Bank Stock Research Articles

Abnormal stock returns of Greek banks during COVID-19: an event study

ABSTRACT This paper aims to disentangle the impact of the COVID-19 crisis on four major Greek bank stocks that were traded in the Athens Stock Exchange during the period the pandemic begun. To this end we employ an event study methodology and estimate Cumulative Abnormal Returns (CARs) that stem from three key announcement dates. These dates include two monetary policy and a major health news announcement. The four banks we focus on are the National Bank of Greece, Eurobank, Alpha Bank and Piraeus Bank. We calculate abnormal stock returns in windows of ±10, ±5, ±2, ±1 and day of the announcements, and report that monetary policy announcements either do not affect stock performance or their effect is only mild, while health news have a positive impact on stock returns.

NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange

This is an effort to analyze the reaction of stock prices of Indian public and private banks listed in NSE and BSE to the announcement of seven best case news events. Several recent studies have analyzed the correlation between stock prices and news announcements; however, there is no evidence on how private and public sector Indian bank stocks react to important news events independently. We examine these features by concentrating on a sample of banking and government news events. We classify these news events to create a group of negative and a group of positive tone of announcements (sentiments). The statistical results show that the negative banking news announcements had a one-month impact on private banks, with statistically significant negative mean CARs. However, with highly statistically substantial negative mean CARs, the influence of the negative banking news announcements on public banks was observed for two months after the news was published. Furthermore, the influence of the positive banking news on private banks persisted a month after the news was published. Positive banking news events had an influence on public banks for five days after they were published. The study concludes that public bank stocks react more to negative news announcements than positive news announcements in the same manner as the sentimental polarity of the news announcements as compared to private bank stocks. First, we retrieved the news articles published in prominent online financial news portals between 2017 and 2020, and the seven major news events were extracted and classified using multi-class text classification. The Random Forest classifier produced a significant accuracy of 94% with pre-trained embeddings of DistilBERT, a neural network model, which outperformed the traditional feature representation technique, TF-IDF. The training data for the classifier were balanced using the SMOTE sampling technique.

The Impact of Profitability, Leverage and Non-Performing Loan on Banking Stock Return

This study aims to investigate several factors that influence stock returns on banks listed on the Indonesia Stock Exchange, including profitability, leverage, and Non-Performing Loans. Profitability is measured by net profit margin, leverage is measured by debt to equity ratio, and non-performing loans are a proxy of the amount of bad credit. The method used in this study is a quantitative method with research data obtained from 2014 to 2017. The analysis uses panel data regression analysis. The results showed that Net Profit Margin had no significant effect on Stock Returns, while leverage and Non-Performing Loans had a significant effect on Stock Returns.

Big Data Analytics Applications and challenges in Real Time

The phrase "Big Data" was used to describe a large amount of data that cannot be managed using typical data handling methods or procedures. In numerous industries, including agro, banking, data mining, education, chemistry, finance, cloud computing, marketing, and health care stocks, the field of big data is indispensable. Big data analytics is a technique for analysing large amounts of data to find hidden patterns, mysterious relationships, and other crucial information that can be used to make better decisions. Because of its rapid development and wide range of applications, there has been a steadily growing interest in big data. Key words: Big data, Analytics, Healthcare, weather-forecast, Education, Government, Hdfs

Asset Allocation in Indonesian Stocks Using Portfolio Robust

The mean-variance portfolio has several weaknesses. It does not accommodate the uncertainty of parameters, tends to be sensitive to the changes of parameter input, and tends to be unreliable on extreme observations. Moreover, it cannot accommodate the changes in investor preferences regarding the evidence of abnormal and asymmetric asset return distribution. To overcome these three weaknesses, we can use the robust mean-variance portfolio that is based on the uncertainty of parameters. However, the robust mean-variance portfolio has not included skewness in its optimization. Therefore, here in this paper, we use the robust mean-variance-skewness portfolio which includes skewness in its optimization. So it can be used for the condition where the data return is skewed asymmetric and contains extreme values. An empirical study of robust mean-variance and robust mean-variance-skewness portfolios has been conducted on four banking stocks in Indonesia, i.e AGRS.JK, BTPN.JK, BBNI.JK, and BBCA.JK. The data used in this study is the daily closing price of the company's stock price for the period January 2, 2020 – January 2, 2022 (489 days) obtained from Yahoo! Finance. From the results of the data analysis, it can be concluded that the variance still plays an important role in determining the weight of the allocation of a portfolio. Meanwhile, the large value of skewness leads to the allocation of the same weight for each stock in a portfolio.

Monetary policy effects in times of negative interest rates: What do bank stock prices tell us?

This paper investigates bank stock performance following different monetary policy actions in times of positive and negative interest rates. Controlling for the broader stock market, monetary policy announcements that cause an unanticipated downward shift in the yield curve and a flattening of the shorter-end of the yield curve are found to persistently reduce bank stock prices once the interest rate environment is negative. Consistent with the deposits channel of monetary policy, the effects are larger and more persistent for banks that are relatively dependent on deposit funding. By contrast, a surprise movement in the slope of the longer-end of the yield curve does not impact bank stock prices in times of negative interest rates. Accounting data confirm that a parallel drop in the yield curve following a monetary policy decision in a negative interest rate environment hurts banks through shrinking deposit margins.

Tail connectedness between lending/borrowing tokens and commercial bank stocks

This paper explores a fresh topic about the tail connectedness between decentralized- lending/borrowing tokens and centralized-commercial bank stocks, regarded as substitutes. Using the methodological approach proposed by Ando et al. (2022), we compare connectedness results at extreme (lower and upper) quantile levels. DeFis and traditional bank stocks may show positive but low spillovers, thus DeFi lending tokens would constitute a new commercial banking asset class. In addition, the tails of the distribution would show excess return (static and dynamic) spillover compared to the mean and median, indicating an increased sensitivity in the extreme market conditions (such as the COVID-19 pandemic), especially in the left tail. The dynamic net spillovers may vary over time for all markets and increase during periods of uncertainty, in line with very recent studies. Also, RTD (Relative Tail Dependence) rejects quasy-symmetry due to its time-variation, ranging between positive and negative values. Therefore, traders and portfolio managers would need to adjust their positions depending on the time-varying net spillovers.

BWM—RAPS Approach for Evaluating and Ranking Banking Sector Companies Based on Their Financial Indicators in the Saudi Stock Market

Seeking the greatest possible return on long-term investments, investors naturally seek equities of the best-performing companies that fit their investment timeframe. Long-term investment success rests on selecting the best companies, which requires a challenging analysis reviewing voluminous and often-conflicting data about companies and understanding broader economic forecasts. This paper undertook a case study deployment of MCDM methodologies to examine the suitability and effectiveness of Multi-Criteria Decision-Making (MCDM) methods in assessing and ranking the best stocks for portfolio inclusion. A combination of MCDM techniques comprised a methodology to evaluate and rank Saudi Arabian banking stocks based on their performance in the Saudi stock market. Specifically, the paper combined the Best–Worst Method (BWM) and Ranking Alternatives by Perimeter Similarity (RAPS) for the analysis. BWM calculated each criterion’s relative impact (weight) in selecting a stock. RAPS then used the weighting to rank the results of the investigation. The study’s findings yielded encouraging results regarding using an integrated MCDM technique to derive optimal banking sector securities in the expansive Saudi stock market. The novel application of the robust RAPS technique combined with BWM encourages continued and increased use of MCDM techniques in financial matters and broader application in evaluating equities.

Analysis of Risk on Common Stock of Commercial Bank in Nepal

The study was conducted to analyze the risk on common stock of commercial bank in Nepal and to understand the risks and movements of the stock in relation to the market. The main objective of any investor in the market is to maximize the profit and reduce the risk. The paper helps the investor to know the snapshot of various statistical tools to be used to analysis the risk and return of the stocks. The study is based on conceptual research so the various research papers written from 2002 to 2021 have been reviewed. It is found that the various tools are used to measure the risk like standard deviation, beta, and coefficient of variation. The investor always needs to find a combination of higher return with low risk. The beta is useful to judge Systematic Risk. Thus, this study helps the investor to arrive at a conclusion and also to provide information about the performance of various banking securities in the market in terms of return and risk.

Access to capital markets and the geography of productivity leaders and laggards

AbstractThis paper examines whether access to the capital market of convertible and nonconvertible bonds affects total factor productivity (TFP) for the population of Italian joint stock manufacturing companies, based in highly segmented local financial markets, between 2007 and 2017. The hypothesis, well grounded in the literature, is that long‐term capital favors investment in intangibles and other risky assets necessary for productivity growth. To identify this effect, we exploit the exogenous shock of the Italian banking deregulation of the mid‐1990s as an instrument for firm‐level access to capital, interacted with distance from logistic networks. These reforms changed the distribution of the type of branches at the local level, increasing the share of joint stock banks, which have high connections to international capital markets. This geographical reallocation of banking activities ultimately affected firms' financial structure, favouring their access to capital, even when based in peripheral financial areas. Firms which issued instruments of market debt achieved higher levels of productivity and a higher probability to reach top percentiles of productivity distribution.

Investment Decision Using Capital Asset Pricing Model (CAPM) in Indonesia’s Banking Sector

The focus of this research was to determine and investigate the application of the Capital Asset Pricing Modeling (CAPM) technique in analyzing investment decisions in particular banking stocks that specialize in digital banking operating models. Investors generally follow the IT or digital sector (Tech stocks) due to the sector's track record of delivering high returns and the promise for even greater returns in the future. In the banking sector, investors continue to pursue digital bank stocks as their holdings because they believe they may create value and expand. Six digital banking stocks were chosen for this study, all of which are listed on the Indonesian Stock Exchange and have an observation period of April 2021 to March 2022. These stocks are Bank Jago Tbk, Bank Neo Commerce Tbk, Bank Danamon Tbk, Bank Permata Tbk, Bank BTPN Tbk, and Bank OCBC NISP Tbk. This research uses linear regression analysis to determine the beta coefficient for the Capital Asset Pricing Modeling (CAPM) method and compares the expected return to the stock market's rate of return during the observation period in order to further differentiate between undervalued and overvalued stocks. The study found that two of the six digital banking companies had higher returns than expected (undervalued/efficient stocks), namely Bank Jago and Bank Neo Commerce, with the remaining four categorized as overvalued/inefficient.

The Sensitivity of Islamic Banking Stock Prices During the Pandemic Covid-19

Economic growth has contracted due to the COVID-19 pandemic. Based on the history of previous economic and financial crises, the banking sector is one of the most sensitive sectors during a crisis. Investors consider these conditions for their investment decisions. This paper aims to find the factors that determine the stock price of Islamic banking on three observation variables: profit, debt equity ratio (DER), and cash flow from operating (CFO). This study used a quantitative approach with secondary data sources from financial statements and company stock prices. The sample was selected purposively from Islamic banking stocks listed on the Indonesian Sharia Stock Index (ISSI). Data analysis used panel data regression using the E-Views 10 application. The findings of this study are that earnings and DER significantly determine the share price of Islamic banking based on a partial test. Profit has a positive effect, while DER has a negative impact. These findings illustrate that expectations for investment in Islamic banking stocks remained stable during the pandemic. The company's fundamental factors determine the demand for Islamic bank shares.

The impact of the sanctions on the economy of the Russian Federation

The article analyses the impact of the sanctions of civilized countries on the Russian economy. Particular attention is paid to sectoral sanctions on the markets of oil and oil products, in the banking and financial sectors, as well as on the market of transport services.
 The authors analysed the prospects for expanding sanctions on the oil market for Russia in the context of setting a maximum price for the export of Russian oil by the main buyers. Conclusions are drawn not only about the impact of sanctions on Russian oil production, but also about a possible decrease in prices for oil and other energy resources in the region.
 The authors positively assessed the US and EU anti-Russian sanctions in the financial sector. The impact of sanctions on the Russian banking system, insurance, and stock markets was studied. Particular attention was paid to the impact of disconnecting the banking system of the Russian Federation from the international payment system SWIFT.
 The authors also focused on the gradual division of the Russian economy under the influence of sanctions and trends in the energy market into the energy sector and the rest. It was found that the most affected by the sanctions were air transportation, mechanical engineering, and ferrous metallurgy sectors.
 Conclusions are drawn about the need to continue sanctions pressure on the Russian economy. High energy prices during the 200 days of the war with Ukraine allowed the Russian Federation to subsidize economic sectors that suffered from the war at the expense of the energy sector. A market reduction of the oil prices or their correction with the help of the sanctions policy should cause significant and irreversible damage to the Russian economy.

Do environmental technology and banking sector development matter for green growth? Evidence from top-polluted economies.

Pursuing green growth is imperative to cope with the climate change battle. Green growth in top-polluting economies is being encouraged. The underlying work is aiming to investigate the impact of environmental technology and banking sector on green growth. More precisely, the study employs CS-ARDL and PMG-ARDL methods for empirical assessment. The FMOLS and DOLS techniques have been used to perform the sensitivity analysis for CS-ARDL and PMG-ARDL results. Empirical evidence of both the CS-ARDL and PMG-ARDL models reveals that banking sector development and environmental technology promote green growth. In detail, the insights reveal the significant and positive effect of environmental innovations and technology on green growth in both long-run as well as in short-run. Moreover, the findings of the study also disclose the significant and positive effect of banking sector and stock market developments on green growth in both long-run and short-run. Sensitivity analysis confirmed and improved our findings. Based on these effects, the study delivers policy implications for the promotion of environmental-based technological innovations and financial sector development to enhance green growth in top-polluted economies.

Financial intermediaries and speculation in the foreign exchange market: the role of monetary policy in Iran’s economy

Given the high fluctuations of the general price level, the importance of investment, and accessing sufficient financial resources, we examine the effects of monetary policy on the nominal (Inflation) and real (Investment) sectors of a developing economy, like Iran, through the channel of the relationship between bank deposits, stock market, and speculation in the foreign exchange market (FEM). For this purpose, due to regime switching in Iran’s economy, the non-linear relation between the variables has been investigated by applying Markov switching models and annual data of 1988–2018. The results show that the financial intermediaries (FIs) have not had a complementary relationship in both bear and bull market and high and low banks deposits regimes. Both FIs have negatively impacted speculation in the FEM in high and low exchange rates difference regimes. The difference between official and unofficial exchange rates negatively impacted the FIs in bear market and low banks deposits regimes; on the contrary, a positive effect in a bull market and high bank deposits. The effects of both FIs on investment have been positive in high and low investment regimes and negative on inflation rates in high and very high inflation regimes. In comparison, the impact of speculation in the FEM has been negative on investment and positive on inflation. Therefore, in the event of the lack of limitations regarding using interest rate (IR) and the policy of exchange rate unification, the central bank would be able to affect the nominal and real sectors of the economy suitably through the channels of the FIs and the FEM.

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.

Forecasting crash risk in U.S. bank returns—The role of credit booms

We propose a new credit variable called probability of aggregate loan trend deviations (pltd) that forecasts crash risk in U.S. bank stock returns. Compared to more classic measures of credit, the main innovation in pltd is that it incorporates a large amount of macroeconomic information in the form of money and interest rate variables known to drive the aggregate loan supply. Positive changes in pltd increase the level of systemic risk in the economy and also trigger lower bank returns and cash flows up to horizons of one year. To disentangle the effect our credit variable has on future bank returns we break down the total variance into individual components and estimate series for news of discount-rates and cash flows. We find that pltd only leads the cash-flow news component without significant effects on the discount-rate news. Finally, we show that pltd is not only useful for predicting the mean of future bank stock returns in-sample and out-of-sample but is also an important driver of the tail of the return distribution proxied by CATFIN.

The Effect of Regulations on Stock Market Risk (Volatility) in Nigeria

Recent regulations are directed at mitigating financial market risk, because risks, especially volatility dampen investors’ confidence, and hinder firms’ ability to raise funds at the exchange. Though, volatility had been investigated in the past, the joint utilization of micro and macro regulatory tools to address it after the global crisis is rare. It is on this backdrop that this study investigates the effect of regulations on stock market risk (volatility) in Nigeria. Thirteen interest charging banks listed in the Nigerian Exchange Limited for the period of 2010-2020 were investigated, because bank stocks are mostly traded at the exchange. Data for this study were collected from the banks’ annual reports, stock exchange official daily price lists as well as the Central Bank of Nigeria Statistical Bulletin various issues. The first difference generalized method of moments (DGMM) and the dynamic model were engaged in the investigation. Results of this study reveal that regulatory liquidity ratio and monetary policy rate positively and significantly impact stock market risk (volatility), while prescribe cash reserve ratio has negative and significant effects. The implication of this finding is that regulations except cash reserve instruments constitute frictions impacting equity market risk. Therefore, it is recommended that caution is exercised in the use of micro and macro regulatory weapons. Otherwise, investors’ confidence will decline and investments will reduce.

The dynamic of bank stock price and its fundamentals: Evidence from Indonesia

The dynamic of bank stock price and its fundamentals: Evidence from Indonesia

An empirical analysis of impact of banking sector on Indian stock market

PurposeThis study attempts to examine the relationship between the banking sector and stock market development in India.Design/methodology/approachTo analyze the relationship between banks and stock market development, the ratio of stock market capitalization to GDP is proxied by stock market development. The determinants of the stock market development are used for analysis namely domestic credit to the private sector as a ratio of GDP is used as a proxy for the development of banks, saving rate, per capita real GDP, and inflation. The autoregressive distributed lag (ARDL)-Bounds testing approach is used for the analysis. The paper also used the unrestricted error correction model and CUSUM and CUSUM square test to check the stability of the model.FindingsThe ARDL bounds test found that there is a long-run relationship between stock market development and bank-centered financial development. The results also revealed that the stock market is positively influenced by the development of banks, savings, and per capita real GDP in the short-run as well as long-run.Research limitations/implicationsThis paper suggests that improvement of banking sector plays an important role to increase liquidity of the capital market development in India. This paper also suggests that the economic growth and savings rate have positive impact to induce the capital market growth in both short run and long run.Originality/valueThe study has investigated the empirical relationship between the banking sector and the stock market development in a different methodological approach by using an ARDL model which is appropriate for a small sample size. There are few studies related to bank-centered financial development and stock market development in the context of India.