F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.151628.1 Research Article Articles The dynamics of financial performance and market performance in the context of Indian banking industry

[version 1; peer review: 2 approved with reservations, 1 not approved]

 Sar Ashok Conceptualization Methodology Supervision Writing – Review & Editing https://orcid.org/0000-0002-7861-9060 a 1 Panigrahi Kshirod Data Curation Formal Analysis Writing – Original Draft Preparation 1 School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India aksar@ksom.ac.in

No competing interests were disclosed.

 18 6 2024 2024 13 657 28 5 2024 Copyright: © 2024 Sar A and Panigrahi K 2024 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

This study aims to gain insight into the effect of banks’ financial performance on their market performance. We conceptualized the research subject on the assumption that the financial performance of an organization is the most important criterion for triggering movement in its stock price. We explored various models and parameters to evaluate financial performance of banks and found CAMELS being one of the most comprehensive and appropriate model. We considered share price growth of banks to measure their stock market performance

 Methods

We collected financial and stock market data pertaining to 32 listed Indian banks for the period 2018 to 2022. The study has employed multiple linear regression analysis of panel data for evaluating the relationship between independent and dependent variables. We adopted panel regression for data analysis and used the Prais- Winsten regression with panel corrected standard errors, as the data suffers from contemporaneous cross-sectional correlation.

 Results

The results show that net non-performing assets, net interest margins, and return on capital have a significant negative impact on share price growth. The capital adequacy ratio and the current and savings account deposit ratios have a positive insignificant impact. The liquid asset-to-total asset ratio has a negative, insignificant impact. The coefficient of determination indicates that the share price growth of banks is more dependent on other factors which are not included in the regression analysis of this study.

 Conclusion

This study helps investors and bankers understand the limited impact of financial parameters on banks’stock prices and to look for other parameters which explain the stock price movement better.

 financial performance; stock market performance; capital adequacy ratio ; net non- performing asset ; CASA ratio; return on capital employed; net interest margin ; liquid asset to total asset; growth in share price The author(s) declared that no grants were involved in supporting this work. 1. Introduction

Despite the global issues of inflation and recession resurfacing, rallies in Indian banking stocks have continued. The banking sector is the foundation of the Indian economy as it channelizes the surplus in the form of investment to entrepreneurs in the form of credit. With the introduction of cutting-edge technology-driven business models, Indian banking has undergone transformation. Additionally, the government has been working to clean up the stressed balance sheets of public-sector banks. The “Make in India” initiative and the ongoing push for infrastructure investment are expected to increase credit demand. Banks will benefit substantially from this huge upsurge in credit-off takes. In 2022, the Nifty Bank index gained more than 11%, whereas the benchmark Nifty gained only 1.56%. Some of the most crucial measures for evaluating banking stock include net interest margins, credit-to-deposit ratios, capital adequacy ratios, return on equity ratios, and return on asset ratios. As the prospects of banks are heavily influenced by macro events and growth cycles, banking sector stocks can be highly volatile. One should invest in banking businesses if one is comfortable with the risks involved. Despite the fact that a slowdown in the global economy appears imminent, Indian banking stocks have been defying global trends. Accelerating credit demand and improving asset quality are anticipated to be favorable factors for these stocks. However, prior to making an investment, comprehensive research is required to avoid getting carried away by the excitement.

It has been a matter of great concern for investors to determine the most important factors influencing stock prices. Investors are engaged in analyzing the volume of data to detect predictable patterns of stock price movements. Ramakrishnan and Toppur (2016) state that stock prices are volatile, uncertain, complex, and ambiguous. Therefore, predicting the future performance of stocks is difficult and almost impossible. Anwaar (2016) maintains that stock prices are largely influenced by financial, monetary, and foreign trade policies, as well as other macroeconomic factors. He emphasized that investors make investment decisions mostly because of the financial information of a firm. Pradhan et al. (2014) find a causal relationship between economic, financial, and political risk factors and stock prices. Narayan et al. (2014) observed that industrial production, which was a surrogate for economic growth and the rate of exchange (currency depreciation), had a positive impact, whereas interest rate increase had an adverse impact on stock prices. Given the riskier nature of stock investments, it is imperative to understand the market dynamics and price determinants of a particular stock. Sharif et al. (2015) observed that internal factors, such as firm performance, management capability, corporate planning, and strategy, which are firm-specific, and external factors, such as macroeconomic parameters, government regulations, and market conditions, decide the price of a stock.

The price of a stock can be determined based on the current financial performance, consistency of past performance, and future revenue-generating potential of the firm. Consequently, the reaction of the market to financial performance should be linear across the firm, subject to other factors remaining constant. From this notion, we can reasonably assume that the financial performance of banks and their stock prices are strongly associated. We need to check the extent of the overall correlation and evaluate each financial performance dimension with respect to its impact on stock prices.

The Indian stock market is evolving rapidly, keeping pace with growing GDP. The market is maturing and offers new avenues of investment for both retail and institutional investors. Businesses find it easy to mobilize equity funding through the market. Against this backdrop, it is essential to understand the details of market performance and the dynamics thereof. This understanding will help investors make informed decisions about their portfolios. There has been more than 100 percent growth in the number of dematerialized accounts in just two years. The number was 40.9 million in March 2020, which has become 108.0 million in December 2022. As per the current market statistics, retail investors conduct 52% of daily transactions as against 29% and 19% by domestic and foreign institutional investors, respectively.

This study intends to assess the impact of the financial results of banks listed on the Indian stock exchange on the performance of their stock. The Indian banking industry has been chosen purposefully, as it is moving towards consolidation. Mergers of state-owned banks have resulted in a decrease in the number of public-sector banks and an increase in the size and business volume of anchor banks. As India is poised to register higher economic growth in the next ten years leveraging high domestic demand, Indian banks will play a significant role. Two Indian banks already appear on the list of the top 20 banks in terms of market capitalization. This number is expected to rise further as India is on the cusp of becoming 3 rd largest GDP by 2030, overtaking Japan and Germany, as per the S&P Global and Morgan Stanley report.

The second section covers previous studies and their contributions to the subject. The design, method, and approach are discussed in section. section explains the result of the study. A thorough discussion and analysis of the findings are covered in the fifth part. The conclusions of this study are discussed in the final section.

 2. Literature review 2.1 Financial performance

Sar (2017, 2018, 2019) states that a firm’s financial performance is assessed at the aggregate level using return on invested capital (ROIC), return on equity (ROE), the spread between ROE and the cost of equity, or the spread between ROIC and weighted average cost of capital (WACC). A firm is said to have superior financial performance if its ROE, ROIC, or economic rate of return compare well with a benchmark. Financial performance has a wider connotation, and can be evaluated using various parameters. Suhadak et al. (2019) suggest that financial performance is an indicator of the effectiveness and efficiency of an organization. Effectiveness indicates the capability of management to select the correct objective and tools to accomplish the objective. Efficiency refers to the optimum input utilization to produce the desired output. Previous studies have used several financial ratios, such as liquidity, leverage, activity, profitability, growth, and valuation, to evaluate financial performance.

The CAMELS rating system is an internationally acknowledged and accepted risk rating system employed by the central bank to evaluate the overall health of banks. Regulators and rating agencies use these to assess the strength of banks. Venkatesh and Suresh (2014) mention that the framework was first used in 1979 by the Federal Financial Institutions Examination Council (FFIEC). As stated by Banu and Vepa (2021) this model was recommended for measuring financial strength and weakness of banks by the Basel committee on banking supervision of the Bank of International Settlements’ (BIS) in 1988. In 1997, the model was expanded to include a sixth parameter, which is sensitive to market risk. In 1995, the Padmanabhan Committee recommended implementing the model in Indian Banks.

The model comprises six parameters: capital adequacy, asset quality, Management, Earnings, Liquidity, and Sensitivity". The strength of this model lies in its comprehensiveness in assessing capital sufficiency, operational efficiency, managerial capability, profit-generating ability, and financial stability. Unlike other ratings or regulatory ratios, the CAMELS rating is not made public. The regulatory authorities and top management of the respective banks use it only to understand and control potential risks. The Indian Reserve Bank of India (RBI), which is the central bank, uses a five-point rating system of five point scale, with one representing the highest rating and five representing the lowest.

Nguyen et al.(2020) narrating on the efficacy of the model articulated that the model was being adopted by three U.S. watchdogs. Nhan et al. (2021) emphasized the importance of this model as a barometer to check the overall performance and risk mitigation capability of banks. Describing the significance of the model, Handorf (2016) explained this as the most effective test to measure the soundness of a bank, and Dang (2011) considered this to be the most reliable tool for checking the health and safety of banks. The CAMELS model was chosen because it is the most acceptable and comprehensive framework for evaluating a bank’s financial performance and vulnerability to risk.

 2.2 Market performance

Sar (2019) maintains that the market performance of a public limited company is assessed using measures associated with the performance of its shares in stock markets. Hobarth (2006) stated that market value versus book value and Tobin's ratio are measures used to evaluate a company's market performance. Market value to book value ratio indicates the market valuation of a company in comparison to its book value. Tobin’s Q is the ratio between the total market capitalization and the book value of an enterprise’s assets. He uses the following mathematical formula to measure the market performance of a company: Δ Market value t 1 = Market value t 1 − Market value t − 1 Market value t − 1 × 100

This formula calculates the share price growth of a company in percentage terms from t -1 to t 1. This is the most common indicator used to evaluate market performance. The stock price of a firm is the true value, as the buyer of the stock paid the price for owning the share of that company. Though speculators manipulate stock prices in the short run, the price reflects the real value of the stock in the long run. The market performance of an enterprise is accurately reflected in its share price, as is the outsider’s view of the company. Unlike financial performance, market performance is an investor’s perspective on the company and industry. Stowe et al. (2010) mentioned two types of return from an investment in equity: capital gain, which is the amount of appreciation in the stock price, and dividends earned from equity during the ownership period of equity. Sharpe (1964) and Lintner (1965) propounded the capital asset pricing model (CAPM) based on the premise that stock prices are a function of the risk attached to it. The model explains that the expected yield from a stock or portfolio of stocks is the sum of the risk-free rate and risk premium attached to the risk of that stock or portfolio. The theory assumes that markets are efficient and investors are rewarded with a return equivalent to the quantum of risk. This model measures the systematic risk of a stock using its beta coefficient, which refers to the sensitivity of the return of a particular stock to the market portfolio. The CAPM model was challenged by Basu (1977), who found that CAPM failed to predict returns accurately for stocks with a high price earnings (PE) ratio because it underestimated the returns of those stocks. Banz (1981) also observed that the CAPM underestimated the average return of stocks with smaller market capitalization. Bhandari (1988) maintains that, for stocks, with a high debt-equity ratio, the actual return is higher than the return estimated based on the market beta. Rosenberg et al. (1985) and Stattman (1980) state that the average yields of equity with high book-to-market ratios are higher than those indicated by their betas. Fama and French (1992) commented on the empirical failure of CAPM and observed that the estimation of the expected return forecast by market beta is supplemented by factors such as size, PE ratio, debt equity and book to market ratio.

 2.3 Impact of financial performance on market performance

Sarjono and Suprapto (2020) in an attempt to assess the impact of CAMEL rating model on bank’s share price in Indonesian stock exchange found that movement of stock price is positively correlated with capital adequacy ratio (CAR), return on asset (ROA), return on equity (ROE) and net interest margin (NIM). In another study on the Indonesian stock exchange, Nugroho et al.(2020) found that, except for capital adequacy, the other four variables representing CAMELS have a negligible impact on stock prices. Ikechukwu and Owualah (2022) observed that, while capital adequacy, asset quality, and earnings have no impact, management capability and liquidity have significant negative influences on share prices. While conducting an impact assessment study of the CAMELS model on the share prices of Jordanian banks, Awwad (2022) found that asset quality, sensitivity, and liquidity have positive and substantial impacts. However, capital adequacy, management ability, and earnings have no impact.

Riani et al. (2020) observed that net profit margin, price-earnings ratio, total assets turnover ratio, and return on equity have a positive and significant relationship with share price, whereas debt equity and the current ratio have an insignificant impact on stock prices. Sumantri (2020) finds that stock prices are substantially impacted by ROE and ROA. Wuryani et al. (2021) discover that capital adequacy affects stock prices, whereas profitability and liquidity do not. Awalakki and Archanna (2021) analyzed the impact of 11 independent variables on stock returns and concluded that price to book value (PB) and ROE are two parameters which have positive and significant impact on performance of stock.

Sharif et al. (2015) assessed the impact of eight variables on share prices and found that all eight variables have a positive and significant relationship with dividend yield (DY), which has a negative relationship. Anwaar (2016) examined the relationship between five independent variables displaying a firm’s performance and one dependent variable, stock returns, and found that return on assets (ROA) and net profit margin (NPM) have significant and positive effects on stock performance.

Nureny (2019) found that the (CAR) has a significant impact on share prices. In a similar study on the Indonesian stock exchange, Rusdiyanto et al. (2019) found that CAR and non-performing loans (NPL) have a substantial impact on stock prices. Benyamin and Endri (2019) and Al-qudah (2020) found that share price is strongly and positively impacted by Return on Equity (ROE).

Hatem (2017) considered share price growth an appropriate parameter to measure stock market returns. He observed that growth in share price (SPG) was positively and strongly impacted by return on equity (ROE). However, a similar relationship was not found between return on asset (ROA) and SPG. Jape and Pauldhas (2021) conducted a study to understand the impact of economic variables such as the G-sec coupon rate, FII funds flow, and exchange rate on stock price movement. Trần Nha Ghi (2015) and Fathony et al. (2020) sought to determine the impact of a company's financial success on its stock return. In a similar effort to evaluate how different financial ratios affect stock returns, Chabachib et al.(2020) used “growth in share price” to measure stock performance.

 2.4 Research gap

A review of past studies sheds light on the relationship between return on stocks and financial performance of various industries, including banks, using two to three sets of dependent variables. Many of these studies have suggested expanding the work in future research by using a greater number of variables. For further research, Rusdiyanto et al. (2019) suggested including more variables to determine stock price movement. Awalakki and Archanna (2021) find it very difficult to rely on a few parameters or a fixed model to predict stock prices. The results vary and financial parameters do not appear to have a uniform impact on stock prices. Although studies have measured the financial strength of Indian banks using the CAMELS parameters, the model has not been used to measure the impact on stock prices in the Indian banking industry. Many of these studies have suggested extending the use of the CAMELS model to assess market performance dynamics. Although the model has been extensively used to evaluate the overall performance of banks, it has not been used to assess the stock market performance of Indian banks.

Based on result of past studies, we formulated the following hypothesis:

- There is no relationship between financial parameters consisting of CAMELS model and performance of stocks of Indian banks.

 3. Methods 3.1 Sample and context

Despite global macroeconomic issues such as rising inflation, rising interest rates, and sluggish growth, the Indian banking industry is showing all healthy symptoms, such as adequate capital, rising profit, and steep fall in nonperforming assets. There are 12 state-owned banks, 22 private sector banks, 46 foreign banks, 12 small finance banks, four payment banks, and 43 regional rural banks. The total banking sector deposit is INR 177.34 trillion and bank credit is INR 133.04 trillion as on December 2022.

The Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) are the two Indian stock exchanges from which we obtain our sample. While collecting data, we found that there are a total of 32 banks listed on these two stock exchanges for at least five years. Therefore, the sample size for the study was equal to that of the whole population. For five years, stock market data were not available for the other banks, and they were not considered for the study.

 3.2 Measures

Six independent variables were selected based on the CAMEL model. The last parameter of the CAMELS model - sensitivity to risk–has not been considered for the analysis, as appropriate data were not readily available for this parameter. The remaining five CAMEL parameters are measured using six financial ratios. Stock performance is measured by growth in share prices. Detail structure of the Table 1.

Constructs, measures and references.
Construct Measures Reference
C-Capital Adequacy Capital Adequacy Ratio (CAR) Al Zaidanin (2020), Rusdiyanto et al. (2019), Banu & Vepa (2021), Sarjono & Suprapto (2020), Dang (2011), Nugroho et al. (2020), Maude et al. (2020), Sumantri (2020), Nureny (2019), Thisaranga & Ariyasena (2021), Wuryani et al. (2021)
A-Asset Quality Net Non Performing Asset (NNPA) Nureny (2019), Thisaranga & Ariyasena (2021) Banu & Vepa (2021), Sarjono & Suprapto (2020), Dang (2011), Nugroho et al. (2020), Nureny (2019)
M-Management efficiency CASA Ratio (CASA) Sarjono & Suprapto (2020), Indrajaya et al. (2022)
Cost to Income ratio ( CTI) Al Zaidanin (2020), Thisaranga & Ariyasena (2021)
Return on Asset (ROA) Anwaar (2016), Sarjono and Suprapto (2020), Maude et al. (2020), Nugroho et al. (2020), Sumantri (2020), Awalakki & Archanna (2021)
E-Earning ability Return on Capital Employed (ROCE) Banu & Vepa (2021), Das (2017), Hamidah (2015)
Net Interest Margin (NIM) Al Zaidanin (2020), Sumantri (2020), Sarjono & Suprapto (2020), Venkatesh & Suresh (2014), Dang (2011), Thisaranga & Ariyasena (2021), Nureny (2019)
Return on Equity (ROE) Anwaar (2016), Sarjono & Suprapto (2020), Muriithi (2018), Muriithi (2018), Sumantri (2020), Awalakki & Archanna (2021)
Net profit Margin (NPM) Anwaar (2016), Nugroho et al. (2020), Al Zaidanin (2020)
L-Liquidity Liquid Asset to Total Asset (LATA) Awwad (2022), Maude et al. (2020), Thisaranga & Ariyasena (2021), Trivedi (2013), Al Zaidanin (2020), Sathyamoorthi et al. (2017)
Performance of stock Growth in share price (SPG) Chabachib et al. (2020), Khan & Naz (2013), Hatem (2017), Anwaar (2016), Trần Nha Ghi (2015)

Brief description of each variable is mentioned below.

Capital Adequacy Ratio: This ratio computes overall capital as a proportion of the bank's total risk-weighted assets. A higher ratio indicates better health. Capital acts as a shock absorber for banks during a liquidity crisis.

Net NPA: NPA stands for Non-performing assets, which are any asset that does not generate income for a stipulated period, as per the guidelines issued by the RBI. We obtain the Net NPA by deducting provisions from Gross NPA. Gross NPA consists of substandard, doubtful, and lost assets. A lower net NPA value is better for banks. The net NPA is always shown as a percentage of the total asset portfolio to understand the quality of a bank’s asset portfolio.

CASA Ratio: The ratio of demand deposit (current and savings) to the total deposit (demand plus term deposit) of a bank. A higher CASA ratio is desirable because demand deposits are less costly than time deposits. Therefore, a bank with a higher CASA ratio has a lower funding cost and a higher interest margin. The bank must provide a customized solution to capture the flow of funds and augment its service quality and product range to mobilize high-demand deposits. This reflects the efficiency of management.

Return on Capital Employed: This ratio signifies the earning capacity of a firm. Total earnings before interest and taxes are expressed as a percentage of the total capital employed. Total capital is computed by deducting current liabilities from total assets. A higher ROCE ratio indicates that companies efficiently utilize capital to obtain higher return.

Net Interest Margin: It calculated by deducting interest expenses from a bank’s interest income. It is computed by dividing the gap between investment returns and interest expenses by average earnings assets. This ultimately determines a bank’s profitability.

Liquid Asset to Total Asset: This ratio of current assets to total assets. The current asset indicates a bank’s liquidity strength. A higher liquidity ratio is considered to be healthier for banks.

Growth in share price: This measures the percentage growth in share price over the last year’s price. Growth was calculated based on the closing price of the stock.

 3.3 Data collection

The data were collected from 32 Indian banks listed on either of the two Indian stock exchanges.

NSE and BSE for 5 year time period from 2018 to 2022. Since we need six years of data to calculating 5 years share price growth, the closing share price of each bank from 2017 to 2022 was collected for the computation of stock price growth. Information was collected from the following websites: a list of 32 banks is given in Table 2.

List of banks.
SL No Name of Bank Types of Bank
1 State Bank of India Public Sector Bank
2 Punjab National bank
3 Canara Bank
4 Bank of Boroda
5 Bank of India
6 Indian Bank
7 Union Bank of India
8 Central Bank of India
9 Indian Overseas Bank
10 Punjab & Sind Bank
11 Bank of Maharastra
12 Uco bank
13 HDFC Bank Private Sector Bank
14 ICICI Bank
15 Axis bank
16 Kotak Mahindra Bank
17 Indisind Bank
18 Yes Bank
19 RBL Bank
20 City Union Bank
21 DCB Bank
22 Dhanalaxmi Bank
23 Federal Bank
24 IDBI Bank
25 J & K Bank
26 Karur Vyasya Bank
27 Bandhan Bank
28 South Indian Bank
29 CSB Bank
30 Karnatak Bank
31 IDFC Bank
32 Ujjivan Small Finance Bank Limited Small Finance Bank

https://www.moneycontrol.com/; https://economictimes.indiatimes.com/; /https:/www.nseindia.com/; https://www.bseindia.com/.

 3.4 Data analysis

Keeping in view the nature of the data, a panel regression model was chosen to evaluate the effect of Indian Banks’ financial results on stock market performance. This study adopted a multiple linear regression analysis of panel data to measure the relationship between the explanatory and predictor variables. The regression equation is constructed using share price growth as the dependent variable and the six CAMEL parameters as independent variables. spg it = α + β 1 ( car ) it + β 2 ( nnpa ) it + β 3 ( casa ) it + β 4 ( roce ) it + β 5 ( nim ) it + β 6 ( lata ) it + Ɛ it

(i = 1,2,3 … . 32 and t = 2018, 2019, … . 2022)

spg it refers to the share price growth of bank i for period t. where Î ± is the intercept of the regression model. β 1 to β 6 are the slope coefficient for the independent variables and Ɛ it is the error term.

 4. Results

The observable explanatory variables were carefully chosen to ensure that they did not suffer from multicollinearity. It is known that multicollinearity adversely impacts the efficacy of the regression model, reduces the accuracy of estimation of the coefficient, and produces a distorted p-value that cannot be relied upon. Therefore, to determine the extent of correlation among the explanatory variables, a multicollinearity test using Variance Inflation Factors (VIF) was performed. Four variables with a VIF score of > 3 were eliminated, and six variables with VIF scores of < 3 and mean VIF score of 1.78. were considered for the regression analysis. The VIF details are given in Table 3.

Variance Inflation Factors (VIF) result.
Variable VIF 1/VIF
nim 2.92 0.341903
car 2.82 0.354705
roce 2.26 0.442378
nnpa 1.56 0.642735
casa 1.05 0.950788
lata 1.03 0.970061
Mean VIF 1.94

We ensured that the data did not suffer from heteroscedasticity, contemporaneous cross-sectional correlation, or autocorrelation in the residuals, as the presence of these elements in the standard error could produce biased statistical inference. Cameron and Trivedi (2005) suggest that independent observations provide better information than correlated observations. Therefore, we cannot ignore the possible correlation between regression disturbances over time and between subjects. Bera et al. (2000) maintained that the standard error component model also addresses the serial correlation problem. Baltagi (2005) also confirmed that it addresses heteroscedasticity.

In our research, the data included time-series data. We must verify the degree of correlation between the values of the same variable over successive periods. This is known as an autocorrelation or serial correlation. The dataset exhibits first-order autocorrelation as per the Wooldridge test for autocorrelation. The associated details are presented in Table 4.

Econometrics test results.
Test Description Statistics Probability
Wooldridge test for autocorrelation 11.478 0.0019
Modified Wald test for groupwise heteroscedasticity 1078.16 0.0000
Pesaran test of cross sectional dependence (FE) 11.742 0.0000
Pesaran test of cross sectional dependence (RE) 15.558 0.0000
Average correlation across units (FE) 0.532
Average correlation across units (RE) 0.565

Regression analysis assumes that the residuals or errors are homogeneous. The absence of this condition is known as heteroscedasticity. To ensure that the random error components are identically and independently distributed across the independent variables, we must verify the heteroscedasticity present in the collected data. The presence of heteroscedasticity in the panel data was validated using a modified Wald test for group-wise heteroscedasticity in a fixed-effect regression model, as suggested by Greene (2000). The associated details are presented in Table 5.

Another important assumption of the panel data model is that the disturbances or errors available in the cross-section are independent. The cross-sectional dependence in the panel data is attributed to unobserved common elements in the error term. We used the CD test, as suggested by Pesaran (2004), to determine cross-sectional dependence. The test results demonstrate a high degree of dependence among cross-sectional data. Since the p-value is less than 0.001, we can reject the null hypothesis that there is weak cross-sectional dependence. If we assume that the unobserved common elements that are responsible for the cross-sectional dependence have no relationship with the independent variables, then the standard fixed-effects (FE) and random-effects (RE) models could be used, and the bias in the standard errors could be corrected by adopting the method of Driscoll and Kraay (1998). On the contrary, if these unobserved common elements that are responsible for the cross-sectional dependence are found to be correlated with the independent variables, then the FE and RE estimators will be unreliable or inconsistent and biased, and the approach proposed by Pesaran (2006) will be used as per De Hoyos and Sarafidis (2006). However, when the relationship between the two is not known, the choice of method is not clear.

Beck and Katz (1995) used Monte Carlo simulations to test the accuracy of panel corrected standard errors (PCSE) and to verify the efficiency of ordinary least squares (OLS) estimators. They were in favor of using OLS with PCSE instead of FE and RE models as well as the generalized least squares (GLS) estimator for panel data sets showing both heteroscedasticity and cross-sectional dependence.

Since all three problems (heteroscedasticity, contemporaneous cross-sectional correlation, and autocorrelation) are found in the error structure of our data, the ordinary least squares (OLS) with Therefore, PCSE was not appropriate for our study. Ardizzi et al. (2014) used the Prais–Winsten regression with Panel-Corrected Standard Errors (PCSE) when similar issues are observed in panel data. Therefore, we adopted the same model to adjust the standard errors appropriately.

The outcome demonstrates that the model is statistically significant because the p-value is less than 0.05. The regression model was a good fit for this study. Because the p-value of 0.0209 was < 0.05, the null hypothesis was rejected. This leads us to believe that there is a relationship between the CAMEL parameters and stock returns in Indian banks. The R 2 value signifies that 21.27% of the variance in share price growth is explained by six independent variables, whereas 78.73% of the variance is explained by variables not included in the regression model. The combined effect of all CAMEL parameters has a moderating effect on share price growth in the banking industry.

Prais-Winsten regression, correlated panels corrected standard errors (PCSEs).
spg Coef. Std. Err. Z statistics P value Lower 95% Upper 95%
car .0316538 .0192501 1.64 0.100 -.0060757 .0693833
nnpa -.0499094 .0244486 -2.04 0.041 -.0978277 -.0019911
casa .0108027 .0075503 1.43 0.152 -.0039957 .025601
roce -.1076648 .041892 -2.57 0.010 -.1897716 -.025558
nim -.0613966 .0270188 -2.27 0.023 -.1143524 -.0084408
lata -.0469504 .1087801 -0.43 0.666 -.2601554 .1662546
_cons -.216544 .5617477 -0.39 0.700 -1.317549 .8844613
Prob > chi 2 0.0209
R-squared 0.2127
Adj R-squared 0.0237
5. Discussion and further research

CAR has a regression coefficient of 0.0316 with a p-value of 0.10, which is > 0.05. This implies that for a 1% increase in the (CAR) the share price growth (SPG) will be impacted by 3.16%. Because the p-value is greater than 5%, the relationship is not significant. The NNPA has a regression coefficient of -0.0499 with a p-value of 0.04, which is < 0.05. This result indicates that a 1% increase in net Non-performing Asset (NNPA) causes a 4.99% change in share price growth (SPG). The negative sign indicates that when NNPA increases, the SPG is negatively impacted. The relationship between NNPA and SPG was significant, with a p-value > 0.05. The CASA has a positive impact on share price growth, but the relationship between the two is insignificant. ROCE, NIM, and LATA have a negative impact on SPG, and the relationship between ROCE and NIM with SPG is significant, as the p-value is < 0.05. The relationship between LATA and SPG was insignificant.

The findings of our analysis with respect to CAR are corroborated by the results of Sarjono and Suprapto (2020), Nugroho et al.(2020), Ikechukwu and Owualah (2022), and Nureny (2019). Our results regarding NNPA match those of Sarjono and Suprapto (2020) and Rusdiyanto et al. (2019). Ikechukwu and Owuala (2022) discovered a negative correlation between asset quality and share price. However, the relationship was found to be insignificant, whereas our results show that it is significant. The findings regarding LATA are supported by those of Anwaar (2016). Similar to our findings, Ikechukwu and Owualah (2022) also found a negative relationship between liquidity and share price, but unlike ours, their relationship is statistically significant. Our findings on managerial efficiency (CASA) and earning ability (ROCE and NIM) do not come from previous studies.

This study recommends further research on the subject to understand the key factors responsible for the performance of banking stocks. The study was not able to include sensitivity to market risk parameters in the CAMEL model. It also does not consider other important macroeconomic parameters, such as inflation and exchange rates, which are expected to impact stock returns. The impact of structural changes, such as the merger of public sector banks and COVID-19, was not included in the study. Stock market correlation and co-movement across geographies are also important factors that influence stock price movement, as illustrated by Evans and McMillan (2009) and Dos Santos and Lagoa (2017).

 6. Conclusion

Based on the results, the predictor variables selected for the study as per the CAMEL model have a moderate impact on the explanatory variable, that is, share price growth. This implies that there are other variables not considered in our study that are responsible for share price growth in the banking industry in India. As evident from the regression estimation, Net Non-performing asset (NNPA) measuring asset quality has a negative and significant impact on share price growth. This inference is statistically proven and commonsensical, as we know that any increase in nonperforming assets results in erosion of banks’ earnings. The net interest margin (NIM) and return on capital (ROCE) have negative and significant impacts. Surprisingly, these two variables, identified to measure earning ability, have a negative impact. This could be attributed to structural issues such as mergers in public sector banks and pandemics, which might have influenced stock prices. The impact of capital adequacy ratio (CAR) and CASA ratios is positive, but insignificant. A threshold Capital adequacy ratio being the regulatory requirement, and almost all banks maintain a reasonable level of CAR. Therefore, it might not be a powerful indicator of stock prices. The CASA ratio influences the cost of funds and the earnings of a bank. A higher CASA ratio entails lower interest expenses and lower cost of funds. However, the impact is positive but negligible. Liquid assets to total assets (LATA), which measures liquidity, has a negative and insignificant relationship with stock prices. The major takeaway is to make the research more comprehensive and include more valid explanatory variables to better predict stock prices.

 Ethics and consent

Ethical approval and written informed consent were not applicable.

 Data availability statement Underlying data

Figshare: Banking Industry Performance.xlsx, https://doi.org/10.6084/m9.figshare.25859644.v1 ( Sar, 2024)

Data is available under the terms of CC By 4.0

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Risk Management & Analysis in Financial Institutions EJournal. 2014;1–42. 10.2139/ssrn.2418144 Wuryani E Handayani S Mariana : The Effect of Financial Performance and Bank Size on Banking Stock Prices. Proc. Int. Jt. Conf. Art. Humanit. 2021;618:975–979. 10.5256/f1000research.166288.r339596 Reviewer response for version 1 Rut Damayanti Cacik 1 Referee Brawijaya University, Malang, East Java, Indonesia

Competing interests: No competing interests were disclosed.

 3 12 2024 Copyright: © 2024 Rut Damayanti C 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Introduction:

The paragraph should have references, especially when the authors describe data in the banking sector.

The introduction should clearly describe the research problem in India, whether India has an issue with financial and market performance, to justify the importance of conducting research in this country.

The research gaps and novelty of this research should be illustrated in the introduction, such as how the CAMELS model has been under utilized for market performance analysis in the Indian banking sector.

While the abstract mentions the CAMELS model as a tool for assessing financial performance, it needs to justify why this model is particularly suitable for the study or how it stands out compared to other approaches.

Avoid generalizations, such as "Indian banking stocks have defied global trends," without providing data or citations to support these claims.

 Literature Review

The review needs a more precise thematic structure. Discussions on financial performance, market performance, and the CAMELS model are scattered, making it hard for readers to follow the logical progression. I suggest organizing the literature review into clearly defined themes, such as: (a) Financial performance metrics. (b) Market performance and stock price determinants. (c) Prior studies using the CAMELS model. (d) Studies specific to the Indian banking sector.

The author should discuss the CAMELS model and how its application in past studies has been explored in depth. The review mentions the model but needs to evaluate its application across various contexts or its limitations critically and provides a detailed analysis of how the CAMELS model has been used in different studies to assess financial performance and market behavior. Highlight its strengths, weaknesses, and gaps in its application, especially in the Indian context.

The literature review primarily summarizes prior studies without critically analyzing their methodologies, findings, or relevance to the current research.

The review relies heavily on generalized studies, with limited inclusion of studies specific to the Indian banking sector or stock market.

 Methods

The chapter briefly mentions the data sources (e.g., NSE and BSE websites) but does not provide enough detail on verifying the data’s accuracy or completeness. There is also no mention of handling potential biases in the data. I suggest including a detailed discussion of data quality assurance, such as cross-checking data across multiple sources or handling missing or outlier data points.

 I recommend conducting robustness checks, such as using alternative models (e.g., fixed effects, random effects, or dynamic panel models) or testing the results on different time frames or subsets of data to assess the reliability of the findings.

While the CAMELS model is central to the study, the chapter needs to adequately justify why specific variables (e.g., NNPA, ROCE) were selected or how they align with the study objectives.

The chapter mentions 32 listed banks as the sample but needs to discuss their representativeness or provide details about sample characteristics (e.g., public vs. private banks, size, regional focus).

 Result

While the statistical significance of variables (e.g., NNPA, ROCE, and NIM) is discussed, the meaning of these findings in the context of the Indian banking sector is minimally interpreted.

Variables such as CAR, CASA, and LATA are found to be statistically insignificant, but their implications are not adequately explored.

The results are not compared with findings from previous studies, making it hard to contextualize the significance of the current study.

The chapter reports significance levels (p-values) but does not discuss their thresholds, the potential for Type I or Type II errors (e.g., 0.05 or 0.10), and the likelihood of false positives or negatives.

The chapter focuses on statistical findings but needs to discuss their practical implications for stakeholders like investors, policymakers, or bankers.

Summarize the results in the context of the hypotheses (e.g., "H0 is rejected for NNPA and NIM but not for CAR").

The negative impact of ROCE and NIM is surprising but should be discussed in more detail. I suggest discussing unexpected findings thoroughly, including potential reasons and their implications for the banking sector.

The chapter does not acknowledge any limitations in the results, such as potential biases, omitted variables or model restrictions.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Corporate governance, accounting and finance, sustainability.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

 10.5256/f1000research.166288.r328301 Reviewer response for version 1 Yudaruddin Rizky 1 Referee https://orcid.org/0000-0002-0850-9747 Mulawarman University, Samarinda, Indonesia

Competing interests: No competing interests were disclosed.

 18 11 2024 Copyright: © 2024 Yudaruddin R 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

Summary of the Article

This study aims to understand the effect of banks' financial performance on their market performance, based on the assumption that financial performance is a key driver of stock price movement. Using the CAMELS model to assess financial performance, the study analyses financial and market data from 32 listed Indian banks over the period 2018 to 2022 through multiple linear regression on panel data.

Strengths of the Article

The strength of this article lies in the use of the CAMELS model to measure the impact of financial performance on stock price movements in the Indian banking industry.

Weaknesses of the Article

1. Important variables such as the health crisis (COVID-19) have been ignored by the author

2. Endogeneity problem

3. Using stock performance measurements with the percentage growth in share price over the last year's price, completely ignores the dynamics of stock price fluctuations.

Constructive Suggestions for Improvement

1. The research period 2018-2022, is the period before and during COVID-19. The author needs to involve control variables such as COVID-19.

2. The author needs to explain the existence of Endogeneity problems and how to overcome them

3. The author needs to use daily data

4. The author needs to do a robustness check

5. The author needs to prepare a clear statement about the contribution of this study

Dharani, M. et al., 2023 (Ref 1)

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

No

Reviewer Expertise:

finance and banking

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

References : Covid-19 pandemic and stock returns in India. Journal of Economics and Finance .2023;47(1) : 10.1007/s12197-022-09586-8 251-266 10.1007/s12197-022-09586-8 Sar Ashok School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

Competing interests: No competing interests were disclosed.

 12 1 2025

I have addressed the 5 constructive suggestions in the revised manuscript. Size of the bank as a Control variable has been incorporated. Endogeneity problem has been discussed. Daily data for independent variables is not available as the financial parameters are available in the balance sheet and profit and loss statement of banks. With respect to robustness check Multiculinearity test through Variance Inflation Factors (VIF) was done. CD test as suggested by MH Pesaran (2004) has been done to find out the cross sectional dependence. Contribution of this study has been mentioned in a separate section.

 10.5256/f1000research.166288.r314161 Reviewer response for version 1 Subburayan Baranidharan 1 Referee https://orcid.org/0000-0002-7780-4045 Christ University, Bangalore, Karnataka, India

Competing interests: No competing interests were disclosed.

 26 8 2024 Copyright: © 2024 Subburayan B 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Summary of the Article  - The dynamics of financial performance and market performance in the context of Indian banking industry

The article examines the intricate relationship between financial performance and market performance in the Indian banking industry. By analyzing various financial metrics such as profitability, liquidity, and capital adequacy, alongside market indicators like stock prices and market capitalization, the authors aim to understand how these factors interact and influence each other. The findings suggest that financial performance significantly impacts market performance, with implications for both investors and banking institutions.

Strengths of the Article

Comprehensive Coverage 

The article covers a wide range of financial metrics, providing a holistic view of the banking industry’s financial health and its impact on market performance. This comprehensive approach adds depth to the analysis.

Novelty and Relevance 

The study addresses a relevant and timely issue in the Indian banking sector, which is crucial given the ongoing changes and challenges in the industry. The focus on the dynamics between financial and market performance adds a fresh perspective to the existing literature.

Methodological Rigor 

The methodology is sound, with clear articulation of the data collection process, analytical techniques, and statistical methods used. The use of appropriate models to analyze the relationship between financial and market performance enhances the study’s credibility.

Weaknesses of the Article

Literature Review

One of the significant weaknesses of the article is the literature review, which relies on research up to 2021. While the literature review is thorough, it lacks the inclusion of more recent studies that could provide updated insights and strengthen the foundation of the research. The use of present tense in some parts of the literature review is inconsistent with academic writing norms, where past tense is typically used to discuss previous research. This inconsistency may detract from the article’s professionalism and clarity.

Implications for Practice and Research 

The article would benefit from a more explicit discussion of the implications of the findings for both practitioners in the banking industry and future researchers. Adding this section before the conclusion would help to contextualize the findings and provide actionable insights.

Constructive Suggestions for Improvement

Update the Literature Review The authors should consider including more recent studies, particularly those published after 2021. This would make the research more relevant and ensure that it reflects the latest trends and developments in the field. Consistency in Tense Usage Revising the literature review to ensure consistent use of past tense when referring to previous studies will improve the article’s readability and adherence to academic standards.

Implications Section Introducing a section that discusses the managerial and research implications of the findings would greatly enhance the article. This would provide practical insights for industry professionals and highlight areas where further research is needed.

The article makes a valuable contribution to the understanding of how financial performance influences market performance in the Indian banking industry. The comprehensive analysis and novel focus on this dynamic relationship are commendable. However, the inclusion of more recent literature, consistent tense usage, and a discussion of the practical and research implications would significantly strengthen the paper. The decision to approve the article with reservations is appropriate, given the need for these improvements. The suggestions provided here are intended to help the authors refine their work and ensure that it meets the highest academic standards.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Dr. S. Baranidharan, specializes in financial analytics, stock market analysis, financial econometrics, and economics.  The practical applications of financial theories, including the use of advanced statistical models and econometric techniques to analyze market trends, manage financial risks, and optimize investment strategies. He is also deeply involved in the study of derivatives markets, hedging strategies, and the development of financial tools for decision-making in uncertain environments.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Sar Ashok School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

Competing interests: No competing interests were disclosed.

 12 1 2025

I have addressed the three sets of suggestions in the revised manuscript.