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USING THE ALTMAN Z-SCORE MODEL TO TEST BANKRUPTCY IN THE
OIL INDUSTRY
Stewart Morrison Bracegirdle
A dissertation submitted in partial fulfilment of the requirements for the Degree
of Master of Oil and Gas Accounting, Dundee Business School, University of
Abertay Dundee
13th
September 2013

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ABSTRACT
The purpose of this research is to evaluate whether company size is significant
in determining the potential for bankruptcy in the oil and gas industry. More
specifically, do larger independent oil and gas companies experience higher z-
scores, and therefore a lower risk of bankruptcy, than their smaller competitors?
The Altman z-score bankruptcy model is used as the statistical tool for
determination of bankruptcy in the sample of independent oil and gas
companies. It was found that for the most part, the larger companies did indeed
experience less risk of bankruptcy, but the findings were inconclusive.
Interestingly, certain smaller companies performed as well as, or in some
cases, better than the largest companies in the sample. There is potential for
the Altman z-score model to be adapted and tailored more specifically for the oil
and gas industry, which may lead to adoption of bankruptcy prediction models
as a performance indicator. Using the Altman z-score model does indeed
highlight influential characteristics in the determination of bankruptcy in the oil
and gas industry.

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ACKNOWLEDGEMENT
I would first like to thank my primary supervisor Mr Peter Morrison to whom I
owe a debt of gratitude for his time and help during the course of my
dissertation and academic studies. Thanks also go to my second supervisor Mr
Neil McGregor for his assistance in my dissertation. Particular recognitions
should be given to my lecturers, and the staff of the University of Abertay
Dundee: Dr Greg Bremner, Dr Nat Jack, Prof Reza Kouhy, Mr Neil McGregor,
Dr Zahid Muhammad and Mr Andrew Seenan.
My profound appreciation goes to my parents (Innes and Peter), sister (Fiona)
and extended family for their guidance, patience, and support throughout the
course of my academic undertakings. I would also like to give a special mention
to my parents for being willing to financially support me through my studies.
Similarly, I would like to give thanks to my partner Mhiara-May Mackenzie for
her significant help, support, and encouragement for the entirety of my Masters
degree. My friends and colleagues also deserve recognition for their advice and
concern in my decision to pursue my Masters qualification.

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DEDICATION
This research is dedicated to my late family members: Alexander Adamson,
John Adamson, Margaret Adamson and Margaret Bracegirdle.
Without their profound impact and influence on my life, I would not have been
able to achieve the success I have enjoyed in my academic career thus far.

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TABLE OF CONTENTS
CHAPTER ONE 1
INTRODUCTION 1
1.1 Background to the study 1
1.2 Characteristics of the oil industry 2
1.3 Aim and objective of the research 3
1.4 Research question 4
1.5 Motivation and significance of the study 4
1.6 Conduct of the study 6
1.7 Scope and limitations 7
1.8 Structure of the rest of the study 7
CHAPTER TWO 8
LITERATURE REVIEW 8
2.0 Introduction 8
2.1 The oil industry 8
2.1.1 Oil companies 10
2.2 Components of the annual report and ratios 11
2.2.1 Annual report 11
2.2.2 The balance sheet 11
2.2.3 The income statement 12
2.3 Bankruptcy 12
2.3.1 Implication of bankruptcy 13
2.3.2 when companies decide to use bankruptcy models 14
2.3.3 Reason for testing bankruptcy 15

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2.3.4 Factors which influence potential for bankruptcy 15
2.4 The Beaver Model 16
2.4.1 The Altman model 18
2.4.2 Updated studies based on original models 20
2.5 Choice of ratios 22
2.6 The z-score 24
2.7 Predictability of models 26
2.8 Conclusion 27
CHAPTER THREE 28
METHODOLOGY 28
3.0 Introduction 28
3.1 Research philosophy 29
3.1.1 Research paradigm 29
3.1.2 Research methodology 29
3.2 Software used for analysis 30
3.3 Rationale for using quantitative method of analysis 31
3.4 Sources and nature of data 32
3.5 Research design 33
3.5.1 Deductive nature of research 34
3.6 Categorising companies into bankrupt or non-bankrupt sectors 35
3.6.1 Use of methodology from previous study 35
3.7 Population and sample of the study 36
3.7.1 Sampling frame 37
3.7.2 Use of both FTSE indices 38

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3.7.3 Sample 39
3.8.1 Method for calculating the z-score 40
3.8.2 Working capital/total assets 41
3.8.3 Retained earnings/total assets 42
3.8.4 EBIT/total assets 42
3.8.5 The market value of equity/book value of debt 43
3.8.6 Sales/total assets 44
3.8.7 Exchange rate 45
3.9 Generalities 45
CHAPTER FOUR 46
DATA PRESENTATION AND ANALYSIS 46
4.0 Introduction 46
4.1 Descriptive analysis of data 46
4.2 Overview and analysis of the Altman z-score results 48
4.2.1 2008 z-score analysis 48
4.2.4 Unfavourable results for BP in 2010 51
4.2.7 Coastal Energy 55
4.3 Overview of large company results 56
4.4 Overview of Gulf Keystone 58
4.5 Conclusion 61

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CHAPTER FIVE 62
CONCLUSION AND RECOMMENDATIONS 62
5.1 Summary of main findings 62
5.2 Wider issues in the oil industry 63
5.3 A reconsideration of the objective set 63
5.4 Recommendations 64
5.5 Limitations of the dissertation project 66
5.6 Learning gained from doing the research 66
BIBLIOGRAPHY 68
APPENDICES 81

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LIST OF TABLES
Table 3.1 Sample oil companies and their average total assets (in $ million) for
the research period, 2008-2012.
Table 4.1 z-scores for the sample oil companies.

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LIST OF FIGURES
Figure 4.1 Altman z-scores for BP 2008-2012.
Figure 4.2 Altman z-scores for the small sized sample companies for 2012.

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LIST OF ACRONYMS
BVD – Book value of debt.
EBIT – Earnings before interest and taxes.
FTSE – Financial Times and Stock Exchange.
IOC – Independent Oil Companies.
LSE – London Stock Exchange.
MDA – Multiple discriminant analysis.
MVE – Market value of equity.
NOC – National Oil Companies.
OECD - The Organisation for Economic Co-operation and Development.
OPEC - Organization of Petroleum Exporting Countries.
PLC – Public Limited Company.
RDS – Royal Dutch Shell.
RE – Retained earnings.
UK – United Kingdom.
US – United States.

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CHAPTER ONE
INTRODUCTION
1.1 Background to the study
According to Li and Tang (2007), research has shown that larger companies
tend to outperform smaller companies when considering: profitability, growth of
share capacity, efficiency of operating practices and financial security. This
statement prompted the researcher to consider whether this is indeed true for
companies in the oil and gas industry. One method of testing performance of
companies is to assess their potential for bankruptcy.
The original research to test company performance by assessing bankruptcy
probability was conducted in the late 1960s: Beaver (1966) and Altman (1968).
Several modern studies have been completed using the pioneering statistical
models from the original research papers. One study in particular - Sena and
Williams’ “Using the Altman bankruptcy model to analyse the performance of oil
companies” - adopted the original statistical models to assess the influence of
company size on overall performance, and risk of bankruptcy. To the
knowledge of the researcher, no further studies have been conducted using a
bankruptcy model to test oil company performance. Therefore, the decision was
made to conduct a modern adaptation of the 1998 Sena and Williams’ study.
The influence company size has on potential for bankruptcy is ultimately the
proposed question of this dissertation.

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1.2 Characteristics of the oil industry
The oil and gas industry is associated with high risk, high level of investment
and the potential for vast returns (Wright and Gallun, 2008). Ward (1994)
suggests that cash flow from investment activities in the extractive industries
ought to be valuable in the prediction of bankruptcy, or financial distress, based
on the substantial investments in lasting tangible assets. If companies decide
not to invest in long-term assets (or are requiring to sell existing assets in order
to achieve cash flow equilibrium) it can be assumed that they will be more
susceptible to experience financial distress in times to come.
As one of the most important purposes of any business is to make money, it is
interesting to consider the oil and gas industry where money exchanged in
projects is vast (Wright and Gallun, 2008). It would be conceivable - based on
the Li and Tang (2007) research - to assume that major oil and gas companies
such as Royal Dutch Shell and BP, would perform better than smaller oil and
gas companies. Additionally, this could suggest that larger companies would
feel only minor pressure in the face of a global recession. However, since the
majority of economies, and companies, have been seen to be affected in some
way by the global economic crisis this may be an inappropriate stance to take
(Al-Khatib and Al-Horani, 2012). As the majority of world economies were
affected by the recession, conducting a study to assess how important
company size is in the face of financial adversity is possible, and should reveal
some interesting results.

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Past studies have used a bankruptcy model to test manufacturing and retail
businesses, since these industries are more susceptible to bankruptcy (Altman,
Haldeman and Narayanan, 1977). The oil industry has only been tested once
using bankruptcy as a performance indicator, so, as this industry becomes
increasingly important in the continuing development and expansion of the
emerging economies, such as China and India (BP, 2011) there is need to
adopt alternative criteria to assess performance. The steady, but increasing
demand for oil, has prompted demand and supply shocks on a global basis.
Supply shortages are a catalyst for oil price rises - such as the $147 per barrel
peak of late 2008 (Chen and Lee, 1993), which could not be managed over a
sustained period of time. It is therefore important to address the current
situation of the oil industry and whether there may be an imminent threat of
bankruptcy for companies related to size.
1.3 Aim and objective of the research
The comprehensive aim and objective of this research is to establish whether
independent oil company size is influential in the company’s ability to avoid
bankruptcy. To achieve this goal, the following objective will be followed.
To assess whether large independent oil and gas companies have better z-
scores than the smaller oil and gas companies, and consequently, less likely to
file for bankruptcy.

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1.4 Research question
In pursuance of achieving the aim and objective of this research, the following
research question is addressed:
Do large independent oil and gas companies have better z-scores than smaller
oil and gas companies?
To evaluate this question in an empirical manner, the aforementioned question
will be tested using a specific bankruptcy model. A thorough dissection of the
resulting z-scores will allow inferences to be made. Specifically, how important
company size is to survival in the oil and gas industry. Suggestions about the
state of the oil and gas industry can be drawn from the findings of the resultant
z-scores.
1.5 Motivation and significance of the study
The initial motivation of this research is to discover whether oil company
performance can be assessed through testing the potential for bankruptcy. As
the bankruptcy model includes ratios addressing key performance indicator
components of companies, assessing the threat of bankruptcy should indeed
show how well the companies are performing. As the oil and gas industry is key
to the global economy, determining whether performance of oil companies is
promising, or worrying, may highlight areas for improvement in practices.
Although only one study has been conducted to test the performance of oil
companies using a bankruptcy model, it is useful to discover how company size
affects a business’s operation and survival. The findings of this research would

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prove useful for the sample companies that are in a position of imminent
bankruptcy. It would then allow the management to take actions to avoid failure.
This research may supplement the existing body of work and will give scope for
future research to be conducted.
The ambition of this research is to assess the impact of company size and
continuing performance before, during and after the recent global financial
crisis. As the oil and gas industry is significantly important to the global
economy, it will similarly be interesting to understand how the companies react
and perform in unfavourable economic conditions.
The study is significant in the consideration of bankruptcy measurement as, to
the knowledge of the researcher, there is only one article which specifically
tests the petroleum industry using the Altman’s z-score model. This dissertation
should therefore build on the work of Sena and Williams (1998) but give a fresh
and updated perspective of the global oil and gas industry and the companies
involved.
The bankruptcy model uses ratios, and ratio analysis, to determine the overall
z-score of a company. The data required for the ratio calculations is found in
easily accessible company reports. The components of the ratios test key areas
of performance such as: profitability, liquidity, productivity, and the sales
generating ability of a company’s assets (Carstea et al., 2010; Sena and
Williams, 1998). When all these key performance indicators are combined, it
gives an overall score, which is used to determine the potential for bankruptcy.

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1.6 Conduct of the study
The methodology of the study will follow a similar structure to the one proposed
in the Sena and Williams’ (1998) study. The period of study was from 2008-
2012 as data before 2008 was not readily available. The sample includes
nineteen public limited companies listed on the London Stock Exchange with
total assets ranging from less than $1 billion to greater than $50 billion. The
data required for the ratio calculations was taken from the annual reports and
the London Stock Exchange. Once the data was collected, ratios of working
capital/total assets; retained earnings/total assets; earnings before interest and
taxes/total asset; market value of equity/book value of debt; and sales/total
assets were calculated. The resulting ratios are put into the following equation
and the z-score is found:
Z = 1.2*X1 + 1.4*X2 + 3.3*X3 + 0.6*X4 + 0.999*X5
Where the values for each of the X components are as follows: X1 = working
capital/total assets X2 = retained earnings/total assets X3 = earnings before
interest and taxes/total assets X4 = market value of equity/book value of debt
X5 = sales/total assets (Carstea et al., 2010).
Once the z-scores were calculated for each sample company, over the five-year
research period, threshold ranges determine the potential for bankruptcy. A z-
score below 1.81 is the bankrupt sector, between 1.81 and 2.99 is the grey
sector, and above 2.99 is the non-bankrupt sector (Altman, 1968). Tables of the
z-scores were created, and conclusions drawn on the potential for bankruptcy,
and company size.

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1.7 Scope and limitations
Research on the topic of bankruptcy in the oil and gas industry is scant and
apparently overlooked in the body of literature available. Hence, the literature
reviewed gives a more general view of bankruptcy, and the bankruptcy model in
other industry sectors.
The findings of the importance of company size in corporate endurance are
discussed in the latter chapters of the research. Due to the time constraints of
the research (three months) it was not conceivable to cover a vast sample of
companies. In the future, further studies could be conducted with a larger
sample, or indeed the complete population of oil companies on the London
Stock Exchange.
The research does consider two important aspects of company performance:
size (based on total assets), and bankruptcy. The two aspects are important
considerations for every company and in the research time granted it is hoped
that results provide an insight into the risk of bankruptcy in the oil industry.
1.8 Structure of the rest of the study
The remainder of this dissertation will be structured as follows: Chapter two will
review the relevant literature of the topic. The literature covers conceptual and
investigative aspects of the subject matter. Chapter three will detail the
methodology used to conduct the research. Chapter four presents the data, and
a thorough analysis of the main findings is shown. Chapter five concludes, and
proposes recommendations for further research.

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CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter begins with an overview of the oil and gas industry, specific
terminology, and fundamental knowledge deemed necessary for the reader.
This is followed by a thorough review of the previous research conducted into
the bankruptcy of companies, and highlights the theoretical foundation for
conducting this study. Accordingly, definitions of bankruptcy, past findings,
statistical models, and the accuracy of previous results are detailed. This
underlines the scope, and reasoning, behind the research topic of this study.
2.1 The oil industry
The oil industry is characterised by high-risk ventures in the extraction of
hydrocarbons (Suslick and Schiozer, 2004). The investments required in
exploration, appraisal, development and production phases of the oil industry
are vast, and extend over a long period of time. Therefore, there is a need to
assess the level of risk involved. According to Wright and Gallun (2005) there
are two sectors which oil and gas companies can be involved in: the upstream
and downstream. The upstream sector is concerned with exploration
(searching) and production (producing hydrocarbons) activities, whereas the
downstream sector is focused upon the transporting, refining, and marketing of
petroleum and petroleum based products (Wright and Gallun, 2005).
The reason the oil industry is so influential on the world economy is due to the
demand for oil on a global basis. The associated oil price can have a

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substantial effect on the economies that are heavily reliant on the revenues
generated by oil. The oil price, and other commodity prices have experienced
sharp rises and falls over the past sixty years. The first commodity boom was
particularly due to the build up of raw materials as a result of the war in Korea,
during 1950-51. The second commodity boom, occurring in 1973-74, was
driven by inconsistent crop yields and in OPEC’s mismanagement of oil
supplies, prompting the oil price to triple. The third boom began in 2004 and is
on-going. This can be attributed to the sustained, and aggressive economic
growth of China and India and their increasing demand for raw materials
(including oil) (Radetzki, 2006). In more recent times (late 2008), the oil price
soared to $147/barrel, but this peak did not last for long, and shortly after the
price fell drastically to $40/barrel causing major disruptions for all economies
and companies involved (Mohanty, Nandha and Bota, 2010; Pirog, 2012).
According to Mohanty, Nandha and Bota (2010) many factors influenced the
volatile oil price over the previous decade (2000-2010). The staggering growth
of emerging economies such as China and India occurred at a time when
production had plateaued causing oil demand shocks. There were also issues
in the supply of oil with the U.S. war on terror in Iraq. The recession in the U.S.
and other OECD economies of late 2008 exacerbated the oil price rises
resulting from the global financial crisis and the demise of the Lehman Brothers
in 2008 (Mohanty, Nandha and Bota, 2010). During the period between August
2008 and March 2010, the global financial crisis had an undesirable effect on
the prices of commodities as well as equity values of companies in the oil and
gas industry and global stocks.

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2.1.1 Oil companies
Independent oil companies (IOCs) have the incentive to maximise shareholder
wealth, and if the companies do not provide a better rate of return than the
market, money must be returned to shareholders (Stevens, 2008). According to
Villalonga (2000) IOCs possess certain features, such as: private ownership (in
the form of tradable shares), takeover threats and the potential for bankruptcy,
which helps these companies to align their interests with the shareholders. On
the other hand, National Oil Companies (NOCs) are more likely to be driven by
the personal or political goals of the country of ownership (Eller, Hartley and
Medlock, 2007; Bernard and Weiner, 1996). These can include: national
employment; public infrastructure, and a number of other goals, not stringently
associated with fundamental oil sector activities (Victor, 2007). NOCs do not
usually possess tradable shares and are reluctant to publish information on
their financial performance. The reason IOCs were considered in this study is
that information on their financial statements is readily available, there are
tradable shares - a necessary requirement for one of the calculations in the
study - and the information published is audited, giving a certain level of
reliability.
Independent oil companies can experience substantial unpredictability in their
profit and cash flows. This is because they are subject to fluctuations in
commodity prices and the significant investments required to aid the
replacement of reserves (DBRS, 2011). There are issues concerning
independent oil companies accessing reserves. The majority of the world’s oil

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reserves are located in the Middle East and Africa where frequent political
disturbances can have a detrimental impact on the global supply and demand
of commodities. This can also affect the global oil prices underlining the
influence of political processes contributing to instability in the oil and gas
industry (DBRS, 2011).
2.2 Components of the annual report and ratios
2.2.1 Annual Report
All independent oil companies record their yearly results in an annual report
(Walton, 2000). When assessing a company, it is useful to consider the
components of the annual report and financial statements. Ratio analysis is a
technique used to highlight the performance of a company where ratios are
calculated from important records in the annual reports namely: the income
statement and the balance sheet (Walton, 2000; Atrill and McLaney, 2008;
Dunn, 2010). The income statement is sometimes referred to as the profit and
loss account; and the balance sheet, the statement of financial position (Walton,
2000; Atrill and McLaney, 2008; Dunn, 2010).
2.2.2 The balance sheet
The purpose of the balance sheet is simple, “to set out the financial position of a
business at a particular moment in time” (Atrill and McLaney, 2008). This will
usually be at the end of the year - the 31st
of December (Walton, 2000; Gibson,
2009). There are two specific categories on the balance sheet: assets of the

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business, and claims against the business (Atrill and McLaney, 2008).
2.2.3 The income statement
The income statement records how much profit, or loss, the business has made
over a specific period of time. It is a summary of the revenues and expenses of
a business (Gibson, 2009). To measure the profit generated requires a record
of the total revenue - total number of goods and services sold to customers
(Gibson, 2009). The revenue (expenses) can be defined as the incoming
(outgoing) flows of economic benefits as a result of the normal activities of a
business. The income statement reports the total revenue generated and
deducts the total expenses in generating that revenue. If the total revenue is
greater (less) than the total expenses, there will be a profit (loss) for the
business (Atrill and McLaney, 2008).
2.3 Bankruptcy
According to Chen and Lee (1993) bankruptcy (financial distress) occurs when
a company is unable to fulfil its financial commitments. In an operational sense,
a company will be deemed in financial difficulty once one of the ensuing
proceedings, the first to affect the business, has happened: “1 filing for
protection under Chapter 11 of the U.S. Bankruptcy Code or, for Canadian
firms, going into receivership; 2 Defaulting on the payment of principal or
interest; Suspending preferred stock dividends” (Chen and Lee, 1993). This
definition is very similar to the one observed in the Beaver (1966) study, where

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he signals failure events as: “bankruptcy; bond default; an overdrawn bank
account; or non-payment of a preferred stock dividend” (Beaver, 1966). Other
studies define a company as failing when it enters into procedures of
bankruptcy or discussions with the financers to help minimise the debts of the
company (Edmister, 1972; Blum, 1969; Altman, 1968). Deakin (1972) deems
failure to include only companies that experience “bankruptcy, insolvency, or
were otherwise liquidated for the benefit of creditors” (Deakin, 1972). The
aforementioned are United States based definitions. According to
PricewaterhouseCoopers UK bankruptcy is defined as “a company becomes
insolvent if it does not have enough assets to cover its debts and/or it cannot
pay its debts on the due dates” (PricewaterhouseCoopers, 2009).
2.3.1 Implication of bankruptcy
Deakin (1972) and Doukas (1986) note the effect failure can have on a
company, specifically the considerable losses experienced by owners of the
business (stockholders and other investors). Barbuta-Misu (2011) reports that
using bankruptcy risk as a means of assessing the financial health of a
company is justified, as a company with a minimal probability of failure is
deemed efficient in financial standings. As the failure of a business has
significant implications for shareholders, and the reputations of the company
representatives, the prediction of bankruptcy can help managers take steps to
avoid failure such as: consideration of merger or divestment, revaluation of
financial structure, and how to improve efficiency in their respective industry

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(Ohlson, 1980; Agarwal and Taffler, 2007). The severity of bankruptcy, and
corporate failure was recognised by Beaver (1966) and Altman (1968). Both
researchers deemed the risk worthy to develop models to predict failure based
on the annual and financial reports of the companies concerned (Deakin, 1972).
If models could be developed with the predictive power to observe initial signs
of failure up to five years prior to bankruptcy, then it would allow managers to
initiate proceedings to evade failure, thus, allowing the company to continue
operations and keep the shareholders happy.
2.3.2 when companies decide to use bankruptcy models
Most studies after the original Beaver (1966) and Altman (1968) models were
conducted when economies, and/or companies were faced with adversity.
Shirata (1999) reports that after the economic distress of 1990, the Japanese
economy experienced a period of financial turmoil and many companies
succumbed to bankruptcy. He suggests the need for the development of a new
Japanese model of bankruptcy predictability as only a small number of studies
to assess Japanese company bankruptcy had been conducted and due to the
small sample sizes, generalities could not be made. Also, the accuracy in past
Japanese bankruptcy prediction models was lower than desired, whereas the
newer Shirata model boasts more than 86.14% accuracy (Shirata, 1999).
Additionally Doukas (1986) observed that following the recession of 1980-82 a
substantial number of studies were conducted into the forecast of failure. It
seems that as long as companies are making a profit and not facing adverse
conditions then there is no need to determine the risk of bankruptcy.

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2.3.3 Reason for testing bankruptcy
Chen and Lee (1993) set the initial measuring time in the survival period of oil
and gas companies at December 1981. This was due to the oil price reaching a
peak in mid-1981. Likewise, 2008 was chosen as the beginning point of the
study for this research due to the high oil price of $147 in that year. Al-Khatib
and Al-Horani (2012) noted that even in today’s economy, studies of bankruptcy
prediction hold value, as companies are still susceptible to the detrimental
effects of a financial crisis on their ability to survive. The vast majority of the
world’s economies were affected by the global financial crisis of 2008, and
many public limited companies fell victim to bankruptcy in the United States,
Europe, Asia and other countries (Al-Khatib and Al-Horani, 2012; Carstea et al.,
2010). In light of these events, many analysts, economists and academics have
questioned companies’ ability to endure a recession. This led to substantial
curiosity into the paramount methods and indicators, which can aid in the
forecasting of financial failure in companies. In light of the events at the turn of
the century where Enron and WorldCom met their demise, this acted as a
catalyst for global economies to take more care and prompted rehabilitated
concern for credit risk assessment (Aziz and Humayon, 2006; Agarwal and
Taffler, 2007).
2.3.4 Factors which influence potential for bankruptcy
In line with Li and Tang (2007), research shows that companies of a greater
magnitude (based on total assets or market capitalization for instance) tend to

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outperform their smaller counterparts when considering: profitability, growth of
share capacity, efficiency of operating practices and financial security.
Corporate endurance can vary vastly from company to company, depending on
a number of factors: the capital structure of a company; size of company;
efficiency of operational practices; and industry sector (Ohlson, 1980, Chen and
Lee, 1993, Shirata, 1999). For the extractive industries it is: size, age of entity,
and successful exploration that are key determinants in the endurance of
companies. As stated by Ohlson (1980) there are four main factors, which affect
a company’s likelihood of failure these are: “1 - size of company, 2 - a
measure(s) of the structure, 3 - a measure(s) of performance, 4 - a measure(s)
of current liquidity” (Ohlson, 1980). In this dissertation the focus is on the size of
company but due to the independent variables included in the z-score model
each of the above factors will be assessed indirectly.
2.4 The Beaver Model
Beaver used univariant discriminant analysis, meaning he tested one variable at
a time, in the determination of bankruptcy (Barbuta-Misu, 2011). The variables
used in his study were specific key financial ratios. The study was based on the
earlier investigative work of Patrick (1932) on the usefulness of ratios. The
result of Patrick’s study showed that indeed ratios, and associated analytical
methods, could be used as powerful instruments of assessment. According to
Barnes (1987), financial ratios are used for a wide variety of purposes: by
accountants for forecasting future financial performance and more recently by
researchers in statistical models (z-score) to determine bankruptcy, credit rating

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and the valuation of risk. Beaver (1966) used this study to develop a model for
assessing companies’ risk of bankruptcy through ratio analysis (Abor and
Appiah, 2009). In the 1930s it had been discovered that companies
experiencing financial turmoil display substantial differences in the measured
ratios compared with firms experiencing strong financial performance (Altman,
1968). The Beaver model not only based predictions of financial distress on
bankruptcy but also compared distressed and non-distressed companies. This
research concurs with the later exploratory works of Giroux and Wiggings
(1984) and DeAngelo and DeAngelo (1990) (Ward, 1994). According to Platt
and Platt (1990) there is an abundance of literature based upon the original
framework set by Beaver in 1966 using the univariant methodology to predict
failure. Deakin (1972) argues that while the Beaver model is unquestionable in
the predictive ability of its results, the later Altman model has greater perceptive
uses, and popularity (Deakin, 1972).
Beaver (1966) defines a financial ratio as a “quotient of two numbers, where
both numbers consist of financial statement items” (Beaver, 1966). This is a
very succinct definition. The aim of his study was not only to create a model for
the prediction of failure but also to ultimately examine the worth of ratios and
the accounting data used in their calculation. It is suggested that further
research could be done using multiratio analysis, where numerous ratios are
used to determine the potential of bankruptcy in companies. Beaver thought this
might prove more useful and even better than using single ratios (Beaver,
1966). This suggestion paved the way for Edward Altman to develop his

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bankruptcy prediction model. However, the Beaver model is still recognised as
a pioneering work, with its greatest addition being the development of a method
to evaluate accounting data for any use, not only for corporate endurance
(Beaver, 1966).
2.4.1 The Altman model
The Altman bankruptcy model is regarded by many as the pioneering research
into the development of a model to predict the probability of failure in corporate
entities (Abor and Appiah, 2009; Platt and Platt, 1990; Chen and Lee, 1993;
Sena and Williams, 1998; Barbuta-Misu, 2011; Deakin, 1972; Doukas, 1986;
Carstea et al., 2010). In Doukas’ (1986) opinion, the Altman model of 1968 has
progressed and is “deemed as the yardstick of predictability models because of
its straightforwardness of understanding and pertinence” (Doukas, 1986). This
is in a similar vein to Moyer (1977) who states that further studies have not
provided adequately improved results to render the Altman model obsolete and
in need of adaptations. Since its conception, the z-score model has been tried
and tested in various academic works. The outcomes show that the original
model is precise and dependable, and is still widely utilised to determine
financial distress, in spite of developments throughout the past thirty years
(Agarwal and Taffler, 2007; Carstea et al., 2010). Edmister (1972) reports the
interesting predictive power of ratios is cumulative – the predictive ability
increases with successive additions of other ratios. The additional ratios will
only add to the predictive power if they are indeed relevant, significant, and do
not overlap other ratios. He also notes that some ratios are not actually

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significant predictors of bankruptcy by themselves but aid the improved
discriminant capability when included in the z-score function (Edmister, 1972).
Altman drew on the suggestions made in the 1966 paper, that it may be
possible to achieve greater accuracy in bankruptcy prediction should a variety
of ratio be used at one time – multivariate analysis. Although Altman recognised
that the Beaver study showed irrefutable evidence that using ratios in analysis
can indeed predict potential failure of firms, he wanted to mould the model to
give greater accuracy. The Beaver study prompted Altman to develop a
statistical technique to test companies known to have failed against those which
have not – Multiple Discriminant Analysis (MDA). According to Altman (1968),
the possible principal advantage of multiple discriminant analysis in determining
issues of classification (into bankrupt and non-bankruptcy companies) is the
ability to evaluate an absolute set of variables simultaneously, as opposed to
consecutively examining singular features of the test subject (Altman, 1968).
Altman attempted to assess the quality of ratio analysis as an analytical tool in
predicting bankruptcy of firms ranging in size from $0.7 million to $25.9 million
in assets. His sample was from a population of the manufacturing industry and
consisted of thirty-three companies declaring bankruptcy under Chapter X over
the period 1946-1965. He paired the bankrupt companies with a sample of
thirty-three firms not declaring bankruptcy. From an original list of twenty-two
ratios based on earlier research (notably Beaver 1966), Altman selected the five
ratios he deemed most appropriate in the prediction of bankruptcy. These five
ratios were as follows: working capital/total assets, retained earnings/total
assets, earnings before interest and taxes/total assets, market value of

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equity/book value of debt, and sales/total assets (Altman, 1968; Edmister, 1972;
Chen and Shimerda, 1981; Sena and Williams, 1998; and Carstea et al., 2010).
An interesting example of the power of MDA is with the sales to total asset ratio.
This ratio exemplifies a company’s ability to generate sales. When considered
on an individual basis, it is the least significant, and would have been omitted
from the study. However, due to the unique interaction it has with other ratios in
the z-score model, it actually is positioned second in its influence on the overall
predictability of the model. Altman suggested that the ratio of market value of
equity/book value of debt, gave a market approach to his predictive model
rather than relying solely on the reported figures in the financial statements.
Interestingly, Altman’s model is only really useful in predicting the likelihood of
bankruptcy in public limited companies (due to the need for the market value of
equity ratio component). When Doukas (1986) conducted his study involving
privately owned firms only, the information required to calculate the market
value of equity was unavailable and this ratio was omitted from his research. He
instead used the book value of equity. This shows the limitations of ratios and
the availability of data in conducting studies.
2.4.2 Updated studies based on original models
According to Aziz and Humayon (2006) the most common statistical method
used in failure prediction is ratio analysis. It was discovered that, of the eighty-
nine empirical past bankruptcy prediction studies, sixty per cent reportedly used
financial ratios. As reported by Abor and Appiah (2009) there are a substantial

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number of bankruptcy models present today, all adopting slightly different
methods. However, it must be recognised that the majority, if not all models are
based, in some way or another, upon the originally conceived study of Altman in
1968 (Abor and Appiah, 2009). These models adopted similar methods to
Altman but are modified in such a way to suit their specific needs. The models
include: Deakin (1972); Altman et al. (1977); Keasey and Watson (1986);
Gentry et al. (1987); Balwin and Glezen (1992) and Aly et al. (1992). As stated
by Barbuta-Misu (2011) there is a substantial number of studies based on the
original papers of Beaver and Altman in the development of bankruptcy risk
models. Specific to bankruptcy prediction are, for example: Edmister (1972), the
Diamond model (1976), Deakin probabilistic model (1977), Springate model
(1978), the Ohlson model (1982), and the Fulmer model (1984) (Barbuta-Misu,
2011). It should be noted that, to the knowledge of the researcher, there are few
research studies developing bankruptcy models in the 2000s. A possible reason
is that the original models (and aforementioned updated studies), are
adequately addressing the bankruptcy issues of the 21st
century. This is the
notion of Aziz and Humayon in their 2006 study, who indicate that due to
prominent use of multiple discriminant analysis in past studies, it is the most
appropriate method to use for their study (Aziz and Humayon, 2006).
There is an interesting study conducted by Shirata (1999) on the prediction of
failure in Japan. The model Shirata constructed was loosely based on the
Altman model but chose to omit ratios of profitability and liquidity. The reason
why profitability was omitted is due to the fact that even if a Japanese company
experiences a decrease in profitability, it can still have an abundance of

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cumulative profitability, and subsequently may not go bankrupt (Shirata, 1999).
Therefore profitability is not a significant determinant of bankruptcy in Japanese
firms. A notable outcome of this study was that the model used is a universal
model and not heavily influenced by size of company or the business sector it
operates. As the research of this dissertation is concerned with the influence
size of company has on corporate endurance, the Shirata model would not be
appropriate to use. However it is worth mentioning for any researchers wishing
to pursue further research on companies regardless of industry sector or
company size.
2.5 Choice of ratios
According to Horrigan (1968) the state of ratio analysis at the time of his paper
was missing an unambiguous speculative structure to adhere to. As a result the
researcher conducting the analysis has to rely on the worthiness of a writer’s
experience in the field. Hence, ratio analysis is made up of unproven
declarations regarding which ratios to use, alongside the expected relations
between ratios. From the Chen and Shirmerda (1981) study, it was identified
that considering a sample of twenty-six separate studies, sixty-five financial
accounting ratios appeared. From this sample, forty-one of the ratios were
deemed significant for the researchers. Provided with such a substantial and
varied collection of financial ratios, researchers conducting studies may find it
difficult to select ratios most useful to address their research objective(s). In this
research, it was deemed appropriate to follow the methods adopted by Sena
and Williams (1998) using the five ratios considered most effective by Altman

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(1968). This bestows a certain level of trust in the aforementioned studies and
following the methodology and framework of the Sena and Williams study,
alleviated bias towards the choice of ratios. This made it easier to decide on
using only five ratios and not consider all forty-one. As the Sena and Williams
study is the only one found which uses the Altman model for oil and gas
company performance, it is sensible to adopt a similar approach.
Another method of selecting ratios is to look at overlaps in the financial data
used in the calculations. In Deakin (1972), the univariant study calculated 28
separate ratios respectively, however this was made up of only ten different
quantifiable items of data. Similarly the later study by Elam (1975) used 18
separate data elements in the calculation of twenty-eight ratios. Observing any
overlapping data would help eliminate less useful ratios and allow the
construction of a set of consistent useful financial ratios (Chen and Shirmerda,
1981). The main concern with overlapping in ratios is the presence of
mutlicollinearity – where two separate ratios have a very strong relationship with
one another, rendering their contribution to the model substantially less
significant. In a similar vein, Altman utilised this method of overlapping to
cleanse his list of twenty-two ratios to a selection of five he regarded as the
most powerful in their relevance and predictive ability in the determination of
failure. Edmister (1972) recognised that conducting analysis using ratios can be
exceedingly perceptive to “either or both the purpose of the analysis and the
population studied” (Edmister, 1972).

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2.6 The z-score
The z-score function takes the following form: Z = vixi + v2x2+…+vnxn, where vi,
v2,… vn = discriminant coefficients, xi, x2,… xn = independent variables. The
discriminant coefficients are the factors each independent variable is multiplied
by. The independent variables are the ratios: working capital/total assets,
retained earnings/total assets, retained earnings before profit and taxation,
market value of equity/book value of debt, and sales/total assets (Altman, 1968;
Sena and Williams, 1998; Carstea et al., 2010). The lower the resultant z-score
a company receives, the greater the potential for bankruptcy. If the model is
created in a systematic and intelligent manner, the independent variables
(ratios) characteristically address essential aspects of business performance
(profitability, liquidity, efficiency, sales ability among others) (Agarwal and
Taffler, 2007).
As stated in Agarwal and Taffler (2007) the definition of the standard z-score is
given as “the distillation into a single measure of a number of appropriately
chosen financial ratios, weighted and added” (Agarwal and Taffler, 2007). This
allows the researcher to not only test the predictive value of one ratio, but
several at once. Carstea et al. (2010) suggests z-scores are extremely useful in
allowing the financial health of a company to be determined by a single figure
depending on the thresholds of bankruptcy or non-bankruptcy. This is the
premise for using discriminant analysis as it allows several different influential
constituents to be amalgamated to form one single determinant score. Taffler
(1977) used multiple discriminant analysis to construct a model for failed and

xxxv

non-failed companies in the United Kingdom (Abor and Appiah, 2009). The
major impact of his research was the expansion of a specific z-score model for
bankruptcy prediction in the United Kingdom.
There are three categories that companies can be separated into, based on
their z-score: bankrupt or distressed zone; the grey area – inconclusive zone,
and the non-bankrupt or safe zone. The bankrupt zone includes any company
that reports a z-score of below 1.81; the grey area includes any company
reporting a z-score between 1.81 and 2.99; and any company reporting a z-
score above 2.99 is in the “safe zone” (Altman, 1968, Carstea et al., 2010; Sena
and Williams, 1998). These threshold values were determined by the firm’s
resultant z-scores in Altman’s original study.
As reported by Deakin (1972), observing the resultant scores of firms and
classifying them based on thresholds is a sufficient method of failure prediction,
yet it may omit certain relative scores from the study, hence misclassifying
some of the companies. He refers to the studies of Altman (1968), Frishkoff
(1970) and Frank and Weygandt (1971) as examples of this method. In the
Sena and Williams (1998) study it was discovered that of their sample, two
companies were in the bankrupt zone, six were in the safe zone, and the
majority (eighteen companies) were situated in the grey zone. Companies
falling into the grey zone are susceptible to errors in classification whether there
is an imminent threat of bankruptcy or not. This is a limitation of the model and it
may be useful to revise the thresholds, which determine the predictability of
failure. With the time limitations of this study, calculating new threshold z-scores
is beyond the scope of the research, but could be an option for future studies.

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The study of Agarwal and Taffler (2007) illustrates the predictive abilities of the
z-score model, in comparison with supplementary prediction models. This study
also emphasises that using published financial accounts gives a certain level of
reliability and validity in the ratios, and therefore the overall z-score.
2.7 Predictability of models
The Taffler (1977) model, adopting MDA, can provide a significant level of
accuracy in prediction of failure in a business, not only in the year prior to
bankruptcy but also in two or three years before. Deakin (1972) reports that the
original work of Altman was over 90% effective in the selection of future
bankruptcy in firms in the years prior to bankruptcy (Chen and Shimerda, 1981).
To emphasise how accurate his predictions were, it is noted that the firms
Altman predicted to fail, did so, on average, “seven and one-half months after
the close of the last fiscal year for which reports were prepared” (Deakin, 1972).
It has been discovered that the predictive power of these models can highlight
potential for bankruptcy up to three years prior to failure, with a high level of
accuracy, thus allowing managers to take steps to avoid looming failure. The
later revised study of Altman (1968) by Altman, Haldeman and Narayanan
(1977) argued that using the z-score method could categorise companies as
bankrupt up to five years prior to failure. This paper also suggested an accuracy
of 92.8% determination of bankruptcy in companies in the year prior to financial
distress (Al-Kahtib and Al-Horani, 2012).

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2.8 Conclusion
This chapter has provided an overview of the oil and gas industry, bankruptcy
and associated issues, and has shown the power and predictive ability of
bankruptcy models. The literature review revealed how past studies have
analysed and emphasised the importance of bankruptcy prediction for
businesses. A dissection of the z-score model and ratio components is
explained and analysed. Furthermore, the original and pioneering studies of
Altman and Beaver have been discussed in detail to provide the reader with a
thorough account of the basis of future research. By building on the methods
and theories developed in the previous studies, the researcher will utilise the
bankruptcy model to determine the performance of independent oil companies.

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CHAPTER THREE
METHODOLOGY
3.0 Introduction
Firstly, this chapter opens with a discussion and reasons why the study was
chosen, followed by an overview of the research paradigm, research philosophy
and the methodology used. Secondly, information on the nature of the research
and the data will be presented. Thirdly, the sample and population are
addressed including explanations of the ratios utilised in the calculations. The
final section considers how the research was conducted, highlighting any issues
arising and how these were overcome.
To the knowledge of the researcher there are no articles after the Sena and
Williams’ study which use the Altman bankruptcy model to assess the
performance of oil companies (Sena and Williams, 1998). Since the 1998 study
was based on a sample of oil companies between 1986-1995, it was feasible to
adopt a similar methodology over a different longitudinal time frame. This
dissertation considers the performance of oil companies between 2008 and
2012 using the Altman z-score model. This period allowed conclusions to be
drawn on the impact of company size relative to overall performance and
potential for bankruptcy. In addition, it allowed consideration of the reasons
behind companies of a similar size, experiencing better performances than
others. A thorough discussion of the results and findings is addressed in
Chapter four.

xxxix

3.1 Research philosophy
Lewis, Saunders and Thornhill (2012) describe a research philosophy as an all-
encompassing phrase related to “the development of knowledge and the nature
of that knowledge” (Lewis, Sanders and Thornhill, 2012). In other words, the
process of a study is to increase understanding of a specific topic. In this
dissertation, the purpose, and ambition of addressing a modest, yet important
problem for a specific population has indeed shown a development of
knowledge.
3.1.1 Research paradigm
The paradigm adopted was positivism, described by Bryman and Bell (2011) as
“an epistemological position that advocates the application of the methods of
the natural sciences to the study of social reality and beyond” (Bryman and Bell,
2011). There is no single conclusive definition of positivism and many authors
will discuss it in similar fashion of either a paradigm or philosophy (Collis and
Hussey, 2009 p. 56; Lewis, Saunders and Thornhill, 2012 p. 134; Jankowicz,
2005 p. 110; Bryman and Bell, 2011 p. 15) but the general concept and nature
is the same.
3.1.2 Research methodology
According to Lewis, Saunders and Thornhill (2011), the methodology is the
research strategy for how the researcher will conduct the study and answer the
research question (or questions) proposed. According to Denzin and Lincoln
(2005) cited in Lewis, Saunders and Thornhill (2011) p. 173, the research

xl

strategy is the connection amidst the research philosophy and the ensuing
decision of how to gather and examine the data. The structure of the
methodology will include: clearly defined objectives fashioned from the research
question(s); an outline of precisely where the data will be gathered from; how
the researcher suggests to gather and examine the data; discussions of any
ethical concerns; and the limitations of the research such as availability of data,
time constraint and financing the project (Lewis, Saunders and Thornhill, 2011).
3.2 Software used for analysis
The Microsoft Excel programme was used to input, and analyse all the data
collected. One workbook was used, where each company had a separate
worksheet for the respective data. Once the ratio component figures for working
capital, total assets, market value of equity, book value of debt, sales, retained
earnings, and the earnings before interest and taxes were found, or calculated,
they were entered into the worksheet. The aforementioned figures were found
for each year of the study for all nineteen sample companies. This took
considerable time due to the number of companies and five-year study period.
Thorough checks were completed to make sure the information included in the
worksheets was consistent, accurate, and reliable. Independent auditors
audited the complete sample financial reports. Having an independent audit
gives a level of validity, and reliability, in the reported figures. The companies
used for the audit were major accounting firms such as: Deloitte LLP, Ernst and
Young, and PricewaterhouseCoopers LLP.

xli

Once all the data had been calculated and input in separate worksheets in
EXCEL, the z-scores were calculated. This was done by taking each ratio
component for the first company (Afren) in 2008 and multiplying them by the
specific factor proposed in the z-score model. Once the z-score had been
calculated for 2008, the 2009 results were calculated, and so on. This method
of calculation was replicated for the entire sample of the study. Once all the z-
scores were calculated, a table was constructed for the results of each year.
The mean yearly value and mean company value was calculated in EXCEL.
Although the Sena and Williams’ study used the mean z-score values to assess
the performance of the companies, it was not possible to replicate, as the
timeframe of this dissertation was not sufficiently long. It proved more applicable
to look for trends in each company’s z-score over the five-year period, or
alternatively to compare companies with other companies’ yearly z-score in the
sample. Full details of the z-score calculations and results can be found in the
appendices section (Appendix A). As the main question raised is concerned
with company size, analysis of each group (small, medium and large
companies) was made. It was discovered that some companies significantly
outperformed others in their size grouping or even in other grouping categories.

3.3 Rationale for using quantitative method of analysis
The study is concerned with the size of independent oil companies, based on
total assets. The use of numeric data in the ratio calculations means it is
sensible to follow a quantitative method of analysis. Quantitative refers to any
data that has been enumerated (numeric data). This analysis allows ratios to be

xlii

calculated from audited financial statements, providing reliability in the figures.
A qualitative method (concerning non-numeric data) using a questionnaire, or
survey for instance, could have been adopted to gain the opinion of the
management of the companies in the sample. This would have provided an
interesting insight into the opinion of management on bankruptcy issues in the
oil and gas industry, however, due to the time limitation (three months) and lack
of contacts in the positions required, this method could not be fulfilled.
3.4 Sources and nature of data
According to Lewis, Saunders and Thornhill (2011) data are “facts, opinions and
statistics that have been collected together and recorded for references or for
analysis” (Lewis, Saunders and Thornhill, 2011). Data can be divided into two
broad groups: primary data and secondary data. Primary data is any data,
which has been gathered explicitly for the study being conducted. This data is
new, and is collected by the researcher, through the use of questionnaires,
surveys, interviews, and focus groups (Collis and Hussey, 2009). Contrarily,
secondary are data originally gathered for a different intention to the specific
purpose of the research in this study. The data can be subject to additional
analysis to postulate further and deeper knowledge of a topic. It may also allow
the researcher to draw conclusions. Sources of secondary data include any
information already gathered and reported, examples such as: journals;
publications; databases and company’s annual reports; and other company
documents (Collis and Hussey, 2009). The main advantage of secondary data
is that it is already available, and in abundance, thus alleviating issues of time

xliii

and monetary constraints, apparent in some researchers’ academic studies
(Ghauri and Grønhaug, 2010 cited in Lewis, Saunders and Thornhill, 2012).
Secondary data will also allow the analysis of a much larger sample, providing
the researcher with the option of making generalities about the wider subject
area. It will also allow for longitudinal studies to be conducted, that is, a study
conducted over a specific period of time. There are however, disadvantages in
secondary data, most prevalently that the data was not collected for the specific
purpose of the researcher’s study. Also, there is no guarantee that the data
gathered is of high quality and reliable.
The data from the companies’ annual reports was used for the components of
the ratio calculations. The ratios used considered liquidity; profitability;
productivity of assets; solvency; and sales generating ability of assets. There
are indeed limitations to using ratio analysis: there is no universally accepted
set of ratios to use for assessment; a single ratio doesn’t provide enough
information to make a thorough assessment and ratios are only as reliable as
the source of data they have been retrieved and calculated from (Atrill and
McLaney, 2011).

3.5 Research design
It was decided that a positivist philosophy, involving experimental research, was
the most appropriate for the research. The reason for choosing a positivist
study is because the research is based on the collection, and analysis, of
secondary quantitative data and adopts, as far as applicable, a value neutral

xliv

approach to the research. Value neutral means that the researcher was
independent of the research subject (Collis and Hussey, 2009). The researcher
has aimed to follow this approach as closely as possible, but there are some
limitations in adopting this mentality such as: the choice of issues addressed;
the aims of the research; and which data to collect and analyse. Therefore the
approach cannot be fully value neutral. This approach involves the collection of
data about a reality deemed observable and the pursuit of consistencies and
contributory associations in the data to establish generalities similar to those
fashioned by scientists (Gill and Johnson, 2010).

3.5.1 Deductive nature of research
The research was conducted in a deductive manner, because it is concerned
with the use of data to test a theory or question(s). Deductive research is most
common to scientific experiments and the natural sciences, where theories are
put through substantial tests to predict the reliability of validity of the theory.
As the study proposed an association between size and potential for bankruptcy
to form a conclusion it follows the nature of deductive research. Other research
approaches, such as inductive and abductive, could have been used but were
analysed and subsequently rejected. Inductive research begins with the
collection of data to investigate an occurrence, and then theories are built from
the findings – quite frequently in the development of a “conceptual framework”
(Lewis, Saunders and Thornhill, 2011). The abductive approach to research
uses data to investigate an occurrence, recognise similarities and repetitions, to
allow the generation of a new, or adapt a current theory further tested using

xlv

additional data. As neither of these alternative approaches was applicable, the
approach was indeed deductive.
3.6 Categorising companies into bankrupt or non-bankrupt sectors
Z-score values, as recommended in the Altman study of 1968, were proposed
to assess companies’ probability of bankruptcy. The categories are as follows: a
z-score equal or greater than 2.99 means a company fits into the non-bankrupt
sector; a z-score equalling between 1.81 but less than 2.99 is in the “grey area”
and a company with a z-score less than 1.81 is in the bankrupt sector. The grey
area is the section where a company’s potential for bankruptcy is undetermined.
It must be made clear that the z-score is merely a performance indicator and
does not provide an absolute guarantee that companies will ultimately go
bankrupt or not, if they report scores below 1.81 (bankrupt) or above 2.99 (non-
bankrupt).
3.6.1 Use of methodology from previous study
The Altman z-scores for the sample of oil companies were calculated, and are
documented in figure 4.1, found in Chapter four. The originally proposed period
of study for this dissertation considered a time frame over a ten-year period
between 2003 and 2012. This was later revised, and shortened, to a study from
2008 to 2012 due to lack of available data from all sample companies
concerned. If a researcher had full access to historic company accounts, then
the originally proposed ten-year study period could be possible. The chosen
five-year period allowed a sufficient analysis of oil companies of varying size,

xlvi

based on their average total assets. The reason for choosing this period was to
evaluate company size and the potential for bankruptcy in independent oil
companies. This may also help to highlight the effect the global financial crisis
and oil price shocks of late 2008 had upon independent oil companies.
Choosing independent oil companies with varying size: large (with average total
assets >$50 billion), medium sized (between $1 billion and $50 billion average
total assets) and small (below $1 billion total assets) allowed comparisons to be
drawn on the potential for bankruptcy relative to company size.
The data was then used to compare the sample over the five-year period using
Altman’s arrangement and modelling technique. This technique involved the
calculation of the five specific ratios: working capital/total assets; retained
earnings/total assets; earnings before interest and taxes/total assets; market
value of equity/book value of debt; and sales/total assets, for each company in
the sample. The ratios were then multiplied by a specific factor and combined to
provide an overall z-score. The z-score is the determination of the probability of
bankruptcy within a company. The value for these ratios can be found in
summarised form, of annual and company results, in the appendices section
(Appendix A).

3.7 Population and sample of the study
The population of a study is defined in slightly different ways by academics.
Bryman and Bell (2011) describe it as “the universe of units from which a
sample is to be selected”; Lewis, Saunders and Thornhill (2012) describe it as

xlvii

“the complete set of cases or group members” (Bryman and Bell, 2011; Lewis,
Saunders and Thornhill, 2012). Although these definitions vary slightly, the
general idea of a population is the complete group of the test subject (for
example: retail stores, oil and gas companies, banks and others).
It would not be possible, given the time allotted (three months), to analyse the
complete population of all independent oil companies. Thus, a sample is
selected from the population. A sample is a smaller “sub-group or part of a
larger population” (Lewis, Saunders and Thornhill, 2012). This alleviates the
impracticability of testing a population of hundreds of companies in the limited
time available. An appropriate sample was selected to represent the population.
This was prepared under the associated method of probability (or
representative) sampling, whereby implications need to be made from the
selected sample, about the population. The sample is prepared in order to
address, and answer, the question(s) set by the researcher and ultimately
achieve the objective(s) set.
3.7.1 Sampling frame
To allow a sample to be selected, the sampling frame was established. The
sampling frame is a “complete list of all the cases in the population from which
your sample will be drawn” (Lewis, Saunders and Thornhill, 2012). To allow
generalities to be drawn from the sample, about the population as a whole,
companies needed to be varied in size from small, medium, to large. Initially the
sample was chosen using a method of only including companies on the London
stock exchange with a market capitalisation of greater than £100 million as it

xlviii

helped to reduce the companies which were too small for the study. It was
discovered that the majority of companies with a market capitalisation of less
than £100 million were oil investment companies, not independent oil
companies as required for the research. If these companies were included in
the sample, the mean values would be erroneous and inconsistent and the
overall sample results would be unreliable. Considering the total number of oil
companies listed on the London Stock Exchange (FTSE all share Index and
FTSE AIM all Share Index) gave a population of 115 companies (ninety-seven
from the FTSE AIM all share and eighteen from the FTSE all share).
3.7.2 Use of both FTSE indices
The reason for considering both FTSE indices is that large and medium sized
companies are listed on the all share index, and smaller companies are listed
on the AIM index, thus providing an ample range of companies of varying size
to assess potential for bankruptcy. The initial sample included thirty-five
companies, nineteen from the FTSE AIM all share index and sixteen from the
FTSE all share index. The average total assets of each company were
calculated (over 2008-2012) to give an indication of relative size. This was
consistent with the past study of Sena and Williams, which based the sample of
company size on total assets. Once the average total assets had been
calculated from the initial sample, a further nine companies were subsequently
omitted, when it was discovered that the historic annual reports did not go back
to 2008 as required. Another reason for omitting the nine companies was that
the figures were quoted on the 31st
of March. As the vast majority of companies

xlix

report their figures on the 31st
of December, any company reporting financial
results on another date was omitted, to ensure the results were consistent. The
annual reports were sourced through the companies’ websites and
http://www.northcote.co.uk - a database of historical annual reports. The latter
source was used only when the annual reports could not be sourced directly
from the companies’ website. Data required for the market value of equity (the
share price) was gathered from the London Stock Exchange website.
3.7.3 Sample
The companies in the initial sample were: Afren, Amerisur Resources, BG
Group, BP, Cairn Energy, Circle Oil, Coastal Energy, Exillion Energy, Faroe
Petroleum, Geopark, Gulf Keystone, Heritage Oil, Igas Energy, Iofina, Ithaca
Energy, JKX, Ophir Energy, Petroceltic International, Premier Oil, Providence
Resources, Royal Dutch Shell, Salamander Energy, San Leon, Soco
International, Tullow Oil and Xcite Energy. Unfortunately, data from: Amerisur
Resources, Exillion Energy, Igas Energy, Iofina, Providence Resources, San
Leon and Xcite Energy was not available, or consistent with the rest of the
sample, hence they were subsequently removed. There was no share price
information before February 2009 for Providence Resources meaning the
market value of equity could not be calculated. Amerisur Resources’ annual
report was for a period ending on the 31st
of March. A similar inconsistency
issue arose with Igas Energy where the annual reports were given for the year
ending on the 31st
of March. Similar inconsistences, or lack of available data
were the reason the abovementioned companies were omitted.

l

The final sample size consisted of nineteen companies, listed below in Table
3.1. The company size was based on the samples’ average total assets ($
millions). It can be observed that this sample is indeed an accurate
exemplification of the population as it contains oil majors (BP and Royal Dutch
Shell), and a mix of medium-sized companies and smaller, less well-known oil
and gas companies.
Company Name Total Assets
($m)
Company Name Total Assets
($m)
Afren 1,997 Heritage Oil 1,405
BG Group 71,217 Ithaca Energy 593
BP 265,946 JKX 525
Cairn Energy 4,893 Ophir Energy 637
Circle Oil 186 Petroceltic International 383
Coastal Energy 482 Premier Oil 3,150
Faroe Petroleum 440 Royal Dutch Shell 320,545
Geopark 336 Salamander Energy 1,029
Gulf Keystone 470 Soco International 1,186
Tullow 10,794
Table 3.1: sample oil companies and their average total assets (in $ million)* for
the research period, 2008-2012 (Source: Annual accounts of companies
concerned 2008-2012).
*Companies with total assets greater than $50 billion, 3; between $1 and 50
billion, 7; and less than $1 billion, 9.
3.8.1 Method for calculating the z-score
To calculate the z-score, the formula originally proposed by Altman (1968) was
used. This formula was used primarily for the manufacturing industry, and after
a thorough search of literature and past studies an oil industry specific z-score
model could not be found. The z-score formula is as follows:

li

Z = 1.2*X1 + 1.4*X2 + 3.3*X3 + 0.6*X4 + 0.999*X5
The values for each of the X components are as follows:
X1 = Working capital/total assets
X2 = Retained earnings/total assets
X3 = Earnings before interest and taxes/total assets
X4 = Market value of equity/book value of debt
X5 = Sales/total assets
3.8.2 Working capital/total assets
The working capital was calculated by taking the total current assets and
subtracting the total current liabilities. This calculation was completed for each
sample company for each year. The figures for the current assets and current
liabilities were clearly located and retrieved from the companies’ balance sheet.
When the working capital is divided by the total assets, the result gives a
measure of the liquidity of the business. According to Atrill and McLaney (2008),
liquidity is a measure of how many liquid resources (money) a company has
available in order to pay what they owe (Atrill and McLaney, 2008). There are
two other commonly used ratios for measuring the liquidity of a company: the
current ratio (current assets/current liabilities) and the quick ratio (current assets
less stocks/current liabilities), however both alternatives were discovered to be
less statistically significant than the working capital/total assets.

lii

3.8.3 Retained earnings/total assets
The retained earnings (or accumulated losses) are a measure of the profitability
and are a major foundation of finance for the majority of businesses (Atrill and
McLaney, 2008). If earnings are retained by the business, as opposed to
releasing to the shareholders in a dividend (share of a companies profits paid to
owners quarterly), the available funds for the business are improved. The
retained earnings figure is located on the balance sheet under the heading of
equity.
3.8.4 EBIT/total assets
Earnings before interest and taxes (also know as EBIT) is a measure of how
efficient the business is utilising its assets. The continuation of a business is
built on the earning ability of its assets. In this study the operating profit was
used for the figure of EBIT, as this is the wealth generated during a specific
period from regular activities carried out by the business. In the case of
bankruptcy, insolvency can occur when the total liabilities exceed the
businesses’ total assets (the earning ability of the assets). As many of the
companies reported very different forms of interest values, it proved consistent
to use operating profit for the EBIT component of the ratio as all companies
reported the it clearly in the income statement. The ratio of earnings before
interest and taxes/total assets carries the highest contribution and inclusive
determinant ability of bankruptcy prediction in the model.

liii

3.8.5 The market value of equity/book value of debt
The book value of debt was calculated by summing all the liabilities on the
companies’ balance sheet (both current and long-term). The market value of
equity is also referred to as the market capitalisation of the company. This is
calculated by taking the total number of outstanding shares of the company and
multiplying by the share price for the date the accounts are reported (31st
December). This value took a considerable period of time to calculate as the
historic share price was required for each company, found on the London Stock
Exchange website. As this information is from the LSE, the share price is
quoted in British pence. All the other figures are reported in US Dollars ($),
therefore the share price had to be converted from Pounds (£) to Dollars ($).
The conversion was achieved by taking the share price in pence, dividing it by
100 to get the value in Pounds, then converted to Dollars using the middle (+/-
0%) rate from the www.oanda.com website. For each year (2008-2012) the
exchange figures were as follows: 1.44727; 1.59257; 1.54679; 1.54261 and
1.61533 respectively. The figures from 2008, 2009, 2010 and 2012 are for the
31st
of December. The 2011 figure is for the 30th
of December as there is no
share price quoted for the 31st
of December as this fell on a Saturday, when the
stock exchange was closed.
By including the market value of equity to book value of debt ratio, it gave a
market value aspect to the overall z-score model. The outstanding shares figure
is found under the ‘share-based payments’ section in the annual reports. This
details the outstanding shares at the beginning and end of each year. The latter

liv

value is required for the market value of equity calculation. A similar, more
common ratio (book value of net worth/book value of total debt) could have
been used, but it was deemed that including the market value dimension was
more influential in bankruptcy prediction (Sena and Williams, 1998). There was
only one company, which posed an issue when calculating the market value of
equity – Royal Dutch Shell. As RDS possesses class ‘A’ and class ‘B’ shares,
investigation was done to find out which class of share would be most
appropriate. It was discovered that dividends paid on class ‘A’ shares follow a
Dutch tax regime. Class ‘B’ shares, on the other hand, receive dividends from a
dividend access mechanism. Any dividends paid through this mechanism will
have a UK source for both Dutch and UK taxes. Hence, the class ‘B’ share price
and number of outstanding shares was used in the calculation for market value
of equity for RDS (Shell, 2013).
3.8.6 Sales/total assets
The sales (also known as turnover or revenue) of a company are known as a
measure of the inflow of capital from the ordinary operating activities of a
business (Atrill and McLaney, 2008). The sales to total assets ratio highlights
the sales creation capability of the company’s assets. This is a common
accounting ratio, deemed appropriate to measure the aptitude of the
management’s ability to deal with competitive market environments (Edmister,
1972; Sena and Williams, 1998).

lv

3.8.7 Exchange rate
Three of the companies in the sample (BG Group, Faroe Petroleum and Tullow
Oil) reported all their figures in British Pounds, hence the OandA website was
used to convert the figures to US Dollars. This was achieved using the middle
(+/- 0) exchange rate. The exchange rates in 2008, 2009, 2010, 2011 and 2012
(1.44727; 1.59257; 1.54679; 1.54531; 1.61533 respectively) were used for this
conversion. Once the z-scores for each company were calculated the
information was collated into one table. This table included all the z-scores, a
yearly mean and company mean for each of the sample companies.
3.9 Generalities
It is important to note that any generalities made from the sample findings are
only concerned with companies listed on the London Stock Exchange.
Inferences cannot be made about all independent oil and gas companies on a
global basis unless the number of companies assessed was a higher proportion
of the population and was based on a sample from all listed independent oil and
gas companies globally.

lvi

CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.0 Introduction
The proposed aim of this chapter is to report and evaluate the data collected in
the study. Initially, the findings will be presented in a table of the overall z-
scores for the sample companies over each year. This will give values for the
company mean and yearly mean allowing further analysis to be conducted. By
presenting the figures for each company in a table allows easy to understand
results and trends to be analysed. The second section of this chapter will delve
into the analysis of the findings and provide guidelines on the reasons why the
trends are occurring and ultimately enable the research question to be
addressed, and answered. Generalities about the population will be proposed in
the conclusion of this chapter.
4.1 Descriptive analysis of data
The data from the study is collected and amalgamated into a table to highlight
the trends in the figures for each company over the period studied. The
companies are detailed in alphabetical order, as it would not be possible to
arrange them according to z-score as each year’s results differ substantially. As
it was not possible to address the companies over a longer period of time, the
trends are over a five-year period. This however gives a good range of results
and allows for a thorough analysis.
Table 4.1 below gives a full table of all companies and their associated z scores
over the research period.

lvii

Company
2008 z
score
2009 z
score
2010 z
score
2011 z
score
2012 z
score
Mean
company
z score
2008 2009 2010 2011 2012
Afren -0.352 0.481 0.326 0.443 1.190 0.417
BG Group 1.381 1.010 0.833 0.903 0.770 0.980
BP 3.289 2.859 2.094 2.798 2.630 2.734
Cairn Energy 1.187 0.757 1.649 1.516 1.247 1.271
Circle Oil -0.229 0.620 1.362 0.977 1.381 0.816
Coastal Energy 0.110 2.379 2.320 1.474 2.684 1.793
Faroe Petroleum -0.742 -0.076 0.345 0.948 0.506 0.196
Geopark 1.394 2.023 2.420 1.585 1.521 1.789
Gulf Keystone -1.803 -6.962 0.778 -0.263 -0.300 -1.219
Heritage Oil -0.370 0.225 1.439 1.300 0.091 0.537
Ithaca Energy -0.237 1.130 3.742 1.406 1.287 1.466
JKX 2.635 2.400 1.485 1.683 1.331 1.907
Ophir Energy -1.491 0.170 -0.557 0.616 -0.236 -0.300
Petroceltic
International 2.081 -0.287 0.324 -0.133 1.724 0.742
Premier Oil 1.704 0.923 0.810 0.665 0.932 1.007
Royal Dutch Shell 3.158 2.154 2.470 2.970 2.853 2.721
Salamander
Energy -0.135 0.130 -0.426 0.510 0.138 0.043
Soco International 1.423 1.379 1.330 1.743 2.664 1.708
Tullow Oil 0.749 0.697 0.705 0.993 1.285 0.886
Yearly Mean 0.722 0.632 1.234 1.345 1.389
Table 4.1: z-scores for the sample oil companies (Annual accounts of
companies concerned, 2008-2012; London Stock Exchange, 2013).
Based on the company mean z-scores in all years, it can be observed that
100% of the sample is either categorised in the grey or bankrupt sectors. From
these findings it suggests that the oil and gas industry (for the companies listed
on the London Stock Exchange) was in an unfavourable position of financial
health. This could be due to the fact that the global recession affected every
industry, even one as lucrative and expansive as oil and gas.

lviii

4.2 Overview and analysis of the Altman z-score results
4.2.1 2008 z-score analysis
It proved more beneficial to assess the financial health of the industry year by
year to evaluate trends. This also showed that in some cases, companies were
in the non-bankrupt sector, when if the mean value was observed, the
companies would appear to be in the grey or bankrupt sectors. From the 2008
z-scores, two companies were in the non-bankrupt sector, two in the “grey”
sector and fifteen in the bankrupt sector. This shows a majority (79%) of
companies in the bankrupt sector – emphasising that the oil and gas industry
was not in a good financial position. Of the companies in the bankrupt sector,
eight reported negative z-scores (Afren, Circle Oil, Faroe Petroleum, Gulf
Keystone, Heritage Oil, Ithaca Energy, Ophir Energy and Salamander Energy).
This was a very worrying situation for these companies to be in but can be
explained as a result of negative retained earnings and negative earnings
before interest and taxes reported for that year. Circle Oil also reported zero
sales in 2008; further enhancing the less favourable reported results, and
overall z-score. It was interesting to note that seven of the eight companies
reporting negative z-scores, have total assets less than $1 billion, categorising
them as small sized companies. One company (Salamander Energy) is a
medium sized company, giving an indication that even slightly larger companies
still face precarious results.

lix

With the recession taking hold in late 2009, the resultant z-scores for all
companies would be expected to decrease accordingly, as the financial
environment was inauspicious. This is indeed the case, and no single company
appears in the non-bankrupt zone with a z-score of 2.99 or greater. Royal Dutch
Shell, in fact, experienced a decrease in z-score of over 1.000, from 3.158 to
2.154 in the space of one year. Five companies fall into the grey sector, with a
decrease in the percentage of companies in the bankrupt sector by 5%. Of
these five companies, only three reported negative figures - an improvement on
the 2008 results. The companies, which reported negative z-scores, are again
small sized companies with total assets below $1 billion (Faroe Petroleum, Gulf
Keystone and Petroceltic International). The reason for this is due to the
negative retained earnings and negative earnings before interest and taxes for
all three companies. Petroceltic International experienced a loss of $6.1 million
as a result of higher administration costs, largely due to broadening all areas of
the company’s operations (Petroceltic International, 2009). Faroe Petroleum
recorded a loss of £6.0 million in 2009, largely as a result of lower than
predicted reserves in the Topaz gas field after drilling of the well earlier that
year (Faroe Petroleum, 2009). Gulf Keystone reported a severe negative z-
score in 2009 of -6.962 mainly due to the working capital value of -$221.23
million. This was possibly due to a diversification of the assets in Kurdistan, to
allow the company to gain exposure not only to exploration opportunities but,
production, appraisal and development (London South East 2013). A decision
was made by the management to restrict investment in Algeria and begin a

lx

tactical withdrawal from the country. This contributed heavily in the company’s
2009 loss of $96.3 million, where the financial charges involved in the Group’s
exit from Algeria were $73.9 million. Algeria had been a key area of exploration
and success for Gulf Keystone for many years but the investments required to
develop projects had increased significantly. This makes development projects
in Algeria only applicable to larger oil companies with an abundance of financial
resources (Gulf Keystone, 2009).
A surprising result occurred in the 2010 reports with one company (Ithaca
Energy) rising into the non-bankrupt sector with a score of 3.742, the highest
reported z-score over the period of this research. From the annual reports, and
individual ratio calculations, Ithaca Energy experienced a large rise in the
number of outstanding shares (from 162,361,975 in 2009 to 255,789,464 in
2010); this was coupled with an end of year share price of $1.97. The increased
number of outstanding shares of the company could be attributed to a UK
private placement (Ithaca Energy, 2010). A private placement is adopted by
smaller companies and involves the issue of shares to a select, and
sophisticated group of investors, in order to raise capital (Sherman, 1991). The
company issued over 90 million shares in the private placement, generating
$153.2 million (Ithaca Energy, 2010). When the market value of equity was
calculated, from the aforementioned figures, and divided by the book value of
debt, the result was large, having a substantial influence on the increased
overall z-score. The working capital (current assets less current liabilities) for

lxi

Ithaca Energy in 2010 also increased, nearly four times the value reported in
2009. Of the $224.9 working capital value, $195.6 million was free cash balance
meaning this money was available immediately, if required. From the Ithaca
Energy ‘Management’s Discussion and Analysis’ report for 2010, the improved
results could be attributed to the increased production in the Beatrice and Jacky
fields (Ithaca Energy, 2011). Repairs and modifications were made in early
2010, which allowed the facilities to run at higher capacity, and subsequently
increase production. A higher oil price in 2010, compared with 2009 also helped
Ithaca Energy report a highly successful year. The overall picture for the
industry was improved from 2009, with one company in the non-bankrupt zone,
four in the grey zone and fourteen in the bankrupt zone. Of all the companies,
only two reported negative z-scores.
4.2.4 Unfavourable results for BP in 2010
The results of BP were interesting to consider as the z-score changed from one
of the highest in the study (2.859 in 2009) dropping by 0.765 to a low of 2.094 in
2010. Even though BP did not score low enough to be in the bankrupt sector at
any point, this change is noteworthy. The low score was substantial and the
trend for BP’s z-score results needs mentioning. The trend can be observed in
Figure 4.1 below:

lxii

Figure 4.1 Altman z-scores for BP 2008-2012 (BP, 2008-2012; London Stock
Exchange, 2013).
The significant decrease experienced in 2010 can be mainly attributed to the
Deepwater Horizon oil spill occurring in the Gulf of Mexico on the 20th
of April
2010. The incident involved a failure of the equipment used to maintain the
integrity of the well. Following this breakdown, the equipment used to control
the flow of hydrocarbons from the well failed and hydrocarbons leaked into the
Gulf of Mexico (BP, 2013). BP reports that on the 31st
December 2012, over
$14 billion has been spent on activities to rectify the damage caused. This has
involved close engagement with governments, indigenous people, company
shareholders, BP employees, the oil industry as a whole and the media (BP,
2013). According to a recent Financial Times report, the compensation
payments continue for BP. Although it states that since the accident, BP has
paid $11 billion to remedy the damage caused, it entered a settlement whereby
3.289

2.859

2.094

2.798

2.630

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

2008
2009
2010
2011
2012

Altman

z
-‐score

Year

BP
Z-‐scores
2008-‐2012

lxiii

billions more are to be paid to businesses and indigenous people located in the
five states on the Gulf of Mexico (Financial Times, 2013c). Although, BP’s z-
score in 2011, returned to a figure nearing the results of 2009, it is unclear how
dramatic the effect of the on-going settlements will have on the overall results
for BP in the future.
The 2011 z-scores highlight the worst yearly overall performance of all
companies with a staggering 89.5 per cent of companies falling into the
bankrupt sector. The only exceptions were the two largest companies – BP and
Royal Dutch Shell – even then these companies were in the grey zone, and not
the non-bankrupt zone. This year saw a high in the oil prices with the spot price
of Brent crude averaging a value of $111.26 per barrel, an increase of forty per
cent for the previous year (U.S. EIA, 2012; BP, 2012). This average was the
highest oil price reported since 2008. It can be explained by significant events
occurring during 2011. The civil war in Libya broke in February, which caused a
decrease in the supply of oil from Libya to the world oil markets (1.5 million
barrels per day in exports). This forced the oil price to rise and relied on the
input of OPEC to increase supply to the world markets. The oil price peaked in
April of 2011 as a result of the disruptions in Libya and ensuing loss of supply of
exports (BP, 2012). This, coupled with the continuing increased demand from
the emerging economies, such as China and India, put strain on the major oil
exporting countries and the price of oil. China’s share of global energy
consumption in 2011 was at a staggering 20.3 per cent, emphasising the

lxiv

demand for oil in emerging economies (BP, 2011). As the focus of this research
is on companies listed on the London Stock Exchange, a major factor in the
results may be characterised somewhat by the debt crisis in Europe. This crisis
had an impact on not only the European markets but also the global economy
as a whole. In particular, stunted growth of the organisation for economic co-
operation and development (OECD) countries was observed (U.S. EIA, 2011).
Of the seven medium sized companies in the sample, Premier Oil is the third
largest in terms of total assets. In 2011, the reported z-score was 0.665, similar
to the results of smaller medium sized companies. According to the Financial
Times report, Premier Oil was experiencing some operational issues with the
Huntington field in the North Sea. The repercussions of this was a lower level of
production than was originally expected. This led to a decrease in the pre-tax
profit from $111.6 million to $32.5 million over the course of the first two
quarters of 2011. Premier Oil’s share price dropped by twenty-two per cent in
August 2011, unusual for a company listed on the main FTSE index (Financial
Times, 2011).
There were four companies listed in the grey zone and fifteen in the bankrupt
zone in 2012. Coastal Energy experienced a high z-score result in 2012, higher
than that of its much larger counterpart, BP. With a z-score result of 2.684, it
was the second highest in 2012, behind Royal Dutch Shell. Coastal Energy is
categorised as a small company so the results were remarkable when

lxv

compared with the other small companies in the sample. The results are shown
below in figure 4.2:
Figure 4.2 Altman z-scores for the small sized sample companies for 2012*
(Annual accounts of companies concerned, 2012; London Stock Exchange,
2013).
*Full details of the Altman z-score results for the small sized sample over the
entire research period can be found in Appendix B (App 21).
4.2.7 Coastal Energy
From the annual report and financial statements Coastal Energy recorded a
fantastic year of results. The total assets increased from $518.731 million to
$894.193 million during 2011 and 2012. The sales revenue generated was
remarkable and increased over two times in the space of one year from
$297.104 million to $666.902 million. The most staggering result observed in
-‐0.500

0.000

0.500

1.000

1.500

2.000

2.500

3.000

Altman

z
-‐scores

Companies

Small
sized
companies
Altman
z-‐scores
for

2012

2012
Altman
z-‐scores
for
small
size
companies.

lxvi

the results is for the retained earnings increasing over ten times in 2012
($193.88 million) compared with the 2011 results ($17.63 million). The earnings
before interest and taxes increased over four times from 2011 to 2012 (Coastal
Energy, 2012). This was mainly driven by higher production levels - averaging
20,000 barrels of oil per day - together with increased global commodity prices
(The Wall Street Journal, 2013). Increased reserve bases also assisted in the
momentous results for Coastal Energy. The production and cash flow for
Coastal Energy was strong for the fourth-year running, represented in the vast
financial statement results (Financial Times, 2013b). The future looks bright for
Coastal Energy with the signing of a contract to develop reservoirs in Malaysia.
Coastal Energy has committed to the hire of a rig for a daily rate of $145,000 for
this project. It is proposed, by Investec, that this commitment should result in
the sales increasing to $936 million in 2013 (Financial Times, 2012). Coastal
Energy’s CEO Randy L. Bartley reports projected expansion in the future and
has the opportunity to work as the operator on fields owned by PETRONAS in
Malaysia (Coastal Energy, 2013). Even in their short history their results should
give investors the confidence to continue to support the business’s operations.
For a company of Coastal Energy’s size to report a better z-score than a major
company – such as BP – shows that larger companies do not necessarily
perform better than their smaller counterparts.
4.3 Overview of large company results
Of the three large sample companies - BG Group, BP, and Royal Dutch Shell –
BG Group reported low z-scores across the entirety of the study. Given the size

lxvii

and diversity of these companies’ global operations, one would expect all to
report large earnings, generation of sales revenue and z-score. However, the
BG Group results give a different picture. Although BG Group is around twenty-
two per cent and twenty-seven per cent of the size of BP and Royal Dutch Shell
respectively, and significantly larger than other companies in the sample, the z-
scores reported are worrying. The yearly z-scores for BG Group were 1.381,
1.010, 0.833, 0.903, and 0.770, which places them in the bankrupt sector for
the entirety of the study. The z-score for 2008 is not a major concern with the
development of large Brazilian oil fields, coupled with sizeable profits from
liquefied natural gas (LNG) projects increasing four-fold. However, the share
price decreased due to the speculation of a higher price required for the
acquisition of the Australian based company, Origin Energy (Financial Times,
2008). According to a report by The Guardian, in the following year, a discovery
of the Guara field in the Santos basin off Brazil possesses the opportunity for
BG to make substantial cash flow and increase production levels for years to
come (The Guardian, 2009). The main reason for the lower z-score results is
attributed to the low market value of equity based on the shares outstanding
being only 45.0 million for 2009 compared with its larger counterpart BP that
had 18,732 million shares outstanding for the same year. This may highlight
that share price has a significant impact on the overall z-score value. If time
permitted, a thorough investigation would be conducted into the correlation
between share price and z-score.
Of all the years, BG Group reported its lowest z-score in 2012 of 0.770. This
could mainly be down to the announcement of no production increase for 2012.

lxviii

This led to a drop in BG shares by greater than eighteen per cent. Several
issues limited its ability to meet the production targets set, such as the halt on
operations on the Elgin/Franklin field in the North Sea, experiencing a large leak
– meaning no production would take place until some point in 2013. In addition,
the expected start date in production of the Jasmine field was delayed from the
original date of late 2012, to now begin in 2013. Operations in Egypt contributed
to the less favourable performance of BG Group in 2012, where reservoir
decline had lowered the production levels. The strategy implemented to halt the
decline was less effective than planned and production levels still remained low.
Additionally, the continuing political and social unrest with the presidential and
parliamentary elections made project execution more difficult (BG Group, 2012).
Despite the abovementioned issues, the future looks good for BG Group, with
large projects in both Brazil and Australia expected to increase cash flow and
production levels in 2014 and 2015 (BG Group, 2012). Also, a twenty-year deal
agreed with the Chinese oil giant CNOOC to supply LNG from a project in
Australia, supports this notion (The Telegraph, 2012). Although BG Group
reported low z-scores for each year, it is clear that the company does not look
as if it will go bankrupt in the near future. Thus, emphasising that z-scores are
merely an indicator of potential bankruptcy and not an absolute guarantee that
bankruptcy is immanent for companies reporting a z-score lower than 1.81.
4.4 Overview of Gulf Keystone
It must be noted that some companies reported negative z-score values,
emphasising a severe threat of bankruptcy might be immanent. It would not be

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