Evaluating factors of profitability for Indian banking sector: a panel regression
Abstract
Purpose
The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose.
Design/methodology/approach
The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ^{2}, PP – Choi Zstatistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series.
Findings
Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks.
Originality/value
No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.
Keywords
Citation
Bansal, R., Singh, A., Kumar, S. and Gupta, R. (2018), "Evaluating factors of profitability for Indian banking sector: a panel regression", Asian Journal of Accounting Research, Vol. 3 No. 2, pp. 236254. https://doi.org/10.1108/AJAR0820180026
Download as .RISPublisher
:Emerald Publishing Limited
Copyright © 2018, Rohit Bansal, Arun Singh, Sushil Kumar and Rajni Gupta
License
Published in the Asian Journal of Accounting Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Indian banking sector is one part of the shifting business paradigms across the globe. The sector is passing through from an era of high competition, regulatory changes and the slow growth of the Indian economy, which has affected it. The recent few events have affected the bankingrelated operations, i.e. NPAs, demonetization, digit India, payment wallet, goods & service tax and payment banks in India. The RBI has extended the timeline for Basel III compliance and licenses to private sector entities. So, competition is going to intensify. Increasing competition is going to be a problem as well as an opportunity also to explore new area and scope of banking services. Customer satisfaction, service innovation and technologydriven banking would be the focus points. Economic solidity remains engrained though, with inflation continuing moderate, and the fiscal deficit on the path of consolidation.
In this reading, we want to discover the factors of profitability for private and public sector banks and would like to elaborate, which factors are affecting profit margin (PM), interest income, deposits and certain expenses. Financial performance is the key indicator for any business organization. The future evolution and present operations of corporate would be influenced by profitability. The profitability is the ratio which supports to quantify the financial performance of business uniqueness of this study, which divides whole banking data into three categories, i.e. private bank, public bank and combined (private and public). Amandeep (1983) studied several variables that affect the profitability with the help of regression analysis. The author had tried to define various factors that affect the dependent variable, i.e. profitability, also used trend analysis and ratio analysis for commercial banks in India. Mishra (1992) studied and evaluated the profitability of scheduled commercial banks considering the interest and noninterest income and interest expenditure, manpower expenses and other expenses. He said that the growing preemption of funds in the form of liquidity ratio, cash reserve ratio, as compared to the income, advances and total investment than interest income has contributed to the deteriorating profitability of Indian commercial banks. Ramamoorthy (1998) studied profitability and productivity for the Indian banking sector during the period of 1993–1996. The author evaluated and tested profitability as well as the efficiency of Indian banks with its global equals. The results disclosed that Indian banks have higher interest spread than banks abroad, higher operating cost banks in foreign countries and higher risk provision levels. Vennet (2002) described the cost and profit efficiency of European financial conglomerates and universal banks. The author concluded that operational efficiency has become the major determinant of bank profitability. Goddard et al. (2004a, b) revealed that the relationship between the capitalassets ratio and profitability is positive. Satish et al. (2005) scrutinized the performance of 55 banks for the period 2004–2005. They concluded that the Indian banking system gazes rigorous and upcoming advances technology which will assist the banking system to propagate in the coming era. Bhayani (2006) tried to analyze the performance of new private banks through the help of the CAMEL model. The author found the satisfactory performance of these private banks in the Indian scenario.
Vyas and Dhade’s (2007) study was primarily focused on the State Bank of India (SBI), as to how much it had been affected by the entry of new private sector banks. The authors used several financial ratios and applied the ttest to evaluate the changes in the business of SBI, especially before and after the entry of private sector banks. Singla (2008) had tried to analyze the role of financial management in the growth of banking. The study was related to measuring the profitability of selected 16 banks for a period from 2000–2001 to 2006–2007. The study concluded that the profitability position was reasonable when likened with the preceding years. Rao (2008) pointed few observations during their research and found that following factors, such as competition, new information technologies and falling costs, have all frolicked a major role for public sector banks in India. The author had taken public, private and foreign banks working in India for a period from 2005–2006 to 2010–2011. Pat (2009) illustrated the various groups of banks and some financial ratio like net profit, return on assets (ROA) and return on equity. The author reported the improvements in net profits margin, ROA and return on equity.
Prasad and Chari (2011) described the financial performance of four major banks in India. The following variables had been taken for the study, spread ratios, burden ratios and profitability ratios. The study brings out the comparative efficiency of SBI, PNB, ICICI and HDFC. Devanadhen (2013) discussed about the financial soundness of the Indian banking sector. He had included 14 public and 3 private banks from April 1, 2000 to March 31, 2011 in their study. Central Bank of India erected last in the total performance and SBI exhibited better performance than ICICI Bank. Barua et al. (2017) found a negative link between profitability and market concentration. Other findings suggested that capitalization, credit risk, leverage and ownership structure are the most important elements of the viability of Indian banks. Ozili (2017) investigated the determinants of African bank profitability. Using static and dynamic panel estimation techniques, the conclusions specify that bank size, total regulatory capital and loan loss provisions are substantial elements of the ROA of listed banks compared to nonlisted banks.
Research problem and objectives
to examine the determinants of profitability for the banking companies listed on Bombay stock exchange (BSE) by taking PM and ROA as a dependent variable;
identify various financial ratios, affecting the measurements of profitability;
apply Hausman’s test to measure panel regression;
to quantify various determinates for the profitability of listed public banks (PSUs) and private banks; and
compare the determinants of profitability between public banks and private banks and identify those elements that are moving the productivity of listed banks in India.
Research methodology
This research paper’s purpose is to quantify the determinants of PM of the Indian banking sector that is listed on the BSE for the period of April 2012 to March 2017. As a research procedure, we have obtained the income and the financial statements for the five periods (April 2012–March 2017) of the listed banking companies from BSE and the company’s website. Financial analysis for Indian companies is based on the data of the financial year ending on March 31. Financial ratios were collected from the company’s financial statements, then brief to arise with profitability and other activity ratios that were used in the analysis phase. Therefore, a sample of 39 listed banking companies was selected. Current study excludes eight companies as they do not have audited income statement and financial statement. Finally, only 41 listed oil companies have been included in our study for analysis purpose. Dougherty (2007) recommended a regression model in panel data approaches, i.e. fixed effects (FE) and random effects (RE) panel. After applying both panel data approaches authors must run Hausman’s specification test, if this test provides a significant result, then they should reject the following null hypothesis, “difference in coefficients not systematic” (Table I).
The FE model
The FE model is a specific set of N firms, i.e. private and public listed banks on BSE, and our inference is limited to the behavior of these groups of companies. Inference is conditional on the particular N firms, companies that are observed.
FE regression equation Model A:
FE regression equation Model B:
The RE models
There are unique, time constant attributes of individuals that are the results of random variation and do not correlate with the individual regressors. We have included private and public listed banks on BSE.
RE regression equation Model A:
RE regression equation Model B:
The Hausman’s test
The Hausman’s (1978) test compares the RE and FE estimators, since the key consideration in choosing between an RE and FE approaches is whether c_{i} and x_{it} are correlated, it is important to have a method for testing this assumption (Tables II and III).
Empirical results
Panel unit root test
The value of fisher χ^{2} test (PP) statistic is 59.91 and Choi Zstat. is 5.88. All of the results indicate the nonpresence of a unit root, as both Fisher and Choi Ztests do not fail to reject the null of a unit root. We can say that unit root problem does not exist into the Indian banking panel data.
Hadri panel unit root test
The Hadri panel unit root test has a null hypothesis of no unit root in any of the series in the panel. The test is based on the residuals from the individual OLS regressions of on a constant, or on a constant and a trend:
The Hadri Zstatistic value is 10.67 and Heteroscedastic consistent Zstat. is 10.54. At the preceding, all of the results indicate the presence of a stationarity, as the Hadri tests do not reject the null of a stationarity. We conclude that the Indian banking panel data are stationarity data set.
Crosssection regression results
Results and findings
Table VIII signifies the results of FE and RE panel regression for the private banking sector in India. Net profit margin (LOGPM) has been used as a dependent variable, whereas IITF, interest expended interest earned (IEIE), credit deposit ratio (CRDR), CDR, CAR, advances to loans funds (ALF), quick ratio (QR), total debt to owners fund (TDOF) and total income to capital employed (TICE) have been used as an independent variable. The total number of observations under this panel is 195, and 39 is included as a crosssection. Five years of data from 2012 to 2017 have been booked in this study.
Out of all variables, IEIE and CRDR are found significant with the probability value of 0.069 and 0.02, respectively, under the FE regression model for the private banks in India. There is a negative statistically significant relationship between IEIE and CRDR and the viability of the Indian banking sector. Although other independent variables, i.e. IITF ratio, cash deposit ratio (CDR), capital adequacy ratio (CAR), advances to loans funds, QR, total debt to owners fund and total income to capital employed, have been found insignificant with the net PM, these variables did not influence the profitability of the banking sector in India. The R^{2} of this FE panel model is 81.00 percent, while adjusted R^{2} of this panel is 77.00 percent. The R^{2} explains 81.00 percent variations in the profitability in this panel from 2012 to 2017. Adjusted R^{2} of this panel explains the 77.00 percent variations in the profitability. Model is acceptable as Ftest is 23.27. The value of Durbin–Watson stat. is 02.09, which explains there is no autocorrelation problem exists in this FE panel model, and this model is also permitted from hetroscadisticity.
Under the RE regression model, IITF ratio, IEIE, CRDR, CAR and ALF are found significant with the probability value of 0.095, 0.007, 0.011, 0.001 and 0.020, respectively. We found a positive significant relationship between IITF and ALF and the profitability of the private banking sector. However, there is a negative statistically significant connection between IEIE, CRDR and CAR with the profitability of the private banking sector. Although other independent variables, i.e., CDR, QR, total debt to owners fund and total income to capital employed, have been found insignificant with the net PM by the RE regression model, these variables did not influence the profitability of banking sector in India. The R^{2} of this RE panel model is 49.00 percent, while adjusted R^{2} of this panel is 42.00 percent. The R^{2} explains 49.00 percent variations during 2012–2017. Adjusted R^{2} of this panel explains 42.00 percent variations in that model. However, the model is not acceptable as Ftest is 06.58. The value of Durbin–Watson stat. is 01.62, which explains there is a positive autocorrelation problem exists in this panel model (Table IX).
As out of the above two models (FE and RE), the χ^{2} value of this test 47.43 under FE model is significant at the 1 percent level of significance. The FE model has two significant variables which include IEIE and CRDR of the firm, whereas other independent variables, i.e. interest income to total funds ratio, CDR, CAR, advances to loans funds, QR, total debt to owners fund and total income to capital employed, have been found insignificant with the net PM.
Results and findings
Table X indicates the results of FE and RE panel regression for the public banking sector in India. Net profit margin (LOGPM) has been used as a dependent variable under FE and RE panel, whereas IITF, IEIE, CRDR, CDR, CAR, ALF, QR, total debt to owners fund (TDOF) and total income to capital employed (TICE) have been used as independent variables. The total number of observations under this panel is 195, and 39 included as a crosssection. Five years of data from 2012 to 2017 have been used in this study.
Out of all variables, IEIE, CRDR, CDR, advances to loans funds and QR are found significant with the probability values of 0.01, 0.07, 0.07, 0.00 and 0.08, respectively, under the FE regression model for the public banks in India. We have found a negative statistical association between IEIE, CRDR and QR and the viability of public banks in India. However, there is a positive association between CDR and advances to loan funds with the profitability of public banks in India. Although other independent variables, i.e. IITF ratio, CAR, total debt to owners fund and total income to capital employed, have been found insignificant with the net PM for the public banks, these variables did not influence the profitability of banking sector. The R^{2} of this FE panel model is 74.00 percent, while adjusted R^{2} of this panel is 65.00 percent. The R^{2} explains the 74.00 percent existence of included variables from 2012 to 2017. Adjusted R^{2} of this panel explains the 65.00 percent variations. Model is acceptable as Fstat. in 08.21. The value of Durbin–Watson stat. is 01.71, which explains there is no autocorrelation problem exists in this FE panel model and this model is also permitted from hetroscadisticity.
Under the RE regression model, interest income to total funds ratio, CRDR, CAR, advances to loans funds and total income to capital employed are found significant with the probability values of 0.04, 0.01, 0.00, 0.00 and 0.05, respectively. We found a negative association between IITF ratio and CRDR with the profitability of public banks in India. These ratios reduced the PM of public banks in India. However, we find a positive relationship between CAR, ALF and TICE and the profitability of public sector banks. Although other independent variables, i.e. IEIE, CDR, QR and total debt to owners fund, have found insignificant with the PM of public sector banks by the RE regression model, these variables did not influence the profitability of public banks. The R^{2} of this RE panel model is 57.00 percent, while adjusted R^{2} of this panel is 54.00 percent. The R^{2} explains 57.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel is explain 54.00 percent variations in the profitability. However, the model is a good fit as Fstat. is 17.44. Conversely, the value of Durbin–Watson stat is 01.75, which explains there is a positive autocorrelation problem exists in this RE panel (Table XI).
As out of the above two models (FE and RE), the χ^{2} value of this test 31.01 is significant and substantial at the 1 percent level of importance under FE. For the checking validity of these two models, we run a Hausman’s specification test in order to decide the one appropriate model from two possible options. The FE model explains that variables which include IEIE, CRDR, CDR, ALF and QR are significant with the net PM for the public sector banks in India whereas other independent variables, i.e. IITF ratio, CAR, total debt to owners fund and total income to capital employed, have been found insignificant.
Results and findings
Table XII indicates the results of FE and RE panel regression for the private banking sector in India. LOGROA has been used as a dependent variable under FE and RE, whereas IITF, IEIE, CRDR, CDR, CAR, advances to loans funds (ALF), QR, total debt to owners fund (TDOF) and total income to capital employed (TICE) have been used as an independent variable. The total number of observations under this panel is 195, and 39 included as a crosssection. Five years of data from 2012 to 2017 have been used in this study.
Out of all variables under the FE regression model, QR, CRDR, CDR, advances to loans funds and total debt to owners fund have found significant with the probability values of 0.09, 0.00, 0.09, 0.06 and 0.09, respectively, for the private banks in India using ROA as a dependent variable. We find a negative significant association between CRDR, and total debt to owners fund and the return of asset’s ratio of private banks in India. These two ratios have affected the ROA for the private sector banks. Nevertheless, QR, CDR and advances to loan funds found to have a positive association with the return of asset’s ratio of private banks in India. These variables increased the return of private sector banks. Although other independent variables, i.e. IITF, IEIE, CAR and total income to capital employed, have been found insignificant with the ROA for the private banks, these variables did not influence the return of private banking in India. The R^{2} of this FE panel model is 84.00 percent, while adjusted R^{2} of this panel is 74.00 percent. The R^{2} explains 84.00 percent deviations. Adjusted R^{2} of this panel explains 74.00 percent variations. The model is acceptable as Fstat. in 08.61. The value of Durbin–Watson stat is 01.28, which explains there is a serial autocorrelation problem exists in this FE panel.
Under the RE regression, only CAR has found significant with the probability value of 0.01. We have found a positive association between CAR and with the ROA for private banks. These ratios continue the return in India. Though other independent variables, i.e. QR, IITF ratio, IEIE, CRDR, CDR, ALF, total debt to owner’s fund and total income to capital employed, have found insignificant with the ROA for private sector banks by RE regression model, these variables did not influence the return of private sector banks in India. The R^{2} of this RE panel model is 21.00 percent, while adjusted R^{2} of this panel is 09.00 percent. The R^{2} explains 21.00 percent variations in this panel from 2012 to 2017. Adjusted R^{2} of this panel explains 09.00 percent variations. However, the model is not a good fit as Ftest is 01.77. The value of Durbin–Watson stat. is 01.24, which explains that there is a positive autocorrelation problem exists in this RE panel (Table XIII).
As out of the above two models (FE and RE), the FE model is significant. The outcome suggests that the most appropriate model is the FE model because the χ^{2} value of this test 14.40 is significant at the 10 percent level of significance. The FE model with these variables, i.e. QR, CRDR, CDR, ALF and total debt to owners, and IITF ratio, IEIE, CAR and total income to capital employed have been found insignificant with the ROA for the private banks in India.
Results and findings
Table XIV indicates the results of FE and RE panel regression for the public sector banking in India. LOGROA has been employed as a dependent variable under FE and RE panel, IITF, IEIE, CRDR, CDR, CAR, advances to loans funds (ALF), QR, total debt to owners fund (TDOF) and total income to capital employed (TICE) have been used as an independent variable. The total number of observations under this panel is 195, and 39 is included as a crosssection. Five years of data from 2012 to 2017 have been used in this study.
On the view of all variables under the FE regression model, not a single variable has found significant for the public banks using ROA as a dependent variable. Accordingly, all independent variables, i.e. QR, CRDR, CDR, ALF, TDOF, IITF ratio, IEIE, CAR and TICE, have been found insignificant with the ROA for the public sector banks in India. These variables did not influence the return of the public banking sector in India. The R^{2} of this FE panel model is 89.00 percent, while adjusted R^{2} of this panel is 84.00 percent. The R^{2} explains 89.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel explains the 84.00 percent variations in profitability. Model is a good fit as Fstat. is 21.41. The value of Durbin–Watson stat. is 01.20, which explains there is a serial autocorrelation problem exists in this FE.
Under the RE regression, only CRDR has found significant with the probability value of 0.09. We have found a positive association between CRDR and with the ROA for public banks. These ratios continue the return of public banks in India. Though other independent variables, i.e. QR, IITF ratio, IEIE, CAR, CRDR, ALF, TDOF and TICE, have found insignificant with the ROA for public sector banks by RE regression model, these variables did not influence the return of public banks in India. The R^{2} of this RE panel model is 14.00 percent, while adjusted R^{2} of this panel is 07.00 percent. The R^{2} explains 14.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel explains 07.00 percent variations in the profitability. However, the model is not a good fit as Fstat. is 02.01. The value of Durbin–Watson stat is 00.96, which explains there is a positive autocorrelation problem exists in this RE panel (Table XV).
As out of the above two models (FE and RE), none of these two models are significant at the desired level of significance. The outcome suggests that the χ^{2} value of this test 11.68 is insignificant at the 1, 58 and 10 percent levels of significance according to the criteria of selecting a model described earlier.
Results and findings
Table XVI indicates the results of FE and RE panel regression for overall banking sectors in India. Net profit margin (LOGPM) has been used as a dependent variable under FE and RE panels, whereas IITF, IEIE, CRDR, CDR, CAR, ALF, QR, TDOF and TICE have been used as independent variables. The total number of observations under this panel is 195, and 39 included as a crosssection. Five years of data from 2012 to 2017 have been used in this study.
Out of all variables under the FE regression model, advances to loans funds, CDR, CRDR, interest expended to interest earned and total debt to owners fund are found significant with the probability values of 0.00, 0.02, 0.02, 0.00 and 0.06, respectively, under the FE regression model for total banks in India. We find a negative significant association between the CRDR and interest expended to interest earned with the profitability of all banks in India. However, advances to loan funds, CDR and total debt to owner fund found to be positively associated with the profitability of all banks. Although other independent variables, i.e. CAR, interest income to total funds ratio, QR and total income to capital employed, have been found insignificant with the PM for all the banks in India, these variables did not influence the profitability of all banks in India. The R^{2} of this FE panel model is 80.00 percent, while adjusted R^{2} of this panel is 74.00 percent. The R^{2} explains 80.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel explains the 74.00 percent variations in profitability. The model is a good fit as Fstat. is 12.75. The value of Durbin–Watson stats is 01.94, which explains there is no autocorrelation problem exists in this FE panel model, and this model is also permitted from hetroscadisticity.
Under the RE regression model, advances to loans funds, CAR, CRDR and interest expended to interest earned are found significant with the probability values of 0.01, 0.00, 0.02 and 0.01, respectively. We found a negative association between the CRDR and interest expended to interest earned with the profitability of all the banks in India. These ratios reduced the PM of banks in India. However, there is a positive significant association between the advances to loans funds and CAR with the effectiveness of all banks in an Indian context. Although other independent variables, i.e., CDR, IITF ratio, QR, TDOF and TICE, have found insignificant with the PM of all the banks by the RE regression model, these variables did not influence the profitability of banks in India. The R^{2} of this RE panel model is 57.00 percent, while adjusted R^{2} of this panel is 55.00 percent. The R^{2} explains 57.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel explains the 55.00 percent variations in profitability. However, the model is a good fit as Fstat. is 27.53. Conversely, the value of Durbin–Watson stat. is 01.36, which explains that there is a positive autocorrelation problem exists in this RE panel (Table XVII).
As out of the above two models (FE and RE), the FE model is significant at the 1 percent level of significance. The outcome suggests that the FE model is more relevant because the χ^{2} value of this test is 41.30. The Husaman’s test recommends that the FE model is suitable for this study. We have found the following important variables which include advances to loans funds, CDR, CRDR, interest expended to interest earned and total debt to owners fund are found significant, and other independent variables, i.e. CAR, interest income to total funds ratio, QR and total income to capital employed, found insignificant with the PM for all the banks in India.
Results and findings
Table XVIII indicates the results of FE and RE panel regression for the overall banking sector in India. LOGROA has been used as a dependent variable under FE and RE panel, whereas interest income to total funds (IITF), interest expended to interest earned (IEIE), CRDR, CDR, CAR, advances to loans funds (ALF), QR, total debt to owners fund (TDOF) and total income to capital employed (TICE) have been used as independent variables. The total number of observations under this panel is 195, and 39 included as a crosssection. Five years of data from 2012 to 2017 have been used in this study.
Out of all variables under the FE regression model, advances to loans funds, CAR and CRDR are found significant with the probability values of 0.08, 0.06 and 0.07, respectively, under the FE regression for total banks in India. We have found a negative statistical association between the CRDR and the ROA of all the banks in India. However, advances to loan funds and CDR found to be positively associated with the profitability of all the banks in India. Although other independent variables, i.e. CDR, IEIE, IITF ratio, QR, TDOF and TICE, have been found insignificant with the ROA for all the banks in India, these variables did not influence the profitability of the banking sector in India. The R^{2} of this FE panel model is 83.00 percent, while adjusted R^{2} of this panel is 77.00 percent. The R^{2} explains 83.00 percent variations. Adjusted R^{2} of this panel explains 77.00 percent variations in the profitability. The model is a good fit as Fstat. is 14.78. The value of Durbin–Watson stat. is 01.87, which explains that there is no autocorrelation problem exists in this FE panel model, and this model is also permitted from hetroscadisticity.
Under the RE regression model, all the independent variables have been found insignificant with the ROA of all the banks in India. These variables did not influence the profitability of banks in India. The R^{2} of this RE panel model is 05.00 percent, while adjusted R^{2} of this panel is 01.00 percent. The R^{2} explains 05.00 percent variations from 2012 to 2017. Adjusted R^{2} of this panel explains the 01.00 percent variations in profitability. However, the model is not a good fit as Fstat. is 01.19. Conversely, the value of Durbin–Watson stat is 01.71, which explains there is a positive autocorrelation problem exists in this RE (Table XIX).
The FE model is significant, the χ^{2} value of this test is 17.60 at the 5 percent level of significance. The Husaman’s specification test recommends that the FE model is suitable for this study. The FE model is significant with the following variables which include advances to loans funds, CAR and CRDR, whereas other independent variables, i.e. IEIE, CDR, ALF, QR, IITF ratio, TDOF and TICE, have been found insignificant with the ROA for all the banks in India.
Conclusion and implication
We had applied panel data regression for the profitability measures of the Indian banks, panel regression is quite authenticated and reliable analysis techniques. Subsequently after conducting an inclusive profitability analysis of the Indian banking sector (private and public), we arrived at the following conclusions: IEIE ratio and CRDR are reducing the profitability of private banks. On the other side, interest earned ratio, CRDR and QR are reducing the effectiveness of public banks. It seems that public banks are not capable to maintain their QR as compare to private banks up to a standard limit, so that it is reducing their profitability. However, public banks are focusing on CDR and advances to loan funds, increasing their profitability. Results describe that there is a positive association between CDR and advances to loan funds with the profitability of public banks. Findings reveal that interest expended to CRDR and total debt to owners fund are reducing the profitability of private banks in India. On the other side, CRDR, advances to loan funds and QR are increasing the profitability of private banks in India. It seems that private banks are able to maintain their ROA ratio in good condition as compare to public banks. However, results describe that there is no association between various financial ratio and with the profitability of public banks, while taking ROA as a profitability measure.
Indian companies should also try different strategies like offering more options to consumers, lenders, and borrowers to try and generate more revenue. We recommend that PSU’s banks should and be competitive and must allocate some funds to improve their image. Finally, we suggest that private banks should try to boost the CRDR and interest expended to interest earned to generate more revenue than to spend on various services (Tables XX and XXI).
Determinants of viability
Variable  Sign  Formula 

Profit margin  PM_{it} 

Return on assets  ROA_{it} 

Interest income to total funds ratio  IITF_{it} 

Total income to capital employed  TICE_{it} 

Capital adequacy ratio  CAR_{it} 

Advances to loans funds  ALF_{it} 

Credit deposit ratio  CRDR_{it} 

Cash deposit ratio  CDR_{it} 

Total debt to owners fund  TDOF_{it} 

Quick ratio  QR_{it} 

Interest expended to interest earned  IEIE_{it} 

Note: Profit margin has been used as a dependent variable and debtors turnover, working capital turnover and assets turnover ratio have been used as independent variables in this study, their descriptions are provided in the table
The Hadri Zstatistic matrix
H_{0} is true  H1 is true  

b_{1} (RE estimator)  Constant effective  Unreliable 
b_{0} (FE estimator)  Constant ineffective  Reliable 
Companies included into study
S. No.  Security code  Security ID  Security name  Type  ISIN No. 

1  532480  ALBK  Allahabad Bank  PSU  INE428A01015 
2  532418  ANDHRABANK  Andhra Bank  PSU  INE434A01013 
3  532134  BANKBARODA  Bank of Baroda  PSU  INE028A01039 
4  532149  BANKINDIA  Bank of India  PSU  INE084A01016 
5  532525  MAHABANK  Bank of Maharashtra  PSU  INE457A01014 
6  532483  CANBK  Canara Bank  PSU  INE476A01014 
7  532885  CENTRALBK  Central Bank of India  PSU  INE483A01010 
8  532179  CORPBANK  Corporation Bank  PSU  INE112A01023 
9  532121  DENABANK  Dena Bank  PSU  INE077A01010 
10  500116  IDBI  IDBI Bank Ltd  PSU  INE008A01015 
11  532814  INDIANB  Indian Bank  PSU  INE562A01011 
12  532388  IOB  Indian Overseas Bank  PSU  INE565A01014 
13  500315  ORIENTBANK  Oriental Bank of Commerce  PSU  INE141A01014 
14  533295  PSB  Punjab & Sind Bank  PSU  INE608A01012 
15  532461  PNB  Punjab National Bank  PSU  INE160A01022 
16  532218  SOUTHBANK  South Indian Bank Ltd  PSU  INE683A01023 
17  501061  SBBJ  State Bank of Bikaner & Jaipur  PSU  INE648A01026 
18  500112  SBIN  State Bank of India  PSU  INE062A01020 
19  532200  MYSOREBANK  State Bank of Mysore  PSU  INE651A01020 
20  532191  SBT  State Bank of Travancore  PSU  INE654A01024 
21  532276  SYNDIBANK  Syndicate Bank  PSU  INE667A01018 
22  532505  UCOBANK  UCO Bank  PSU  INE691A01018 
23  532477  UNIONBANK  Union Bank of India  PSU  INE692A01016 
24  533171  UNITEDBNK  United Bank of India  PSU  INE695A01019 
25  532401  VIJAYABANK  Vijaya Bank  PSU  INE705A01016 
26  532215  AXISBANK  Axis Bank Ltd  Private  INE238A01034 
27  532210  CUB  City Union Bank Ltd  Private  INE491A01021 
28  532772  DCBBANK  DCB Bank Limited  Private  INE503A01015 
29  532180  DHANBANK  Dhanlaxmi Bank Limited  Private  INE680A01011 
30  500469  FEDERALBNK  Federal Bank Ltd  Private  INE171A01029 
31  500180  HDFCBANK  HDFC Bank Ltd  Private  INE040A01026 
32  532174  ICICIBANK  ICICI Bank Ltd  Private  INE090A01021 
33  532187  INDUSINDBK  Indusind Bank Ltd  Private  INE095A01012 
34  532209  J&KBANK  Jammu & Kashmir Bank Ltd  Private  INE168A01041 
35  532652  KTKBANK  Karnataka Bank Ltd  Private  INE614B01018 
36  590003  KARURVYSYA  Karur Vysya Bank Ltd  Private  INE036D01028 
37  500247  KOTAKBANK  Kotak Mahindra Bank Ltd  Private  INE237A01028 
38  534690  LAKSHVILAS  Lakshmi Vilas Bank Ltd  Private  INE694C01018 
39  532648  YESBANK  Yes Bank Ltd  Private  INE528G01019 
Income statement and financial statement of the following companies are not available  
40  539437  IDFCBANK  IDFC Bank Ltd  Private  INE092T01019 
41  540065  RBLBANK  RBL Bank Ltd  Private  INE976G01028 
42  580001  STAN  Standard Chartered PLC  Private  INE028L21018 
Notes: This table characterizes all banks listed on Bombay stock exchange (BSE) India from April 2012, out of the total samples 39 companies have been listed on the basis of their security code, security ID, name, type and ISIN number. Income statement and financial statement have been taken from BSE of all respective companies from April 2012 to March 2017. Total 42 companies had been listed under the banking sector (given in the table) on BSE but the unavailability of the income statement and financial statement of three companies, only 39 companies were selected in the final sample for the research purpose. Panel data have been created for these 39 companies from 2012 to 2017
Descriptive statistics for listed PSU’s and private banks
TICE  TDOF  ROA  NPM  IITF  IEIE  CRDR  CDR  CAR  ALF  QR  

Mean  9.95  15.51  319.15  7.51  8.96  70.28  75.69  5.52  12.74  73.14  23.9 
Median  9.79  15.76  173.38  7.50  8.88  71.43  74.76  5.26  12.40  73.34  24.0 
Max.  19.25  29.94  1,584.3  22.76  17.5  82.24  105.0  9.82  18.8  96.23  49.9 
Min.  6.89  0.00  19.12  −19.4  6.25  53.79  59.86  3.08  7.51  55.78  6.20 
SD  1.20  4.90  317.32  7.96  1.06  6.19  7.66  1.08  2.08  6.13  6.94 
Skewness  2.51  −0.19  1.54  −0.87  2.76  −0.54  1.12  1.20  0.85  −0.28  −0.06 
Kurtosis  20.57  3.37  4.76  4.51  24.4  2.70  5.80  5.22  3.54  3.86  3.38 
Jarque–Bera  2,713.84  2.20  102.61  43.23  3,994  10.30  104.3  86.6  25.62  8.54  1.30 
Observation  195  195  195  195  195  195  195  195  195  195  195 
Notes: This table includes descriptive statistics for listed banks on Bombay stock exchange from April 2012 to March 2017. It contains a number of variables which have been used in this study, i.e. total income to capital employed (TICE), total debt to owners fund (TDOF), return on assets (ROA), net profit margin (NPM), interest income to total funds ratio (IITF), interest expended to interest earned (IEIE), credit deposit ratio (CRDR), cash deposit ratio (CDR), capital adequacy ratio (CAR), advances to loans funds (ALF) and quick ratio (QR). Total 195 numbers of samples have been used to comprise a balanced panel of banking sector from the years 2012–2016. Descriptive statistics have been employed in this study. Mean of net profit margin is 7.51, and the return of assets is 319.15
Pearson correlation coefficient matrix
LOGPM  LOGALF  LOGCAR  LOGCDR  LOGCRDR  LOGIEIE  LOGIITF  LOGQR  LOGTDOF  LOGTICE  

LOGPM  1.00  
LOGALF  0.55  1.00  
LOGCAR  0.58  0.36  1.00  
LOGCDR  0.18  0.25  0.16  1.00  
LOGCRDR  0.36  0.60  0.49  0.34  1.00  
LOGIEIE  −0.60  −0.44  −0.59  −0.09  −0.40  1.00  
LOGIITF  0.18  0.30  0.16  0.19  0.15  −0.12  1.00  
LOGQR  −0.33  −0.15  −0.48  −0.26  −0.41  0.49  0.15  1.00  
LOGTDOF  −0.44  −0.29  −0.60  −0.11  −0.45  0.48  −0.46  0.29  1.00  
LOGTICE  0.33  0.36  0.37  0.17  0.25  −0.19  0.54  −0.14  −0.59  1.00 
Notes: This table represents the calculation of Pearson’s correlation coefficient matrix. Before examining the panel data models, it is important to estimate the correlation among variables in order to the presence of multicollinearty. The outcomes authorize that there is no cause of multicollinearty in the models as the values of correlation do not surpass from a cut point 0.70. At the end, we conclude that all the variables, i.e. total income to capital employed (TICE), total debt to owners fund (TDOF), net profit margin (NPM), IITF, IEIE, CRDR, CDR, capital adequacy ratio (CAR), advances to loans funds (ALF) and quick ratio (QR) have been taken in this study are free from multicollinearty
Summary
LOGPM  LOGROA  

Method  Statistic  Prob.  Method  Statistic  Prob. 
PP – fisher χ^{2}  59.9178  0.9361  PP – fisher χ^{2}  89.6914  0.1721 
PP – Choi Zstat.  5.89951  1.0000  PP – Choi Zstat.  0.7305  0.7675 
Notes: This table calculated these types of panel unit root tests: fishertype tests using the PP method, and PP – Choi Zstat. Null hypothesis: unit root (individual unit root process). Newey–West automatic bandwidth selection and Bartlett kernel. Total (balanced) observations: 195, crosssections included: 39
Panel unit root test by Hadri Zstat.
LOGPM  LOGROA  

Method  Statistic  Prob.  Statistic  Prob. 
Hadri Zstat.  10.6738  0.0000  07.33698  0.0000 
Heteroscedastic consistent Zstat.  10.5478  0.0000  10.4470  0.0000 
Notes: This table presents the panel unit root test by Hadri Zstat. and heteroscedastic consistent Zstat. Hadri (2000) accept that there is a stationarity process so that is identical across crosssections. Under the null hypothesis, there is a stationarity, while under the alternative, there is no stationarity. Null hypothesis: stationarity. Sample: 2012 2017 Newey–West automatic bandwidth selection and Bartlett kernel. Total (balanced) observations: 195, crosssections included: 39
Private banks with profit margin as a dependent variable
Fixed effect panel regression  Random effect panel regression  
Variable  Coefficient  tStat.  Prob.  Coefficient  tStat.  Prob. 
C  21.56  2.20  0.032  8.14  1.51  0.13 
LOGIITF  05.87  1.55  0.127  −3.58  −1.69  0.095*** 
LOGIEIE  −03.29  −1.85  0.069***  −2.64  −2.78  0.007* 
LOGCRDR  −02.46  −2.26  0.02**  −2.12  −2.62  0.011** 
LOGCDR  0.38  1.06  0.317  −0.03  −0.08  0.928 
LOGCAR  −0.04  −0.06  0.952  2.31  4.19  0.001* 
LOGALF  0.76  0.77  0.439  1.86  2.37  0.020** 
LOGQR  −0.17  −0.68  0.496  −0.07  −0.43  0.667 
LOGTDOF  0.19  0.33  0.741  0.25  0.53  0.591 
LOGTICE  −4.88  −1.35  0.181  3.43  1.51  0.138 
Effects specification  Effects specification  
R^{2}  0.81  R^{2}  0.49  
Adjusted R^{2}  0.77  Adjusted R^{2}  0.42  
Durbin–Watson  2.09  SE of regression  0.39  
SE of regression  0.30  Fstatistic  6.58  
Fstatistic  23.27  Prob. (Fstatistic)  0.02  
Prob. (Fstatistic)  0.00  Durbin–Watson stat  1.62 
Notes: The fixed effect panel equation LOGPM_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it} + β_{9}LOGIEIE_{it} + u_{it} and random effect panel equation LOGPM_{it}=β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it} + β_{9}LOGIEIE_{it} + u_{it}+ e_{it} have been used in this table for regression analysis purpose. Panel EGLS (crosssection random effects) method has been employed to quantify the relationship. Crosssection random and idiosyncratic random effects have been done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also been used for testing a good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test for private banks with profit margin
Test summary  χ^{2} statistic  χ^{2} df  Prob. 

Crosssection random  47.4335  9  0.00 
Public banks with profit margin as a dependent variable
FE panel regression  RE panel regression  
Variable  Coefficient  tStatistic  Prob.  Variable  Coefficient  tStatistic  Prob. 
C  −3.46  −0.29  0.76  C  −25.78  −3.44  0.00 
LOGIITF  −2.05  −0.49  0.61  LOGIITF  −5.74  −2.04  0.04** 
LOGIEIE  −4.96  −2.55  0.01**  LOGIEIE  −0.05  −0.05  0.95 
LOGCRDR  −4.23  −1.77  0.07***  LOGCRDR  −4.2  −3.77  0.01** 
LOGCDR  0.75  1.81  0.07***  LOGCDR  0.21  0.8  0.42 
LOGCAR  0.31  0.37  0.71  LOGCAR  2.43  4.24  0.00* 
LOGALF  9.35  6.14  0.00*  LOGALF  9.1  7.93  0.00* 
LOGQR  −0.72  −1.73  0.08***  LOGQR  −0.04  −0.19  0.84 
LOGTDOF  0.22  1.04  0.29  LOGTDOF  0.04  0.29  0.76 
LOGTICE  3.77  0.88  0.37  LOGTICE  5.66  1.91  0.05** 
Effects specification  Effects specification  
R^{2}  0.74  R^{2}  0.57  
Adjusted R^{2}  0.65  Adjusted R^{2}  0.54  
Durbin–Watson stat.  1.71  Durbin–Watson stat.  1.75  
Fstatistic  8.21  Fstatistic  17.44  
Prob. (Fstatistic)  0.02  Prob. (Fstatistic)  0.01 
Notes: The fixed effect panel equation LOGPM_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it} + β_{9}LOGIEIE_{it} + u_{it} and random effect panel equation LOGPM_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it} + β_{9}LOGIEIE_{it} + u_{it}+ eit have been used in this table for panel, and idiosyncratic random effects had done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also been used for testing the good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test for public banks with profit margin as a dependent variable
Test summary  χ^{2} statistic  χ^{2} df  Prob. 

Crosssection random  31.017754  9  0.0003 
Private banks with return on assets as a dependent variable
Fixed effect panel  Random effect panel  
Variable  Coefficient  tStatistic  Prob.  Variable  Coefficient  tStatistic  Prob. 
C  26.17  1.35  0.18  C  20.98  1.82  0.07 
LOGQR  0.74  1.67  0.09***  LOGQR  0.48  1.28  0.21 
LOGIITF  5.75  0.94  0.35  LOGIITF  −1.13  −0.25  0.80 
LOGIEIE  −5.12  −1.55  0.13  LOGIEIE  −3.62  −1.77  0.08 
LOGCRDR  −5.24  −2.97  0.00*  LOGCRDR  −2.60  −1.62  0.11 
LOGCDR  1.76  1.70  0.09***  LOGCDR  0.74  1.12  0.27 
LOGCAR  0.68  0.53  0.60  LOGCAR  2.90  2.68  0.01** 
LOGALF  3.28  1.92  0.06***  LOGALF  −0.12  −0.08  0.94 
LOGTDOF  −1.91  −1.71  0.09***  LOGTDOF  0.76  0.82  0.42 
LOGTICE  −2.63  −0.44  0.66  LOGTICE  0.59  0.13  0.90 
Effects specification  
R^{2}  0.84  R^{2}  0.21  
Adjusted R^{2}  0.74  Adjusted R^{2}  0.09  
Durbin–Watson stat.  1.28  Durbin–Watson stat.  1.24  
Fstatistic  8.61  Fstatistic  1.77  
Prob. (Fstatistic)  0.02  Prob. (Fstat)  0.09 
Notes: The fixed effect panel equation LOGROA_{it}=β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it} and Random effect panel equation LOGROA_{it}=β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it}+ eit have been used in this table for the regression analysis purpose. Panel EGLS (crosssection random effects) method has been employed. Crosssection random, and Idiosyncratic random effects have been done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also used for testing good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test
Test summary  χ^{2} Statistic  χ^{2} df  Prob. 

Crosssection random  24.406952  9  0.086 
Public banks with return on assets as a dependent variable
Fixed effect panel  Random effect panel  
Variable  Coefficient  tStatistic  Prob.  Variable  Coefficient  tStatistic  Prob. 
C  −1.97  −0.21  0.83  C  −2.28  −0.26  0.79 
LOGQR  0.41  1.21  0.23  LOGQR  0.09  0.31  0.76 
LOGIITF  −0.80  −0.24  0.81  LOGIITF  −2.51  −0.81  0.42 
LOGIEIE  −1.67  −1.08  0.28  LOGIEIE  −2.01  −1.44  0.15 
LOGCRDR  1.85  0.98  0.33  LOGCRDR  2.73  1.70  0.09*** 
LOGCDR  −0.29  −0.87  0.39  LOGCDR  −0.13  −0.42  0.67 
LOGCAR  0.06  0.09  0.93  LOGCAR  −0.08  −0.13  0.90 
LOGALF  0.73  0.60  0.55  LOGALF  0.88  0.76  0.45 
LOGTDOF  0.12  0.73  0.47  LOGTDOF  0.04  0.26  0.79 
LOGTICE  1.71  0.51  0.61  LOGTICE  2.75  0.85  0.40 
Effects specification  
R^{2}  0.89  R^{2}  0.14  
Adjusted R^{2}  0.84  Adjusted R^{2}  0.07  
Durbin–Watson stat.  1.20  Durbin–Watson stat.  0.96  
Fstatistic  21.42  Fstatistic  2.01  
Prob. (Fstatistic)  0.001*  Prob. (Fstatistic)  0.04** 
Notes: The fixed effect panel equation LOGROA_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it} and random effect panel equation LOGROA_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it}+ eit have been used in this table for regression analysis purpose. Crosssection random and idiosyncratic random effects have been done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also been used for testing the good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test
Test summary  χ^{2} statistic  χ^{2} df  Prob. 

Crosssection random  11.684047  9  0.134 
Total banks with profit margin as a dependent variable
Fixed effect panel  Random effect panel  
Variable  Coefficient  tStat.  Prob.  Variable  Coefficient  tStat.  Prob. 
C  1.71  0.20  0.84  C  −5.86  −1.27  0.21 
LOGALF  5.10  5.23  0.00*  LOGALF  4.75  6.57  0.01* 
LOGCAR  0.71  1.25  0.21  LOGCAR  2.63  6.83  0.00* 
LOGCDR  0.76  2.36  0.02**  LOGCDR  0.31  1.35  0.18 
LOGCRDR  −3.01  −2.38  0.02**  LOGCRDR  −2.71  −3.78  0.02** 
LOGIEIE  −4.30  −3.08  0.00*  LOGIEIE  −2.31  −3.18  0.01* 
LOGIITF  4.60  1.52  0.13  LOGIITF  −2.00  −1.16  0.25 
LOGQR  −0.36  −1.31  0.19  LOGQR  −0.03  −0.16  0.87 
LOGTDOF  0.35  1.87  0.06***  LOGTDOF  0.08  0.56  0.57 
LOGTICE  −1.49  −0.47  0.64  LOGTICE  2.57  1.42  0.16 
Effects specification  
R^{2}  0.80  R^{2}  0.57  
Adjusted R^{2}  0.74  Adjusted R^{2}  0.55  
Durbin–Watson stat.  1.94  Durbin–Watson stat.  1.36  
Fstatistic  12.75  Fstatistic  27.53  
Prob. (Fstatistic)  0.00  Prob. (Fstatistic)  0.00 
Notes: The fixed effect panel equation LOGPM_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it} and random effect panel equation LOGPM_{it}=β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it}+ eit have been used in this table for the regression analysis purpose. Panel EGLS (crosssection random effects) method has been employed. Crosssection random, and Idiosyncratic random effects has been done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also been used for testing the good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test??
Test summary  χ^{2} statistic  χ^{2} df  Prob. 

Crosssection random  41.300533  9  0.004 
All banks with return on assets as a dependent variable
Random effect panel  Random effect panel  
Variable  Coefficient  tStatistic  Prob.  Variable  Coefficient  tStatistic  Prob. 
C  2.00  0.32  0.78  C  4.09  0.55  0.58 
LOGALF  1.75  1.79  0.08***  LOGALF  1.47  1.6  0.11 
LOGCAR  1.08  1.9  0.06***  LOGCAR  0.8  1.55  0.12 
LOGCDR  −0.11  −0.34  0.74  LOGCDR  0.13  0.45  0.65 
LOGCRDR  −2.3  −1.82  0.07***  LOGCRDR  −1.12  −1.07  0.29 
LOGIEIE  0.37  0.26  0.79  LOGIEIE  −0.59  −0.51  0.61 
LOGIITF  1.79  0.59  0.55  LOGIITF  0.67  0.27  0.79 
LOGQR  0.3  1.1  0.27  LOGQR  0.05  0.21  0.84 
LOGTDOF  0.14  0.77  0.45  LOGTDOF  0.03  0.16  0.87 
LOGTICE  −0.7  −0.22  0.83  LOGTICE  −0.71  −0.27  0.79 
Effects specification  
R^{2}  0.83  R^{2}  0.05  
Adjusted R^{2}  0.77  Adjusted R^{2}  0.01  
Durbin–Watson stat.  1.87  Durbin–Watson stat.  1.61  
Fstatistic  14.78  Fstatistic  1.19  
Prob. (Fstatistic)  0.00*  Prob. (Fstatistic)  0.3** 
Notes: The fixed effect panel equation LOGROA_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it} and random effect panel equation LOGROA_{it} = β_{0i} + β_{1}LOGTICE_{it} + β_{2}LOGIITF_{it} + β_{3}LOGCAR_{it} + β_{4}LOGALF_{it} + β_{5}LOGCRDR_{it} + β_{6}LOGCDR_{it} + β_{7}LOGTDOF_{it} + β_{8}LOGQR_{it}+ β_{9}LOGIEIE_{it} + u_{it}+ eit have been used in this table for the regression analysis purpose. Crosssection random, and Idiosyncratic random effects have been done under effects specification module. Durbin–Watson test has been used for checking autocorrelation and hetroscadisticity. Anova Ftest has also used for testing good fit of this model. *,**,***Significant at the 1, 5 and 10 percent levels, respectively
Hausman’s test
Test summary  χ^{2} statistic  χ^{2} df  Prob. 

Crosssection random  17.603842  9  0.0401 
Summary for profitability (net profit margin) with private, public and total banks
Dependent variable (net profit margin)  Private bank  Public bank  All banks  

Independent Variables  Fixed panel  Random panel  Fixed panel  Random panel  Fixed panel  Random panel 
Interest income to total funds ratio (IITF)  No effect  Positive  No effect  Negative  No effect  No effect 
Interest expended to interest earned (IEIE)  Negative  Positive  Negative  No effect  Negative  Negative 
Credit deposit ratio (CRDR)  Negative  Positive  Negative  Negative  Negative  Negative 
Cash deposit ratio (CDR)  No effect  No effect  Positive  No effect  Positive  No effect 
Capital adequacy ratio (CAR)  No effect  Negative  No effect  Positive  No effect  Positive 
Advances to loans funds (ALF)  No Effect  Positive  Positive  Positive  Positive  Positive 
Quick ratio (QR)  No effect  No effect  Negative  No effect  No effect  No effect 
Total debt to owners fund (TDOF)  No effect  No effect  No effect  No effect  Positive  No effect 
Total income to capital employed (TICE)  No effect  No effect  No effect  Positive  No effect  No effect 
Notes: Summary reveals that IEIE ratio and CRDR are reducing the profitability of private banks in India. On the other side, interest earned ratio, credit deposit ratio and quick ratio are reducing the effectiveness of public banks. It seems that public banks do not control their quick ratio as compare to private banks up to a standard limit, so that it is reducing their profitability. However, public banks are focusing on cash deposit ratio and advances to loan funds, increasing their profitability. Results describe that there is a positive association between cash deposit ratio and advances to loan funds with the profitability of public banks
Summary for profitability (return on assets) with private, public and total banks
Dependent variable (return on assets)  Private bank  Public bank  All banks  

Independent variables  Fixed panel  Random panel  Fixed panel  Random panel  Fixed panel  Random panel 
Interest income to total funds ratio (IITF)  No effect  No effect  No effect  No effect  No effect  No effect 
Interest expended to interest earned (IEIE)  No effect  No effect  No effect  No effect  No effect  No effect 
Credit deposit ratio (CRDR)  Negative  No effect  No effect  Positive  Negative  No effect 
Cash deposit ratio (CDR)  Positive  No effect  No effect  No effect  No effect  No effect 
Capital adequacy ratio (CAR)  No effect  Positive  No effect  No effect  Positive  No effect 
Advances to loans funds (ALF)  Positive  No effect  No effect  No effect  Positive  No effect 
Quick ratio (QR)  Positive  No effect  No effect  No effect  No effect  No effect 
Total debt to owners fund (TDOF)  Negative  No effect  No effect  No effect  No effect  No effect 
Total income to capital employed (TICE)  No effect  No effect  No effect  No effect  No effect  No effect 
Notes: Summary reveals that interest expended to credit deposit ratio and total debt to owners fund are reducing the profitability of private banks in India. On the other side, credit deposit ratio, advances to loan funds and quick ratio are increasing the profitability of private banks in India. It seems that private banks are able to maintain their return on assets ratio in good condition as compare to public banks. However, results describe that there is no association between various financial ratio and with the profitability of public banks, while taking return on assets as a profitability measure
References
Amandeep (1983), Profits and Profitability in Commercial Banks, Deep & Deep Publications, New Delhi.
Barua, R., Roy, M. and Raychaudhuri, A. (2017), “Structure, conduct and performance analysis of Indian Commercial Bank”, South Asian Journal of Macroeconomics and Public Finance, Vol. 5 No. 2, pp. 157185.
Bhayani, S.J. (2006), “Performance of the New Indian Private Sector Banks: a comparative study”, ICFAI Journal of Management, Research, Vol. 5 No. 11, pp. 5370.
Devanadhen, K. (2013), “Performance evaluation of largesize commercial Banks in India”, Indian Journal of Finance, Vol. 7 No. 1, pp. 516.
Dougherty, C. (2007), Introduction to Econometrics, Department of Sociology, Baylor University, Oxford University Press, Waco, TX.
Goddard, J., Molyneux, P. and Wilson, J.O.S. (2004a), “The profitability of European banks: a crosssectional and dynamic panel analysis”, The Manchester School, Vol. 72 No. 3, pp. 363381, doi: 10.1111/j.14679957.2004.00397.
Goddard, J.A., Molvneux, P. and Wilson, J.O.S. (2004b), “Dynamics of growth and profitability in banking”, Journal of Money, Credit, and Banking, Vol. 36 No. 6, pp. 10691090, doi: 10.1353/mcb.2005.0015.
Hadri, K. (2000), “Testing for stationarity in heterogeneous panel data”, Econometric Journal, Vol. 3 No. 2, pp. 148161.
Hausman, J.A. (1978), “Specification tests in econometrics”, Econometrica, Vol. 46 No. 6, pp. 12511271.
Mishra, M.N. (1992), “Analysis of profitability of commercial banks”, Indian Journal of Banking and Finance, Vol. 5.
Ozili, K. (2017), “Bank profitability and capital regulation: evidence from listed and nonlisted banks in Africa”, Journal of African Business, Vol. 18 No. 2, pp. 143168, doi: doi.org/10.1080/15228916.2017.1247329.
Pat, K.A. (2009), “Why Indian banks are healthy in this global crisis?”, Economic and Political Weekly, Vol. 44 No. 17, pp. 2122.
Prasad, K.V.N. and Chari, A.A. (2011), “Financial performance of public and private sector banks: an application of posthoc Tukey HSD test”, Indian Journal of Commerce & Management Studies, Vol. 2 No. 5, pp. 114.
Ramamoorthy, K.R. (1998), “Profitability and productivity in Indian Banking”, Charted Financial Analyst, Vol. February No. 2, pp. 5354, available at: http://shodhganga.inflibnet.ac.in/bitstream/10603/38111/13/13_bibliography.pdf
Rao, S.C.D. (2008), Banking Reforms in Indiaan Evaluate Study of the Performance of Commercial Banks, Regal Publications, New Delhi, pp. 978981.
Satish, D., Sharath, J. and Surender, V. (2005), “Indian banking – performance and development 200405”, Chartered Financial Analyst, Vol. 11 No. 10, pp. 615.
Singla, H.K. (2008), “Financial performance of banks in India”, The IUP Journal of Bank Management, No. 1, pp. 5062, available at: https://ideas.repec.org/a/icf/icfjbm/v7y2008i1p5062.html
Vennet, V.R. (2002), “Cost and profit efficiency of financial conglomerates and universal banks in Europe”, Journal of Money, Credit, and Banking, Vol. 34 No. 1, pp. 254282, doi: 10.1353/mcb.2002.0036.
Vyas, R. and Dhade, A. (2007), “A study on the impact of new private sector banks on State Bank of India”, The ICFAI Journal of Bank Management, Vol. 6 No. 3, pp. 6176.
Further reading
Afia, A. and Khaled, M. (2014), “Liquidityprofitability relationship in Bangladesh banking industry”, International Journal of Empirical Finance, Vol. 2 No. 4, pp. 143151.
Badola, B.S. and Verma, R. (2006), “Determinants of profitability of banks in India a multivariate analysis”, Delhi Business Review, Vol. 7 No. 2, pp. 7989.
Gujarati, N. (2006), Basic Econometrics, 4th ed., Tata McGrawHill, New Delhi, pp. 178180.
Jain, D. and Shaikh, N. (2012), “Nonperforming assets of private sector Banks in India”, International Journal of Economics, Vol. 1 OctoberDecember.
Kumbirai, M. and Webb, R. (2010), “A financial ratio analysis of commercial bank performance in South Africa”, African Review of Economics and Finance, Vol. 2 No. 1, pp. 3053.
Prasad, K.V.N., Ravinder, G. and Reddy, D.M. (2011), “A CAMEL model analysis of public & private sector banks in India”, Journal of Banking, Financial Services & Insurance Research, Vol. 1 No. 5, pp. 5072.
Internet sources.
www.dnaindia.com/money/interviewindiaisamarketripeforaninnovationsconsultant2025561
Department of Industrial Policy and Promotion (DIPP) statistics report 2016.
Department of Information and Technology (DIT) annual reports 2016.
Media Reports, Press Information Bureau (PIB), December 2016 Edn.