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Indian Journal of Finance and Banking; Vol. 4, No. 1; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 

117 

 

The Role of Internal Control and Firm-Specific Characteristics on Firm 
Value: Evidence from Indian Financial Services Sector 

 
 

Anju Kalluvelil Janardhanan PhD 
Lecturer 

Crown Institute of Higher Education 
North Sydney, New South Wales, Australia 

E-mail: anju.kj@cihe.edu.au 
 
 

Uma V R PhD 
Associate Professor  

Department of Commerce 
CHRIST (Deemed to be University) 

Bengaluru, Karnataka, India 
E-mail: uma.vr@christuniversity.in 

 
 
Abstract 
This research determines the role of firm-specific characteristics such as firm size, firm age, liquidity, firm complexity, board 
independence, institutional ownership, non-performing assets, annual volatility of stock returns, leverage and internal control 
represented by Enterprise Risk Management (ERM) and Big4 auditor on the firm value measured using Tobin’s Q, Return 
On Equity (ROE) and Return On Assets (ROA). This proposition is addressed with the sound statistical investigation of 
67 companies listed in the NSE financial services sector by utilizing annual panel data for 11 years from 2007-17. The 
important findings of the study are that the purchasers consider firm size, firm age, liquidity, the volatility of stock returns, 
and non-performing assets. ROA shows that the management has to focus on firm size, firm age, and volatility of stock 
returns. ROE informs that the investors will look into firm size, firm age, institutional ownership, non-performing assets, 
leverage, firm complexity, and volatility of stock returns.  

 
1. Introduction 
Firm value is considered as a vital aspect in analyzing a company’s financial health. It is an estimate of the total value of a 
company. Firm value is measured using a three-dimensional approach – from the purchasers, investors, and operational 
perspective (Adetunji & Owolabi, 2016). Tobin's Q incorporates market performance into the measurement of firm 
performance and shows the firm's effectiveness from a purchaser’s perspective. The Return on Equity (ROE) shows the return 
that the investors get for their capital investments to the company. From the investor’s perspective, it is an essential component 
that helps in measuring a firm’s performance against its competitors. The Return on Assets (ROA) shows the efficiency of a 
company to utilize its assets to make profits unaffected by management financing decisions. To enhance the firm value, it is 
imperative to understand the factors that play a major role in affecting it.  

The concept of internal control has gained attention among the public and worldwide regulators because numerous 
international organizations have declined due to incompetent risk management (Beasley, Branson & Hancock, 2010). Many 
businesses have collapsed and the economic crisis in 2008 could be connected with inadequate internal control mechanisms 
and risk management (McConnell, 2009). Through internal control, an organization diagnoses threats to explore alternatives 
and alleviate its risks. Consequently, in any dynamic business environment, internal control is a vital and challenging concern 
for the organization in each sector (Gordon, Loeb & Tseng, 2009). As per the Committee of Sponsoring Organizations of 
the Treadway Commission (2004) an essential part of the internal control is enterprise-wide risk management and external 
audit. The presence of Enterprise Risk Management (ERM) and external audit by Big4 auditors can help the organization in 
the potential improvement of firm value to recognize and prevent numerous risks and to accommodate sustainability (Beasley, 
Clune & Hermanson, 2005). External auditors of a firm will critically review the quality of internal control. Deloitte, PwC, 
Ernst & Young, and KPMG are the Big4 auditor firms. A robust internal control mechanism lies in the proper analysis of the 
enterprise’s risk appetite (Walker, Shenkir & Barton, 2003).  

The purchasers will be interested in acquiring a firm with high Tobin’s Q, the investors will prefer a company with 
high ROE, and a greater ROA will reveal the managerial efficiency of a firm. The financial services sector contributes 21% 
to India’s Gross Domestic Product. So, to enhance the firm value, it is essential for the management to know the numerous 
factors affecting it.  

 

  
Keywords:    Firm Value, Internal Control, Tobin’s Q, ERM, ROA, ROE, Big4 Auditor, Firm-specific Characteristics. 

 
    



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2. Review of Empirical Findings 
2.1 Effect of ERM on Firm Value 
One of the objectives upon the inception of a firm is to create value by giving priority to its owners. It is a reward for their 
investments in the firm. Studies establish that a reasonable increment of the firm value is the organization's long term 
objectives. Owners required affirmation upon their investment that they have contributed and return on their investments. 
The organization will endeavor to expand the firm value, by extending the financial performance. The review of prior studies 
on ERM and firm value came up with mixed results. The following studies didn’t find any evidence that risk management is 
value-creating. Danisman and Demirel (2019) investigated Turkish non-financial companies for the five years using mixed 
research methods and established that ERM doesn't influence Q. They identified the reasons as inadequate risk management 
disclosure, managerial risk version motives, no clarity on risk management concepts, misuse of financial hedging instruments, 
and no support from management for effective implementation of ERM.  

Sayilir and Farhan (2017) focused on 26 firms in the manufacturing industry of Turkey during the period 2008-
13 and regression analysis established that there is no connection between Q, ROA and ERM. They mentioned that there 
was resistance to change, and the organizational structure did not support ERM implementation. The path analysis study 
conducted by Agustina and Baroroh (2016) from 2011 to 2013 in the Indonesian banking companies revealed that ERM 

doesn't influence ROE as they consider it as a compliance requirement with banking regulations. Sprčić, Žagar, Šević and 
Marc (2016) showed that ERM does not add to the Q of an organization in the long term. Regression results supported that 
the market reaction for the ERM announcement had a positive effect only for a shorter period. The study was conducted 
from 2003 to 2012 on 258 non-financial US companies. Research conducted in the Netherlands on 39 insurance companies 
from 2005 to 2008 by Eikenhout (2015) proved that ERM didn't affect ROA and ROE. Further, the results of multiple 
regression identified the negative impact of ERM on ROA. Laisasikorn and Rompho (2014) identified that the ERM System 
and Performance Management System (PMS) have a weak significant correlation with the ROA and ROE in Thailand. 
Structural Equation Modeling (SEM) technique was used. They found evidence that firms that had ERMS and PMS achieved 
good financial performance. But as the implementation of ERMS and PMS requires a substantial amount of a firm’s resources, 
it does not generate more financial benefits in the short run.  

Manab and Ghazali (2013) were of the view that though ERM affected firm value, it isn’t the prime factor that 
prompted value creation (EPS). The regression results revealed that ERM helped in better corporate governance in the 
financial companies when compared to non-financial companies. The sample consisted of 417 public listed Malaysian 
companies. Ballantyne (2013) also supported that ERM adoption isn’t related to Q, ROA and ROE. They used mixed-
method research and collected data from 137 public listed companies in the US. Their study identified that the effectiveness 
of ERM depends on business leadership and cultural integration. Lin, Wen and Yu (2011) observed that insurers who had 
adopted ERM incurred a decrease of 11.1% in Q and 5.35% in ROA. They investigated 85 PC insurer firms in the US 
during 2000-07. The identified that it is because the implementation costs of ERM are higher than the benefits derived from 
it.  

On the other hand, the following studies could substantiate that ERM creates value. Bohnert, Gatzert, Hoyt and 
Lechner (2019) supported that ERM increased Q. The regression results also showed that companies with ERM had on an 
average 6.5% higher Tobin’s Q than non-ERM integrated companies. This empirical study focused on 41 European insurance 
companies from 2007 to 2015. Lechner and Gatzert (2018) regressed 160 German listed firms from 2009 to 2013 and 
exhibited that firms with ERM had an increase in Q. One of the significant impediments of the examination was the 
nonappearance of ERM implementation disclosure. Florio and Leoni (2017) believed that firms with more level of ERM 
practices had more prominent ROA and Q from the financial and market perspective in Italy. They conducted a study on 
non-financial companies from 2011 to 2013. The data collected by Ping and Muthuveloo (2015) through questionnaire and 
quantitative analysis on 103 public listed Malaysian firms in 2015 showed that the usage of ERM impacted Q. The data was 
analyzed by using PLS and SEM. Gates, Nicolas and Walker (2012) collected data through a questionnaire in 2004 and 
conducted Partial Least Squares (PLS) analysis on 150 companies. The Conference Board members in the US. The reflective 
method suggested that the use of ERM has multifold benefits such as better managerial decisions, risk awareness and enhanced 
accountability. Studies conducted by McShane, Nair and Rustambekov (2010) in 2008 among US 82 publicly traded insurers 
showed that ERM positively impacted Q. They also observed that firms with higher ERM ratings did not have any additional 
increase in Q. 

Silva, Silva and Chan (2019) revealed the ERM impacted Q positively in Brazilian stock exchange during 2004-
13. The regression study used the Generalised Linear Model (GLM) and focused on 80 publicly-traded companies listed on 
the IbrX100 index. A large portion of the organizations considered ERM as a part of internal control which upgraded the 
standard of strategic decisions made and subsequently improve firm value. They observed that this could be a reason for the 
absence of CRO in firms with ERM. As per the regression study done in 68 publicly-traded Taiwanese financial industry 
between 2001-2016 by Chen, Chuang, Huang, and Shih (2019), Q of financial companies with ERM was 5.37% more than-
ERM financial companies. They also found that ERM improves revenue and creates cost efficiency too. Hoyt and Liebenberg 
(2011) focused on 275 publicly-traded insurance firms in the US from 1995 to 2005 and found that ERM improved Q. 
The regression coefficient results supported that ERM insurers had approximately 20% more firm value than non-ERM 
insurers after controlling endogeneity bias and other determinants of firm value.  

 
 



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2.2 Effect of Audit Committee and Big4 auditor on Firm Value 
Without an audit committee (AC), it is complicated for the success of any organization in the current hostile environment 
(Lloyd & Fanning, 2007). Independent members in AC were observed to be more accountable and transparent as they are 
free from management intervention. A large number of members in AC might be ineffective when compared to smaller 
committees (García, Barbadillo & Parez, 2012). AC is crucial to overseeing the risk management systems, and they play a vital 
role in risk management (Turley & Zaman, 2004), and they can influence the board for the successful implementation of risk 
management (Paape & Speklé, 2012). The AC must elevate corporate governance standards to secure public interest (Vasile 
& Croitoru, 2013). External auditors report their opinions on the internal control quality of a firm. More specifically, they 
examine and express their views on annual accounts, consolidated financial statements, as well as the board of directors and 
administration of CEO. The panel data regression results of the study conducted by Chan and Li (2000) in the fortune 200 
companies showed that experts and independence of the audit committee increased Q whereas research led by Yermack (1996) 
revealed a negative connection between AC size and Q of an organization. The study was conducted on 452 large scale US 
industrial companies from 1984-91. On the other hand, the size of the audit committee revealed a positive relationship with 
firm value in the study conducted by Szczepankowski (2012). The results of audit committee activity in 69 Polish public 
stock companies during 2009-10 were presented in the study. The Big4 (Deloitte, PwC, Ernst & Young and KPMG) are 
classified as excellent audit quality in most of the previous studies. DeAngelo (1981) identified Big4 auditors to have better 
monitoring power that facilitates greater credibility of the information. This gives positive signals to stakeholders about the 
goodwill of the company and greater market response from the users of financial statements. Big4 auditors are considered to 
identify and report any misrepresentation of financial statements diligently (Gounopoulos & Pham, 2017).  
 
2.3 Effect of Firm-specific Characteristics on ERM and Firm Value 
Beasley et al. (2005) and Ghosh (2013) revealed that the independence of the board would bring better risk governance and 
thus enhance the scope of ERM implementation. Florio and Leoni (2017) found that board independence negatively impacts 
ROA but has a positive impact on Q. The more the number of subsidiaries, the more is the firm complexity. McShane, Nair 
and Rustambekov (2010) identified a positive impact of firm complexity on firm value. Gordon, Loeb and Tseng (2009) 
found that firm complexity can influence the relationship between ERM and firm value. Capasso, Gallucci and Rossi (2015) 
found that firm value is positively related to firm age in the Italian wine industry, which contradicted the findings of Adetunji 
and Owolabi (2016) and Rajesh Kumar and Sujit (2018) found out that firm size is an essential determinant of firm value. 
Most prior studies find that size is negatively associated with the firm value (Lang & Stulz, 1994; Sekerci, 2016; Sayilir & 
Farhan, 2017) while studies conducted by (Jin & Jorion, 2006; Hoyt & Liebenberg, 2011; Ballantyne, 2013; Mohamad, 
2018) found a positive relationship between firm size and firm value. Florio and Leoni (2017) and Adetunji and Owolabi 
(2016) identified that firm size is negatively related to Q and has a positive relation to ROA. Institutional investors act as a 
monitoring agent for a company. The presence of large outside ownership will pave way for the implementation of ERM as 
they will pressurise the management to publish all the information (Liebenberg & Hoyt, 2003). Studies conducted by Marcia, 
Marcus, Saunders and Tehranian (2007), and Chaganti and Damanpour (1991) identified that institutional ownership has a 
positive impact on firm value. Organizations with higher financial leverage instigate greater deficit risk and thus more 
significant financial distress. Studies conducted by Hoyt and Liebenberg (2011), McShane, Nair and Rustambekov (2010), 
Mohamad (2018) and Adetunji and Owolabi (2016) revealed that leverage negatively impacts firm value while Winarto 
(2015) found a positive influence to firm value. Ballantyne (2013) found that leverage didn't impact firm value. Jin and Jorion 
(2006), and Sekerci (2016) find that leverage is positively related to firm value. An organization that has a higher volume of 
cash produced from its internal activity is probably going to have more prominent slack accessible which it can use for ERM 
implementation. Along these lines, Ghosh (2013) supported that higher liquidity can encourage a firm to embrace ERM 
Winarto (2015) found liquidity posits a negative impact on firm value while Mohamad (2018) proved a positive influence 
on firm value. An organization may flag more severe risks connected with its performance due to instability in stock returns. 
In this way, they might have higher motivating factors to put resources into ERM to minimize the risks which can upset the 
accomplishment of organizational objectives. Many studies have hypothesized the relationship between the volatility of stock 
returns and ERM implementation (Liebenberg & Hoyt, 2003). The volatility of stock returns is negatively related to firm 
value (McShane, Nair & Rustambekov, 2010). 
 
2.4 Research Gap 
Prior literature shows that the relationship of firm value with ERM, Big4 auditor, firm-specific characteristics such as Firm 
Size, Firm Age, Liquidity, Firm Complexity, Board Independence, Institutional Ownership, Non-Performing Assets, Annual 
Volatility of stock returns, and Leverage are contentious. The Companies Act 2013 requires each organisation to have a risk 
management committee and audit committee for better internal control. This cost will influence firm value. So, it is essential 
to consider the role of internal control and firm-specific characteristics on firm value. Moreover, in emerging economies like 
India, only a few empirical studies are available on the influence of internal control and firm- specific characteristics on firm 
value. This investigation along these lines endeavours to fills the gap in the existing empirical literature on Indian financial 
services from the perspective of purchasers, management and investors.  
 
 
 



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3. Research Objectives 
▪ To identify the companies which have adopted ERM and Big4 auditor in the Indian financial services sectors. 
▪ To explore the changes in the firm value based on the adoption of ERM during the study period. 

▪ To investigate the changes in firm value based on the Big4 auditors. 
▪ To analyze the impact of the adoption of ERM, the Big4 Auditor and the firm-specific characteristics on firm value. 

 
4. Hypotheses Development 
The relationships explored in the existing literature were used to formulate the following research hypotheses 
 

▪ H01 - There is no significant difference in Q between ERM and non-ERM observations. 

▪ H02 - There is no significant difference in ROA between ERM and non-ERM observations. 
▪ H03 - There is no significant difference in ROE between ERM and non-ERM observations. 
▪ H04 - There is no significant difference in Q between Big4 and non-Big4 observations. 

▪ H05 - There is no significant difference in ROA between Big4 and non-Big4 observations. 
▪ H06 - There is no significant difference in ROE between Big4 and non-Big4 observations. 

 
5. Conceptual Framework 

 
Figure 1. Conceptual Framework 

6. Methodology 
▪ Research design - The research is empirical in nature, quantitative approach, deductive logical reasoning. 
▪ Paradigm - Positivist philosophy. 

▪ Secondary data – Necessary data support was taken from secondary sources of information such as annual reports, 
company websites, journals, articles and online databases like CMIE PROWESS and Ace Analyser. 

▪ Population Study – Out of the 76 companies in NSE Financial services sector, nine companies were excluded due 
to the unavailability of data. This population study comprises of 67 companies with 737 firm-year observations. 

▪ The frequency of data – Annual 

▪ Type of data – Panel 
▪ Statistical analysis – Descriptive Statistics such as frequency, percentage, mean and standard deviation, Content 

Analysis, Trend analysis, One-way ANOVA, Correlation analysis, Hausman test, Multicollinearity test, Likelihood-
Ratio Test, Wooldridge Test and Panel data regression analysis. 

▪ Statistical software packages – SPSS, EViews and STATA. 

▪ Period of study – This study covers the period of 11 years from April 2007 to March 2017.  
 
    
 
 
 



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Table 1. Description of variables 
 

Source. Prepared by author 
 
7. Model Specification 
The multivariate OLS regression models (general form) developed from Anju and Uma (2017) is used to test the impact of 
firm value and its determinants. In this study, firm value is measured using Tobin’s Q, ROA and ROE as in Adetunji and 
Owolabi (2016). 
 
Model 1 

Qit = α + β1 ERMit + β2 ACit + β3 SIZEit + β4 AGEit + β5 LIQit + β6 BODit + β7 INSOWNit + Β8 NPAit +   

              β9 LEVit + β10 FCit + β11 VOLit + εit 
 
Model 2 

ROAit = α + β1 ERMit + β2 ACit + β3 SIZEit + β4 AGEit + β5 LIQit + β6 BODit + β7 INSOWNit + Β8 NPAit +   

              β9 LEVit + β10 FCit + β11 VOLit + εit 
 
Model 3 

ROEit = α + β1 ERMit + β2 ACit + β3 SIZEit + β4 AGEit + β5 LIQit + β6 BODit + β7 INSOWNit + Β8 NPAit +   

              β9 LEVit + β10 FCit + β11 VOLit + εit 
where, 

α = Coefficient of intercept (constant) 

β1 – β11 = Regression coefficients 

εi = Error term 

t = Sub-indices represent firm and time respectively 

 
8. Analysis and Interpretation 
8.1 Descriptive Statistics 
The summary statistics of all the 14 variables, i.e., independent, dependent and firm-specific variables, have been shown in  
Table 2. It consists of 737 firm-year observations of 67 companies across 11 years.  
 
 
 

Acronym  Type of Variable Measurement 

 Dependent Variables 

Qit  Tobin’s Q (Total Assets + Market Capitalization - Net Worth) / Total Assets 

ROAit  Return on Asset Net Income / Average Total Assets 

ROEit  Return on Equity Net Income / Shareholder’s Equity 

 Independent Variables 

ERMit  The existence of 
ERM/RMC/CRO 

Dummy variable. Value = 1 if the firm has ERM/RMC/CRO, 0 
otherwise 

 

ACit  The existence of Big 4 Auditor Dummy variable. Value = 1 if the firm has Big4 Auditor, 0 otherwise 

 Firm-Specific Characteristics  

SIZEit  Firm Size Natural log of the book value of total assets 

AGEit  Firm Age Number of years from inception to date 

LIQit  Liquidity Net cash flow from operating activities divided by the total Assets 

BODit  Board independence Percentage of independent directors over the total number of directors 
on the Board of the company 

INSOWNit  Institutional Ownership Percentage of shares held by institutional investors 

NPAit  The net value of Non-
Performing Assets 

(Gross NPA’s – Provisions) / (Gross Advances - Provisions) 

LEVit  Leverage Total Assets/Net worth 

FCit  Firm Complexity Number of subsidiaries 

VOLit  Volatility in daily stock returns (Standard deviation of Daily Returns) x 365 



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Table 2. Descriptive Statistics of the Variables 
 

Variable Mean Std. Dev. Min Max 

Dependent variables 

Tobin’s Q (Q) 1.40 1.40 0.02 16.99 

Return on Assets (ROA) 2.86 6.87 -45.30 120.20 

Return on Equity (ROE) 14.90 13.22 -124.65 133.71 

Independent variables 

Enterprise Risk Management (ERM) 0.93 0.25 0.00 1.00 

Big 4 Auditor (AC) 0.10 0.30 0.00 1.00 

Firm-Specific Characteristics (Control Variables) 

Firm Size (SIZE) 12.50 2.07 6.65 17.12 

Firm Age (AGE) 45.42 35.24 2.00 152.00 

Liquidity (LIQ) -0.10 0.90 -12.91 5.58 

Board Independence (BOD) 41.07 26.19 0.00 100.00 

Institutional Ownership (INSOWN) 26.88 20.05 0.00 88.39 

Non-Performing Assets (NPA) 15873.41 50812.88 0.00 582774.00 

Leverage (LEV) 11.41 7.76 1.00 42.67 

Firm Complexity (FC) 4.99 7.80 0.00 62.00 

Volatility of Stock Returns (VOL) 2.46 1.11 0.00 6.93 

Source. Authors’ compilation 
 
8.2 Findings for Objective 1 
Content analysis of 737 annual reports and 67 company websites were used to identify the existence of ERM and the presence 
of Big4 audit firms in the audit committee in the financial services sector. The keywords ‘Risk Management’, ‘Chief Risk 
Officer’, ‘Enterprise Risk Management’, ‘COSO’ were searched to identify the existence of ERM and to determine the 
presence of Big4 auditors, Deloitte, KPMG, Ernst & Young, and PwC were searched.  
 

 
Figure 2. Number of Companies with ERM 

 

 
Figure 3. Number of Companies with Big4 auditors 

 

61 61 61 61
62 62

64 64 64

66 66

58

60

62

64

66

68

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

C
o
m

p
an

ie
s

Year

ERM in Financial Services Sector

4

6 6
5

6 6
7

6
7

9
8

0

2

4

6

8

10

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

N
o
. o

f 
co

m
p

an
ie

s 

Year

Big4 Auditors in Financial Services Sector



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    There is an increase in the existence of ERM during the study period. Sixty-one banks were having ERM for the past 
11 years, i.e., 2007-17. The Reserve Bank of India had released a notification in 2007 on compliance function in banks which 
emphasized on enterprise-wide risk management framework. In 2017, 66 banks had ERM. On the other hand, though there 
is an increasing trend in the presence of Big4 audit firms in the audit committee, only eight companies had a Big4 auditor in 
2017. 
 
8.3 Findings for Objective 2  
A one-way ANOVA was performed to decide if the firm value measured by Q, ROA and ROE differed between ERM (N 
= 689) and non-ERM (N = 48) companies.  
 
Table 3. Descriptive Statistics of Q, ROA, ROE and ERM 
 

 N Mean Std. 
Deviation 

Std. Error 95% Confidence  Minimum Maximum 

Lower 
Bound 

Upper 
Bound 

Q 0 48 1.78 1.40 0.20 1.37 2.18 0.33 7.55 

1 689 1.38 1.40 0.05 1.27 1.48 0.02 16.99 

Total 737 1.40 1.40 0.05 1.30 1.51 0.02 16.99 

ROA 0 48 6.53 17.39 2.51 1.48 11.58 0.40 120.20 

1 689 2.60 5.36 0.20 2.20 3.00 -45.30 36.30 

Total 737 2.86 6.87 0.25 2.36 3.35 -45.30 120.20 

ROE 0 48 17.86 19.27 2.78 12.27 23.46 0.48 133.71 

1 689 14.69 12.69 0.48 13.74 15.64 -124.65 63.31 

Total 737 14.90 13.22 0.49 13.94 15.85 -124.65 133.71 

Source. Authors’ Research 
 
Table 4. Comparison of Firm value among ERM and Non-ERM observations 
 

Note. *Significant at 10%   
Source. Authors’ Compilation 

During the study period,  
▪ There is a difference in Q between ERM and non-ERM observations [F(1,735)=3.65,p=0.05] 
▪ There is a difference in ROA between ERM and non-ERM adoption [F(1,735)=14.94,p=0.00] 

▪ There is a difference in ROE between ERM and non-ERM observations [F(1,735)=2.59,p=0.10] 
 
Of all the three measures, ROA has a higher value than all other measures of firm value. ROA indicates how 

profitable are the firm’s assets in generating income. ERM helps to safeguard the assets and create firm value to their owners. 
ANOVA table suggests that there is a significant difference in the ROA among the companies that have integrated ERM and 
not integrated ERM. Non-ERM observations have a higher mean than ERM observations. It means that the companies that 
do not have ERM in place can generate better ROA. Implementation of ERM will reduce the net income as it involves a large 
amount of investments. ROE is a measure that indicates how well a company uses its investments in generating earnings. 
Tobin’s Q is the measure of market capitalization on the replacement value of assets. The firm value measured by Q and ROE 
is different between the ERM and non-ERM observations. It implies that the investors and purchasers of the company 
consider the presence of ERM before making an investment or calculating the market value of the firm. 
 
 
 

Firm 
Value 

ANOVA Sum of Squares df Mean Square F Sig. 

Q Between Groups 7.14 1.00 7.14 3.65 .05* 

 Within Groups 1438.35 735.00 1.96    

Total 1445.49 736.00      

ROA Between Groups 691.32 1.00 691.32 14.94 .00* 

Within Groups 34013.06 735.00 46.28    

Total 34704.37 736.00      

ROE Between Groups 451.47 1.00 451.47 2.59 .10* 

Within Groups 128230.29 735.00 174.46    

Total 128681.75 736.00      



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8.4 Findings for Objective 3  
A one-way ANOVA was administered to determine if the firm value measured by Q, ROA and ROE varied among Big4 
(N = 73) and non-Big4 (N = 664) companies.  

 
Table 5. Descriptive Statistics of Q, ROA, ROE and Big4 
 

 N Mean Std. 
Deviation 

Std. 
Error 

95% Confidence Interval for 
Mean 

Minimum Maximum 

Lower 
Bound 

Upper 
Bound 

Q 0 664 1.41 1.46 0.06 1.30 1.52 0.02 16.99 

1 73 1.34 0.67 0.08 1.18 1.50 0.03 5.11 

Total 737 1.40 1.40 0.05 1.30 1.51 0.02 16.99 

ROA 0 664 3.02 7.09 0.28 2.48 3.56 -45.30 120.20 

1 73 1.41 4.05 0.47 0.47 2.36 -23.00 6.70 

Total 737 2.86 6.87 0.25 2.36 3.35 -45.30 120.20 

ROE 0 664 15.15 13.44 0.52 14.13 16.18 -124.65 133.71 

1 73 12.57 10.80 1.26 10.05 15.09 -30.00 30.47 

Total 737 14.90 13.22 0.49 13.94 15.85 -124.65 133.71 

Source. Authors’ Research 
 
Table 6. Comparison of Firm value among Big4 and Non-Big4 observations 
 

Source. Authors’ Research 
During the study period,  

▪ There is no difference in Q between Big4 and non-Big4 observations [F(1,735)=0.16,p=0.686] 

▪ There is no difference in ROA between Big4 and non- Big4 observations [F(1,735)=3.61,p=0.58] 
▪ There is no difference in ROE between Big4 and non- Big4 observations [F(1,735)=2.52,p=0.113] 

 
In Table 6 firm value is not different between the Big4 and non-Big4 observations. Only 9% of the firms (73 firm-

year observations) showed the presence of Big4 auditor in the audit committee. It implies that investors and purchasers of the 
company are not dependent on whether the company has incorporated Big4 in the audit committee for making investment 
decisions. So, in the long term, when more firms include Big4 audit firms in their audit committee, it may affect firm value. 
 
8.5 Findings for Objective 4  
8.5.1 Correlation Analysis 
Pearson Product Moment Correlation has been utilized to gauge the linear connection between the variables.  
 
Table 7. Pearson Correlation Coefficients of the variables 
  

Q ROA ROE ERM AC SIZE AGE LIQ BOD INS NPA LEV FC VOL 

Q 1 
             

ROA 0.46* 1 
            

ROE 0.19* 0.65* 1 
           

ERM -0.07* -0.14* -0.06* 1 
          

AC -0.02 -0.07* -0.06 0.09* 1 
         

Firm 
Value 

ANOVA Sum of Squares df Mean Square F Sig. 

Q Between Groups 0.32 1 0.32 0.16 0.686 

 Within Groups 1445.17 735 1.97     

Total 1445.49 736       

ROA Between Groups 169.58 1 169.58 3.61 0.58 

Within Groups 34534.8 735 46.99     

Total 34704.37 736       

ROE Between Groups 439.17 1 439.17 2.52 0.113 

Within Groups 128242.58 735 174.48     

Total 128681.75 736       



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SIZE -0.37* -0.38* -0.11* 0.26* -0.02 1 
        

AGE -0.18* -0.18* -0.07* 0.09* -0.14* 0.49* 1 
       

LIQ 0.05 -0.00 -0.07 -0.02 0.02 -0.00 0.07 1 
      

BOD 0.07* 0.02 0.00 -0.08* 0.10* -0.21* -0.28* -0.01 1 
     

INS -0.03 -0.05 0.06 0.02 0.26* 0.38* 0.06* 0.15* 0.35* 1 
    

NPA -0.06* -0.09* -0.22* 0.07* -0.05 0.40* 0.22* 0.07* -0.20* 0.05 1 
   

LEV -0.32* -0.32* -0.03 0.14* -0.18* 0.66* 0.63* 0.10* -0.40* -0.01 0.24* 1 
  

FC 0.14* 0.00 -0.15* -0.01 0.21* 0.04 -0.19* 0.09* 0.11* 0.15* 0.18* -0.40* 1 
 

VOL -0.02 -0.11* -0.14* -0.18* -0.06* 0.03 0.05 0.03 0.24* 0.20* -0.02 0.05 0.08* 1 

Note. Results computed using Stata14; * Significant at 10% 
Source. Authors’ compilation 

8.5.2 Specification Test 
 
Table 8. Hausman Test 

 

Dependent variable Chi-Square Statistics Degree of Freedom  p-value 

Q 65.794388 11  0.0000* 

ROA 30.2713 11  0.0014* 

ROE 97.935982 11  0.0000* 

Note. * denotes statistically significant at 10% level. 
Source. Authors’ analysis 
 

   Table 8 shows the results of the Hausman test, which rejects the null hypothesis and concludes that the Fixed Effects 
Model (FEM) is appropriate for each of the models under study. 
 
Table 9. Co-linearity Statistics 
 

Variance Inflation Factor 

Variable VIF Tolerance = 1/ VIF 

Leverage (LEV) 3.17 0.315593 

Firm Size (SIZE) 3.03 0.329679 

Institutional Ownership (INSOWN) 1.71 0.584355 

Firm Age (AGE) 1.71 0.585256 

Board Independence (BOD) 1.51 0.660176 

Firm Complexity (FC) 1.37 0.731093 

Non-Performing Assets (NPA) 1.28 0.779768 

Big4 Auditor (AC) 1.18 0.849524 

Volatility of Stock Returns (VOL) 1.15 0.865915 

Enterprise Risk Management (ERM) 1.11 0.900284 

Liquidity (LIQ) 1.1 0.907586 

Mean VIF 1.67 
 

Source. Authors’ analysis 
 

Table 9 shows the VIF statistics of the independent variables. The VIF mean is 1.67, which indicates that there is no 
multicollinearity. 
 
Table 10. Likelihood-Ratio Test for Panel Level Heteroskedasticity 
 

Dependent variable Chi-Square Statistics Degree of Freedom p-value 

Q 819.718773 66 0.0000* 

ROA 359.215112 66 0.0000* 

ROE 405.919127 66 0.0000* 

 Note. * significant at 10%. 
Source. Authors’ analysis 

 
Table 10 confirms the presence of heteroskedasticity at a 1% level of significance, rejecting the null hypothesis. 



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Table 11. Wooldridge Test for Autocorrelation in Panel Data 
 

Dependent variable F-Statistics df p-value 

Q 5.938 66 0.0175* 

ROA 1.111 66 0.2958 

ROE 4.539 66 0.0369* 

 Note. * Significant at 10% 
Source. Authors’ analysis 

 
    Table 11 shows the results of autocorrelation in panel data. The Wooldridge test for autocorrelation rejects the null 

hypothesis that there is no first-order autocorrelation in model 1 and model 3. There is the first-order autocorrelation in 
model 2. Hence the panel data regression model uses cluster-robust standard errors to control autocorrelation in model 1 and 
model 3. 
 
8.5.3 Regression Analysis 
Table 12 shows the results of panel data regression estimated using OLS, FEM, and REM models using Q, ROA,  
and ROE as the dependent variable. The Hausman test shows a significant p-value that indicates FEM is appropriate for 
Model 1, 2, and 3. 

 
Table 12. Regression results using Q as the dependent variable 
  

Model 1- Q Model 2 - ROA Model 3 - ROE 

Variables OLS    FEM REM OLS FEM REM OLS FEM REM 

Enterprise Risk  
Management  
(ERM) 

1.27 0.86 1.66 -1.23 0.25 -0.64 -1.09 0.45 -0.81 

  -0.205 -0.393 (0.098)* -0.22 -0.804 -0.524 -0.28 -0.66 -0.42 

Big4 Auditor (AC) -2.61 -0.89 -0.21 -3.64 0.3 -1.11 -2.8 1.38 -0.97 

  -0.009 -0.378 -0.831 (0.00)* -0.767 -0.269 (0.01)* -0.17 -0.33 

Firm Size (SIZE) -7.74 -2.84 -1.24 -6.37 1.85 -3.43 -2.9 1.75 -3.38 

  (0.000)* (0.006)* -0.214 (0.00)* (0.065)* (0.001)* (0.00)* (0.08)* (0.00)* 

Firm Age (AGE) 1.55 2.62 1.2 1.11 -3.32 -0.21 -1.64 -3.88 -2.21 

  -0.122 (0.011)* -0.23 -0.27 (0.001)* -0.832 (0.10)* (0.00)* (0.03)* 

Liquidity (LIQ) 0.29 -2.3 -0.65 -0.32 -0.52 -0.57 -2.34 0.01 -1.53 

  -0.768 (0.025)* -0.519 -0.75 -0.602 -0.566 (0.02)* -0.99 -0.13 

Board 
Independence 
 (BOD) 

-1.62 1.06 2.2 -2.85 -0.47 -0.68 -1.43 0.74 0.15 

  (0.105)* -0.295 (0.027)* (0.01)* -0.635 -0.498 -0.15 -0.46 -0.88 

Institutional 
 Ownership 
 (INSOWN) 

3.4 -0.05 -0.26 3.71 1.48 2 5.28 2.98 4.42 

  (0.001)* -0.961 -0.797 (0.00)* -0.14 (0.045)* (0.00)* (0.00)* (0.00)* 

Non-Performing 
Assets 
 (NPA) 

1.74 -1.69 0.31 1.51 0.31 -0.22 -4.55 -2.3 -7.23 

  (0.083)* (0.096)* -0.754 -0.13 -0.756 -0.824 (0.00)* (0.02)* (0.00)* 

Leverage 
 (LEV) 

-1.45 0.97 -2.61 -3.14 -0.99 -1.37 2.34 2.17 5.47 

  -0.148 -0.337 (0.009)* (0.00)* -0.324 -0.171 (0.02)* (0.03)* (0.00)* 

Firm Complexity  
(FC) 

3.56 -0.33 1.45 -0.39 0.08 -0.3 -1.56 2.61 -0.02 

  (0.000)* -0.74 -0.147 -0.7 -0.933 -0.768 -0.12 (0.01)* -0.98 

Volatility (VOL) -0.68 2.14 1.71 -2.97 -2.65 -2.14 -4.84 -2.52 -4.14 

  -0.498 (0.036)* (0.088)* (0.00)* (0.008)* (0.032)* (0.00)* (0.01)* (0.00)* 

R-squared 0.1868 0.7326 0.03227 0.2005 0.509 0.0513 0.1243 0.4952 0.1502 

Hausman Test   65.80 
(0.00)* 

    30.27 
(0.00)* 

    97.935982 
 (0.000)* 

  



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F Test   23.45 
(0.00)* 

    8.88 
(0.00)* 

    8.3944
13 
(0.000
* 

  

Note. Results computed using Stata14; p-value is in parenthesis with * Significant at 10%; OLS 
– Ordinary Least Square, FEM – Fixed effects, REM – Random effects. 

Source. Authors’ analysis 
 
Table 13. Fixed Effect (within) Regression adjusted for robust standard error  
 

Variable Coef. Robust Std. Err. p-value 

Enterprise Risk Management (ERM) 0.2314 0.2695 0.39 

Big4 Auditor (AC) -0.0667 0.0752 0.38 

Firm Size (SIZE) -0.3360 0.1182 0.01* 

Firm Age (AGE) 0.1212 0.0463 0.01* 

Liquidity (LIQ) -0.0992 0.0432 0.03* 

Board Independence (BOD) 0.0031 0.0029 0.30 

Institutional Ownership (INSOWN) -0.0002 0.0043 0.96 

Non-Performing Assets (NPA) -1.7800 0.0000 0.09* 

Leverage (LEV) 0.0108 0.0111 0.34 

Firm Complexity (FC) -0.0045 0.0135 0.74 

Volatility (VOL) 0.1516 0.0709 0.04* 

CONSTANT -0.6843 1.0752 0.53 

Note. * Significant at 10%. Source. Authors’ analysis 
 

            Panel Data Fixed Effect Model 1 is significant and has explanatory power with a good fit as F-statistic = 23.45 
and p-value = 0.000, R-squared = 0.73 indicating that the dependent variables predict 73% of the variances in Q. 

In table 13, the results of the FEM show that there is a significant relationship between the firm size, firm age, 
liquidity, non-performing assets, volatility and the firm value measured using Q. The results show that the NPA has the 
highest impact as it decreased the firm value by 1.78. If NPA rises, they cannot recover their interest income as borrowers do 
not pay interests and installments. This creates a negative impression in the minds of the purchasers and reduces the market 
value of the company. The size of the firm measured by total assets has a negative impact on Q by 0.34. This is consistent 
with the findings of Florio and Leoni (2017) and Adetunji and Owolabi (2016). If the total assets are more in a financial 
services company, it signals the purchasers that the company does not have investment opportunities, so that it is assessed as 
unfavorable. The volatility of stock returns increases Q by 0.15 from 2007-17. In this study, there are 372 firm-year 
observations in the financial services sector with high volatility in daily stock returns that indicate the demand for the firms’ 
share. This creates a positive impression in the minds of the purchasers and influences the market estimation of the company. 
As the financial service sector firm grows old, it can create a better image in the minds of purchasers and thus improve market 
value. Firm age has a positive approach to Q throughout the study. It increases Q by 0.12. Financial firms' liquidity is affected 
if NPA increases. The results show that liquidity decreases the firm value by 0.09 during the study period. So, to improve 
liquidity, firms should make regular efforts to reduce bad debt and mobilize additional resources. 
 
Table 14. Fixed Effect (within) Regression using ROA 
 

Variable Coef. Std. Err. p-value 

Enterprise Risk Management (ERM) 0.3577 1.4432 0.80 

Big4 Auditor (AC)  0.3915 1.3181 0.77 

Firm Size (SIZE) 0.9717 0.5254 0.07* 

Firm Age (AGE) -0.4229 0.1275 0.00* 

Liquidity (LIQ) -0.1900 0.3643 0.60 

Board Independence (BOD) -0.0063 0.0132 0.64 

Institutional Ownership (INSOWN) 0.0361 0.0244 0.14 

Non-Performing Assets (NPA) 0.0000 0.0000 0.76 

Leverage (LEV) -0.0832 0.0843 0.32 

Firm Complexity (FC) 0.0043 0.0503 0.93 

Volatility (VOL) -0.6576 0.2483 0.01* 

CONSTANT 11.330 4.0730 0.01 

Note. * Significant at 10%. Source. Authors’ Compilation 



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         Panel Data Fixed Effect Model 2 is significant and has explanatory power with a good fit as F-statistic = 8.88 and 
p-value = 0.000, R-squared = 0.51 indicating that the dependent variables predict 51% of the variances in ROA. 

In table 14, the results of the FEM show a significant relationship between the firm size, firm age, volatility and 
ROA. As the assets of the firm increases, the management can generate greater profits. That is why firm size has a positive 
impact on ROA. It increases the firm value by 0.97 during the study period. There are 372 firm-year observations with the 
high volatility of stock returns indicating that any change in the value of the share will negatively affect the ROA by 0.66. 
Firm age has decreased ROA by 0.42. In this study, there are 470 firm-year observations with less than 45 years of mean 
industry age. Only when the company grows older, it will be able to understand the business environment better and manage 
the assets more efficiently. On the contrary, the existence of ERM, the presence of Big4 auditor, board independence, firm 
complexity, institutional ownership and leverage have an insignificant impact on ROA. It means that the total assets and net 
income of the company are not affected by the presence of ERM, Big4 audit firms in the audit committee, number of 
independent directors on the board, number of subsidiaries, the percentage of institutional investors and the percentage of 
debt-equity for the period 2007 to 2017. 
 
Table 15. Fixed Effect (within) Regression adjusted for robust standard error 
 

Variable Coef. Robust Std. Err. p-value 

Enterprise Risk Management (ERM) 0.9118 2.0459 0.66 

Big4 Auditor (AC)  1.7912 1.2976 0.17 

Firm Size (SIZE) 4.1562 2.3728 0.08* 

Firm Age (AGE) -2.0476 0.5278 0.00* 

Liquidity (LIQ) 0.0052 0.9743 1.00 

Board Independence (BOD) 0.0321 0.0432 0.46 

Institutional Ownership (INSOWN) 0.1856 0.0623 0.00* 

Non-Performing Assets (NPA) -0.0001 0.0000 0.03* 

Leverage (LEV) 0.4798 0.2208 0.03* 

Firm Complexity (FC) 0.1831 0.0700 0.01* 

Volatility (VOL) -3.1104 1.2325 0.01* 

CONSTANT 50.7192 13.0070 0.00 

Note. * Significant at 10%. Source: Authors’ Compilation 
 

        Panel Data Fixed Effect Model 3 is significant and has explanatory power with a good fit as F-statistic = 8.3944 
and p-value = 0.000, R-squared = 0.49 indicating that the dependent variables predict 49% of the variances in ROE. 

In table 15, the results of the FEM show that there is a significant relationship between firm size, firm age, 
institutional ownership, non-performing assets, leverage, firm complexity, volatility, and firm performance measured using 
ROE. On the contrary, the existence of ERM, the presence of Big4 auditor, board independence, and liquidity has an 
insignificant impact on firm value. For firm size, the beta values show an increase of 4.16 in ROE during the study period. 
Investors look forward to investing in firms with a large asset base, as it will generate more returns for their investment. That 
is why firm size has the highest impact on ROE. There are 372 firm-year observations that high volatility in daily stock 
returns. It has decreased the ROE of 3.11. Investors prefer low-volatility stocks to minimize risk in their portfolios. When 
the company grows old, it can create a better image in the minds of the investor. But here, there are 470 firm-year observations 
with less than 45 years of mean industry age. Hence it decreases the ROE by 2.05. There are 407 firm-year observations with 
low leverage, indicating that there is more equity than borrowings, which signals the investors that the company can generate 
more income from its investments in the long run. Hence leverage increases ROE by 0.48 during the study period. The 
number of subsidiaries indicates the company’s vision for growth and expansion to the investors. So, these have a positive 
effect on ROE. During the study period, the percentage of institutional investors and the number of subsidiaries increase the 
ROE by 0.19 and 0.18 respectively. NPA creates a negative effect on the minds of investors. Hence it decreases ROE. 
 
Table 16. Results of Hypotheses Testing 
 

      Hypotheses p-value Results 

H01 0.05 Rejected 

H02 0.00 Rejected 

H03 0.10 Rejected 

            H04  0.69 Accepted 

H05 0.58 Accepted 

H06 0.11 Accepted 

Source. Authors’ compilation 
        



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    The results of hypotheses testing are shown in Table 16. Thus, in the financial services sector, it is evident there are 
differences in firm value for ERM and non-ERM companies. But on the other side, there is no difference in firm value for 
Big4 and non-Big4 companies. The firm value measured by Q showed that the purchasers consider firm size, firm age, 
liquidity, the volatility of stock returns, and non-performing assets. ROA indicated that the management has to focus on firm 
size, firm age, and volatility of stock returns. ROE pointed out that the investors will look into firm size, firm age, institutional 
ownership, non-performing assets, leverage, firm complexity. and volatility of stock returns. 
 
9. Discussion and Conclusions 
This research determined the role of firm-specific characteristics and internal control represented by ERM and big4 auditor 
on the firm value measured using a three-dimensional approach – from the purchaser, management and investor perspective.  

 
9.1 Purchaser’s Perspective 
For ERM, the study reveals that there is an increase in the Beta coefficient, which means the adoption of ERM, increased the 
Q value by 0.23. It implies that the existence of ERM will improve the confidence of purchasers in shares of the company 
and thus boost the market value of the company. The volatility of stock returns increases Q by 0.15. In this study, there are 
372 firm-year observations in the financial services sector with high volatility in daily stock return, which indicates the demand 
for the firms’ share. This creates a positive impression in the minds of the purchasers and thus affects the market value of the 
company. The findings are consistent with that of Fang, Noe and Tice (2009). As the financial service sector firm grows old, 
it can create better confidence in the minds of purchasers and thus improve market value. Firm age has a positive approach to 
Q throughout the study. It increases Q by 0.12. Leverage has a positive impact on the minds of the purchasers as it helps to 
expand the firm’s asset and generate returns on risk capital. It increases Q by 0.01. Similarly, as with any other organization, 
banks with high leverage ratio is viewed as more secure. The bank needs to utilize its cash-flow to provide loans or sell its 
risky assets or make investments. This will create less impact if, in future, the creditors fail to repay their loans or the economy 
faces depression. The presence of Big4 audit firms in the audit committee seems to have a negative impact on Q. The huge 
resource commitment reduces the firm value by 0.07. It implies that the purchasers consider other factors apart from the audit 
quality by the Big4 auditors. Financial firms' liquidity is affected if NPA increases. The results show that liquidity decreases 
the firm value by 0.09, which is similar to the findings of Winarto (2015). So, to improve liquidity, firms should make regular 
efforts to reduce bad debt and mobilize additional resources. The size of the firm measured by total assets has a negative 
impact on Q by 0.34. This is consistent with the findings of Florio and Leoni (2017) and Adetunji and Owolabi (2016). If 
the total assets are more in a financial services company, it signals the purchasers that the company does not have investment 
opportunities, so that it is assessed as unfavorable. The results show that the NPA decreased the firm value by 1.78. If NPA 
rises, they cannot recover their interest income as borrowers do not pay interests and installments. This creates a negative 
impression in the minds of the purchasers and reduces the market value of the company. Results show that the purchaser does 
not consider the number of independent directors on the board, the number of subsidiaries and the percentage of institutional 
investors in the financial services sector. So, it does not help to increase the market value of a company measured by Q. 

 
9.2 Management’s Perspective 
As the assets of the firm increases, the management can generate greater profits. That is why firm size has a positive impact 
on ROA. It increases the firm value by 0.97 during the study period. This result is consistent with that of Florio and Leoni 
(2017) and Adetunji and Owolani (2016). The regression coefficients indicate that the existence of ERM and the presence 
of Big4 auditors have a positive impact on ROA during the study period. When ERM is in place, the management can 
anticipate risks, prevent losses and increase profits. Also, the Big4 auditor ensures audit quality. Though the implementation 
of ERM and Big4 auditor involves huge resource commitment, it is observed that it will help the management to increase 
ROA in the long run by 0.36 and 0.39, respectively. On average, institutional investors hold 0.27 of the shares. The 
institutional investors have more resources than the individual investor. During the study period, institutional investors help 
to increase ROA by 0.04. The presence of prudent and effective institutional investors will motivate the management to 
perform efficiently, which will help to increase the ROA. NPA indicates the inefficiency of management to prevent bad debts. 
It doesn't seem to affect ROA. 243 firm-year observations have subsidiaries more than the industry mean. It has increased 
ROA. It shows the management’s efficiency to expand and grow. The number of independent directors on the board helps 
to improve the efficiency of management through better monitoring and governance. There are 330 firm-year observations, 
which is highly leveraged, indicating that there are more borrowings than equity, which will affect the net income and assets 
of the firm. Hence leverage decreases ROA by 0.08. Negative operating cash flow indicates the inefficiency of the management 
in meeting its operating expenses by generating profits from total assets. In this study, liquidity denotes the net cash flow from 
operating activities divided by the total assets. 278 firm-year observations are denoting negative liquidity. For liquidity, the 
study reveals that there is a decrease in ROA by 0.19. Firm age has decreased ROA by 0.42. In this study, there are 470 firm-
year observations with less than 45 years of mean industry age. Only when the company grows older, it will be able to 
understand the business environment better and manage the assets more efficiently. This supports the findings of Capasso et 
al. (2015), Dogan (2013) and Coad, Segarra and Teruel (2012). There are 372 firm-year observations with the high volatility 
of stock returns indicating that any change in the value of the share will negatively affect the ROA by 0.66. 
 
 



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9.3 Investor’s Perspective 
For firm size, the beta value shows an increase of 4.16 in ROE. Investors look forward to investing in firms with a large asset 
base, as it will generate more returns for their investment. That is why firm size has the highest impact on ROE. Though 
ERM and Big4 auditors do not ensure guaranteed return on equity, it is evident from the results that their presence in the 
organization gains the trust and confidence of investors. The study period shows it will increase ROE by 0.91 and 1.79, 
respectively. There are 407 firm-year observations with low leverage, indicating that there is more equity than borrowings, 
which signals the investors that the company can generate more income from its investments in the long run. Hence leverage 
increases ROE by 0.48 during the study period. The investors look into the number of independent directors on the board 
and the percentage of institutional investors before investing in the company. It implies the safety of their investments, as 
there are good governance and monitoring in the firm. The number of subsidiaries indicates the company’s vision for growth 
and expansion to the investors. So, these have a positive effect on ROE. During the study period, the percentage of institutional 
investors improved the ROE by 0.19. This supported the findings of Masry (2016). Also, the number of institutional 
investors and the percentage of independent directors on the management board increases the ROE by 0.18 and 0.03, 
respectively. The capacity to pay estimated expenses like providing loans or paying debts using liquid assets decides a bank's 
liquidity. Here liquidity decreases ROE by 0.005 during the study period. Investors will be interested when a bank maintains 
a liquidity level that allows it to pay unexpected expenses without liquidating other assets. So, a negative operating cash flow 
indicates the investors that the company doesn't have sufficient liquid assets to meet its operating expenses. NPA creates a 
negative effect on the minds of investors. Adebisi and Matthew (2015), Sharifi and Akhter (2016) and Nyarko-Bassi (2018) 
also agreed on the negative effect of NPA on ROE. As the company grows old, it can create a better image in the minds of 
the investor. But here, there are 470 firm-year observations with less than 45 years of mean industry age. Hence it decreases 
the ROE by 2.05. This finding is consistent with Susanti and Restiana (2018) and Ilaboya and Ohiokha (2016). 372 firm-
year observations have high volatility in daily stock returns. It has decreased the ROE of 3.11. When the value of the share 
fluctuates erratically with a rapid increase and immediate falls, it is a high stock. Low volatility stocks help to minimize risks 
in investor’s portfolios as it is steady thus investors prefer it.  
 
10. Implications of the Study 
The findings of this study are more reliable, accurate, and represent all firms in the financial services sector. The results from 
the analysis respond to the study’s research questions and are of particular interest to investors, researchers and practicing 
managers in the above sector.  

▪ This study helps to understand that the amendments in the Companies Act led to an increase in Q and a decrease in 
ROA and ROE. Stringent internal control through ERM and Big4 auditor in the Audit committee builds confidence 
in the purchasers. But the huge resource commitment in implementing ERM and the adoption of Big4 auditors 
reduce the net income, which affects the returns for management and investors. 

▪ From this study, it is clear that there is an increase in the adoption of ERM from 61 to 66 firms and from 4 to 8 in 
the adoption of Big4 auditors.  

▪ The outcome of this study explained that are differences in the firm value among companies that have integrated 
ERM and not integrated ERM. The companies that do not have ERM in place have higher ROA. Implementation 
of ERM will reduce the net income as it involves a large number of investments. The investors and purchasers of the 
company do not consider the presence of ERM before making an investment or calculating the market value of the 
firm. 

▪ The investors and purchasers of the company are not dependent on whether the company has incorporated Big4 in 
the audit committee for making investment decisions. So, in the long term, when more firms include Big4 audit firms 
in their audit committee, it may affect firm value. 

▪ The study showed that the age of the firm and the number of subsidiaries creates confidence in the minds of the 
purchasers and thus influence Q.  

▪ The outcome of this study indicated that the volatility of stock returns and the firm age influenced ROA.  
▪ The results of the study indicated that the firm size, volatility and firm age affected ROE. 

▪ Data from this study shows that NPA has the highest impact on Q, and firm size has a major effect on ROA and 
ROE. 
 

11. Recommendations  
This study has contributed to the existing literature by identifying the factors that will impact firm value from three dimensions 
– purchaser, management and investor in the financial services sector.  

▪ NPA had the highest impact on firm value (Q). To build the confidence of a prospective purchaser, the bankers have 
to focus on stringent credit policies and debt collection policy to reduce the bad debts. The firm value of the banks 
will increase if there are fewer bad debts written off from the profits of the banks.  

▪ The companies should invest in ERM even it involves large investment as it helps to increment firm value (Q, ROA 
and ROE). 

▪ The purchaser, management and investor are interested in a firm with a huge asset base. Hence, every firm should try 
to increase its size as it helps to improve firm value. 



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▪ The review done by Big4 auditor helps to enhance the credibility of financial statements, which in turn improve the 
report quality. It increases the firm value (ROA and ROE) from the management and investor perspective.  
 

12. Limitations 
▪ This research study is restricted to companies in the NSE Financial Services during the period 2007-2017 only.  

▪ The findings are based on the financial statements and annual reports published by the organizations. 
▪ The dichotomous ERM variable neglects to measure the varying level of ERM implementation across organizations. 

 
13. Scope for Further Research 

▪ A prospective direction for future investigations would be to extend this study to different sectors or stock exchanges 
of other countries or different period as well. It will help to check the validity of the findings in this study.  

▪ Future research could include data from organization surveys and spotlight on developing an ERM index to measure 
the level of ERM implementations in organizations.  

 
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