crude oil price and operating performance of listed oil and gas … · 2017-09-21 · crude oil...

Sahel Analyst: ISSN 1117-4668 Page 41

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

Sahel Analyst: Journal of Management Sciences (Vol.15, No.4, 2017), University of Maiduguri


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

Sahel Analyst: ISSN 1117- 4668 Page 43

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.


Sahel Analyst: ISSN 1117-4668 4

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



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.



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


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%



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


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


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



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


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


30.3

R Square WITHIN = 0.1138

BETWEEN = 0.2502

OVERALL = 0.0578

0.0483



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



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.

References

Arouri, M. E., & Nguyen, D. K. (2010). Oil prices, stock markets and

portfolio investment: Evidence from sector analysis in Europe over the

last decade. Energy Policy, 38 (8), 4528-4539.

Asaolu, T. O., & Ilo, B. M. (2012). The Nigerian Stock Market and Oil price:

A cointegration analysis. Kuwait Chapter of Arabian Journal of

Business and Management Review, 1 (6), 109-118.



Daddikar, P. V., & Rajgopal, M. (2016). Impact of crude oil price volatility on

firm's financial performance: Empirical evidence from Indian

Petroleum Refining sector. Imperial Journal of Interdisciplinary

Research, 2 (10), 1728-1742.

Dayanandan, A., & Donker, H. (2011). Oil prices and accounting profits of oil

and gas companies. International Review of Financial Analysis, 20,

252–257.

Ford, G. S. (2011). An investigation into the relationship of retail gas prices

on oil company profitability. Applied Economics, 43, 4033-4041.

Gogineni, S. (2010). Oil and the stock market: An industry level analysis. The

Financial Review, 45, 995–1010.

Hazarika, I. (2015). Performance analysis of top oil & gas companies

worldwide with reference to oil prices. Journal of Energy & Economic

Development, 1 (1), 62-78.

Hesse, H., & Poghosyan, T. (2016). Oil prices and bank profitability:

Evidence from major oil-exporting countries in the Middle East and

North Africa. In A. V. Gevorkyan, & O. Canuto (Eds.), Financial

Deepening and Post-Crisis Development in Emerging Markets (pp.

247-270). Palgrave Macmillan US.

Lele, U. (2016). Impact of oil prices on revenue growth and profitability of

Saudi listed companies in non-financial sectors. International Journal

of Management, Information Technology and Engineering, 4 (6), 13-

20.

Malik, F., & Ewing, B. T. (2009). Volatility transmission between oil prices

and equity sector returns. International Review of Financial Analysis,

18, 95-100.

Mohanty, S. K., Akhigbe, A., Al-Khyal, T. A., & Bugshan, T. (2013). Oil and

stock market activity when prices go up and down: The case of the oil

and gas industry. Review of Quantitative Financial Accounting, 41,

253-272.

Narayan, P. K., & Sharma, S. S. (2011). New evidence on oil price and firm

returns. Journal of Banking and Finance, 35, 3253-3262.



Ramos, S. B., & Veiga, H. (2011). Risk factors in oil and gas industry return:

international evidence. Energy Economics, 33 (3), 525-542.

Sadorsky, P. (2012). Correlations and Volatility Spillovers between Oil Prices

and the Stock Prices of Clean Energy and Technology Companies.

Energy Economics, 34, 248-255.

Wattanatorn, W., & Kanchanapoom, T. (2012). Oil prices and profitability

performance: Sector analysis. The 2012 International (Spring)

Conference on Asia Pacific Business Innovation and Technology

Management Society, Procedia - Social and Behavioral Sciences (pp.

763-767). Elsevier Ltd.

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