WHAT IS THE IMPACT OF THE CRUDE OIL PRICE INDEX ON THE
PERFORMANCE OF OIL AND GAS FIRMS?
By
Y ousef El Kalush
B.Sc. Economics, ih of October University, Libya, 1997

PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF BUSINESS ADMINISTRATION

THE UNIVERSITY OF NORTHERN BRITISH COLUMBIA
October 2009

© Y ousef El Kalush, 2009

Abstract:

This paper empirically investigates the impact of fluctuations of crude oil prices
on the financial performance of the 200 largest oil and gas companies which are listed on
the US Stock Exchange for the period ranging from 1990-2008. The empirical results
provide evidence that an increase in crude oil price positively influences the performance
of oil and gas firms. Results from the panel data regression analysis show a statistically
significant relationship (at 1% level) between the oil and gas index, return on equity
(ROE), the return on asset (ROA) earning before interest, tax, depreciation and
amortization (EBITDATA), and net-income (NI).

Acknowledgement:
I would like to thank my supervisor Prof, Han Donker, Associate Professor in
Accounting at the University of Northern British Columbia, for his expert knowledge,
insightful criticisms and patient engagement, which aided in the writing of this paper.

II

TABLE OF CONTENTS
ABSTRACT ..... ........... ........... . ....... ........ ............. ....................... ... .i
ACKNOWLEDGEMENT .... .. ...... ... ............ .... ................... . ....... .. . ..... ii
SECTION I
INTRODUATION AND BACKGROUND ............................................................. I
SECTION II
LITERATURE REVIEW ... .... . .... ....... .............. . ... ......... ..................... 3
SECTION III
HYPOTHESIS AND RESEARCH QUESTION . ..... ............. .. . .............. ... 8
SECTION IV
METHODOLOGY AND DATA ... ........... ........ .... .... ................. . .. ........ 9
SECTIONV
ANALYSIS AND FINDINGS ...... .. ... ............. .. ....... ........................ . .. 21
KEY METRICS
1. MEAN ....... ..................................... ............ ......... .. ........... 20
2. MINIMUM ... .. .......................... .... .... ................. .... ..... . ...... 21
3. MAXIMUM ..... . .. ......... ..... ................ .... ................. ............. 21
PERFORMANCE INDICATORS
a. ROE .............................................. ... ................ . ... ............ 22
b. ROA ......................... ... ............ ... ........ ... ............. .............. 23
c. EBITDATA ....... .......... .......... ....... ...... ..... ..... .................. .. .. 25
d. NITA ........................................ ..... ...... ................. ............ 26
SECTION VI
CONCLUSION ............... . ... ........ .................... .............. ... .. ........ ..... 29
REFERENCE ..... ....... ............. ..... ... ......... ..... ........... ........... ........... 3
APPENDIX ....... .................... .... ............. .... .............. .. .. ........... .. . .. 33
LIST OF TABLES
TABLE 1 .................... ........... ..... ............ ..... ................ ......... ....... 37
TABLE C ........... ...... ............. .... ..... ... ......................... ....... ....... .. .. 38
TABLE 3 ..... ....... ..... . ...... ...... .......................... .... ........ ...... ... ........ 40
TABLE 4 .................. .. .................................. .... ......... ... ........ ..... .. 41
TABLE 5 ..................... ...... ........................... .... ......... ... ..... .. ........ 42
LIST OF FIGURES
FIGURE 1 ..... ........................ ........... ... .... .... ................ ............ ..... .2
FIGURE 2 ......... ........................ ...... ...... ............ ............. . ...... .. . ..... 10
FIGURE 3 ....... ....................... ...... .................. ...................... .... .. .. 13
FIGURE4 ....... ....................... ...... ................... ..................... ...... . . 19
Ill

SECTION I

I.

INTRODUCTION AND BACKGROUND:

In 2008, crude oil pnces increased in a dramatic way and peaked to
unprecedented levels ($147 per barrel in July); meanwhile, world oil consumption
increased from 75 million barrels per day in 2000 to 87 million barrels per day in 2008,
according to the US Department of Energy (refer to chart of page 2). Most of the high
demand for oil was generated from Brazil, India, Russia, and China (BRIC) countries,
due to their strong economic growth. As a result of this increased demand for oil and the
scarcity of oil resources, profits and stock prices for a number of oil and gas companies
rose noticeably as the energy stock prices increased. However, the relationship between
crude oil price fluctuations and the return on profit for oil companies is still controversial
and debatable.

Several studies have examined and tested the impact of fluctuations of crude oil
prices on the equity returns at country, industry, and individual company levels (Chen et
al. 1986; Al-Mudhaf and Goodwin, 1993; Jones and Kaul, 1996: Faffand Brailsford,
1999; Hammoudeh et al., 2004; Hammoudeh and Li, 2005; Boyerand, Filion, 2007;
Nandha and Hammoudeh. 2007), yet, none of these studies has directly examined the
relationship between crude oil prices and the financial performance of oil and gas firms.

Figure 1
Oil Price, January 2003 - December 2008
w..kty United States Spot l'd c. We5t:ht.cf by knport Vol~ (Dollus per Barrel)
S160

$140

$120

SlOO

"!

.:

!. sso

=i
0

S60

S40

S20

so

This paper analyzes the impact of crude oil pnce index on the financial
performance of 200 largest oil and gas companies listed on the US stock exchange market
between the period of 1990 and 2008. The U.S. oil industry includes companies engaged
in various phases of oil production and processing, operating domestically and
internationally. These companies are grouped into five categories based on the
classification of the S&P oil industry sector stock indices including; oil exploration and
production, oil and gas refining & marketing, oil-domestic integrated, oil-international
integrated, and oil composite.

2

The sample of companies examined includes multinational oil and gas companies,
which are traded publicly on US Stock Exchange. As a requirement, these firms must
publicly disclose all their performance measures between 1990 and 2008. Panel data
regression analysis is the analytical tool employed to facilitate in the analysis and
understanding of the underlying relationship between crude oil prices and the financial
performance of the oil and gas firms. The performance indicators used in this study are
return on equity (ROE), return on assets (ROA), earnings before interest, tax,
depreciation and amortization (EBITDA), and net income (NI). The total observations
for each performance indicator varies between a minimum of 2151 and a maximum of
2494 observations, as shown in the methodology section in table B. This analytical work
is based on the hypothesis that: an increase in crude oil price positively influences the
financial performance of oil and gas firms.

The outcome of this paper should aid in understanding the relationship between
crude oil prices and the financial performance of oil and gas companies that trade on the
US Stock Exchange, and are engaged in different activities, especially exploring
activities in the petroleum industry. Therefore, the remainder of this paper is structured as
follows: Section II reviews existing literature, Section III describes the hypothesis and the
research question formulated, Section IV describes the methodology used and data
collected about the 200 largest oil and gas companies, Section IV analyses the
relationship between the performance indicators and crude oil prices using data collected,
and Section VI offers a conclusion.

3

SECTION II

II.

LITERATURE REVIEW:

There has been sufficient research and analysis performed about the relationship
between the crude oil prices index and the petroleum sector. Chen et al. 1986; Al-Mudhaf
and Goodwin, 1993; Jones and Kaul, 1996; Faffand Brailsford, 1999; Hammoudeh et al.,
2004; Hammoudeh and Li, 2005; Boyerand Filion, 2007; Nandha and Hammoudeh. 2007
studied and analyzed how oil company stocks reacted to the changes in oil prices.
However, none of the theoretically or empirically published research, shows analysis
about the relationship between the change in crude oil prices and financial performance
of the oil and gas firms listed on the US stock market (i.e existing research has not
addressed the topic on a large scale). This research paper therefore, is focused on
investigating the direct impact of fluctuations in crude oil prices on the 200 largest oil
and gas company financial indicators.

According to the literature review, considerable research has been conducted
about how oil prices influence financial markets and stock prices. For example, at the
country level, Jones and Kaul (1996) studied the impact of oil prices across Canada,
Japan, UK and USA, and concluded that the differences in oil prices depended on
different concentrations of resources and industries. In their research, they utilized a
standard cash-flow dividend valuation model as an analytical tool. They found that the
4

response could completely be accounted for by the impact of the oil stocks on real cash
flows; however, the results for Japan and the UK were not as strong as it appeared in
Canada.

Huang, Masulis, and Stoll (1996) used Vector Auto regression (VAR) model to
examine the relationship between daily oil futures returns and daily U.S. stock returns.
They found that oil future returns some individual oil company stock returns but had no
impact on the broad based market index such as the S&P 500. In addition, Hammoudeh
and Li (2005) made a comparison between oil prices, and the return on the stock markets
in oil-based countries, and the world capital market as represented by the MSCI World
Index. They found a positive association between oil prices and the return on stock
market oil- based countries, and a negative association with the MSCI World Index. In
addition, they found a negative relationship between the returns of the US transportation
industry and oil prices

Sadorsky (2001) analyzed oil pnce sensitivity of the Canadian oil and gas
industry by using the APT model where the Toronto Stock Exchange (TSE) Oil and Gas
index is explained by market return, crude oil price, exchange rate and interest rate. He
observed that crude oil prices and market returns have a positive effect on industry
returns. Furthermore, Sadorsky (200 1) also observed that crude oil prices and market
return on the firms listed on the US Stock Exchange have a positive effect on stock prices,
whereas a depreciation of the Canadian dollar, and an increase of interest rates, have a
negative effect on Canadian oil and gas stocks. In addition, he further explained that the
influence of factors (macroeconomics, accounting and others) depends on the timeframe,
5

the measures employed, the database, or simply on the operations of the corporation in
particular.

This implies that different industries react to different factors, for example: a
sudden increase in commodity prices of crude oil should not only lead to an increase in
the market value of the firms producing the commodity, but also lead to a decrease in the
value of net buyers. As Sadorsky (2002) reports, the idea that macroeconomic variables
can help to explain excess returns in equity and bond markets has recently been extended
to commodity futures markets. He also found that the oil and gas firms value are driven
by commodity prices.

At the firm level, AI-Mudhaf and Goodwin (1993) used a multi-factor of the
arbitrage pricing theory (APT) model to analyze and explain the differences in market
and oil price returns in 29 US oil companies in a period surrounding the oil shock of 1973.
They found that oil price shocks drove up returns for oil firms. In addition, Rajgopal and
Venkatachalam (1999) studied 25 petroleum refining companies and concluded that
earnings exhibited a strong correlation with the firms' oil betas (i.e. their sensitivity to
changes in oil prices).

Boyer and Filion (2007) employed the APT model to investigate the determinants
of stock returns of Canadian oil and gas companies. Their results also reveal a significant
relationship between oil price changes and stock returns.

6

However, other researchers argue that the impact of crude oil prices on equity
returns is ambiguous. (Chen et al. (1986) from US, and Hamao (1989) from Japan).
Besides the relation between the oil risk and equity return, some studies focus on the
question whether oil price sensitivity can be seen as an explanatory factor in asset pricing.
According to the semi-strong form of market efficiency, investors should not be able to
trade profitably on the basis of publicly available information. Since the oil price is
publicly available for every investor, it suggests that investors can not earn an extra
return from bearing oil price risk.

Accordingly, Hammoudeh et al. (2004) investigated the dynamic relationship
among five S&P oil sector index and five different oil prices for the US oil markets. The
results show that the West Texas Intermediate (WTI) spot oil prices and New York
Mercantile Exchange (NYMEX) futures prices explain the stock price movement of oilrelated companies.

Using multivariate co-integration techniques and a vector error-correction model,
Lanza et al. (2005) examined the long-run financial determinants of the stock prices of
six major oil companies and found a significant oil risk premium. However, this
contradicts what other researchers have argued. For example, Chen et al. (1986)
examined the impact of an index of oil price changes on asset pricing and found no
overall effect. Given this contrasting evidence, it is worthwhile to have a closer look at
these prevailing issues. This will be achieved by using more recent data for the US and
by using a modem financial approach.

7

Another study that examined the American oil and gas companies brings to
light two important details. Firstly, using the Johansen (1988) co-integration test, Aleisa
et al. (2003) shows that price fluctuations of West Texas Intermediate (WTI) barrel 1month to 4-month futures explain share price movements of firms operating in
exploration, refinery and marketing of oil. In fact, they note that the degree of cointegration varies between crude oil prices and the firm type. Firms included in the S&P
Oil Composite Index, the S&P Oil Domestic Integrated Index and the S&P Oil
International Integrated Index have a stronger link to crude oil prices than firms included
in the S&P Oil and Gas Exploration Index or the S&P Oil and Gas Refining and
Marketing Index.

This paper specifically examines the impact of the fluctuations of crude oil prices on the
oil firms' financial indicators including ROE, ROA, NITA, and EBITDATA, in terms
of how significant they are and whether there is a positive or negative relationship.

8

SECTION III

III.

HYPOTHESIS AND RESEARCH QUESTION:

For purposes of this paper, crude oil price is assumed to have a major influence
on the performance of oil and gas firms. As stated in the literature review, the
relationship between crude oil prices and the performance of the oil and gas companies
has not been clearly and conclusively researched. Consequently, an in-depth research will
be carried out to analyze this relationship and will be based on the following hypothesis:

Hypothesis:
An increase in crude oil price will positively influence the performance of oil and
gas firms

Confirmation or rejection of the above hypotheses will be based on answenng the
following question:

What is the impact of the crude oil price index on the performance of Oil and Gas
firms?

If there is conclusive evidence showing a positive relationship between increase in crude
oil prices and the financial performance of oil and gas firms, then the hypothesis will be
accepted.
9

SECTION IV

IV.

METHODOLOGY ANDDATA:

This empirical research analyzes historical financial data collected from the
financial statements of the 200 largest oil and gas companies listed on the US Stock
Exchange between the periods of 1990 to 2008. As a requirement these companies must
have production and discovery of oil and gas activity in the time series of the study. In
addition to the historical financial data, the crude Oil price from the West Texas
Intermediate oil and gas index as shown in the chart below was obtained. To mitigate the
impact of the fluctuation of crude oil prices, the average price per year for the time period
of examination will be used. In addition, a number of key metrics on current active and
publicly traded firms including mean, minimum, and maximum, will be used to analyze
the relationship between crude oil prices and performance indicators.

10

-

-

~

FIGURE 2:
Crude Oil - \Nest Texas Intermediate Spot Price - Avg. Annual
USD per Barrel
120

100

...

.., 80
G>

·;:::

c..

0

GO

2
<.J

40

- - S c ri cs 1

G>
-.:I

-

20

0

'

'

'

'

'

'

'

~~~~~~~~~~~~
Years

'

~

'

'

'

~~~

'

r

TABLE A:
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Source:

Crude Oil- West Texas Intermediate
Spot Price - A vg Annual USD per Barrel
24.53
21.54
20.58
18.43
17.2
18.43
22.12
20.61
14.42
19.34
30.38
25.98
26.18
31.08
41.51
56.64
66.05
72.34
99.67
United States Energy Information Administration

11

The main financial performance indicators used in this analysis include: return
on equity (ROE), return of asset (ROA), earning before interest, taxes, depreciation, and
amortization (EBITDATA) divided by total assets, net income (NITA) divided by total
assets, gearing (GEARING), and total assets (TA) measured by the (log) of the market
capitalization in US dollars. To diminish the size factor on the performance of the
selected companies, NITA and EBIDT A performance indicators were divided by their
total assets respectively.

These performance indicators were selected to be used as variables in the
hypothesis because they give clear justification about the performance of firms as crude
oil prices fluctuate. In addition, they are commonly used to evaluate the financial
performance and the wealth of companies. In order to have a better understanding of the
relationship between oil price fluctuations and oil and gas companies' performance, extra
variables referred to as dummies (D1, D2, and D3) were added to the analytical model.
Each of these dummies symbolizes a big event that occurred and has had enormous
impact on the global economy such as the Asian Crisis in 1996, the attack on the world
trade center 2001, and the current global financial crisis (2008/2009)

This paper is based on literature review research and secondary resource data.
All data was collected from Online Osiris Database, a reliable and consistent data source,
and was in the form of financial statements of the selected companies which are publicly
available. The historical crude oil price is also available through the oil and index data
resource accessible at. http://tonto.eia.doe.gov/dnav/petlhistlrwtca.htm

12

To test this hypothesis, collected financial data was compared to the crude oil
price for the same period of time in order to find the relationship between or the impact
of the crude oil price index on the performance of oil and gas firms. Note must be taken
that some of the performance indicators such as return on equity (ROE), and return on
assets (ROA), are already provided for in the firms' financial statements. However, other
performance indicators such as NITA and EBIDATA have to be calculated so as to serve
the purpose of this paper.

The data contain observations for the return on assets, return of equity, earning
before interest and taxes, depreciation and amortization, net income, total assets, and
gearing. The maximum number of observations should be 200 * 19 = 3800 for each
indicator as shown for Oil in the table below. However, due to missing data in the
balance sheet, the maximum number of observations for ROE, ROA, EBITDATA,
NITA, GEARING, and TA could not be calculated. Refer to table below:
TABLEB:

Observations

ROE

ROA

2400

2494

EBITDATA NITA

2381

13

2159

GEARING

TA

OIL

2151

2404

3800

FIGURE3

Observations of perfomance Indicators
4000
3500
3000

."'.
c

2500

10

2000

0

~

Ql

..c
"'

0

• Seriesl

1500
1000
500
0
ROE

ROA

EBITDATA

NITA

GEARING

TA

OIL

Pe rformance Indicators

Osiris Online Data Resource: Financial information on 44,000 listed and major unlisted/desisted companies worldwide (34,000 are
non-US companies). The information includes: standardized and as reported financials (including restated reports), SEC filings,
detailed earnings estimates including recommendations, ownership, stock data, news and ratings

Random Effect model is being sued based on the adjusted coefficient (random
Vs fixed effects), some total assets is positive using random effects model

14

In order to analyze the relationship between firm performance and crude oil
price, a cross-sectional model was used in which firm performance measured by ROA,

ROE, NITA, EBTIDATA, is regressed on Crude Oil Price, GEARING, TA and the
dummies Dl, D2 and D3 as shown in the regression model below.

(I)

ROE it= a+ Bt COPit+Bz logTA it +B3 Dl it +B4 D2 it+ BsD3 + B6
GEARINGt

(II)

ROA it= a+ Bt COPit+Bz logTA it +B3 Dl it +B4 D2 it+ BsD3 +
B6GEARINGit

(Ill) NITA it= a+ Bt COPit+Bz logTA it +B3 Dl it +B4 D2 it+ BsD3 + B6
GEARINGt

(IV) EBITDATA it= a+ Bt COPit+Bz logTA it +B3 Dl it +B4 D2 it+ BsD3
+ B6GEARINGit
From the model above, the dependent variables are return on equity (ROE), net
income divided by the total asset for the same year for each firm (NITA), earnings before
interest, taxes, depreciation and amortization divided by total asset (EBITDATA) and
return on assets (ROA) for an individual firm. The independent variables include crude
oil price (COP), which is calculated as the average price over one year and GEARING,
which is defined as long term liabilities divided by equities. Dl, D2, and D3 (dummies)
symbolize a big event that has occurred and has had enormous impact on the global

15

economy such as the Asian Crisis in 1996, the attack on the world trade center 2001, and
the current global financial crisis (2008/2009)

16

Description

Variables
ROE:

Return on equity is defined as NI divided by Equity
= NI I Shareholder's Equity

ROA:

Return on Assets is defined as NI plus interest divided by total assets
=Nil Total asset

INTA:

Net Income divided by total Asset is a measure of the profitably of a company
over a period of time examination
=Net Income I Total Asset

EBITDA T A:

Earnings before interest, taxes, depreciation and amortization divided by total
asset is an indicator of a company's financial performance.
= Revenue- Expenses (excluded

Interest, Taxes, Depreciation, and

Amortization) I Total Asset
GEARING:

is defined as long term liabilities divided by shareholder funds.

COP:

Crude Oil- West Texas Intermediate Spot Price- Avg Annual USD per Barrel
=(Non Current Liabilities+ Loans) I Shareholders Funds* 100

TA:

Total assets

Dl:

The Asian crisis in 1996: Dummy controlled variable

D2:

The 9111 attack of the world Trade Center in 2001: Dummy controlled variable

D3:

The financial crisis in 2008; Dummy controlled variable

17

Key Metrics to be used in the analysis:

In this empirical study, mean, maximum, and minimum metrics were used to
analyze the performance indicators including return on assets (ROA), Return of equity
(ROE), Earnings before interest, taxes, depreciation and amortization (EBITDATA), and
iv) net income (NITA). In addition, the metrics will be used to analyze the control
variables GEARING, OIL, and TA.

18

SECTIONV

V.

ANALYSIS

Based on the framework of my hypothesis, which states that the crude oil price
will positively influence the performance of oil and gas firms, a strong and positive
relationship between the financial performance indicators and the price of crude oil is
expected to be found.

As indicated in the methodology section, the Mean, Median,

Minimum, and the Maximum metrics for both the dependent and independent variables s
performance of the oil firms.
Sample: 1990 2008
ROE

ROA

EBITDATA

NITA

GEARING

TA

OIL

Mean
Median
Maximum
Minimum

10.39
10.89
52.85
-55.57

4.64
4.62
56.93
-47.25

3.85
2.28
56.90
-34.00

1.17
0.57
48.45
-29.56

76.70
72.37
199.65
-189.68

1,262,383.74
56,297.00
228,052,000.00
755,000

34.05
24.53
99.67
14.42

Observations

2,400

2,494

2,381

2,159

2,151

2,404

3,800

Table 1 displays the summary statistics of the depended and independent variables. It
shows their Mean, Minimum, and Maximum values as the crude oil prices change.

Key Metrics
i.

Mean in value of the performance indicators
From table 1 summary statistic, the means of the dependent variables, Return on

equity (ROE) is 10.39 %, Return on assets (ROA) is 4.64%, earnings before interest,
19

taxes, adding depreciation and amortization divided by total assets (EBITADTA) is
3.83% and Net income divided by total assets (NITA) is 1.17%. In addition, the mean of
the independent variable GEARING is 76.70%, and the mean of the crude oil prices is
$34.05
FIGURE 4: Performance Measures and Oil Price (1990-2008)
120
100
80
60
40
20
0
-20

90

92

94

96

98

- - Mean ROA
- - Mean OIL
- - Mean EBITDATA

ii.

00

02

04

06

08

- - Mean ROE
Mean NITA

Minimum in value of the performance indicators
The second key metric that used in this analysis is the minimum. Accordingly,

results indicate that when oil prices dropped to its lowest level at $14 in 1999, the
minimum of all performance indicators have a negative sign. This implies that most oil
20

firms in our sample have negative returns on their assets and/or equity, and preformed
negatively at the lowest price in 1999.

Results from table 1 indicate that the lowest return on equity (ROE), and the
lowest return on assets (ROA) among the 200 firms in the sample, equaling to -55.57%
and -47.25%, respectively. Also, the mean of the dependent variables both EBITDATA
and NITA are negative when the crude oil prices decreased as shown in (table 1), the
minimum values of -34.00% and -29.56% respectively.

iii.

Maximum in value of the performance indicators

The maximum value of the performance indicators was recorded when the crude
oil prices reached its peak. As the table 1 shows, with the maximum metric, all the values
of the performance indicators have a positive sign.

The maximum values of the

dependent variables return on equity ROE amounting to 52.85%, while the largest return
on assets ROA amounting to 56.93%.The maximum EBITDATA value is 56.90%
among the tested sample in this study. The highest maximum NITA amounting to
48.45%.
The outliers have been removed form the sample to have an accurate results
form the regression although they represent a small percentage of the sample. The mean
and the median are very close in terms of value, implying that the outliers had no impact
on the research, for example, an outliers the maximum value for ROE 889% (outlier) and
the minimum is -675% (outlier) whereas the mean is 10.39% and the median is 10.89%
(Refer for table 1).
21

Financial Performance Indicators:

a) -ROE

Table 2 demonstrates the relationship between the dependent variables return on
equity ROE and the independent variables crude oil price, total assets, and gearing. In
addition, it shows the impact of D 1, D2, and D3 on the dependent variable ROE. It is
evident from the results given by the panel data analysis that there is a significant and
positive relationship between the Crude oil price and the ROE performance indicator at
1% level. The coefficient of the GEARING is negative and significant at the 1% level.
The Asian crisis has a positive impact on the ROE (significant at the 1% level).the 9/11
attack on the World Trade Center has a positive and significant impact on the ROE at 5%
level. Finally, the financial crises have a negative impact on the ROE (significant at the
1% level). The adjusted R- squared is 0.086% and the F- statistic for the regression is
32.05 (significant at the 1% level)

22

Table 2 Dependent Variable: ROE
Method: Panel EGLS (Cross-section random effects)
Sample: 1990 2008
Periods included: 19
Swamy and Arora estimator of component variances
Variable

Coefficient

Std. Error

t-Statistic

p-value

c

0.710
0.515***
0.168***
-0.026***
3.849***
2.216**
-10.410***

1.938
0.197
0.016
0.007
1.279
1.048
1.303

0.37
2.61
10.51
-3.96
3.01
2.11
-7.99

0.71
0.01
0.00
0.00
0.00
0.03
0.00

LOG(TA)
OIL
GEARING
D1
D2
03
Adjusted R-squared
F-statistic
Prob(F-statistic)

0.086
32.05
0.00

***

*,**,***significant at the 10,5,1 percent level (two-tailed tested)
D 1 = Asian crisis ,
D2 = 9/ 11
D3 = financial crisis
Gearing = (non-current liabilities + loans)/equity
Size = logarithm of total assets
Oil index = average WTI Crude Oil price

b)-ROA
Results m table 3 show that there 1s a significant and positive relationship
between the crude oil pnce and the ROA at the 1% level. The coefficient of the
GEARING is negative and insignificant. The Asian crisis has a positive and significant
impact at 1% level, and the 9/11 attacks of the trade world center has a positive and
significant impact on the dependent variable ROA at 10% level. It can also be observed
from the table that the current financial crisis has a negative and significant impact on the

23

ROA 1% level. The adjusted R- squared is 0.07% and the F-statistic for the regression is
20.47 (significant at the 1% level)

Table 3 Dependent Variable: ROA
Method: Panel EGLS (Cross-section random effects)
Sample: 1990 2008
Periods included: 19
Swamy and Arora estimator of component variances
Variable

Coefficient

Std. Error

t-Statistic

Pro b.

LOG(TA)
OIL
GEARJNG
Dl
D2
D3

c

-2.282
0.226
0.104***
-0.000
2.295**
1.476*
-7.473***

1.488
0.146
0.01 I
0.005
1.011
0.750
0.850

-1.53
1.55
9.36
-0.06
2.27
1.97
-8.79

0.13
0.12
0.00
0.95
0.02
0.05
0.00

Adjusted R-squared
F-statistic
Prob(F-statistic)

0,07
20.47
0.00

***

*,**,***significant at the 10,5,1 percent level (two-tailed tested)
D 1 = Asian crisis
D2 = 9/ 11
D3 = financial crisis
Gearing = (non-current liabilities+ loans)/equity
Size= logarithm oftotal assets
Oil index = average WTI Crude Oil price

24

c)-EBITDATA

Results from Table 4 shows that there is a significant and positive relationship
between the crude oil price and the financial performance EBITDAT A at the 1% level.
The coefficient of the GEARING is positive and significant relationship with the
EBITDATA at 5% level. Both the Asian crisis and the 9/11 attack on the Trade World
Center have a positive impact on the EBITDATA at 5%, and1% level accordingly. The
current global financial crisis has a negative and significant impact on the EBITDATA at
the 1%. The adjusted R- squared is 0.20% and the F-statistic for the regression is 83.75
(significant at 1% level).

Table 4 Dependent Variable: EBITDATA

Method: Panel EGLS (Cross-section random effects)
Sample: 1990 2008
Periods included: 19
Swamy and Arora estimator of component variances

Variable

Coefficient

Std. Error

t-Statistic

Pro b.

LOG(TA)
OIL
GEARING
Dl
D2
D3

c

19.143***
-1.702** *
0.081***
0.005**
0.937*
1.487***
-2.080***

0.917
0.094
0.007
0.003
0.491
0.398
0.510

20.87
-18.18
12.06
2.04
1.91
3.74
-4.08

0.00
0.00
0.00
0.04
0.06
0.00
0.00

Adjusted R-squared
F-statistic
Prob(F -statistic)

0.20
83.75
0.00

***

*, **, *** significant at the 10,5, 1 percent level (two-tailed tested)

25

d)-NITA
Table 5 shows results from the panel data regression to be similar to the results
obtained from previous regressions of other performance indicators. It can be observed
from Table 5 that the relationship between the independent variable, crude oil price and
the dependent variable, NITA is positive and significant at 1% level. The coefficient of
GEARING has no relationship with NITA. The Asian crisis has not impact on the
deepened variable, NITA . The 9111 attack on the world trade center has no impact on
NITA. Finally, the current global financial crisis is observed to have a negative and
insignificant impact on the NITA at 1% level. The adjusted R- squared is 0.09% and the
F-statistic for the regression is 30.0 and significant at the 1% level.
Table 5 Dependent Variable: NITA
Sample: 1990 2008
Periods included: 19

Method: Panel EGLS (Cross-section random effects)

Swamy and Arora estimator of component variances

Variable

Coefficient

Std. Error

t-Statistic

Pro b.

LOG(TA)
OIL
GEARING
Dl
D2
D3

c

5.968***
-0.535***
0.043***
-0.003
0.554
0.176
-1.963***

0.573
0.057
0.004
0.002
0.356
0.307
0.343

10.41
-9.37
9.89
-1.45
1.56
0.57
-5.72

0.00
0.00
0.00
0.15
0.12
0.57
0.00

Adjusted R-squared
F-statistic
Pro b(F-statistic)

0.09
30.00
0.00

***

*,**,***significant at the 10,5,1 percent level (two-tailed tested)

26

TableD: Summary of the relationship between the dependent
variables and the controlled variables
ROE

ROA

NITA

EBITDA

LOG (TA)

+***

+INS

- ***

- ***

OIL

+ ***

+ ***

+ ***

+ ***

GEARING

- ***

-INS

-INS

+ **

Dl

+ ***

+ **

+ ***

+*

D2

+ **

+*

+INS

+ ***

D3

- ***

- ***

- ***

+ ***

*, **,***significant at the 10, 5, 1 percent level (two-tailed tested) INS= Insignificat
Table D summary of shows the results from the panel data analysis between the
financial performance indicators (dependent variables) and the controlled variables the
(independent variables). It shows how they are related to each other positively or a
negatively, and how significant or insignificant their relationship is. As for the hypothesis
of this study, table C shows a statistically positive andsignificant relationship between
crude oil prices (controlled variable) and, return on equity (ROE), return on asset (ROA)
earning before interest, tax, depreciation and amortization (EBITDATA), and net-income
(NI).

27

SECTION VI

VI.

CONCLUSION:
The global oil and gas industry has experienced rapid increase in demand most

especially from developed and fast developing nations including Brazil, Russia, India,
and China (BRIC). This resulted into a dramatic increase in the price per barrel of oil to a
record $147 in July 2008.
However, upon reviewing existing literature, it became apparent that no research
has conclusively examined the relationship between the crude oil prices and the financial
performance of the oil and gas companies on a large scale. This paper has focused on
examining the impact of crude oil price fluctuations on the financial performance (ROA,
ROE, EBTIDATA, and NITA) of the largest 200, capital based, oil and gas companies
which were listed on the USA stock market between 1990 and 2008.

Analysis reveals that there is a positive and significant relationship between crude
oil price fluctuations and the financial performance indicator. The empirical results
provide evidence that an increase in crude oil price positively influences the performance
of oil and gas firms. Results from the panel data regression analysis show a statistically
significant relationship (at 1% level) between the oil and gas index, return on equity
(ROE), the return on asset (ROA) earning before interest, tax, depreciation and
amortization (EBITDATA), and net-income (NI).

28

Therefore, this study shows that there is a significant positive relationship
between crude oil prices and the performance of oil and gas companies, and it answers
the question, "What is the impact of the crude oil price index on the performance of Oil
and Gas firms?" As a result, the hypothesis that an increase in crude oil price will
positively influence the performance of oil and gas firms is confirmed and accepted.

29

REFERENCES:
Ahn. S.C .. and C. Gadarowski (1999). Two-Pass Cross-Sectional Regression of Factor
Pricing Model:Minimum Distance Approach, Working Paper. Arizona Slate University.
Al-Mudhaf, A., and T.H. Goodwin (1993). "Oil Shocks and Oil Stocks: Evidence from
the 1970s. "Applied Economics 25: 181-190
Bower, D.H .. R.S. Bower, and D.E. Logue (1984) . "Arbitrage Pricing Theory and Utility
Stock Refum/\. ".hurnal of Finance 39: 104!-1054.
Boyer. M.M,. and D. Filion (2007). "Common and Fundamental Factors in Stock Returns
ofCanadianOil and Gas Companies." Energy Economics 29: 428-453.
Brooks. C. (2002). Introductory Econometrics for Finance, Cambridge: Cambridge
University Press.
Burmeister. E .. and K. Wall (1986). "The Arbitrage Pricing Theory and Macroeconomic
Factor Measures."The Financial Review 2\: 1-20.
Burmeisler. E . and M. McElroy (1988). "Joint Eslimation of Factor Sensitivities and
Risk Premia forThe Arbitrage Pricing Theory." Journal of Finance 43: 721 -733.
Chen. N.F .. R. Roll and S.A. Ross (I98fi). "Economic Forces and the Stock Maiket."
Journal of Busine.M 59: 383-403.
Cochrane. J.H. (2001). A. s5sr Pricing, Princeton and Oxford: Princeton University Press.
Elton. E.J.. M.J. Gruber. S.J. Brown, and W.N. Goetzman (2003). Modem Portfolio
Theory and Investmem Anaiysis, New York: John Wiley & Sons. 6"' edition.
Fama. E.F .. and J.D. MacBelh (1973). "Risk, Return, and Equilibrium: Empirical Tests."
Journal of Political Economy S\: 607-636.
Fama, E.F .. and K.R. French (1992). "The Cross-section of Expected Stock ReXums."
Joumat of Finance47: 427-465
Faff. R.W .. and T.J. Brailsford (1999). "Oil Price Risk and the Australian Stock Market.''
Joumai ofEnergy Finance and Development 4: 69-87.
Ferson, W,E,. and C.R. Harvey (1991). •The Variation of Economic Risk Premiums.''
Joumai ojPoUiicalEconomy 99. 385-415.
Geweke.J. and G. Zhou (1996). "Measuring the Price of Arbitrage Pricing Theory."
flmAww/financial Studies 9: 557-.'>87.

30

Gibbons. M.R. (1982). "Multivariate Tests of Financial Models: A New Approach."
Joumai ofFinancial Economics 10: 3-28.
Hamao. Y. (1989). "An Empirical Examination of the Arbitrage Pricing Theory: Using
Japanese Data." Japan and liie World Economy 1: 45-61.
Hammoudeh, S. S. Dibooglu. and E. Aleisa (2004). "Relationships among U.S. Oil Prices
and Oil Industry Equity Indices" Intemationai Review of Economics and Finance 13:
427-453.
Hammoudeh. S .. and H. Li (2005). "Oil Sensitivity and Systematic Risk in Oil-sensitive
Stock Indices." Joumai of Economics and Business 57.
Jones. C. and G. Kaul (1996). "Oil and the Stock Markets." Journal of Finance 5\: 463491
Kaneko. T. and B.S. Lee. B.S. (1995). "Relative Importance of Economic Factors in the
U.S. andJapanese .Stock Markets." Joumai of the Japanese and International Economies
9: 290-307.
Lanza. A., M. Manera. M, Grasso. and M. Giovannini (2003). "Long-run Models of Oil
Stock Prices." Envinmmentai Modetlinf" and Software 20: 1423-1430,
Nandha, M .. and S. Hammoudeh (2007). "Systematic Risk, and Oil Price and Exchange
Rate Sensitivities in the Asia-Pacific Stock Markets." Research in international Business
and Finance 21: 326-341.
Rajgopal, S .. and M. Venkatachalam (1998). Are Earning Sen.iitivily Measures Riskrelevant? The Case of Oil Price Risk for the Petroleum Refining Industry. Working
Papers. Stanford Graduate School of Business.
Sadorsky, P (2001). "Risk Factors in Stock Returns of Canadian Oil and Gas
Companies." Energy
Economics 23 : 17-28.

APPENDIX
31

Company name

Total Asset

228052000
161165000
142868000
34417000
42686000
11150000
28589000
25371346
47247000
10032000
7433000
11149098
13910665
26562143
41537000
24805651
17957535
21657684
14385000
48923000
31908000
5049820
21478700
29186485
26006000
11861000
17885800
38444000
34827699
10816224
3076792
2308249
8300900
10627489
11069902
889262
19822799

EXXON MOBIL CORP
CHEVRON CORPORATION
CONOCOPHILLIPS
VALERO ENERGY CORP
MARATHON OIL CORPORATION
SUNOCOINC
HESS CORPORATION
ENTERPRISE GP HOLDINGS L.P.
ENCANA CORPORATION
PLAINS ALL AMERICAN PIPELI
TESORO CORPORATION
MURPHY OIL CORP
IMPERIAL OIL LIMITED
SUNCOR ENERGY INC.
OCCIDENTAL PETROLEUM CORP
PETRO-CANADA LTD
ENTERPRISEPRODUCTSPARTNE
HUSKY ENERGY INC.
HALLffiURTON CO
ANADARKO PETROLEUM CORP
DEVON ENERGY CORP
TEPPCO PARTNERS LP
NATIONAL OIL WELL VARCO, IN
APACHE CORP
WILLIAMS COMPANIES INC
BAKER HUGHES INC
KINDER MORGAN ENERGY PARTN
CHESAPEAKE ENERGY CORP
CANADIAN NATURAL RESOURCES
SMITH INTERNATIONAL INC
WESTERN REFINING, INC.
SUNOCO LOGISTICS PARTNERS
ENBRIDGE ENERGY PARTNERS,
ENERGY TRANSFER PARTNERS,
ENERGY TRANSFER EQUITY
GLOBALPARTNERSLP
TALISMAN ENERGY INC

7254272
38254000
32185203
15951226
18091622
2018469
1874225
5902371
5321908
23668000

ONEOK PARTNERS, L.P.
XTO ENERGY INC
TRANSCANADA CORPORATION
EOG RESOURCES INC
NEXENINC
FRONTIER OIL CORP
HOLLY CORP
CAMERON INTERNATIONAL CORP
BJ SERVICES CO
ELPASOCORP

32

2413433
1610483
2533266
2546743
4459597
4691660
1017200
3586300
210926
6518500
12384000
5317000
5661440
12585334
14213000
4938762
6092627
2299247
2510264
5830100
7111915
9163178
4760158
6065000
7305000
4548892
2712817
2052200
4825249
5070338
1178674
1580906
3087850
3588045
1670020
2016923
4164933
3263097
3034410
2491633
4822352
2458639
1994877
1512881
1382719
1773061
3462200
3623611
5282798
2013665

ALON USA ENERGY, INC.
CVR ENERGY, INC.
CROSSTEX ENERGY, L.P.
CROSSTEX ENERGY, INC.
NUSTAR ENERGY L.P.
HARVEST ENERGY TRUST
DELEK US HOLDINGS, INC
FMC TECHNOLOGIES INC
ADAMS RESOURCES & ENERGY I
OGE ENERGY CORP
NOBLE ENERGY, INC.
ADDAX PETROLEUM CORPORATION
CANADIAN OIL SANDS TRUST
PENN WEST ENERGY TRUST
DYNEGYINC
DIAMOND OFFSHORE DRILLING
EXTERRAN HOLDINGS, INC.
OIL STATES INTERNATIONAL I
PROVIDENT ENERGY TRUST
ENSCO INTERNATIONAL INC
PLAINS EXPLORATION & PRODU
PIONEER NATURAL RESOURCES
SOUTHWESTERN ENERGY CO
PRIDE INTERNATIONAL INC
NEWFIELD EXPLORATION CO
ROW AN COMPANIES, INC.
PATTERSON UTI ENERGY INC
DRESSER-RAND GROUP INC.
ATLAS AMERICA, INC.
HELIX ENERGY SOLUTIONS GRO
GENESIS ENERGY LP
TARGA RESOURCES PARTNERS LP
GAZ METRO PO LITAIN AND COMP
HELMERICH & PAYNE, INC.
OCEANEERING INTERNATIONAL
KEY ENERGY SERVICES INC
CIMAREX ENERGY CO.
BUCKEYE GP HOLDINGS L.P.
BUCKEYEPARTNERSLP
SUPERIOR ENERGY SERVICES I
EXCO RESOURCES, INC.
REGENCYENERGYPARTNERSLP
COMPLETE PRODUCTION SERVIC
FLINT ENERGY SERVICES LTD.
KEYERA FACILITIES INCOME F
EAGLE ROCK ENERGY PARTNERS
SEACOR HOLDINGS INC.
PATRIOT COAL CORPORATION
FOREST OIL CORP
COPANO ENERGY, L.L.C.

33

5087483
4594724
1808076
1766749
4722020
4342104
2451321
2445533
3589674
2581866
2673054
5562543
2695016
3392793
1180000
2751780
4029081
2996552
2056186
2600708
2296115
3655058
3075862
1003157
3633195
2590895
6907329
1330282
1485594
3368992
1977355
1412624
1310711
2215879
3701664
2701035
3947168
793461
713679
1309608
605225
1006810
1213631
1479939
1517288
1468528
2076792
2216834
2542383
2106003

ENERPLUSRESOURCESFUND
DUNCAN ENERGY PARTNERS L.P.
ENSIGN ENERGY SERVICES INC.
ALTAGAS INCOME TRUST
LINN ENERGY, LLC
PENGROWTH ENERGY TRUST
ATLAS PIPELINE HOLDINGS, L
ATLAS PIPELINE PARTNERS, L
DENBURY RESOURCES INC.
UNIT CORP
MARKWEST ENERGY
RANGE RESOURCES CORP
STMARY LAND & EXPLORATION
MARINER ENERGY, INC.
DCP MIDSTREAM PARTNERS, LP
TIDEWATER INC
WHITING PETROLEUM CORPORAT
PENN VIRGINIA CORP
W&T OFFSHORE, INC.
MAGELLAN MIDSTREAM HOLDING
MAGELLAN MIDSTREAM PARTNER
SANDRIDGE ENERGY, INC.
ARC ENERGY TRUST
SHAWCOR LIMITED
ENCORE ACQUISITION CO
HERCULES OFFSHORE, INC.
PETROHA WK ENERGY CORPORAT1
MCMORAN EXPLORATION CO
GLOBAL INDUSTRIES LTD
INTER PIPELINE FUND
BRISTOW GROUP INC.
TETRA TECHNOLOGIES INC
BASIC ENERGY SERVICES, INC
CONTINENTAL RESOURCES, INC.
CABOT OIL & GAS CORPORATION
CRESCENT POINT ENERGY TRUST
PRECISION DRILLING TRUST
RPCINC
NEWPARK RESOURCES INC
CAL DIVE INTERNATIONAL, IN
ENERFLEX SYSTEMS INCOME FU
TRICAN WELL SERVICE LTD
PARKER DRILLING CO
BA YTEX ENERGY TRUST
SWIFT ENERGY CO
VERMILION ENERGY TRUST
BONAVISTA ENERGY TRUST
BREITBURN ENERGY PARTNERS
BERRY PETROLEUM CO
STONE ENERGY CORP

34

4500571
2124973
2270685
6721600
530718
274593
1928554
861431
1111058
508166
1291819
537815
1520549
2275610
1995063
824479
1402704
1577890
422950
1776200
943409
864254
2554041
1202576
680609
2815203
470524
1099958
658230
1882601
1169096
777182
1154378
439716
1679602
476671
564896
1765533
814856
213485
221096
988565
1585046
350890
1787182
1556967
902898
435560
426139
549279

QUICKSILVER RESOURCES INC
CNX GAS CORPORATION
ATLAS ENERGY RESOURCES, LLC
BOARDWALK PIPELINE PARTNER
LUFKIN INDUSTRIES INC
MATRIX SERVICE CO
PETROBANK ENERGY AND RESOU
ION GEOPHYSICAL CORPORATION
ALLIS-CHALMERS ENERGY INC.
NATCO GROUP INC
WILLIAMS PARTNERS L.P.
INTEROIL CORPORATION
TRINIDAD DRILLING LTD.
ATP OIL & GAS CORP
BILL BARRETT CORPORATION
PIONEER DRILLING COMPANY
PETROLEUM DEVELOPMENT CORP
COMSTOCK RESOURCES INC
MAJOR DRILLING GROUP INTER
ULTRA PETROLEUM CORPORATION
CLAYTON WILLIAMS ENERGY INC
VENOCO, INC.
FORT CHICAGO ENERGY PARTNE
TRICO MARINE SERVICES INC
DRIL-QUIP INC
CONCHO RESOURCES INC.
EVEREADY INC.
ATWOOD OCEANICS INC
SUPERIOR WELL SERVICES, IN
ADVANTAGE ENERGY INCOME FU
CONNACHER OIL AND GAS LIMI
PHI, INC.
ROSETTA RESOURCES INC.
GEOKINETICS INC.
TRISTAR OIL & GAS LIMITED
TESCO CORPORATION
CALFRAC WELL SERVICES LTD.
ARC RESOURCES LTD
ENERGY PARTNERS LTD
CE FRANKLIN LTD
ICO INC
NAL OIL & GAS TRUST
HORNBECK OFFSHORE SERVICES
GULF ISLAND FABRICATION INC
COMPTON PETROLEUM CORPORAT
GULFMARK OFFSHORE INC
PARAMOUNT ENERGY TRUST
HILAND HOLDINGS GP, LP
HILAND PARTNERS LP
CARBO CERAMICS INC

35

----------

-------

847282
781961
1209632

SAVANNA ENERGY SERVICES CO
TRILOGY ENERGY TRUST
OILEXCO IN CORPORATED

36