




































INDIAN JOURNAL OF FINANCE AND BANKING 13(1) (2023), 78-90 

 

 78 

                       FINANCE AND BANKING 

                                                                  IJFB VOL 13 NO 1 (2023) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 
                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 

                                                                                                                                                                                                   Published by CRIBFB, USA 

VALUE DRIVERS OF BANK EQUITY MARKET PRICE  

          
 Chitra Gounder  (a)1   Jyoti Nair (b)     

 

(a) Associate Professor, N L Dalmia Institute of Management Studies and Research School of Management, Mumbai, India; E-mail: 

chitra..gounder@nldlamia.edu.in 
(b) Professor, N L Dalmia Institute of Management Studies and Research School of Management, Mumba, India; E-mail: 

jyoti.nair@nldlamia.edu.in 
 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received: 1st January 2023 

Revised: 20th February 2023 
Accepted: 25th February 2023   

Published: 28th February 2023   

 
Keywords: 

 

Bank equity, Market value, Indian Banks, 

Macro-Economic Factors, Value Drivers  

 

 
JEL Classification Codes: 

  

E52, E58, M41 
 

 

 

 
 
  

 
A B S T R A C T 

 
The Indian banking sector has shown robustness and resilience in the face of challenges on account of 

rising non-performing assets and economic upheavals. The market value of bank equity is a reflection 
of the financial performance of banks and macroeconomic factors. 22 bank-specific variables and 5 

macroeconomic factors representing profit management, capital management, shareholder value 

management and risk and leverage management, India’s Real GDP Growth Rates (Factor Cost), Money 

Supply growth, Bank Credit growth, Deposit growth rate and Inflation are used in this study. 19440 

observations are examined from the financial data of 40 commercial banks (private and public sector 

banks for a period of eighteen years. The data is analyzed using panel regression. Hausman and Pagan's 

test was conducted to find the best-fit model. The results show that Finance Charge Coverage (FCCR), 

Advance Loan funds, Current Ratio, Beta and Asset Turnover have a negative impact on bank equity 
value and Activity Mix, Revenue Efficiency, Earnings Retention, and Cost Management shows a  positive 

significant relationship with equity value. Factors like FCCR affects the market values of public sector 

banks and private sector banks differently Growth in GDP, Inflation shows a positive relationship with 

the market value of bank equity. Growth in Money Supply has a differential impact on the market value 

of private sector banks and public sector banks. The results provide useful insights to understand the 

determinants of the market value of bank equity. It can help bankers frame strategies to maintain and 

enhance the market value of their equity. 
  

 

© 2023 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed 

under the terms and conditions of the Creative C6540mmons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

Bank is a complex system with many performance centers interconnected in a nonlinear fashion, the synergy of this 

performance center will determine the banks overall performance leading to the maximization of bank market value and 

stability. In recent trends India economy has been on high growth trajectory crediting unprecedented opportunities for 

banking sector. Indian banking sector is growing rapidly and performing remarkably well as compared to other banks in 

World, Sengupta and Thomas (2007). 

According to an IBA-FICCI-BCG report titled ‘Being five star in productivity – road map for excellence in Indian 

banking”. India’s gross domestic product (GDP) growth will make the Indian banking industry the third largest in the world 

by 2025”. According to the report, the domestic banking industry is set for an exponential growth in coming years with its 

assets size poised to touch USD 28,500 billion by the turn of the 2025. 

Even though Indian banks are currently facing a great deal of difficulties, including increased capital adequacy 

requirements, improved branch sales and services, improved organizational design, frequent changes in policy rates to 

maintain economic stability, various regulatory requirements using innovative technology through new channels, working 

on lean operations, and managing non-performing assets, Indian banking is still showing signs of growth and has enormous 

potential for expansion in India. 

LITERATURE REVIEW 

There are many studies which indicates and measured the growth in productivity and efficiency of banks and reduction in 

                                                      
1Corresponding Author: ORCID ID: 0000-0001-5174-6740 
© 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v13i1.1977 

 
To cite this article: Gounder, C., & Nair, J. (2023). VALUE DRIVERS OF BANK EQUITY MARKET PRICE. Indian Journal of Finance and Banking, 

13(1), 78-90. https://doi.org/10.46281/ijfb.v13i1.1977 

 

https://orcid.org/0000-0001-5174-6740
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v13i1.1977
https://orcid.org/0000-0001-7948-3188


Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

79 

cost, Tyagarajan (1975), Rangarajan and Mampilly (1972) and Subrahmanyam and Swami (1993), Kumbhakar and Sarkar 

(2003); Shanmugam and Das (2004); Das et al. (2005), Sensarma (2005), Rajan, Reddy, and Pandit (2011), cost 

minimization so as to increase efficiency in immensely competitive and growing trend, Mohan (2008), contribution of Indian 

banks in development of economic activity, Jadhav and Ajit (1996) and bank efficiency in the post liberalization period, 

Mahesh and Bhide (2008). 

As the performance of Indian banking sector is continuously showing a growing trend, investors are interested in 

investing in the banking sector. In order to safeguard the interest of shareholder, framing of Bank Valuation model to drive 

a perfect shareholder value by considering external and internal value drivers of banks suitable to developing economy like 

India has become very essential. 

Creating sustainable and growing shareholder value has been the banks' top priority. Copeland evaluations of banks 

can help them achieve this goal. An integrated model that takes both internal and external value drivers into account and 

performs valuation for the generation of shareholder value and risk management is necessary. Finding these value drivers 

is a crucial first step in enabling banks to manage their businesses with shareholder value as their primary objective, 

according to Rappaport (1999). It is also necessary to choose a model with a significant link to share market price. 

Banks are very different from non-banks when it comes to the construction of their business model Gross (2007). 

Looking at the products, non-banks in general occurs risk as a side-effect of doing business while banks has managing, 

incurring, structuring and assessing financial market risk as one of their core business activities. By taking this risk, they 

provide the service of storing value and extending credit, acting as intermediaries between parties with funding surpluses 

and deficits Koller, Goedhart, and Wessels (2010). This gives banks a very central function in the modern economy but it 

also makes them heavily dependent of the overall economy. As there are few studies have been reported in literature related 

to empirical adequacy of bank valuation context, further scope for research emerges. 

There is also need to know the relationship between the intrinsic value and market price as it can be used for 

forecasting the future market price and also for taking necessary decision regarding investment and key policies by investor, 

management, banks and Government. There is also need to understand and find the key Value drivers impacting and market 

price of banks as it will help to determine and find solution for increase or decrease of value of share 

Value drivers can be defined as all factors that influence the value creation of a bank as value drivers. In order to 

develop model which will help to maximize shareholder value a key internal and external value driver has to be investigated. 

Valuation of bank in particular is highly influenced by the external and internal value drivers which are interconnected and 

thus has valuation of bank depends value drivers such as Internal factors such as Cost management, Capital management, 

Leverage management, Profit management and External factors such as Banking industry structure, its rules & regulation, 

its system, Macroeconomic environment and Economic environment. Empirical evidence for value drivers specific to banks 

is very few and therefore there is a need for empirical studies in which the potential value drivers for different bank types 

are measured and ranked. The implications for bank management derived from such evidence could then serve as a basis 

for value creation within banks. 

Internal value drivers are factors relating to the inherent performance of a bank, whereas external drivers result 

macro-economic factors environment of a bank. External drivers relate to specific factors and trends in the banking industry 

as well as to factors concerning the general economic environment. 

According  to Fiordelisi and Molyneux (2007), various determinants such as market structure, bank efficiency, 

adjusted loan and deposit growth rates, staff costs, financial structure and operational and credit risk impact on shareholder 

value creation in European banking between 1997 and 2002. He found that bank’s cost and profit efficiency have a positive 

influence on shareholder value. Leverage is found to be inversely related to value creation suggesting that highly capitalized 

banks are more likely to generate value for their owners compared with lowly capitalized counterparts. Other factors that 

are found to impact positively on value creation include adjusted annual deposits growth rate, industry concentration, bank 

market share and employee costs. He found that quoted banks (listed banks) are not good at creating shareholder value as 

compared to non-quoted (non-listed banks) counterparts, although they found some substantial variations in shareholder 

value creation across European countries and bank ownership types over time. 

Koller, Goedhart, and Wessels (2010) it is clear from the model, that the cost-income ratio is an important driver 

and calculated as operating expenses divided by net interest income. ROA and revenue growth it will increase shareholder 

value significantly. Looking at the products, non-banks in general occurs risk as a side-effect of doing business while banks 

has managing, incurring, structuring and assessing financial market risk as one of their core business activities. By taking 

this risk, they provide the service of storing value and extending credit, acting as intermediaries between parties with funding 

surpluses and deficits, this gives banks a very central function in the modern economy but it also makes them heavily 

dependent of the overall economy. 

Gross (2007), states that shareholder value has become the pre-eminent performance measure in many industrial 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

80 

companies and it has significantly affected how some banks in recent years have tried to optimize their business. The 

objective of the paper by Gross (2007) is to find the metrics that are able to quantify the story behind shareholder value and 

to understand the fundamental drivers of value. The findings in suggest that only the cost efficiency and the risk capabilities 

relevant drivers for shareholder value in banks. Whereas, both the business mix and the branch structure driver is difficult 

to make any reliable conclusions on, due to their ambiguity. The regression results for the business mix suggest that an 

increased income diversification is value destroying in the short-term. Results for the underlying income cost and risk 

structure for the bank is somewhat controversial as well. Looking at the branch structure there is no empirical evidence for 

the value impact of changes in the branch structure and it is therefore concluded that it has no direct impact on value. 

Potential value implications are instead driven by the interdependence of the branch structure and the different value drivers. 

Baele, De Jonghe, and Vander Vennet (2007), in his studies business mix as first driver, where income diversification (non-

interest income) is used as a measure and finds strongly positive correlations between shareholder value and the degree of 

diversification. In order to measure Leverage he has taken ratio of equity to assets, found it has negative and is not significant 

to shareholder value. In order measure cost efficiency, cost-income ratio was taken and found that it has negative impact on 

value creation of the banks who go for diversification. In order measure risk, loan loss provision was taken as value driver 

and result was that it had positive impact on shareholder value but test was not significant 

Shareholder value management has for many years been a dominating management concept and a performance 

indicator for companies all over the world. However, more than twenty years after the ground-breaking book by Copeland 

Thomas, Koller, and Murrin (1994) still only few articles have discussed shareholder value management in connection to 

banks and none of these have had both an internal and an external view. As very few articles are published from the academic 

side, there was need to study the report published by consulting companies regarding this subject. Globally many consulting 

companies have published report on bank profitability and productivity but only few have worked on creation of shareholder 

value. 

According to the consulting industry, the maximization of shareholder value is therefore a strong performance 

measure where managers are forced to make value creating decisions. Articles from McKinsey & Company and BCG have 

been applied since academia does not discuss their findings on such an operational level. 

Visalli et al. (2011) McKinsey: ROE and cost of equity are the main drivers for creation of shareholder value. The 

increasing regulation is the single most important driver when it comes to profitability. Due to the heavy capital constraints, 

equity capital and funding costs will increase which is expensive for banks. Increased regulatory constraints will impact the 

key drivers through a set of underlying value drivers. ROE will be affected both in the numerator and the denominator. 

Return will be negatively impacted due to the increasing cost of holding equity and higher operational costs whereas the 

common equity in the denominator will increase. This expected decrease in ROE is also the main reason for the negative 

TSR in the years after the crisis. Banking TSR have been significantly higher in those countries that have experienced 

growth compared to those where a flat development have been seen. This indicates that those banks, capable of capturing 

revenue growth will be able to increase shareholder value. The shift in consumer behavior to a more technology-driven 

behavior will affect the cost driven value driver. By closing branches or making them smaller bank managers will be able 

to decrease the cost-income ratio which is also expected to affect shareholder value positively. However, as is always the 

case with change, only the banks capable of adapting to the changing environment will benefit from it. Those banks not 

capable of delivering superior customer experience to a new generation of self-helped customers will have a hard time 

competing. If the country has a low credit rating it will affect the banks credit rating, higher the lending costs and thereby 

make it difficult to compete across borders and be more vulnerable to foreign competitors with higher credit ratings. 

Dayal et al. (2010) (BCG): According to author revenue growth is one of important value driver, based on the 

development in emerging market for creating shareholder value. Achieving revenue growth is however very difficult. The 

reason for this low growth in developed economies is not due to a decreasing focus from the top management but more 

because of the challenges in increasing revenue per customer. Risk cost has been the main driver of negative value creation 

since the start of the crisis and effect can be seen in years to come. 

Black and Wright (1998) (PWC): Author has taken residual income as the dependent variable. According to him 

banks growth can be increased by gaining competitive advantages. The competitive advantage period that the bank has is 

measured as the period where the bank is capable of earning a higher operating return than their cost of capital (positive 

residual income). Other Growth drivers such as operating assets which is made up of loans and other earnings assets (short 

term assets, long term positions in investments, loans to banks etc.) is very key driver for increasing shareholder value as 

even though they do not directly affect the cash flow but indirectly every loan creates deposits and there by creates value 

(given that net interest margin is positive). Net interest margin and on-interest income, which is made up of fees, 

commissions and trading income (this is other operating income in the data) drive value positively. The cost-income ratio 

is considered as best value driver for creation of shareholder value. Regulatory requirements (the amount of equity that is 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

81 

needed to maintain capital adequacy) is found significantly affect the shareholder value. 

 
Figure 1. Framework for determinants of shareholder Value 

 

Proposed framework for the determinants of shareholders value in Indian Banking by Dr Chitra Gounder from this 

study and all other research work done previously related to Bank Value creation. 

 

MATERIALS AND METHODS 

This empirical study which will help the Indian banks to create value for shareholder and also understand the key 

determinants impacting the creation of shareholder value by analyzing and interpreting the impact of Bank specific and 

country Economic value drivers   on market price of equity share. 

The present work has considered 40 Indian commercial banks and the time period is from 2001 to 2018. The macro 

and micro economic data has been collected from Data book of planning commission, RBI, CMIE -prowess and from the 

annual reports of each bank. The data collected for the present analysis is arranged in panel data form as 40 units (banks) 

were considered for 18 years so it is combination of times series data and cross-sectional data. So, the relationship between 

the dependent and independent variables is obtained from a regression model called panel regression analysis. 

Empirical study for all below given was classified in private sector banks, public sector banks and all banks which 

included both private and public sector. The commercial banks are only considered for study in order to avoid the 



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82 

dissimilarity of the banking operations with other type’s banks. Panel data may have group effects, time effects, or the both, 

which are analyzed by pooled effect, fixed effect and random effect models. In order to select appropriate panel regression 

model for estimating result, we had applied   Hausman test and Bruesh and Pegan IM test. 

From detail literature review of academics and reports of analyst determinants are identified which can impact the 

market price. This study is essential as to know exactly which value drivers impacts the value of share so that proper measure 

can have adopted for value creation of share. 

 

Variables Selected for Study 

Dependent Variables: 

 Market price of bank stocks: Market price of equity shares is used to represent market value. 

Independent variables  

 Micro (Bank Specific)  

 Macro-Economic Determinants as already proposed in the study above. 

 

Micro Determinants (Bank Specific) 

This is the internal determinants of individual banks. This is again categorized into five broad categories: 

 

Table 1.  Variables used in the study 

Profit Management:  

a) Profit efficiency (PE) a) Cost Income ratio (CIR)  

b) Activity mix (AM) b) Cost efficiency (CE)  

c) Business mix (BM) c) Cost management (CM)  

d) Net Operating Profit per Share (NOP)   

e) Revenue efficiency  

  

Capital Management: Risk and Leverage Management: 

a) Capital Adequacy Ratio (CAR) a) Beta (Beta) 

b) Financial Charges Coverage Ratio (FCCR):   b) Liquidity 

c) Return on Long Term Fund (%) (RLTF) c)  leverage  

d) Earning Retention Ratio (ERR) d)  Current Ratio (Current ratio) 

e)  Asset Turnover Ratio (ATR) e)  Quick Ratio (Quick ratio) 

 f)  Advances / Loans Funds (%) (AALF) 

Shareholder Management:  

a) Earnings per Share (EPS)  

b) Book Value (BV)  

c) Dividend per Share (DPS)  

 

Macroeconomic Determinants Selected for Study 

a) India's Real GDP Growth Rates (Factor Cost) (GDP %) 

b) Money Supply growth (MSG%) 

c) Bank Credit growth (BCg%) 

d) Deposit growth (Dg%) 

e) Inflation CPI (Average 

 

Panel Regression model for Micro (Bank Specific) Determinants of Bank Market price  

MPit = π0+ π1ISit + π2PEit + π 3 AM it + π4 BM it +………….. .   + €it 

 

Panel Regression model for Macro (Country Specific) Determinants of Bank Market price  

MPit = π0+ π1GDPit + π2MSGit + π 3 BCG it + π4 DG it +………….. .   + €it 

 

RESULTS AND DISCUSSIONS 

Multicollinearity test for identifying of Micro and Macro determinants (bank specific) variables  

 

 Correlation Matrix of Micro Determinants (Bank Specific) for Private Sector Banks 

Through correlation matrix we can see there is high correlation between some variables so the solution for this 

multicollinearity is dropping variables such as CIR, RLTF, EPS, BV and DPS. Accept the variable identified as 

multicollinearity all other variables will be considered for multiple regressions for study of Impact of determinants on MP 

and intrinsic value of bank share. In Case of Macroeconomic variable for private sector, as per multicollinearity test DG% 

was dropped. 

 

 Correlation Matrix of Micro Determinants (Bank Specific) for Public Sector Banks 

Through correlation matrix we can see there is high correlation between some variables So the solution for this 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

83 

multicollinearity is dropping variables such as PE, BM, CE, FCCR, CR, AALF, EPS, BV and DPS .Accept the variable 

identified as multicollinearity all other variables will be considered for multiple regressions for study of Impact of 

determinants on MP and intrinsic value of bank share. In Case of Macroeconomic variable for Public sector, as per 

multicollinearity test DG% & BCG% was dropped. 

 

 Correlation Matrix of Micro Determinants (Bank Specific) for All Banks 

Through correlation matrix we can see there is high correlation between some variables so the solution for this 

multicollinearity is dropping variables such as EPS, BV, BM and DPS. Accept the variable identified as multicollinearity 

all other variables will be considered for multiple regressions for study of Impact of determinants on MP and intrinsic value 

of bank share. In Case of Macroeconomic variable for all banks, as per multicollinearity test DG% was dropped. 

 

 Micro Determinants (Bank Specific) Impacting the Market Price of Private Sector Banks 

 

 Table 2.  Panel Regression Model 1 

 
Particulars Panel Regression Models 

Pooled Regression Model Fixed Effect Model Random Effect Model 

const 857.504 *** 562.498 *** 739.317 *** 

AM 19.9286 *** 13.7116 *** 15.094 *** 

NOP 0.809339 *** 0.7794 *** 0.809956 *** 

RE -37.7454 *** -1.17741   -17.2892   

CM 8.21668 *** 8.56796 *** 7.27919 ** 

FCCR -58.4849   -114.828 *** -97.9053 *** 

RNW 6.25491 * -7.01777 ** -1.62669   

ERR 0.0104349   -0.0715895   -0.0281054   

ATR -0.8382   1.45382   0.25137   

Beta -59.9767   -70.7254   -82.7498 * 

LEV -31.8208 *** -15.9903 *** -25.0803 *** 

CR -241.737   -2679.76 *** -1230.5   

QR -6.13041 * -2.54609   -4.02272   

AALF -2.31539 * -1.17807   -1.54479   

R-squared 0.588532   0.798345       

Adjusted      R-

squared 

0.562182   0.768311       

P-value(F) 1.81E-32   4.32E-51       

Durbin-Watson 0.577492   0.90777       

rho 0.690046   0.513731       

Breusch-Pagan 

test  

p-value = 2.42279e-025  So Random effect model has to be selected  

Hausman test  p-value = 1.00388e-010  So Fixed  effect model has to be selected  

 

In this case Breusch-Pagan test was found significant, RE Model was selected and Hausman test was significant, 

FE Model was selected. So Panel Regression Model showing Impact of Micro Determinants (Bank Specific) on Market 

Price of Private Sector Banks as per Fixed effect model is 

MPit = 562.49 + 13.71AM it + 0.77 NOP it - 1.17RE it + 8.56CM it - 114.82FCCR it  - 7.01RNW it - 0.07ERR it + 

1.45ATR it - 70.72Beta it -15.99LEV it - 2679.76CR it  - 2.54QR it - 1.17AALF it + €it 

 
 

Figure 2.  Micro Determinants- Private sector banks 

In above model, the coefficient of constant parameter of the banks shows a positive figure of 562.498, which 

implies that if all the explanatory variables held constant, the dependent variable RIV increases by 562.498 units. It is 

showing the strong Positive significant relationship at 1%significant level. Variables such as, RE, FCCR, RNW, EV, CR, 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

84 

QR, and AALF are showing strong negative relationship which indicates that this variable are inverse proportionally related 

to dependent variable. Variables such as AM, NOP, CM, ERR, ATR and Beta has s positive significant relationship are 

direct proportionally related to dependent variable. AM, NOP, RE, FCCR, RNW, LEV and CR significant relationship. 

So by above analysis, model can be interpreted as fit model for defining the Impact of micro determinants variable 

on Market price of Private Sector Banks share.  

 

Macro-Economic Determinants Impacting the Market Price of Private Sector Banks 

Table 3.  Panel Regression Model 2 

 
Particulars Panel Regression Models 

Pooled Regression Model Fixed Effect Model Random Effect Model 

const 68.9066   24.6619   18.0255   

GDP_ 22.096 * 23.6716 *** 23.555 *** 

MSG_ -11.3875   -10.6666 * -10.7181 * 

BCG_ -0.655432   -0.888813   -0.869836   

INF 40.2216 *** 43.8855 *** 43.6191 *** 

R-squared 0.122416   0.627211       

Adjusted  

R-squared 

0.106674   0.593158       

P-value(F) 6.96E-06   9.20E-35       

Durbin-Watson 0.2688   0.651749       

rho 0.903232   0.657356       

Breusch-Pagan test  p-value = 2.64614e-101              So Random effect model has to be selected  

Hausman test p-value = 0.767961                    So Random  effect model has to be selected 

   

In this case Breusch-Pagan test was found significant, RE Model was selected and Hausman test was not 

significant, RE Model was selected. So Panel Regression Model for Impact of Macro Economic Determinants on Market 

Price of Shares of Private Sector Banks as per Random effect model is  

 MPit = 18.02 + 23.55 GDPit - 10.71MSGit-0.86 BCG it + 43.61 INF it + €it 

 

 
 

Figure 3.  Macro-economic determinants- Public sector banks 

 

In above model, intercept is showing the non-significant relationship. All independent variable except BCG are 

showing significant relationship. Variables MSG is showing negative significant relationship which indicates that this 

variable is inverse proportionally related to dependent variable market price   of bank share. Variables such GDP, and 

Inflation has strong positive significant relationship are direct proportionally related to dependent variable market price of 

bank share.  

So by above analysis, model can be interpreted as fit model for defining the Impact of macro determinants variable 

on market price of banks share. 

 

Micro Determinants (Bank Specific) impacting the Market Price of Public Sector Banks: 

Table 4.  Panel Regression Model 3 

 
Particulars Panel Regression Models 

Pooled Regression Model Fixed Effect Model Random Effect Model 

const 458.37   905.776   495.26   

IS -0.00492754   0.138773   0.00209788   

AM 7.38801   10.5155   7.60673   

NOP 0.63594 *** 0.659268 *** 0.636824 *** 

RE -139.806 *** -228.98 *** -146.044 *** 



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85 

CAR 52.9508 * 49.991   52.7435 * 

RLTF 2.56887   7.27254 ** 2.87925   

RNW 4.08235   5.40438   4.22253   

ERR 2.519   3.17737   2.5315   

ATR -60.0255 *** -62.5184 *** -60.0942 *** 

Beta 9.7594   87.9809   13.4194   

LIQ -254.319   -237.252   -259.726   

LEV -21.9921   -40.2896 ** -23.2595   

R-squared 0.675659   0.718807       

Adjusted  

R-squared 

0.661955   0.6811       

P-value(F) 2.79E-62   1.54E-53       

Durbin-Watson 0.644112   0.75549       

rho 0.642418   0.579619       

Breusch-Pagan 

test  

p-value = 0.0106292  So Radom effect model has to be selected  

  

Hausman test  p-value = 0.0153358 So  Fixed  effect model has to be selected 

 

In this case of Public Sector, a bank Breusch-Pagan test was found significant, RE Model was selected and 

Hausman test was significant, FE Model was selected. So Panel Regression Model for Impact of Micro Determinants (Bank 

Specific) on Market Price of Public Sector Banks as per fixed effect model is 

MPit = 905.77 + 0.13 IS it + 10.51 AM it + 0.65 NOP it - 228.98 RE it + 49.91 CAR it                         + 7.27 RLTF it 

+ 5.40 RNW it + 3.17 ERR it – 62.51ATR it +87.98 Beta it – 237.25 LIQ it        -  40.28 LEV  + €it 

  

 
  

Figure 4. Micro determinants – Private sector banks 

 

In above model, the coefficient of constant parameter of the banks shows a positive figure of 905.776, which 

implies that if all the explanatory variables held constant, the dependent variable RIV increases by 905.776units 

Determinants such RE, ATR, LEV and LIQ are showing negative relationship which indicates that this variable is inverse 

proportionally related to dependent variable. Determinants such as AM, NOP, CAR, RLTF, RNW, ERR and Beta has 

positive relationship are direct proportionally related to dependent variable.   NOP, RE, RLTF, ATR and LEV are showing 

strong significant relationship with Market price of share. 

So by above analysis, model can be interpreted as fit model for defining the Impact of micro determinants variable 

on Market price of Public Sector Banks share. 

 

Macro Economic Determinants impacting the Market Price of Public Sector Banks  

Table 5.  Panel Regression Model 4 

 
Particulars Panel Regression Model 

Pooled Regression Model Fixed Effect Model Random Effect Model 

              

const -735.495 ** -847.879 *** -863.281 *** 

GDP_ 70.0747 ** 79.9638 *** 78.6145 *** 

MSG_ 43.6008 ** 39.9238 *** 40.6842 *** 

INF -7.00184   7.05021   4.42916   

R-squared 0.052663   0.433246       

Adjusted   

R-squared 

0.043406   0.38136       

P-value(F) 0.00084   1.13E-22       

Durbin-Watson 0.34984   0.596438       



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86 

rho 0.824728   0.688943       

Breusch-Pagan test  p-value = 1.77428e-064  So  Random effect model has to be selected   

Hausman test  p-value = 0.262174  So  Random effect model has to be selected  

 

In the case of macroeconomic determinants impacting market price of public Sector Banks Breusch-Pagan test was 

found significant, RE Model was selected and Hausman test was not significant, RE Model was selected. So Random 

effect model is 

 MPit = - 863.28 + 78.61 GDPit – 40.68 MSGit   -0.86 BCG it + 4.42 INF it + €it 

 

 
 

Figure 5.  Macro determinants – Public sector banks  

 

In above model, intercept is showing the strong negative significant relationship. All independent variable except 

INF are showing significant relationship. Variables such GDP and MSG has strong positive significant relationship are 

direct proportionally related to dependent variable market price of bank share.  

So by above analysis, model can be interpreted as fit model for defining the Impact of macro determinants variable 

on market price of banks share. 

 

Micro Determinants (Bank Specific) impacting the Market Price of All Banks   

Table 6.  Panel Regression Model 6 

 
Particulars Panel Regression Models 

Pooled Regression Model Fixed Effect Model Random Effect Model 

const -283.839   -707.276   -419.729   

PE 16.8065   18.0166   18.0133   

AM 9.88642   10.711   10.0422   

NOP 0.626249 *** 0.678418 *** 0.636224 *** 

RE -2.71906   -128.296 *** -34.2211   

CIR 8.57237   5.13075   7.59354   

CE -71.9356   121.405   -16.1177   

CM -3.5252   0.495391   -2.20992   

CAR 20.4532   27.178 * 22.1132   

FCCR -45.0724   14.2745   -26.8979   

RLTF -1.85817   -1.11198   -1.86326   

RNW -2.69844   2.95222   -1.4901   

ERR -0.0849578   0.0560903   -0.042395   

ATR -40.691 *** -29.8863 ** -37.9275 *** 

Beta 1.10705   24.9892   4.19245   

LIQ -277.041 ** -386.798 *** -304.187 ** 

QR -9.02199 ** -5.14925   -8.14615 ** 

AALF 4.21769 ** 7.81445 *** 5.1931 ** 

IS 0.0437244   0.0206877   0.0298957   

R-squared 0.660891   0.72314       

Adjusted R-

squared 

0.648434   0.688149       

P-value(F) 1.20E-102   1.14E-93       

Durbin-Watson 0.644545   0.779155       

rho 0.643759   0.571797       

Breusch-Pagan 

test  

p-value = 8.65723e-006  So Random effect model has to be selected  

Hausman test  p-value = 3.92761e-005  So Fixed  effect model has to be selected  



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

87 

In case study of impact of micro determinants on all banks market price Breusch-Pagan test was found significant 

so RE Model was selected and Hausman test was significant so FE Model was selected. So Fixed effect Panel Regression 

Model for Impact of Micro Determinants on Market Price of shares of All Banks is 

 

MPit = - 707.27 + 18.01 PE + 10.71 AM it + 0.67 NOP it - 1.28 RE it + 5.13 CIR it + 121.40 CE it   + 0.49 it CM + 27.17 

CAR + 14.27 FCCR it    - 1.11 RLTFit   + 2.95 RNW it + 0.05 ERR it – 29.88 ATR it + 24.98 Beta it -386.79 LEV it - 

2679.76 CR it - 5.14 QR it – 7.81 AALF it + 0.02 IS it €it 

 

 
 

Figure 6.  Micro determinants – All banks  

In above model, the coefficient of constant parameter of the banks shows a negative figure of -707.276 which 

implies that if all the explanatory variables held constant, the dependent variable RIV decreases by 707.276 units. It is 

showing the negative significant relationship with market price. Variables such as RE, RLTF, LIQ,QR, and ATF are showing 

strong negative relationship which indicates that this variable are inverse proportionally related to dependent variable. 

Variables such as IS, AM, NOP, CIR, CE, CAR, FCCR, RNW, Beta, CM, ERR and ATR has s positive significant 

relationship are direct proportionally related to dependent variable.   Determinants such as NOP, RE, CAR, ATR, LIQ and 

AALF is strong significant relationship with market price. 

So by above analysis, model can be interpreted as fit model for defining the Impact of micro determinants variable 

on Market price of all banks. 

 

Macro-Economic Determinants impacting the Market Price of All Banks: 

Table 7.  Panel Regression Model 6 

 
Particulars Panel Regression Models 

Pooled Regression Model Fixed Effect Model Random Effect Model 

const -411.279 * -489.982 *** -502.542 *** 

GDP_ 50.4004 *** 55.2926 *** 54.6378 *** 

MSG_ 20.5637 * 18.8258 * 19.1998 ** 

BCG_ -0.140071 
 

0.0123963 
 

0.0154693 
 

INF 13.3334 
 

22.9672 * 21.3072 * 

R-squared 0.036002 
 

0.433145 
   

Adjusted R-

squared 

0.028781 
 

0.383903 
   

P-value(F) 0.000594 
 

9.65E-39 
   

Durbin-Watson 0.33501 
 

0.579667 
   

rho 0.835981 
 

0.699054 
   

Breusch-Pagan 

test 

p-value = 5.85916e-115 So Random effect model has to be selected 

Hausman test p-value = 0.339098 So  Random effect model has to be selected 

 

In case of Impact on Macro Determinants on  All Banks Market price, Breusch-Pagan test was found significant 

so RE Model was selected and Hausman test was not significant so RE Model was selected. So Random Effect Panel 

Regression Model for Impact of Macro Economic Determinants on Market Price of Shares of All Banks is 

 MPit = - 502.54 + 54.63 GDPit – 19.19 MSGit - 0.01 BCG it + 21.30 INF it +  €it 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

88 

 
             

Figure 7.  Macro determinants- All banks 

 

The coefficient of constant parameter of the banks shows a negative figure of -502.542, which implies that if all 

the explanatory variables held constant, the dependent variable market price decreases by -502.542 units. In above model, 

intercept is showing the non-significant relationship. All independent variable except BCG are showing non-significant 

relationship. Variables such GDP MSG and Inflation has strong positive significant relationship are direct proportionally 

related to dependent variable market price of bank share.  

So by above analysis, model can be interpreted as fit model for defining the Impact of macro determinants variable 

on market price of banks share. 

CONCLUSIONS 

Empirical study regarding Micro (Bank Specific) and Macroeconomic Determinants impacting the market price of equity 

share of Private Sector Banks, Public Sector Banks and All Banks were made. All study models were good fit as p value 

(F) is 0.000. So it can be analyzed that model as fit model for defining the Impact of Micro and Macro determinants variable 

on market price of equity share of banks. 

 

Figure 8.  Determinants of bank equity value 

 

In case of Micro Determinants impacting the market price of equity share of Private Sector Banks share FE Model 

was selected. In above model, the coefficient of constant parameter of the banks shows a positive relation. It is showing the 

strong Positive significant relationship at 1%significant level. Variables such as, RE, FCCR, RNW, EV, CR, QR, and AALF 

are showing strong negative relationship and Variables such as AM, NOP, CM, ERR, ATR and Beta has s positive 

significant relationship are direct proportionally related to dependent variable. AM, NOP, RE, FCCR, RNW, LEV and CR 

significant relationship. 

In case of Macro Determinants impacting the market price of equity share of Private Sector Banks share, in this 

case Breusch-Pagan test was found significant so RE Model was selected and Hausman test was not significant so RE Model 

was selected. In above model, intercept is showing the non-significant relationship. All independent variable except BCG 

are showing significant relationship. Variables MSG is showing negative significant relationship which indicates that this 

variable is inverse proportionally related to dependent variable market price   of bank share. Variables such GDP, and 

Inflation has strong positive significant relationship are direct proportionally related to dependent variable market price of 

bank share.  

In case of Micro Determinants impacting the market price of equity share of Public Sector Banks share in this case 



Gounder & Nair, Indian Journal of Finance and Banking 13(1) (2023), 78-90 

 

89 

of Public Sector banks Breusch-Pagan test was found significant so RE Model was selected and Hausman test was significant 

so FE Model was selected. Determinants such RE, ATR, LEV and LIQ are showing negative relationship which indicates 

that this variable is inverse proportionally related to dependent variable. Determinants such as AM, NOP, CAR, RLTF, 

RNW, ERR and Beta has positive relationship are direct proportionally related to dependent variable.   NOP, RE, RLTF, 

ATR and LEV are showing strong significant relationship with Market price of share.  

In case of Macro Determinants impacting the market price of equity share of Public Sector Banks share, 

macroeconomic determinants impacting market price of public Sector Banks Breusch-Pagan test was found significant so 

RE Model was selected and Hausman test was not significant so RE Model was selected. In above model, intercept is 

showing the strong negative significant relationship. All independent variable except INF are showing significant 

relationship. Variables such GDP and MSG has strong positive significant relationship are direct proportionally related to 

dependent variable market price of bank share.  

In case study of impact of micro determinants on all banks market price Breusch-Pagan test was found significant 

so RE Model was selected and Hausman test was significant so FE Model was selected. It is showing the negative significant 

relationship with market price. Variables such as RE, RLTF, LIQ, QR, and ATF are showing strong negative relationship 

which indicates that this variable are inverse proportionally related to dependent variable. Variables such as IS, AM, NOP, 

CIR, CE, CAR, FCCR, RNW, Beta, CM, ERR and ATR has s positive significant relationship are direct proportionally 

related to dependent variable.   Determinants such as NOP, RE, CAR, ATR, LIQ and AALF is strong significant relationship 

with market price. 

In case of Impact on Macro Determinants on All Banks Market price, Breusch-Pagan test was found significant so 

RE Model was selected and Hausman test was not significant so RE Model was selected. In above model, intercept is 

showing the non-significant relationship. All independent variable except BCG are showing non-significant relationship. 

Variables such GDP MSG and Inflation has strong positive significant relationship are direct proportionally related to 

dependent variable market price of bank share 

Important value driver impacting the shareholder value has been identified as the private sector, public sector and 

all banks operating in India in above chart. It will help Banks to focus on value drivers and proper selection performance 

measures will help to improve and strengthen the competitive position of banks and help them to focus on wealth creation. 

Value is the best metric of performance as it is the only measure that is comprehensive and hence is useful for decision-

making. By increasing shareholder value, companies can maximize the value for other stakeholders (customers, labor, 

government and suppliers) also.  

 

 
Author Contributions: Conceptualization, C.G. and J.N.; Methodology, C.G.; Software, C.G.; Validation, C.G. and J.N.; Formal Analysis, C.G.; 

Investigation, C.G., Resources, C.G. and J.N.; Data Curation, C.G.; Writing – Original Draft Preparation, C.G.; Writing – Review & Editing, C.G. and 
J.N; Visualization C.G. and J.N.; Supervision, C.G. and J.N.; Project Administration, C.G.; Funding Acquisition, C.G. and J.N. Authors have read and 

agreed to the published version of the manuscript.  
Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not applicable. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.  

 

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