to 32 days after the announcement, with the most negative return being -3,75%. Within three days after the spill, shareholders experience a significant negative abnormal return of -1,15%, which also indicates that shareholders experience negative effect immediately after the announcement. Furthermore, the cumulative abnormal return has not been significantly positive for one time in 120 days. Of the 46 responsible companies, on average, more than 28 of them had a negative cumulative abnormal return on a particular day, varying from 1 day after the announcement, up to 35 days. This is roughly 61% of the sample. The abnormal dollar return, the loss of shareholders in US dollars, becomes significant after 19 days, resulting in a significant loss of more than one billion US dollars on the twentieth day. Just as the abnormal return, the significance is no longer present after a month, although the numbers stay negative. Taken all together, this study concludes that the shareholders of responsible companies experience negative economic effects after the announcement of an oil spill.

Hypothesis 2: When an oil spill occurs, the competitors of the responsible company profit economically from this.

This research also tried to find the economic consequences of the announcement of an oil spill for the competitors of the responsible companies of that spill. The shareholders of the

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competitors experience significant and positive abnormal returns up to 120 days after the announcement. The cumulative abnormal return keeps rising, with the highest point after 120 days with a cumulative abnormal return of 4,1%, which suggests that competitors gain an sustained advantage compared to the total market. Furthermore, the shareholders of the competing companies feel a positive effect immediately one day after the announcement, with a significant abnormal return of 0,36%. That day, the shareholders have a significant abnormal dollar return of more than 275 US million dollars. However, the abnormal dollar return is no longer significant after this day. Of the sample of 46 oil spills, on average, approximately 18 times the competing companies had a negative cumulative average abnormal return on a particular day, varying from 1 day after the announcement, up to 35 days. This is roughly 40% of the sample. After 120 days, this is even only 31% of the sample. To conclude, this study finds that the competitors of the responsible companies of an oil spill experience a positive economic impact after the announcement of an oil spill.

Hypothesis 3: The magnitude of the spill has an influence on the economic impact of both the responsible company as their competitors.

This study examined the influence of the size of the spill on the abnormal return of both responsible companies as their competitors. Two different size variables were considered, the estimated loss of oil and the number of hits in the media ten days after the spill. It can be concluded that both size variables influence the abnormal return in event windows of five days after the spill, and ten days after the spill. The estimated loss of oil was also significant for responsible companies in an event window of 35 days after the spill, while this variable was significant for competitors in event window of 120 days after the spill. The size variables were controlled with the market value of the responsible company prior to the oil spill announcement. That way, the relative size of the spill is also considered. The analyses indicate that the relative size of the spill is of importance for both the responsible companies and their competitors. Furthermore, for responsible companies, the estimated loss of oil has slightly more explanatory power, while the media coverage has slightly more explanatory power for competitors. Taken all together, both size variables have explanatory power about the abnormal return of both responsible companies and their competitors.

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6.2 The final answer to the research question In the beginning of this thesis the following research question was formulated:

Does an oil spill have a negative economic effect on either the company responsible for the spill or on their competitors, and does the size of the oil spill affect the outcome?

After the announcement of an oil spill, responsible companies experience negative abnormal returns in first month after the announcement. This also implies an efficient market. The loss of shareholders in US million dollars is also significant after twenty days, until one month after the announcement. These numbers indicate that indeed a negative economic effect for responsible companies occurs after an oil spill. Competitors experience significant and positive abnormal returns up to 120 days after the announcement. Furthermore, these positive returns keep rising. This indicates that competitors gain a sustained advantage compared to the total market after an oil spill of one of their competitors. The profit of the shareholders of the competitors is significant on the first day, once again indicating an efficient market. All these numbers indicate that competitors have a positive economic effect after an oil spill announcement. The size of the oil spill, measured as either the estimated loss of oil or the number of hits in the media ten days after the spill, has an influence on the outcome of the economic effect of both responsible companies and their competitors. The relative size of the spill is also of importance for both the responsible companies and their competitors. For responsible companies, the estimated loss of oil has slightly more explanatory power on the economic effect, while the media coverage has slightly more explanatory power for competitors. The loss of oil could have a better indication about the costs of the spill, which makes it understandable that this has more explanatory power for responsible companies. The media attention could have a bigger influence on competitors, because this information also reaches all investors. Taken all together, it is confirmed that an oil spill has a negative economic effect on the responsible company for one month, while it has a positive economic effect on the competitors for at least 120 days. Furthermore, the size of the oil spill, measured as either the estimated loss of oil or the number of hits in the media ten days after the spill, has an influence on that result. A higher size of the oil spill results in worse economic results for

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responsible companies, and in better economic results for competitors for at least ten days after the spill.

6.3 Concluding remarks It is important to note some remarks about the results and the comparisons made in this research. Furthermore, this study provides material for further research. First of all, this study only examined responsible companies that were publicly listed. However, non-publicly listed companies obviously are responsible for oil spills also. For further research, it is of importance to investigate the responsible private firms, and to compare the results of both groups. Second, oil spills are only included when they were in the media in either the Financial Times, the New York Times, or the Wallstreet Journal. When other sources are used, other oil spills could be included. Furthermore, some oil spills do not occur in the media. It is interesting to compare the results of non-media oil spills with the oil spills that were present in the media. It is also possible to examine if similar results are retrieved when different news sources are used. Third, the group of competitors is chosen out of the six super-majors, excluding responsible companies. However, it is possible to choose a different sample of competitors. Earlier research indicates that results of competitors can differ because of a good reputation on environmental responsibility, the firm size and being active in the same area. Therefore, further research could include deeper research about the competitors of the responsible company. Finally, this study only focused on short- and mid-term effect of an oil spill. However, significant results were still retrieved after 120 days. This makes it important to investigate longer-term event window, for example to find if the sustained advantage of the competitors in this sample, also sustains for a longer period.

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References

Articles Brown, S. & Warner, J. (1980), “Using daily stock returns: the case of event studies,” Journal of Financial Economics , vol. 14, pages 3-31.

Campbell, C., Cowan, A. & Salotti, V., (2010), “Multi-country event study methods,” Journal of Banking and Finance , vol. 34, pages 3078-3090.

Capelle-Blancard, G. & Laguna, M.A. (2006), “How do stock markets react to industrial accidents? The case of chemical and oil industry,” European Ecnomic Association Annual Congress .

Capelle-Blancard, G. & Laguna, M.A. (2008), “The Buncefield oil depot explosion: where there’s smoke, there’s (stock market) fire?” Applied Financial Economics Letters , vol. 4, pages 103-107.

Capelle-Blancard, G. & Laguna, M.A. (2010), “How does the stock market respond to chemical disasters?” Journal of Environmental Economics , vol. 59, pages 192-205.

Herbst, A.F., Marshall, J.F. & Wingerder, J. (1996), “An analysis of the stock market’s response to the Exxon Valdez disaster,” Global Finance Journal, vol. 7, pages 101-114.

Klassen, R.D. & McLaughin, C.P. (1996), “The impact of environmental management on firm performance,” Management Science, vol. 42, pagers 1199-1214.

Patten, D.M. & Nance, J.R. (1998), “Regulatory cost effects in a good news environment: The intra-industry reaction to the Alaskan oil spill,” Journal of Accounting and Public Policy, vol. 17, pages 409-429.

Tansuchat, R., McAleer, M. & Chang, C. (2009). “Volatility spillovers between crude oil futures returns and oil company stock returns,” 18 th World IMACS/MODSIM congress, Cairns, .

White, I.C. & Molloy, F.C. (2003). “Factors that determine the cost of oil spills,” The international tanker owners pollution federation , from the international oil spill conference.

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White, M.A. (1996). “Investor response to the Exxon Valdez oil spill,” Working paper, University of Virginia, Charlottesville .

Worthington, A. & Valadkhani, A. (2005). “Catastrophic shocks and capital markets: A comparative analysis by disaster and sector,” Global Economic review, vol. 34, issue 3.

Websites Deepwater horizon oil spill. Wikipedia . Retrieved from: http://en.wikipedia.org/wiki/Deepwater_Horizon_oil_spill

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Appendices

Appendix 1: Spill characteristics Table 11: Spill characteristics The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. This table contains global information of each oil spill of the sample. The first announcement date found on LexisNexis is the date in the first column. The responsible firm is given in the second column, and the third column shows where the company is listed. The fourth column shows in which country the spill occurred. The fifth column gives the number of hits in the ten days after the announcement date, found on LexisNexis. The last column shows the source of spill, in other words where the spill started initially.

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Appendix 2: Overview variables Table 12: Overview of all variables used in the empirical tests For each variable, the way it is measured is given, just like the importance of the variable and a prediction of the (impact on the dependent) variable is given.

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Table 13: Descriptive statistics for the variables of interest The descriptive statistics like the mean, standard deviation, minimums, and maximums are reported.

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Appendix 3: Analysis results Table 14: Cumulative abnormal returns of responsible companies and their competitors The sample includes all oil spills mentioned in LexisNexis while the Financial Times, the New York Times and the Wallstreet Journal are used as sources. LexisNexis covers news from 1980 until now, but news after 2010 is excluded. Further requirements are that the responsible company is known almost directly after the announcement of the spill, and that responsible company must be listed in the DataStream-database. The six ‘super-majors’ in the petroleum industry are considered as the competitors of the responsible company. Obviously, when one of these companies is the responsible company, this company will be deleted in the competitor sample. CAR (0,+t) shows the cumulative abnormal return of the event period, measured with the market model. For competitors, CAAR (0,+t) shows the average cumulative abnormal return of the event period, measured with the market model. CAR (CAAR) < 0 is the percentage of firms with a negative CAR (CAAR) at that event period.

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