CRUDE OIL PRICE AND OPERATING PERFORMANCE OF LISTED OIL AND GAS … · 2017-09-21 · Crude Oil...
Transcript of CRUDE OIL PRICE AND OPERATING PERFORMANCE OF LISTED OIL AND GAS … · 2017-09-21 · Crude Oil...
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CRUDE OIL PRICE AND OPERATING PERFORMANCE OF LISTED
OIL AND GAS COMPANIES IN NIGERIA.
Ismaila Yusuf1
Samuel Teryima Orshi1
Abdulateef Yunusa1
Abstract
This paper seeks to examine the effect of changes in oil prices on firms’
performance by focusing on how changes in oil prices influence operating
performance of oil and gas companies in Nigeria from 2006 to 2015. This
paper employed descriptive and correlational research design. The paper
adopts the purposive sampling technique to arrive at the sample size of seven
firms from a population of fourteen oil and gas companies quoted in Nigeria
as at 31st December 2015. The Generalised Least Square regression model
was used to ascertain the relationship between changes in the price of crude
oil (∆CPR) and operating performance (OPFR). The paper found that ∆CPR
significantly and positively affects OPFR of the sampled listed oil and gas
firms in Nigeria. This indicates that hedging strategy adopted by oil and gas
companies in Nigeria is very effective. The paper concludes that changes in
the prices of crude oil have a significant impact on the operating performance
of listed oil and gas companies in Nigeria. The study recommended further
study incorporating more companies and analyzing separately the effects of
crude oil price on the performance of oil and gas companies in the
downstream sector on one hand and those in the upstream on the other hand.
Keywords: Crude oil price, oil and gas companies, performance, oil price
shocks, operating performance
Introduction
Changes in the price of crude oil significantly impact on the economy of
countries across the globe. Some of the macroeconomic impact of the
increased price of oil includes an increase in the cost of oil products which in
turns reduce real incomes of households and increased the cost of production
in firms using oil as input thus reduce profitability which leads to a fall in
GDP growth. The impact is determined by a large scale on the status of the
country. A rise in crude oil price positively impacts on a net exporting country
1 Department of Accounting, Federal University Dutsin-Ma, Katsina State, Nigeria
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while a net importing country will be negatively affected by an increase in
crude oil price (Dayanandan & Donker, 2011).
From the micro-economic firm perspective, changes in oil prices affect firms
depending on the sector. For companies in the non-oil sector, increasing oil
prices will result in increased production cost with no compensating increase
in revenue leading to reduced profits. Among oil companies, the impact will
depend largely on whether the company operates in the downstream or
upstream sector. Companies in the upstream are expected to experience
increased in profits following increase oil price while downstream companies,
on the other hand, expects a declined profits as the cost of production will
increase and demand their products reduced. Thus, the more oil-intensive an
industry is, the more is likely the impacts of oil price shocks (Gogineni, 2010).
Dayanandan & Donker (2011) posit that most of the recent global economic
crises are influenced by spikes in oil prices while the public believes that oil
and gas firms benefit from higher oil prices at the expense of other entities.
Studies are bound relating oil prices to stock returns or stock market
performance at the macroeconomic level (Mohanty, Akhigbe, Al-Khyal, &
Bugshan, 2013; Asaolu & Ilo, 2012; Arouri & Nguyen, 2010; Gogineni, 2010;
Malik & Ewing, 2009). Results from these studies pointed to the fact that a net
importing economy experiences the negative impact of oil prices on market
returns while a net exporting economy experiences a positive impact
(Dayanandan & Donker, 2011). At the industry level, Arouri and Nguyen
(2010) examined oil-stock market relationship across various sectors and
found that oil price and the stock return relationship largely depends on the
sector. Several other studies have examined oil-stock market relationship from
industry or a sector perspective (Mohanty et al 2013; Sadorsky, 2012; Malik
& Ewing, 2009). Most of the studies on oil price relationship have been
concentrated on its effects at the macroeconomic level.
There is, however, a dearth of literature examining oil price effect on firm
performance. Very few studies, like (Lele, 2016; Hesse & Poghosyan, 2016;
Wattanatorn & Kanchanapoom, 2012; Dayanandan & Donker, 2011; Narayan
& Sharma, 2011; Ford, 2011) examined oil price change and its impact on
firm performance. However, most of the stated studies examined the impact
across the industry or concentrated on non-oil and gas industry with very few
carried out in emerging oil exporting countries. Changes in crude oil price
effects on macro economy largely depend on whether a country is a net
exporting or net importing, companies in the oil and gas industries and
operating in a net exporting country are likely to be affected differently as
compared to companies, not in the oil and gas and/or not operating in a net
exporting country. To bridge this apparent gap, this study is aimed at
assessing the impact of oil price changes at the firm level within the oil and
Crude Oil Price and Operating Performance of Listed Oil and Gas Companies in Nigeria
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gas industry with data from an emerging oil exporting country of Nigeria. The
study is aimed at examining the impact of the change in crude oil prices on the
performance of oil and gas companies. The study is expected to contribute to
the growing literature on the impact of oil price on firm performance in the oil
and gas industry.
Literature Review
Mohanty et al (2013) examined the asymmetric effects of daily oil price
changes on equity return and the stock market. Using data from the oil and gas
industry in the US, found that firm returns, market betas, oil betas, return
variances respond asymmetrically to changes in oil price. The study also
found that firm-specific factors such as firm size, return on Asset, leverage
and market to book ratio along with relative changes in oil prices determine
the effects of change in oil price on firms’ returns, risks and trading volume of
oil and gas companies. Ford (2011) examined the relationship between oil and
gas companies, gross profit margin and retail gas prices in the US. The results
from the study showed that major integrated oil companies record lower
profits during the period of high gas and oil prices, indicating that large oil
companies record better profits during periods of moderate oil prices. The
resulting point to the fact that the size of the oil and gas companies contributes
to the effect of oil price on its profitability. Dayanandan & Donker (2011)
investigated the relationship between prices of crude oil, capital structure, firm
size and accounting measures of firm performance using sample oil and gas
firms listed on the US stock exchange. Using a panel least square technique,
the study found that crude oil prices positively and significantly impact on the
performance of oil and gas firms in North America. The study also found that
the global financial crises of 2007 and 2008 negatively influenced oil prices
and financial performance while the Asian and 9/11 induced financial crises
did not have a significant impact on the performance of oil and gas
companies. Narayan &
Sharma (2011) using data from 560 US firms listed on the New York Stock
Exchange found that depending on sector oil price affects returns of firms.
They also found that oil price influence on firm returns is strongly controlled
by the firm size. Hazarika (2015) in a study using top five oil and gas
companies to analyze the impact of crude oil prices from 2007 to 2014 on
profitability, liquidity, financial health and efficiency these oil and gas
companies. The study found that fluctuating oil prices do not have a
significant impact on profitability, liquidity, efficiency and financial health of
these companies. Ramos & Veiga (2011) analysed the risk factors of investing
in the oil and gas industry in 34 countries. The study found that oil and gas
sector in developed countries respond more to oil price changes than emerging
markets. They also found that oil price rise has a greater impact than oil price
drops.
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In related studies using data from emerging economies, Lele (2016) examined
the impact of crude oil prices on the profitability of Saudi Listed Companies
in the non-financial sectors. Using revenue growth rate, net margin and return
on equity as performance proxy, the study found that oil prices have a
significant impact on the revenue growth and profitability of companies in
Saudi non-financial sectors. Hesse and Poghosyan (2016) analysed the
relationship between oil price shocks and profitability in the banking industry.
The study used data from oil-exporting countries in the Middle East and North
Africa to assess the direct and indirect effect of oil price shocks on
profitability. The results indicate that indirect effects of oil price shocks on
bank profitability are mediated by macroeconomic and institutional variables
while direct effects were found to be insignificant. In a study using data from
companies listed on the Stock Exchange of Thailand, Wattanatorn &
Kanchanapoom (2012) investigated the impact of crude oil prices on the
profitability of companies across varied sectors. The study found that oil
prices have a significant impact on the profitability of companies in the energy
and food sectors. Thus, indicating that the effect of oil prices is to a large
extent determined by industrial sector.
Daddikar & Rajgopal (2016) study analyzed the impact of crude oil price
volatility on firm’s financial performance using data from top five Indian
firms in the petroleum refining sector listed on national stock exchanges. The
results from the study revealed that crude oil price volatility impacts on firm
value and financial performance of sample firms. However, the impact is
controlled by the ownership pattern, operational diversification, economies of
scale/scope, exposure to international trade and other firm-specific factors.
Methodology
This paper adopted descriptive and correlational research design. The
population of the study comprised of all the 14 oil and gas companies listed on
the floor of the Nigeria Stock Exchange (NSE) as at 31st December 2015. A
purposive sampling technique was utilised, however, Japaul oil and maritime
services Plc were eliminated been a service rendering company. In addition,
availability of trend records during the study period was used as a filter,
resulting in the elimination of 6 additional oil and gas firms. Hence, a sample
size of 7 listed oil and gas companies was drawn, namely: Conoil Plc, Eternal
Plc, Forte Oil Plc, Mobile Oil Nigeria Plc, MRS Oil Nigeria Plc, Oando Plc
and Total Nigeria Plc. The study covers a 10-year period from 2006 to 2015.
Variable Description
Crude Oil Price and Operating Performance of Listed Oil and Gas Companies in Nigeria
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The dependent variable of the study was operating performance (OPFR),
which was measured as the ratio of Turnover to Total Assets. The data are
obtained from annual reports and accounts of the sampled oil and gas
companies for the period of the study.
The independent variable was changed in the price of crude (∆CPR),
measured using the formula (P1 – P0) / P0where P0is the price in the base year
and P1 is the price in the current year. Price of crude is the annual average of
crude oil price obtained from the Central Bank of Nigeria Statistical Database.
In addition, the growth (FGRWTH) of the sampled oil and gas companies
(measured in terms of the increase in turnover) and firm age (FAGE) (proxied
by the natural logarithm of how the firms are since the year of listing on the
floor of the NSE) were used as control variables.
Model Specification
The model specification for this paper was stated as:
Yi t= β0 + β1Xit+ εit........................................................................... (1)
where: Yit = Dependent variable of firm i for time period t;
β0 = Constant;
β1 = Coefficients or Parameters of the Explanatory Variable;
Xit = Independent variable of firm i for time period t;
Ԑit = Error Terms.
The dependent variable (Y) is a function of OPFR while independent variable
(X) is a function of ∆CPR, FGRWTH and FAGE. Therefore, by substitution,
the following working models were developed:
OPFRit = β0 + β1∆CPRit + β2FGRWTHit+ β3FAGEit+ εit............................. (2)
Model (2) was used to test the null hypotheses that: Changes in the price of
crude oil have a significant effect on the OPFR of listed oil and gas companies
in Nigeria.
Data Analysis
The result of descriptive statistics for this paper is shown in Table 1. The table
reports the mean, maximum, minimum, standard deviation, coefficient of
variation, and the number of observations for each variable.
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Table I: Descriptive Statistics
VARIABLE MEAN SD MIN MAX SKEW KURT OBS
OPFR 2.219276 1.337349 0.008488 6.321947 0.482 3.414188 70
∆CPR 0.04991 0.288261 -0.4756 0.4062 -0.5124 2.06345 70
FGRWTH 0.232344 0.686607 -0.94781 2.908233 1.99972 7.486256 70
FAGE 1.374053 0.154741 0.954243 1.579784 -0.6212 2.681105 70
Source: STATA 12.0 Output
Table I shows that the mean OPFR of the sampled listed oil and gas
companies during the period of study was 2.219276 with a standard deviation
(SD) of 1.337349. This indicates that the data for OPFR deviate from both
sides of the mean by 133.73%. OPFR also has a minimum and maximum
value of 0.008488 and 6.321947 respectively, and data for OPFR is positively
skewed with a coefficient of 0.482, meaning that a greater portion of the data
falls on the right side of the normal curve. The kurtosis coefficient of
3.414188 has shown that the data is abnormally distributed, which is
explained by the range 6.313459 (i.e. 6.321947 – 0.008488). The table also
shows that data for ∆CPR during the period give an average of 0.04991 and an
SD of 0.288261, which means that ∆CPR deviate from both sides of the mean
by 28.83%. Data for ∆CPR is negatively skewed at a coefficient of -0.5124,
implying that most of the changes in the price of crude during the study period
are negative, indicating a persistent fall in price. ∆CPR also has a minimum
and maximum value of -0.4756 and 0.4062 respectively, and a kurtosis
coefficient of 2.063454. This also is evidence of abnormality of data for
∆CPR. Moreover, it can be seen in Table I above that the standard deviation
of each variable of this study is less than 2, hence they are acceptable. This
means that the data points are not far away from the variable means. The
summary of the correlation coefficients and p-values is presented in Table II.
Table II: Correlation Matrix VARIABLES OPFR ∆CPR FGRWTH FAGE
1.0000
0.2532 1.0000'0.0345'0.1245 0.3969 1.0000'0.3045' '0.0007'0.0985 -0.2000 -0.2095 1.0000'0.4172' '0.0969' '0.0817'
∆CPR
Source: STATA 12.0 Output
OPFR
FGRWTH
FAGE
Table II shows that ∆CPR has a significant positive relationship with OPFR
and FGRWTH at the coefficients of 0.2532 and 0.3969, and 3.45% and 0.07%
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levels of significance respectively. This means that as changes in the price of
crude oil continue to persist, the OPFR of the sampled listed oil and gas firms
in Nigeria moves in the same direction. However, this may be dependent on
whether the change is positive or negative.
In addition, to further determine the fitness of the model of this study, it
requires diagnostic tests to be conducted. As a result, the Variance Inflation
Factor (VIF) test was conducted to check for multicollinearity among the
explanatory variables of the study. It was expected that the VIF for all
independent variables should be less than 5, while their tolerance levels
should be greater than 0.10. The result of VIF is as presented in Table III.
Table III: Result of Variance Inflation Factor (VIF)
VARIABLE VIF 1/VIF
∆CPR 1.21 0.824844
FGRWTH 1.21 0.828219
FAGE 1.06 0.939887
MEAN VIF
Source: STATA 12.0 Output
1.16
Table III shows the VIF of 1.21 and 1.06 for ∆CPR, FGRWTH and FAGE
respectively, both of which are less than 5 and the tolerance levels greater than
0.10. This indicates that there is an absence of perfect multicollinearity among
the independent variables, indicating the fitness of the data variables for the
model of the study. Similarly, the Shapiro-wilk test for data normality was
conducted to test the null hypothesis that data for the study variables were not
normally distributed at 0.05 levels of significance. The outcome of the test is
presented in Table IV.
Table IV: Shapiro-wilk Data Normality Test
VARIABLE OBS W V Z P-VALUE
OPFR 70 0.96544 2.127 1.641 0.05035
∆CPR 70 0.93415 4.053 3.043 0.00117
FGRWTH 70 0.77591 13.793 5.707 0.00000
FAGE 70 0.94508 3.38 2.649 0.00404
Source: STATA 12.0 Output
Table IV shows that data normality test for all the study variables is
significant at 5%. Therefore, the study accepted the null hypotheses that the
data for OPFR, ∆CPR, FGRWTH and FAGE are not normally distributed and
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rejected the alternative hypotheses that the data are normally distributed.
Therefore, this study required a more generalised least square (GLS)
regression model. In the same vein, the result of tests for Breusch-Pagan and
Cook-Weisberg Heteroscedasticity (Hettest), Hausman Fixed and Random
Effects Specification and Random Effects are presented in Table V.
Table V: Results of Hettest, Hausman’s Specification and Random Effects Tests
STATISTIC P-VALUE
Hettest: Chi Square 0.12 0.7304
Hausman: Chi Square 3.54 0.3162
Random Effect: Chi Square 36.51 0.0000
Source: STATA 12.0 Output
OPFR
TEST
The hettest tests the null hypothesis that there is an absence of
heteroscedasticity among data values, at the 5% level of significance. Table V
shows the Hettest Chi2 of 0.12, which is insignificant at 73.04% level of
significance for fitted values of OPFR. Therefore, the study accepted the null
hypothesis that there is an absence of heteroscedasticity among fitted values
of OPFR. As a result of the abnormality of data on Table IV, the residuals of
the fixed and random-effects GLS regression for fitted values of OPFR were
used to conduct the Hausman Fixed Random Specification test. The result of
the specification test shows the Chi2 of 3.54, which is insignificant at 31.62%
levels of significance. This means that the random effect GLS regression is
more suitable for fitted values OPFR.
Consequently, the result of the Breusch and Pagan Lagrangian Multiplier test
for random effects on Table V shows a Chi2 of 36.51 for fitted values of
OPFR, which is significant at less than 1% level of significance. This
indicates that the Robust Random Effect regression was the most appropriate
for fitted values of OPFR. Table VI presents the result of the Robust Random
Effect regression for fitted values of OPFR.
Table VI: Regression Summary for Fitted Values of OPFR
COEFF Z P-VALUE
CONTS 2.276668 0.73 0.4650
∆CPR 1.253587 5.43 0.0000
FGRWTH -0.091813 -0.54 0.5860
FAGE -0.0717778 -0.03 0.9750
Adj. R Square
Wald Chi Square 0.0000
OPFR
VARIABLE
Source: STATA 12.0 Output
30.3
R Square WITHIN = 0.1138
BETWEEN = 0.2502
OVERALL = 0.0578
0.0483
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Table VI presents the result of robust random effect GLS regression analysis
for fitted values of OPFR. It shows the coefficient of the CONST as 2.276668,
which defines OPFR when there is a unit increase or decrease in any of the
explanatory variables by a unit, while others are held constant. The z-value of
the CONST is 0.73, which is insignificant. Moreover, ∆CPR has a coefficient
of 1.253587, a z-value of 5.43 and p-value of 0.000. This implies that all
things being equal, ∆CPR significantly and positively affects OPFR of the
sampled listed oil and gas firms in Nigeria. This means that positive change in
CPR will cause a positive change in OPFR of the sampled firms by 1.253587.
However, both FGRWTH and FSIZE have a negative and insignificant
contribution to OPFR at the coefficients of -0.091813 and -0.0717778; the z-
values of -0.54 and -0.03; and p-values of 0.586 and 0.975 respectively. This
means that all things being equal, FGRWTH and FSIZE of the listed oil and
gas companies have an insignificantly negative effect on their OPFR to the
extent of -0.091813 FGRWTH and -0.0717778 FSIZE respectively. Hence,
the model is represented as:
OPFR = 2.276668 + 1.253587 ∆CPR – 0.091813 FGRWTH – 0.0717778
FSIZE + e.
In addition, the overall result for fitted values of OPFR the overall R2 is
0.0578 and adjusted R2 is 0.0483. This implies that only 4.83% of changes in
OPFR is explained by changes in the price of crude oil, firm growth and firm
size while 95.17% is explained by other variables. Therefore, based on the Z
value of 5.43 in respect of ∆CPR, which is significant at the p-value of 0.0000
and the Wald Chi2
of 30.30, which is significant at the p-value of 0.0000, this
paper accepted the hypothesis that changes in the prices of crude oil has a
significant impact on the operating performance of listed oil and gas
companies in Nigeria.
Discussion of Findings
The results obtained from the study leads us to a number of conclusions. First,
changes in crude oil prices over the period of the study have been consistently
determined by fall in price. This indicates that oil and gas assets held by the
companies have been negatively affected. Second, changes in crude oil price
positively and significantly impact of operating performance of oil and gas
companies in Nigeria. This indicates that change in the price of crude oil is
expected to impact positively on the operating performance of oil and gas
companies. This also points to the fact that irrespective of the consistent fall in
the price of crude oil over the period, operating performance has not been
negatively affected. The result confirmed studies by (Narayan & Sharma,
2011; Wattanatorn & Kanchanapoom, 2012; Lele, 2016; Daddikar &
Rajgopal, 2016) and contradicts studies conducted by (Dayanandan &
Donker, 2011; Hazarika, 2015; Hesse & Poghosyan, 2016). Third, the study
also confirms the argument posited by Ramos & Veiga (2011) that oil and gas
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companies in developed countries respond more to changes in crude oil price
than companies in emerging markets. This is evidenced by the results as the
sign of the change in crude oil price did not have influenced the direction of
operating performance.
Conclusion and Recommendations
Based on the analysis of data collected for this study, it was found that
positive changes in the price of crude oil influence the operating performance
of listed oil and gas companies in Nigeria. Considering the degree of the effect
reported by the overall R2, it can be deduced that macroeconomic factors such
as exchange rates may also affect the operating performance of the oil and gas
companies. This is due to the fact that the price of crude oil is significantly
determined by the Bonny Light in Dollars per barrel, which may be impaired
by the prevailing Naira – Dollar exchange rate.
Based on the finding, the study concludes that positive changes in the price of
crude oil will significantly improve the operating performance of listed oil and
gas companies in Nigeria and vice versa. This implies that oil and gas
companies in Nigeria understand the dynamics of crude oil prices and are able
to choose strategies that will hedge the effect of oil price shocks.
There is the need for further studies on the effect of crude oil price on the
performance of companies in the oil and gas industry. The result obtained
from this study is limited as the sampled companies are small in number and
the companies sampled include those in the upstream and downstream sector
of the oil and gas industry. A further study incorporating more companies and
analyzing separately the effects of crude oil price on the performance of oil
and gas companies in the downstream sector on one hand and those in the
upstream on the other hand is recommended.
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