Saint Petersburg State University
Graduate School of Management
Master in Corporate Finance
INDUSTRY-SPECIFIC VALUE CREATION DRIVERS OF OIL AND GAS
COMPANIES IN RUSSIA, INDIA AND CHINA
Master’s Thesis by 2nd year student
Concentration – Corporate Finance
Yulia Yarovaya
Research Advisor:
Associate Professor, Tatiana A. Garanina
St. Petersburg
2017
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, студентка второго курса магистратуры направления «Менеджмент», заявляю, что в моей
ВКР на тему «Определение драйверов ценности нефтегазовых компаний России, Индии и Китая»,
представленной в службу обеспечения программ магистратуры для последующей передачи в
государственную аттестационную комиссию для публичной защиты, не содержится элементов
плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских диссертаций
имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что «ВКР
выполняется индивидуально каждым студентом под руководством назначенного ему научного
руководителя», и п. 51 Устава федерального государственного бюджетного образовательного
учреждения высшего профессионального образования «Санкт-Петербургский государственный
университет» о том, что «студент подлежит отчислению из Санкт-Петербургского университета за
представление курсовой или выпускной квалификационной работы, выполненной другим лицом
(лицами)».
(Подпись студента)
28.09.2017
(Дата)
STATEMENT ABOUT THE INDEPENDENT CHARACTER OF THE MASTER THESIS
I, second year master student, program «Management», state that my master thesis on the topic
«Industry-specific value creation drivers of oil and gas companies in Russia, India and China», which is
presented to the Master Office to be submitted to the Official Defense Committee for the public defense,
does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses, PhD and
doctorate theses which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major curriculum
programs of higher and secondary professional education at St.Petersburg University «А master thesis
must be completed by each of the degree candidates individually under the supervision of his or her
advisor», and according to paragraph 51 of Charter of the Federal State Institution of Higher Professional
Education Saint-Petersburg State University «a student can be expelled from St. Petersburg University for
submitting of the course or graduation qualification work developed by other person (persons)».
(Student's signature)
28.09.2017
(Date)
АННОТАЦИЯ
Автор
Яровая Юлия Вячеславовна
2
Название ВКР
«Определение драйверов ценности нефтегазовых
компаний России, Индии и Китая»
Направление подготовки
38.04.02 Менеджмент
Корпоративные Финансы
Год
2017
Научный руководитель
Гаранина Татьяна Александровна
Описание цели, задач и основных
результатов
Нефтегазовая отрасль давно является предметом
многочисленных исследований, однако количество
исследований по развивающимся рынкам довольно
невелико. Сейчас внимание к исследованиям
драйверов ценности в отрасли растет ещё больше на
фоне кризисной ситуации на рынке. В связи с этим,
было решено посвятить данную работу определению
драйверов прибыльности и ценности нефтегазовых
компаний, в частности в России, Индии и Китае –
странах, лидирующих в нефтегазовой индустрии
среди стран с развивающейся экономикой. Для этого
были поставлены следующие задачи. Во-первых,
прове сти обзор литературы по ценно стноориентированному менеджменту, в частности, по
модели остаточной прибыли и модели Дюпон, а
также провести обзор специфических характеристик
нефтегазовой индустрии. Во-вторых, собрать
необходимые данные и сформировать выборку для
проведения регре ссионного анализа с
использованием компонентов модели Дюпон и
специфических для отрасли факторов в выбранных
странах. Наконец, сделать выводы о взаимосвязях
операционных компонентов модели Дюпон и
специфических для отрасли факторов с
прибыльно стью и созданием ценно сти в
проанализированных странах и предоставить
практиче ские рекомендации менеджерам и
инвесторам. В результате анализа, были определены
несколько драйверов прибыльности и ценности для
вошедших в выборку компаний с помощью моделей,
объединяющих показатели первого уровня модели
Дюпон и специфические для отрасли показатели, что
позволило сделать соответствующие управленческие
выводы.
Ключевые слова
Драйверы ценности, создание ценности, оценка
компаний, нефтегазовые компании
3
ABSTRACT
Master Student's Name
Yulia Yarovaya
Master Thesis Title
“Industry-Specific Value Creation Drivers of oil and gas
companies in Russia, India and China”
Main field of study
38.04.02 Management
Corporate Finance
Year
2017
Academic Advisor's Name
Tatiana A. Garanina
Description of the goal, tasks and main
results
Oil and gas industry has always been an attractive field
for research, although the number of studies for
developing markets is not high. Now the importance of
the investigation of value creation drivers in the industry
is only rising, following the crisis situation in the market.
That is why it was decided to devote this work to the
identification of profitability and value creation drivers
in oil and gas companies, in particular in Russia, India
and China as ones of the most prominent players in the
industry and representatives of developing economies. In
order to do this, the following objectives were stated.
Firstly, to conduct a literature review on value-based
management concepts, in particular residual income
valuation model and Dupont model as well as specifics
of oil and gas industry. Secondly, to collect the data and
form the representative dataset for the regression
analysis on the components of DuPont model and
industry-specific factors across the countries. Finally, to
draw conclusions about the relationships of operational
DuPont model components and industry-specific factors
with profitability and value creation in the countries of
analysis and provide practical implications for managers
and investors based on the research. As a result, several
profitability and value creation drivers have been
identified via the models uniting first-level DuPont
components and industry-specific indicators, allowing to
provide corresponding managerial implications.
Keywords
Value drivers, value creation, valuation, oil and gas
4
Table of contents
Generating Table of Contents for Word Import ...
5
Introduction
The concept of value-based management has been established as one of the key concepts in
managing companies’ performance during the recent decades. Starting with the fundamental
works by [Rappaport, 1986; Brayley and Mayers, 1981; Copeland, 1995] it has now become an
integral part of strategic and operating decision-making in business. The principles of valuebased management which set the maximization of the value for the shareholders as a key goal
also serve as a basis for the evaluation of the company’s performance and the identification of
the value drivers.
This master thesis is devoted to the identification of the value drivers in oil and gas industry. The
sector was chosen as one of the most influential in global economy and, due to a recent crisis, the
one that requires a clear understanding of performance and value creation factors. The countries
for the study - Russia, India and China – were identified as those that have been investigated
much less than mature western ones. The identification of value drivers will be performed via
Dupont Model with its operational components: many authors focus on DuPont analysis on the
first level of the model, considering operating margin, assets turnover and leverage, but there is a
limited number of works connected to the operational level which will be explored here.
Therefore, main goal of the research is to identify the main drivers of profitability and value
creation in oil and gas companies in Russia, India and China.
Consequently, the objectives of the research are the following.
•
Conduct a literature review on value-based management concepts, in particular residual
income valuation model and Dupont model as well as specifics of oil and gas industry
•
Collect the data and form the representative dataset for the analysis
•
Carry out the regression analysis on the components of DuPont model and industry-specific
factors across the countries
•
Draw conclusions about the relationships of operational DuPont model components and
industry-specific factors on the profitability and value creation in the countries of analysis
•
Provide practical implications for managers and investors based on the research
6
The results of the work will be useful in the following situations:
•
Firstly, the results of the research will be valuable for managers of oil and gas companies
across countries studied. To get an adequate return on equity and assets, as well as control
the value of the company, it is crucial to understand the drivers that contribute to the
change and take into account the models that can be used for the analysis.
•
Secondly, the results will be useful for investors and investment analytics that are making
forecasts about the companies’ performance compared to the expectations of the market.
While traditional multipliers are often used for the short-term analysis, it would be
beneficial to understand how the components of DuPont model, coupled with industryspecific factors, could contribute to the explanation of profitability and value of the
companies.
7
Chapter 1. Value-based management in oil and gas industry
During recent decades the issue of shareholder value creation has attracted a wide interest in
both academic and business world. Such authors as [Rappaport, 1986; Stewart, 1991;
McTaggert, 1994; Copeland, 1995; Weissenrieder, 1997; Arnold, 1998; Koller and Murrin, 2000;
Young and O’Byrne, 2001] have highlighted the necessity of the value-based approach for the
financial management of the companies, as well as numerous studies were performed across
various markets aimed at the identification of the main drivers of value. Much of literature
published relates to value-based management tools influencing the share price of the company, in
other words, the straightforward value creation. Another topic which is being discussed largely is
implementation of value-based management and its implications on corporate level. Third
direction of the studies conducted is devoted to the conflict between stakeholder and shareholder
view on the firm. Moreover, it has been also highlighted by several researchers that value is one
of the best performance measures as it is basically the only one that requires the analysis of
complete information, which also explains its growing popularity.
Generally, there are two main somehow contradicting concepts defining the goal of the
companies’ existence and the groups interested in their successful performance: shareholder
versus stakeholder perspective. The first implies that the value should be maximized for the
owners of the company and all the providers of equity, while the second is taking into account all
the parties that, according to the definition, affect or can be affected by the organization.
Therefore, in performance management the shareholder perspective implies mostly the usage of
indicators that would reflect the value maximization for investors, and the stakeholder
perspective requires multi-criteria metrics, such as balanced scorecard. Stakeholder approach can
be rather beneficial in certain situations, but the categories of interested parties that need to be
considered are rather blurred, as well as the interests themselves can be contradictory and hard to
weight against each other. The shareholder perspective in that sense is much clearer and
therefore has become the basis for the concept of value-based management.
Nevertheless, stakeholder perspective is connected to the paradigm shift considering
management objectives. While earlier on earnings growth was mostly seen as an ultimate goal, it
has been shown via the residual income model that constant growth does not necessarily
generate the value for the company. As Jensen [2001] states in his work, long-term value
8
maximization as an ultimate goal does not yet provide the management with a strategy to
achieve it, and here is when the stakeholder theory helps to explain the process of value creation.
1.1. Value-based management concept
The concept of value-based management implies that the main goal of the company is to
maximize the value for all the shareholders, which is the main responsibility of the management.
It has various definitions according to different researchers, but all of them concentrate on
several main aspects: value creation (strategy), managing for value (corporate governance
system, organizational structure) and company valuation. In other words, value-based
management suggests a universal metric – value – which serves as a base for all other actions of
the firm and aligns the strategic perspectives with key value drivers. When implemented
accordingly, it unites both financial and non-financial aspects of management and concentrates
on the main value drivers which should be the base for decision-making in the company.
Fundamental works on value based management include publications by [Copeland, Koller,
1994; Stewart, 1991; Rappaport, 1986]. In one of the first works devoted to the topic, Rappaport
[1986] provides a theoretical and a practical approach to value-based management. While
theoretical approach is concentrated on the review of fundamental indicators of shareholder
value creation - planning, evaluation of performance, capital market information - practical
approach is connected to its actual implementation: the author suggests using the marginal
indicators, cost of capital and growth rates in order to set the performance levels.
As identified by Copeland [1995], value-based management is the system of governance when
the aspirations of the firm, analytical tools and management processes are aimed at maximizing
the company value by concentrating on the value drivers. According to Claes [2006], the main
aspects highlighted in all the definitions of value-based management are the following. Firstly, it
is generally distinguished from traditional performance management by the usage of cost of
capital. While net profits cover only the cost of debt, value is created by including both costs of
debt and equity. Secondly, value-based management is aimed at economic value creation via the
residual income approach, which implies that value is created only when all operating costs and
cost of capital are covered. Third, it is a managerial concept, meaning that it is not limited to
calculating the value. It combines a lot of different techniques and tools which help the firm
9
achieve its goals in different organizational areas. Finally, value-based management system is
based on value drivers, involving both financial and non-financial concepts.
The crucial component of value-based management is the identification of the value drivers that
affect the company’s performance and help top-management understand the relation of their
decisions with all the levels of the organization. Value drivers can be analyzed on different levels
according to the decisions that the manager needs to make. In particular, such value drivers as
operating profit margin or sales growth can be used in the analysis of all business units but are
too general to be applied at operational level where other drivers are useful. In one of the first
articles on value-based management, Koller [1994] highlights that value based management
links value as a goal with processes and mechanisms of its implementation. In particular, he
states that value drivers can be divided into three main categories: generic (for return on
investment they consist in operating margins and invested capital), business unit (capacity
management, sales force productivity) and “grass roots” where the drivers are connected to
specific decisions of front-line managers (unit sales, accounts receivable terms and etc.).
Moreover, Koller emphasizes that scenario and sensitivity analyses are the main tools of value
drivers’ identification.
According to the [Bukhvalov, Volkov, 2005] there are two main classes of value indicators:
market and fundamental ones. According to the market approach, the value of the company is
presented as capitalization and is generated by financial market where the ownership rights are
being redistributed among different participants. As for the fundamental value of the company, it
is determined by the cash flows of the company and related to the main activities of the company
and the product market where it operates. Nevertheless, whatever type of value the company is
concentrating on, it is crucial to understand which factors are influencing the financial results
and, consequently, if there is a system that would help to identify the areas of focus.
As highlighted in [Volkov, 2006] value-based management is a system aimed at maximization of
the value created when the evaluation of business performance and remuneration are based on
the indicators of value added. The concept of value-based management unites the following
procedures and decisions: the choice of the model and procedures of identification company’s
value for shareholders; monitoring of the changes in value; identification of the drivers of new
10
value creation and the particular linkages between shareholders value and corporate businessstrategies; development of the financial strategy of the company aimed at value creation;
identification of mechanisms which would align the interests of shareholders and managers, as
well creation a system of measurement of performance.
The process of valuation itself, according to the author [Volkov, 2006], consists of three
important decisions: about the valuation model, the main performance indicators aimed at
monitoring the changes and creation of value drivers. Therefore, one of the most important
questions is the choice of a suitable valuation model and instruments that would help to make
decisions leading to shareholder value maximization on all the levels of the firm. The choice of
the valuation model is implying two important issues: if it is an adequate management tool and if
it is a reliable instrument to explain the changes in the value of the company. As for the first one,
it requires answering a number of questions considering the performance indicators used in the
model: if it reflects correctly the results of the activities of the company; if it is linked to
shareholder value creation and if it can be a base for decision-making system; how it would
solve the agency problem and align the interests of managers and shareholders; if it can be a base
for management incentives system and if it is understandable for managers and investors.
Finally, the author also highlights that the chosen indicator should provide the possibility to build
a system of value drivers upon it, as well as take into account the individual results of every
manager. Regarding the reliability of the model, it means how well the results of fundamental
valuation are linked with the actual market price. Considering performance indicators, the most
common ones such as return on equity, return on assets and the degree of financial leverage will
be discussed further in the work.
1.2. Fundamental value and residual income
One of the main instruments of identification of the value of the company in value-based
management is the calculation of fundamental value via the residual income approach. Residual
income model implies that the fundamental value of the company depends on four main factors:
amount of capital invested, actual return on equity, required return on equity and the stability of
the spread of the results (the ability of the company to achieve the return on equity which is
higher than the required one). According to [Volkov, 2006], Residual Income model includes two
11
main elements: book value of equity and discounted residual income cash flows that contribute
to the increase in the difference between fundamental and book value. Therefore, the central
element of the model is Residual Income that can be presented with the following formula
R
\ Ii = πi − k × Ii−1 (1)
Where R
\ Ii – residual income of the year i
\ i – Net income for the year i
π
k – Required return on capital
I\ i−1 - Investments in the beginning of the year
In this work, residual income will be calculated via the residual earnings of the company which
are computed by subtracting equity cost from net income while investments are understood as
book value of equity. Therefore, here the formula can be presented the following way:
\ Ei = NIi − ke × Ei−1 (2)
R
Where
\ Ei - residual earnings in the period i
R
\ i - net income in the period i
NI
k\ e
- cost of equity
\ i−1 - equity in the period i-1
E
Consequently, the value of the company from the equations above can be represented as follows,
using residual earnings as a basis
VEREM = E0 +
∞
R Ei
∑ (1 + k )i
e
i=1
(3)
Where
\VEREM – value of the company calculated via residual earnings model
\ 0
E
- equity of the company at a period zero
\ Ei - residual earnings at period i
R
k\ e
- cost of equity (return on equity)
12
The return on equity can be calculated via CAPM Model, as it was done in this work, following
the formula below:
r
\
e = rf + (rm − rf ) × β,
(4)
Where
r\ e - required return on equity
r\ f – risk-free rate (in this work computed as average return on government bonds of the
corresponding country for each year: Russian Government 10Y Bond – RUGBITR10Y; India
10Y Government Bond – GINDR10Y; China 10Y Government Bond)1.
r\ m − rf - equity risk premium of the country for each year (in this work - based on the data
provided by A. Damodaran 2)
\ – beta of the stock (measure of the stock volatility relative to the market).
β
Compared to dividend discount model and discounting of cash flows, residual income model is
distinguished by several characteristics. Firstly, residual income presents the result of investment
and operating activities of the company that create the value. Secondly, residual income, based
on accounting principles of gains and losses calculation per period, correctly measures the value
added during the period. Finally, in this model additional investments are treated not as losses
but as factors that add value. Thus, residual income creates value for the company, at the same
time being a metric of financial results. That is why it was used in this work for the calculation
of fundamental value.
1.3. Value drivers tree
One of the most common tools used for a comprehensive analysis of value drivers which affect
the value generated for shareholders and at the same time can serve as instruments for current
and strategic management is Dupont model. It was implemented in 1919 by Donaldson Brown,
finance executive of E.I. du Pont de Nemours in order to identify the drivers of change in
financial performance of the company, and has become extremely popular due to the
1
https://in.investing.com/rates-bonds
2
Equity Risk Premiums: Determinants, Estimation and Implications // New York University, Stern School of
Business
13
representative approach and clear identification of the factors influencing the value on different
levels. It is considered to be one of the clearest and universal means of identification of the value
drivers of the company, as it is the most commonly presented disaggregation scheme for ROA/
ROE. Generally, in a short form, ROE and ROA are calculated the following way
ROE
\
j =
\
ROA
j =
NIj
Ej−1
(5)
NOIj
Aj−1
(6)
Where
\ j is the net income of the company for period j,
NI
\ j−1 stands for equity of the previous period
E
\
NOI
j defines net operating income of the company for period j
and \Aj−1 – total assets for the period j-1.
Return on equity characterizes the ability of the company to pay back the investments of the
owners and the lenders. Nowadays, many companies are paying special attention to this
indicator, for example, by connecting ROE to the salaries of management. In the residual income
model it is used to identify the fundamental value of the company. Return on assets indicates the
level of net income that the assets of the company are generating, which is crucial for
understanding the short-term impacts on the value. It can be also useful in the analysis of
strategic alternatives and in particular mergers and acquisitions as it measures the influence of
the transaction on the total purchase price.
Financial leverage, which “unites” ROE and ROA, is also one of the most popular indicators that
would be used further in the study. It measures the level of financial risk of the company by
identifying the relationship between total assets and shareholders equity, indicating the amount
of equity which was used to purchase the assets. However, financial leverage is often determined
by management for several years ahead and thus is not significantly influenced by market
conditions.
14
I\ FLEV =
ROE
(7)
ROA
Dupont model for these profitability indicators can be presented as follows
\
ROE
= Oper at ing Prof it Margin × Asset T ur n over × Fin a n cial L e ver age (8)
\
ROA
= Oper at ing Prof it Margin × Asset T ur n over (9)
Or, more precisely
\
ROE
=
Oper at ing In com e
Sales
Assets
×
×
(10)
Sales
Assets
Equit y
\
ROA
=
Oper at ing In com e
Sales
×
(11)
Sales
Assets
Further, return on assets component in the model can be presented via the “lowest” level of the
indicators:
\
ROA =
COGS
G en er al ex pen ses
Ma n ager ial Ex pen ses
Ta x
EBI other
Non − cur rent Assets
Ca sh
Non − ca sh Work ing Capital
1−
−
−
−
+
÷
+
+
(12)
)
Sales
Sales
Sales
Sales
Sales ) (
Sales
Sales
Sales
(
Where Non
\
− ca sh WC = Invetor y + Accou nt Receiva ble − Accou nts Pa ya ble (13)
As it is shown in the formula above, DuPont model decomposes return on assets (sales divided
by assets) into asset turnover and profit margin. Return on equity is presented as net return on
sales multiplied by assets turnover and opposite financial leverage.
The main advantage of the Dupont Model is its simplicity as it shows how the key financial
ratios of the company are linked to financial performance. Moreover, it provides the opportunity
to see how changes in the company can affect financial results.
1.4. Application of Dupont model: Empirical studies overview
As one of the most popular instruments for identification of value drivers, DuPont model has
attracted the attention of numerous researchers. There is a number of studies for foreign markets
15
based on DuPont analysis on different levels, with the majority of them concentrated on
explanatory and predictive ability of DuPont model in terms of future earnings of the company
and the applicability of this forecast for residual income valuation.
Overall, prior studies have shown that asset turnover and profit margin hold explanatory power
with the respect to changes of profitability. For example, the study by Nissim and Penman
[2009] is based on the residual income framework where DuPont analysis is used: return on net
operating assets is decomposed into asset turnover and profit margin. This division contributes to
the significant improvement in the forecast, and has a simple explanation: while profit margin
usually represents the pricing power of the company, asset turnover is based on efficiency and
asset utilization. It is logical to expect the competitive environment of the industry to influence
those indicators differently. When the levels of profit margins are high, usually more and more
companies are attracted to the segment, which in turn causes the increase in the rivalry and
return of the profit margins to normal levels. As for asset turnover, it is less likely to change due
to shifts in competition as the efficiency of asset utilization is harder to imitate.
As for the ROE itself, which is also called an accounting rate of return, it is regarded as a
fundamental summary measure in ratio analysis. In his theoretical work, Penman [2001]
examines the role that ROE plays in pricing the stocks. According to Penman, investment
analysis includes investigating two main components: information indicating future earnings and
information implying the discounting rate of the future returns, in other words, risk. The author
states that traditionally ROE is considered as a profitability measure but, nevertheless, ROE also
reflects the expected rate of return: previous studies [Ohlson, 1900] highlight that it is related to
leverage and, consequently, to risk. That is why in his work Penman refers to ROE as an
indicator of both profitability and risk.
Before the study of Penman [2001] just a few studies were conducted on return on equity.
Several authors, for example, [Salamon, 1966; Vatter, 1966; Livingstone and Salamon, 1970]
referred to ROE as an internal rate of return, because under particular conditions it satisfies the
present value criteria of profitability analysis. However, the only work that discussed the price of
stocks in relation to return on equity in equilibrium is the one by Ohlson [1900] which describes
the relationship of book value to price of the company in terms of future earnings and served as a
base for Penman’s paper.
16
Penman [2001] highlights that financial statement analysis, therefore, is the observation of
information that would project further accounting rates of return. He also emphasizes that the
issue of projecting future ROE from current ROE relates to profitability, while the question of
how ROE corresponds to discount rate is associated with risk. In terms of the risk component,
which is usually less discussed, the study concentrates on relationship between ROE and “beta”,
the systematic risk. Results showed little relationship between the two indicators because in
relative terms risk is so small that it cannot be detected. Moreover, it is concluded that ROE by
itself is not a sufficient measure of future profitability, but in case of disaggregation it may give
better results. The study also provides evidence that ROE contributes to the explanation of
change in unrecorded goodwill: it helps to identify when earnings project higher or lower future
earnings and correlates with information other than earnings that can predict future profitability
and thus explain the returns on stocks.
The authors of [Nissim, Penman, 2001] state that the main tool for equity valuation is still
dividend discount model despite of the existence of the variety of other methods. However, they
highlight the importance of residual income method implementation and identification of ratios
which help to forecast residual income. In particular, those ratios include the components of
ROE model. The authors provide an equity valuation approach which is based on residual
income and use Dupont analysis, decomposing return on net operating assets into assets turnover
and profit margin. They highlight that while profit margin comes from “pricing power”, meaning
the product positioning, brand recognition and market niche, as well as measures the ability to
control the costs, assets turnover is about asset efficiency and utilization (efficient usage of
PP&E, inventory processing and other forms of working capital management).
The study by [Lipe, 1986] aimed at investigating the explanatory power of the components of
accounting earnings over the stock returns lead to a positive outcome. It was found that the main
components investigated - gross profit, general and administrative expenses, interest and
depreciation expenses, income taxes – have huge explanatory power over stocks and all provide
different pieces of information to the market. Two main conclusions were drawn considering the
practical implementation. Firstly, it is stated that the earnings reported do not provide a full
summary of accounting information as some information is lost when the components are
aggregated into earnings. Secondly, the results prove that additional information is consistent
with the rational reaction of market participants to time-series characteristics of the components.
17
Thus, the authors highlight that the results show how valuation theories can be implemented to
develop stronger empirical tests of relations between returns and accounting information and the
methodology created can be used in studies with different decomposition of earnings.
Selling and Stickney [1989] also examined ROA, profit margins and asset turnovers of firms in
22 industries from 1977 to 1986 to find out the effects of business environment and strategy on
firm's rate of return on assets. The results of regression analysis revealed that industries with
high operating leverage and huge entry barriers were characterized by lowest asset turnovers and
highest profit margins, while the opposite was true for industries with commodity-like products
and low capital intensity (fixed assets divided by total assets gives a small ratio).
The results of the study [Fairfield, Sweeney, Yohn, 1996], conducted on a sample of 33 334 firmyear observations from 1973 to 1990 with the purpose to find out the influence of accounting
classification on predictive content of earnings, suggest that disaggregation of earnings (into
operating earnings, non-operating earnings and taxes and special items) helps to increase the
quality of ROE forecasts one year ahead. According to the authors, operating earnings should be
given the highest weights, followed by non-operating earnings. Moreover, they highlight that
further insights into earnings classification such as analysis of separate industries can highly
contribute to the results.
Thus, in the study by [Chang et al., 2014], DuPont analysis of value drivers in health care
industry is performed for the period from 1987 to 2010 with 1211 firm-year observations. As a
result of the analysis, a negative correlation has been revealed between PM and ATO, as well as
in the previous studies. However, unlike the studies conducted before, a positive correlation was
revealed between RNOA and PM, as well as between RNOA and ATO in one the cases
investigated (and in the other one the correlation was not significant). Therefore, the assumption
that PM has more influence of RNOA that ATO was validated. It was an expected result because
of the specifics of the industry. Another industry-specific study - performed for manufacturing
and retail sectors in USA and Japan by [Herrman, Inoue, Thomas, 2000] - also proved that the
accuracy of earnings forecast increases with larger disaggregation of earnings components (sales,
cost of goods sold, selling, general and administrative expenses). Moreover, the study revealed
that earnings disaggregation leads to more significant improvements in the accuracy of the
18
forecast for American firms than for Japanese ones because the reporting guidelines are more
detailed in USA and the emphasis on the issue itself is larger there than in Japan.
The research by [Kim, Kross, 2005] has revealed that the predictive ability of earnings is also
increasing over time, which justifies forecasting of future operating cash flows based on current
earnings. It contradicts the results of previous studies which generally lead to a conclusion that
the value relevance of earnings is diminishing over time. In the study performed by [Herciu,
Belascu, Ogrean, 2010] the authors investigate ROE, ROA and ROS for 20 most profitable
companies in the world. They highlight that the absolute terms are often not relevant for the
comparison of several companies, that is why introduction of relative size and efficiency
measures are needed to draw further conclusions.
However, not all the researchers share the same point of view on the role of earnings
decomposition in predicting future returns. For example, [Fairfield, Lombardi, 2001] argue that
there is no evidence proving the usefulness of Dupont model in explanation and forecasting.
Their study reveals that Dupont model does not give sufficient information for forecasting the
change in ROA one year ahead. Nevertheless, it proves that the disaggregation of the change in
ROA into change in PM and ATO still helps to make the forecast about the change in ROA in
one year. Moreover, the authors conclude that the while there is correlation between the change
in ATO and change in ROA, change in PM and change in ROA are not correlated.
According to the results of the work devoted to investigation of industry-specific influence on
profitability by [Fairfield, Ramnath, Yohn, 2009], industry-specific models are in general more
accurate in predicting company growth (especially in terms of sales) but not profitability.
Authors highlight that although industry has a certain impact on firm operations and
performance (demand, business risk, entry barriers), factors within industry itself are more likely
to influence the profitability of the companies. However, they say that industry-level analysis is
still useful in improving profitability predictions when it comes to industries which are less
dependent on overall economic forces, for example regulated industries, those with high entry
barriers and characterized by a wide presence of large companies.
19
Mark Soliman in his study [Soliman, 2007] also reveals that the change in asset turnover is
significant to explain future changes in return on net operating assets (after controlling for
fundamental signals and variables in extended accrual decomposition). First, the author shows
that the information in ROA is a significant accounting signal. Second, the study involves testing
of the immediate and delayed responses of analysts and the forecast errors based on ROA, as
well the study of Dupont analysis usage by stock market participants. The results of the study
[Li, Nissim, Penman, 2014] as well show that DuPont model is helpful in forecasting the
variance in future growth rates of operating profits and variance of stock returns. Furthermore,
the authors state that DuPont model is useful in explaining the implied volatility in option prices.
One of the latest studies on the topic [Berezinets, Udovichenko, Devkin, 2016] is aimed at
identifying whether applying Dupont model would benefit the forecasting of the profitability of
Russian companies. To distinguish operational and financial activities of the companies, return
on net operating assets was forecasted. The methodology of the study is based on econometric
models where RNOA was split into DuPont components. Apart from splitting RNOA into profit
margin and return on assets, the model takes into consideration industry component and specific
component for the company where industry influence is eliminated. The study was conducted on
a sample of 518 Russian companies from eight industries (5019 firm-years). Authors highlight
that the main patterns in behavior of profit margin and return on assets were revealed, which can
be used in accessing the investment attractiveness of the companies, as well implementation of
value-based management techniques in the company. In particular, conclusion is made that when
the industry requires a high level of capital expenditures, it is more likely that the high levels of
return on assets would be achieved via high profit margin, whereas in the industries which do not
require significant capital investments assets turnover plays a bigger role. In general, every
industry can be characterized by a certain combination of assets turnover and profit margin
which mean revert. If there are any deviations in some companies initiated by managerial
decisions, they would also come to median industry levels. Nevertheless, in every case it can be
different, sometimes due to manipulations with “special items” and etc., especially in Russian
practice.
Therefore, based on the studies presented, it can be concluded that DuPont model components
disaggregation is generally a key technique for the identification of profitability and value
20
drivers of companies and, coupled with industry-specific analysis, can provide managers,
investors and analysts with insights for performance evaluation.
1.5. Oil and gas companies in Russia, India and China
The main differentiating feature of oil and gas industry globally is the pricing mechanism: prices
are not set by the companies but are determined by a variety of factors: supply and demand for
the resources on regional and global markets, amounts of reserves and production rates,
investment levels, expected growth rates of economy, geopolitical situation, development of new
technologies and alternative energy sources, government regulations and transportation prices.
Taking into account the strategic importance of the industry in India, China and Russia, as well
as the countries’ significant economic growth which presents enormous opportunities and
challenges for oil and gas companies, those three countries were chosen for the analysis. Now
Russia, India and China are among the first five countries ranked by oil consumption: China
occupies the second place after USA with 10.12 million barrels daily, India is on the fourth place
with 3.51 million barrels, following Japan, and Russia is the fifth with 3.32 million barrels. The
nature of the activities of oil and gas companies is though very different: Russia is the second
biggest exporter, while China and India are second and third biggest importers3.
Considering the situation in the industry during the last three years, 2015 brought the largest
changes. Overall, the growth of global economy has decreased by 2.4%, equal to 1.6% for
developed and 4.3% for developing economies4. The growth rates of the world demand for oil
have continued to decrease, and amounted to 1.5%, mainly due to the decrease in China where
economy has shown the lowest growth during the recent decade (6.9%). The fall of global oil
demand, coupled with the highest extraction volume (3.9 billion tons), has lead to extreme
overbalance of supply and demand, increased competition for export markets and the dramatic
drop in oil prices that have reached the minimal levels for the last ten years. Moreover, the
Organization of Petroleum Exporting Countries decided not to lower the extraction levels in
order to keep the market shares. As a result, it also has reached the highest extraction level (31.7
million barrels). However, while 2015 saw a dramatic fall in oil prices, in 2016 situation has
changed: for the first time since 2008 OPEC countries have established an agreement to lower
3
http://www.globalfirepower.com/
4
Surgutneftegas - Annual Report 2015
21
the oil extraction by 1.2 million barrels daily – to 32.5 million, also negotiating with non-OPEC
countries to lower the production. All this led to an increase in Brent price up to 57 dollars per
barrel5. The impact of all those changes on the countries of the analysis will be discussed further.
Russia
It is widely known that oil and gas industry in Russia plays a crucial role in the strategic
development of the country: natural resources significantly exceed other elements in the trade
balance of the country. Despite the difficulties of 2015, which resulted in devaluation of national
currency and fall in investment activity, the upstream sector has shown significant growth during
the last year: extraction has reached the historical maximum due to opening up of new wells and
increase in extraction coefficient. The majority of oil and gas companies have kept positive
extraction dynamics, with capital expenditures growth of almost 10%, with proved reserves
surplus in regions of Eastern Siberia, Krasnoyarsk region and Far East. According to the
Ministry of Energetics, oil extraction in Russia amounted to 547.5 millions of tons in 2016 due
to high investments during the last years, with export equal to 254.2 millions of tons, thus
making Russia the world leader in oil extraction. Gas extraction amounted to 640 007 billions of
cubic meters6 : total extraction lowered slightly due to the decrease by the main producer –
Gazprom, but oil producers, on the opposite, have increased their gas extraction and production
rates. Moreover, the significant change in ruble-USD exchange rate in 2015 allowed the
exporters to increase the revenues in rubles and decrease the costs in USD which to some extent
was regarded as a positive change for the companies, especially major exporters.
Traditionally, the main volume of oil extraction in Russia is attributed to vertically integrated oil
corporations. In 2015, the half of those companies has shown a slight decrease in the extraction,
while independent producers have increased the production on average by 10%. Moreover, the
extraction in Western Siberia, the main extraction region, is getting lower as the maintenance of
the old fields is becoming more and more costly. Therefore, the new centers of oil extraction are
establishing strong positions: in particular, Eastern Siberia and Far East.
5
https://lenta.ru/news/2017/01/02/oilproduction/
6
https://minenergo.gov.ru/activity/statistic
22
As for natural gas extraction and production, the well-known leader here is Gazprom: the
company strongly occupies the first position by the amount of gas reserves in the world with
11% share of total global extraction and 66% share of extraction in Russia. Inside the country,
the gas is supplied via regulated and unregulated pricing scheme, with Gazprom dominating in
the first one. Other major gas producers include Novatek, Yatek, Sibur (the last one engaged only
in gas processing and transportation), as well as oil and gas companies mentioned above.
Considering downstream activities, here the situation is very different. While Russia has always
been one of the main oil and gas exporters, oil refining has not been developing as fast. During
the recent years, however, more accent has been put on the improvement of current refining
facilities, which total output in 2015 was equal to 249 millions of tons. The processing depth has
also been increasing due to modernization of refining capacities. In 2011, Russian government
encouraged the upgrade of the refining systems by imposing a tax burden on lower quality oil
products which boosted the changes. However, in 2014 the focus again has been switched back
to upstream activities7. Moreover, the tax reform in oil and gas industry in 2015 has for now
significantly reduced the attractiveness of refining compared to export.
Coming back to the growth in upstream sector despite the unfavorable economic situation, it has
been possible due to several reasons. Firstly, capital expenditures in the industry have dropped
by more than 25% exactly because many projects in downstream were delayed, as mentioned
earlier. Moreover, the falling ruble to USD exchange rate has brought benefits to oil exporters
from Russia: revenues in rubles increased and the cost base in dollars almost halved. As a result,
in USD terms operating cash flows of the companies during the three recent years have exceeded
total capital expenditure. The only exception was 2013 due to the purchase of TNK-BP by
Rosneft8. Thus, it can be concluded that Russian oil industry managed to “self-fund” itself
despite the sanctions. Another aspect to be taken into account is the taxation system. Basically,
there are two main types of taxes which significantly influence the financial results of the
companies: Mineral Extraction Tax and Export Duty that are charged at a marginal rate of around
90% but depend on the oil price and have a sliding scale. The rebalancing of the system also took
7
https://www.oxfordenergy.org/wpcms/wp-content/uploads/2017/02/Russian-Oil-Production-Outlook-to-2020OIES-Energy-Insight.pdf
8
https://www.oxfordenergy.org/wpcms/wp-content/uploads/2017/02/Russian-Oil-Production-Outlook-to-2020OIES-Energy-Insight.pdf
23
place with the “Tax Maneuver” reform which lowered the export duty and increased MET, but
the central idea has not changed: companies are more protected when the oil price falls due to
marginal tax rate and sliding scale, and their cash flows are changing much less than the
revenues of the government. Therefore, the favorable taxation system combined with decreased
costs in dollars leads to a low breakeven price for Russian oil companies – around 10$ per barrel
for major fields, allowing to invest high ruble amounts to maintain the output.
Of course, with all the benefits and problems that the fall in oil prices brought to companies, for
state budget it cannot be called favorable: before 2014 the share of the government revenues
attributed to oil and gas was equal to 50%, while in 2012 it fell to 36%. In addition to this, new
sanctions from the West (mainly US) concerning oil and gas sector have brought additional
challenges for the companies in terms of development of projects in partnerships with American
colleagues. Nevertheless, according to the analysts, so far Russian companies have managed to
realize planned investments and open new projects without being dependent on foreign partners9.
For sure, with all the economic and political events that crucially affect the industry such factors
as worsening of the resource base or the drop in the economic effectiveness of the projects also
need to be taken into consideration. Therefore, from oil and gas companies it requires new
strategies to keep the performance levels high. Here one of the key roles is attributed to
successful cost management which would allow companies to stay profitable and sound both in
short- and long-term perspective. As stated in the annual report of Surgutneftegas, [2015], “in
order to ensure the economic effectiveness of oil and gas extraction operations special attention
is paid to cost control”10. The profitability of the industry is significantly pressured, and with the
existing taxation system the main tool for keeping it up with the required levels is to lower the
operating expenses of the company. Thus, now one of the main elements of cost control
programs in oil and gas is import substitution that gives the companies an opportunity to
decrease costs by contributing to the development of Russian economy. As the company
mentions, “we cannot control oil prices, but we can focus on the areas that depend only on us:
increasing the quality and safety of operations, decreasing the costs and lifting the productivity”.
Basically, to some extent it defines the focus of this study too: companies cannot fully predict
9
https://www.ft.com/content/cca94692-2061-11e7-a454-ab04428977f9
10
Surgutneftegas Annual Report 2016
24
how oil prices will change but should be aware of the most influential factors that they can
control in order to develop the strategies accordingly and maintain strong positions without
being fatally affected by unfavorable global economic factors.
India
As for India, it is now the third largest oil consumer in the world as well as one of the largest
exporters of refined products and importers of crude oil. From 1990s, the average growth rate
India’s economy has constituted around 6.5% yearly, second only China among large developing
economies and 2.5 times more than the global average rate. On PPP basis, it has now become the
third largest world economy, which alone contributed more than 9% of global economic output
from 1990s11. Therefore, the demand for energy in the country is creating significant
opportunities and challenges for oil and gas companies. According to International Energy
Agency, India has all the potential to soon overtake China as the principal growth driver in oil
demand across the globe: three years in a row it outpaces China by the oil demand growth. The
country is now becoming one of the main players in global energy market and has all the
potential to replace Russia as the third largest oil refiner by early 2020s.12 Exports of crude and
petroleum products contribute around 19% to total exports of the country13. Now Indian
government owns major stakes in eight largest oil and gas companies of the country, and a large
merger of several state-controlled companies is planned in order to become more successful in
the international competition 14.
As highlighted in the report of BPCL, reliable energy supplies represent a pillar of development
of Indian economy. Rapid growth, experienced now by the country, coupled with fast
industrialization, increasing population and, in particular, growing automobile sector, is implying
an additional challenge for the country that possesses only 3% of oil reserves and represents
17.5% of the world’s population. Therefore, Indian government has implemented several
measures to enhance the development of the industry. Thus, foreign direct investments in
11
India Energy Outlook, World Energy Outlook Special Report 2015
12
http://www.hindustantimes.com/business-news/india-to-replace-russia-as-3rd-largest-oil-refiner-iea/storyp0AG87DyjZCHdwXXy51oKP.html
13
BPCL annual report, 2015
14
https://www.ft.com/content/18d1fd5a-e914-11e6-893c-082c54a7f539
25
refining sector are now encouraged by permitting a 49% share in refining companies. Reforms
are being discussed for upstream sector as well. In addition, in 2014 a large initiative “Make in
India” was announced, aimed at increasing the share of manufacturing GDP up to 25% until
2022. Energy companies, especially oil and gas, were identified as one of the main areas of
change15.
The amount of the resources that India possesses is relatively small, with the majority of oil
reserves located in the western part. So far, the upstream sector has been underperforming
despite the efforts to open it up for private investors: the regulatory requirements still present a
significant obstacle, coupled with the uncertainty about the amount of reserves available and
their correct appraisal. Moreover, upstream is dominated by several state-owned companies:
ONGC and OIL together produce around two-thirds of the crude oil, and the remaining output
can be attributed to joint ventures with national companies. However, considering the instability
in the supplies of some of its major partners such as Iran, Libya or Nigeria, in March 2015 the
government has announced a strategic aim to decrease the reliance on the imports of crude oil by
10% by 2020 16. It does not only concern the development of production, but also enlargement of
storage facilities. In particular, Strategic Crude Oil Storage has been opened recently in order to
be able to store more oil bought in times of low prices.
In contrast, the refining sector has been strengthening significantly: the refining capacities have
almost doubled during the last decade. During the recent several years, India has significantly
expanded its refining capacities, and Indian companies are now becoming more and more
competitive, making the country one of largest centers for petroleum refining. Compared to
upstream, the majority of the companies are private, including such large players as Reliance
Industries or Essar. The refinery assets of the country include the largest refinery in the world,
Jamnagar complex which capacity amounts to 1.2 million barrels of throughput daily, exceeding
India’s domestic crude oil production as well as domestic demand, making it a net exporter of
refined products. The exports are mainly coming from private companies, while national
companies satisfy the domestic demand for refined products. As a result, India’s modern and
highly efficient refineries have been capable of taking the market share from the ones in Europe
15
http://www.makeinindia.com/about
16
https://www.iea.org/publications/freepublications/publication/IndiaEnergyOutlook_WEO2015.pdf
26
and Japan. While production of oil in the country amounted to only to 41 millions of tons in
2015, the total output of refining capacities was equal to 223.3 millions of tons and is expected
to grow further due to the launch of a new Paradip Refinery. Moreover, the project of “West
Coast Refinery” by public sector oil companies (IOCL, BPCL, HPCL, EIL) is now widely
discussed. If implemented, it will become the largest refinery and petrochemical complex in
India.17
The drop in crude prices, coupled with huge dependence of the country on crude oil imports
(estimated 84%) and diesel marketing decontrol, has also resulted in general concerns about
petroleum prices and demanded additional costs constraints18. “With a significant increase in
drilling, service, production and operating costs due to the increased complexities faced by the
sector, cost management has assumed center stage for most companies”19. Therefore, the
attention has been paid to optimization of the processes and the establishment of strong controls
- for example, “leveraging the technology”, outsourcing and introducing shared services. “Oil
companies have little choice but to address the vital existential issues of how to efficiently
manage business in the increasingly carbon-constrained environment” 20.
Natural gas presents only 6% of the energy mix in India. The biggest player in midstream and
downstream gas market is a state-owned company GAIL India, followed by private oil giants
Reliance and Essar starting to pave their way in the sector. In early 2000s, a lot of expectations
have been put on the development of the industry because of large discoveries, but for now not
much is going on due to several constraints, mainly subsurface complexity of opened offshore
fields and low prices for domestic producers.
China
China is the sixth biggest producer and second biggest oil consumer in the world, primarily due
to rising demand for gasoline which is replacing diesel fuel21. However, despite the high oil
17
BP Annual Statistics, 2016
18
Indian Oil Annual Report 2015-2016, p.5
19
BPCL, 2014
20
BPCL, 2014-2013
21
http://www.bakerinstitute.org/media/files/files/e0b5a496/WorkingPaper-ChinaOil-093016.pdf
27
demand, among large international oil and gas players Chinese companies do not have much
influence. Being a major producer of crude oil, the country has become a large oil importer
during the recent decades due to increasing demand as production rates have been falling despite
the significant increase in capital expenditures since 200022. Nevertheless, with the appearance
of the three big state-owned giants – China National Petroleum Corporation (CNPC), Sinopec
and China National Offshore Corporation (CNOOC) – Chinese oil and gas market saw
significant restructuring and is considered to be one of the most promising for foreign investment
attraction. Refinery output in China amounted to 460 millions of tons in 2015 and is also
expected to grow 23. Overall, until 2020, the country is aiming to increase the output of crude oil
to 200 million tones and capacity for natural gas to 360 billion cubic meters. 24
As for demand for gas, it slowed down significantly because of uncompetitive gas prices and
economic downturns. Gas consumption equaled 191 billion cubic meters, 33% of which
imported to the country. In 10 years, its share in China’s energy mix is projected to grow from
5% to 8%. Special attention is paid to shale gas which production is also expected to grow
significantly, making China second largest shale gas producer after US in the long run (in 2035,
according to BP Energy Outlook 2017). Nevertheless, the projections of the exact growth rates
have been lowered during the last two years25.
From 2011 to 2015, oil and gas reserves in China have been steadily growing. Although the
upstream investments were declining, significant discoveries were made, adding 1 billion tons of
oil and 1 trillion cubic meters of gas to the country reserves base. Even though the rising prices
can have a negative impact on the growth of the reserves amount, it is not expected to be
significant since the strategic reserves, which constitute the principal amount of all the reserves
of the country, are so far the area of responsibility of the government26.
22
https://www.platts.com/latest-news/oil/singapore/outlook-2017-changes-for-chinas-oil-sector-in-27741677
23
BP annual statistics
24
http://www.chinadaily.com.cn/china/2017-05/22/content_29437500.htm
25
http://www.bp.com/content/dam/bp/pdf/energy-economics/energy-outlook-2016/bp-energy-outlook-2016country-insights-china.pdf
26
https://www.platts.com/latest-news/oil/singapore/outlook-2017-changes-for-chinas-oil-sector-in-27741677
28
On 21 May 2015, the reform was announced by Chinese government, aimed at increasing the
shares of private ownership in state-owned companies. It is a key element of Five-Year Plan for
2016-2020. The principal focus is put on enhancing the diversification of shareholder base and
deepening the mixed-ownership reform. Moreover, attention is paid to the specialization of the
companies: engineering enterprises as well as oil and gas equipment producers are to act as
independent companies. The three “giants’ of the industry were long been accused of
monopolizing the market and being inefficient, so the reform is supposed to change the situation
and lead to market-based pricing mechanism with government stepping in only in case of
abnormal fluctuations. Large players have already started the cooperation with private
companies. In particular, Sinopec is planning to establish partnerships in refining, while CNPC
announced that it would allow private companies to hold stakes in oil exploration business, but
no more than 49 percent 27.
What is more, in summer 2015 an import quota of about 80 million tons was given to several
“teapot” refineries in Shandong which was open only for national oil companies before, and 49
million were actually imported 28. Consequently, around 90% of oil import growth in China in the
first half of 2016 can be attributed to teapot refineries 29. However, with recently introduced
stricter tax regulations and increased complexity of quota application procedure the situation
may become unfavorable for the independent refineries again. In addition to this, in 2017 the
government withdrew the permission for export from independent refineries which can hinder
their investment plans.
1.6. Specific characteristics of value creation in oil and gas
As mentioned in [Rogova, 2014] one of the most important problems for oil and gas companies
is their profitability and investment appeal growth because of the specifics of their business.
Firstly, fixed assets constitute a large part of the assets of the companies. It implies that the entry
27
http://www.chinadaily.com.cn/china/2017-05/22/content_29437500.htm
28
https://www.platts.com/latest-news/oil/singapore/outlook-2017-changes-for-chinas-oil-sector-in-27741677
29
http://www.reuters.com/article/us-china-economy-trade-crude-idUSKBN1570VJ
29
barriers of the industry are extremely high and the market shares tend to stay constant throughout
the years, as well a high level of capital expenditures that is spent each year to maintain the
facilities. Then, the prices are highly dependent on natural extracting conditions and market
prices basically meaning that no matter how hard a company tries to achieve certain performance
level it always can suffer largely from unexpected changes in the market or unfavorable nature
conditions.
Considering particular value drivers, oil and gas companies have been characterized by
relatively high profit margin and it is as well one of the key indicators that companies highlight
in the reports while assets turnover generally does not have such an attention. However, recent
decade saw several crises when operating profit margins for many companies dropped
significantly. Therefore, the importance of assets turnover needs to be highlighted as well a
crucial metric for the companies which are highly dependent on the assets and how effectively
they generate the profit. For example, in the study by Rogova [2014], asset turnover component
was significant in its relationship with return on equity, opposite to operating profit margin
which showed an insignificant coefficient. As for financial leverage, it is has been generally low
for oil and gas companies since they used to have enough funds to maintain their operating and
investment activities - though crisis changed the picture largely, plus the situation is different for
developed and emerging markets. Moreover, high volatility in oil prices and consideration of
additional risks incurred with loans also make companies keep financial leverage low.
Considering the studies of oil and gas industry which are especially close to this work, here two
somehow complementing works can be mentioned. First, the study by Rogova [2014] which was
already cited above was based on the companies from “Energy Intelligence Top 100” rating,
2008-2012 and aimed at demonstrating the impact of DuPont components on return on equity
and, thus, investment appeal of the companies. As a result, 4 components turned out to be
significant: earnings before interest and taxes margin, interest burden, tax burden and assets
turnover. Thus, it is highlighted that those factors should be especially considered by the
managers of oil extracting companies in order to increase ROE and draw investors’ attention, and
by investors who can evaluate the companies based not only on ROE itself but the factors
revealed. The author also states that it would be interesting to create a comprehensive valuation
model based on DuPont components and other efficiency ratios.
30
Concerning the “other” side of oil and gas companies performance evaluation, namely industryspecific factors such as amounts of reserves and extraction, the study by [Ewing, Thompson,
2016] was aimed at identifying the role of proved reserves and production in market
capitalization of oil and gas exploration and production companies. The results of the research
revealed that there is an “optimal tradeoff between current and future production” given current
volume of reserves which are positively valued by the market. The findings also prove the
importance of capital structure in capital intensive oil and gas industry, and are industry-unique,
being useful for executives of oil and gas companies as well as investors.
Therefore, the analysis of previous studies and the market situation in oil and gas industry shows
that DuPont model disaggregation can be a valuable tool for the investigation of the reasons for
profitability and value changes of the companies, but alone it does not provide the full picture.
That is why DuPont components combination with industry-specific factors in oil and gas is
suggested for more precise identification of value drivers.
31
Chapter 2. Empirical Study
2.1. Methodology
As it was stated in the objectives of the work, the relationship of the industry-specific drivers
with the profitability and value of the companies are to be identified in the following models.
Return on equity and return on assets of the company were taken as the measures of profitability,
while fundamental value and market value were identified as the main value indicators based on
the previous research. As for independent variables, for ROA and ROE those were primarily
operating profit margin, assets turnover and leverage as first level components of Dupont Model,
as well as exploration expenses, amount of reserves, extraction and production, share of
government ownership and export sales. EBITDA and exploration expenses per barrel were not
included in the models since the calculation of ROA already includes operating profit, while
calculation of ROE – net income, which would automatically result in significant coefficients but
would not add much value to the analysis. Those factors were included further, to market
capitalization, residual income and fundamental value models.
The following factors were chosen to be used for the modeling. For all the indicators initially
presented in absolute values – fundamental and market value, amounts of reserves and
production, exploration expenses – natural logarithms were taken to be included in the models.
The correlations between the variables were also checked so that there is no correlation higher
than 50% for the variables included in one model.
Table 1. Variables description
ROA
Return on Assets = Operating Income/ Total Assets
ROE
Return on Equity = Net Income/ Equity
Fundvalue
Fundamental Value of the company
Marcap
Market capitalization of the company
Oilres
Amount of proved oil reserves in barrels of oil equivalent
Gasres
Amount of proved gas reserves in cubic meters
Restot
Amount of total reserves in tons
Oilprod
Amount of oil produced (extracted) during the year, in barrels of oil equivalent
Gasprod
Amount of gas produced during the year, in cubic meters
Prodtot
Amount of total production during the year, in tons
32
Oilref
Amount of oil refined during the year, in barrels of oil equivalent
Explorexp
Exploration expenses incurred by the company during the year, thousands of
US dollars
Ebbarprod
EBITDA divided by the amount of barrels of oil produced
Explorexpbar Exploration expenses per barrel produced
Government
Share of government ownership in the ownership of the company
Exportshare
Share of foreign sales in total sales of the company
Crisis
Dummy variable, attributed to years 2014-2015
Control variables included logarithms of the revenue of the company and total assets. However,
since the reserves are already somehow the measure of the size and assets of the company, they
can be themselves treated as control mechanisms of the models and therefore it was not
necessary to include size and/ or total assets together with them. Initially, it was planned to
include such variables as annual reserves surplus or growth of extraction/ refining amounts, but
those values were often negative and logarithms could not be taken.
The models were constructed for total amounts of reserves and production (with barrels and
cubic meters converted into metric tons) as well as separately for oil and gas components of
business, taking into account the reserves, extraction and refining of oil/ amounts of gas reserves
and production. However, since the models built separately for oil and gas components still
referred to the same companies and gave generally the same results as the models including total
amounts of reserves and production, they are not presented as separate ones here – the results
can be found in the Appendix 3.
Table 2. Models tested
D e p e n d e n t
Independent variables
variable
Profitability
!
ROA
it
\α + β × OPM + β × ATO + β × L E VER AGE + β × r est ot + β × pr o d t ot + β × ex plorex p + β × gover n m ent + β × ex por tsh are + β × si ze + υ
1
it
2
it
3
it
4
it
5
it
6
it
7
it
8
it
9
it
it
ROE
\
it
Value
33
Fun
d va l ueit
\
α
\ + β1 × OPMit + β2 × ATOit + β3 × L E VER AGEit + β4 × r est otit
\
Ma
r ca pit
\
+ β
× pr o d t otit + β6 × ex plorex pit + β7 × gover n m entit + β8 ×
5
ex
\ por tsh areit + β9 × si zeit + υit
Fun
d va l ueit
\
\
Ma
r ca pit
α
\ + β1 × OPMit + β2 × ATOit + β3 × L E VER AGEit + β4 ×
\
ebba
r pr o dit + β5 × ebba r r e fit + β6 × si zeit + β7 × r est otit +
\ 8 × gover n m entit + β9 × ex por tsh areit + υit
β
Fundamental value computation
As for calculation of residual income and fundamental value of the companies, they were
computed according to the formulas mentioned in the first chapter. In order to forecast residual
income for five years ahead for every of the companies, the corresponding values of each
element of the income statement was forecasted according to its historical growth during the last
3-5 years: revenue, cost of goods sold, selling, general and administrative expenses,
depreciation, interest income/expense, in some cases research and development expenses. In
cases of a growth which was too high compared to other historical periods due to some one-time
events, the forecast rate was adjusted accordingly and thus the influence was mitigated.
In the following tables, risk-free rates and equity premiums used are presented:
Table 3. Risk-free rates 2011-2015 for Russia, India and China
Russia
India
China
2011
7.87%
8.68%
3.89%
2012
7.86%
8.27%
3.47%
2013
7.19%
8.14%
3.83%
2014
9.39%
8.58%
4.17%
2015
11.25%
7.76%
3.40%
Table 4. Equity risk premiums 2011-2015 for Russia, India and China
Russia
India
China
2011
8.80%
6.56%
7.60%
2012
7.49%
6.10%
6.75%
34
2013
7.34%
5.72%
7.81%
2014
7.37%
6.25%
9.61%
2015
9.56%
9.11%
6.71%
Beta was computed for each company for every year as the standard deviation of the company’s
stock return divided by the standard deviation of the index of the corresponding stock exchange
(New York Stock Exchange or Shanghai/ Bombei/ Moscow Stock Exchange) and multiplied by
the correlation of these changes. Then, with the cost of capital obtained, terminal value and
fundamental value were calculated for each year and compared with market value. Generally,
Indian companies were undervalued in the beginning – 2011-2012, and overvalued in the end –
2014-2015, while Chinese companies seem mostly undervalued during the period of study, and
for Russian companies no clear tendency can be traced: depends on the year and the company.
2.2. Data
As mentioned earlier, the study is based on 24 companies engaged in oil and gas sector, from
year 2011 to 2015, 120 observations. Initially, the sample was supposed to be larger and include
all the companies presented in DataStream database, namely 120 companies. Nevertheless, after
a more precise look it was identified that the half of those companies belong to oil transportation
industry as well as the production of equipment for oil and gas companies. It was decided to
eliminate them from the research since the nature of their operations is rather different and the
comparison with oil and gas producers would not add any value to the study. Secondly, the
companies left included both the “main” market players and a lot of their subsidiaries, which
often did not have all the data needed available in public sources. Since leaving both would have
been incorrect for the analysis, small subsidiaries were excluded as well.
The names of the companies as well as the quantity was checked with stock exchanges where the
shares of the companies are traded in order to make sure that DataStream data includes all the
available companies and that nothing was missed. Therefore, the final number of companies
allowed conducting a more thorough analysis and examining different data points that would
take too long to collect if the number of the observations was much larger. While all the financial
data was extracted from Thompson Reuters Database, the majority of non-financial factors such
as reserve base, production levels, ownership structure were found in the annual reports of the
companies since in most cases they are not available in the databases. In Appendix 1, a short
35
description of every company included in the sample is presented: 10 Indian companies, 3
Chinese and 11 Russian. Among Indian companies 6 are vertically integrated, 2 are engaged in
upstream and 2 in downstream, among Russian – 9 vertically-integrated and 2 downstream, and
all 3 Chinese companies are vertically integrated as well. It would have been beneficial for the
study to include not only the largest Chinese players but also the smaller refineries that are much
more successful in downstream segment, but unfortunately the data could not be found in open
sources.
The descriptive statistics of the variables is presented in the table below. The statistics for each of
the countries separately is presented in the Appendix 2.
Table 5. Descriptive statistics
Minimum
Maximum
Mean
S t a n d a r d
deviation
ROA
-8.046
0.516
-0.004
0.808
ROE
-4.514
0.617
0.034
0.620
OPM
-0.23
0.57
0.17
0.19
AT
0.23
1.91
0.74
0.55
Leverage
1.13
3.60
1.88
0.73
Resinc, th USD
-14 443 132
16 010 313
849 589
4 262 770
Fundvalue, th USD
-16 520
1 116 164 431 126 921 257
239 945 223
Marcap, th USD
40 550.75
318 869 900
41 801 560
61 502 080
TA, th USD
23 759
408 515 300
66 139 950
10 050 300
Oilres, mln barrels
0.04
125 607
12 085
29 440
Gasres, mln cubmeters
1.992
23 705 000
2 333 837
5 973 279
Restot, mln tons
5.551
57 130 390
4 219 177
1 267 989
Oilprod, mln barrels
0.007
1478
218
322
Gasprod, mln cubmeters
0.648
513 200
51 435
118 588
Prodtot, mln tons
0.023
1 236 844
82 669
240 142
Oilref, mln barrels
5
713
255
195
36
Explorexp, th USD
1 343
218 357 100
4 596 114
245 874 800
Ebbarprod, USD
-5 266
147
-62
614
Explorexpbar, USD
0.022
1 619
58
234
Government
0
1
0.25
0.35
Exportshare
0
0.86
0.281
0.3
In order to make to the scale of the variables comparable, logarithms of all the specific factors
such as reserves, production and refining were taken, as well as the revenue, total assets, residual
income, fundamental value, market capitalization. As for EBITDA per barrel produced or
refined, for the companies which are engaged in different segments (vertically integrated
companies which represent two thirds of the sample) revenue structure was found with the
percentages of each segment contribution: thus, EBITDA was multiplied by the percentage of
exploration and production/ refining in total sales of the company and only then divided by the
amount of barrels produced and refined, in order to take into account only those activities but not
marketing or transportation.
First of all, ROA and ROE showed the following traits throughout the period examined. As for
ROA, its average value during the period is equal to -0.004. However, it was influenced a lot by
the negative value for Hindustan Oil in 2015 - -8.046: if to exclude the company from the
sample, the average ROA would have been equal to 0.096. The maximum average value among
companies is 0.26 belonging to gas company Novatek, followed by Basneft (0.18). Similar
situation characterizes ROE: average value is equal to 0.028 due to extremely low values
generated by Hindustan Oil, NNK Khabarovsky and Orsknefteorgsintez. When those values are
excluded, the mean is equal to 0.14, maximum average ROE equals 0.29 for Novatek, followed
by Yatek, Bashneft and others. 5Y average ROA and ROE for all of the companies in the sample
are presented in the Appendix 2. The charts below represent annual average ROA and ROE for
the three countries examined with the largest outliers excluded from the calculations. Decreasing
trend can be observed for both cases during the period studied. However, a decrease of around
10% characterizes all three countries studied, while the decrease in ROE is more moderate in
India than in China or Russia.
37
ROA annual average
0,18
0,14
India
Russia
China
0,09
0,05
0,00
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.1 ROA annual average
ROE annual average
0,22
0,17
India
Russia
China
0,11
0,06
0,00
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.2 ROE annual average
Secondly, the “core” variables included into every model are operating profit margin, asset
turnover and leverage as the components of DuPont identity. The highest maximum value of
operating profit margin belong to an Indian company (0.57), followed by Russia (0.47) and then
China (0.28), with the same tendency among the mean values (0.19, 0.16 and 0.13). Overall,
however, the level of profit margin in Indian companies can be divided in two groups: there are
companies that have a very high relative level of profit margin and those with only 1-2%, while
Russian companies are more diverse in values. As for Chinese companies, operating profit
margin of CNOOC is much higher than of the other two. In asset turnover, Indian companies are
clearly the “leaders” with the highest value of 1.91 and the mean of 1.15, followed by Chinese
(1.49 and 0.7) and then Russian companies (1 and 0.38). Generally, asset turnover of Indian
companies is higher than 1, while it equals 1.5, 0.3, 0.31 for Chinese companies and stays around
0.3 for Russian. The explanation of this tendency may lay in the nature of operations of the
companies: Indian companies are more engaged in refining business while Russian companies
are focused more on upstream activities. Leverage is again the highest among Indian companies
38
with maximum value of 3.6 and a mean of 2.41 followed by Chinese companies (2.78 and 1.8)
and then Russian (2.25 and 1.42). Therefore, it shows that Indian companies tend to attract more
external financing than Russian and Chinese oil and gas companies.
Charts representing the average values of operating profit margin, asset turnover and leverage for
each of the companies in the sample are provided in the Appendix, the average values for each of
the countries per year are presented below, with the largest outliers excluded from the
calculation. Here the average values of operating profit margin followed decreasing pattern for
all three countries, with the most sharp for Russian companies. Assets turnover has been
decreasing for Chinese companies, while for Russian and Indian stayed almost stable. As for
leverage, it has been increasing throughout the period for India which shows that each year
Indian companies on average have been attracting more and more external financing, probably as
a means to cope with the crisis. Leverage of Chinese companies has been slightly increasing, of
Russian companies – till 2013, coming back to 2011 level in 2015. Overall, Russian companies
are distinguished by the highest level of operating profit margin (with China having the lowest),
especially during the first years of analysis, and the lowest value of assets turnover (with India
and China staying on the same level almost twice as high) which demonstrates the differences in
the nature of the companies’ operations.
OPM annual average
0,300
0,225
India
Russia
China
0,150
0,075
0,000
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.3 OPM annual average
39
AT annual average
1,400
1,050
India
Russia
China
0,700
0,350
0,000
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.4 AT annual average
Leverage annual average
3,400
2,550
India
Russia
China
1,700
0,850
0,000
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.5 Leverage annual average
As for the industry-specific factors in the sample, the highest amount of oil reserves belongs to
Lukoil, while the largest gas reserves base – to Gazprom. The holder of the lowest amount of oil
reserves is Yatek – Russian company engaged mainly in gas production. However, the sample
includes several Indian companies that operate only in refining segment and do not have or do
not disclose the amount of oil reserves. Similarly, the lowest amount of gas reserves belongs to
an Indian company Hindustan Oil which activities lay primarily in the segment of oil extraction
and production, not gas. The amount of oil refined (among those who are engaged in oil refining)
is the lowest for Orsknefteorgsintez – 5.24 million barrels due to the size of the company. The
largest amount of oil refined belongs to Chinese China Petroleum which operates more in the
sector of oil refining than oil and gas exploration and production. Share of the refining activities
is generally higher among Indian companies which include companies 100% engaged in refining
activities, while among Chinese companies the share of refining segment in revenue is around
40
35%-45%. Russian companies are relying more on upstream segment (the maximum amount of
oil production in the sample generated by Rosneft and of gas – by Gazprom) but the share of oil
refining in total operations around 45% is also common, with Lukoil being the leader – refining
segment constitutes around 75% in total earnings of the company.
Exploration expenses, among the 16 companies for which it was possible to find them, are the
highest for Lukoil (in 2013 in particular), but the difference in not huge among the large
companies. The lowest exploration expenses belong to Selan Exploration (in particular, in 2013),
just because of the size of the company (to avoid the inconsistency in the results caused by the
differences in the size of companies logarithms of such indicators were applied). Below, the
graph representing the average annual amount of exploration expenses per country of the
analysis is presented.
Exploration expenses annual average,
thousands USD
30000000
India
Russia
China
Total
22500000
15000000
7500000
0
\
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.6 Exploration expenses annual average
As for EBITDA per barrel of oil produced and exploration expenses per barrel, the
difference is also quite huge. For example, for Hindustan oil it was negative, more than minus
5000 dollars per barrels because of the negative EBITDA during several years and low amount
of production. The highest value of EBITDA and exploration expenses per barrel as well belong
to Hindustan Oil in 2011, due to relatively low production amounts. Therefore, corresponding
adjustments were made and such outliers eliminated from the analysis.
As for ownership structure, the share of the government, it is the highest for Chinese
government-owned companies, as well as several Indian ones. In Russia, share of direct
government ownership in the companies is generally lower, except for Rosneft where the
government still owns around 70% of the equity. As for the share of exports in the sales of the
company, it is different across the companies but is generally higher for Russian companies than
41
for Indian ones, often constituting more than 50% of all the sales. Among the Chinese ones, it is
quite high for Petro China compared to the other two companies.
The company with the lowest market capitalization is Gujarat Natural Resources, while the one
with the highest is PetroChina, overall and in 2014 in particular. Fundamental value is sometimes
consistent with the market value and sometimes differs significantly, as it was expected. Below,
annual values in millions USD for each country are presented.
Market capitalization annual average,
mln USD
180000
135000
India
Russia
China
Total
90000
45000
0
Y2011
\
Y2012
Y2013
Y2014
Y2015
Fig.7 Market capitalization annual average
Fundamental value annual average,
mln usd
700000
India
Russia
China
Total
525000
350000
175000
0
!
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.8 Fundamental value annual average
42
Residual income annual average,
mln usd
7000
5250
India
Russia
China
Total
3500
1750
0
-1750
-3500
-5250
-7000
!
Y2011
Y2012
Y2013
Y2014
Y2015
Fig.9 Residual income annual average
Residual income of the companies was not always positive throughout the period studied. While
Russian companies have been performing rather strongly and only 8 out of 55 observations
showed negative value, mainly during the last two years (those companies which do not have a
high share of export in sales suffered losses), among Indian companies 25 out of 50 observations
were negative. It is connected to the high required rate of return on equity (because of rather high
equity risk premium and high risk free rate, coupled with high degree of risk for some companies
measured as high beta), and also low net income which was not enough to generate positive
residual income. Nevertheless, the most negative residual income was generated by Lukoil in
2015: the required return on equity got higher due to higher risk free rate and equity risk
premium, and the net income was still not enough to offset total cost of equity. The highest
residual income among all of the observations belongs to Gazprom in 2011 when the net income
of the company got to a level twice as high as the previous year and equity increased only 15%.
As for fundamental value, the lowest value of all was generated by Indian company Hindustan
Oil because of a negative net income in 2015, while the highest belongs to Chinese PetroChina
which equity simply does not “allow” the fundamental value to be negative even when the
residual income is negative.
2.3. Research questions
Based on the previous research, the following assumptions about the relationships between the
variables can be made.
43
•
Significant positive relationship can be assumed between dependent variables and operating
profit margin/ assets turnover [Stickney et al., 1989; Berezinets et al., 2016], negative and
probably insignificant relationship with leverage [Rogova, 2014].
•
As for industry-specific indicators - amounts of reserves and production, they are also
expected to be related positively to profitability and value metrics [Ewing, Thompson, 2016],
as well as the composite factor of EBITDA per barrel produced [Herciu et al., 2010].
•
Exploration expenses can be expected to be positively related only to market capitalization as
a positive sign of the companies’ development, while their association with other dependent
variables might be negative due to the problems in exploration efficiency discussed in the
first chapter (when the revenue from production does not cover exploration costs). As for
exploration expenses per barrel, a negative relationship with dependent variables also can be
assumed as companies are generally working on decreasing the expenses per barrel to
achieve higher profitability and, in a more long-term perspective, value.
•
Government ownership, though indicated by several studies to have a positive relationship
with profitability of the companies on developing markets, is expected to be negatively
associated with the dependent variables if the opinions of analytics are considered. As for the
share of exports and refining in sales, they are expected to have a positive relationship with
profitability and value creation indicators, as mentioned in [Oxford Energy Annual Outlook,
2016].
Below, a table with the assumptions about the models is presented.
Table 6. Assumptions about the relationships between variables
ROA
ROE
Marcap
OPM
positive
AT
positive
Leverage
negative
Restot
positive
Prodtot
positive
Fundvalue
Explorexp
negative
negative
positive
negative
Explorexpbar
negative
negative
negative
negative
Ebbar
positive
Government
negative
44
Exportshare
positive
Refshare
positive
2.4. Results of regression analysis
As a result of regression analysis, several significant models have been identified: some of the
assumptions were confirmed while several unexpected relationships (or their absence) were
revealed. All the results are summarized in the table that can be found in the Appendix 3, while
here each model for each dependent variable is presented separately, followed by the discussion
of the results. The main models are presented below – firstly for the whole sample, then countryspecific, with all the coefficients before the industry-specific variables always significant except
for constant term.
First of all, return on assets has shown a positive relationship with assets turnover and
operating profit margin, while the relationship with the degree of financial leverage turns out
to be negative, which is an expected result. The coefficient before operating profit margin is
twice as high as before assets turnover: it confirms the fact that oil and gas companies,
characterized by a high level of capital investments and long-term horizon of the projects, are
more likely to increase their profitability due to high product markup than due to higher assets
turnover ratio.
As for the industry-specific factors, a positive relationship also was identified between return on
assets and the amount of gas reserves as well as total reserves but not particularly oil reserves
which indicates that the relationship is more applicable to gas companies where prices are
generally more predictable than for oil companies. Initially, an attempt was made to include not
the absolute values (or their logarithms) in the regression analysis but the reserves surplus.
However, in almost half of the cases the surplus turned out to be negative: since it was not
possible to derive a natural logarithm from negative values and the number of positive values
was not sufficient for a proper analysis, it was decided to stick to total number of reserves
annually.
Considering production levels, positive relationship has been identified between ROA and gas
production per year, as well as oil production and total production. Therefore, it can be
concluded that higher production amounts generate higher sales, and thus, higher return on
assets. This is a rather straightforward conclusion which, from the first sight, does not require
45
any regression analysis to be proved. Nevertheless, it is not always true as sales are still quite
dependent on different market conditions, including the volatility in prices, balance of supply
and demand, political situation and etc., and thus high production amount does not always lead
to higher profitability. Consequently, positive relationship between production levels and ROA
means that despite all the difficulties oil and gas companies have been capable to keep the sales
consistent with the increase in production. At the same time, the problem of overproduction and
its negative influence on the overall situation also should not be disregarded.
In terms of the amount of oil refined (see in the Appendix 3), no significant relationship with
profitability indicators has been found, unlike for oil and gas production amounts. Thus, the
differences between companies depending on their segmental focus can be seen. Probably the
coefficient in front the amount of oil refined is insignificant since for most of the companies
refining output is held constant during the years and the changes are not large enough to affect
ROA or ROE somehow.
Next, in all of the models with return on assets, negative relationship was identified with the
amount of exploration expenses (total amount and expenses per barrel – all the results are as
well presented in the Appendix 3). Thus, the higher is the level of exploration expenses for the
companies in the sample, the lower is return on assets. On the one hand, it can be considered as
an unexpected result as it can be assumed that exploration expenses would lead to increase in
production levels and, consequently, profitability. On the other hand, such an assumption is
somehow flawed since the expenses incurred during the year of observation generate the actual
results only a year or two after. Even though the amount of exploration expenses is not
dramatically different across the years for a particular company and sometimes the return on the
investments in exploration can be seen during the same period already, the drawbacks of such
straightforward judgment should be considered when drawing conclusions about the results of
the analysis. Moreover, since exploration expenses sometimes are not included in the income
statement but are capitalized as exploration assets in the total assets of the company, it is clear
that with all other components held constant the increase in exploration expenses will lead to
increase in total assets and thus the decrease in ROA itself. At the same time, their share in total
assets is rather low and would not have significant influence on ROA, especially coupled with
other changes generated by increased exploration and development activities. Finally, the
exploration expenses per barrel is one the indicators that the companies are trying to decrease,
and its negative association with ROA serves as a clear illustration of the reason behind it.
46
As for other significant coefficients, a positive relationship was identified between return on
assets and the share of exports in the companies’ sales. As dummy variable “crisis” was not
significant the appreciation of USD exchange rate to the countries’ currencies during the last two
years did not influence the situation significantly. Therefore, it can be concluded that there are
factors stronger than changes in the exchange rate that contribute to the positive relationship.
Possible explanation could have been the size of the companies but it is not the case: both larger
and smaller companies of the sample include those with a high share of exports.
Considering the share of the government in the equity, no significant relationship with
profitability ratios studied was identified. It is often stated that government ownership can
prevent companies from performing as well as their privately-owned peers do, but for oil and gas
companies which generally operate in a strategically important sector it is not the case as far as
the results show for this particular sample. The influence, however, can probably be
demonstrated for other performance indicators which were not included in the study.
ROA
\ it = 0.131 + 0.399 × OPMit + 0.183 × ATit − 0.125 × L E Vit + 0.011 × l n(r est ot)it − 0.029 × l n(ex pl or ex p)it + 0.134 × ex por t sh a r eit + υit
ROA
\ it = 0.178 + 0.404 × OPMit + 0.18 × ATit − 0.119 × L E Vit + 0.012 × l n( pr o d t ot)it − 0.028 × l n(ex pl or ex p)it + 0.122 × ex por t sh a r eit + υit
As for return on equity, in terms of significant coefficients the results are different from ROA –
none of the relationships mentioned above was the same for ROE: there was no relationship
identified with the amounts of reserves, production or refining. It was not expected, but the
reason for such a result can be the accounting of those specific factors – they are mostly included
into assets of the company. Moreover, exploration expenses coefficient is positive compared to
ROA case. Significant relationship was identified between ROE and EBITDA per barrel/
exploration expenses per barrel. Therefore, it can be clearly seen that the picture is quite
different when the assets themselves are not used in the computation of the dependent variable.
!
ROE
it = − 0.109 + 0.578 × OPMit + 0.07 × ATit − 0.008 × L E Vit − 0.001 × ebb a rit + 0.011 × ex pl or ex pb a rit
+ !υit
Moving to the valuation part and, particularly, market capitalization, positive relationship was
identified with the production amount – which implies that investors pay attention to the level
of production of oil and gas companies, opposite to the levels of reserves which showed a
negative with market capitalization. Still, positive relationship was identified with exploration
expenses per barrel of oil produced and total exploration expenses. This result probably
speaks in favor of the fact that investors’ attention is drawn to exploration expenses as to a
47
positive sign but at the same time indicates that exploration expenses per barrel are probably not
the factor investors pay attention to. Also the relationship with EBITDA per barrel is significant
but negative. The reason behind it can be the exceeding increase in the production levels over the
increase in EBITDA: even if the later is increasing, a positive relationship between market
capitalization and production level probably does not “allow” the relationship with the indicator
to be positive up to a certain point. Considering DuPont model multipliers, a positive
relationship was found with operating profit margin and assets turnover, and negative with
leverage, which corresponds with the results for previous dependent variables. Relationship with
government ownership and share of exports in sales was also proved to be positive, thus
probably being treated by the market as positive signs.
!
Ma r ca pit = 0.73 + 0.045 × OPMit + 0.611 × ATit − 0.409 × L E Vit − 0.102 × l n(r est ot )it + 0.055 × l n(ex pl or ex p)it + 1.607 × ex por t sh a r eit + υit
!
Ma r ca pit = 11.42 + 0.062 × OPMit + 0.414 × ATit − 0.243 × L E Vit + 0.162 × l n( pr o d t ot )it + 2.26 × g over n m en tit + 2.35 × ex por t sh a r eit + υit
!
Ma r ca pit = 3.62 + 1.34 × OPMit − 0.229 × ATit − 0.567 × L E Vit − 0.004 × ebb a rit + 0.007 × ex pl or ex pb a rit + 0.837 × si z eit + υit
Finally, fundamental value has shown, firstly, positive relationship with the amount of total
reserves and production which is consistent with the results for profitability of the companies.
A positive relationship was also identified between fundamental value and EBITDA per barrel
and total amount of exploration expenses, and negative with exploration expenses per barrel.
The positive relationship with EBITDA per barrel comes from the calculation of fundamental
value where EBITDA is involved and reserves amount is not. Positive relationship with
exploration expenses indicates that they probably can affect fundamental value even in short
term, while negative with exploration expenses per barrel shows the importance of
differentiating those two metrics. Moreover, it illustrates the importance of expense
management: with exploration expenses per barrel getting lower, the fundamental value of the
company is increasing. Relationship with DuPont model components is similar to the one of
market capitalization: positive coefficient of operating profit margin and assets turnover and
negative of leverage.
Fun
! d va l ueit = 9.7 − 0.185 × OPMit + 1.424 × ATit − 0.686 × L E Vit + 0.244 × l n(r est ot )it + 3.87 × ex por t sh a r eit + υit
Fun
! d va l ueit = 6.85 − 0.148 × OPMit + 2.169 × ATit − 0.561 × L E Vit + 0.391 × l n(oi l pr o d )it + 0.244 × l n(ex pl or ex p)it + υit
!
Fun
d va l ueit = 7.74 + 0.025 × OPMit + 1.535 × ATit − 0.191 × L E Vit + 0.309 × l n( pr o d t ot )it + 4.11 × ex por t sh a r eit + υit
! d va l ueit = − 1.51 + 2.15 × OPMit − 0.49 × ATit − 0.593 × L E Vit + 0.019 × ebb a rit − 0.031 × ex pl or ex pb a rit + 1.177 × si z eit + υit
Fun
48
All the models also included a dummy variable – crisis, which took value 0 from 2011 to 2013
and 1 in 2014 and 2015 as the years when the situation in the industry became less favorable
then the three years before. However, the coefficient before this variable was not significant in
any of the cases, implying that it did not anyway influence the whole picture largely, though this
conclusion is debatable. It is important to mention that whenever size was not included into the
final model but possibly could have influenced the significance and signs of the coefficients, the
same model with size/ total assets logarithm factor was tested in order to check that its inclusion
does not significantly change the results. At the same time, if the results with size component
were different, it was included into the models.
In the table below, the average coefficients from all the models for the dependent variables are
presented - since the coefficients generally do not differ largely and thus can be put in such form
to give an overview of the results for each of the variables. The detailed results for every model
are presented in the Appendix 3.
Table 7. Relationships identified – overall average coefficients
ROA
ROE
Fundamental value
Market value
opm
0.4
0.58
-0.18
0.5
at
0.18
0.07
1.5
0.61
leverage
-0.12
-0.008
-0.65
-0.3
ln (restot)
0.011
-
0.244
-0.102
ln (prodtot)
0.012
-
0.309
0.162
ln (explorexp)
-0.03
-
0.244
0.055
explorexpbar
-0.0007
0.011
-0.031
0.007
exportshare
0.13
-
4
2
ebbar
-
-0.001
0.019
-0.004
government
-
-
-
2.26
Then, a separate country-based analysis was done: since the number of Chinese companies does
not allow to perform any analysis in Stata, only samples for India and Russia were tested.
The results are rather different for India compared to the results for the whole sample: a
significant positive relationship was identified between market capitalization/ fundamental
49
value of the companies and the amount of total production, included in the models together
with DuPont multipliers, as well as between market capitalization and government ownership
and share of exports in the sales of the companies. The coefficients before any factors in
profitability models were not significant. It can be so because generally Indian companies had
the lowest ROA and ROE in the sample which were not determined only by the amounts of
production of the companies but other external factors.
Ma
\ r ca pit = 11.13 + 0.059 × OPMit − 0.276 × ATit − 0.242 × L E Vit + 0.161 × l n( pr o d t ot)it + 2.88 × g over nmen tit + 2.93 × ex por t sh a r eit + υit
Fun
d va l ueit = 5.24 + 0.024 × OPMit − 1.264 × ATit − 0.092 × L E Vit + 0.432 × l n( pr o d t ot)it + υit
\
Then, the analysis for Russian sample showed negative relationship between the ROA and
exploration expenses – total amount and per barrel, reserves and production, as well as
positive relationship between ROE and exploration expenses and negative between ROE and
total amounts of reserves and production. Negative relationship was found between fundamental
value and the amount of oil production, share of exports and refining segment in the sales of
the company. Positive relationship was identified between fundamental value and amount of oil
reserves, total production and EBITDA per barrel. This is consistent with the results for the
united sample and indicates that in terms of those coefficients it is largely influenced by the
Russian part: while for Indian companies which are more engaged in refining the amount of
exploration expenses and the amount of reserves are important but not critical, Russian
upstream-oriented companies show a certain degree of dependency between profitability and
valuation and those variables. Moreover, negative relationship between total production and
return on equity was identified which is not the case for the united sample.
\ it = 0.127 + 0.547 × OPMit + 0.079 × ATit + 0.011 × L E Vit − 0.006 × l n( pr o d t ot)it − 0.005 × ex pl or ex pba rit −0.05 × r e f sh a r eit + υ
ROA
it
ROE
\
it = − 0.146 + 0.897 × OPMit + 0.241 × ATit + 0.033 × L E Vit − 0.006 × l n(oi l r es)it +υit
ROE
\
it = 0.092 + 0.854 × OPMit + 0.06 × ATit + 0.024 × L E Vit − 0.016 × l n( pr o d t ot)it + 0.015 × l n(ex pl or ex p)it +υit
Ma
r ca pit = 9.52 + 2.257 × OPMit + 0.554 × ATit − 0.625 × L E Vit − 0.118 × l n( pr o d t ot)it + υit
\
Fun
d va l ueit = 4.25 − 0.526 × OPMit + 0.516 × ATit − 0.355 × L E Vit + 0.451 × l n( pr o d t ot)it + υit
\
\ d va l ueit = − 13.49 − 0.734 × OPMit + 0.569 × ATit − 0.777 × L E Vit + 0.086 × ebba rit − 5.083 × ex por t sh a r eit − 3.654 × r e f sh a r eit + υit
Fun
Detailed results of the regression analysis can be also found in the Appendix 3, the average
coefficients are presented in the below for an overview.
50
Table 8. Relationships identified – country-specific average coefficients
India
Russia
ROA
ROE
Fund
Market
ROA
ROE
Fund
Market
opm
-
-
0.024
0.059
0.547
0.854
-0.6
2.257
at
-
-
-1.264
-0.276
0.079
0.06
0.52
0.554
leverage
-
-
-0.092
-0.242
0.011
0.024
-0.5
-0.625
ln (restot)
-
-
-
-
-
-
-0.006
-
ln (prodtot)
-
-
0.432
0.161
-0.006
-0.016
-0.451
-0.118
ln (explorexp) -
-
-
-
-
0.015
-
-
explorexpbar
-
-
-
-
-0.005
-
-
-
exportshare
-
-
-
2.93
-
-
-5.083
-
ebbar
-
-
-
-
-
-
0.086
-
government
-
-
-
2.88
-
-
refshare
-
-
-
-
-0.05
-
-3.654
-
2.5. Findings discussion
The results of the analysis have shown the importance of industry-specific factors consideration
when determining the drivers of profitability and value of oil and gas companies. Several groups
of factors can be identified depending on their relationships with profitability and value
indicators.
First of all, the relationship discovered with the first level components of DuPont model
corresponds to expectations: operating profit margin and assets turnover are positively related to
profitability and value, while leverage is characterized by a negative relationship. Moreover, the
degree of the relationship (the coefficient) with operating profit margin is generally higher for
profitability while with assets turnover for value, both market and fundamental. It can be
explained just by the fact that operating income itself is a profitability indicator while asset
turnover turns out to be more closely related to value generation. Then, in some models on
fundamental value, the coefficient before operating profit margin also turned out to be negative:
this may have taken place due to the Indian part of the sample that is characterized by high assets
turnover and low fundamental value due to the crisis.
51
Considering industry-specific factors such as amount of reserves or production, their
relationship with the dependent variables in almost all of the models turned out to be positive,
implying primarily the same direction of changes between the variables. However, growing level
of production at some point can start affecting the profitability and value negatively, especially
taking into account the situation of global imbalance of oil supply and demand. Positive
relationship means that overall companies manage to keep their production on the level which
only benefits their performance when profitability and value are growing, and, at the same time,
the fall in production would most likely affect ROE, ROA, market and fundamental value.
Then, the results obtained for exploration expenses are different for the variables: while ROA
and fundamental value are characterized by negative relationship with the indicator, the
coefficient is positive for ROE and market capitalization. As mentioned before, it may take place
due to several reasons; it may happen because of the strong association of exploration expenses
with assets of the company where they are capitalized, which are the core for the calculation of
return on assets, as well as their significant influence on the calculation of residual income.
However, the explanation from the crisis point of view is more probable: while companies were
increasing the exploration expenses, ROA and fundamental value have been falling due to
unfavorable economic events. Positive relationship with the other two indicators is leading to the
conclusion that probably the amount of exploration expenses is perceived by investors as a sign
of growth, and, therefore, coupled with other factors boosts the growth of market value of the
company.
As for the operational level of DuPont model components – EBITDA per barrel and exploration
expenses per barrel – which were initially considered as the main focus of the work, their
relationship with the dependent variables is significant in several models presented. The
relationship of exploration expenses per barrel with ROA and fundamental value is negative,
while relationship with ROE and market capitalization is positive, probably due to the reasons
mentioned above for the total amount of exploration expenses. Relationship of EBITDA per
barrel, on the opposite, is negative with ROE and market capitalization - probably as a result of
the crisis situation that did not allow EBITDA to “take over” the power of influence from barrels
produced, while it is still positive for fundamental value.
52
Factors which generally did not change as much as others during the time observed were
government share in ownership, share of exports in the sales of the company and share of
refining segment in sales. Coefficient of government ownership share is significant only in one
case – positive relationship was discovered with market capitalization, which actually can
indicate that investors treat it as a sign of security and stability. Positive relationship of the share
of exports in sales was revealed with return on assets, market capitalization and fundamental
value – the possible reason is, again, the crisis situation and the fall of exchange rates that
benefited somehow the companies with a huge export market, allowing them to diversify the
risks and increase revenues in foreign currency. As for refining segment, positive relationship
was identified only with residual income. Taking into account that the majority of the companies
with positive residual income in the sample are Russian, it can be concluded that those that
actually take steps in increasing the share of refining activities in the portfolio are thus paving
the way for higher returns – not as high now to affect the value and profitability, but probably in
the future and supported by more favorable economic conditions.
But with all the results obtained, it should not be forgotten that there are always factors that are
not included into analysis but may affect the situation, especially in oil and gas industry which is
largely influenced by different macroeconomic, political and other factors. Even though the
relationships identified are based on strong arguments, they can be as well strongly connected to
other factors which are not considered in the model.
2.6. Managerial implications
The results of the analysis have proved that models based on the combination of DuPont Model
components and industry-specific factors are a key tool to discover the main drivers’ influence
on profitability and value creation, allowing to see their relationship with profitability and value
indicators to form managerial and investment decisions accordingly.
First of all, the assumptions about DuPont model first-level components and their relationship
with profitability and value were confirmed for the sample studied, representing different degree
of relationship with dependent variables and thus the degree of those indicators’ importance for
53
the change in profitability and value creation factors. Thus, as mentioned earlier, operating profit
margin is characterized by a stronger relationship with ROA and ROE, while assets turnover –
with market and fundamental value. It provides the managers with an insight depending on the
perspective of the performance evaluation and the strategies they need to develop: changes in
ROA and ROE would more probably be attributed to operating profit margin while fundamental
value changes are more closely linked to assets turnover, as well as market capitalization despite
the belief that investors generally follow operating profit margin characteristics. Therefore, in
short-term perspective operating profit margin would be the more appropriate metric for
performance measurement while assets turnover is more of a long-term value metric. Investors,
in their turn, also would benefit from being attentive to the particular indicators depending on
their investment position. As for leverage, its negative relationship with all the dependent
variables and higher association with fundamental value indicates the importance of its
monitoring as with the influence of unfavorable economic events companies are relying more on
debt which can affect the value negatively.
Moreover, the role of industry-specific factors was demonstrated indicating the need for their
consideration: reserves base, production amount proved to be significant in their relationship
with profitability and value indicators. Thus, positive relationship of reserves and production
amounts with dependent variables also may indicate that the companies tend to keep levels of
production corresponding to the changes in market situation. The fact that no such relationship
was identified for Russian companies can be explained via the specifics of the industry: since it
is dominated by the upstream sector, planned production levels cannot be changed easily – here
the increasing involvement in refining sector can be considered as a hedging strategy. In addition
to this, negative association indentified between the amount of oil reserves and return on assets
can overall speak for performance management problems of oil giants.
Nevertheless, as it was mentioned earlier, as much as it is important for managers to maintain
those indicators on a certain level, it is essential to coordinate it with the demand and monitor
exploration costs: since they showed negative relationship with ROA and fundamental value it
may indicate that high costs do not result in corresponding output. For each company this issue
should be investigated individually to control the underlying reasons. The positive association of
exploration expenses with the market value of the company can indicate that large exploration
54
expenses are accessed by the market as a positive sign but its negative relationship with return on
assets shows that the exploration expenses do not appear to generate desired profit levels as high
investments are followed by a low output. Therefore, it can be concluded that the absolute
amount of exploration expenses should not be used as a separate metric for investment appeal
and different macro factors should be considered. As for investors, it is important to know that
high levels of production and reserves base can be generally considered as a positive sign, while
exploration expenses tend to relate differently to different indicators and thus a closer attention to
the situation in a particular company should be paid.
Next, positive relationship was also revealed between the dependent variables and the
characteristics of ownership structure (government ownership), export orientation (share of
exports in sales) and nature of operations (share of refining activities in sales). For managers
those are additional points for consideration in long-term strategy planning: while government
ownership is considered to be not beneficial for the performance of the companies, it apparently
provides companies with benefits that outweigh the disadvantages. Situation with export
orientation though is debatable since it is not that easy to increase the export share, as well the
share of refining activities due to different external constraints. Nevertheless, when comparing
other factors with those of competitors, their degree of involvement in export, level of state
ownership and business portfolio should be considered as additional variables that can affect the
differences apart from the overall exploration, production and refining management. As for
investors, the implication is rather straightforward: the abovementioned factors should be
considered when choosing the companies for investment among those from the sample.
Considering composite indicators of the operational level of DuPont model, it was expected
that the relationship would be significant and positive in the majority of the cases. Nevertheless,
it was the case only for several models, otherwise the coefficients were insignificant. Although
the variable “crisis” was not significant neither, the issue still can be attributed to the negative
situation in the market, especially for EBITDA per barrel, meaning that here the task of
managers is basically the mitigation of the unfavorable conditions. For investors, on the other
hand, it means that the fall in the indicator does not mean that there are problems with
production management inside the company but more a general landscape in the industry. As for
55
exploration expenses per barrel, this factor showed the same dynamics as total exploration
expenses which were discussed above.
In particular, the negative relationship of EBITDA per barrel, the most popular performance
indicator in oil and gas industry, with ROA, ROE and market capitalization indicates that it may
not reflect the market changes – in relative terms meaning that production is generally falling
faster than the earnings are growing. Thus, it may be concluded that growing EBITDA per barrel
is not an attractive investment or benchmarking metric during crisis.
Moreover, the results of the study showed differences and similarities between the countries:
while the production/ refining amount as well as government and export share were positively
related with almost all the dependent variables of all samples, composite variables’ coefficients
were significant only for Russian sample. For investors it means that composite indicators can be
applied for comparison only between Russian companies, while other indicators can be used for
cross-country comparisons. In addition to this, the calculation of fundamental value showed that
Indian companies generated negative fundamental value much more often than others, especially
during the last 2 years of the study, meaning that investors generally should be careful with
buying the stocks of Indian oil and gas companies. Although a separate reliable analysis of
Chinese companies could not be performed, it was crucial to take those companies into account
when forming the overall sample.
To sum up, the results obtained provide managers and investors with a combination of factors
that can be used to investigate the reasons of change in profitability and value of companies,
conduct comparisons between companies and countries to benchmark the factors of the
company’s performance against its peers, get corresponding insights for managerial decisions on
operational and strategic levels and for the choice of partner companies or investment targets.
2.7. Limitations and further research suggestions
First of all, the number of the observations in the sample was lower than expected initially: it
included all the available information, thus allowing to obtain 120 observations which was
enough to perform statistical analysis and draw conclusions for the countries studied.
56
Concerning data collection process, sometimes it was hard to get the data and annual reports not
only for relatively small companies but also for large players, especially in India and China, and
therefore it took a lot of time to get the numbers for all the variables and analyze companies one
by one. That is why 120 observations was actually a perfect number as given the time constraints
it would have been impossible to get correct reliable data on all the indicators. Nevertheless, if
time allowed, it would have been interesting to look into more countries – for example, BRICS –
and compare their performance between themselves or with highly developed oil companies
coming from USA and Norway. Moreover, the number of years could be enlarged to in order to
perform not only descriptive analysis but also be able to make long-term forecasts based on
historical data with dynamic panel data models. It would also allow to enlarge separate samples
for each country and make the country-specific results more reliable. Considering such industryspecific factor as reserves base, there also have been a lot of discussions on whether their reevaluation is sometimes too subjective and does not reflect the reality. It would possibly be
interesting to look into the issue more precisely but this requires a completely separate study.
In addition to this, even though all the main industry-specific factors were included, there always
can be factors which also influence the situation but were not considered in the model, from
highly technical characteristics to a variety of external factors. Talking about the variables that
cannot be influenced by the companies themselves, the main one is for sure the price of oil. In
this work, it was considered somehow with the variable “crisis” which did not turn out to be
significant for the sample due to differences between the companies, although it is clear that the
dramatic changes in oil prices affected the situation largely. On the other hand, inclusion of such
variable as the corridor of oil price changes just as separate regression factor probably also
would not help a lot. Thus, it would be beneficial to see how the advanced methods of oil price
modeling would contribute to the analysis of the performance of the companies of this particular
sample.
57
Conclusion
Value-based management has become one of the most important concepts for the evaluation of
performance of companies and choice of particular value factors that are crucial to achieving
profitability and shareholder value creation. DuPont model, as the comprehensive tool for
analysis, can contribute significantly to value drivers’ identification up to operational level,
especially coupled with industry-specific factors, although the number of studies in the field is
quite limited for developing countries and particular industries. That is why it was decided to
concentrate on value drivers’ identification of oil and gas companies in India, China and Russia:
oil and gas industry is generally characterized by high dependency on macroeconomic factors
and the question of how companies can control profitability and value despite the changes that
cannot be managed is always topical. In addition, oil and gas companies in Russia, China and
India play a crucial strategic role in the development of the economy and, considering recent
crisis situation, are facing additional challenges that need to be addressed via the identification of
crucial profitability and value creation factors.
The study was based on 10 Indian, 11 Russian and 3 Chinese companies, from year 2011 to
2015, in total 120 observations. The overall situation for oil and gas companies in the countries
of the analysis was investigated, as well as a review on value-based management and existing
studies on DuPont model application in value drivers’ identification conducted. Then, regression
analysis was performed to identify the relationships between return on assets, return on equity,
residual income, market value and fundamental value as dependent variables and DuPont second
level components as well as industry-specific factors and composite indicators as independent
variables. As a result, significant relationships were identified based on the models including
combination of factors, providing managers of oil and gas companies and investors with the
insights for decisions concerning the companies in the countries studied. Nevertheless, it is
important to mention that the research was limited by the time and information available and
serves as a base for further studies where more factors can be included as well as larger time
frame and bigger number of countries considered to allow for more tools of the analysis to be
used and discover the relationships (or their absence) that have not been identified in the current
study.
58
Appendix
Appendix 1. Description of the companies
India
Indian Oil
One of the leading Indian oil and gas companies engaged in exploration,
production, refining and marketing. Owns and operates over 10 refineries.
Reliance Industries
Another vertically-integrated oil and gas leader on Indian market
Selan Exploration
Company is principally engaged in exploration and production of oil and gas
Cairn India
Company’s main activities lay in extraction of crude oil and natural gas, as
well as refining and marketing of the products.
G u j a r a t N a t u r a l Company mainly involved in oil and gas exploration, operating basins all
Resources
over India
Mangalore Refinery Holding company engaged in crude oil refining and manufacturing of refined
and Petrochemicals
petroleum products
Bharat Petroleum
Company operates in segments of Exploration and Production of
Hydrocarbons and Downstream Petroleum, including refining and marketing.
Hindustan Oil
Oil and gas company which main activities are related to exploration and
production of hydrocarbons.
Hindustan Petroleum
Vertically-integrated company with a main focus on downstream segment
Oil and Gas Natural
Global vertically integrated energy company
China
China National Oil The largest producer of offshore crude and natural gas in China as well as
Offshore Corporation one of the biggest independent oil and gas exploration and production
(CNOOC)
companies in the world.
China Petroleum & Energy (vertically integrated with a focus on refining) and Chemical
Chemical Corporation
Company
PetroChina Company
Company is engaged in oil and gas production and distribution, including
Limited
Exploration and Production, Refining, Chemical, Marketing and
Transportation Segments
Russia
ANK Bashneft’
Company is principally involved in extraction, exploration and production of
crude oil and oil products. In 2015 was acquired by NK Rosneft’
Yatek OAO
Company engaged in extraction, processing and marketing of natural gas and
gas condensate
Novatek OAO
Natural gas producer operating in segments of exploration, production,
processing, transportation and marketing of natural gas.
59
NK Rosneft’
Vertically integrated oil and gas company which operates via many
subsidiaries in Russia and abroad
N N K K h a b a r o v s k y Company mainly operating in segments of processing and refining of crude
NPZ AO
oil
O r s k n e f t e o r g s i n t e z Company involved in refining and manufacturing of petroleum products,
PAO
active both in Russia and abroad
Gazprom PAO
One of the largest companies engaged in exploration, production,
transportation and sales of natural gas domestically and internationally, as
well as crude oil production
Gazpromneft’ PAO
Vertically integrated oil company operating in Russia and abroad
Lukoil
Oil company involved in exploration, production, refining, marketing and
distribution of oil and refined products, domestically and internationally
Surgutneftegas
Vertically integrated oil company
Tatneft’
Oil and gas company operating in segments of exploration and production,
refining and marketing and petrochemicals
Appendix 2. Descriptive statistics of variables
India
Russia
China
Min
Max
Min
Max
Min
Max
ROA
-8.04
0.52
-0.47
0.41
0.024
0.24
ROE
-2.98
0.25
-4.5
0.62
0.03
0.26
OPM
-20.34
0.69
-1.49
0.81
0.023
0.38
AT
0.05
2.49
0.17
1.45
0.26
2.37
Leverage
1.11
7.88
1.13
6.31
0.14
2.44
Resinc, th. USD
- 2 014 2 860 028
282
-7 731 179
16 010 313
- 1 4 4 3 3 7 426 257
Fundvalue, th. USD
-16 520
132
92 432 389 253 036
691 437 024
56 488 826 1 166 164
431
Marcap, th. USD
40 550
1 0 8 6 7 0 71 920
137 943 988
86 263 255 3 1 8 2 6 9
000
TA, th. USD
23 759
894
81 007 272 152 901
408 515 319
61 053 401 3 8 7 6 7 6
855
Oilres, mln
5.36
3 796
0.04
125 607
2 165
11 128
Gasres, mln
1.99
459 332
122 682
23 705000
171 319
2 195 499
Restot, mln
5.55
1 107 507
277
57 130385
413 181
5 292 346
60
Oilprod, mln
0.0067
164
29.45
1 477
0.88
404
Gasprod, mln
0.648
77 405
925
513 200
5 250
242 267
Prodtot, mln
0.023
186 574
0.73
1 236 844
12 654
583 865
Oilref, mln
91.6
489
5.24
712.86
186.43
1689
Explorexp, th. USD
1 343
2 882 812
1 990
218 357142
1 612 305
17 156511
Ebbarprod, USD
-5 265
146
0.05
20.63
3.4
72.65
Explorexpbar, USD
2.35
1 619
0.02
15.35
1.07
49
Government
0
1
0
0.75
0.28
1
Exportshare
0
0.57
0
0.81
0
0.86
India
Russia
China
Mean
St dev
Mean
St dev
Mean
St dev
ROA
-0.17
1.23
0.12
0.13
0.09
0.058
ROE
0.018
0.46
0.03
0.81
0.13
0.06
OPM
-0.36
2.98
0.16
0.31
0.13
0.13
AT
1.14
0.94
0.71
0.35
1.16
0.73
Leverage
3.11
2.07
1.94
0.91
1.56
0.94
Resinc, th USD
274,846
883 703
1 735 240
5 338 145
109 914
6 700 540
Fundvalue, th USD
20 877 326 26 821 851 1 2 6 3 6 1 1 6 9 6 7 2 4 8 2 0 8 1 4 2 4 1 9 8
221
Marcap, th USD
104
16 964 889 27 817 835 31 513 644 31 824 545
138
896
1 5 5 4 5 4 84 706 307
230
TA, th USD
20 900 347 23 650 064 65 185 276 98 496 341
Oilres, mln barrels
064
808
1 537
21 355
39 606
5 476
3 697
Gasres, mln cubmeters 139 956
189 410
5 012 331
8 617 895
794 854
880 723
Restot, mln tons
270 006
429 026
7 550 963
17 335746
1 916 364
2 123 041
Oilprod, mln barrels
29
54
424
379
109
160
m l n 12 770
25 886
77 603
163 917
67 702
86 696
Prodtot, mln tons
18 485
50 350
119 060
326 347
163 178
208 928
Oilref, mln barrels
250
159
258.13
223.62
944.7
606.5
Explorexp, th.USD
527 041
903 342
5 997 452
32 666079
5 817 528
5 002 805
Ebbarprod, USD
-110
775
9.2
6.2
24.94
28.16
Explorexpbar, USD
189
415
2.19
3.72
13.32
17.84
Government
0.28
0.37
0.1
0.23
0.69
0.44
Gasprod,
1 218
220 439 116 865
cubmeters
61
Exportshare
0.10
0.18
0.4
0.29
0.29
0.37
Appendix 3. Regression analysis results
United sample (120 observations, 2011 to 2015)
ROA (1)
ROA (2)
opm
0.3931966***
0.3989332*** 0.402***
0.3989981*** 0.4046958***
at
0.1650579***
0.1837132*** 0.125***
0.1602562*** 0.1805186***
leverage
-0.1513986 ***
-0.1255528**
*
-0.1539682**
*
ln (gasres)
0.0259008***
ln (restot)
ROA (3)
ROA (4)
-0.151***
ROA (5)
-0.1185472**
*
0.011435**
ln (oilprod)
0.00947**
ln (gasprod)
0.0348118***
ln (prodtot)
0.0120764***
ln (explorexp)
-0.0336366***
exportshare
-0.0291745**
*
-0.0389816**
*
0.1344957**
-0.0283104**
0.1217334**
const
-0.0844863
0.1310065
0.144
0.1304474
0.1784778**
P-value
0.0000
0.0000
0.0000
0.0000
0.0000
R-squared
0.9825
0.9803
0.9776
0.9819
0.9787
№ of obs
55
75
90
65
80
ROA (6)
ROE (1)
opm
0.3960615
***
0.5779177* 0.0757625* 0.04533
**
**
0.0357801* 0.0653284*
**
**
at
0.0395684
0.0702955* 0.5684649
**
0.6118083
-0.4267596
**
0.8717175*
leverage
-0.0038047
-0.0087242
-0.4096449*
**
-0.5877297
***
-0.4028881
***
ln (oilres)
Marcap
(1)
-0.5919297
***
Marcap (2) Marcap
(3)
Marcap
(4)
0.1871175*
*
ln (gasres)
ln (restot)
ln (oilprod)
0.0157487
**
0.1019673**
-0.1503172
***
62
ln
(gasprod)
0.1407274*
**
ln
(explorexp)
0.0653168
ebbar
explorexpb
ar
0.0552874
-0.0014012
**
-0.0007458
***
0.0103838*
*
-0.0099586
government
2.64686**
exportshare
1.482806** 1.607321**
0.8422035*
**
size
const
0.4251752
**
-0.1087147
***
11.70025** 0.7309***
*
6.7752***
13.50279**
*
P-value
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
R-squared
0.9867
0.5741
0.7289
0.7309
0.9451
0.6775
№ of obs
65
65
75
75
75
80
* meaning 10% significance level
** - 5% significance level
*** - 1% significance level
Marcap (5) Marcap (6) Marcap
(7)
Fundvalue
(1)
Fundvalue(2
)
opm
0.0625987** 1.343979*** 0.297851
*
-0.1833883
-0.2103443
at
0.4144901** -0.2293206
-0.34866**
*
1.461976**
1.276699**
leverage
-0.2433974
***
-0.2182625
***
-0.9500784*
**
-0.6365753
-0.5670692*
**
ln (oilres)
0.3832916**
*
ln (gasres)
0.2950954***
ln (restot)
ln (prodtot) 0.1625925**
*
0.1411173*
**
63
ln
(explorexp)
0.1722523
ebbar
-0.0037155
0.0359713*
**
ebbarref
0.0069017**
*
explorexpb
ar
government 2.265004***
0.8386544*
**
exportshare 2.351691***
0.8374707**
*
size
const
11.42419*** 3.620419*** 14.11173**
*
7.376066
9.925555***
P-value
0.0000
0.0000
0.0000
0.0000
0.0006
R-squared
0.7789
0.9163
0.9099
0.6237
0.5785
№ of obs
110
65
50
73
59
Fundvalue (3)
Fundvalue(4)
Fundvalue(5)
Fundvalue(6)
Fundvalue(7)
opm
-0.1845112
-0.1477923
-0.1422419
0.025536
2.150652**
at
1.423936***
2.168869***
1.877714***
1.535122***
-0.4920481
leverage
-0.6863884***
-0.5616098
-0.8562958** -0.1913988
*
ln (restot)
0.2442216***
-0.5928116**
0.391181***
ln (oilprod)
0.3593319***
ln (gasprod)
0.3099384***
ln (prodtot)
0.2444916***
ln (explorexp)
0.0529173
ebbar
0.0188193**
explorexpbar
-0.0305621***
exportshare
3.877187***
2.977115***
4.110099***
1.176849***
size
const
9.695047***
6.848355***
7.533071***
7.741636***
-1.515168***
P-value
0.0000
0.0000
0.0000
0.0000
0.0000
R-squared
0.7418
0.6901
0.7985
0.7848
0.9177
№ of obs
78
73
64
108
61
64
India
Marcap(1)
Marcap (2)
Fundvalue
opm
0.0593043***
0.0163077
0.0247683
at
0.2760338
-1.187897***
1.263822***
leverage
-0.2420234***
-0.4151585
0.0917393
0.6138252***
ln (oilprod)
ln (prodtot)
0.1608729**
0.4316282***
government
2.881759**
5.18106***
exportshare
2.934685**
6.673276***
const
11.12676***
5.931621***
5.241088***
P-value
0.0000
0.0000
0.0000
R-squared
0.7905
0.9558
0.6387
№ of obs
50
35
48
Russia
ROA (1)
ROA (2)
ROA (3)
ROA (4)
ROA (5)
opm
0.4537168***
0.4977919***
0.4317333***
0.3576801***
0.5478823***
at
0.1081124***
0.0440817
0.072251***
0.1053592***
0.0789999***
leverage
-0.000973
0.0103899
0.0234357***
-0.0183137***
0.0112648
-0.0057***
-0.006394***
ln (oilres)
-0.0051691***
ln (restot)
-0.0265902***
ln (oilprod)
ln (prodtot)
ln (explorexp)
-0.0113605***
-0.0047594***
explorexpbar
refshare
0.0634955
-0.0923937
-0.0497374
const
0.1095416**
0.1261047
0.4503942***
0.1864967***
0.1265585***
P-value
0.0000
0.0000
0.0000
0.0000
0.0000
R-squared
0.8333
0.77
0.8358
0.9074
0.8867
№ of obs
45
40
40
54
35
ROE (1)
ROE (2)
ROE (3)
ROE (4)
Marcap
0.8979765***
0.8169381***
0.8549997*** 0.5267011*** 2.257373***
opm
65
at
0.2419167***
0.0290428
0.0604749
0.1206272*** 0.5543983
leverage
0.0325749***
0.0461548**
0.0242571
0.0102911
ln (oilres)
-0.0054247***
ln (gasprod)
-0.6251828*
-0.0246534***
ln (prodtot)
-0.0159604**
*
0.1175643***
ln (oilref)
ln (explorexp)
0.0178462***
0.0145099**
explorexpbar
0.0118415***
const
-0.1455059***
0.234138
0.0923238
-0.0582655
9.519604***
P-value
0.0000
0.0000
0.0000
0.0000
0.0000
R-squared
0.8773
0.7412
0.7983
0.6890
0.7560
№ of obs
40
35
45
35
40
Fundvalue (1)
Fundvalue (2)
Fundvalue (3)
Fundvalue (4)
opm
-0.7856602
0.7117249
-0.5257345
-0.7339036
at
-0.0676162
0.6804048
0.5158992***
0.5687197
leverage
-0.8263207***
-0.3927556
-0.3550182
-0.7769248***
ln (oilres)
0.3745573***
ln (oilprod)
-0.8590605***
ln (prodtot)
0.4513059***
ebbar
0.0857721***
exportshare
-5.082932***
refshare
-3.654061***
size
1.759186***
2.099186
const
11.27718***
4.144994
4.250984
-13.49787
P-value
0.0000
0.0000
0.0058
0.0000
R-squared
0.8288
0.6369
0.3468
0.7270
№ of obs
40
40
45
35
66
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