Financial performance of Iranian banks from 2013 to 2019: A panel data approach

Ebrahimi, Pejman; Fekete-Farkas, Maria; Bouzari, Parisa; Magda, Róbert Article Financial performance of Iranian banks from 2013 to 2019: A panel data approach Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ebrahimi, Pejman; Fekete-Farkas, Maria; Bouzari, Parisa; Magda, Róbert (2021) : Financial performance of Iranian banks from 2013 to 2019: A panel data approach, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 6, pp. 1-15, https://doi.org/10.3390/jrfm14060257 This Version is available at: https://hdl.handle.net/10419/239673 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article Financial Performance of Iranian Banks from 2013 to 2019: A Panel Data Approach Pejman Ebrahimi 1,* , Maria Fekete-Farkas 2, Parisa Bouzari 3and Róbert Magda 2,4   Citation: Ebrahimi, Pejman, Maria Fekete-Farkas, Parisa Bouzari, and Róbert Magda. 2021. Financial Performance of Iranian Banks from 2013 to 2019: A Panel Data Approach. Journal of Risk and Financial Management 14: 257. https:// doi.org/10.3390/jrfm14060257 Academic Editors: Peter J. Stauvermann and Ronald Ravinesh Kumar Received: 13 April 2021 Accepted: 5 June 2021 Published: 8 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöll˝o, Hungary 2Institute of Economic Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöll˝o, Hungary; [email protected] (M.F.-F.); [email protected] (R.M.) 3Department of Industrial Management, Lahijan Branch, Andishmand University, 44 Lahijan, Iran; [email protected] 4Vanderbijlpark Campus, North-West University, Vanderbijlpark 1900, South Africa *Correspondence: [email protected]; Tel.: +36-707-193-926 Abstract: It is widely believed that the financial system is dependent on the banking industry, and its strength and development are vital for economic prosperity. This paper tried to show the financial performance of Iranian banks listed on the Tehran Stock Exchange (TSE) during 2013–2019, as the research population. The statistical population included 18 banks listed on the TSE from 2013 to 2019, which were sampled using a screening method. The results indicated a significant relationship between explanatory variables of capital ratio and the financial performance of banks in all models. However, a significant negative relationship was found between the inflation rate and the financial performance of banks in all models. Furthermore, it seems that banks with high asset strength are more profitable than the others. Regulators should guarantee that banks remain highly capitalized for a viable banking sector in Iran. Keywords: financial performance; bank-specific factor; macroeconomic factors; panel data 1. Introduction It is widely believed that the financial system is dependent on the banking industry and its strength and development are vital for economic prosperity (Bouzari et al. 2020). The efficiency of banks has been reported as one of the important factors of economic success. Moreover, as different banks have different management levels, the various types of financial intermediation are expected to have different performances (Chen 2020). According to Rengasamy, the word “performance” means carrying into the execution or achievement or performing specific activities or fulfilling obligations. Therefore, “bank performance” can be defined as “the reflection of the way a bank uses its resources in a form which enables it to achieve its goals. In addition, the bank performance also implicates employing a series of indicators to reflect the status of the bank and, in a way, its ability to achieve the desired objectives (Rengasamy 2012). It is possible to investigate the financial performance of the banks of an economy to evaluate their economic health (Haque and Sharma 2011). It has been reported that bank profitability can be assessed at the micro and macrolevels of the economy. At the microlevel, profit is vital for a competitive banking institution and the most inexpensive source of funds. It is also a requirement for successful banking in a period of growing competition in financial markets (Aburime 2008). At the macrolevel, a profitable banking sector is capable of bearing the negative shocks and it can help boost the stability of the financial system (Athanasoglou et al. 2008). Banks are looking for new techniques for boosting their services. To figure out the achievement of superior performance, managers and policymakers have raised this question: “What drives performance?” To answer this question, researchers are looking for the operational details (Soteriou and Zenios J. Risk Financial Manag. 2021,14, 257. https://doi.org/10.3390/jrfm14060257 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 257 2 of 15 1999). An important prerequisite for answering this question is to measure profitability. Return on total assets, return on total equity and net interest margin are the main tools for evaluating Islamic and Conventional banks (Abduh and Alias 2014;Robin et al. 2018). According to the recent profitability of the banks in Iran, some elements have been observed clearly by The Central Bank of Iran (CBI) as the specifications of banks (such as reassessment of assets and increased capital, significant growth of common and noncommon properties, sale of excess property, clearance of government debts, and overdue receivables with overdraft from the central bank, increased net income and profit, and positive monetary indicators). Thus, these banks boosted their status in the national banking system by enhancing their performance indicators, reforming the structure, and standardizing the financial statements. Iran’s economy has faced sanctions against the banking system since 2006. Due to its economic structure (the bank-oriented system), the performance of this sector will be very effective in the general situation of Iran’s economy. Therefore, the possibility of Iran’s economic vulnerability to sanctions increased because of the extensive financial support of the banking system for the government (Keimasi et al. 2016). The financial sanctions exclude Iran from the worldwide messaging system used to arrange international money transfers, making international payments very difficult and constraining other bilateral economic flows (Dizaji and Van Bergeijk 2013). Despite these important changes in the Iranian banking system, there has been no empirical research about the impact of the sanctions on the profitability of the Iranian banking industry due to the lack of accurate and publicly available statistics. It should be noted that Iranian banks are in specific conditions and their performance cannot be compared with other groups of banks worldwide because of some reasons, e.g., crippling US sanctions, no sustainable economy, and unstable inflation. Furthermore, it should be mentioned that some previous studies have emphasized capital adequacy (positive), loan intensity (negative), management efficiency (negative), lagged GDP growth (positive) and real interest rate (positive) had the same significant effect on banks’ profitability in Iran. Meanwhile, liquidity had the positive effect on banks’ profitability in Iran. On the other hand, size, credit risk and industry concentration had opposite effect on banks in Iran (Al-Harbi 2019). Meanwhile, Ebrahimi et al. (2016) shown that internal factors—the amount of capital and the size of the bank—have a positive impact on profitability. Besides, structural factors including market share and concentration are shown to have a positive impact on profitability whereas ownership appears to have no significant impact. Furthermore, inflation and economic cycles—among environmental factors—exhibit a positive impact on profitability. Banks affect economic growth mainly through the capital accumulation channel. While it appears that the stock market does cause growth through the productivity channel as well (Taghipour 2009), Iranian banks can reduce transaction and acquisition costs by acquiring information about investment opportunities, aggregating and equipping savings, monitoring investments and corporate governance, facilitating the exchange of goods and services, distributing and managing risk. Finally, it leads to better allocation of resources and, ultimately, to increased economic growth. This paper aims to show the financial performance and profitability of Iranian banks from 2013 to 2018 using a panel regression framework. Although the financial performance has been comprehensively studied in the theoretical and empirical literature, there is scant specific research simultaneously investigating the impact of bank-specific, industry-related, and macroeconomic factors on the financial performance in Iran. The study applied a distinctive balanced panel data set covering bank-level annual data of Iranian banks. Contrary to numerous empirical studies on profitability, corporate governance along with variables controlling for other bank-specific, industry-related, and macroeconomic factors are also taken into consideration in this framework, following Robin et al. (2018) and Ekinci and Poyraz (2019). In the second part of the study, the focus is on a literature review related to the topic. The third section provides information related to data collection and methods. In the fourth J. Risk Financial Manag. 2021,14, 257 3 of 15 section, a panel data approach is used to analyze the data obtained from banks listed on the Tehran Stock Exchange (TSE), which were sampled using a screening method. The conclusion and discussion parts are focused on important notes, managerial implications, some limitations, and suggestions for future research. 2. Literature Review It has been argued many times that financial development enhances economic growth by enabling efficient intertemporal allocation of resources, capital accumulation, and technological innovation. The activities of the banking sector further accelerated through an extended banking network and credit expansion with strengthened risk management practices as depicted in the improved asset quality, healthy liquidity ratios, adequate profitability, and high-quality capital levels ensuring sufficient risk absorption capacity (Jahfer and Inoue 2014). Banking supervision is an essential aspect of modern financial systems, seeking crucially to monitor risk taking by banks to protect depositors, the government safety net, and the economy as a whole against systemic bank failure and its consequences (Davis and Obasi 2009). Effective banking supervision is one of the basic preconditions for ensuring the correct functioning of the country’s economic system. The main purpose of banking supervision is to maintain the stability of the financial system and increase its confidence by reducing the risk for depositors and other creditors. The bank profitability and performance shows the use of the bank resources to achieve its goals (Mirbargkar et al. 2020) and covers a set of indicators showing the bank status and its ability to achieve the objectives (Memmel and Raupach 2007). The performance of banks is assessed to determine their operational results and their overall financial condition, measure their asset quality, management quality, efficiency, and the achievement of their objectives, and determine their earning quality, liquidity, capital adequacy, and the level of bank services (Kamandea et al. 2016). So far, several ratios, such as return on assets (ROA) (Flamini et al. 2009), return on equity (ROE) (Saona Hoffmann 2011), and the net interest margin (NIM) (Ben Naceur and Goaied 2008) have been applied for the bank profitability measurements. Banks are different in terms of profitability. Several factors affect the profitability and financial performance of banks (Tharu and Shrestha 2019). Empirical studies investigating the financial performance of banks use variables that fall into three groups: (1) individual bank-specific factors, (2) banking sector/industry-specific factors, and (3) macroeconomic indicators (Alfadli and Rjoub 2020). The bank performance determinants have been investigated by several empirical studies (Bourke 1989;Athanasoglou et al. 2008;Salim et al. 2016). There are internal and external determinants, with the former covering bank-specific management decisions, for instance, the level of liquidity, credit exposure, capital ratio, operational efficiency, and bank size. The external determinants are industry-related, such as reform policies or regulations, ownership or concentration, and macroeconomic indicators, e.g., inflation, GDP growth, and broad money growth (Robin et al. 2018). The management of commercial banks, stakeholders, and other interest groups, such as the central bank and the government, can benefit from identifying the bank-specific factors and their influences on the bank profitability and performance. Several internal bank-specific factors, external, and industry-specific factors (Kamandea et al. 2016) have been identified by evaluating the internal aspects that determine the profitability and financial performance of commercial banks. Profitability can be influenced by several macroeconomic indicators, such as economic growth (Kosmidou 2008), financial market structure, and macroeconomic conditions (Pasiouras and Kosmidou 2007). According to Kosmidou (2008), a significant negative effect of inflation on profitability was reported in Greek banking during the EU financial integration. According to Athanasoglou et al. (2008), macroeconomic factors shape the profitability of Greek banks. A study by Sufian and Kamarudin (2012) revealed that profitability was affected greatly by the J. Risk Financial Manag. 2021,14, 257 4 of 15 growth in GDP and inflation. Ongore and Kusa (2013) found that macroeconomic variables did not influence the performance of commercial banks in Kenya at a 5% significance level. Gautam (2018) believed that gross domestic product could significantly affect the financial performance of commercial banks. A nonsignificant positive relationship was found between the GDP growth rate and the performance of banks, whereas this parameter was negatively and nonsignificantly influenced by the interest rate (Nyabakora et al. 2020). Both the inflation rate and the exchange rate had nonsignificant negative influences on the bank performance at a 10% significance level. It has been reported that the capital and assets of banks are significantly involved in determining profitability (Robin et al. 2018). Bourke (1989) and Molyneux and Thornton (1992) reported a positive relationship between the level of capital (capital ratio) and profitability. Jha and Hui (2012) showed that the capital adequacy ratio (CAR) negatively influenced ROA while it had a positive influence on ROE. According to Adam (2014), the financial performance of the Erbil Bank was influenced by the positive behavior of its financial position and some variables of its financial factors. Then, it was reported that the total financial performance of the Erbil Bank was boosted regarding liquidity ratios, asset quality ratios or credit performance, and profitability ratios (NPM, ROA, and ROE). According to Alshatti (2016), bank profitability is influenced positively by the variables of capital adequacy, capital, and leverage, but it is negatively influenced by the variable of asset quality. Profitability is believed to be driven mainly by capital strength and asset quality in Bangladesh (Robin et al. 2018). Therefore, a suitable banking policy or raising capital base and asset quality are essential to guarantee a viable banking sector in this country. Gautam (2018) found a positive relationship between ROA and CDR, with the latter affecting the financial performance of commercial banks; the interest margin was positively affected by the bank size (Demirgüç-Kunt and Huizinga 1999). Pasiouras and Kosmidou (2007) argued that the specific characteristics of banks are influenced by the profitability of both domestic and foreign banks. According to Athanasoglou et al. (2008), bank-specific and macroeconomic factors, except for bank size, form the profitability of Greek banks, and other industry structure variables do not significantly influence the profitability. Higher levels of technical efficiency can be observed in larger and more profitable banks. Olson and Zoubi (2011) conducted an empirical study on MENA banks and revealed a positive correlation between bank size and accounting profitability. Sufian and Kamarudin (2012) reported that profitability was influenced by bank size. According to Tharu and Shrestha (2019), bank size is not affected by profitability (ROA). Rao and Lakew (2012) argued that the key determinants of bank profitability in Ethiopia were the internal factors being under the control of the bank management. Bouaziz and Triki (2012) highlighted a significant effect of board features on the financial performance of Tunisian companies. Ongore and Kusa (2013) revealed that the board and management decisions were the key drivers of the financial performance of commercial banks in Kenya. AlQudah et al. (2019) showed that politically connected directors were a stumbling block in the way to positively improve performance. They also found board independence was no significantly linked with ROA. Haris et al. (2019) argued that the presence of politically connected directors in the board negatively influenced the bank profitability. Haris et al. (2020) reported an inverted U-shaped relationship between capital ratio and profitability. This indicates profitability increases with an increase in capital ratio up to a certain level, while a further increase in capital ratio beyond that level decreases profitability. Lucky and Nwosi (2015) showed a significant relationship between asset quality and the profitability of commercial banks in Nigeria. Mule et al. (2015) reported a positive association between ROE, profitability, and firm size. Ali and Puah (2019) also indicated that bank size, credit risk, funding risk, and stability had statistically significant impacts on profitability. The term “concentration” originates from the structure-conduct performance theory, indicating that a high concentration is positively related to profitability. Ekinci and Poyraz (2019) found a significant positive relationship between bank concentration (CR3) J. Risk Financial Manag. 2021,14, 257 5 of 15 and profitability. Stanˇci´c et al. (2014) found that the proportion of independent directors on the board is negatively but insignificantly related to bank profitability. Kaymak and Bektas (2008) and Pathan et al. (2007) presented evidence of a significant positive relationship between the board independence and the performance of Turkish and Thai banks. Al-Harbi (2019) suggested that equity, foreign ownership, real gross domestic product growth, and concentration could foster bank profitability. Ameur and Mhiri (2013) and Yanikkaya et al. (2018) reported a negative correlation between profitability and GDP growth. Rahman et al. (2015) found that GGDP to be an important factor for NIM and conversely, inflation was found as an important determinant of ROA and ROE. Aburime (2008) revealed that political affiliation had a positive nonsignificant impact on the bank profitability in Nigeria. Saeed (2014) concluded that the inflation rate negatively affected bank profitability whereas it had a positive influence on bank size. Nouri Nouri Borojerdi et al. (2010) showed that the banking industry concentration had a positive relationship with bank profitability. In the banks of Iran, Arjomandi et al. (2012) showed that the banking industry’s technical efficiency level—which had improved between 2003 and 2006—deteriorated after regulatory changes were introduced in Iran. The results obtained also show that during 2006–2007, the industry’s total factor productivity increased by 32 percent. Hami (2017) showed that inflation has a negatively significant effect on financial depth and also a positively significant effect on the ratio of total deposits in banking system to nominal GDP in Iran during the observation period. Moreover, the existence of an equilibrium relationship between inflation and other three indicators of Iran‘s financial development used in this study was rejected. Shahchera and Jozdani (2012) indicated profitability increased up to a certain level with an increase in the capital ratio, while a further increase in the capital ratio beyond that level decreased the profitability. The current study addresses the following hypothesis: There is a significant relationship between the explanatory variables (capital ratio, asset quality, bank size, concentration ratio, political director, independent director, GDP growth rate, and Inflation) and the profitability of banks. 3. Methodology 3.1. Data Collection There are 8 public banks and 18 private banks operating in Iran, among which only 19 banks are listed on the Tehran Stock Exchange. The required information was obtained from the TSE software. All data are categorized in this software for every year and every bank separately. Thus, the statistical population included the banks listed on the TSE from 2013 to 2019, which were sampled using a screening method. Due to the severe financial sanctions against Iran, especially the banking sanctions, the focus of the present research was on the selected years. In the last 7 years, Iranian banks have experienced various conditions after the crippling financial sanctions. Although it was hoped that the situation would improve with the advent of the joint comprehensive plan of action (known as Barjam in Iran), the results of internal and statistical analyses show that Iranian banks have experienced complex conditions in the context of financial sanctions, which was the main reason for reviewing the data in the selected period. Because the statistical population was probably limited, the following inclusion criteria were considered for sample selection: 1. The final fiscal year of the bank should be until the last day of the year. 2. The bank should have unceasingly operated in the TSE from 2013 to 2019. 3. Comprehensive information and notes, along with the financial statements of the bank, should be accessible. 4. The equity share of the bank should be positive during the study period. 5. The fiscal year of the bank should be unchanged during the study period. Ultimately, data from 18 banks were analyzed after screening the banks. The websites of TSE (www.tse.ir) (accesses on 15 February 2019) and CBI (www.cbi.ir) (accesses on 15 February 2019) were visited to gather the data related to the research variables. J. Risk Financial Manag. 2021,14, 257 6 of 15 3.2. Variable Description 3.2.1. Dependent Variables This study employs three measures of profitability as follows. ROA is defined as the ratio of net profit after tax divided by total assets (Rivard and Thomas 1997;Pasiouras and Kosmidou 2007), ROE is measured by net profit after tax to shareholders’ equity, and net interest margin (NIM) is measured by net interest income (interest income minus interest expense) divided by total assets (Dietrich and Wanzenried 2011). 3.2.2. Independent Variables Following the literature discussed in Section 2, and based on the empirical studies of Robin et al. (2018) and Ekinci and Poyraz (2019), the major factors influencing profitability measures are listed as follows: Capital ratio (TC/TA): This reflects the bank’s capability to absorb losses incurred due to poor asset quality. The capital ratio is measured as the total capital divided by total assets. Asset quality (TL/TA): This variable, which is used to represent the asset quality, is also an indicator of liquidity. It is defined as the ratio of total loans to total assets. Bank size (SIZE): Bank size is measured by the natural logarithm of total assets. Concentration ratio (CR3): The three-bank deposit concentration ratio (CR3) is included in our model to capture the effect of market concentration. Political director in the bank board (PD): This variable is a dummy variable defined as PD = 1 if any politically linked person is on the bank board and zero otherwise. Independent director in the bank board (ID): This variable is a dummy variable defined as ID = 1 if any independent director is on the bank board and zero otherwise. GDP growth rate (GDPG): This variable is measured by the real GDP growth rate. Inflation (INF): CPI inflation rate is used as a proxy. 3.3. Data Analysis The main research hypothesis was tested using the panel data approach (Al-Homaidi et al. 2020) initiated by doing the unit root test for stationary. To ensure the use of the panel method, a likelihood ratio test (LRT) was used (Ebrahimi et al. 2019). A Hausman test (Hausman 1978) was used to differentiate between fixed and random effects. In addition, Pearson’s correlation test was done (Appendix A) to eliminate any multicollinearity. The research model is based on those introduced by (Trabelsi and Trad 2017) and (Tan and Floros 2012). The relationship between research variables was tested using the following model based on (Robin et al. 2018). Zit =β0+γ1(TC/TA)it +γ2(TL/TA)it +γ3SIZEit +γ4CR3t+γ5PDit +γ6IDit +γ7GDPGt+γ8INFt+ eit (1) where z is expressed as the measure of profitability in terms of either ROA, ROE, or NIM. The explanatory variables are capital ratio (TC/TA), asset quality (TL/TA), bank size (SIZE), concentration ratio that is calculated based on deposits (CR3), a dummy for political director (PD) in the bank board, a dummy for independent director (ID) in the bank board, GDP growth rate (GDPG), and inflation (INF). 4. Results Table 1shows the descriptive statistics of the research variables. In the first step, it is essential to test the stationary of the series using the LLC test (Levin et al. 2002). Table 2 shows that all variables are stationary. J. Risk Financial Manag. 2021,14, 257 7 of 15 Table 1. Descriptive statistics of research variables. Variables Mean Std. Dev. Min. Max. ROA 0.149 0.196 0.001 0.859 ROE 0.284 0.257 0.001 1.054 NIM 0.235 0.238 0.035 1.243 TC/TA 1.514 3.852 0.001 22.410 TL/TA 0.447 0.481 0.001 3.607 SIZE 6.318 0.760 5.232 8.014 CR3 88.259 19.834 52.635 111.000 PD 0.634 0.483 0.000 1.000 ID 0.619 0.487 0.000 1.000 GDPG 437.107 25.994 385.874 467.414 INF 18.471 9.510 9.000 34.700 Table 2. Unit root test for stationary. Variables Trend & Intercept Decision Level LLC Test ROA −16.716 I(0) (0.000) * ROE −8.198 I(0) (0.000) * NIM −9.638 I(0) (0.000) * TC/TA −31.694 I(0) (0.000) * TL/TA −10.433 I(0) (0.000) * SIZE −16.091 I(0) (0.000) * CR3 −4.185 I(0) (0.000) * PD −8.848 I(0) (0.000) * ID −8.396 I(0) (0.000) * GDPG −7.782 I(0) (0.000) * INF −6.456 I(0) (0.000) * Note: * signify 1%. Prob. values are shown in brackets and the other values are the statistics. Before the model estimation, the presence/absence of multicollinearity between independent variables was verified using Pearson’s correlation. Here, H 0 and H 1 show the absence and presence of multicollinearity between the independent variables, respectively. H 0 is accepted, rejecting the presence of multicollinearity between independent variables that have values less than 0.8 (Tabachnick et al. 1996;Ebrahimi and Mirbargkar 2017). Afterward, considering the significance of cross-section F (prob. < 0.05) in the tests of redundant fixed effects (Table 3), the Hausman test was done for selecting the model type in the panel. The probability of cross-section random (prob. < 0.05) in the Hausman test inspires the fixed effect model. Table 3shows the analysis of fixed effect panel data regression with the use of WLS linear regression to overcome the equality of variances between series. J. Risk Financial Manag. 2021,14, 257 8 of 15 Table 3. Estimation results of the panel regression analysis. Variables Model 1: ROA Model 2: ROE Model 3: NIM TC/TA 3.292 1.636 4.327 (0.001) * (0.088) *** (0.000) * TL/TA 0.655 0.039 0.289 (0.513) (0.968) (0.772) SIZE −7.812 1.450 0.855 (0.000) * (0.149) (0.394) CR3 −1.501 3.212 1.042 (0.136) (0.001) * (0.299) PD −0.448 0.499 1.390 (0.654) (0.618) (0.167) ID 3.279 2.012 1.441 (0.001) * (0.000) * (0.243) GDPG 1.434 1.188 3.261 (0.154) (0.236) (0.001) * INF −1.663 −1.674 −3.129 (0.099) *** (0.096) *** (0.002) *** C7.561 5.565 4.454 (0.000) * (0.000) * (0.000) * R-squared 58.6% 50.6% 51.4% Probability (F) 0.000 0.000 0.000 Durbin-Watson (DW) 1.778 2.030 1.715 Probability (Cross-section F) 0.008 0.000 0.000 Probability (Hausman test) 0.000 0.000 0.006 Total observations 126 126 126 Note : * and *** represent 1% and 10%, respectively. Prob. values are shown in brackets and the other ones are the t-statistic values. Table 3shows the results of the model estimation using the cross-section method and fixing the heteroscedasticity problem through cross-section weights. According to the F-statistic significance level, the model is verified at a 99% confidence level. The results show that (a) the R-squared value of 58.6 reveals that 58.6% of the data fit the ROA’s regression model, (b) the R-squared value of 50.6 reveals that 50.6% of the data fit the ROE’s regression model, and (c) the R-squared value of 51.4 reveals that 51.4% of the data fit the NIM’s regression model. Moreover, Durbin-Watson statistics (DW) did not show any autocorrelation There is a significant relationship between the explanatory variables with the capital ratio and the financial performance of banks in all models (ROA: t-statistic = 3.292; prob = 0.001; ROE: t-statistic = 1.636; prob = 0.088; NIM: t-statistic = 4.327; prob = 0.000). Size has a significant relationship with the financial performance of banks (t-statistic = − 7.812; prob = 0.000) only in the ROA model. A negative coefficient shows that the financial performance of banks decreases with increased size. A significant relationship is established between three-bank deposit concentration ratio and the financial performance of banks in the ROE model (t-statistic = 3.212; prob. = 0.001). ID has a significant positive relationship with the financial performance of banks in ROA and ROE models. GDP growth rate has a significant relationship with dependent variables (t-statistic = 3.261; prob = 0.001) only in the NIM model. There is a significant negative relationship between inflation rate and the financial performance of banks in all models (ROA: t-statistic = − 1.663; prob = 0.099; ROE: t-statistic =−1.674; prob = 0.096; NIM: t-statistic = −3.129; prob = 0.002). J. Risk Financial Manag. 2021,14, 257 15 of 15 Saeed, Muhammad Sajid. 2014. Bank-Related, Industry-Related and Macroeconomic Factors Affecting Bank Profitability: A Case of the United Kingdom. Research Journal of Finance and Accounting 5: 42–50. Salim, Ruhul, Amir Arjomandi, and Juergen Heinz Seufert. 2016. Does Corporate Governance Affect Australian Banks’ Performance? Journal of International Financial Markets, Institutions and Money 43: 113–25. [CrossRef] Saona Hoffmann, Paolo Rodrigo. 2011. Determinants of the Profitability of the US Banking Industry. International Journal of Business and Social Science 2: 255–69. Shahchera, Mahshid, and Nasim Jozdani. 2012. The Impact of Capital Ratio on Iranian Banking Profitability (1380–1388) (in Persian). Monetary & Banking Research 4: 19–44. Smirlock, Michael. 1985. Evidence on the (non) relationship between concentration and profitability in banking. Journal of Money, Credit and Banking 17: 69–83. [CrossRef] Soteriou, Andreas, and Stavros A. Zenios. 1999. Operations, Quality, and Profitability in the Provision of Banking Services. Management Science 45: 1221–38. [CrossRef] Stanˇci´c, Predrag, Milan ˇ Cupi´c, and Vladimir Obradovi´c. 2014. Influence of Board and Ownership Structure on Bank Profitability: Evidence from South East Europe. Economic Research-Ekonomska Istraživanja 27: 573–89. [CrossRef] Sufian, Fadzlan, and Fakarudin Kamarudin. 2012. Bank-Specific and Macroeconomic Determinants of Profitability of Bangladesh’s Commercial Banks. The Bangladesh Development Studies 35: 1–28. Tabachnick, Barbara G., Linda S. Fidell, and Jodie B. Ullman. 1996. Using Multivariate Statistics. Northridge: Harper Collins. Taghipour, Anoshirvan. 2009. Banks, stock market and economic growth: The case of Iran. Iranian Economic Review 14: 20–40. Tan, Yong, and Christos Floros. 2012. Bank Profitability and Inflation: The Case of China. Journal of Economic Studies 39: 675–96. [CrossRef] Tharu, Nanda Kumar, and Yogesh Man Shrestha. 2019. The Influence of Bank Size on Profitability: An application of Statistics. International Journal of Financial, Accounting, and Management 1: 81–89. Trabelsi, Mohamed Ali, and Naama Trad. 2017. Profitability and Risk in Interest-Free Banking Industries: A Dynamic Panel Data Analysis. International Journal of Islamic and Middle Eastern Finance and Management 10: 454–69. [CrossRef] Yanikkaya, Halit, Nihat Gumus, and Yasar Ugur Pabuccu. 2018. How Profitability Differs Between Conventional and Islamic Banks: A Dynamic Panel Data Approach. Pacific-Basin Finance Journal 48: 99–111. [CrossRef]