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