key: cord-0636584-9c1xy2au authors: Hossain, Md Saimum; Ahamed, Faruque title: Comprehensive Analysis On Determinants Of Bank Profitability In Bangladesh date: 2021-05-29 journal: nan DOI: nan sha: 572e7444721becfc413737780e93d5ea96f6c255 doc_id: 636584 cord_uid: 9c1xy2au The study investigates the relationship between bank profitability and a comprehensive list of bank specific, industry specific and macroeconomic variables using unique panel data from 23 Bangladeshi banks with large market shares from 2005 to 2019 employing the Pooled Ordinary Least Square (POLS) Method for regression estimation. The random Effect model has been used to check for robustness. Three variables, namely, Return on Asset (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), have been used as profitability proxies. Non-interest income, capital ratio, and GDP growth have been found to have a significant relationship with ROA. In addition to non-interest income, market share, bank size, and real exchange rates are significant explaining variables if profitability is measured as NIM. The only significant determinant of profitability measured by ROE is market share. The primary contribution of this study to the existing knowledge base is an extensive empirical analysis by covering the entire gamut of independent variables (bank specific, industry related, and macroeconomic) to explain the profitability of the banks in Bangladesh. It also covers an extensive and recent data set. Banking sector stakeholders may find great value from the outputs of this paper. Regulators and policymakers may find this useful in undertaking analyses in setting policy rates, banking industry stability, and impact assessment of critical policy measures before and after the enactment, etc. Investors and the bank management are to use the findings of this paper in analyzing the real drivers of profitability of the banks they are contemplating to invest and managing on a daily basis. From a global perspective, banks' profitability and drivers have been a topic of curiosity among academicians and practitioners. Several studies have been carried out to pinpoint the factors capable of explaining the profitability of banks and the exact nature of the relationship between these two sets of variables. The current study is an addition to the long list of studies already available on this topic. However, it focuses on doing comprehensive research on the banking sector of Bangladesh. This study finds it relevant because Bangladesh has a primarily bank-dominated financial system since its birth in 1971. The Bangladeshi banking sector has seen many changes, from the nationalization of all banks to financial liberalization. Bangladesh nationalized all the banks right after the liberation war of 1971. However, Private Commercial Banks (PCBs) have been being patronized since 1981. Table 1 shows a matrix of the different types and number of banks in the country: (06) Privately-owned (43) Scheduled banks (06) Locally owned (40) NRB banks (03) Specialized banks (03) Conventional banks (33) Shariah-based Islamic banks (10) Foreign-owned (09) Apart from the above, five non-scheduled banks and 34 non-bank financial institutions operate in the country. 4 Studies on determinants of bank profitability can be categorized across two types of variables: the development stage of the economy and the type of the variable used to explain the profitability measure. Table 2 below shows the common types found in earlier literature: There's a dearth of studies on the profitability determinants of the Bangladesh banking sector. The ones available either cover a relatively older data set or test only one category of independent variables against the profitability measures. The primary contribution of this study to the existing knowledge base is an extensive empirical analysis by covering the entire gamut of independent variables (bank-specific, industry-related, and macroeconomic) to explain profitability of the banks in Bangladesh. It also covers a relatively wide (15 years) and recent data set (till 2019). This combination of extensive data and a comprehensive selection of bank-related, industry-specific and macroeconomic factors enable us to accomplish this objective. The output from this paper is a valuable tool for related stakeholders. Regulators and policymakers may find this useful in undertaking analyses in setting policy rates, banking industry stability, and impact assessment of critical policy measures before and after the enactment etc. Investors and the bank management are to use the findings of this paper in analyzing the real sources/drivers of profitability of the banks they're contemplating to invest and managing on a day-to-day basis. This paper is organized as follows: Section II covers a review of the relevant literature, section III presents the methodology, including the econometric model, data, and empirical model description, section IV discusses the results, and section V concludes the paper and suggests scope for further research. We can classify the previous literature on bank profitability determinants from several angles: some researchers have studied only bank-specific or internal variables. Some others have studied external (industry-related and macroeconomic) variables and their effect on bank profitability. Many studies cover single-country data, whereas studies done in a multi-country or multi-continent setting are also common. Lastly, researchers focused on undertaking studies based on the life-cycle stage of economiesdeveloped, developing, emerging, etc. We present a review of the previous literature based on geographic classification: Studies looking at both bank-specific and macroeconomic factors are widespread for different economies in the world. For bank-specific factors which have a strong influence on the profitability of banks, studies were conducted by Bhatia et al. (2012) ; Sufian and Noor (2012) in India; Liu and Wilson (2010) in Japan; Shoaib et al. (2015) in Pakistan; Sufian and Chong (2008) in the Philippines; Macit (2012); Alper and Anbar (2011); Alp et al. (2010) in Turkey; Kosmidou et al. (2005) ; Sufian (2011) in Korea, Saeed (2014) in the United Kingdom, etc. Sufian (2011) used 251 bank information of Korea from 1992-2003 and found that liquidity had negative and noninterest income has a positive relationship with profitability. Goddard, Molyneux, and Wilson (2004) conclude that banks with higher liquidity witness lower profits. Al-Jarrah et al. (2010) conducted a study using the cointegration and error correction models to identify the determinants of profitability on all Jordanian banks over 2000-2006. According to the study, loans and advances outstanding to total assets ratio, noninterest or operating expenditures ratio, the capital arrangement, and the deposit to asset ratio are important internal determinants of profitability. Macit (2012) conducted a study using quarterly unconsolidated balance sheets of participating banks that operated between 2005 and 2010 in Turkey. The study found that the equity to total asset ratio has a positive impact on profitability. In contrast, the ratio of nonperforming loans to total outstanding loans and advances has a negative relationship. Gul et al. (2011) used the pooled Ordinary Least Square (POLS) method to identify the relationship between bank-specific and macroeconomic characteristics over bank profitability by using data of top 15 Pakistani commercial banks over [2005] [2006] [2007] [2008] [2009] . They identified that assets, loans, equity, and deposits positively impact all three profitability indicators, i.e., ROA, ROE, and NIM. Shoaib et al. (2015) conducted a study through the POLS regression model by using the panel data of all scheduled banks of Pakistan from 2006-2013. The empirical results show that banks' profitability is adversely affected by liquidity, nonperforming loans, and administrative expenses and positively affected by capital adequacy. An increase in operating expenses causes the profitability of Turkish banks to fall, commented Alp et al. (2010) . They also identified that there does not exist any statistically significant relationship between total loans and receivables to total assets ratio with the indicators of profitability. Growe et al. (2014) conducted a study during 1994-2011 over U.S. regional banks using the Generalized Method of Moments (GMM) estimator technique. They found that the level of nonperforming assets is negatively related to all measures of profitability. Acaravci and Çalim (2013) explained that in the case of private commercial banks, the volume of deposits has an insignificant impact on profitability, and higher nonperforming loans reduce profitability by a large extent. In contrast, capital adequacy has a significant and positive impact on profitability. According to Hassan and Bashir (2003) , bank profitability measures respond positively to the increases in capital. Kosmidou et al. (2005) studied U.K.-owned commercial banks during 1995-2002 to identify bankspecific characteristics, macroeconomic conditions, and financial market structure on banks' profits and found that capital strength and efficiency in expenses management positively and leading influence on their performance. Kosmidou (2006) ; Pasiouras et al. (2006) reveal an adverse effect of liquidity on bank profitability. Vieira (2010) found a weak short-run positive relationship between ROA and liquidity. According to Lee and Hsieh (2013) ; Menicucci and Paolucci (2016) , a high volume of deposits leads to higher profits. Similar results were found by Saeed (2014) in his study. However, Demirguç-Kunt and Huizinga (1998) found a mixed relationship between deposit and profitability. Saeed (2014) investigated the impact variables of profitability on 73 U.K. commercial banks from 2006 to 2012 and concluded that capital ratio, loan outstanding, the volume of deposit deposits, amount of liquidity, and interest rate positively impact ROA ROE. Sufian and Chong (2008) examine the performance determinants of banks in the Philippines during the period 1990-2005. The study suggests that operating expense is negatively related to ROA and ROE while the capital and Non-interest income positively impact profitability. Bhatia et al. (2012) tried to examine the private sector banks' profitability determinants from 2006-07 to 2009-10. Backward Stepwise Regression Analysis has been conducted on 23 banks to identify the relationship of these determinants and banks' performance. The study reveals that loan and advances outstanding to deposit ratio, Capital adequacy ratio, and non-interest income directly impact Return on Assets. In another study in the Indian banking sector from 2000 to 2008, Sufian and Noor (2012) liquidity and operating expenses significantly impacted profitability. Batten and Xuan (2019) conducted a study on Vietnam using the panel data method that suggested a substantial impact on profitability from variables like bank size, risk, expense, productivity, capital adequacy, etc. In contrast, industry-related features and macroeconomic variables negatively affect the profitability measures of a bank. Besides, the causality direction is not consistent across profitability measuring proxies. Rani and Zergaw (2017) conducted their study on Ethiopian banks using multiple regression models to analyze the bank-specific and industry and macroeconomic specific determinants of profitability. The study showed a negative impact of internal and industry-related variables on profitability. In contrast, macroeconomic determinants showed a positive but somewhat insignificant relationship with the net profit margin of the Ethiopian banks. Bolarinwa et al. (2018) conducted a study on Nigeria using the system generalized method of moments, which showed that cost-efficiency works as a strong determinant in attaining profitability in developing countries. Hasanov et al. (2018) conducted their study in Azerbaijan, which carries an oil-dependent economy implementing the Generalized Method of Moments that indicated internal and external variables like bank size, asset, and liability, oil price, inflation rate, economic cycle, etc. have a positive relationship with profitability. On the other hand, deflation of the exchange date, amount of deposit, and risk regarding the liquidity can negatively affect profitability measures. Topak and Talu (2017) , based their study on Turkey implementing the balanced panel data from 2005 to 2015, find a significant and positive impact of bank-based variables like net interest margin, commissions, etc., on profitability in return on assets and equity. On the other hand, the ratio of NPL and other operating expenses, capital adequacy have a negative relationship with profitability. Belke and Unal (2017) conducted their study on 23 deposit banks in Turkey using the panel regression method. According to the study, bank size, capital, inflation rate, economic growth, market concentration, exchange and policy rate, etc., have a significant impact on bank profitability. However, the impact and influence differ in terms of listed and non-listed banks. Hasan et al. (2020) demonstrated the effect of bank profitability in terms of two variables: return on asset and return on equity following the Model Panel Data methodology in the context of Indonesia. For the return of equity, variables like net interest margin, capital adequacy ratio, loan to deposit Ratio etc., be significant. Ali and Puah (2018) conducted a panel regression analysis of 24 Pakistani commercial banks for the 2007-2015 periods and found a statistically substantial impact of bank size, credit and funding risk on profitability. Liquidity risk had no significant statistical impact. Another study on bank profitability in Sri Lanka was approached by Kawshala and Panditharathna (2017) , implementing the panel data method on 12 Sri Lankan domestic, commercial banks. The study reveals that variables such as capital ratio, deposit ratio, etc., have a significant and positive relationship with bank profitability and liquidity negatively associated with profitability. Menicucci, and Paolucci (2016) found that higher equity ratio on total assets can be an essential factor on the profitability of banks in Europe. Sahyouni and Wang (2018) conducted their study using the panel data fixed effect technique on 11 developed and emerging countries for the 2011-2015 period. They concluded that management, capital ratio, and bank size indicate a positive relationship with profitability, whereas banks that generate higher liquidity are likely to achieve lower profitability. Boateng (2018) conducted a comparative study on 20 India and Ghana-based banks (10 banks from each country) using the multiple regression method. According to the study, macroeconomic and bank-specific variables like credit risk, net interest margin, liquidity, capital adequacy ratio, bank size, etc., had a remarkable impact on the profitability measure (return on asset) of Indian and Ghanaian banks. However, bank size and cost to income ratio had a significant effect on Ghana's profitability rate and comparatively insignificant influence in terms of India. Özsarı, et al. (2018) conducted their research on 13 post-Soviet countries using the Generalized Method of Moments and panel regression. They found a positive relationship of economic growth and non-interest bank loan with profitability and a negative association of loan-to-GDP with profitability. Islam and Rana (2017) Taking a 2012-2016 dataset of the top 15 private commercial banks in Bangladesh by asset size, Hossain and Ahamed (2015) find that bank earnings, asset quality, management efficiency, capital strength, size, and asset structure have a significant impact on bank profitability. In another study, found a positive correlation between liquidity and profitability using the annual data for 2005-2018. took data from 1997 to 2004 and analyzed it using the Lease Squares and Fixed Effect model. They find a negative correlation between profitability and a bank-specific variable (bank size) and a macroeconomic variable (inflation). (2009); Abdullah et al. (2014) conducted studies with a similar focus on a specific cluster of variables in explaining bank profitability. The study investigates the relationship between bank profitability and bank-specific, industryspecific, and macroeconomic variables. The data is collected from the annual financial statements of To identify the relationship between the profitability of bank and the bank-specific, industryspecific and macroeconomic variables we estimate the following linear regression model: Yij= α + β1NIIij + β2DPSTij + β3OPEXij+ β4CAPRij+ β5LTARij+ β6SIZEij+ β7MKTij+ β8INFij +β9GDPij+ β10EXHij+ ϵ In this equation i refers to a specific bank, j refers to a year, Yij refers to bank profitability and is the observation of bank i in a particular year j. and ϵ is a normally distributed random variable disturbance term or error term with zero variance. In the literature, three measures of profitability, such as Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), are used and expressed as a function of the internal and external determinants. Return on Assets (ROA) shows the profit of the company over its assets. It measures the efficiency of utilizing assets to generate income for a company. ROA is a better measure of the ability of the firm to generate returns on its portfolio of assets. ROA is used to identify the operational performance, competence, and efficiency of a bank. ROA is used by many researchers in previous literatures. Return on Equity (ROE) measures the amount of income a company generates against its equity. It explains how effectively managing a company is using the shareholders' equity capital to earn profit. ROE does not account for financial leverage, so the ratio tends to be higher than the ROA. ROE measures how successful a company uses its investment funds to cause earnings growth. The net Interest Margin (NIM) variable is calculated by dividing the net interest income by the total assets. The net interest income is found by subtracting the total interest expense from the total interest income. NIM is a good measure of profitability as it shows the interest profit earned by the bank by using funds of the depositors and shareholders. Internal determinants involve the factors influenced by the bank management's decisions, efficiency, policy, and objectives. The external determinants can be comprised of both industry and macroeconomic variables that display the banks' economic, legal, and competitive environments. The independent variables fall into three categories of bank-specific variables, industry-specific variables, and macroeconomic variables. All these variables have an independent effect on the profitability of the bank. The bank-specific variables include earnings, asset structure, asset quality, management efficiency, liquidity, and capital strength. Non-interest income has been used to measure earnings. Operating expenditure, total deposit, nonperforming loans, outstanding loans, and shareholders' equity are used to measure the management efficiency, asset structure, asset quality, liquidity analysis, and capital strength, respectively. It is noteworthy that the bank-specific variables are scaled using comprehensive variables like Total Asset or Total Loans to create comparability of data for the sample banks. Non-interest income is the proxy variable of earnings. Exchange and brokerage commission, fees, investment income, foreign exchange profit, service charge, dividend income, gain from the asset sale, etc., are considered the source of non-interest income. Deposit is the primary source of bank funding, so it is directly correlated with bank profitability. The deposits are used as a proxy of the bank asset structure. It shows the diversification of the assets of the banking business. The more the deposit amount, the higher the opportunity to earn profit by disbursing loans and advances. Nonperforming loans are considered loans and advances which do not generate any income for the bank. Bank must keep provisions from profit against the nonperforming loans. The loan loss provision reduces the distributable profit of the bank. The loan loss provisions reduce the liquidity and affect the disbursement ability of new loans and investments. Operating expense or the Noninterest expense is the measurement of operational efficiency of the management. Equity is used as a proxy of total capital and defines the general safety and soundness of the financial institutions. Higher equity indicates the ability of the bank to absorb losses and handle significant threat and vulnerabilities arising from the business operation. Large, capitalized banks are able to absorb shocks at different levels from various risk factors and perform well in the long run. Loans and Advances are the primary sources of earnings for the banks. Generally Loans and Advances are less liquid than other asset components hence higher loans and advance to asset ratio implies less liquidity. Bank size variable is one of the crucial factors that impact the profitability. Several previous studies in empirical research found that size is a determinant of bank profitability. Large banks have strong capital and asset base which allows them to disburse more loans and invest in various securities. Greater market share increases efficiency, generate fund at lower costs and poses strong market power. However, excessively large size of a bank can lead to greater inefficiency and rise agency cost. Market Share ratio can be calculated by dividing the individual bank assets with the total banking asset. This variable identifies the effect of competition in the banking industry. Macroeconomic factors are considered as the signaling points of economic growth of the country. The basic estimation strategy is to pool the observation across banks and apply the regression analysis on the pooled sample. The study uses the least squares method of the random effects (RE) model where the standard errors are calculated by using White's 1980 transformation to control for cross sectional heteroskedasticity. The random effects model has been chosen over fixed effects model by using the Hausman test. In the study, a total of three models have been developed considering the endogenous variable ROA, ROE and NIM as proxy for profitability. The output from both the random effect model and pooled ordinary least square method depicts consistency, proving the robustness of the dataset. The strong R-squared and adjusted R-squared suggests that all three models explained most of the variation of bank specific, industry specific and macroeconomic variables. In model one, POLS finds that non-interest income, capital ratio and bank size have positive relationship whereas deposit size, operating expense, loan outstanding, market share, inflation, GDP growth and exchange rate has negative relationship with ROA. However, among the explanatory variables non-interest income, capital ratio and GDP growth has significant relationship. In case of RE model non-interest income, operating expense, capital, market share and bank size positively related with profitability whereas deposit size, loan outstanding, market share, inflation, GDP growth and exchange rate depicts negative relationship. RE models also finds non-interest income, capital ratio, market share, GDP growth and exchange rate to be significant for return on assets. The empirical results suggests that non-interest income, deposit size and inflation have positive association whereas operating expense, capital ratio, loan outstanding, market share, bank size and GDP growth has negative correlation with ROE under both POLS and RE model. POLS method finds operating expense to have inverse relation but RE model finds it positively associated. Market share identified to be significant under both methods, but non-interest income is significant just under RE model. The POLS methods exhibit that non-interest income operating expense, market share and GDP growth has positive relationship with NIM whereas deposit size, capital ratio, loan outstanding, bank size and inflation has negative relation. The results from RE model suggest that non-interest income operating expense, capital, market share, loan outstanding and GDP growth is positively associated with profitability where deposit size, bank size and inflation have negative relation. POLS method finds that non-interest income, market share, and bank size is significant for profitability. In RE model non-interest income, market share, bank size and exchange rate proving to be significant. The outbreak of Covid-19 adversely affected both the banking industry and the economy. In 2020, demand and time deposits grew by 25.06% and 12.16%, respectively, compared to 2019. Loans and advances grew by just 9.05% in 2020 which is far less than projected. Weighted Average Rate of Interest on Deposits and Advances decreased by 89 basis points to 2.98% in December 2020 compared to the same period of the previous year. The plummeted demand for loans and advances shrinks the interest rate spread in the year 2020. The expanded gap between deposits and loans & advances growth forced the banks to hold more liquidity and reduction in profitability. refinancing schemes were also initiated to provide liquidity in the banking sector and support the borrowers. This enhanced amount of liquidity along with low credit demand shrinks the opportunity to generate enough return to cover the cost of capital. The study examined the relationship between bank profitability and a comprehensive list of bankspecific, industry-specific and macroeconomic variables using unique panel data from 23 Bangladeshi banks with large market shares from 2005 to 2019 employing the Pooled Ordinary Least Square (POLS) Method for regression estimation. The random effect model was used to check for robustness. Three variables (ROA, ROE, and NIM) were used as bank profitability proxies following standard literature. It was concluded that non-interest income, capital ratio, market share, bank size, GDP growth, and real exchange rates were significant to differing degrees in explaining the profitability of the sampled Bangladeshi banks. The chief policy implication of this study is that in a regimented industry where price competition is not a viable source of competitive advantage, empirical test results exhibit that efficient allocation of resources to a supplementary source of earnings, non-interest income, can significantly enhance earnings as well as profitability. Banks that will pursue this income-source diversification strategy will be a gainer in the longer term and receive favorable ratings from investors and analysts. Large bank size and higher market share provide access to higher liquidity, investments, and financing facilities. Although this study found the mixed direction of the sign of the predictor variable, larger banks and market leaders generally face complex new regulations and low growth opportunities in the industry, which decreases marginal profit. This finding has important implications for managers running banking operations in Bangladesh as they navigate through the new regulations-growth nexus in a way that optimizes profitability. Business environment variables such as GDP growth and exchange rate can affect supply and demand shock, affecting profitability. Therefore, policymakers in charge of the country's macroeconomic management, especially the central bank, need to formulate economic growth-focused policies and manage the real exchange rate that works in favor of bank profitability. Although the deposit is hypothesized to be positively correlated with profits, this study revealed that not all the deposits are profitable for the banks. A proxy for managerial efficiency, operating expenses such as the expansion of brank's branches, recruiting extra workforce for better services, aggressive sales and marketing, etc. can drive the costs up while boosting earnings at the same time. Higher capital and equity provide a cushion against adverse financial conditions. The cost of equity is higher than the costs of other sources of financing, which can drag the profit downwards. Higher outstanding loans and advances also enhance the chances of greater non-performing loans and provision requirements. Thus, the overall balance between loan outstanding and non-performing loan ratio is an element in profitability. Holding substantial funds can reduce the loanable funds and create liquidity excessiveness. However, interestingly, none of the three profitability proxies was affected by the liquidity variable in the current context. It is consistent with a previous study by Hossain and Ahamed (2015) that concluded that this phenomenon indicates Bangladeshi banks not pursuing systematic and modern Balance Sheet management strategies. Finally, although this study provided a strong foundation in understanding the determinants of Bangladeshi banks' profitability, it has its limitations. There are some structural issues rather unique to the Bangladeshi banking industry like the six-nine interest rate regime fixed arbitrarily by the chief executive of the country, absence of natural functioning of the market forces in allowing poorly performing banks to go bankrupt or merge with banks with stronger fundamentals, exogenous influence on the bank management in disbursing low-quality assets etc. that need to be tackled separately to better understand the issue at hand. This is a crucial direction for future studies. Bank Specific, Industry Specific and Macroeconomic Determinants of Commercial Bank Profitability: A Case of Bangladesh Turkish Banking Sector's Profitability Factors Determinants of Liquidity Risk in the Commercial Banks in Bangladesh Macroeconomic Impact of Covid-19: A case study on Bangladesh The internal determinants of bank profitability and stability: An insight from banking sector of Pakistan The determinants of the Jordanian's Banks profitability: A cointegration approach Internal determinants of profitability in Turkish banking sector Bank specific and macroeconomic determinants of commercial bank profitability: Empirical evidence from Turkey Determinants of bank profitability for the selected private commercial banks in Bangladesh: a panel data analysis. Banks and Bank Systems Major Economic Indicators Determinants of bank profitability: evidence from listed and nonlisted banks in Turkey The determinants of commercial bank interest margin and profitability: Evidence from Tunisia Determinants of profitability of private sector banks in India Determinants of Bank Profitability: A Comparative Study of Indian and Ghanaian Banks. Canara Bank School of Management Studies Re-examining the determinants of bank profitability in Nigeria Financial System: Bank and IFs Determinants of commercial bank interest margins and profitability: Some international evidence Profitability of Commercial Banks in Bangladesh: A Multivariate Analysis Determinants of bank profitability before and during the crisis: evidence from Switzerland What explains the low profitability of Chinese banks? Dynamics of growth and profitability in banking The Profitability and Performance Measurement of U.S. Regional Banks Using the Predictive Focus of the "Fundamental Analysis Research Factors affecting bank profitability in Pakistan Determinants of Bank Profitability with Size as Moderating Variable Bank-Specific and Macroeconomic Determinants of Bank Profitability: Evidence from an Oil-Dependent Economy Determinants of Islamic Banking Profitability Determinants of bank profitability: A study on the banking sector of Bangladesh Determinants of bank profitability for the selected private commercial banks in Bangladesh: A panel data analysis. Banks and Bank Systems Determinants of Bank Profitability-Evidence from Vietnam, Emerging Markets Finance and Trade The Factors Effecting on Bank Profitability The Determinants of Banks' Profits and Margins in Greece during the period of EU financial integration Determinants of profitability of domestic UK commercial banks: Panel evidence from the period Beyond bank competition and profitability: Can moral hazard tell us more? Bank specific and macroeconomic determinants of profitability: Evidence from participation banks in Turkey The Bank-Specific Factors Affecting the Profitability of Commercial Banks in Bangladesh: A Panel Data Analysis Determinants of bank performance: evidence for Latin America The determinants of bank profitability: Empirical evidence from European banking sector An analysis of the deposits and lending behaviours of banks in Nigeria Factors influencing the profitability of domestic and foreign banks in the European Union Determinants of Bank Profitability: Empirical Evidence from Bangladesh Bank-related, industry-related and macroeconomic factors affecting bank profitability: A case of the United Kingdom The determinants of Bank Profitability: Does Liquidity Creation matter? Bank Specific, Industry Specific and Macroeconomic Determinants of Bank Probability in Ethiopia Determinants of bank's profitability in Pakistan: A latest panel data evidence Profitability of the Korean banking sector: Panel evidence on bank-specific and macroeconomic determinants Bank specific and macroeconomic determinants of bank profitability: Empirical evidence from the China banking sector Determinants of bank profitability in a developing economy: Empirical evidence from Philippines Determinants of bank profitability in a developing economy: Empirical evidence from Bangladesh Determinants of bank performance in a developing economy does bank origins matters? Bank Specific and Macroeconomic Determinants of Bank Profitability: Evidence from Turkey The relationship between liquidity and profitability Determinants of Profitability in the Banking Sector On behalf of all authors, the corresponding author states that there is no conflict of interest.