determinants of bank profitability before and during the crisis

Determinants of bank profitability before and during the crisis: Evidence from Switzerland

Andreas Dietrich <img src="http://origin-cdn.els-cdn.com/sd/entities/REemail.gif" alt="E-mail the corresponding author">,

Gabrielle Wanzenried <img alt="Corresponding author contact information" src="http://origin-cdn.els-cdn.com/sd/entities/REcor.gif">, <img src="http://origin-cdn.els-cdn.com/sd/entities/REemail.gif" alt="E-mail the corresponding author">

Lucerne University of Applied Sciences and Arts, Institute of Financial Services IFZ, Grafenauweg 10, 6304 Zug, Switzerland

Received 19 January 2010, Accepted 26 November 2010, Available online 7 December 2010

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http://dx.doi.org/10.1016/j.intfin.2010.11.002

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Abstract

Using the GMM estimator technique described by Arellano and Bover (1995), this paper analyzes the profitability of 372 commercial banks in Switzerland over the period from 1999 to 2009. To evaluate the impact of the recent financial crisis, we separately consider the pre-crisis period, 1999–2006, and the crisis years of 2007–2009. Our profitability determinants include bank-specific characteristics as well as industry-specific and macroeconomic factors, some of which have not been considered in previous studies. The inclusion of these additional factors as well as the separate consideration of the crisis years allow us to gain new insights into what determines the profitability of commercial banks.

JEL classification

E44;

G21;

G32;

L2;

C23

Keywords

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Banking profitability;

Macroeconomic impact on banking profitability;

Financial crisis;

Market structure;

Ownership

1. Introduction

Using data from the Swiss banking market, this paper examines bank profitability as a measure of how well a bank is run. We examine whether, for banks operating in similar environments, one can make judgments concerning the success of their competitive strategies and other management-determined factors by using profitability measures. Likewise, for banks that are essentially similar, this paper examines whether environmental factors have an impact on bank profitability. Given the importance of profitability for the stability of the banking industry, and the impact of the banking industry on the capital markets and the economy as a whole, these questions are of vital importance. This is especially true in light of the recent global financial crisis, for which we include a separate analysis for the data recorded for the Swiss banking industry during the crisis years of 2007–2009.

Research on the determinants of bank profitability has typically focused on both the returns on bank assets and equity, and the net interest margins as dependent variables. More recent studies have expanded the number of factors considered. Thus, scholars (Staikouras and Wood, 2004, Athanasoglou et al., 2008, Brissimis et al., 2008 and García-Herrero et al., 2009) have examined the effect of bank-specific (i.e. capital ratio, operational efficiency, bank size), industry-specific (i.e. ownership and concentration) and macroeconomic (i.e. inflation and cyclical output) determinants on bank performance. Until now, few papers have analyzed the impacts of the recent financial crisis on the determinants of bank profitability. Likewise, no econometric study has yet considered the determinants of profitability for the Swiss commercial banking system, which is both diverse and strong, although not immune to fluctuations in the global market. However, to appreciate the value of the Swiss commercial banking system for research purposes, some background information is required.

Below the level of its national government, Switzerland is organized into 26 regional governments known as cantons. From canton to canton, certain aspects of the banking environment, such as market growth, bank competition, and tax regimes can differ. However, across all cantons, some factors do remain constant. Therefore, the analysis of banking profitability in the Swiss banking market offers insight on how the variation of a particular factor can affect bank profitability. In addition, unlike banking sectors in other countries, the Swiss banking industry includes several different types of bank. These institutions differ from each in important characteristics, such as ownership (state-owned versus privately owned), business model, and economic environment. These aspects are expected to affect bank performance and are captured in our study. Finally, analyzing bank performance within such a diverse context and during periods of dramatically different levels of economic prosperity is a promising way to better understand the underlying mechanisms of bank performance in developed countries.

The present paper builds on the work by Athanasoglou et al. (2008) and García-Herrero et al. (2009). We empirically assess the main factors that determine the profitability of banks in Switzerland. To that end, we use data from 372 commercial banks and for the longest relevant period (from 1998 to 2009). To account for profit persistence and potential endogeneity problems, we apply a GMM technique to our panel of Swiss banks.

Our results show that profitability is, for the most part, explained by five factors: operational efficiency, the growth of total loans, funding costs, the business model, and the effective tax rate. We find, not surprisingly, that operationally efficient banks are more profitable than banks that are less operationally efficient. Likewise, we find that above-average growth in loan volume affects bank profitability positively, while higher funding costs result in a lower profitability. The interest income share also has a significant impact on profitability. Banks that are heavily dependent on interest income are less profitable than banks whose income is more diversified. Furthermore, the separate consideration of the time periods before and during the crisis provides new insights with respect to the underlying mechanisms that determine bank profitability. Our results provide empirical evidence that, for the Swiss market during the financial crisis, state-owned banks are more profitable than privately owned banks. We believe that, during this time of turmoil, state-owned banks were thought of as safer and better banks in comparison to privately owned institutions. The loan loss provisions relative to total loans ratio, which is a measure of credit quality, did not have a statistically significant effect on bank profitability before the crisis. However, the loan loss provisions have significantly increased during the crisis, and this is reflected in its negative impact on profitability during the crisis years. The yearly growth of deposits has had a significant and negative impact on bank profitability, and this effect is seen mainly in the crisis years. Banks in Switzerland were not able to convert the increasing amount of deposit liabilities into significantly higher income earnings during the recent time of turmoil.

The paper is structured as follows. Section 2 surveys the relevant literature on banking profitability. Section 3 outlines our model and the dependent and independent variables used in our analyses. Section 4 describes the data sample and methodology used. Section 5 presents the results of our empirical analysis, and Section 6 concludes.

2. Theoretical background

This section reviews the relevant literature on the determinants of banking profitability.

2.1. Literature on determinants of bank profitability

Following early work by Short (1979) and Bourke (1989), a number of more recent studies have attempted to identify some of the major determinants of bank profitability. The respective empirical studies have focused their analyses either on cross-country evidence or on the banking system of individual countries. The studies by Molyneux and Thornton (1992), Demirguc-Kunt and Huizinga (1999), Abreu and Mendes (2002), Staikouras and Wood (2004), Goddard et al. (2004), Athanasoglou et al. (2006), Micco et al. (2007) and Pasiouras and Kosmidou (2007) investigate a panel data set. Studies by Berger et al. (1987), Berger (1995), Neely and Wheelock (1997), Mamatzakis and Remoundos (2003), Naceur and Goaied (2008), Athanasoglou et al. (2008) and García-Herrero et al. (2009) focus their analyses on single countries. The empirical results of these above-mentioned studies do vary, which is to be expected, given the differences in their datasets, time periods, investigated environments, and

countries. However, we found some mutual elements that we used to categorize further the determinants of banking profitability.

Bank profitability is usually measured by the return on average assets and is expressed as a function of internal and external determinants. The internal determinants include bank-specific variables. The external variables reflect environmental variables that are expected to affect the profitability of financial institutions.

In most studies, variables such as bank size, risk, capital ratio and operational efficiency are used as internal determinants of banking profitability. Pasiouras and Kosmidou (2007) find a positive and significant relationship between the size and the profitability of a bank. This is because larger banks are likely to have a higher degree of product and loan diversification than smaller banks, and because they should benefit from economies of scale. Other authors, such as Berger et al. (1987), provide evidence that costs are reduced only slightly by increasing the size of a bank and that very large banks often encounter scale inefficiencies. Micco et al. (2007) find no correlation between the relative bank size and the ROAA for banks, i.e. the coefficient is always positive but never statistically significant. Another determinant of bank profitability is risk. Abreu and Mendes (2002), who examined banks in Portugal, Spain, France and Germany, find that the loans-to-assets ratio, as a proxy for risk, has a positive impact on the profitability of a bank. Bourke (1989) and Molyneux and Thornton (1992), among others, find a negative and significant relationship between the level of risk and profitability. This result might reflect the fact that financial institutions that are exposed to high-risk loans also have a higher accumulation of unpaid loans. These loan losses lower the returns of the affected banks.

Empirical evidence by Bourke (1989), Demirguc-Kunt and Huizinga (1999), Abreu and Mendes (2002), Goddard et al. (2004), Naceur and Goaied, 2001 and Naceur and Goaied, 2008, Pasiouras and Kosmidou (2007) and García-Herrero et al. (2009) indicate that the best performing banks are those who maintain a high level of equity relative to their assets. The authors explain this relation with the observation that banks with higher capital ratios tend to face lower costs of funding due to lower prospective bankruptcy costs. Furthermore, there is also empirical evidence that the level of operational efficiency, measured by the cost-income ratio (Goddard et al., 2009) or overhead costs over total assets (Athanasoglou et al., 2008) positively affects bank profitability (Athanasoglou et al., 2008 and Goddard et al., 2009). A further bank-specific variable is the ownership of a bank. Micco et al. (2007) found that whether a bank is privately owned or state-owned does affect its performance. According to their results, state-owned banks operating in developing countries tend to have a lower profitability, lower margins, and higher overhead costs than comparable privately owned banks. In industrialized countries, however, this relationship has been found to be much weaker. Iannotta et al. (2007) point out that government-owned banks exhibit a lower profitability than privately owned banks. Demirguc-Kunt and Huizinga (1999) suggest that the international ownership of banks has a significant impact on bank profitability. Foreign banks are shown to be less profitable in developed countries. In contrast, Bourke (1989) as well as Molyneux and Thornton (1992) report that the ownership status is irrelevant for explaining bank profitability. They find little evidence to support the theory that privately owned banks are more profitable than state-owned banks. Furthermore, Beck et al. (2005) controlled for the age of the bank, since longer established banks might enjoy performance advantages over relative newcomers. Their results for the Nigerian market indicate that older banks did not perform as well as newer banks, which were better able to pursue new profit opportunities.

Previous studies also include external determinants of bank profitability such as central bank interest rate, inflation, the GDP development, taxation, or variables representing market characteristics (e.g. market concentration). Most studies have shown a positive relationship between inflation, central bank interest rates, GDP growth, and bank profitability (e.g. Bourke, 1989, Molyneux and Thornton, 1992, Demirguc-Kunt and Huizinga, 1999, Athanasoglou et al., 2008 and Albertazzi and Gambacorta, 2009). Furthermore, there is some evidence that the legal and institutional characteristics of a country matter. The study of Demirguc-Kunt and Huizinga (1999) reports that taxation reduces bank profitability. Another study by Albertazzi and Gambacorta (2009) concludes that the impact of taxation on banking profitability is small because banks can shift a large fraction of their tax burden onto depositors, borrowers, or purchasers of fee-generating services. Overall, although fiscal issues are likely to exert a significant influence on the behavior of a bank, the taxation of the financial sector has received little attention.

To measure the effects of market structure on bank profitability, the structure–conduct–performance (market-power) hypothesis states that increased market power yields monopoly profits. According to the results of Bourke (1989) and Molyneux and Thornton (1992), the bank concentration ratio shows a positive and statistically significant relationship with the profitability of a bank and is, therefore, consistent with the traditional structure–conduct–performance paradigm. In contrast, the results of Demirguc-Kunt and Huizinga (1999) and Staikouras and Wood (2004) indicate a negative but statistically insignificant relationship between bank concentration and bank profits. Likewise, the estimations by Berger (1995) and Mamatzakis and Remoundos (2003) contradict the structure–conduct–performance hypothesis.

The literature on the impact of the recent financial turmoil on the determinants of bank profitability is relatively sparse. Xiao (2009) runs qualitative and quantitative analyses to examine the performance of French banks during 2006–2008. She concludes that French banks were not immune but proved relatively resilient to the global financial crisis reflecting their business and supervision features. A paper by Millon Cornett et al. (2010), looking at internal corporate governance mechanisms and the performance of publicly traded U.S. banks before and during the financial crisis, finds that banks of all size groups suffered bank performance decreases. However, they find that the largest banks faced the largest losses. Beltratti and Stulz (2009) find that large banks with more Tier 1 capital and more deposit financing at the end of 2006 exhibited significantly higher returns during the crisis.

To our knowledge, no previous study has investigated the profitability of Swiss commercial banks. Studies on Swiss banking are only very loosely related to our paper and have instead focused on the relationship between the size of a bank and its efficiency (e.g. Hermann and Maurer, 1991 and Rime and Stiroh, 2003), or cost efficiency (Bikker, 1999 for banks in nine European countries).

To conclude, the existing literature provides a comprehensive examination of the effects of bank-specific, industry-specific, and macroeconomic determinants on bank profitability. However, the impact of the crisis on the determinants of bank profitability has not yet been widely analyzed. Furthermore, our study fills an important gap in the literature because no study has yet analyzed the profitability determinants of Swiss commercial banks. These two novelties should make an important addition to the extensive literature on the determinants of bank profitability.

3. Determinants of bank profitability and variable selection

In this section, we describe both the dependent and independent variables that we selected for our analysis of bank profitability. See Table 1 for a summary of the variables described below.

Table 1.

Definition of variables.

Variables Description

Dependent variables: bank profitability

ROAA Net profits over average total assets (%)

ROAE Net profits over average total equity (%)

NIMNet interest margin (in %), defined as net interest income divided by total assets

Independent variablesExpected effect

Bank-specific characteristics (internal factors)

Equity over total assetsEquity over total assets (%). This is a measure of capital adequacy, respectively the bank risk. The higher this ratio, the lower the risk of the bank

+/−

Cost-income ratioTotal expenses over total generated revenues as a measure of operational efficiency (%)

–

Loan loss provisions over total loans

Loan loss provisions over total loans (%). This is a measure of credit quality

–

Yearly growth of deposits Annual growth of deposits (%) +/−

Difference between bank and market growth of total loans

Difference between the annual growth of a bank's lending volume relative to the average growth rate of the market lending volume (%)

+

Bank sizeDummy variables for different bank size categories. Bank size is measured by the accounting value of the bank's total assets

+/−

Interest income share Total interest income over total income (%) –

Funding costs Interest expenses over average total deposits (%) –

Bank age Dummy variable for different bank age groups +

Variables Description

Bank ownership

Dummy variable: Public bank if public sector owns more than 50% of the shares

+/–

Dummy variable: Listed bank if institution is listed at the stock exchange

+/–

NationalityDummy variable: Foreign bank if at least 50% of the bank's stocks are in foreign hands

–

Macroeconomic and industry-specific characteristics (external factors)

Effective tax rate Total taxes over pretax profit (%) –

Real GDP growth The yearly real GDP growth (%) +

Term structure of interest rates

The difference between the interest rate of a 5-year and a 2-year treasury bill in CHF issued by the Swiss government (%)

+

Herfindahl indexThe market shares in the mortgage business of all in Switzerland acting commercial banks by region (market structure variable)

+/−

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3.1. Dependent variables

We use the return on average assets (ROAA) as our main measure of bank profitability. The ROAA is defined as the ratio of net profits to average total assets expressed as a percentage. As alternative profitability measures, we use the return on average equity (ROAE), which is the ratio of net profits to average equity expressed as a percentage, as well as the net interest margin (NIM). The latter is defined as the net interest income divided by total assets.

The ROAA reflects the ability of a bank's management to generate profits from the bank's assets. It shows the profits earned per CHF of assets and indicates how effectively the bank's assets are managed to generate revenues. To capture changes in assets during the fiscal year, our study relies on the average assets value. As Golin (2001) points out, the ROAA has emerged as the key ratio for the evaluation of bank profitability and has become the most common measure of bank profitability in the literature.

Our second measure of profitability is the return on average equity (ROAE), which is the return to shareholders on their equity. Although the financial literature commonly uses the ROAE to measure profitability, we find that it is not the best indicator of profitability. For example, banks with a lower

leverage ratio (higher equity) usually report a higher ROAA but a lower ROAE. However, the ROAE disregards the higher risk that is associated with a high leverage and the effect of regulation on leverage. Thus, in our analyses, we consider the ROAA as the better measure of profitability and use it as the main dependent variable, although we also report the results for the ROAE.

Finally, the net interest margin serves as our third performance measure. As a measure of the return on assets, the net interest margin has been used in many studies of bank performance. While the ROA measures the profit earned per dollar of assets and reflects how well bank management uses the bank's real investment resources, the NIM focuses on the profit earned on interest activities.

3.2. Independent variables: determinants of bank profitability

This section describes the independent variables that we used to analyze bank profitability. They include bank-specific (internal) and market-specific (external) factors.

3.2.1. Bank-specific determinants

As bank-specific determinants of bank profitability, we use the following variables:Equity over total assets: As a proxy for a bank's capital, we use the ratio of equity to assets. Anticipating the net impact of changes in this ratio is complex. For example, banks with higher capital-to-asset ratios are considered relatively safer and less risky compared to institutions with lower capital ratios. In line with the conventional risk-return hypothesis, we expect banks with lower capital ratios to have higher returns in comparison to better-capitalized financial institutions. In contrast, highly capitalized banks are safer and remain profitable even during economically difficult times. Furthermore, a lower risk increases a bank's creditworthiness and reduces its funding cost. In addition, banks with higher equity-to-assets ratios normally have a reduced need for external funding, which has again a positive effect on their profitability. From this point of view, a higher capital ratio should have a positive effect on profitability. Given that we have anticipated effects pointing in opposite directions, the impact of a bank's capitalization on its profitability cannot be anticipated theoretically.Cost-income ratio: The cost-to-income ratio is defined as the operating costs (such as the administrative costs, staff salaries, and property costs, excluding losses due to bad and non-performing loans) over total generated revenues. This ratio measures the effect of operating efficiency on bank profitability. We therefore expect higher cost-income ratios to have a negative effect on bank profitability.

Loan loss provisions over total loans: The ratio of loan loss provisions over total loans is a measure of a bank's credit quality. The loan loss provisions are reported on a bank's income statement. A higher ratio indicates a lower credit quality and, therefore, a lower profitability. Thus, we expect a negative effect from the loan loss provisions relative to total loans on bank profitability.

Yearly growth of deposits: We measure a bank's growth by the annual growth of its deposits. One might expect that a faster growing bank would be able to expand its business and thus generate greater profits. However, the contribution to profits that derives from an increase in deposits depends upon a number of factors. First, it depends on the bank's ability to convert deposit liabilities into income-earning assets, which, to a certain extent, also reflects a bank's operating efficiency. It also depends on the credit quality of those assets. Growth is often achieved by investing in assets of lower credit quality, which has a negative effect on bank profitability. In addition, high growth rates might also attract

additional competitors. This again reduces the profits for all market participants. Therefore, the sign of this variable cannot be anticipated theoretically.

Difference between bank and market growth of total loans: We include a variable measuring the growth of a bank's loan volume relative to the average market growth rate of loans. From a theoretical viewpoint, the impact of changes of this variable on bank performance is very difficult to anticipate. For example, one might expect that a bank with a higher growth rate of its loan volume (relative to the market's growth rates) would be more profitable due to the additional business generated. However, if the bank realized its growth through lower margins, one might expect a negative impact on profitability. Furthermore, a high growth of the loan volume might also lead to a decrease in credit quality and thus to a lower profitability. Given that we have effects pointing in opposite directions, the overall effect on bank profitability cannot be anticipated theoretically.

Bank size: We measure bank size by total assets. To identify potential size effects, we build dummy variables for small, medium, and large banks. One of the most important questions in the literature is which bank size maximizes bank profitability. For example, Smirlock (1985) argue that a growing bank size is positively related to bank profitability. This is because larger banks are likely to have a higher degree of product and loan diversification than smaller banks, which reduces risk, and because economies of scale can arise from a larger size. Because reduced risk and economies of scale lead to increased operational efficiency, we expect a larger size to have a positive effect on bank profitability, at least up to a certain point.

However, banks that have become extremely large might show a negative relationship between size and profitability. This is due to agency costs, the overhead of bureaucratic processes, and other costs related to managing extremely large firms (e.g. Stiroh and Rumble, 2006 and Pasiouras and Kosmidou, 2007). Accordingly, the overall effect is indeterminate from a theoretical point of view. As a robustness test, we use total assets as an alternative size variable in our analyses.

Interest income share: Swiss commercial banks in our sample generate a large fraction of their total income through traditional commercial banking activities (interest operations) and to a lesser extent through “fee and commission income” and “trading operations.” Because margins in fee and commission income and trading operations are usually higher than margins in interest operations, we expect reduced profitability of banks with a higher share of interest income relative to their total income. This variable represents an item that is off the balance sheet and is a good proxy for the bank's business model.

Funding costs: Funding costs are defined as interest expenses over average total deposits and are mainly determined by a bank's credit rating, competition, market interest rates, and by the composition of the sources of funds and its relative importance.

Overall, we expect better profits from banks that are able to raise funds more cheaply.

Bank age: We classify banks into three age groups. The first group includes banks founded after 1990, the second group includes those institutions founded between 1950 and 1990, and the third group refers to banks established before 1950. We expect older banks to be more profitable due to their longer period of service, during which the banks could build up a good reputation.

Bank ownership: In our model, a bank is either privately owned or state-owned. We classify a bank as state-owned if the public sector owns more than 50% of the bank. From a theoretical viewpoint, the effect of differences in bank ownership on performance is indeterminate, and there is even disagreement among the empirical studies. Some studies (e.g. Bourke, 1989 and Molyneux and Thornton, 1992) find no significant relationship between the ownership status and the performance of a bank. However, Micco et al. (2007) and Iannotta et al. (2007), find strong empirical evidence that ownership does affect bank profitability. Furthermore, we investigate whether being listed at a stock exchange has an impact on bank profitability. As a potentially positive impact, listed banks face greater pressure for being profitable from their shareholders, the analysts, and the financial markets overall. As a potentially negative impact, listed banks, in contrast to unlisted banks, face many reporting and other requirements, which create significant additional costs. Therefore, the overall effect is indeterminate and remains to be answered empirically.

Nationality: We consider whether the nationality of the bank owner, i.e. whether a bank is Swiss-owned or foreign-owned, has an effect on profitability. An institution is defined as a foreign bank if at least 50% of the bank's stocks are in foreign hands. We expect foreign banks to be less familiar with the Swiss environment and, therefore, to be less profitable than Swiss-owned banks.

3.2.2. Macroeconomic and industry-specific characteristics (external factors)

In addition to the bank-specific variables described above, our analysis includes a set of macroeconomic and industry-specific characteristics that we expect to have an impact on bank profitability.

Effective tax rate: The effective tax rate, defined as taxes paid divided by before-tax profits, reflects the explicit taxes paid by the banks (mostly corporate income taxes). This variable is not uniform across the Swiss market, where tax rates vary widely among the Swiss cantons. This variation provides an opportunity to see whether the differences in effective tax rates affect the profitability of the banks. In cantons with higher effective tax rates, we would expect banks to shift a large fraction of their tax burden onto their depositors, borrowers, and purchasers of fee-generating services. This would protect those banks from the full impact of the higher tax burden, but it would not eliminate the impact entirely. Thus, consistent with the results of Demirguc-Kunt and Huizinga (1999), we expect a higher effective tax rate to have a negative impact on bank profitability.

Real GDP growth: GDP growth, which varies over time but not among the Swiss cantons, is expected to have a positive effect on bank profitability according to the literature on the association between economic growth and financial sector profitability (e.g. Demirguc-Kunt and Huizinga, 1999, Bikker and Hu, 2002 and Athanasoglou et al., 2008). Accordingly, because the demand for lending increases during cyclical upswings, we expect a positive relationship between bank profitability and GDP development.

Term structure of interest rate: We use the difference between the interest rate of a 5-year and a 2-year treasury bill in CHF issued by the Swiss government and as published by the Swiss National Bank as proxy for the term structure of interest rates. Again, this variable varies over time but not across cantons. Commercial banks usually use short-term deposits to finance long-term loans. This maturity transformation is an important function of commercial banks and is influencing its profitability. Thus, we expect a steeper yield curve to affect the profitability positively.

Herfindahl index: We measure the market structure in the banking industry by means of the Herfindahl–Hirschman-Index, which is defined as the sum of the squares of the market shares of all the banks within the industry, where the market shares are expressed as fractions. As the lending business of commercial banks in our sample is locally oriented, market structure and competition also vary by region. Therefore, we compute the Herfindahl index by region, measuring the market shares in the mortgage business of all commercial banks acting in Switzerland (see Piazza, 2008). According to the structure–conduct–performance hypothesis, banks in highly concentrated markets earn monopoly rents, because they tend to collude (e.g. Gilbert, 1984). Because collusion may result in higher rates being charged on loans and lower interest rates being paid on deposits, we expect that a higher bank concentration has a positive impact on profitability. On the other hand, a higher bank concentration might be the result of a tougher competition in the banking industry, which would suggest a negative relationship between performance and market concentration ( Boone and Weigand, 2000). As a result, the overall effect of market concentration on banking performance is indeterminate and remains to be answered empirically.

For a summary of the definitions of our dependent and explanatory variables, see Table 1.

4. Data and methodology

This section identifies the sources of our data, presents the data itself, and describes the regression model we use to investigate the effects of internal and external factors on bank profitability.

4.1. Data

Our main data source for the bank-specific characteristics is the Fitch-IBCA Bankscope (BSC) database, which provides annual financial information for banks in 179 countries around the world. Coverage by the Bankscope database is comprehensive, with the included banks accounting for roughly 90% of the assets of all banks. Information about bank age, bank ownership, and the nationality of the bank owners were taken from the Swiss National Bank and from the web pages of the respective institutions. In addition to the bank-specific data, we use a set of macroeconomic and industry-specific variables to explain bank profitability. The real GDP growth and the interest rates for 5-year and a 2-year treasury bills issued by the Swiss Government were taken from the Swiss National Bank. The data for the Herfindahl index, measuring the market shares in the mortgage business of all commercial banks acting in Switzerland, stems from Piazza (2008).

To use the data of the Bankscope database (BSC) for our statistical analysis, we had to edit the data carefully in the following ways. Given that our focus lies on commercial banks in Switzerland, we start by excluding the Swiss National Bank, investment banks, securities houses and non-banking credit institutions. We also exclude the big banks Credit Suisse and UBS AG from our sample (see also below for a detailed description of the included institutions). In a further step, we eliminate duplicate information. If BSC reports both unconsolidated and consolidated statements, we dropped the unconsolidated statement.

Similarly, we needed to make a choice concerning data from the banks with balance sheet data reported at the aggregated level. BSC builds aggregated statements by combining the statements of banks that have merged or are about to merge. Aggregated statements may then report the data of groups of affiliated banks that neither have financial links nor form a legal entity. As a result, a given bank might be reported several times in database, namely as an independent unit by its consolidated as well as by its

unconsolidated statements. As Micco et al. (2007) outline, there are two ways to deal with banks that have aggregated statements. The first is always to work with the aggregated statement and drop the observations for the individual banks. The second is to drop the aggregated statement and work with the individual banks up to the time of the merger and then, starting from the year of the merger, with the new bank. We use the first strategy and work with the aggregated statements.

Our sample is an unbalanced panel dataset of 372 commercial banks in Switzerland, consisting of 1639 observations over the years from 1999 to 2009. To investigate the impact of the recent financial crisis, we split the sample into two time periods: the period from 1999 to 2006, the pre-crisis period; and the years 2007, 2008 and 2009, the post-crisis period. In our sample, we include the cantonal banks, regional and savings banks, and Raiffeisen banks according to the official statistics maintained by the Swiss National Bank. Furthermore, foreign-owned banks that are active in the traditional commercial banking activities, and other banks active in traditional lending business (mainly category 5.11 commercial banks, as defined by the Swiss National Bank) are considered in our analyses (see Table 2).

Table 2.

Number of banks and observations by bank category.

Cantonal banks

Regional and savings banks

Raiffeisen banks

Other banks

All

No. of banks 24 86 169 93 372

No. of observations

156 417 691 375 1639

This table reports the number of banks and the number of observations by bank category, as defined by the Swiss National Bank (SNB, 2010).

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Note that we explicitly exclude the two big banks Credit Suisse and UBS from our sample, and this for the following reasons: First, they provide all banking services (private banking, institutional asset management, investment banking and commercial banking); second, a large share of their lending activities is abroad; and third because commercial banking is not a predominant part of their revenues on the accounted group level. Due to these reasons, it is hard to compare these two big banks with the other commercial banks in Switzerland, especially also with respect to profitability considerations. Overall, the banks in our sample have a clear focus on commercial banking activities, with a median of roughly 85% of their income generated in the traditional field of interest income. For a more detailed description of our sample, please check “Appendix A”.

Table 3 reports the descriptive statistics for the variables used in our analyses. Let us briefly highlight a few interesting facts. On average, the banks in our sample have a ROAA of 0.63% over the entire period

from 1999 to 2009. The difference between mean and median indicates that there exist large profitability differences among the banks in our sample. The same holds true for our second profitability measure, the ROAE, which amounts to 7.31% on average. The banks in our sample exhibit an average net interest margin of 1.73%. On average, the capitalization of banks is 8.55%, which, however, differs among banks, like the other variables as well. The best-capitalized bank in our sample, for instance, has a capital ratio of 87%, whereas, for the least-capitalized institutions, total equity only covers 0.5% of total assets. The loan loss provision relative to total loans, which is an indicator of the quality of the credit portfolio, amounts to 0.35% on average, which seems quite low, but there exist again large differences among the banks in our sample with respect to this variable. As pointed out above, a substantial part of the total income of the commercial banks in our sample stems from interest operations. This interest–income share amounts to almost 74% on average. The median of this variable is even 86%. The effective tax rate as one of the macroeconomic factors amounts to 29.9% on average. This variable reflects the tax burden across the different Swiss cantons, which may differ significantly. Finally, note that the Herfindahl index as our measure of bank concentration is 2450, which is quite high compared to bank concentration ratios in other countries (see, e.g. Beck et al., 2006). The correlation matrix for the independent variables can be found in Table 4.

Table 3.

Descriptive statistics.

Dependent variables: bank profitability Mean Median Std. dev.

ROAA 0.63 0.31 1.18

ROAE 7.31 6.57 5.86

NIM 1.73 1.67 0.56

Independent variables Mean MedianStd. dev.

Bank-specific characteristics (internal factors)

Equity over total assets 8.55 6.42 9.68

Cost–income ratio 64.97 62.44 16.51

Loan loss provisions over total loans 0.35 0.05 1.74

Yearly growth of deposits 8.16 5.00 16.65

Difference between bank and market growth of total loans 2.68 1.08 17.51

Dummy: large bank: total assets > 512 m. USD 0.37 0 –

Dummy: medium bank: total assets between 212 m and 512 m. USD (reference category)

0.38 0

Independent variables Mean MedianStd. dev.

Dummy: small bank: total assets < 212 m. USD 0.25 0 –

Interest income share 74.36 86.04 27.36

Funding costs 2.30 2.03 1.34

Bank age

Dummy: bank was founded before 1950 (reference category) 0.70 1 –

Dummy: bank was founded between 1950 and 1989 0.20 0 –

Dummy: bank was founded after 1990 0.10 0 –

Bank ownership

Dummy: bank is privately owned (reference category) 0.90 1 –

Dummy: bank is (co-)owned by a state or city 0.09 0 –

Dummy: bank is not listed at a stock exchange (reference category) 0.94 1

Dummy: bank is listed at a stock exchange 0.06 0 –

Nationality

Dummy: bank is a Swiss bank (reference category) 0.82 1 –

Dummy: bank is a foreign bank 0.18 0 –

Macroeconomic and industry-specific factors (external factors)

Effective tax rate 29.94 26.32 18.75

Real GDP growth 2.12 2.53 1.24

Term structure of interest rates 0.51 0.45 0.29

Herfindahl Index 2449.2 2545 797.11

The table reports the descriptive statistics of the variables used in the regression analyses. For the notation of the variables see Table 1.

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Table 4.

Cross-correlation matrix of independent variables.

Eq. o. TA

CI ratio

Loan loss prov o. TA

Yr growth deposits

Growth tot.loans

Dum large bank

Dum small bank

int.inc. sh

Fund costs

Dum bank age middle

Dum bank age young

Dum state bank

Dum list. bank

Dum for. bank

Eff. tax rate

Real gdp growth

Term structure

HHI


0.22

0.15

1

Yr growth of deposits

−0.07

0.09

0.05

1

Diff. growth total loans

−0.05

0.06

−0.02

0.21 1

Dum large bank

0.05

−0.07

−0.02

−0.02 0.01 1

Dum small bank

0.09

0.08

0.09

0.02 0.01−0.27

1

Interest income share

−0.26

−0.22

−0.20

−0.06 0.02−0.21

0.04

1

Funding costs

−0.03

−0.14

0.09

0.12 0.150.20

−0.12

0.18 1

Dum bank age

0.31

0.07

0.13

0.05 0.02 0.13

−0.08

−0.41

0.04

1

Eq. o. TA

CI ratio


Yr growth deposits

Growth tot.loans

Dum large bank

Dum small bank

int.inc. sh

Fund costs

Dum bank age middle

Dum bank age young

Dum state bank

Dum list. bank

Dum for. bank

Eff. tax rate

Real gdp growth

Term structure

HHI

middle

Dum bank age young

0.31

0.25

0.09

0.08 0.070.04

0.09

−0.31

−0.06

−0.16

1

Dum state bank

−0.01

−0.11

−0.03

−0.08 −0.060.29

−0.12

0.050.09

−0.13

−0.11

1

Dum listed bank

0.02

−0.05

−0.02

−0.04 −0.050.31

−0.12

−0.05

0.06

−0.08

−0.05

0.28

1

Dum foreign bank

0.35

0.16

0.18

0.11 0.060.24

0.08

−0.32

0.06

0.28 0.28−0.14

−0.09

1

Effective tax rate

−0.22

−0.01

−0.07

−0.01 −0.02−0.32

0.13

0.23−0.17

−0.01

−0.10

−0.32

−0.16

−0.11

1

Real gdp growth

0.02

−0.16

−0.05

−0.06 −0.24−0.05

0.02

−0.02

−0.08

−0.02

0.01−0.02

−0.02

−0.02

0.07

1

Term structure of int.

−0.11

0.14

0.04

0.09 0.11−0.03

0.01

0.05−0.24

−0.02

−0.04

−0.01

−0.01

−0.06

0.01

−0.44 1

HHI−0.03

−0.03

0.01

−0.01 −0.02−0.02

−0.03

0.010.05

−0.04

−0.08

0.21

0.02

−0.05

−0.09

0.04 −0.03 1

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4.2. Methodology

To empirically investigate the effects of internal and external factors on bank profitability, we follow Athanasoglou et al. (2008) and García-Herrero et al. (2009) and use a linear model given by (1):

equation(1)

<img height="51" border="0" style="vertical-align:bottom" width="366" alt="View the MathML source" title="View the MathML source" src="http://origin-ars.els-cdn.com/content/image/1-s2.0-S1042443110000831-si1.gif">PERFit=c+δPERFi,t−1+∑j=1JβjXitj+∑l=1LβjXitj+εit

Turn MathJax on

PERFi,t is profitability of bank i at time t , with i = 1,…,N , t = 1,…,T , c is a constant term, Xit's are the bank-specific and market-specific explanatory variables as outlined above, and ɛ i.t is the disturbance, with v i the unobserved bank-specific effect and u it the idiosyncratic error. This is a one-way error

component regression model, where v i ∼ (IIN(0, <img height="19" border="0" style="vertical-align:bottom" width="34" alt="View the MathML source" title="View the MathML source" src="http://origin-ars.els-cdn.com/content/image/1-s2.0-S1042443110000831-si2.gif">0,σv2)) and

independent of u it ∼ (IIN( <img height="19" border="0" style="vertical-align:bottom" width="34" alt="View the MathML source" title="View the MathML source" src="http://origin-ars.els-cdn.com/content/image/1-s2.0-S1042443110000831-si3.gif">0,σu2)). Bank profits show a tendency to persist over time, reflecting impediments to market competition, informational opacity and/or sensitivity to regional/macroeconomic shocks to the extent that these are serially correlated ( Berger et al., 2000). As a consequence, we specify a dynamic model by including a lagged dependent variable among the regressors, i.e. PERFi,t−1 is the one-period lagged profitability and δ the speed of adjustment to equilibrium. A value of δ between 0 and 1 implies persistence of profits, but they will eventually return to their normal level. A value close to 0 indicates an industry that is fairly competitive, while a value close to 1 implies a less competitive structure. 1

Given the dynamic nature of our model, least squares estimation methods produce biased and inconsistent estimates (see Baltagi, 2001). Therefore, we use techniques for dynamic panel estimation that are able to deal with the biases and inconsistencies of our estimates. Another challenge with estimation of bank profitability refers to the endogeneity problem. As García-Herrero et al. (2009) outline, more profitable banks, for example, may also be able to increase their equity more easily by retaining profits. Similarly, they could also pay more for advertising campaigns and increase their size, which in turn might affect profitability. However, the causality could also go in the opposite direction, because more profitable banks can hire more personnel, and thus reduce their operational efficiency. Another important problem is unobservable heterogeneity across banks, which definitively also exists in the Swiss banking industry, differences in corporate governance, which we cannot measure well.

Following García-Herrero et al. (2009), we address these problems by employing the generalized method of moments (GMM) following Arellano and Bover (1995), also known as system GMM estimator. This methodology accounts for endogeneity. The system GMM estimator uses lagged values of the dependent variable in levels and in differences as instruments, as well as lagged values of other regressors which could potentially suffer from endogeneity.

We instrument for all regressors except for those which are clearly exogenous. The variables treated as endogenous are shown in italics in the result tables below. The system GMM estimator also controls for unobserved heterogeneity and for the persistence of the dependent variable. All in all, this estimator yields consistent estimations of the parameters.

In a first step, we estimate our model over the entire time period from 1999 to 2009. In order to investigate the impact of the recent financial crisis on the determinants of banking profitability, we additionally split up the sample into two time periods, namely the pre-crisis period ranging from 1999 to 2006, and the crisis and post-crisis period including the years 2007, 2008 and 2009.

Finally, the simultaneous inclusion of certain variables may raise concerns of multicollinearity. As noted later on, we computed several tests in order to make sure that our results are not affected by multicollinearity issues.

5. Empirical results

Table 5 summarizes the empirical results for our main profitability measure ROAA. The first two columns report the results when including all eleven years in our sample. In order to investigate the impact of the recent financial crisis on the banks’ profitability determinants, we further split up the sample: Columns three and four refer to the period before the crisis (up to 2006). Columns five and six report the estimates for the years of the financial crisis, namely 2007–2009. In order to identify the stability of the coefficients and their significance, we first include only the bank-specific determinants into our model (columns one, three and five). In a second step, we report the estimates of the full model with the bank- and market-specific factors (columns two, four and six).

Table 5.

Regression results for returns on average assets (ROAA) as dependent variable.

Dependent variable: ROAA

All yearsBefore the financial crisis: 1999–2006

During the financial crisis 2007–2009

Bank-specific factors

Bank- and market-specific factors





L.ROAA0.087*** (0.023)

0.090*** (0.023)

0.044 (0.029)

0.051* (0.030)

0.156***(0.049)

0.131*** (0.050)

Equity over −0.011 −0.014** 0.005 0.005 −0.023** −0.028***










total assets (0.007) (0.007) (0.011) (0.011) (0.010) (0.010)

Cost-income ratio

−0.028*** (0.001)

−0.027*** (0.001)

−0.025*** (0.002)

−0.025*** (0.002)

−0.027*** (0.001)

−0.028*** (0.002)


0.005 (0.010)−0.000 (0.010)

−0.016 (0.013)

−0.021 (0.013)

−0.143** (0.060)

−0.138** (0.061)

Yearly growth of deposits

−0.005*** (0.001)

−0.006*** (0.001)

−0.003 (0.002)

−0.004* (0.002)

−0.010*** (0.002)

−0.011*** (0.002)

Diff. between bank and market growth of total loans

0.008*** (0.001)

0.008*** (0.001)

0.010*** (0.001)

0.011*** (0.002)

0.009*** (0.001)

0.007*** (0.002)

Dummy: large bank: total assets>512 m. USD

−0.155 (0.132)

−0.118 (0.133)

0.436** (0.185)

0.419** (0.187)

−0.929*** (0.268)

−0.871*** (0.272)

Dummy: small bank: total assets<212 m. USD

0.758*** (0.136)

0.817*** (0.139)

1.377*** (0.189)

1.420*** (0.191)

−0.227 (0.253)−0.067 (0.262)


−0.005*** (0.001)

−0.004*** (0.001)

−0.017*** (0.004)

−0.019*** (0.004)

−0.011*** (0.002)

−0.012*** (0.002)










Funding costs

−0.053*** (0.015)

−0.031* (0.017)

−0.154*** (0.022)

−0.151*** (0.024)

0.058 (0.121) 0.070 (0.112)

Dummy: bank was founded between 1950 and 1989

0.687*** (0.148)

0.614*** (0.152)

0.997*** (0.217)

1.006*** (0.223)

0.991*** (0.284)

0.834*** (0.297)

Dummy: bank was founded after 1989

0.301* (0.165)

0.300* (0.166)

0.056 (0.225)

0.003 (0.227)

1.237*** (0.307)

1.105*** (0.318)

Dummy: bank is (co-)owned by state or city

1.336*** (0.293)

1.105*** (0.308)

1.001 (0.925)

0.443 (0.551)

1.803*** (0.399)

1.750*** (0.433)

Dummy: bank is listed at stock exchange

−1.930*** (0.337)

−1.852*** (0.346)

−1.653*** (0.464)

−1.341*** (0.476)

−1.468*** (0.569)

−1.388** (0.586)

Dummy: bank is a foreign bank

−0.318** (0.135)

−0.285** (0.137)

−0.326* (0.191)

−0.198 (0.197)

−0.133 (0.270)−0.053 (0.275)

Effective tax rate

−0.007*** (0.001)

−0.007*** (0.001)

−0.008*** (0.002)

−0.009*** (0.002)

−0.009*** (0.002)

−0.008*** (0.002)

Real gdp growth

–0.037*** (0.013)

–0.006 (0.016)

–−0.032 (0.044)











–0.193*** (0.067)

–0.038 (0.089)

–0.282** (0.135)

Herfindahl Index

–0.000** (0.000)

–0.000*** (0.000)

– 0.000 (0.000)

Constant4.185*** (0.210)

3.643*** (0.293)

3.452*** (0.311)

2.346*** (0.418)

3.459*** (0.454)

3.923*** (0.594)

Number of observations

1639 1639 981 981 658 658

Number of banks

372 372 318 318 263 263

Wald-testχ2(22) = 1898.26

χ2(25) = 1886.68

χ2(22) = 966.46

χ2(25) = 963.19

χ2(22) = 1232.55

χ2(25) = 1238.11

Hansen test (p-value)

(1.000) (1.000) (1.000) (1.000) (1.000) (1.000)

AB test AR(1) (p-value)

(0.002) (0.13) (0.002) (0.002) (0.03) (0.039)


(0.144) (0.13) (0.58) (0.503) (0.961) (0.884)

The table reports results from GMM estimations of the effects of bank- and market-specific characteristics on bank profitability. The dependent variable is the return on average assets ROAA. For the notation of the variables see Table 1. The full sample includes 1639 observations from 372 banks. The period covers the years 1999 to 2009. Variables in italics are instrumented through the GMM procedure following Arellano and Bover (1995). Robust standard errors are in brackets. Coefficients that are significantly different from zero at the 1%, 5%, and 10% level are marked with ***, **, and *

respectively. The Hansen test is the test for over-identifying restrictions in GMM dynamic model estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 respectively of order 2 is 0 (H0: no autocorrelation); p-values in brackets.

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Our estimation results point out to stable coefficients. Also, the Wald-test indicates fine goodness of fit and the Hansen test shows no evidence of over-identifying restrictions. The equations indicate that a negative first-order autocorrelation is present. However, this does not imply that the estimates are inconsistent. Inconsistency would be implied if second-order autocorrelation was present (Arellano and Bond, 1991). But this case is rejected by the test for AR(2) errors.

Our lagged dependent variable, which measures the degree of persistence of our profitability measure ROAA, is statistically significant across all models, indicating a high degree of persistence of bank profitability and justifying the use of a dynamic model.

Overall, we observe some significant differences between the estimation results of the different time periods, both with respect to the significance and the size of the coefficients. The capital ratio, which is defined as equity over total assets, does not have a significant impact on bank profitability before the crisis. However, it has a negative and significant effect on bank profitability as measured by ROAA during the financial crisis 2007–2009. One of the main reasons for this relation is that safer banks in Switzerland were attracting additional saving deposits (mainly from UBS) during the crisis. However, they were not able to convert the substantially increasing amount of deposits into significantly higher income earnings as the demand for lending decreased in this period. Even though total earnings for these banks often slightly increased during this time of turmoil, profitability decreased as banks did not find attractive investment opportunities or were lowering net interest margins in order to lend their additional deposits.)

The coefficient of the cost-to-income ratio, our operational efficiency measure, is negative and highly significant for all different time period. The more efficient a bank is the higher is its profitability. This result meets our expectation and stands in line with the results of Athanasoglou et al. (2008).

The loan loss provisions relative to total loans ratio, which is a measure of credit quality, do not have a statistically significant effect on bank profitability before the crisis. This is not surprising, given that banks in Switzerland had very low loan loss provisions before the financial crisis. However, the loan loss provisions have significantly increased during the crisis, and this is reflected in its negative impact on profitability during the crisis years, with the coefficients being significant at the 5% level.

The yearly growth of deposits has a significant and negative impact on bank profitability, and this effect is mainly driven by the crisis years. Banks in Switzerland were not able to convert the increasing amount of deposit liabilities into significantly higher income earnings above all in recent time of turmoil. However, and most interestingly, banks with relatively higher lending growth rates (in comparison to the market) were more profitable than slowly growing banks in all considered time periods. The effect of a

faster growing loan volume seems to over-compensate the risk that a too fast growth in loans may lead to a decrease in credit quality.

As to bank size, which we track by dummy variables, we find some empirical evidence that larger and smaller commercial banks were more profitable than medium-sized banks (reference category) before the crisis. This gives some indication that larger banks were able to benefit from higher product and loan diversification possibilities, and/or economies of scales (see Smirlock, 1985 and Bikker and Hu, 2002). However, large banks in Switzerland were less profitable than small and medium-sized bank during the past 3 years of the financial crisis.2 The main reasons for this negative relationship between size and profitability are that larger banks in Switzerland had relatively higher loan loss provisions during the crisis and that larger banks were found to have significantly lower net interest margins in times of turmoil than smaller banks (see below). This might also be a consequence of some reputational problems that mainly larger banks in Switzerland faced during the recent crisis.

On average, 75% of total income for the commercial banks in our sample consists of traditional banking income (interest income) and to a lesser extent of fee and commission income as well as trading operations. Our findings show that banks with a higher share of interest income relative to the total income are significantly less profitable, and this holds before as well as during the crisis, with the effect being significant at the 1% level. The reason for this coherence is that profit margins of fee, commission and also trading operations are usually higher than profit margins in interest operations, and that many banks could benefit from a positive development of the stock market and a higher stock exchange turnover.

Funding costs have a significantly negative impact on the return on assets before the crisis, i.e. banks that raise cheaper funds are more profitable. However, this does not hold anymore during the crisis, where funding costs dropped anyway to a historically low level. Furthermore, bank age has a significant impact on banking profitability in the whole sample period. In contrast to our hypothesis as formulated above, older banks are not more profitable than recently founded banks or banks founded between 1950 and 1989. Newer banks seem to be even more profitable than older banks. This means that newer banks are able to pursue successfully new profit opportunities and that a longer tradition of service and, in this context, a better reputation does not positively affect the profitability of a bank. Also, younger banks may be more efficient in terms of their IT infrastructure, which is reflected in the profitability measure as well.

Our results regarding the impact of ownership on profitability before the crisis support the findings of Bourke (1989), Molyneux and Thornton (1992) and Athanasoglou et al. (2008) that the ownership status (private or state-owned banks) is irrelevant for explaining profitability. Our results from Switzerland stand thus in contrast to the findings of Micco et al. (2007) and Iannotta et al. (2007), who point out that government-owned banks exhibit a lower profitability than privately owned banks. However, things look different for the crisis years, which is quite interesting. Our results provide empirical evidence for the Swiss market that state-owned banks are more profitable than privately owned banks during the financial crisis. In this time of turmoil, state-owned banks were considered as safer and better banks in comparison to privately owned institutions. International ownership of a bank seems to have a significant impact on bank profitability when considering the whole model. In fact, foreign-owned banks in Switzerland seem to be less profitable than their Swiss competitors. This result confirms the findings of Demirguc-Kunt and Huizinga (1999), who find evidence that foreign-owned banks are less profitable

in developed countries than domestic banks. Furthermore, there is some empirical evidence that banks listed on the stock exchange are slightly less profitable than banks that are not. This holds for all specifications.

Considering the external factors related to the macroeconomic environment and the financial structure in Switzerland, our study finds that taxation negatively affects bank profitability in Switzerland, with the coefficients being significant at the 1% level in all specifications. Our results confirm the findings of Demirguc-Kunt and Huizinga (1999) that higher tax rates lead to a lower post-tax profit. This result is of specific importance in Switzerland, where tax rates vary widely across the Swiss cantons, all of which have their own tax regime. However, the impact of taxation on banking profitability is rather small. Overall, it seems that banks are able to shift a large fraction of their tax burden onto their depositors, borrowers, and purchasers of fee-generating services.

The business cycle significantly affects bank profits when considering all years. Bank profits seem to be pro-cyclical as the demand for lending increases during cyclical upswings and thus lead to more and more profitable business (Athanasoglou et al., 2008 and Albertazzi and Gambacorta, 2009). The term structure of interest rates, measured by the difference between the 5-year and 2-year treasury bills in CHF issued by the Swiss government, positively affects the profitability of Swiss banks overall and in particular during the financial crisis. Commercial banks in Switzerland use short-term deposits to finance long-term loans. A steeper yield curve, as during the financial crisis years, thus affects the profitability positively.

Furthermore, the impact of the market structure, approximated by the Herfindahl index seems to have a significant and positive effect on bank profitability before the crisis, but not thereafter. Accordingly, we do find some support for the structure-conduct-performance hypothesis. These findings are in line with the results of Bourke (1989) and Molyneux and Thornton (1992), even though the effect seems to be rather small.

Table 6 reports the regression results for our second profitability measure return on average equity ROAE. Again, we estimate the model for the entire time period considered, and then separately for the two subsamples pre-crisis and crisis years.

Table 6.

Regression results for returns on average equity (ROAE) as dependent variable.

Dependent variable: ROAE









L.ROAE0.181*** (0.024)

0.180*** (0.024)

0.123*** (0.033)

0.128*** (0.033)

0.086* (0.045)

0.092** (0.045)










Equity over total assets

−0.023 (0.056)

−0.036 (0.057)

−0.055 (0.097)

−0.054 (0.098)

−0.050 (0.058)

−0.065 (0.059)

Cost-income ratio

−0.144*** (0.009)

−0.140*** (0.009)

−0.208*** (0.016)

−0.203*** (0.016)

−0.084*** (0.008)

−0.088*** (0.008)


−0.078 (0.081)

−0.090 (0.082)

−0.176 (0.116)

−0.197* (0.119)

−0.535* (0.325)

−0.474 (0.333)


0.024** (0.011)

0.017 (0.012) 0.006 (0.019) 0.004 (0.019)−0.004 (0.011)

−0.001 (0.012)


0.025*** (0.007)

0.031*** (0.008)

0.044*** (0.012)

0.046*** (0.014)

0.020*** (0.007)

0.008 (0.009)

Dummy: large bank: total assets > 512 m. USD

2.412** (1.093)

2.647** (1.101)

8.654*** (1.717)

8.587*** (1.736)

−4.462*** (1.323)

−4.580*** (1.355)

Dummy: small bank: total assets < 212 m. USD

3.289*** (1.131)

3.521*** (1.153)

9.298*** (1.765)

9.345*** (1.778)

−0.962 (1.344)

−0.726 (1.413)


−0.014*** (0.010)

−0.012*** (0.011)

−0.219*** (0.033)

−0.223*** (0.034)

−0.052*** (0.009)

−0.060*** (0.010)

Funding costs−0.400*** (0.123)

−0.293** (0.133)

−1.247*** (0.194)

−1.186*** (0.210)

−0.245 (0.181)

−0.198 (0.183)

Dummy: bank 0.475 (1.199) 0.290 (1.231) 3.407* 3.326* −0.441 −0.844










was founded between 1950 and 1989

(1.900) (1.943) (1.593) (1.681)

Dummy: bank was founded after 1989

−2.388* (1.376)

−2.412* (1.381)

−3.002 (2.022)

−3.035 (2.043)

−1.896 (1.773)

−2.171 (1.839)


−2.365 (2.211)

−3.204 (2.307)

−1.885 (4.442)

−3.362 (4.585)

−1.188 (2.158)

−1.149 (2.304)


2.724 (2.600) 3.186 (2.672) 1.633 (4.102) 2.563 (4.196)6.919** (3.091)

7.523** (3.156)


−0.240 (1.141)

−0.231 (1.157)

0.221 (1.757) 0.457 (1.798)3.303** (1.572)

3.846** (1.633)

Effective tax rate

−0.022** (0.010)

−0.021** (0.010)

−0.011 (0.016)

−0.012 (0.016)

−0.070*** (0.009)

−0.067*** (0.009)

Real gdp growth

– 0.264** – 0.104 (0.142) –−0.370 (0.250)


– 0.715 (0.542) – 0.417 (0.782) – 0.368 (0.716)

Herfindahl Index

– 0.001 (0.001) – 0.001 (0.001) –−0.000 (0.000)

Constant25.116*** (1.734)

22.024*** (2.366)

20.559*** (2.749)

17.455*** (3.521)

30.667*** (2.607)

32.572*** (3.330)











1639 1639 981 981 658 658

Number of banks

372 372 318 318 263 263

Wald-testχ2(22) = 911.46

χ2(25) = 914.37

χ2(22) = 660.12

χ2(25) = 653.21

χ2(22) = 812.15

χ2(25) = 801.76


(1.000) (1.000) (1.000) (1.000) (1.000) (1.000)


(0.015) (0.01) (0.085) (0.08) (0.04) (0.042)


(0.405) (0.35) (0.41) (0.342) (0.739) (0.527)

The table reports results from GMM estimations of the effects of bank- and market-specific characteristics on bank profitability. The dependent variable is the return on average equity ROAE. For the notation of the variables see Table 1. The full sample includes 1639 observations from 372 banks. The period covers the years 1999–2009. Variables in italics are instrumented through the GMM procedure following Arellano and Bover (1995). Robust standard errors are in brackets. Coefficients that are significantly different from zero at the 1%, 5%, and 10% level are marked with ***, **, and * respectively. The Hansen test is the test for over-identifying restrictions in GMM dynamic model estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 respectively of order 2 is 0 (H0: no autocorrelation); p-values in brackets.

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Overall, the results of these regressions confirm to a large extent the above-discussed key results. There are, however, also some differences. In contrast to the results for our main profitability measure ROAA, differences exist related to our ownership variables. The ownership status (private or state-owned banks) has no impact on bank profitability when measured by the ROAE. This does not only hold for the

period before the crisis, but also thereafter. Banks listed at the stock exchange are more profitable during the crisis than unlisted banks. This result might be driven by the fact that the return on equity reflects shareholder maximization attempts, which is a common practice of some of the listed banks. In particular, some listed banks in Switzerland may have effectively lowered their equity capital in order to increase the ROAE. Also, there is empirical evidence that foreign-owned banks are not less profitable over the whole period when the ROAE is our dependent variable. Furthermore, there is no significant impact of the market structure, approximated by the Herfindahl index, on bank profitability before the crisis.

Table 7 reports the regression results for another profitability measure, the net interest margin. Again, we estimate the model for the entire time period considered, and then separately for the two subsamples pre-crisis and crisis years. Analyzing the determinants of the net interest margin helps us to better understand some results of the ROAA specifications. For instance, it is interesting to see that larger banks have a significantly lower net interest margins during the financial crisis than medium- or small-sized banks. This might also explain why large banks were less profitable in the referring years. Furthermore, banks with a high relative loan growth exhibit higher net interest margins. This is also a possible explanation for the positive relationship between the relative loan growth and bank profitability when measured by both the ROAA and the ROAE. Furthermore, banks with a higher share of interest income of total income have lower net interest margins. This might be another reason why banks with a higher interest income share are less profitable than banks that have a better income diversification.

Table 7.

Regression results net interest margin (NIM) as dependent variable.

Dependent variable: NIM









L.NIM0.668*** (0.018)

0.704*** (0.018)

0.621*** (0.023)

0.653*** (0.024)

0.668*** (0.041)

0.652*** (0.041)

Equity over total assets

0.018*** (0.003)

0.019*** (0.003)

0.027*** (0.004)

0.029*** (0.004)

0.003 (0.005)0.004 (0.005)

Cost-income ratio

−0.004*** (0.000)

−0.003*** (0.000)

−0.006*** (0.001)

−0.004*** (0.001)

−0.002*** (0.001)

−0.002** (0.001)


0.017*** (0.004)

0.012*** (0.004)

0.020*** (0.005)

0.014*** (0.005)

0.009 (0.028)0.001 (0.028)











−0.001 (0.001)

−0.002*** (0.001)

0.001 (0.001)−0.001 (0.001)

−0.006*** (0.001)

−0.006*** (0.001)


0.001* (0.000)

0.003*** (0.000)

0.003*** (0.001)

0.006*** (0.001)

0.003*** (0.001)

0.004*** (0.001)

Dummy: large bank: total assets > 512 m. USD

−0.162*** (0.052)

−0.090* (0.053)

−0.084 (0.067)

−0.034 (0.068)

−0.242** (0.122)

−0.235* (0.123)

Dummy: small bank: total assets < 212 m. USD

0.117* (0.060)

0.108* (0.061)

0.144* (0.079)

0.176** (0.080)

−0.378*** (0.122)

−0.354*** (0.122)


−0.001** (0.000)

−0.000 (0.001)

−0.009*** (0.001)

−0.007*** (0.001)

−0.002*** (0.001)

−0.002* (0.001)

Funding costs

0.042*** (0.006)

0.045*** (0.006)

0.031*** (0.008)

0.028*** (0.009)

0.031* (0.017)

0.030* (0.017)

Dummy: bank was founded between 1950 and 1989

0.197*** (0.060)

0.251*** (0.062)

0.018 (0.081) 0.109 (0.083)−0.223 (0.144)

−0.143 (0.152)

Dummy: bank was founded

0.021 (0.066) 0.020 (0.066) 0.084 (0.086) 0.025 (0.087) −0.288** (0.142)

−0.288** (0.142)










after 1989


0.729*** (0.109)

0.659*** (0.115)

0.606*** (0.156)

0.389** (0.170)

0.013 (0.207)−0.017 (0.214)


−0.883*** (0.123)

−0.700*** (0.125)

−0.969*** (0.151)

−0.667*** (0.157)

0.252 (0.295)0.196 (0.297)


−0.060 (0.052)

−0.101* (0.053)

−0.103 (0.066)

−0.098 (0.069)

0.203 (0.132)0.129 (0.132)

Effective tax rate

0.000 (0.001) 0.001 (0.001) 0.001 (0.001) 0.001 (0.001) 0.001 (0.001)0.000 (0.001)

Real gdp growth

0.047*** (0.005)

0.040*** (0.006)

0.032 (0.021)


0.080*** (0.028)

0.118*** (0.034)

0.056 (0.065)

Herfindahl Index

0.000 (0.000)0.000*** (0.000)

0.000 (0.000)

Constant0.565*** (0.079)

0.007 (0.120)0.699*** (0.110)

−0.074 (0.159)

0.923*** (0.221)

0.608** (0.276)


1639 1639 981 981 658 658

Number of banks

372 372 318 318 263 263










Wald-testχ2(22) = 3251.90

χ2(25) = 3420.79

χ2(22) = 2183.61

χ2(25) = 2274.52

χ2(22) = 1392.62

χ2(25) = 1441


(1.000) (1.000) (1.000) (1.000) (1.000) (1.000)


(0.001) (0.002) (0.003) (0.001) (0.003) (0.001)


(0.987) (0.49) (0.94) (0.801) (0.657) (0.389)

The table reports results from GMM estimations of the effects of bank- and market-specific characteristics on bank profitability. The dependent variable is the net interest margin NIM. For the notation of the variables see Table 1. The full sample includes 1639 observations from 372 banks. The period covers the years 1999 to 2009. Variables in italics are instrumented through the GMM procedure following Arellano and Bover (1995). Robust standard errors are in brackets. Coefficients that are significantly different from zero at the 1%, 5%, and 10% level are marked with ***, **, and * respectively. The Hansen test is the test for over-identifying restrictions in GMM dynamic model estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 respectively of order 2 is 0 (H0: no autocorrelation); p-values in brackets.

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6. Conclusions

This paper has examined how bank-specific characteristics, industry-specific and macroeconomic factors affect the profitability of 372 commercial banks in Switzerland over the period from 1999 to 2009. To account for the impacts of the recent financial crisis, we separately considered the years before and during the crisis, namely the period up to 2006, and the crisis years 2007, 2008 and 2009. To date, no econometric study has examined the determinants of profitability for the Swiss banking market, which is surprising given that Switzerland is one of the most important banking centers in the world. Similarly,

there exist very few papers that investigate the impact of the recent financial crisis on bank performance.

We use a dynamic model specification that allows for profit persistence. Our results clearly show that there exist large differences in profitability among the banks in our sample and that a significant amount of this variation can be explained by the factors included in our analyses. In particular, bank profitability is mainly explained by operational efficiency, the growth of total loans, funding costs and the business model. Efficient banks are more profitable than banks that are less efficient. An above-average loan volume growth affects bank profitability positively; higher funding costs result in a lower profitability. The interest income share also has a significant impact on profitability. Banks that are heavily dependent on interest income are less profitable than banks whose income is more diversified. We also find some evidence that ownership is an important determinant of profitability. Furthermore, the separate consideration of the time periods before and during the crises provides new insights with respect to the underlying mechanisms that determine bank profitability. The results outlined in this paper provide some evidence that the financial crisis did indeed have a significant impact on the Swiss banking industry and on bank profitability in particular.

Overall, our results provide some interesting new insights into the mechanisms that determine the profitability of commercial banks in Switzerland. Our findings are relevant for several reasons. First, our estimation results confirm findings from former studies on bank profitability. Second, we consider a larger set of bank- and market-specific determinants of bank profitability, which extends our knowledge of bank profitability with respect to several important dimensions. These extensions let us generate some new and interesting findings. Third, we consider the years from 1999 to 2009. Not only do we provide evidence for a recent period, but these years were also characterized by some important changes in the banking industry. In addition, by dividing the sample into pre-crisis and post-crisis segments, we gain additional insights into the impacts of the financial crisis on financial institutions. Finally, by using the system GMM estimator developed by Arellano and Bover (1995), we apply an up-to-date econometric technique that addresses the issue of endogeneity of regressors, which, in this type of study, can lead to inconsistent estimates. Also, our dynamic model specification allows for the fact that bank profits show a tendency to persist over time and tend to be serially correlated, reflecting impediments to market competition, informational opacity, and sensitivity to regional and/or macroeconomic shocks.

Even though our sample includes a large fraction of all commercial banks active in Switzerland and considers the main bank profitability determinants as well as factors related to the institutional and macroeconomic environment, it has certain limitations. Including additional aspects in our analyses, such as the impact of mergers, would help us to understand even better the determinants of bank profitability. In addition, it could be fruitful to integrate specific information on management and board members, e.g. education, skill level, experience, independence, all of which are increasingly important factors in understanding bank profitability. Some of these issues will be addressed in future work.

Acknowledgements

We would like to thank participants of the 2009 Annual Meeting of the European Financial Management Association in Milano, the 12th conference of the Swiss Society for Financial Market Research (SGF) in Geneva in 2009 and the Brown Bag seminar at the Institute of Financial Services Zug, Urs Birchler and

Kevin Walsh for helpful suggestions, and the Lucerne University of Applied Sciences and Arts for their financial support.

Appendix A. The Swiss banking market

The Swiss banking system is based on the concept of universal banking, i.e. all banks may offer all banking services. As of 2009, there are 278 authorized banks and securities dealers in Switzerland, ranging from the two “big” banks down to small banks serving the needs of a single community or a few special clients. Swiss banks vary of the degree to which they use the option to engage in all financial activities. Some banks really do offer universal services, while other institutions specialize either in traditional banking or in asset management. In the official statistics maintained by the Swiss National Bank, Swiss banks are classified into seven major groups: the (two) big banks, the cantonal banks, the regional and savings banks, the Raiffeisen banks, the foreign-owned banks, the private bankers, and other banks. To better understand our sample and subsequent empirical work, we provide a brief description of each type below.

The two “big” banks, UBS AG and the Credit Suisse Group, are the largest and second largest Swiss banks. Both banks have extensive branch networks throughout the country and most international centers. We do not include the two big banks in our sample because they pursue all lines of financial activities (private banking, institutional asset management, investment banking and commercial banking), because a large share of their lending activities is abroad and because commercial banking is not a predominant part of their revenues on group level.

Cantonal banks are state-owned, either entirely or partially, and the majority of a cantonal bank's capital is owned by the sponsoring canton, which also guarantees the bank's liabilities. According to cantonal law, the objective of a cantonal bank is to promote the canton's economy, although cantonal banks must comply with commercial principles in their business activities. Collectively, the cantonal banks account for around 30% of banking business in Switzerland and have a combined balance sheet total that is greater than 300 billion Swiss francs. Formerly, there were at least one or two cantonal banks per canton. Today, there are only 24 cantonal banks (in Switzerland's 26 cantons and half-cantons). Cantonal banks vary both in size and in their business activities. They are engaged in all banking businesses, with an emphasis on lending/deposit business, and they operate primarily in the market of their home canton. Because these banks are active mainly in the traditional commercial banking business, we include all 24 cantonal banks in our sample.

Regional and savings banks are typically small banks focusing on traditional banking, and their business is often limited to very small geographical areas. We have included almost all Swiss regional and savings banks in our sample.

Raiffeisen Switzerland, the third largest bank group in Switzerland, is comprised of 390 member banks, most of which are located in rural areas, and each of which is run as a cooperative. Collectively, the 390 Raiffeisen banks control a network of 1154 branch offices, the largest such network in Switzerland, and count 1.4 million Swiss citizens as members, hence co-owners, of the cooperative. As a group of banks with the largest branch network in Switzerland, 390 Raiffeisen banks with totally 1154 branches together form Raiffeisen Switzerland. Raiffeisen Switzerland coordinates the group's activities, creates the conditions for the business activities of the local Raiffeisen banks and advises and supports them in many issues. The bank group is organized as a cooperative and has positioned and established itself as

the third largest bank group in Switzerland. As one of Switzerland's leading retail banks, Raiffeisen is mainly focusing on mortgage lending. Raiffeisen meanwhile counts 1.4 million Swiss citizens as members of the cooperative and hence co-owners of their Raiffeisen bank. However, the Raiffeisen banks are still legally independent small banks located and active mainly in rural areas. Due to their legally independent status, our sample includes each of the Raiffeisen member banks individually.

Foreign banks are institutions operating under Swiss banking law, but whose capital is primarily foreign controlled. Foreign control means that foreigners with qualified interests hold over half of the company's votes. The national origin of these foreign-owned banks is predominantly European (over 50%) and Japanese (around 20%). These banks differ widely in size and activities. Some qualify as universal banks while others focus on asset management. Our sample includes only those foreign-owned banks that are active in the traditional banking activities. Our sample excludes foreign-owned banks that are active only in asset management for private clients.

Private bankers are among the oldest banks in Switzerland. They are unincorporated firms, primarily active in asset management for private clients. Private bankers are subject to unlimited subsidiary liability with their personal assets. Because these private banks, which do not publicly offer to accept savings deposits, are not active in the traditional banking field, and because they do not have to publish data, we do not include them in our analysis.

The group, “other banks,” includes banks with various business objectives, such as institutes specializing in the stock exchange, securities, and asset management. For our sample, only banks active in traditional lending (mainly category 5.11 commercial banks, as defined by the Swiss National Bank) are considered in our analyses.

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1

See also Athanasoglou et al. (2008) for further details.

2

As a robustness test, we alternatively measure bank size by total assets instead of the dummy variables for the different size categories. The effect of total assets on the ROAA is negative and statistically significant at the 10% level, which confirms our results from the dummy approach. Note that the advantage of including the dummy variables for size is that we have obtained more information about the impact of size on the return on bank profitability.