An Empirical Analysis of Financial Performance of Selected Oil Exploration and Production Companies in India

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-12, Issue-3 (June 2022)
www.ijemr.net https://doi.org/10.31033/ijemr.12.3.16
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An Empirical Analysis of Financial Performance of Selected Oil
Exploration and Production Companies in India
P.Venkataraman1
and Dr. K.Subramaniam2
1
Part time Ph.D Scholar, Sri Krishna Arts and Science College Erode Arts and Science College (Autonomous) Erodem,
INDIA
2
Assistant Professor, PG and Research Department of Commerce, Erode Arts and Science College (Autonomous) Erode,
INDIA
1
Corresponding Author: venkatca94@Gmail.com
ABSTRACT
After the United States, China, and Japan, India
was the world's fourth biggest consumer of oil and
petroleum products. The nation is significantly reliant on
crude oil imports, the majority of which come from the
Middle East. The Indian oil and gas business is one of the
country's six main sectors, with important forward links to
the rest of the economy. More than two-thirds of the
country's overall primary energy demands are met by the
oil and gas industry. The industry has played a key role in
placing India on the global map. India is now the world's
sixth biggest crude oil user and ninth largest crude oil
importer. In addition, the country's portion of the
worldwide refining market is growing. India's refining
industry is now the world's sixth biggest. With plans for
Reliance Petroleum Limited to commission another
refinery with a capacity of 29 MTPA next 16 to its 33
MTPA refinery in Jamnagar, Gujarat, this position is
projected to be enhanced. As a consequence, the Reliance
refinery would be the biggest single-site refinery in the
world. Based on secondary data gathered from CMIE, the
current research examines the ratios influencing the
profitability of selected oil exploration and production
businesses in India during a 10-year period.
Keywords-- Consumer of Oil and Petroleum Products,
Middle East, Crude Oil Importer, Refining Industry,
Reliance Petroleum Limited, Profitability, CMIE
I. INTRODUCTION
India, being a growing country and one of the
world's biggest, is a significant importer of oil and
petroleum products from Gulf countries. Despite the fact
that our nation has a large oil resource, it is insufficient
to meet our population's domestic demands. In recent
years, there has been an inexorable increase in the price
of gasoline, which has had a significant impact on
transportation charges. Among the key economic sectors,
the oil industry is one of the largest employers,
employing thousands of people. Petroleum products
represent 17 percent of export revenues, making them a
major contribution to our country's GDP. Among the
eight crore industries in our nation, the oil and gas
industry is the most important. It has a significant role in
influencing our country's economy. The Industrial Policy
Resolution of 1954 established the groundwork for
India's oil and gas industry, with the government
declaring petroleum to be the primary sector. In 2020-21,
India will be the world's third biggest energy consumer
of oil and gas, accounting for 4.81 percent of total global
oil consumption and 1.41 percent of total global natural
gas consumption.
Oil and gas firms have been around since 1867,
when the first oil well was discovered in Digboi, Assam.
The Assam Oil Company was founded in 1889.
Previously, Burmah Oil firms controlled the industry.
During the year of independence, foreign businesses
dominated the oil and gas sector, which had a capacity of
250 lac tonnes per year. Oil India Limited (OIL) and
Indian Oil Corporation (IOL), two Indian oil businesses,
were founded in 1955. Indian Refineries Limited was
founded in 1959. Major oil deposits are found in the
underdeveloped areas of Mumbai High, Rajasthan, and
the Bay of Bengal.
The ONGC, a government-owned firm founded
in 1956, supplies almost 75 percent of India's oil
demands. National Oil Companies met over 75 percent
of the standards in the early 1970s (NOCs). Four Indian
oil firms, including OIL and ONGC, have made
significant measures to extract oil from several rivers in
India, but local demand has not been satisfied. Following
Liberalization Many differences were seen when the
government implemented liberalization programs.
Private businesses were also encouraged to explore oil
reserves once the liberalization policy was implemented
in 1991. Following the adoption of reforms in the years
following 1990 and before 1994, the businesses were
tasked with oil exploration. In 1997-98, the New
Exploration and Licensing Policy (NELP) was
implemented, resulting in substantial improvements in
exploration and attracting a small number of private
sector investments. The Ministry of Petroleum and
Natural Gas controls the oil and gas business in terms of
exploration, refining, distribution, marketing, exports,
and imports, as well as saving oil resources to fulfill
public demand. Gas and Authority of India Limited
(GAIL), Bharat Petroleum Corporation Limited (BPCL),
Hindustan Petroleum Corporation Limited (HPCL), Oil
and Natural Gas Corporation (ONGC), and Indian Oil
Corporation are the primary firms involved (IOC). The

oil and gas sectors rely heavily on private sector
corporations such as Adani Gas, Essar Oil Limited,
Shell, and BP. The refineries in India play a vital part in
the exploration of oil and gas reserves since they are
involved in the production of petroleum and gas.
II. REVIEW OF LITERATURE
Surender Kumar (2005) studied the impact of
oil price changes on the Indian economy's industrial
output development during a 30-year period starting in
1975. Vector auto regressions were employed to assess
the impact of oil price variations on macroeconomic
variables. He demonstrated that; first, stating oil prices in
US dollars had a smaller effect than expressing them in
Indian rupees (INR). Second, changes in oil prices cause
an increase in industrial output. Third, rising real oil
prices have a beneficial influence on the short-term
interest rate and the rate of inflation. Finally, an oil price
shock in a stable economy has a higher economic impact
than one in an unstable pricing one.
Dr. Pratapsinh Chauhan (2012) selected
seven public and private sector enterprises in India to
analyze shareholder value generation in the petroleum
industry from 2001 to 2011. For the empirical study, he
employed EVA, MVA, PAT, EPS, NOPAT, and Market
Capitalization. The data was analyzed using statistical
methods such as descriptive statistics, correlation, T-test,
and chi-square test for analyzing the increase and trend
of value adds in terms of MVA and EVA. The findings
reveal that firms in both the private and governmental
sectors have favorable EVA and MVA. More crucially,
EVA of both sectors' businesses demonstrated a
substantial link with OP, EPS, NOPAT, Market
Capitalization, and MVA. Furthermore, the findings
show that both sectors performed admirably;
nevertheless, the private sector petroleum businesses'
MVA and EVA trends were stronger than those of the
public sector. Finally, he came to the conclusion that
MVA and EVA are strongly linked to the market
capitalization of enterprises in both sectors.
Khatik and Thakur (2017) used selected
criteria such as acid test ratio, current ratio, and debtors
to sales ratio to examine the liquidity management and
its influence on the profitability of ONGC in India. The
relevant data was gathered from the company's annual
reports for a period of ten years beginning in 2005, and
the data was analyzed using the ratio analysis approach.
ONGC Ltd's total liquidity condition was adequate
throughout the study period, according to this analysis.
Using monthly data from 1996 to 2008,
Agusman & Deriantino (2005) investigated the
association between stock return and oil price variations
in nine Indonesian industrial sectors. Oil price variations
had little effect on the stock returns of the sectors
studied, according to this research. However, although
the government's move to liberalize domestic oil prices
has a favorable and large influence on the mining
sector's stock return, it has a negative and major impact
on the trading sector's stock return. Furthermore, the
findings suggest that the increase in oil prices as a
consequence of the government's liberalization policies
has a negative and considerable effect on the
infrastructure and consumer sectors. The data also show
that, although the sensitivity of oil price fluctuations and
stock returns in these sectors is asymmetric, a drop in oil
price has little influence on these sectors' stock returns.
As a result, it is possible to conclude that a drop in oil
prices is not necessarily a sign of good news for
investors.
Tayyaba (2013) investigated the association
between various forms of leverage and EPS in 25
Pakistani oil and gas businesses. For the examination of
data obtained from firms' official websites over a six-
year period beginning in 2007, the researcher employed
descriptive statistics, correlation, and regression. The
researchers observed that whereas ROA and DFL have a
positive association, ROA and DOL have an adverse
relationship. The research also discovered an inverse
association between ROI and DFL, as well as an inverse
relationship between ROI and DOL. Furthermore,
although there is a negative association between EPS and
DOL, there is a positive link between DFL and EPS.
III. SAMPLING DEFINITION
For conducting the present study the researcher
has selected three three oil exploration and production
companies listed in BSE And NSE including Reliance
Industries, ONGC,and Oil India. The financial data for
the same is extracted from CAPITALINE and CMIE for
the period of 10 years from 2011-12 to 2020-21 and the
relevant statistical tools have been applied
IV. OBJECTIVES
To study the influence of financial ratios on the
profitability of selected oil exploration companies
operating in India
Tools used: Multiple regression analysis and
ANOVA
V. MULTIPLE REGRESSION
ANALYSIS
Multiple Regression Analysis is a statistical
method for predicting dependent variables by combining
numerous independent variables. It's a functional
connection between a dependent variable and many
independent factors in which the impact of the
independent variables on the dependent variables
(profitability) is determined via analysis. The present
research used this technique to seek for a new
combination of factors that may explain the differences
in profitability. In the chosen car industry organisations,
Multiple Regression is used using current ratio as the

dependent variable and all other factors as independent
variables. Multiple regression analysis is utilised in this
research to determine the link between variables and the
factor impacting profitability.
Table 1: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – Reliance Industries
S.No. Ratio of
Multiple
Regression
Co-efficient
t’ value p-value
X1 Quick ratio
.965 11.694 .000**
X2 Interest Coverage Ratio
.065 .767 .463
X3 Net working capital to sales
.105 1.030 .330
X4 Raw material turnover ratio
-.002 -.019 .985
X5 Debtors turnover ratio
.050 .372 .719
X6 Creditors turnover ratio
.126 1.263 .238
X7 Distribution expenses ratio
-.129 -1.458 .179
X8 Miscellaneous expenditure ratio
.027 .285 .782
X9 Operating expenses ratio
-.078 -.743 .476
X10 Net fixed assets turnover ratio
.013 .132 .898
X11 Debt to equity ratio
.025 .278 .787
X12 Inventory turnover ratio
-.001 -.017 .987
**significant at 1% level. * Significant at 5% level
Table 2: Anova
Sum of
Squares
df Mean Square F p-val S/NS
Regression .363 1 .363 136.741 .000 S
Residual .027 10 .003
Total .389 11
**significant at 5% level. * Significant at 1% level S- significant NS – Not significant
Table 3: Model Summary
MODEL R R SQUARE
1 0.965 0.932

It is clear that, the multiple regression co-
efficient values of Reliance Industries These presented
values indicate that one variable is individually
contributes significantly to variations in the current ratio
when influence of other variables are kept constant. The
t and Sig (p) values give a rough indication of the impact
of predictor variable namely, Quick ratio (t- 11.694, p
0.00, p< 0.01 ). In connection with this, the R2
value in
terms of these variables is 93.2 percent. Overall ANOVA
results, the p-value is less than the 0.01(p<0.01).Hence,
this model is statistically significant. So Quick ratio has
significant impact on profitability of Reliance Industries
during the study period.
Table 4: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – ONGC Ltd.
S.No. Ratio of
Multiple
Regression
Co-efficient
t’ value p-value
X1 Quick ratio
.015 .325 .756
.043 .488 .643
.667 9.469 .000*
.022 .331 .752
.033 .414 .693
.434 6.279 .000*
X7 Distribution expenses ratio
-.147 -2.379 .049**
.281 4.037 .005*
.045 .685 .519
.005 .075 .943
.001 .009 .993
-.004 -.054 .959
Table 5: Anova
Sum of
Squares
df Mean Square F P-val S/NS
Regression .368 4 .092 165.704 .000** S
Residual .004 7 .001
Total .371 11
MODEL R R SQUARE
1 0.995 0.990

efficient values of ONGC Ltd. These presented values
indicate that three variables are individually contributing
significantly to variations in the current ratio when
influence of other variables are kept constant. The t and
Sig ( p) values give a rough indication of the impact of
each predictor variable namely, Net working capital to
sales (t 9.469, p .000 , p< 0.01), Creditors turnover
ratio(t 6.279, p .000 , p< 0.01), Distribution expenses
ratio(t -2.379, p .049, p< 0.05), Miscellaneous
expenditure ratio(t 4.037, p .005, p< 0.01). In connection
with this, the R2
value in terms of these variables is 99
percent. Overall ANOVA results, the p-value is less than
the 0.01(p<0.01) except Miscellaneous expenditure
.Hence, this model is statistically significant. So
working capital to sales , Creditors turnover ratio ,
Distribution expenses ratio and Miscellaneous
expenditure ratio have significant impact on profitability
of ONGC during the study period.
Table 7: Multiple Regression Analysis of the Selected Variables with the Ratio of Current Ratio – India Oil Ltd
S.No. Ratio of
Multiple
Regression Co-
efficient
t’ value p-value
X1 Quick ratio
.281 4.037 .005*
-.101 -.760 .469
.105 1.030 .330
-.195 -1.160 .280
-.034 -.230 .824
.031 .263 .799
.122 1.041 .328
-.062 -.203 .844
-.196 -.600 .565
-.093 -.702 .503
.222 .493 .635
Table 8: Anova
Sum of Squares df Mean Square F P S/NS
Regression .641 2 .321 38.961 .000 S
Residual .074 9 .008
Total .715 11
MODEL R R SQUARE
1 0.947 0.896

efficient values of India Oil Ltd . These presented
values indicate that one variable is individually
contributing significantly to variations in the current
ratio when influence of other variables are kept constant.
The t and Sig (p) values gives the indication of the
impact of the predictor variable namely, Quick ratio(t
4.037, p .005, p< 0.01). In connection with this, the R2
value in terms of these variables is 89 percent. From the
overall ANOVA results, the p-value is less than the
0.01(p<0.01). Hence, this model is statistically
significant So Quick ratio has significant impact on
profitability of India Oil Ltd during the study period.
VI. CONCLUSION
Findings from oil exploration companies proves
that , the companies have to concentrate mostly on the
short term assets and short term liabilities to the utmost
satisfaction of the productive operations. The cash and
outsiders’ funds are to be availed for the continuous flow
of operations. The ratios which provides positive
influence has the significance contribution in the
financial performance of the selected oil production and
exploration companies.
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