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

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) mentioned 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 were 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:

This formula calculates the share price growth of a company in percentage terms from t_-1 to t₁. 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 were a function of the risk attached to it. The model explained that the expected yield from a stock or portfolio of stocks was the sum of the risk-free rate and risk premium attached to the risk of that stock or portfolio. The theory assumed that markets were efficient and investors were rewarded with a return equivalent to the quantum of risk. This model measured the systematic risk of a stock using its beta coefficient, which referred 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) maintained that, for stocks, with a high debt-equity ratio, the actual return was higher than the return estimated based on the market beta. Rosenberg et al. (1985) and Stattman (1980) stated that the average yields of equity with high book-to-market ratios were 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 was 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 was 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 had a negligible impact on stock prices. Ikechukwu and Owualah (2022) observed that, while capital adequacy, asset quality, and earnings had no impact, management capability and liquidity had 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 had positive and substantial impacts. However, capital adequacy, management ability, and earnings had no impact.

Riani et al. (2020) observed that net profit margin, price-earnings ratio, total assets turnover ratio, and return on equity had a positive and significant relationship with share price, whereas debt equity and the current ratio had an insignificant impact on stock prices. Sumantri (2020) found that stock prices were substantially impacted by ROE and ROA. Wuryani et al. (2021) discover that capital adequacy affected stock prices, whereas profitability and liquidity did 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 were two parameters which had 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 had a positive and significant relationship with dividend yield (DY), which had 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) had significant and positive effects on stock performance.

Nureny (2019) found that the (CAR) had 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) had a substantial impact on stock prices. Benyamin and Endri (2019) and Al-qudah (2020) found that share price was strongly and positively impacted by Return on Equity (ROE). Agustyawati and Rais (2023) found that Earning per share (EPS), Net profit margin (NPM), and current ratio had significant impact on stock prices whereas Debt equity ratio and price earnings ratio had insignificant impact on stock prices. Gea and Tobing (2022) observed that return on asset (ROA) and earnings per share (EPS) influenced stock prices positively.

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 Control variable

Nielsen and Raswant (2018) noted that control variables helped us measure the impact of extraneous variables which could influence the relationship between predictor and outcome variables. Bernerth and Aguinis (2016) remarked that it enabled the researcher to eliminate the scope of any alternative explanation other than the hypothesized relationship between the independent and dependent variables. Curado et al. (2024) viewed that it restricts the role of any other variable which does not explain the causal relationship among variables. It improves the efficiency of the model in terms of estimation of values of coefficient by removing statistical noise.

Based on previous studies, we found size of the bank would be the most appropriate control variable which might impact stock price of banks. Elsas et al. (2010) observed that diversified income streams and size of the bank impacted valuation of the bank. Goddard et al. (2004) and Mercieca et al. (2007) discovered that there was a positive relationship between size and profitability of banks. Similarly Petria et al. (2015) noticed positive relationship between size and return on average asset of banks. Researchers have observed that large banks leverage on economies of scale, economies of scope and diversification to augment their valuation. There is also contrary view which suggests an adverse relationship between size of the bank and its market valuation. Avramidis et al. (2018) discerned an inverse relationship between these two. Sakawa et al. (2020) found that large banks were engaged in more risky activities and hence there valuation was low. The studies which have found negative relationship between the two attributed it to higher cost of operation and lower return on asset.

2.5 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:

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.

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.

Size of the bank: We have taken the asset size of the bank to measure the size of the bank. The natural logarithm of the asset volume has been used for estimation.

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.

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.

(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. β₁ to β₆ are the slope coefficient for the independent variables and Ɛ_it is the error term.