




































American Finance & Banking Review 

 Vol. 6, No. 1; 2021 

ISSN 2576-1226    E-ISSN 2576-1234 

Published by CRIBFB, USA 

 

26 

RELATIVE STANDING OF ROE AND ROCE IN EFFECTIVE 

LIQUIDITY MANAGEMENT: EVIDENCE FROM 

BANGLADESHI COMMERCIAL BANKS 

 
Dr. Nazneen Jahan Chaudhury  

Associate Professor  

Department of Business Administration 

International Islamic University Chittagong, Chattogram, Bangladesh 

E-mail: nazneenchy@yahoo.com 

https://orcid.org/0000-0002-1172-3576 

 
  

Received: September 23, 2021     Accepted: November 12, 2021     Online Published: November 27, 2021 

 

DOI: 10.46281/amfbr.v6i1.1459            URL: https://doi.org/10.46281/amfbr.v6i1.1459 

 

 

ABSTRACT 

This study has been designed for examining the effectiveness of liquidity management through 

the relative standing of ROE and ROCE of Nationalized Commercial Banks in Bangladesh for 

the duration of 2008–2018. Six NCBs are selected purposively as sample. The study relies on a 

balanced panel data set of 66 observations which are gathered from the annual reports of banks 

and analyzed by random effects regression model. However, the research only examined 

a few variables. The empirical results reveal that the selected NCBs have been portraying better 

standing in case of ROE than ROCE in effective liquidity management. The value of R2 of ROE is 

75.25%; it signifies that the explanatory measures could clarify 75.25% of the variations in 

ROE. Among the liquidity measures, Assets/Shareholders Equity has highly significant negative 

effect; Tier 1 Capital/Risk Weighted Assets has highly significant positive effect; Deposits/Assets 

have some significant positive and Bank Size in terms of Deposits has some significant negative 

effect on ROE of the selected NCBs.  

 

Keywords: Bangladesh, Commercial Bank, Effective Liquidity Management, ROE, ROCE. 

 

JEL Classification Codes: E44, G14, G21, O16. 

 

PRELUDE 

Banking system is one of the major components of financial system for ensuring its continued 

existence. According to Wilner (2000), efficient banking system acts as a means for the 

accumulation of deposit funds and investing those funds in a productive manner. Without 

effective liquidity management, banks with intense foundation and bright future may not be 

profitable (Jose et al., 1996). Therefore liquidity management involves the strategic 

administration of inflows and outflows of banks’ funds that will concurrently maintain liquidity, 

profitability and solvency of banks. Agbada and Osuji (2013) articulated that with a view to 

achieving these conflicting objectives, liquidity management requires to be efficient in the 

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circulation of liquidity compatible with a preferred level of cash without twisting the profitability 

and functions of the bank.  

 

Banking structure of Bangladesh 

Bangladesh has an assorted banking sector of 60 scheduled banks consisting of 06 Nationalized 

Commercial Banks, 03 Specialized Banks, 42 Private Commercial Banks and 09 Foreign 

Commercial Banks. Out of 42 Private Commercial Banks, 32 banks are interest based whereas 8 

are interest free (www.bb.org.bd/fnansys/bankfi.php). 

 

LITERATURE REVIEW 
Basel Committee on Banking Supervision (2008) explained that bank’s sustainability is 

dependent on the liquidity position as it measures the role of bank in maintaining cash flow. 

According to Central Bank of Barbados (2008), liquidity problem may usually influence a bank’s 

profits and capital and exceptional circumstance, due to liquidity problem a solvent bank may 

become insolvent. Jenkinson (2008) found that liquidity problem simultaneously affects both the 

performance and reputation of a bank. According to Aspachs et al. (2005), banks can use three 

methods to indemnify against liquidity risks. Such as (i) seizing bumper of liquid assets (ii) 

relying on interbank market (iii) relying on central bank-a lender of last resort. 

Hirigoyen (1985) agreed that low liquidity leads to low profitability. Chandra (2001) 

claimed that usually high liquidity acts as a signal of financial strength. AssafNeto (2003) argued 

that both high and low liquidity are undesirable. According to Goddard et al. (2004), holding 

liquidity causes an opportunity cost and thereby having a negative consequence on profit 

making ability of banks. Gup and Kolari (2004) claimed that bank management should extend a 

liquidity plan that makes a balance between risks and returns. Koch (1992); Sufian and Chong 

(2008) believed that as maintenance of liquidity bears both risk and return, a trade-off between 

liquidity and profit base can minimize the conflict.  

Repullo (2003) found that liquidity management depends on hybrids of theories that are 

typically employed to obtain optimality. Considering Shiftability theory, Dodds (1982) suggested 

that assets must meet three conditions-“shiftability, marketability or transferability” in order to 

ensure convertibility of the assets. Based on Anticipated Income theory, Nzotta (1997) identified 

two fundamental factors-earning capability and creditworthiness of a borrower which can be 

regarded as the undertaking for assuring sufficient liquidity. According to Liability 

Management theory, Emmanuel (1997) suggested that the central bank or sister banks can come 

forward to lend money if other banks urgently require funds. He also propagated a very 

conservative outlook when he asserted that the banks do not lend money for long term, due to the 

long payback period. Hence Commercial Loan theory is only applicable for short period, self-

liquidating loans. 

Agbada and Osuji (2013) experienced that the soundness of banks can be enhanced by 

efficient liquidity management. According to Kurawa and Abubakar (2014); Obida and Owolabi 

(2012), liquidity management measures the growth and financial performance of a bank. As per 

Nwankwo (1991), liquidity management requires assessing liquidity needs and meeting up those 

needs at all times without incurring any significant costs. It is asserted that adequate liquidity at 

normal market interest rate is crucial for both large and small banks to meet all types of 

liabilities. It is also said that adequate liquidity may help a bank to face three kinds of 

liquidity risks: (i) risk of funding (ii) default risk (iii) risk of failure to honor maturity 

obligations of customers.  

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According to Lartey et al. (2013), there is a very insignificant but positive association 

between optimum levels of liquidity and profit base. According to Osborne et al. (2012), the 

association between liquidity and profit base becomes more positive in the distress of banking 

sector. Arif and Anees (2012); Datta et al. (2011); Adebayo et al. (2011) observed that profit 

base is significantly influenced by liquidity in the form of deposits. Rauch et al. (2010) found 

that profit base is negatively associated with liquidity. Other things held constant, adequate 

liquidity can improve profit base of banks (Bernanke, 2008). Flannery and Rangan (2008) 

asserted that due to sudden shock or regulatory requirements, if liquidity of banks exceeds their 

optimum level reduces profit base only. Jahangir et al. (2007) argued that loan deposit ratio 

works as an important determinant of banks’ profitability. Hossain (2000) indicated that high 

percentage of fixed to total deposits influence the profitability performance of NCBs.  

Lucy et al. (2018) found that liquidity has significant positive effect on ROCE. Agbada 

and Osuji (2013) explored that adequate capital is a must for maximizing ROCE. Raza, Farhan, 

and Akram (2011) affirmed that ROCE is one of the major measures to measure profitability. 

Alshatti (2015) recommended that quick ratio and investment ratio are positively associated with 

ROE whereas capital ratio and liquid asset ratios are negatively associated with ROE. According 

to Bassey (2015), cash ratio, loan to deposit ratio, current ratio, loan to asset ratio and liquid ratio 

are significantly related with ROE. Olarewaju and Adeyemi (2015) stated that there is no causal 

relationship between loan deposit ratio and ROE. According to Abdullah and Jahan (2014), all 

other things held constant, the more liquid bank tends to have lower ROE. Tabari, Ahmadi, and 

Emami (2013) acknowledge that ROE is one of the substitutes to measure profit base of a bank. 

Ramadan et al. (2011) showed that there is an association between ROE and the efficacy of 

credit management. According to Ahmed et al. (2006), in order to fortify the economic 

conditions of a country, NCBs must improve their performance evaluation metrics such as NPL, 

ROA and ROE. Siddique (2004) recognized that NPL is one of the outcomes of poor bank fund 

management. 

In the above sections the researcher has conducted an extensive review of literatures over 

banks’ liquidity management nationally and internationally. It is found that maximum studies 

have been conducted over management of liquidity and banking performance, liquidity-

profitability relationship, and role of liquidity on banks’ profitability. However, relative standing 

of profitability measures in effective liquidity management in Bangladesh remains an unexplored 

area. Hence, the present study has been carried out to evaluate the relative standing of ROE and 

ROCE of NCBs in efficient liquidity management. 

 

OBJECTIVES OF THE STUDY 

The key objective of this study is to analyze the relative prominence of ROE and ROCE in 

effective liquidity management of NCBs. This objective basically attempts: 

 To evaluate the descriptive features of both liquidity and profitability measures of 

respective banks; 

 To analyze the liquidity measures that influence both the ROE and ROCE; 

 To measure the association between liquidity measures and selected profitability 

measures; 

 To appraise the comparative position of ROE and ROCE in ensuring effective liquidity 

management of selected NCBs; 

 

 



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THEORETICAL FRAMEWORK 

 

Independent Variables (Liquidity Indicators)                 

                                                                    

                                                                                             

               

                             

                                                                          Dependent Variables (Profitability indicators)  

                                        

                                                                                      

                                           
 

 

 

 

 

        

                                                                               

                                                               

 

 

 

 

 

 

 

 

Figure 1. Theoretical Framework 

 

METHODOLOGY OF THE STUDY 

Research Design  

The study is descriptive as well as analytical in nature. It is descriptive as it demonstrated the 

effectiveness of liquidity management of NCBs in Bangladesh through the measures of relative 

standing of its ROE and ROCE. This study is analytical in the sense that it carried out a panel 

data analysis for testing hypothesis and interpreting relationship by analyzing available 

information. Basically, the study intends to provide an appropriate strategy for the effective 

liquidity management of NCBs in Bangladesh. 

 

Hypotheses of the Study 

𝐻01:  There is no statistically significant relationship between liquidity and ROE of NCBs  

 𝐻11:  There is a statistically significant relationship between liquidity and ROE of NCBs  

𝐻02:  There is no statistically significant relationship between liquidity and ROCE of NCBs  

Market rate 

of Interest  

TAs/SE 

T1C/RWA

A 

LQA/TAs 

RRP/TLs 

 

 

ROE  

&  

ROCE  

  

 

 

 

LQAs/TDs 

 
BDO/TAs 

 

 

TDs/TAs 

NPL/GLA 

 

Bank Size  



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 𝐻12:  There is a statistically significant relationship between liquidity and ROCE of NCBs  

 

Specification of the Model  

 

Model 1: ROE= A+ β1 (TAs/SE) + β2 (T1C/RWA) + β3 (LQA/TAs) + β4 (RRP/TLs) + β5 

(LQA/TDs) + β6 (BDO/TAs) + β7 (TDs/TAs) + β8 (NPL/GLA) + β9 (MRI) + β10 (BSZ) + e 

 

Model 2: ROCE= A+ β1 (TAs/SE) + β2 (T1C/RWA) + β3 (LQA/TAs) + β4 (RRP/TLs) + β5 

(LQA/TDs) + β6 (BDO/TAs) + β7 (TDs/TAs) + β8 (NPL/GLA) + β9 (MRI) + β10 (BSZ) + e 

 

Where, ROE and ROCE are the dependent variables, A is the constant term, β’s measures the 

coefficient of explanatory variables, variables in the parenthesis are the explanatory variables 

and ‘e’ denotes the stochastic disturbance term.  

 

 Table 1. Elucidation of dependent and independent variables and their replacements  

Source: Compiled by the researcher from the annual reports of banks 

 

Choice of Population, Sample Size and Sampling method 

According to Bangladesh Bank, the no. of scheduled NCBs in Bangladesh is 06. Accordingly the 

researcher opted for 06 NCBs purposively.  

 

Collection and Analysis of Data 

The study is mainly dependent on secondary data that have been gathered from yearly published 

information of selected NCBs. This balanced panel data sets of 66 observations throughout the 

period of 2008-2018 have been evaluated by some descriptive statistics, pairwise correlation, 

ratio analysis and ultimately, random effects regression model.  

 

 

 

Variables Formula/ Definition Unit 

Dependent Variables 

ROE Net income/ Shareholders equity % 

ROCE EBIT/(Total assets-current liabilities) % 

Independent Variables 

TAs/SE Total assets/Shareholders equity % 

TIC/RWA Tier 1 capital/Risk weighted assets % 

LQA/TAs Liquid asset/Total assets % 

RRP/TLs Reverse Repo Purchase/Total liabilities % 

LQA/TD Liquid asset/Total deposits % 

BDO/TAs Balance due other banks/Total assets % 

TDs/TAs Total deposits/Total assets % 

NPL/GLA Nonperforming loan/Gross loan & advances % 

MRI Market rate of interest % 

BSZ Bank size in terms of total deposits Natural logarithm 

of total deposit 



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FINDINGS AND DISCUSSIONS 

Analysis of Features of Liquidity Indicators and Profitability Indicators of selected NCBs 

 

Table 2. Descriptive Analysis of related variables  

 

Measu

res 

ROE ROC

E 

TAs

/ 

SE 

TIC

/ 

RW

A 

LQ

A/ 

TAs 

RR

P/ 

TLs 

LQ

A/ 

TD 

BD

O/ 

TAs 

TDs

/ 

TAs 

NPL/

GLA 

MRI BSZ 

Mean 0.18 1.99 17.4

4 

5.14 9.22 5.30 12.7

4 

1.20 80.3

2 

18.90 11.7

8 

5.54 

Range 301.1

8 

19.77 68.6

0 

30.9

5 

12.6

6 

5.98 18.8

7 

5.44 15.5

2 

29.98 4.19 1.19 

Max. 41.28 9.85 52.8

7 

9.78 17.2

5 

9.64 26.1

7 

5.44 85.9

3 

35.28 13.7

5 

6.04 

Min. -

259.9

0 

-9.92 -

15.7

3 

-

21.1

7 

4.59 3.66 7.30 0.00 70.4

1 

5.30 9.56 4.85 

Std. 

Dev. 

43.70 2.83 10.2

8 

5.52 2.87 1.08 4.27 1.29 3.78 7.60 1.32 0.30 

CV 247.4

5 

1.42 0.59 1.07 0.31 0.20 0.34 1.08 0.05 0.40 0.11 0.05 

Notes: i) Data have been compiled by the researcher 

ii) Analysis Mode: SPSS (version 24.0) 

 

From the above analysis the study has found that the selected banks have maintained the 

highest average liquidity ratio i.e 80.32% in terms of TDs/TAs which is authenticated by 

standard deviation of 3.78%, and range of 15.52%; and the lowest average liquidity ratio i.e 

1.20% in terms BDO/TAs which is authenticated by standard deviation of 1.29%, and range of 

5.44% during study periods. Between these two liquidity ratios, TDs/TAs indicates minimum 

risk in terms of CV i.e 0.05%. It has been observed that average rate of profitability in terms of 

ROE and ROCE is 0.18% and 1.99% respectively. Between two profitability indicators, the 

maximum & minimum ratio of ROE has shown maximum fluctuation during the study periods. 

This situation is authenticated by standard deviation of 43.70%, and range of 301.18%. It 

indicates that selected banks are exposed to more risk in generating profit in terms of ROE than 

ROCE while managing liquidity. This signifies that selected NCBs should give due 

consideration to ROE particularly in making profit while constructing liquidity management 

policies & implementing thereof. 

 

Regression Analytical Tests 

Normality Test 

 

 

 



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Table 3.  Shapiro-Wilk Test for Normality 

 

Variable p value 

ROE/ROCE 0.12 / 0.08 

TAs/SE 0.08 

TIC/RWA 0.43 

LQA/TAs 0.06 

RRP/TLs 0.20 

LQA/TD 0.22 

BDO/TAs 0.11 

TDs/TAs 0.06 

NPL/GLA 0.58 

MRI 0.18 

BSZ 0.21 

Notes: i) Data have been compiled by the researcher 

ii) Analysis Mode: STATA (version 12.0) 

 

From table 3, it is clear that the data used in this study is normally distributed as p value > 0.05. 

 

Multicollinearity Test 

 

Table 4. Analysis of Correlation between Liquidity indicators and Profit base of selected NCBs 

 

 
ROE 

RO

CE 

TAs/ 

SE 

TIC/ 

RWA 

LQA/ 

TAs 

RRP/ 

TLs 

LQA/ 

TD 

BDO/ 

TAs 

TDs/ 

TAs 

NPL/ 

GLA MRI BSZ 

ROE 1  

          ROC

E 
.101 

1 

          TAs/ 

SE 

-

.572** 

-

.352* 1 
         

TIC/ 

RWA 
.378* -.178 .312* 1 

        

LQA/ 

TAs 
.037 -.165 .160 .157 1 

       

RRP/ 

TLs 
.079 -.173 -.055 .136 .356* 1 

      

LQA/ 

TD 
.099 -.077 .098 .268 .788** .226 1 

     

BDO/ 

TAs .065 .237 
-

.394*

* 

-

.483** 
.105 .043 -.023 1 

    

TDs/ 

TAs 
.126 -.192 .006 -.073 .435** .235 .196 .347* 1 

   

NPL/ -.258 -.144 .144 -.190 .038 .205 -.004 -.113 .017 1 
  



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GLA 

MRI -.237 .201 .057 -.219 -.348* -.181 
-

.481** 
.182 -.212 -.067 1 

 

BSZ 

-.047 

-

.464*

* 
.287 .488** .184 .409** .222 

-

.490** 
.168 .333* 

-

.324* 
1 

Notes: i) Data have been compiled by the researcher 

ii) Analysis mode: SPSS (version 24.0) 

iii) *Correlation is significant at the 5% level 
** Correlation is significant at the 1% level 

 

             From table 04, it is observed that as the correlation between the variables is below the 

maximum limit of 0.80, so the variables are beyond multicollinearity problem which is supported 

by Kennedy (2008).  

Another important method of identifying multicollinearity is the Variance Inflation 

Factor (VIF). According to Gujarati and Sangeetha (2008), if the VIF value exceeds 10, the 

variable is considered to be highly collinear. The VIF test result for all the explanatory variables 

are shown in appendix 1A where there is no existence of multicollinearity problem as the VIF 

value of the variables is less than 10. 

 

Heteroskedasticity Test 

White’s heteroskedasticity test is applied to measure the difficulty of heteroskedasticity in a 

linear regression model (Gujarati, 1995). The outcomes of both analysis indicate that there is no 

evidence for the existence of heteroskedasticity, as the calculated values 44.00 < the critical 

value 79.49 as shown in appendix 1B and 1C.  

 

Test of Autocorrelation  

One of the most popular tests for the detection of autocorrelation is Wooldridge (2002) statistic 

which signifies that there is no autocorrelation, as presented below: 

 

Table 5. Wooldridge Test for Autocorrelation 

 

ROE ROCE 

Ho: no first –order autocorrelation 

F (1, 3) = 4.733 

Prob > F = 0.1178 

Ho: no first –order autocorrelation 

F (1, 3) = 1.736 

Prob > F = 0.2792 

Notes: i) Data have been compiled by researcher 

         ii) Analysis mode: STATA (version 12.0) 

 

Hausman Specification Test  

A Hausman Test (1978) has been applied to measure the appropriateness of Random versus 

Fixed effects model which is reported in appendix 1D and 1E. After running the Hausman test, 

both Prob>chi2 value of ROE and ROCE are 0.3881 and .8494 respectively, which are greater 



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than 0.05. Therefore the Random Effects model is statistically appropriate for both the model of 

the study.  

 

Random Effect (RE) tests on Model 1 (ROE) and Model 2 (ROCE) 

 

Table 6. Position of ROE and ROCE in the Effectiveness of Liquidity Management 

 

Variables Model 1 (ROE) Model 2 (ROCE) 

Constant 79.3944 23.9873* 

TAs/SE -3.2398*** -.0787 

TIC/RWA 6.0589*** .0709 

LQA/TAs .1159 -.1708 

RRP/TLs -.4326 .0154 

LQA/TD -.5235 .1532 

BDO/TAs -3.5083 -.0177 

TDs/TAs 3.1273** -.032 

NPL/GLA .5269 .0333 

MRI -2.5484 .3275 

BSZ -49.2252** -4.2098* 

F 10.03 1.271 

R2 .7525 .2493 

Adjusted R2 .5925 .0530 

No. of 

observations 

66 66 

Notes: i) Data have been compiled by researcher 

         ii) Analysis mode: STATA (version 12.0) 

                                          iii) Standard errors in parentheses p* < 0.10, p** < 0.05, p*** < 0.01 

 

From RE regression Model 1, it has been observed that the variables TAs/SE & 

TIC/RWA are significant at 1% level and TDs/TAs & BSZ at 5% level. The rest of the variables 

are found insignificant. The value of R2 is 75.25%; it signifies that the explanatory variables 

could clarify 75.25% of the variations in ROE. F value of statistics is also found significant. F 

value of statistics is also found significant. 

From RE regression Model 2, it has been observed that almost all the explanatory 

variables are found insignificant even BSZ as it is significant at 10% level. The value of R2 is 

24.93%; it signifies that the explanatory variables could clarify only 24.93% of the variations in 

ROCE. F value of statistics is also found insignificant. 

These imply that standing of ROE compared to ROCE is more significant in the 

effectiveness of liquidity management of selected NCBs in Bangladesh. Accordingly, in case of 

Model 1 the null hypothesis is rejected and alternative hypothesis is accepted. That is, there is a 

statistically significant association between ROE and liquidity of NCBs. On the other hand, the 

null hypothesis of Model 2 is accepted indicating that there is no statistically significant 

association between ROCE and liquidity of NCBs. 

 

 

 



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CONCLUSION AND RECOMMENDATIONS 

The study is attempted to figure out the relative standing of ROE and ROCE in the effectiveness 

of liquidity management of NCBs in Bangladesh. It is found that ROE represents better standing 

than ROCE in case of effective liquidity management. The ROE is mainly influenced by 

TAs/SE, TIC/RWA, TDs/TAs and BSZ liquidity measures. Among the explanatory measures, 

TAs/SE has highly significant negative and TIC/RWA has highly significant positive effect on 

ROE; TDs/TAs has some significant positive and BSZ has some significant negative effect on 

ROE of the selected NCBs. 

The conclusion recommends that NCBs that can increase its core capital will act as a 

defender in protecting consumers against unexpected losses and to exude financial strength. Papa 

(2012) said that banks can simply drive up ROE by holding relatively higher levels of Tier 1 

capital.  According to Abugamea (2018); Ramadan et al. (2011), profitability in terms of ROE 

tends to be associated with well capitalized banks. Abugamea (2018) also asserted that ROE is 

inversely related to deposits which is supported by the ratio BSZ in terms of total deposits. But 

higher deposits to assets ratio is linked with improved financial sufficiency, as deposits are the 

stable sources of fund for funding assets (Mwangi et al., 2015). There is a common phenomenon 

that banks’ profitability is extensively affected by its higher total assets provided the assets are 

financed by stock rather than debt. Ramadan et al. (2011) showed that there is an association 

between ROE and the efficacy of credit management.  

Based on the empirical assessment, the researcher recommends that NCBs should accept 

a more professional liquidity management strategy to insure such conflicting objectives of banks-

liquidity, profitability and solvency. Agbada and Osuji (2013) articulated that with a view to 

achieving these conflicting objectives, liquidity management requires to be efficient in the 

circulation of liquidity compatible with a preferred level of cash without twisting the profitability 

and functions of the bank. Without effective liquidity management, banks with intense 

foundation and bright future may not be profitable (Jose et al., 1996).  

 

LIMITATIONS OF THE STUDY 

The study has considered a limited no. of variables and only four selected NCBs in Bangladesh 

as sample. Therefore the size of the sample might be increased for measuring the actual effect of 

exogenous variables on the dependent variable. New research could be suggested for measuring 

the relative standing of other profitability measures not only on NCBs but also on other banking 

structure in Bangladesh to appraise whether the liquidity is effectively managed or not.  

 

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APPENDICES 

Appendix-1 A: VIF Results 

 

Variable VIF value Tolerance 

LQA/TAs 3.92 0.26 

LQA/TDs 3.57 0.28 

BSZ 2.64 0.38 

BDO/TAs 2.28 0.44 

TIC/TRW 1.97 0.51 

TDs/TAs 1.83 0.55 

MRI 1.57 0.64 

RRP/TLs 1.53 0.65 

TAs/SE 1.47 0.68 

NPL/GLA 1.46 0.68 

Mean VIF 2.22  

Notes: i) Data have been compiled by researcher 

ii) Analysis mode: STATA (version 12.0) 

 

Appendix-1 B: White’s Heteroskedasticity Test on ROE  

 

Source Chi2 df p 

Heteroscedasticity 66.00 43 0.4290 

Skewness 31.78 10 0.0004 

Curtosis 0.89 1 0.3460 

Total 76.67 54 0.0229 

Notes: i) Data have been compiled by researcher 

ii) Analysis mode: STATA (version 12.0) 

 

Appendix-1C: White’s Heteroskedasticity Test on ROCE  

 

Source Chi2 df p 

Heteroscedasticity 66.00 43 0.4290 

Skewness 8.99 10 0.5330 

Curtosis 1.50 1 0.2212 

Total 54.49 54 0.4559 

Notes: i) Data have been compiled by researcher 

ii) Analysis mode: STATA (version 12.0) 

 



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Appendix-1D:  Hausman Test on ROE 

ROE Coefficients 

b 

Fixed 

B 

Random 

b-B 

Differences 

sqrt (diag(V_b-

V_B)) SE 

X1: TAs/SE  -3.2444 -3.2398 -.0047 .1409 

X2: TIC/TRW  5.8043 6.0589 -.2546 .2472 

X3: LQA/TAs  1.9815 .1159 1.8656 1.1810 

X4: RRP/TLs  .2120 -.4326 .6446 1.7642 

X5: LQA/TD  .3758 -.5235 .8993 .6923 

X6: BDO/TAs  -6.4881 -3.5083 -2.9798 2.2446 

X7: TDs/TAs  3.5190 3.1273 .3917 .4181 

X8: NPL/GLA  .6512 .5269 .1242 .8021 

X9: MRI  -2.9912 -2.5484 -.4428 .8258 

X10: BSZ  -102.4167 -49.2252 -53.1915 31.7284 

Notes: i) Data have been compiled by the researcher 

ii) Analysis Mode: STATA (version 12.0) 

iii)  b = consistent under Ho and Ha; obtained from xtreg 

B= inconsistent under Ha, efficient under Ho; obtained from xtreg 

iv) Test: Ho: difference in coefficients not systematic 

Chi2 (3) = (b-B)1[(V_b-V_B)^(-1)] (b-B)=3.02 

Prob>chi2 = 0.3881 

 

Appendix-1E:  Hausman Test on ROCE 

ROCE Coefficients 

b 

Fixed 

B 

Random 

b-B 

Differences 

sqrt (diag(V_b-V_B)) 

SE 

X1: TAs/SE  -.0784 -.0787 .0002 .0153 

X2: TIC/TRW  .0804 .0709 .0095 .0269 

X3: LQA/TAs  -.1229 -.1708 .0480 .1283 

X4: RRP/TLs  .0717 .0154 .0563 .1917 

X5: LQA/TD  .1267 .1532 -.0264 .0752 

X6: BDO/TAs  .1107 -.0177 .1284 .2439 

X7: TDs/TAs  -.0016 -.0320 .0304 .0454 

X8: NPL/GLA  .0108 .0333 -.0225 .0872 

X9: MRI  .3137 .3275 -.0138 .0897 

X10: BSZ  -4.3824 -4.2098 -.1726 3.4475 

Notes: i) Data have been compiled by the researcher 

ii) Analysis Mode: STATA (version 12.0) 

iii)  b = consistent under Ho and Ha; obtained from xtreg 

B= inconsistent under Ha, efficient under Ho; obtained from xtreg 

iv) Test: Ho: difference in coefficients not systematic 

Chi2 (3) = (b-B)1[(V_b-V_B)^(-1)] (b-B)=0.80 

Prob>chi2 = 0.8494 
 



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AUTHOR CONTRIBUTIONS  

Conceptualization:  Nazneen Jahan Chaudhury  

Data curation: Nazneen Jahan Chaudhury  

Formal analysis: Nazneen Jahan Chaudhury  

Funding Acquisition: Nazneen Jahan Chaudhury  

Project Administration: Nazneen Jahan Chaudhury  

Software: Nazneen Jahan Chaudhury  

Validation: Nazneen Jahan Chaudhury  

Writing – Original Draft: Nazneen Jahan Chaudhury  

Writing – Review & Editing: Nazneen Jahan Chaudhury  

 

CONFLICT OF INTEREST STATEMENT 

The authors declare that they have no competing interests.  

 

COPYRIGHTS  

Copyright for this article is retained by the author(s), with first publication rights granted to the 

journal. This is an open-access article distributed under the terms and conditions of the Creative 

Commons Attribution license (https://creativecommons.org/licenses/by/4.0). 


