Nexus between monetary policy and bank growth: Swedish evidence

Monetary policy and particularly the interest rates set by the central bank is one of the most important factors explaining the profitability of banks (Altavilla et al., 2018; Borio et al., 2017; Brei, 2020; Claessens et al., 2018; Kumar et al., 2020). Commercial banks use intermediation processes such as changes in loans and deposits in line with the monetary policy regimes to enhance their profitability (Dzeha et al., 2023; Kusi et al., 2020). Therefore, the monetary policies applied by central bank, specifically interest rate decisions, significantly affects the profitability of commercial banks across various economies (Brei, 2020; Kumar et al., 2020).

Nonetheless, the current literature on the effect of interest rate on the performance of commercial banks is relatively limited and not conclusive (Borio et al., 2017). There are many studies which have shown the positive relationship between the policy rate of the central bank and banks’ profitability (e. g., Aydemir and Ovenc, 2016; Borio et al., 2017; Dzeha et al., 2023), while some others have reported negative correlation (e.g. Aharony et al., 1986; Campmas, 2020; Stráský and Hwang, 2019; Zimmermann, 2019). However, there are also some research indicating that the relationship between interest rates and bank profitability is negligible or even not significant – whether positive or negative (e.g. Bikker and Vervliet, 2018; Kosmidou, 2008).

This study, therefore, seeks to contribute to this field through examining the link between monetary policy and profitability of commercial banks within the Swedish context. The banking sector in Sweden has special characteristics that makes it a valuable case for studying the relationship between central bank’s monetary policy and banks’ profitability. Unlike the financial systems in the United States, the United Kingdom and many other countries that are market-oriented, Sweden is often referred to as a “bank-dominated economy” where commercial banks have played a major role in providing of finance to the business sector (Larsson and Wallerstedt, 2015; Sjögren and Zackrisson, 2005). The Swedish situation is especially relevant since the Swedish central bank (Riksbank) has implemented several unconventional monetary policy tools, such as negative interest rates and quantitative easing, which can significantly affect the profitability of Banks (Flug and Honohan, 2022).

Against this backdrop, the study uses data from 20 banks over the period 2007–2022 to check the effect of the interest rates from the Swedish central bank on the banking industry. Applying Ordinary Least Squares (OLS), fixed-effects and Feasible Generalized Least Squares (FGLS) regression models, the analysis does not find a statistically significant relationship between central bank policy rates and banks’ profitability. Moreover, the results of this study reveal the importance of organizational level factors such as persistent profitability, capital adequacy ratio, size and growth in determining banks’ profitability. These factors seem to be more influential than the interest rates set by the central bank in the context of the examined banks.

The outcome of this research helps in understanding the relationship between the monetary policies set by central banks and the performance of banks which is of significance to the investors. The findings of this paper thus enable these stakeholders to understand the effects of economic changes on their investments. Furthermore, the study emphasizes the significance of organizational factors in explaining the profitability of banks. In this regard, not only it increases our understanding of the internal dynamics driving banks performance but also provides us a foundation for making decisions aimed at improving financial stability. Finally, the study also provides a basis for fostering sustainable economic growth through developing more effective regulatory policies aligned with broader macroeconomic objectives.

The following sections of this study start with a review of previous literature and hypotheses development in section 2. Section 3 describes the research methodology, sample construction and data collection. The results of the empirical analysis are presented in section 4, while sections 5 provides a discussion of the findings as well as the limitations of the study and possible future researches.

From an empirical perspective, central bank interest rate has often been reported as a significant factor influencing the profitability of commercial banks sectors across many economies (Brei, 2020; Kumar et al., 2020). These previous studies can be categorized into three different contexts; developed economies, developing economics and studies particularly conducted within the Nordic and Swedish context.

Studies from developed countries have reported both positive and negative and in the short term and long term. For example, a study by Borio et al. (2017), focusing on 109 large international banks across 14 developed economies from 1995 to 2012, indicates a positive correlation between short-term interest rates and bank profitability (return on assets, ROA). Similarly, another study by Brei et al. (2020) using data from 113 international banks across 14 developed economies from 1994 to 2015 reported that, on average, a 1% reduction in policy rates resulted in a 0.93% increase in revenue from fees and commissions. Campmas (2020) explored the effect of European policy interest rates on banking profitability across 445 banks and 26 European economies from 1999 to 2015 and found a negative impact of monetary policy on bank profitability. While lower interest rates positively affect net interest margins, but negatively impact return on assets and equity. Furthermore, the study by Zimmermann (2019) across 17 advanced economies from 1970 to 2015 revealed that contractionary monetary policy increases deposit and lending spreads but reduces banking profitability. Studies on single country context have also reported different types of association between central banks interest rates and banks profitability. A study by Kumar et al. (2020) based on a panel of 19 banks from New Zealand between 2006 to 2018 reveal that an increase in short-term rates correlates with enhanced banking profitability, whereas long-term rate increases correspond to reduced profitability. Similar findings were observed for UK banks (English et al., 2018).

Previous research on developing countries have also largely reported a significant association between the policy rate and bank profitability. For example, a study conducted by Ezirim and colleagues (2020) in Nigeria from 2007 to 2017 revealed a positive correlation between bank profitability and the policy rate. Similarly, a study by Mbabazize et al. (2020) in Uganda based on a data from commercial banks covering 9 years from 2010–2018, revealed that monetary policy in terms of its link to the lending rate has a significant effect on banks profitability. Regarding studies on bank profitability incorporating interest margin or spread, Raharjo et al. (2014) found a positive and robust relationship between profitability and net interest margin in Indonesian banks, while Zaman et al. (2014) reported a positive association between net interest margin and profitability in Pakistan banks. Furthermore, Owusu-Antwi et al. (2017) reported that approximately 45% of banks’ profits in Ghana are attributed to interest rate differentials.

Few previous studies on the Nordic and particularly Swedish context have also reported the existence of a significant association between central bank’s monetary policy and banks profitability. For example, Madaschi and Nuevo (2017) found that bank profitability increased during periods of negative interest rates in Sweden and Denmark. Thus, while there has also been few studies that found no significant relationship between central bank’s interest rate and banks profitability (e.g. Kosmidou, 2008; Stráský and Hwang, 2019), but based on the results of the majority of previous research particularly in the Nordic an Swedish context, we also predict the existence of a significant relationship between Swedish central bank (Riksbank) interest rate and the profitability of banks in Sweden. Therefore, we propose the first hypothesis as bellow:

The effect of persistent profitability on bank growth is one of the key issues in the field of financial economics with the focus on banking institutions. This relationship focuses on how profitability in the past affects the banks’ future profitability, their strategies, movements in the market and competitive edge. Earlier research has established the importance of persistent profitability for bank growth with specific focus on its effect on current profitability (e. g. Klein and Weill, 2022; Öhman and Yazdanfar, 2018). Although some research have reported negative correlation (e.g. Al-Jafari and Alchami, 2014; Klein and Weill, 2022), the previous literature provides support for positive and significant relationship between lagged profitability and current performance (e.g. Dietrich and Wanzenried, 2011; Trujillo-Ponce, 2013; Öhman and Yazdanfar, 2018).

For example, Öhman and Yazdanfar (2018) based on bank-level panel data from 2005 to 2014 reported a positive relationship between persistent profitability and banks’ current profitability among Swedish banks. Similarly, Shehzad and colleagues (2013), using a dynamic panel data from more than 15,000 banks across 148 countries between 1988 to 2010, found a significant positive relationship between lagged profitability and banks’ current profitability. The findings are consistent with the earlier studies by Goddard et al. (2004a, b) who applied dynamic panel and cross-sectional regressions to assess growth and profitability of commercial, savings and cooperative banks in five major European countries in the mid 1990s. The study, which involved 583 banks over the period 1992–1998, established that profitability is one of the most important factors that determine future growth of a bank even after controlling firm level, industry level and macroeconomic variables. More recently, O’Connell (2022) applied the generalized method of moments (GMM) technique to a panel of UK banks covering the period 1998–2018. The study reported a highly significant coefficient (0.53) for the relationship between persistent profitability (measured through lagged profitability) and current bank profitability, underscoring the enduring impact of profit persistence in the UK banking sector.

Based on the results of prior research, it can be suggested that persistent profitability also has a positive effect on the growth of banks in the Swedish context. Therefore, the following hypothesis is suggested:

Bank capital includes shareholders’ funds which comprise of ordinary shares and retained earnings and serve as a safety net against losses that may arise from non-performing loans (NPLs). During financial crises of 2007–2009, capital adequacy has been found to play a critical role in increasing a bank’s probability of survival and maintaining market share (Abreu and Gulamhussen, 2015; Assaf et al., 2019; Berger and Bouwman, 2013; Xu et al., 2019). Thus, regulatory frameworks such as Basel Committee on Banking Supervision have increasingly demanded more capital reserve from banks as well as additional safeguards against the potential banking losses.

Previous studies on the relationship between capital adequacy and bank profitability have reported mixed findings. The majority of these studies have found a positive influence of banks capital ratio on their profitability (e.g. Charitou, 2019; Demirgüç-Kunt and Huizinga, 1999; Dietrich and Wanzenried, 2011; Goddard et al., 2004b; Hutchison and Cox, 2007; Rahman et al., 2015; Xu et al., 2019). These studies highlight that banks with high capital ratio benefit from lower funding as they are perceived with lower risk among depositors and creditors, leading to higher net interest margins and profitability. Although, there are some other studies arguing that excessive capital requirements can constrain banks’ lending capabilities and reduce their overall performance (e.g. Anginer et al., 2018; Berger and Bouwman, 2013; Mendicino et al., 2020).

Within the Swedish context, few previous research has also examined the relationship between capital adequacy and profitability (e.g. Bengtsson et al., 2012; Nilsson et al., 2014; Öhman and Yazdanfar, 2018). For instance, Öhman and Yazdanfar (2018) identified a positive relationship between higher capital ratios and increased profitability in Swedish banks. However, studies such as those by Karlsson et al. (2021) suggest a more nuanced relationship, with moderate capital levels enhancing profitability, while excessively high requirements may constrain lending and limit overall bank performance.

Although the findings of the previous research have not been conclusive, theoretically it is assumed that capital adequacy and bank profitability to be positively related. Therefore, based on the previous research findings particularly within the Swedish context, the following hypothesis is proposed:

It is commonly believed that large banks can reduce their fixed costs due to their large number of transactions (Berger and Humphrey, 1992). Moreover, larger banks are associated with easier access to resources such as increased investment options, larger portfolio, strong brand identity and easier access to equity capital and other sources of financing (Aggarwal and Jacques, 2001). These advantages enable larger banks to reduce their operation costs (Goddard et al., 2004a) and ensure their profitability through diversifying their revenue streams (Demsetz and Strahan, 1997).

Empirical studies have reported a positive relationship between a bank size and its profitability. For example, Goddard et al. (2004a) using a panel of European banks discovered that larger banks are more profitable than smaller banks due to enhanced market power and efficiency. Similarly, Jiang et al. (2019), in a study of Asian banks revealed a positive relationship between size and profitability where the relationship is more evident in the countries with well-developed financial systems. Goetz (2018) also showed that larger banks are able to diversify their portfolios and as a result are not as affected by risk and enjoy higher profitability. Dietrich and Wanzenried (2011) conducted a study of banks in Switzerland, which revealed the importance of size in explaining banks’ profitability with large banks performing better than small ones. Within the U.S. context, the study by Berger and Humphrey (1994) has also revealed better performance for large banks comparing to small banks due to their ability to realize economies of scale.

Thus, building upon the theoretical frameworks and previous empirical findings, this study proposes the following hypothesis:

In line with the theoretical framework, higher revenue growth creates possibilities for more resource acquisition which create competitive advantage and enhances profitability (Goddard et al., 2004a; Yazdanfar and Öhman, 2015). In the developed economies, research evidence is quite conclusive of the positive relationship between revenue growth and bank profitability (e.g. Demirgüç-Kunt and Huizinga, 1999; Doumpos et al., 2016; Lepetit et al., 2008; Öhman and Yazdanfar, 2018). For example, Saunders and his colleagues (2020) found that non-interest income sources such as trading and fees improve profitability and decrease the risk-adjusted losses of the banks in the U.S. Likewise, Lepetit et al. (2008) using European data from the period 1996–2002 found out that income diversification especially in smaller banks increases profitability but may increase insolvency risk based on the activity. Trading income, however, has fewer risk implications than fee-based activities. According to Doumpos et al. (2016), effective risk management enhances the profitability of revenue diversification in European banks.

In emerging markets, the connection between revenue growth and profitability is not always clear. For instance, Asif and Akhter (2019) discovered that revenue diversification increased profitability only when the market conditions were favorable but had the opposite effect when the market was volatile. Sanya and Wolfe (2011) analyzed 226 banks across 11 emerging economies and found that diversification helps in reducing insolvency risks and improves profitability of the banks especially in low financial stability context.

The analysis of the Swedish market shows that research works done by different authors support the hypothesis that revenue growth is linked with bank profitability. Öhman and Yazdanfar (2018) established that revenue growth has a positive relationship with profitability among the Swedish banks.

Altogether, previous research determines the revenue growth, especially lending and non-interest income, as the major factor that affects bank profitability. Therefore, this study also posits the following hypothesis regarding the relationship between revenue growth and bank profitability:

In the Swedish banking sector there are four main types of banks namely commercial banks, foreign banks, savings banks and cooperative banks. The present study focuses on Swedish commercial banks and the data used in this study has been obtained from the annual reports of these banks which include the income statements as well as the balance sheets. Missing data and outliers are common when using panel data which is derived from financial statements. To overcome these challenges, cases with missing data and banks with less than ten years of data were removed from the analysis. Therefore, the final dataset comprises 20 commercial banks over 15 years from 2007 to 2022 which gives a total of 320 observations. This longitudinal dataset helps in analyzing the differences in the performance of banks of different sizes and different operational environment.

The study applies three types of data analysis models. Ordinary least squares (OLS) regression served as the foundation for the multivariate analysis. Moreover, to address the potential endogeneity that may arise from unobserved heterogeneity, we employ a fixed-effects (FE) model. The model controls for time-invariant bank-specific characteristics such as managerial styles, corporate cultures or structural factors that may influence profitability but are unobserved or difficult to measure. In this way, the FE estimator isolates the effects of time-varying independent variables, thereby reducing omitted variable bias, a key source of endogeneity (Wooldridge, 2002). While previous studies have mainly applied OLS or fixed-effect models, the current study also employs feasible generalized least square (FGLS) to deal with problems such as heteroscedasticity and autocorrelation which may lead to biased results in cross-sectional panel data (Wooldridge, 2002). By introducing FGLS, this study makes a novel contribution to the existing literature by incorporating FGLS analysis, leading to a more precise examination of the major contractual determinants of profitability among banks.

The following OLS model, Model 1, was formulated to identify the variables that explain bank profitability within the sample

Where PF_i,t profitability of bank i at time t measured through return on assets (ROA). ROA is widely used to evaluate a bank’s efficiency in generating returns from its available assets. This metric indicates how well a bank uses its resources to create profits. ROA has been extensively used in many studies as a powerful proxy for operational efficiency and profitability more broadly (e.g. Athanasoglou et al., 2005; Almaqtari et al., 2018). CBIR_t denotes central bank interest rate at time t, while PF_i,t-1 denotes banks persistent profitability measured through lagged profitability, that is bank’s ROA in the previous period. CA_i,t represents capital adequacy of bank i at time t measured through the ratio of equity to total assets (book value). SZ_i,t indicates the size of bank i at time t, measured as the natural logarithm of the bank’s book value of total revenue while GR _i,t denotes the growth of bank i at time t measured as the percentage change in revenue (book value). μ_i,t is the error term and α_t is the constant. These variables together provide a robust framework for understanding the factors influencing bank profitability. Table 1 provides a summary of all variables used in this study.

The parameters of the fixed-effects model, Model 2, were similar to those of the OLS model, with the exception of the term ηi, which represents the unobservable heterogeneity (individual effects) specific to each entity (Wooldridge, 2002). Accordingly, the fixed-effects model was formulated as follows:

The variables included in the FGLS model (model 3) were similar to those included in the OLS model (model 1).

Table 2 provides a summary of the descriptive statistics pertaining to the variables used in both correlation and regression analyses. Across the 2007–2022 timeframe, the average annual profitability (ROA) of the sampled banks hovered around 2%, with a standard deviation close to 5%. Meanwhile the average annual central bank interest rate was around 0.008%, with a standard deviation exceeding1.35%. Furthermore, the average capital adequacy ratio of the sampled banks during the study period was approximately 12%, with a standard deviation of about 10%. Revenue growth, on average, registered an approximately 12% annual increase, albeit with a standard deviation exceeding 32%. The average size of commercial banks, represented by the natural logarithm of the bank’s book value of total revenue, fluctuated during the study period, with an average around 24 and a standard deviation of 2.3. The average capital adequacy ratio stood at 12%, with a standard deviation around 10%. The lagged profitability displayed approximately similar mean value and standard deviation as the current profitability. This consistency implies that past performance may be a strong predictor of future profitability, indicating stability in the financial conditions of the banks over time.

Table 3 presents the results of the correlation analysis among the variables. The correlation coefficients illuminate the direction and strength of associations between the variables. The central bank interest rates, capital adequacy, growth and lagged profitability exhibit significant and positive correlations with profitability (ROA), suggesting that these factors generally contribute to enhancing profitability over the study period. Conversely, the correlation between size and ROA is negative and significant, indicating that larger bank size may be associated with lower profitability.

Furthermore, the analysis reveals weak correlations among independent variables, implying that multicollinearity issues are minimal and that the variables are largely independent in their relationships. This is crucial for the robustness of subsequent regression analyses.

Table 4 presents the results of the Ordinary Least Squares (OLS), fixed-effects and Feasible Generalized Least Squares (FGLS) analyses. The models notably yield high explanatory power, with adjusted R² values ranging from 77% to 78%, suggesting that the independent variables explain a substantial proportion of the variation in bank profitability. Furthermore, the results from the fixed-effects and FGLS regressions closely align with those of the OLS regressions, reinforcing the robustness of the findings across different regression techniques.

The results reveal a non-significant relationship between the central bank interest rate and bank profitability. This result is consistent across all modeling techniques, including the fixed-effects model. Therefore, based on this result, the first hypothesis suggesting a statistically significant positive relationship between central bank interest rates and bank profitability is rejected.

Importantly, the results from all three models indicate a statistically significant and positive relationship between lagged profitability and capital adequacy with profitability (ROA) at a 1% significance level. That is, banks exhibiting persistent profitability and higher capital ratio tend to demonstrate greater profitability. These results support the second and third hypothesis of the study.

However, results regarding the influence of size on profitability are not consistent across three models. While the results of OLS and FGLS models show a significant and positive relationship between size and profitability, the fixed-effect model demonstrates a statistically significant and negative relationship with profitability. This suggests that while larger banks tend to have higher levels of profitability in OLS and FGLS models, the fixed-effects model suggests diminishing returns to size. Thus, hypothesis H4 is refuted due to the observed negative relationship between size and profitability. Regarding the influence of revenue growth, the results of the three models are not consistent. While the OLS and FGLS models supports a positive and significant influence of revenue growth on banks profitability, the fixed-effect model does not show any significant relationship.

To further evaluate the robustness of the regression analysis, several relevant post-estimation diagnostic tests were conducted following the model estimation. These tests confirm the reliability and validity of the regression results, ensuring that the estimates are not affected by model misspecification or unaccounted variables. The results of the diagnostic tests are shown in Table 5. Based on the outcomes of these diagnostic tests, it can be concluded that the estimated model satisfies all relevant diagnostic criteria, thereby enhancing confidence in the robustness of the regression estimates. Particularly, the high adjusted R² values across all models indicate a substantial explanatory power of the independent variables on profitability (ROA). Furthermore, the results of the LM test, Hausman test and Durbin–Watson statistic support the validity and reliability of the models (Hill et al., 2018), verifying that the regression estimates are not subject to major issues like model misspecification or autocorrelation. Additionally, the low Variance Inflation Factor (VIF) values further suggest that multicollinearity is not an issue here, ensuring the stability of the regression coefficients.

Collectively, these diagnostic results not only support the reliability of the findings but also improve the credibility of the conclusions drawn from the analysis. Hence, the models can be considered robust, with strong explanatory power and well-tested assumptions.

The results of this study have significant theoretical and practical implications. According to the literature, the determinants of bank profitability can be classified into two broad categories: internal factors and external factors (Sufian and Habibullah, 2009) While the internal determinants of banks are variables, their managerial influence on decision-making makes them contingent on the effectiveness of managerial actions (Athanasoglou et al., 2005). In fact, under this umbrella is control over key variables such as capital adequacy and bank size, which are within the realm of managerial discretion and ultimately such measures are usually the result of painstaking analysis of bank financial statements. These determinants contribute at the organization level and type of bank that drive profitability. In contrast, external factors, such as regulatory frameworks and macroeconomic variables (e.g. interest rates), lie beyond the direct control of bank management but significantly impact the operational environment. This study found that internal factors such as persistent profitability, capital adequacy, size and revenue growth have a more significant impact on current profitability than external factors, such as central bank interest rate.

In addition, this research challenges the traditional view about the relationship between bank size and profitability. The negative correlation detected between size and profitability in the fixed-effects model suggests that larger banks may experience diminishing returns to scale. This outcome is in contrasts with the assumption that larger banks benefit from economies of scale (Hughes and Mester, 2013). The study suggests that operational complexity, systemic risks and regulatory burdens associated with larger banks may offset the benefits of size. These mixed findings highlight the complexity of the relationship between macroeconomic conditions, bank size and profitability.

The findings of this study have also several practical implications for bank management, policymakers and investors. The positive relationship between capital adequacy and profitability emphasizes the importance of maintaining robust capital buffers. Well-capitalized banks are perceived as safer and better positioned to weather crises, enhancing profitability and stability. This finding aligns with Coccorese and Girardone (2017), who noted the stabilizing effect of strong capital during crises. It is also important that policymakers encourage banks to favor its behavior around international standards, that is Basel III, without discouraging so high capital buffers that reduce banks profitability (Brogi and Langone, 2016). There needs to be a balance between resilience and profitability.

The persistence of profitability, where past profits forecast future performance, underscores the importance of long-term strategies for operational efficiency. Thus, managers should leverage their track record to attract investment and ensure continued profitability, focusing on risk management and optimizing asset quality. The results of the study also contradict the idea that economies of scale only benefit big banks. While larger banks benefit from efficiencies, there are higher risks associated with operation, systemic risks and regulation costs, which reduce profitability according to Curti et al. (2016). Furthermore, Stimpert and Laux (2011) pointed to diminishing returns to scale as the banks became larger. Thus, bank managers should adjust the benefits of expansion with the risks of complexity, focusing on improving efficiency and managing regulatory burdens.

The study also provides practical implications for investors and their investment decisions. Investors should consider capital adequacy, lagged profitability and bank size when assessing bank stocks. Strong capital positions and profitability tenacity are key indicators of long-term financial health. The study also cautions investors against assuming that larger banks always provide higher returns, as operational risks and regulatory costs may reduce profitability.

Nonetheless, the study also has some limitations which might be addressed by future studies. Since the study is conducted within the Swedish contexts, the results may not be generalizable to other contexts. Moreover, the control variables in this study largely includes the internal factors influencing a bank’s profitability. Thus, future research may also control for external factors such as macro-economic variables, industry specific variables or financial market variables. We also acknowledge the possibility of reverse causality between profitability and some of our explanatory variables (e.g. capitalization or asset quality), where higher profitability could lead to improved capital buffers, and vice versa. Future studies could strengthen causal inference by employing instrumental variable (IV), Granger causality tests or dynamic panel models (e.g. system GMM) to better isolate the effect of each determinant and ensure that our results are not driven by feedback loops from profitability back onto the explanatory factors.

Future studies may also include other types of banks such as saving banks or public banks as this study was only focused on commercial banks. Moreover, future studies might examine how central bank interest rates affect banks of different sizes and other financial institutions, for example pension funds and microfinance providers, to understand how monetary policy influences the different layers of the financial sector.