determinants of bank profitability before and during the crisis: evidence from switzerland

Int. Fin. Markets, Inst. and Money 21 (2011) 307–327

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Determinants of bank profitability before and during thecrisis: Evidence from Switzerland

Andreas Dietrich, Gabrielle Wanzenried ∗

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

a r t i c l e i n f o

Article history:Received 19 January 2010Accepted 26 November 2010Available online 7 December 2010

JEL classification:E44G21G32L2C23

Keywords:Banking profitabilityMacroeconomic impact on bankingprofitabilityFinancial crisisMarket structureOwnership

a b s t r a c t

Using the GMM estimator technique described by Arellano andBover (1995), this paper analyzes the profitability of 372 commer-cial banks in Switzerland over the period from 1999 to 2009. Toevaluate the impact of the recent financial crisis, we separatelyconsider the pre-crisis period, 1999–2006, and the crisis years of2007–2009. Our profitability determinants include bank-specificcharacteristics as well as industry-specific and macroeconomic fac-tors, some of which have not been considered in previous studies.The inclusion of these additional factors as well as the separate con-sideration of the crisis years allow us to gain new insights into whatdetermines the profitability of commercial banks.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Using data from the Swiss banking market, this paper examines bank profitability as a measureof how well a bank is run. We examine whether, for banks operating in similar environments, onecan 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,

∗ Corresponding author. Tel.: +41 417246537; fax: +41 417246550.E-mail addresses: [email protected] (A. Dietrich), [email protected] (G. Wanzenried).

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308 A. Dietrich, G. Wanzenried / Int. Fin. Markets, Inst. and Money 21 (2011) 307–327

this paper examines whether environmental factors have an impact on bank profitability. Given theimportance of profitability for the stability of the banking industry, and the impact of the bankingindustry on the capital markets and the economy as a whole, these questions are of vital impor-tance. This is especially true in light of the recent global financial crisis, for which we include aseparate analysis for the data recorded for the Swiss banking industry during the crisis years of2007–2009.

Research on the determinants of bank profitability has typically focused on both the returns onbank assets and equity, and the net interest margins as dependent variables. More recent studies haveexpanded the number of factors considered. Thus, scholars (Staikouras and Wood, 2004; Athanasoglouet al., 2008; Brissimis et al., 2008; 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 concentra-tion) and macroeconomic (i.e. inflation and cyclical output) determinants on bank performance. Untilnow, few papers have analyzed the impacts of the recent financial crisis on the determinants of bankprofitability. Likewise, no econometric study has yet considered the determinants of profitability forthe Swiss commercial banking system, which is both diverse and strong, although not immune tofluctuations in the global market. However, to appreciate the value of the Swiss commercial bankingsystem for research purposes, some background information is required.

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

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 thatend, 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 toour panel of Swiss banks.

Our results show that profitability is, for the most part, explained by five factors: operational effi-ciency, the growth of total loans, funding costs, the business model, and the effective tax rate. Wefind, not surprisingly, that operationally efficient banks are more profitable than banks that are lessoperationally efficient. Likewise, we find that above-average growth in loan volume affects bank prof-itability positively, while higher funding costs result in a lower profitability. The interest income sharealso has a significant impact on profitability. Banks that are heavily dependent on interest income areless profitable than banks whose income is more diversified. Furthermore, the separate considerationof the time periods before and during the crisis provides new insights with respect to the underlyingmechanisms that determine bank profitability. Our results provide empirical evidence that, for theSwiss market during the financial crisis, state-owned banks are more profitable than privately ownedbanks. We believe that, during this time of turmoil, state-owned banks were thought of as safer andbetter banks in comparison to privately owned institutions. The loan loss provisions relative to totalloans ratio, which is a measure of credit quality, did not have a statistically significant effect on bankprofitability before the crisis. However, the loan loss provisions have significantly increased duringthe crisis, and this is reflected in its negative impact on profitability during the crisis years. The yearlygrowth of deposits has had a significant and negative impact on bank profitability, and this effect isseen mainly in the crisis years. Banks in Switzerland were not able to convert the increasing amountof 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.

A. Dietrich, G. Wanzenried / Int. Fin. Markets, Inst. and Money 21 (2011) 307–327 309

Section 4 describes the data sample and methodology used. Section 5 presents the results of ourempirical 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 haveattempted to identify some of the major determinants of bank profitability. The respective empiricalstudies have focused their analyses either on cross-country evidence or on the banking system ofindividual 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), Athanasoglouet al. (2006), Micco et al. (2007) and Pasiouras and Kosmidou (2007) investigate a panel data set. Studiesby 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 theiranalyses on single countries. The empirical results of these above-mentioned studies do vary, whichis 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 thedeterminants of banking profitability.

Bank profitability is usually measured by the return on average assets and is expressed as a func-tion 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 offinancial institutions.

In most studies, variables such as bank size, risk, capital ratio and operational efficiency are usedas internal determinants of banking profitability. Pasiouras and Kosmidou (2007) find a positive andsignificant relationship between the size and the profitability of a bank. This is because larger banksare likely to have a higher degree of product and loan diversification than smaller banks, and becausethey should benefit from economies of scale. Other authors, such as Berger et al. (1987), provide evi-dence that costs are reduced only slightly by increasing the size of a bank and that very large banksoften encounter scale inefficiencies. Micco et al. (2007) find no correlation between the relative banksize 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 banksin Portugal, Spain, France and Germany, find that the loans-to-assets ratio, as a proxy for risk, hasa 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 profitabil-ity. This result might reflect the fact that financial institutions that are exposed to high-risk loansalso have a higher accumulation of unpaid loans. These loan losses lower the returns of the affectedbanks.

Empirical evidence by Bourke (1989), Demirguc-Kunt and Huizinga (1999), Abreu and Mendes(2002), Goddard et al. (2004), Naceur and Goaied (2001, 2008), Pasiouras and Kosmidou (2007) andGarcía-Herrero et al. (2009) indicate that the best performing banks are those who maintain a high levelof equity relative to their assets. The authors explain this relation with the observation that banks withhigher 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 bythe 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; Goddard et al., 2009). A furtherbank-specific variable is the ownership of a bank. Micco et al. (2007) found that whether a bank isprivately owned or state-owned does affect its performance. According to their results, state-ownedbanks operating in developing countries tend to have a lower profitability, lower margins, and higheroverhead costs than comparable privately owned banks. In industrialized countries, however, thisrelationship 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) aswell as Molyneux and Thornton (1992) report that the ownership status is irrelevant for explainingbank profitability. They find little evidence to support the theory that privately owned banks are moreprofitable 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. Theirresults 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 inter-est rate, inflation, the GDP development, taxation, or variables representing market characteristics (e.g.market concentration). Most studies have shown a positive relationship between inflation, centralbank interest rates, GDP growth, and bank profitability (e.g. Bourke, 1989; Molyneux and Thornton,1992; Demirguc-Kunt and Huizinga, 1999; Athanasoglou et al., 2008; Albertazzi and Gambacorta,2009). Furthermore, there is some evidence that the legal and institutional characteristics of a countrymatter. The study of Demirguc-Kunt and Huizinga (1999) reports that taxation reduces bank prof-itability. Another study by Albertazzi and Gambacorta (2009) concludes that the impact of taxation onbanking profitability is small because banks can shift a large fraction of their tax burden onto depos-itors, borrowers, or purchasers of fee-generating services. Overall, although fiscal issues are likely toexert a significant influence on the behavior of a bank, the taxation of the financial sector has receivedlittle attention.

To measure the effects of market structure on bank profitability, thestructure–conduct–performance (market-power) hypothesis states that increased market poweryields 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 prof-itability of a bank and is, therefore, consistent with the traditional structure–conduct–performanceparadigm. 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 andbank 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 profitabilityis relatively sparse. Xiao (2009) runs qualitative and quantitative analyses to examine the performanceof French banks during 2006–2008. She concludes that French banks were not immune but provedrelatively resilient to the global financial crisis reflecting their business and supervision features. Apaper by Millon Cornett et al. (2010), looking at internal corporate governance mechanisms and theperformance of publicly traded U.S. banks before and during the financial crisis, finds that banks ofall size groups suffered bank performance decreases. However, they find that the largest banks facedthe largest losses. Beltratti and Stulz (2009) find that large banks with more Tier 1 capital and moredeposit 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 therelationship between the size of a bank and its efficiency (e.g. Hermann and Maurer, 1991; Rime andStiroh, 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, theimpact of the crisis on the determinants of bank profitability has not yet been widely analyzed. Fur-thermore, our study fills an important gap in the literature because no study has yet analyzed theprofitability determinants of Swiss commercial banks. These two novelties should make an importantaddition 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 ouranalysis of bank profitability. See Table 1 for a summary of the variables described below.


3.1. Dependent variables

We use the return on average assets (ROAA) as our main measure of bank profitability. The ROAAis defined as the ratio of net profits to average total assets expressed as a percentage. As alternativeprofitability measures, we use the return on average equity (ROAE), which is the ratio of net profitsto average equity expressed as a percentage, as well as the net interest margin (NIM). The latter isdefined 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. Itshows the profits earned per CHF of assets and indicates how effectively the bank’s assets are managedto generate revenues. To capture changes in assets during the fiscal year, our study relies on the averageassets value. As Golin (2001) points out, the ROAA has emerged as the key ratio for the evaluation ofbank 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 toshareholders on their equity. Although the financial literature commonly uses the ROAE to measureprofitability, we find that it is not the best indicator of profitability. For example, banks with a lowerleverage ratio (higher equity) usually report a higher ROAA but a lower ROAE. However, the ROAE dis-regards 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 themain 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 returnon assets, the net interest margin has been used in many studies of bank performance. While the ROAmeasures the profit earned per dollar of assets and reflects how well bank management uses the bank’sreal 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. Theyinclude bank-specific (internal) and market-specific (external) factors.

3.2.1. Bank-specific determinantsAs 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 netimpact of changes in this ratio is complex. For example, banks with higher capital-to-asset ratiosare considered relatively safer and less risky compared to institutions with lower capital ratios. Inline with the conventional risk-return hypothesis, we expect banks with lower capital ratios to havehigher returns in comparison to better-capitalized financial institutions. In contrast, highly capitalizedbanks are safer and remain profitable even during economically difficult times. Furthermore, a lowerrisk increases a bank’s creditworthiness and reduces its funding cost. In addition, banks with higherequity-to-assets ratios normally have a reduced need for external funding, which has again a positiveeffect on their profitability. From this point of view, a higher capital ratio should have a positive effecton profitability. Given that we have anticipated effects pointing in opposite directions, the impact of abank’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, andproperty 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 highercost-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 ofa bank’s credit quality. The loan loss provisions are reported on a bank’s income statement. A higherratio indicates a lower credit quality and, therefore, a lower profitability. Thus, we expect a negativeeffect 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. Onemight expect that a faster growing bank would be able to expand its business and thus generate greaterprofits. However, the contribution to profits that derives from an increase in deposits depends upona 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 dependson the credit quality of those assets. Growth is often achieved by investing in assets of lower creditquality, which has a negative effect on bank profitability. In addition, high growth rates might alsoattract 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 thegrowth of a bank’s loan volume relative to the average market growth rate of loans. From a theoreticalviewpoint, 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 (relativeto 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 impacton profitability. Furthermore, a high growth of the loan volume might also lead to a decrease in creditquality 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 dummyvariables for small, medium, and large banks. One of the most important questions in the literatureis which bank size maximizes bank profitability. For example, Smirlock (1985) argue that a growingbank size is positively related to bank profitability. This is because larger banks are likely to have ahigher degree of product and loan diversification than smaller banks, which reduces risk, and becauseeconomies of scale can arise from a larger size. Because reduced risk and economies of scale lead toincreased 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 sizeand profitability. This is due to agency costs, the overhead of bureaucratic processes, and other costsrelated to managing extremely large firms (e.g. Stiroh and Rumble, 2006; Pasiouras and Kosmidou,2007). Accordingly, the overall effect is indeterminate from a theoretical point of view. As a robustnesstest, 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 theirtotal income through traditional commercial banking activities (interest operations) and to a lesserextent through “fee and commission income” and “trading operations.” Because margins in fee andcommission 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 totalincome. This variable represents an item that is off the balance sheet and is a good proxy for the bank’sbusiness model.

Funding costs: Funding costs are defined as interest expenses over average total deposits and aremainly determined by a bank’s credit rating, competition, market interest rates, and by the composi-tion 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 thirdgroup refers to banks established before 1950. We expect older banks to be more profitable due totheir 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 bankas 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 dis-agreement among the empirical studies. Some studies (e.g. Bourke, 1989; 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 ownershipdoes affect bank profitability. Furthermore, we investigate whether being listed at a stock exchangehas an impact on bank profitability. As a potentially positive impact, listed banks face greater pres-sure for being profitable from their shareholders, the analysts, and the financial markets overall. As apotentially negative impact, listed banks, in contrast to unlisted banks, face many reporting and otherrequirements, which create significant additional costs. Therefore, the overall effect is indeterminateand remains to be answered empirically.


Table 1Definition of variables.

Variables Description

Dependent variables: bank profitabilityROAA Net profits over average total assets (%)ROAE Net profits over average total equity (%)NIM Net interest margin (in %), defined as net

interest income divided by total assets

Independent variables Expected effect

Bank-specific characteristics (internal factors)Equity over total assets Equity over total assets (%). This is a measure of

capital adequacy, respectively the bank risk.The higher this ratio, the lower the risk of thebank

+/−

Cost-income ratio Total expenses over total generated revenuesas a measure of operational efficiency (%)

–

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

–

Yearly growth of deposits Annual growth of deposits (%) +/−Difference between bank and market

growth of total loansDifference between the annual growth of abank’s lending volume relative to the averagegrowth rate of the market lending volume (%)

+

Bank size Dummy variables for different bank sizecategories. Bank size is measured by theaccounting 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 +Bank ownership Dummy variable: Public bank if public sector

owns more than 50% of the shares+/–

Dummy variable: Listed bank if institution islisted at the stock exchange

+/–

Nationality Dummy 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 issuedby the Swiss government (%)

+

Herfindahl index The market shares in the mortgage business ofall in Switzerland acting commercial banks byregion (market structure variable)

+/−

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 atleast 50% of the bank’s stocks are in foreign hands. We expect foreign banks to be less familiar withthe 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 macroe-

conomic 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 acrossthe Swiss market, where tax rates vary widely among the Swiss cantons. This variation provides anopportunity 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 theirtax 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 theimpact entirely. Thus, consistent with the results of Demirguc-Kunt and Huizinga (1999), we expecta 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 expectedto have a positive effect on bank profitability according to the literature on the association betweeneconomic growth and financial sector profitability (e.g. Demirguc-Kunt and Huizinga, 1999; Bikker andHu, 2002; Athanasoglou et al., 2008). Accordingly, because the demand for lending increases duringcyclical 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 Bankas proxy for the term structure of interest rates. Again, this variable varies over time but not acrosscantons. Commercial banks usually use short-term deposits to finance long-term loans. This maturitytransformation 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 theHerfindahl–Hirschman-Index, which is defined as the sum of the squares of the market shares ofall the banks within the industry, where the market shares are expressed as fractions. As the lendingbusiness of commercial banks in our sample is locally oriented, market structure and competitionalso vary by region. Therefore, we compute the Herfindahl index by region, measuring the marketshares 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 marketsearn monopoly rents, because they tend to collude (e.g. Gilbert, 1984). Because collusion may resultin higher rates being charged on loans and lower interest rates being paid on deposits, we expect thata higher bank concentration has a positive impact on profitability. On the other hand, a higher bankconcentration might be the result of a tougher competition in the banking industry, which would sug-gest 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 indeterminateand 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 regressionmodel 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 roughly90% of the assets of all banks. Information about bank age, bank ownership, and the nationality of thebank owners were taken from the Swiss National Bank and from the web pages of the respective insti-tutions. In addition to the bank-specific data, we use a set of macroeconomic and industry-specificvariables to explain bank profitability. The real GDP growth and the interest rates for 5-year and a2-year treasury bills issued by the Swiss Government were taken from the Swiss National Bank. Thedata for the Herfindahl index, measuring the market shares in the mortgage business of all commercialbanks acting in Switzerland, stems from Piazza (2008).

To use the data of the Bankscope database (BSC) for our statistical analysis, we had to edit thedata 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-bankingcredit institutions. We also exclude the big banks Credit Suisse and UBS AG from our sample (seealso below for a detailed description of the included institutions). In a further step, we eliminateduplicate information. If BSC reports both unconsolidated and consolidated statements, we droppedthe unconsolidated statement.


Table 2Number 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 372No. 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).

Similarly, we needed to make a choice concerning data from the banks with balance sheet datareported at the aggregated level. BSC builds aggregated statements by combining the statements ofbanks that have merged or are about to merge. Aggregated statements may then report the data ofgroups of affiliated banks that neither have financial links nor form a legal entity. As a result, a givenbank might be reported several times in database, namely as an independent unit by its consolidatedas well as by its unconsolidated statements. As Micco et al. (2007) outline, there are two ways to dealwith banks that have aggregated statements. The first is always to work with the aggregated statementand drop the observations for the individual banks. The second is to drop the aggregated statementand work with the individual banks up to the time of the merger and then, starting from the year ofthe 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 of1639 observations over the years from 1999 to 2009. To investigate the impact of the recent financialcrisis, 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 cantonalbanks, regional and savings banks, and Raiffeisen banks according to the official statistics maintainedby the Swiss National Bank. Furthermore, foreign-owned banks that are active in the traditional com-mercial banking activities, and other banks active in traditional lending business (mainly category 5.11commercial banks, as defined by the Swiss National Bank) are considered in our analyses (see Table 2).

Note that we explicitly exclude the two big banks Credit Suisse and UBS from our sample, and thisfor the following reasons: First, they provide all banking services (private banking, institutional assetmanagement, investment banking and commercial banking); second, a large share of their lendingactivities is abroad; and third because commercial banking is not a predominant part of their revenueson the accounted group level. Due to these reasons, it is hard to compare these two big banks with theother 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 ofroughly 85% of their income generated in the traditional field of interest income. For a more detaileddescription of our sample, please check “Appendix A”.

Table 3 reports the descriptive statistics for the variables used in our analyses. Let us briefly high-light a few interesting facts. On average, the banks in our sample have a ROAA of 0.63% over the entireperiod from 1999 to 2009. The difference between mean and median indicates that there exist largeprofitability differences among the banks in our sample. The same holds true for our second profitabil-ity measure, the ROAE, which amounts to 7.31% on average. The banks in our sample exhibit an averagenet interest margin of 1.73%. On average, the capitalization of banks is 8.55%, which, however, differsamong 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 ofthe credit portfolio, amounts to 0.35% on average, which seems quite low, but there exist again largedifferences among the banks in our sample with respect to this variable. As pointed out above, a sub-stantial 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 variablereflects the tax burden across the different Swiss cantons, which may differ significantly. Finally, notethat the Herfindahl index as our measure of bank concentration is 2450, which is quite high comparedto bank concentration ratios in other countries (see, e.g. Beck et al., 2006). The correlation matrix forthe independent variables can be found in Table 4.


Table 3Descriptive statistics.

Dependent variables: bank profitability Mean Median Std. dev.

ROAA 0.63 0.31 1.18ROAE 7.31 6.57 5.86NIM 1.73 1.67 0.56

Independent variables Mean Median Std. dev.

Bank-specific characteristics (internal factors)Equity over total assets 8.55 6.42 9.68Cost–income ratio 64.97 62.44 16.51Loan loss provisions over total loans 0.35 0.05 1.74Yearly growth of deposits 8.16 5.00 16.65Difference between bank and market

growth of total loans2.68 1.08 17.51

Dummy: large bank: totalassets > 512 m. USD

0.37 0 –

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

0.38 0

Dummy: small bank: totalassets < 212 m. USD

0.25 0 –

Interest income share 74.36 86.04 27.36Funding costs 2.30 2.03 1.34

Bank ageDummy: bank was founded before

1950 (reference category)0.70 1 –

Dummy: bank was founded between1950 and 1989

0.20 0 –

Dummy: bank was founded after1990

0.10 0 –

Bank ownershipDummy: bank is privately owned

(reference category)0.90 1 –

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

0.09 0 –

Dummy: bank is not listed at a stockexchange (reference category)

0.94 1

Dummy: bank is listed at a stockexchange

0.06 0 –

NationalityDummy: 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 18.75Real GDP growth 2.12 2.53 1.24Term structure of interest rates 0.51 0.45 0.29Herfindahl 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 seeTable 1.

4.2. Methodology

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

PERFit = c + ıPERFi,t−1 +J∑

j=1

ˇjXjit

+L∑

l=1

ˇjXjit

+ εit (1)

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Table 4Cross-correlation matrix of independent variables.

Eq. o.TA

CI ratio Loanlossprov o.TA

Yrgrowthdeposits

Growthtot.loans

Dumlargebank

Dumsmallbank

int.inc.sh

Fundcosts

Dumbankagemiddle

Dumbankageyoung

Dumstatebank

Dumlist.bank

Dumfor.bank

Eff. taxrate

Realgdpgrowth

Termstruc-ture

HHI

Loan lossprov o. TA

0.22 0.15 1

Yr growth ofdeposits

−0.07 0.09 0.05 1

Diff. growthtotal loans

−0.05 0.06 −0.02 0.21 1

Dum largebank

0.05 −0.07 −0.02 −0.02 0.01 1

Dum smallbank

0.09 0.08 0.09 0.02 0.01 −0.27 1

Interestincomeshare

−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 0.15 0.20 −0.12 0.18 1Dum bank

age middle0.31 0.07 0.13 0.05 0.02 0.13 −0.08 −0.41 0.04 1

Dum bankage young

0.31 0.25 0.09 0.08 0.07 0.04 0.09 −0.31 −0.06 −0.16 1

Dum statebank

−0.01 −0.11 −0.03 −0.08 −0.06 0.29 −0.12 0.05 0.09 −0.13 −0.11 1

Dum listedbank

0.02 −0.05 −0.02 −0.04 −0.05 0.31 −0.12 −0.05 0.06 −0.08 −0.05 0.28 1

Dum foreignbank

0.35 0.16 0.18 0.11 0.06 0.24 0.08 −0.32 0.06 0.28 0.28 −0.14 −0.09 1

Effective taxrate

−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 gdpgrowth

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

Termstructureof 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 0.01 0.05 −0.04 −0.08 0.21 0.02 −0.05 −0.09 0.04 −0.03 1


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 thebank-specific and market-specific explanatory variables as outlined above, and εi.t is the disturbance,with vi the unobserved bank-specific effect and uit the idiosyncratic error. This is a one-way error com-ponent regression model, where vi ∼ (IIN(0, 0, �2

v )) and independent of uit ∼ (IIN(0, �2u )). Bank profits

show a tendency to persist over time, reflecting impediments to market competition, informationalopacity and/or sensitivity to regional/macroeconomic shocks to the extent that these are serially cor-related (Berger et al., 2000). As a consequence, we specify a dynamic model by including a laggeddependent variable among the regressors, i.e. PERFi,t−1 is the one-period lagged profitability and ı thespeed of adjustment to equilibrium. A value of ı between 0 and 1 implies persistence of profits, butthey will eventually return to their normal level. A value close to 0 indicates an industry that is fairlycompetitive, 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 andinconsistent estimates (see Baltagi, 2001). Therefore, we use techniques for dynamic panel estimationthat are able to deal with the biases and inconsistencies of our estimates. Another challenge withestimation 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 byretaining 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 existsin 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 generalizedmethod of moments (GMM) following Arellano and Bover (1995), also known as system GMM esti-mator. This methodology accounts for endogeneity. The system GMM estimator uses lagged values ofthe dependent variable in levels and in differences as instruments, as well as lagged values of otherregressors which could potentially suffer from endogeneity.

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

In a first step, we estimate our model over the entire time period from 1999 to 2009. In order toinvestigate the impact of the recent financial crisis on the determinants of banking profitability, weadditionally split up the sample into two time periods, namely the pre-crisis period ranging from 1999to 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. Asnoted later on, we computed several tests in order to make sure that our results are not affected bymulticollinearity issues.

5. Empirical results

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

Our estimation results point out to stable coefficients. Also, the Wald-test indicates fine goodness offit and the Hansen test shows no evidence of over-identifying restrictions. The equations indicate thata negative first-order autocorrelation is present. However, this does not imply that the estimates are

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

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319Table 5Regression results for returns on average assets (ROAA) as dependent variable.

Dependent variable: ROAA All years Before the financial crisis: 1999–2006 During the financial crisis 2007–2009

Bank-specificfactors

Bank- andmarket-specific factors





L.ROAA 0.087*** (0.023) 0.090*** (0.023) 0.044 (0.029) 0.051* (0.030) 0.156***(0.049) 0.131*** (0.050)Equity over total assets −0.011 (0.007) −0.014** (0.007) 0.005 (0.011) 0.005 (0.011) −0.023** (0.010) −0.028*** (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)Loan loss provisions over total loans 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 loans0.008*** (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>512m. 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<212m. 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)

Interest income share −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 19890.687*** (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 city1.336*** (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 stockexchange

−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)Term structure of interest rates – 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)Constant 4.185*** (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 658Number of banks 372 372 318 318 263 263Wald-test �2(22) = 1898.26 �2(25) = 1886.68 �2(22) = 966.46 �2(25) = 963.19 �2(22) = 1232.55 �2(25) = 1238.11Hansen 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)AB test AR(2) (p-value) (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 assetsROAA. 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 areinstrumented 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) referto 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.


inconsistent. Inconsistency would be implied if second-order autocorrelation was present (Arellanoand 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 mea-sure ROAA, is statistically significant across all models, indicating a high degree of persistence of bankprofitability and justifying the use of a dynamic model.

Overall, we observe some significant differences between the estimation results of the differenttime 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 profitabilitybefore the crisis. However, it has a negative and significant effect on bank profitability as measuredby ROAA during the financial crisis 2007–2009. One of the main reasons for this relation is that saferbanks 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 signifi-cantly higher income earnings as the demand for lending decreased in this period. Even though totalearnings for these banks often slightly increased during this time of turmoil, profitability decreased asbanks did not find attractive investment opportunities or were lowering net interest margins in orderto lend their additional deposits.)

The coefficient of the cost-to-income ratio, our operational efficiency measure, is negative andhighly 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 nothave a statistically significant effect on bank profitability before the crisis. This is not surprising, giventhat banks in Switzerland had very low loan loss provisions before the financial crisis. However, theloan loss provisions have significantly increased during the crisis, and this is reflected in its negativeimpact 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, andthis effect is mainly driven by the crisis years. Banks in Switzerland were not able to convert theincreasing amount of deposit liabilities into significantly higher income earnings above all in recenttime 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 timeperiods. The effect of a faster growing loan volume seems to over-compensate the risk that a too fastgrowth 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 largerand 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 productand loan diversification possibilities, and/or economies of scales (see Smirlock, 1985; Bikker and Hu,2002). However, large banks in Switzerland were less profitable than small and medium-sized bankduring the past 3 years of the financial crisis.2 The main reasons for this negative relationship betweensize and profitability are that larger banks in Switzerland had relatively higher loan loss provisionsduring the crisis and that larger banks were found to have significantly lower net interest margins intimes of turmoil than smaller banks (see below). This might also be a consequence of some reputationalproblems 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 traditionalbanking income (interest income) and to a lesser extent of fee and commission income as well astrading operations. Our findings show that banks with a higher share of interest income relative tothe total income are significantly less profitable, and this holds before as well as during the crisis, withthe 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 stockexchange turnover.

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


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 thecrisis, where funding costs dropped anyway to a historically low level. Furthermore, bank age has asignificant impact on banking profitability in the whole sample period. In contrast to our hypothesis asformulated above, older banks are not more profitable than recently founded banks or banks foundedbetween 1950 and 1989. Newer banks seem to be even more profitable than older banks. This meansthat newer banks are able to pursue successfully new profit opportunities and that a longer traditionof 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 theprofitability measure as well.

Our results regarding the impact of ownership on profitability before the crisis support the findingsof Bourke (1989), Molyneux and Thornton (1992) and Athanasoglou et al. (2008) that the owner-ship status (private or state-owned banks) is irrelevant for explaining profitability. Our results fromSwitzerland stand thus in contrast to the findings of Micco et al. (2007) and Iannotta et al. (2007), whopoint 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 provideempirical evidence for the Swiss market that state-owned banks are more profitable than privatelyowned banks during the financial crisis. In this time of turmoil, state-owned banks were consideredas safer and better banks in comparison to privately owned institutions. International ownership of abank seems to have a significant impact on bank profitability when considering the whole model. Infact, foreign-owned banks in Switzerland seem to be less profitable than their Swiss competitors. Thisresult 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 issome empirical evidence that banks listed on the stock exchange are slightly less profitable than banksthat are not. This holds for all specifications.

Considering the external factors related to the macroeconomic environment and the financial struc-ture 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 find-ings of Demirguc-Kunt and Huizinga (1999) that higher tax rates lead to a lower post-tax profit. Thisresult 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 israther small. Overall, it seems that banks are able to shift a large fraction of their tax burden onto theirdepositors, borrowers, and purchasers of fee-generating services.

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

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

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

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 measureROAA, 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 onlyhold for the period before the crisis, but also thereafter. Banks listed at the stock exchange are more

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21 (2011) 307–327Table 6Regression results for returns on average equity (ROAE) as dependent variable.

Dependent variable: ROAE All years Before the financial crisis: 1999–2006 During the financial crisis 2007–2009







L.ROAE 0.181*** (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)Loan loss provisions over total loans −0.078 (0.081) −0.090 (0.082) −0.176 (0.116) −0.197* (0.119) −0.535* (0.325) −0.474 (0.333)Yearly growth of deposits 0.024** (0.011) 0.017 (0.012) 0.006 (0.019) 0.004 (0.019) −0.004 (0.011) −0.001 (0.012)Diff. between bank and market growth

of total loans0.025*** (0.007) 0.031*** (0.008) 0.044*** (0.012) 0.046*** (0.014) 0.020*** (0.007) 0.008 (0.009)


2.412** (1.093) 2.647** (1.101) 8.654*** (1.717) 8.587*** (1.736) −4.462*** (1.323) −4.580*** (1.355)


3.289*** (1.131) 3.521*** (1.153) 9.298*** (1.765) 9.345*** (1.778) −0.962 (1.344) −0.726 (1.413)

Interest income share −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 was founded between

1950 and 19890.475 (1.199) 0.290 (1.231) 3.407* (1.900) 3.326* (1.943) −0.441 (1.593) −0.844 (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)Dummy: bank is (co-)owned by state

or city−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)

Dummy: bank is a foreign bank −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)Term structure of interest rates – 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)Constant 25.116*** (1.734) 22.024*** (2.366) 20.559*** (2.749) 17.455*** (3.521) 30.667*** (2.607) 32.572*** (3.330)Number of observations 1639 1639 981 981 658 658Number of banks 372 372 318 318 263 263Wald-test �2(22) = 911.46 �2(25) = 914.37 �2(22) = 660.12 �2(25) = 653.21 �2(22) = 812.15 �2(25) = 801.76Hansen test (p-value) (1.000) (1.000) (1.000) (1.000) (1.000) (1.000)AB test AR(1) (p-value) (0.015) (0.01) (0.085) (0.08) (0.04) (0.042)AB test AR(2) (p-value) (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 equityROAE. 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 instrumentedthrough 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 theArellano–Bond test that average autocovariance in residuals of order 1 respectively of order 2 is 0 (H0: no autocorrelation); p-values in brackets.

A.D

ietrich,G.W

anzenried/Int.Fin.M

arkets,Inst.andM

oney21 (2011) 307–327

323Table 7Regression results net interest margin (NIM) as dependent variable.

Dependent variable: NIM All years Before the financial crisis: 1999–2006 During the financial crisis 2007–2009







L.NIM 0.668*** (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)Loan loss provisions over total loans 0.017*** (0.004) 0.012*** (0.004) 0.020*** (0.005) 0.014*** (0.005) 0.009 (0.028) 0.001 (0.028)Yearly growth of deposits −0.001 (0.001) −0.002*** (0.001) 0.001 (0.001) −0.001 (0.001) −0.006*** (0.001) −0.006*** (0.001)Diff. between bank and market growth

of total loans0.001* (0.000) 0.003*** (0.000) 0.003*** (0.001) 0.006*** (0.001) 0.003*** (0.001) 0.004*** (0.001)


−0.162*** (0.052) −0.090* (0.053) −0.084 (0.067) −0.034 (0.068) −0.242** (0.122) −0.235* (0.123)


0.117* (0.060) 0.108* (0.061) 0.144* (0.079) 0.176** (0.080) −0.378*** (0.122) −0.354*** (0.122)

Interest income share −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 19890.197*** (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 after 1989 0.021 (0.066) 0.020 (0.066) 0.084 (0.086) 0.025 (0.087) −0.288** (0.142) −0.288** (0.142)Dummy: bank is (co-)owned by state

or city0.729*** (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)

Dummy: bank is a foreign bank −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)Term structure of interest rates 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)Constant 0.565*** (0.079) 0.007 (0.120) 0.699*** (0.110) −0.074 (0.159) 0.923*** (0.221) 0.608** (0.276)Number of observations 1639 1639 981 981 658 658Number of banks 372 372 318 318 263 263Wald-test �2(22) = 3251.90 �2(25) = 3420.79 �2(22) = 2183.61 �2(25) = 2274.52 �2(22) = 1392.62 �2(25) = 1441Hansen test (p-value) (1.000) (1.000) (1.000) (1.000) (1.000) (1.000)AB test AR(1) (p-value) (0.001) (0.002) (0.003) (0.001) (0.003) (0.001)AB test AR(2) (p-value) (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 instrumentedthrough 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 theArellano–Bond test that average autocovariance in residuals of order 1 respectively of order 2 is 0 (H0: no autocorrelation); p-values in brackets.


profitable during the crisis than unlisted banks. This result might be driven by the fact that the returnon equity reflects shareholder maximization attempts, which is a common practice of some of thelisted banks. In particular, some listed banks in Switzerland may have effectively lowered their equitycapital in order to increase the ROAE. Also, there is empirical evidence that foreign-owned banks arenot 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 bankprofitability 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 twosubsamples pre-crisis and crisis years. Analyzing the determinants of the net interest margin helps usto better understand some results of the ROAA specifications. For instance, it is interesting to see thatlarger 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 referringyears. Furthermore, banks with a high relative loan growth exhibit higher net interest margins. Thisis also a possible explanation for the positive relationship between the relative loan growth and bankprofitability when measured by both the ROAA and the ROAE. Furthermore, banks with a higher shareof interest income of total income have lower net interest margins. This might be another reason whybanks with a higher interest income share are less profitable than banks that have a better incomediversification.

6. Conclusions

This paper has examined how bank-specific characteristics, industry-specific and macroeconomicfactors affect the profitability of 372 commercial banks in Switzerland over the period from 1999 to2009. To account for the impacts of the recent financial crisis, we separately considered the yearsbefore 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 bankingmarket, which is surprising given that Switzerland is one of the most important banking centers in theworld. Similarly, there exist very few papers that investigate the impact of the recent financial crisison bank performance.

We use a dynamic model specification that allows for profit persistence. Our results clearly showthat there exist large differences in profitability among the banks in our sample and that a significantamount of this variation can be explained by the factors included in our analyses. In particular, bankprofitability is mainly explained by operational efficiency, the growth of total loans, funding costs andthe 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 lowerprofitability. The interest income share also has a significant impact on profitability. Banks that areheavily 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 withrespect to the underlying mechanisms that determine bank profitability. The results outlined in thispaper provide some evidence that the financial crisis did indeed have a significant impact on the Swissbanking industry and on bank profitability in particular.

Overall, our results provide some interesting new insights into the mechanisms that determinethe 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, weconsider a larger set of bank- and market-specific determinants of bank profitability, which extendsour knowledge of bank profitability with respect to several important dimensions. These extensionslet 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 someimportant changes in the banking industry. In addition, by dividing the sample into pre-crisis andpost-crisis segments, we gain additional insights into the impacts of the financial crisis on financialinstitutions. 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 specificationallows for the fact that bank profits show a tendency to persist over time and tend to be seriallycorrelated, reflecting impediments to market competition, informational opacity, and sensitivity toregional and/or macroeconomic shocks.

Even though our sample includes a large fraction of all commercial banks active in Switzerland andconsiders the main bank profitability determinants as well as factors related to the institutional andmacroeconomic 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 bankprofitability. In addition, it could be fruitful to integrate specific information on management andboard members, e.g. education, skill level, experience, independence, all of which are increasinglyimportant factors in understanding bank profitability. Some of these issues will be addressed in futurework.

Acknowledgements

We would like to thank participants of the 2009 Annual Meeting of the European Financial Manage-ment 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, UrsBirchler and Kevin Walsh for helpful suggestions, and the Lucerne University of Applied Sciences andArts 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 allbanking 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 fewspecial clients. Swiss banks vary of the degree to which they use the option to engage in all financialactivities. Some banks really do offer universal services, while other institutions specialize either intraditional banking or in asset management. In the official statistics maintained by the Swiss NationalBank, 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 briefdescription of each type below.

The two “big” banks, UBS AG and the Credit Suisse Group, are the largest and second largest Swissbanks. Both banks have extensive branch networks throughout the country and most internationalcenters. We do not include the two big banks in our sample because they pursue all lines of finan-cial activities (private banking, institutional asset management, investment banking and commercialbanking), because a large share of their lending activities is abroad and because commercial bankingis 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’scapital is owned by the sponsoring canton, which also guarantees the bank’s liabilities. According tocantonal law, the objective of a cantonal bank is to promote the canton’s economy, although cantonalbanks must comply with commercial principles in their business activities. Collectively, the cantonalbanks account for around 30% of banking business in Switzerland and have a combined balance sheettotal that is greater than 300 billion Swiss francs. Formerly, there were at least one or two cantonalbanks 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 allbanking businesses, with an emphasis on lending/deposit business, and they operate primarily in themarket of their home canton. Because these banks are active mainly in the traditional commercialbanking business, we include all 24 cantonal banks in our sample.

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


Raiffeisen Switzerland, the third largest bank group in Switzerland, is comprised of 390 memberbanks, most of which are located in rural areas, and each of which is run as a cooperative. Collec-tively, the 390 Raiffeisen banks control a network of 1154 branch offices, the largest such network inSwitzerland, 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 totally1154 branches together form Raiffeisen Switzerland. Raiffeisen Switzerland coordinates the group’sactivities, creates the conditions for the business activities of the local Raiffeisen banks and advises andsupports them in many issues. The bank group is organized as a cooperative and has positioned andestablished itself as the third largest bank group in Switzerland. As one of Switzerland’s leading retailbanks, Raiffeisen is mainly focusing on mortgage lending. Raiffeisen meanwhile counts 1.4 millionSwiss 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 banksindividually.

Foreign banks are institutions operating under Swiss banking law, but whose capital is primarilyforeign controlled. Foreign control means that foreigners with qualified interests hold over half ofthe 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 qualifyas 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-ownedbanks that are active only in asset management for private clients.

Private bankers are among the oldest banks in Switzerland. They are unincorporated firms, primar-ily active in asset management for private clients. Private bankers are subject to unlimited subsidiaryliability with their personal assets. Because these private banks, which do not publicly offer to acceptsavings deposits, are not active in the traditional banking field, and because they do not have to publishdata, we do not include them in our analysis.

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

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