Bank Stock Prices Research Articles

The Analysis Relates to the Impact Caused by the Capital Adequacy Ratio and Loan to Deposits Ratio Which Mediated by Return on Assets Towards the Stock Prices

The research purpose was to discover and analyze the role of the Capital adequacy ratio and loan to deposits ratio towards Stock prices which are mediated by return on assets. The population in this research were 4 (four) state-owned banks and the sample process through purposive sampling technique with the total number of elements in the population was a sample whose subjects had met the required criteria. The research data was obtained through the publication of Financial Services Authority related to the financial statements of conventional banks and the yahoo finance website. The analysis technique used was panel data regression and data processing through Microsoft Excel 2020 and Eviews 10 applications. The results show that the Capital adequacy ratio had a positive and significant impact on stock prices, loan to deposits ratio and return on assets have no effect on stock prices. The Capital adequacy ratio had a negative and significant impact towards return on assets, loan to deposits ratio had no impact towards return on assets, return on assets is able to partially mediate the impact from Capital adequacy ratio towards stock prices and return on assets is unable to mediate the impact from loan to deposits ratio towards stock prices of state-owned banks in Indonesia during this research period.

Fundamental Factor Analysis On Banking Stock Price In LQ45

This study analyzes the influence of fundamental factors (inflation, exchange rates, ROA, ROE, and BOPO) on the LQ 45 Banking Stock Price. The research subjects are banks listed in LQ 45 for the quarterly period 2015-2019, using Panel Data Regression. The analysis model is the Common Effect Model (CEM). This study uses 2 models where the first model tests all variables except ROE, and the second model tests all variables except ROA. The results showed that the inflation and exchange rate variables significantly affect the LQ45 Banking Stock Price. On the other hand, the variables of ROA, ROE, and BOPO did not significantly affect the LQ45 banking financial stock price. The research results simultaneously showed that in model 1, the variables of inflation, exchange rate, ROA, and BOPO significantly affect the LQ45 banking stock price. Whereas in model 2, jointly, the variables of inflation, exchange rate, ROE, and BOPO significantly affect the LQ45 banking stock price.

Unrecognized Expected Credit Losses and Bank Share Prices

ABSTRACTAccounting for credit losses under U.S. GAAP is transitioning from an incurred to an expected loss model. The model change was motivated by concerns that reporting only incurred losses does not provide investors with sufficient and timely information about banks’ credit risk. In this paper, I develop a measure of lifetime expected credit losses using vintage analysis to examine whether stock prices reflect information about unrecognized expected credit losses in an incurred loss regime. Consistent with investors being able to obtain information about expected losses that are not recognized in the financial statements, I find that unrecognized expected credit losses are negatively associated with bank stock prices. The pricing of these losses is stronger for larger banks, consistent with lower costs of obtaining this information for banks with better information environments. I also find that recorded allowances were less than estimated expected losses, on average, consistent with concerns that implementing the expected loss model will adversely impact regulatory capital adequacy.

Size Anomalies in U.S. Bank Stock Returns and Banking Reforms

Using unique data on over-the-counter bank stock prices and balance sheet information we explore bank funding cost differentials using the risk-adjusted return gap between the largest and the smallest depository institutions. We find that the largest commercial bank stocks, ranked by market value or gross deposits, have significant lower risk-adjusted annual returns than do small sized bank stocks even after controlling for standard risk factors including size. This return difference is one of the first instances of preferential treatment for large banks and can be attributed to the Banking Act of 1935 which authorized the FDIC to act as the liquidator for all insured banks, and to choose which bank would be resolved by purchase and assumption and which by liquidation. Failures of larger institutions tended to be resolved by purchase and assumption, which is preferred because it preserves the value of the going concern, while failures of smaller institutions tended to be resolved by payoff and liquidation. When examined during the period of 1926 to 1939, when there are no such guarantees, we do not find any risk-adjusted returns differences between different sizes of banks.

Brexit and the Banking Sector: The Stock Market Reaction of UK and European Banks

We examine the impact of three events related to Brexit on stock prices of UK and European banks: i) the announcement of the referendum date, ii) the referendum result, and iii) the appointment of Theresa May as the British Prime Minister. Our results show that bank shareholders reacted positively to the announcement of the referendum date and to the election of Theresa May as Prime Minister, whereas their reaction to the referendum outcome was negative. The analysis also demonstrates that the impact of different stages of Brexit on the stock market is more dependent on geographical factors than on firm-specific characteristics. The only exception is bank size, which positively affects bank shareholder choices. Our results have important regulatory and managerial implications: new political risks should be appropriately included into scenario analysis and the ability to assess and manage this risk should be taken into account in strategic planning and risk management.

Prediction of Banking Stock Prices Using Naïve Bayes Method

Bank Rakyat Indonesia (BRI) is one of the largest state-owned banks in Indonesia. It has prompted both local and foreign investors to buy shares in the largest bank in the country. Reading stock price trends is very important for investors to buy or sell the shares they own. The method usually used by investors is fundamental analysis and technical analysis. Analysis by reading financial reports is quite complicated and requires high accuracy. Besides that, it takes much time because of the large amount of data available. A banking stock price prediction can be a solution to make it easier for investors to read stock price movements. In this study, the authors will design a web-based system to predict banking stock prices using the Naïve Bayes method. This system can provide investors with ease and effectiveness in reading stock price movements.

Impact of COVID-19 on the Stock Market: Evidence from the Banking Sector of UAE

The aim of this research is to analyze the impact of COVID-19 on the stock prices of UAE banks. The regressions were applied to estimate the impact of COVID-19 data on the banks’ stock. The data include information about confirmed COVID-19 and the daily prices of the banks from October till December of 2020. The empirical results show that the stock prices of the banks in UAE are negatively and significantly impacted by the reported number of daily Covid-19 cases. However, the daily reported covid-19 fatalities have no significant impact on the stock price. Besides some implications, such as future pandemics should have a similar impact on the stock market of UAE can be predicted based on the empirical results. These results would help the government and financial regulators in undertaking in advance the controlling measures to curb down the spread of contagions to sustain the banking sector performance in the stock market. Hence, this research paper will be beneficial for the banks globally and for governments, to understand the overall impact of pandemics on the financial market and the greater economy.

Why Did Bank Stocks Crash during COVID-19?

We provide evidence consistent with a “credit-line drawdown channel” to explain the large and persistent crash of bank stock prices during the COVID-19 pandemic. Stock prices of banks with large ex-ante exposures to undrawn credit lines and large ex-post gross drawdowns declined more, especially of banks with weaker capital buffers. These banks reduced new lending, even after stabilization policies and even if drawdowns were accompanied by deposit inflows. Bank provision of credit lines appears akin to writing deep out-of-the-money put options on aggregate risk; we show how the resulting risks can be incorporated tractably into bank capital stress tests.

Banking stock price movement and macroeconomic indicators: k-means clustering approach

This study investigates the price movement characteristics of banking issuers listed on the Indonesia Stock Exchange with macroeconomic indicators as an exogenous variable. By using the k-means clu...

Technical Analysis and Malaysian Banking Sector during COVID-19 Pandemic

The announcements of Movement Control Order and Loan Moratorium caused a significant impact on the stock prices of Malaysian banks during the COVID-19 pandemic. This study aims to investigate the effectiveness of technical analysis in predicting the stock price movement and the ability of the technical analysis in generating returns. In doing so, six moving average rules used as the proxy of technical analysis and tested in this study. Majority of the MA rules shown positive returns before the various announcements dates. Specifically, this study revealed that MA rules of (2,5) and (2,10) were among the best performing MA rules during the COVID-19 pandemic. This study also recommends the investors to use the signals emitted by the technical indicator as the reference for their investment decision in the banks’ stock.

The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models

Research Background: The banking sector plays a crucial role in the world?s economic development. This research paper evaluates the volatility spillover, symmetric, and asymmetric effects between the macroeconomic fundamentals, i.e., market risks, interest rates, exchange rates, and bank stock returns, for the listed banks of Pakistan.
 Purpose of the article: The main purpose of this study is to examine the volatility of Pakistani banking stock returns due to the influence of market risk, interest rates, and exchange rates. Pakistan is selected for the study because the volatility of its banking stock returns is strongly influential in achieving sustainable economic development.
 Methods: By applying the OLS with the Heteroskedasticity and Autocorrelation Consistent (HAC) covariance matrix, the GARCH (1, 2), and the EGARCH (1, 1), analysis is conducted for the period from January 1, 2009 to December 31, 2019 using samples of 13 listed banks.
 Findings & Value added: The ARCH parameter is significant in the OLS with the HAC covariance matrix estimation, which is a clear indication of the existence of heteroskedasticity in the squared residuals and the inaccuracy of the OLS with the HAC covariance matrix. The results of the OLS with the HAC covariance matrix suggest using the GARCH model family to accurately measure the volatility of bank stock prices. The results of the mean equation in the GARCH (1, 2) and EGARCH (1, 1) indicate the positive significance of market risk and the low significance of interest and exchange rates, confirming that market returns strongly affect the sensitivity of bank stock returns compared to interest and exchange rates. It should be noted that the ARCH (?) and GARCH (?) parameters of the variance equation fulfill the non-negative conditions of the GARCH model. Furthermore, the leverage parameter (?) is found to be positively significant for all banks, and volatility is found to be influenced by positive shocks compared to negative shocks. Conclusively, it can be stated that market returns determine the dynamics of the conditional returns of bank stocks. Nevertheless, the interest and exchange rate volatilities determine the conditional bank stock returns? volatility.

Effect of CAMELS Ratio on Indonesia Banking Share Prices

The research was conducted with the aim of knowing the effect of the CAMELS ratio either partially or simultaneously on stock prices. The CAMELS ratio (Capital, Asset Quality, Management, Earning, Liquidity) is used to measure the soundness of a bank, where by the better the soundness of the bank, the more profitable the bank will be for potential investors and other interested parties. The population of this research consists of the four state banks documented on the Indonesia Stock Exchange over the 2012-2019 period. The sample selection technique is a saturated sampling. This study provides the results that partially CAR has a significant effect on the share price of government banks listed on the IDX. Meanwhile, NPL, NPM, ROA, and LDR do not have a significant effect on stock prices of state banks listed on the IDX. The results of the regression analysis show that, together the CAMELS ratio, which is proxied by CAR, NPLS, NPM, ROA, and LDR has a positive and significant influence on the share price of state-owned banks documented on the Indonesia Stock Exchange, so this can be used as a reference for investors in predicting the share price of a state-owned bank before investing in shares.

Investigating the Impact of the Announcement of Loan Moratorium on Stock Prices: Evidence from Indian Public Sector Banks

The present article applies event study methodology in an attempt to investigate the impact of the announcement of 3-month moratorium by Reserve Bank of India on Indian public sector bank equity returns. For the present study, the estimation period is considered to be 120 trading days while the event window is considered to be 21 trading days. To compute the expected returns, the study uses a single-index model or the market model proposed by Fama [Fama, E., 1976. Foundations of finance. Basic Books]. The findings of the study suggest that the market responded to the news relating to the liquidity infusion by the Reserve Bank of India, falling global indices, development of potential coronavirus vaccine, and the announcement of 3 weeks period lockdown. The study further concluded that the market anticipated that the government may announce loan moratorium since industry bodies like The Associated Chambers of Commerce and Industry of India and The Federation of Indian Chambers of Commerce and Industry have recommended loan moratorium in order to safeguard the business enterprises especially the micro-, small- and medium-enterprise sector. Thus, the adjustment in the bank stock prices occurred before the announcement of the 3-month loan moratorium and as a consequence the average annual return on day ED-0 is found to be insignificant.

Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.

Impact of Selected Variables on Stock Price With reference to Nepalese Commercial Banks

This study is entirely based on secondary sources of data. The data has been collected from the Nepal Stock Exchange (NEPSE). The descriptive and analytical research designs have been used to analyze the sample. The data has-been examined to determine the financial health of Nepalese commercial banks. Financial ratios help in predicting the stock price. The twenty-seven commercial banks are taken as a sample for the study. The information and data has been collected for the year 2065/066. The independent variables earning per share, price earnings ratio, dividend per share, return on equity and book value per share and the dependent variable is market price per share. The financial ratios have been collected from the financial statements of the sample banks and NEPSE. The theoretical framework determines the dependent variable and independent variables. Hence, this event has been used to analyze the financial ratio and variables as independent variables to predict the dependent variable e.g., stock price. In this research five independent variables has been used e.g., price earnings ratio, earning per share, dividend per share, return on equity, and book value per share with the dependent variable e.g., stock Price.
 The descriptive statistics, correlation analysis and multiple regression models is used to states the impact of independent variables on dependent variable empirically. These models aroused as a strong tool to establish and find if the relationship or correlation exists between the variables. This research also throws light on the ratios can help to predict the stock return for the next period. Results give a clear indication that the P/E Ratio and EPS have the positive impact on the stock price in the NEPSE. The Nepalese investors can rely on the price earnings ratio and the earning per share to predict the market price per share. Hence, the overall conclusion of this research is that the financial variables direct the investors in NEPSE. If the answer is yes investors can rely on P/E ratio and EPS on the confidence level is 99 percent and significance level is below 0.01 which gives clear indication that price earnings ratio and earnings per share may influence the stock price of commercial banks in Nepal.

A cautionary tale of two extremes: The provision of government liquidity support in the banking sector

Using U.S. bank holding company data, we study the impact of the crisis liquidity programs initiated by the U.S. Federal Reserve on bank-specific information production. We find empirical evidence that following the receipt of liquidity support there was a pervasive decrease in bank stock price informativeness that increased market synchronicity and crash risk. Our findings further suggest that these effects are mainly driven by bank participation in the Discount Window (DW) and Term Auction Facility (TAF) programs. On the bright side, we confirm that the liquidity programs served their purpose in targeting and supporting illiquid banks with low core stable funding sources through the crisis.

Long-Term Analysis Banking Share Price: Application of Data Panel Regression Model

The objective of this research is to estimate and analyze the factors that the banking stock price listed in Indonesia Stock Exchange during 2006-2016 period using panel data regression method with eleven banks selected as research sample. The results showed that the variable NIM, CAR, LDR, ROA, SBI significantly affect the movement of banking stock prices. Of the variables that influence significantly, the LDR ratio variable is the most dominant variable of influence, while the NIM ratio variable is the least influence variable. All independent variables consisting of; NIM, CAR, NPL, LDR, BOPO, ROA, SBI, inflation, and exchange rates simultaneously affect the banking stock price significantly.

Non-traditional monetary policy and the future of the financial industries

This paper investigates how expansionary monetary policy after the Global Financial Crisis (GFC) has affected the U.S. banking sector. In response to the GFC, the Federal Reserve first lowered the overnight federal funds rate from 5.25% in August 2007 to zero in December 2008. It then turned to quantitative easing, purchasing housing agency debt, mortgage-backed securities, and longer-term Treasury bonds to stimulate the economy. While these policies helped the overall economy to recover, they may have harmed the banking sector. Banks accept safe short-term deposits and transform these into risky longer-term loans. They make a profit on the difference between the interest rate they earn on longer-term assets and the rate they pay of short-term deposits (the net interest margin). Low short-term interest rates and compressed spreads between long- and short-term interest rates may impair bank profitability. Bernanke and Gertler (J Econ Perspect 4:27–48, 1995) showed that reduced bank profitability can hinder their ability to extend loans. Bernanke (FRBNY Q Rev 18:50–70, 1993) noted that this is problematic because banks play a special role in channeling savings to promising borrowers. Financial markets are plagued by information imperfections. Savers release funds today for the promise of obtaining funds later. Whether they get repaid depends on the character of the borrower, the quality of the investment, the collateral that the borrower can provide, and other factors. The lender needs to consider these items and not just interest rates. Asymmetric information can thus hinder the flow of funds from savers to small businesses and other borrowers whose quality is hard to evaluate. Banks can bridge imperfect information problems because they have a comparative advantage because of the following reasons: (1) economies of specialization, as lending officers gain expertise in a particular industry; (2) economies of scale, as it is cheaper for bank to evaluate a loan than for small savers to; and (3) economies of scope, as it is cheaper to provide lending services together with other services. This paper investigates how lower short-term rates and falls in the spread between long-and short-term rates affect bank profitability. To do this, it investigates how these variables affect bank stock prices. Stock prices provide valuable information since they are the expected present value of future cash flows. The results indicate that falls in short rates and in the spread have caused large drops in bank stock returns after the GFC. Banks are also facing competitive pressures from Fin Tech firms and big technology firms. Their performance after the GFC has lagged other parts of the U.S. economy. They are thus vulnerable to negative shocks that could arise during a downturn or a crisis. The Fed should take account of the impact of their policies on the banking sector, since an interruption on the flow of credit through the financial system could prevent funds from going to the most promising firms. This misallocation of resources could then hinder long-term economic growth.

Further Effects of Six Macro Variables on Vietin Bank Stock Price – A Case in Commercial Banking Industry in Vietnam

Further Effects of Six Macro Variables on Vietin Bank Stock Price – A Case in Commercial Banking Industry in Vietnam

Influence of fundamental analysis and systematic risk towards stock price of banks in Indonesia

This research aims to identify and gives the empirical proven of significant influences in partial and simultaneously of four independent variables towards the stock price. In addition, this research chooses the population in the banking industry listed on the LQ45 index during the period of 2011-2014. Through purposive sampling and pooling data, this research has got 96 observations from six companies in four years with a quarterly basis that fulfills the criteria. Based on the result, there is debt to equity ratio, earnings-per-share, and return on equity which having significant influences towards the stock price. On the other hand, systematic risk has no significant influence on the stock price. Simultaneously, those four independent variables provide 69.7% influences to the dependent variable. The rest, 30.3%, is influenced by other factors outside this research. Moreover, earnings-per-share is chosen as the most significant factor that influences stock price.