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- 1. Analysis of Factors affecting Net Profit of ONGC Submitted By => KARAN SHAH Enrollment No. => 1011517039 Date => 06/09/2016 Submitted To => Dr. Abhay Raja
- 2. Objective of the Study The objective of this study is to analyze the impact of Sales, Interest Exp. and Average Oil Prices (USD/barrel) on the Quarterly net profits of ONGC. Also, to study whether each quarter significantly impacts the Net Profit of the company.
- 3. Identifying Dependent and Independent Variable Dependent Variable: - Net Profit after Tax of ONGC for last 15 Quarters Independent Variables:- Sales, Interest Exp. and Average Oil Prices (USD/barrel) Dummy Variables:- Q1, Q2, Q3 and Q4
- 5. Dependent Variable: NET_PROFIT Method: Least Squares Date: 09/05/16 Time: 21:59 Sample: 6/01/2001 3/01/2016 Included observations: 60 Variable Coefficient Std. Error t-Statistic Prob. C 223.3362 513.3234 0.435079 0.6652 SALES 0.177025 0.046482 3.808433 0.0003 INTEREST_EXP_ -0.618125 6.693777 -0.092343 0.9268 OIL_PRICES_$_BBL_ 16.43866 7.052968 2.330744 0.0234 R-squared 0.643520 Mean dependent var 3964.752 Adjusted R-squared 0.624423 S.D. dependent var 1646.938 S.E. of regression 1009.314 Akaike info criterion 16.73627 Sum squared resid 57048076 Schwarz criterion 16.87589 Log likelihood -498.0881 Hannan-Quinn criter. 16.79088 F-statistic 33.69724 Durbin-Watson stat 1.854935 Prob(F-statistic) 0.000000
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- 7. Dependent Variable: NET_PROFIT Method: Least Squares Date: 09/05/16 Time: 22:02 Sample: 6/01/2001 3/01/2016 Included observations: 60 Variable Coefficient Std. Error t-Statistic Prob. C 195.6652 413.1428 0.473602 0.6376 SALES 0.178715 0.042355 4.219452 0.0001 OIL_PRICES_$_BBL_ 16.34077 6.911926 2.364141 0.0215 R-squared 0.643466 Mean dependent var 3964.752 Adjusted R-squared 0.630956 S.D. dependent var 1646.938 S.E. of regression 1000.498 Akaike info criterion 16.70309 Sum squared resid 57056763 Schwarz criterion 16.80781 Log likelihood -498.0927 Hannan-Quinn criter. 16.74405 F-statistic 51.43629 Durbin-Watson stat 1.850975 Prob(F-statistic) 0.000000 As we can see the significance levels of Sales and Avg. Oil Prices (USD/Barrel) have changed but still they significantly impact the Net Profit of ONGC.
- 8. Regression Output after adding Dummy Variables As we can see that even after adding dummy variables “Interest Exp.” is not significant Dependent Variable: NET_PROFIT Method: Least Squares Date: 09/05/16 Time: 22:08 Sample: 6/01/2001 3/01/2016 Included observations: 60 Variable Coefficient Std. Error t-Statistic Prob. C 247.1278 514.1621 0.480642 0.6327 SALES 0.172291 0.043671 3.945242 0.0002 INTEREST_EXP_ 0.511423 6.353854 0.080490 0.9362 OIL_PRICES_$_BBL_ 17.15152 6.628872 2.587397 0.0124 DUMMY_QRT_1 -631.7632 345.4176 -1.828984 0.0730 DUMMY_QRT_2 11.05170 347.8075 0.031775 0.9748 DUMMY_QRT_3 552.5482 346.0343 1.596802 0.1163 R-squared 0.709052 Mean dependent var 3964.752 Adjusted R-squared 0.676114 S.D. dependent var 1646.938 S.E. of regression 937.2884 Akaike info criterion 16.63314 Sum squared resid 46561007 Schwarz criterion 16.87748 Log likelihood -491.9942 Hannan-Quinn criter. 16.72871 F-statistic 21.52714 Durbin-Watson stat 1.800765 Prob(F-statistic) 0.000000
- 9. Output Without Int. Exp. Dependent Variable: NET_PROFIT Method: Least Squares Date: 09/05/16 Time: 22:12 Sample: 6/01/2001 3/01/2016 Included observations: 60 Variable Coefficient Std. Error t-Statistic Prob. C 269.5238 428.3717 0.629182 0.5319 SALES 0.170908 0.039777 4.296679 0.0001 OIL_PRICES_$_BBL_ 17.22663 6.502214 2.649348 0.0106 DUMMY_QRT_1 -628.6620 340.0897 -1.848518 0.0700 DUMMY_QRT_2 8.697621 343.3726 0.025330 0.9799 DUMMY_QRT_3 554.4691 342.0199 1.621160 0.1108 R-squared 0.709016 Mean dependent var 3964.752 Adjusted R-squared 0.682073 S.D. dependent var 1646.938 S.E. of regression 928.6260 Akaike info criterion 16.59993 Sum squared resid 46566698 Schwarz criterion 16.80936 Log likelihood -491.9979 Hannan-Quinn criter. 16.68185 F-statistic 26.31544 Durbin-Watson stat 1.805818 Prob(F-statistic) 0.000000
- 10. Interpretation of the Output As we can see the probability values of all the dummy variables are greater than 0.05, which means all the dummy variables are insignificant at 95% confidence level. Hence, we can say that with the change in quarter of a year, the net profit of ONGC does not get impacted significantly or else we can say that net profit is not dependent on the quarter of a year. Also, with the addition of dummy variables the other independent variables (Sales and Oil Prices) remain significant.
- 11. Regression Equation Net Profit = 269.52+0.1709(Sales) + 17.2266(Oil Prices USD/bbl) - 628.662(Q1Profit) + 8.697 (Q2 Profit) + 4.4691(Q3 Profit)
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- 15. Dependent Variable: NET_PROFIT Method: Least Squares Date: 09/05/16 Time: 22:43 Sample: 6/01/2001 3/01/2016 Included observations: 60 Weighting series: SALES Weight type: Variance (average scaling) White heteroskedasticity-consistent standard errors & covariance Variable Coefficient Std. Error t-Statistic Prob. C 236.5418 244.4042 0.967830 0.3374 SALES 0.184491 0.051022 3.615944 0.0007 OIL_PRICES_$_BBL_ 14.87050 8.996688 1.652886 0.1042 DUMMY_QRT_1 -525.0760 264.7367 -1.983389 0.0524 DUMMY_QRT_2 -44.10333 248.3946 -0.177554 0.8597 DUMMY_QRT_3 453.8547 192.3781 2.359180 0.0220 Weighted Statistics R-squared 0.768712 Mean dependent var 3627.495 Adjusted R-squared 0.747296 S.D. dependent var 1066.254 S.E. of regression 806.7917 Akaike info criterion 16.31865 Sum squared resid 35149297 Schwarz criterion 16.52808 Log likelihood -483.5594 Hannan-Quinn criter. 16.40057 F-statistic 35.89493 Durbin-Watson stat 1.770084 Prob(F-statistic) 0.000000 Weighted mean dep. 3426.924 Wald F-statistic 49.35065 Prob(Wald F-statistic) 0.000000 Unweighted Statistics R-squared 0.706290 Mean dependent var 3964.752 Adjusted R-squared 0.679095 S.D. dependent var 1646.938 S.E. of regression 932.9654 Sum squared resid 47002915 Durbin-Watson stat 1.778441
- 17. Interpretation All the prob. levels are greater than 0.05, hence we accept the null hypothesis that “there is no auto correlation between the observations”. Hence, this assumption of CLRM is fulfilled.
- 18. 3. TEST OF NORMALITY 0 2 4 6 8 10 -3000 -2000 -1000 0 1000 2000 Series: Residuals Sample 6/01/2001 3/01/2016 Observations 60 Mean -3.52e-13 Median -8.215070 Maximum 2216.133 Minimum -2766.761 Std. Dev. 888.4065 Skewness -0.171539 Kurtosis 3.818639 Jarque-Bera 1.969682 Probability 0.373499
- 19. Interpretation To test the normality of the data, we use Jarque – Bera test. Here, the prob. of Jarque Bera is 0.3734, which is greater than 0.05, hence we reject the null hypothesis that “data is not normal”. The Kurtosis Value is 3.8186, which is marginally greater than 3, hence we can safely assume that data is normal. Thus, this assumption of CLRM
- 20. 4. Test of Multicollinearity Variance Inflation Factors Date: 09/05/16 Time: 23:09 Sample: 6/01/2001 3/01/2016 Included observations: 60 Coefficient Uncentered Centered Variable Variance VIF VIF C 183502.3 12.76765 NA SALES 0.001582 27.34730 2.693477 OIL_PRICES_$_BBL_ 42.27879 15.90254 2.702630 DUMMY_QRT_1 115661.0 2.011854 1.508891 DUMMY_QRT_2 117904.7 2.050882 1.538162 DUMMY_QRT_3 116977.6 2.034756 1.526067
- 21. Interpretation As we have removed the “Interest Exp.” variable because its insignificance came up in the first part of our analysis, running Multicollinearity test by including it would be useless. From, the above analysis we see that there is a 78.73% linear relationship between the two variables, but the Centered VIF of Sales and Oil Prices is 2.693 and 2.702 resp. so we can safely say that the Multicollinearity can be ignored between the two variables. Also, logically we can’t remove either of the variables because both of them are significant in predicting the net profit of the company.
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