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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
113 This work is licensed under Creative Commons Attribution 4.0 International License.
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
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
114 This work is licensed under Creative Commons Attribution 4.0 International License.
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
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
115 This work is licensed under Creative Commons Attribution 4.0 International License.
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
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
116 This work is licensed under Creative Commons Attribution 4.0 International License.
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
X2 Interest Coverage Ratio
.043 .488 .643
X3 Net working capital to sales
.667 9.469 .000*
X4 Raw material turnover ratio
.022 .331 .752
X5 Debtors turnover ratio
.033 .414 .693
X6 Creditors turnover ratio
.434 6.279 .000*
X7 Distribution expenses ratio
-.147 -2.379 .049**
X8 Miscellaneous expenditure ratio
.281 4.037 .005*
X9 Operating expenses ratio
.045 .685 .519
X10 Net fixed assets turnover ratio
.005 .075 .943
X11 Debt to equity ratio
.001 .009 .993
X12 Inventory turnover ratio
-.004 -.054 .959
**significant at 5% level. * Significant at 1% level
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
Table 6: Model Summary
MODEL R R SQUARE
1 0.995 0.990
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
117 This work is licensed under Creative Commons Attribution 4.0 International License.
It is clear that, the multiple regression co-
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*
X2 Interest Coverage Ratio
-.101 -.760 .469
X3 Net working capital to sales
.105 1.030 .330
X4 Raw material turnover ratio
-.195 -1.160 .280
X5 Debtors turnover ratio
-.034 -.230 .824
X6 Creditors turnover ratio
.031 .263 .799
X8 Miscellaneous expenditure ratio
.122 1.041 .328
X9 Operating expenses ratio
-.062 -.203 .844
X10 Net fixed assets turnover ratio
-.196 -.600 .565
X11 Debt to equity ratio
-.093 -.702 .503
X12 Inventory turnover ratio
.222 .493 .635
**significant at 5% level. * Significant at 1% level
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
Table 9: Model Summary
MODEL R R SQUARE
1 0.947 0.896
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
118 This work is licensed under Creative Commons Attribution 4.0 International License.
It is clear that, the multiple regression co-
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.
REFERENCES
[1] Kumar, Surender. (2005). The macroeconomic effects
of oil price shocks: Empirical evidence for India.
Available at: https://ssrn.com/abstract=900285 or
http://dx.doi.org/10.2139/ssrn.900285.
[2] Chauhan, P. (2012). Shareholders value creation in
Indian petroleum industry: An empirical analysis.
International Conference on Business, Economics,
Management and Behavioral Sciences (ICBEMBS 2012),
pp. 483–486.
[3] Khatik, S. K. & Thakur, V. (2017). Liquidity
management-A case study of oil and natural gas
corporation limited. The Indian Journal of Commerce,
70(2), 244–253.
[4] Agusman, A. & Deriantino, E. (2005). Oil price and
industry stock returns: evidence from Indonesia. SSRN
Electronic Journal, 10.2139/ssrn.1253904.
[5] Tayyaba, K. (2013). “Leverage”–An analysis and its
impact on profitability with reference to selected oil and
gas companies. International Journal of Business and
Management Invention, 2(7), 50-59.
[6] Ansari, M. & Shivalingegowda, M. (2017). Impacts
of asset tangibility and capital structure on financial
performance of listed oil and gas companies in India.
EPRA International Journal of Economic and Business
Review, 5(12), 110-114.
[7] Collins, D. W., Rozeff, M. S. & Dhaliwal, D. S.
(1981). The economic determinants of the market
reaction to proposed mandatory accounting changes in
the oil and gas industry: A cross-sectional analysis.
Journal of Accounting and Economics, 3(1), 37-71.
[8] Garcia, J. P. (2016). Financial performance in
upstream, downstream, and integrated oil companies in
response to oil price volatility. Finance Available at:
https://scholarworks.uark.edu/finnuht/34.
[9] Hazarika, I. (2015). Performance analysis of top oil
and gas companies worldwide with reference to oil
prices. Journal of Energy and Economic Development,
1(1), 62-78.
[10] Lanza, A., Manera, M., Grasso, M. & Giovannini,
M. (2005). Long-run models of oil stock prices.
Environmental Modelling & Software, 20(11), 1423-
1430.

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

  • 1. 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.12.3.16 113 This work is licensed under Creative Commons Attribution 4.0 International License. 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
  • 2. 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.12.3.16 114 This work is licensed under Creative Commons Attribution 4.0 International License. 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
  • 3. 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.12.3.16 115 This work is licensed under Creative Commons Attribution 4.0 International License. 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
  • 4. 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.12.3.16 116 This work is licensed under Creative Commons Attribution 4.0 International License. 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 X2 Interest Coverage Ratio .043 .488 .643 X3 Net working capital to sales .667 9.469 .000* X4 Raw material turnover ratio .022 .331 .752 X5 Debtors turnover ratio .033 .414 .693 X6 Creditors turnover ratio .434 6.279 .000* X7 Distribution expenses ratio -.147 -2.379 .049** X8 Miscellaneous expenditure ratio .281 4.037 .005* X9 Operating expenses ratio .045 .685 .519 X10 Net fixed assets turnover ratio .005 .075 .943 X11 Debt to equity ratio .001 .009 .993 X12 Inventory turnover ratio -.004 -.054 .959 **significant at 5% level. * Significant at 1% level 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 Table 6: Model Summary MODEL R R SQUARE 1 0.995 0.990
  • 5. 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.12.3.16 117 This work is licensed under Creative Commons Attribution 4.0 International License. It is clear that, the multiple regression co- 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* X2 Interest Coverage Ratio -.101 -.760 .469 X3 Net working capital to sales .105 1.030 .330 X4 Raw material turnover ratio -.195 -1.160 .280 X5 Debtors turnover ratio -.034 -.230 .824 X6 Creditors turnover ratio .031 .263 .799 X8 Miscellaneous expenditure ratio .122 1.041 .328 X9 Operating expenses ratio -.062 -.203 .844 X10 Net fixed assets turnover ratio -.196 -.600 .565 X11 Debt to equity ratio -.093 -.702 .503 X12 Inventory turnover ratio .222 .493 .635 **significant at 5% level. * Significant at 1% level 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 Table 9: Model Summary MODEL R R SQUARE 1 0.947 0.896
  • 6. 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.12.3.16 118 This work is licensed under Creative Commons Attribution 4.0 International License. It is clear that, the multiple regression co- 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. REFERENCES [1] Kumar, Surender. (2005). The macroeconomic effects of oil price shocks: Empirical evidence for India. Available at: https://ssrn.com/abstract=900285 or http://dx.doi.org/10.2139/ssrn.900285. [2] Chauhan, P. (2012). Shareholders value creation in Indian petroleum industry: An empirical analysis. International Conference on Business, Economics, Management and Behavioral Sciences (ICBEMBS 2012), pp. 483–486. [3] Khatik, S. K. & Thakur, V. (2017). Liquidity management-A case study of oil and natural gas corporation limited. The Indian Journal of Commerce, 70(2), 244–253. [4] Agusman, A. & Deriantino, E. (2005). Oil price and industry stock returns: evidence from Indonesia. SSRN Electronic Journal, 10.2139/ssrn.1253904. [5] Tayyaba, K. (2013). “Leverage”–An analysis and its impact on profitability with reference to selected oil and gas companies. International Journal of Business and Management Invention, 2(7), 50-59. [6] Ansari, M. & Shivalingegowda, M. (2017). Impacts of asset tangibility and capital structure on financial performance of listed oil and gas companies in India. EPRA International Journal of Economic and Business Review, 5(12), 110-114. [7] Collins, D. W., Rozeff, M. S. & Dhaliwal, D. S. (1981). The economic determinants of the market reaction to proposed mandatory accounting changes in the oil and gas industry: A cross-sectional analysis. Journal of Accounting and Economics, 3(1), 37-71. [8] Garcia, J. P. (2016). Financial performance in upstream, downstream, and integrated oil companies in response to oil price volatility. Finance Available at: https://scholarworks.uark.edu/finnuht/34. [9] Hazarika, I. (2015). Performance analysis of top oil and gas companies worldwide with reference to oil prices. Journal of Energy and Economic Development, 1(1), 62-78. [10] Lanza, A., Manera, M., Grasso, M. & Giovannini, M. (2005). Long-run models of oil stock prices. Environmental Modelling & Software, 20(11), 1423- 1430.