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DECLARATION

This work has not previously been accepted in substance for any degree and is not

being currently submitted in candidature for any degree.

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Date…………………………………………

STATEMENT 1

This work is the result of my own investigations, except where otherwise stated.

Where correction services have been used, the extent and nature of the correction is

clearly marked in a footnote(s).

Other sources are acknowledged by footnotes giving explicit references. A

bibliography is appended.


Date…………………………………………

STATEMENT 2

I hereby give consent for my work, if accepted, to be available for photocopying and

for inter-library loan, and for the title and summary to be made available to outside

organisations.


Date…………………………………………

1



Table of Content

1. Introduction ……………………………………………….…..................…… 3

2. Literature review ………………………………………….…...……………... 6

2.1. Overview …..………………………………………….…..…………… 6

2.2. Internal determinants ……………….……....……………….……........ 6

2.3. External determinants …………...……..…............................................ 8

2.3.1. Market Characteristics …………………….…....………….....… 8

2.3.2. Macroeconomic characteristic ……………………………....… 10

2.4. Studies on Swiss banking system ………………………………...…... 11

2.5. Studies of bank profitability ………………....……………………..… 12

3. Switzerland Banking Sector …………………………....…....…………..….. 14

3.1. An overview of the Swiss economy …………………..…………..….. 14

3.2. History of Swiss Banking ……………………………....….….....…… 16

3.3. Understanding the Swiss Banking System ………………....….….….. 17

3.4. Future of the Swiss banking system ………………………….…….… 22

4. Determinants and variable selection ………………………………………... 24

4.1. Dependent variable …………………………….……..……………..... 24

4.2. Independent variables ………………………………..…….………… 26

4.2.1. Bank-specific determinants of profitability …………….…..…... 26

4.2.2. Macroeconomic determinants ………………………...….……... 314.3. Conclusion ……………………………………………………….….... 33

5. Methodology and data description ……………………………...…………... 36

5.1. Data …………………………………………………………………… 36

5.2. The regression model …………………………………….…….……... 37

5.3. Econometric specification ……………………………………...……... 39

5.4. Data description …………………………………………………....….. 43

5.5 Conclusion …………………………………………………………..…. 45

6. Empirical results ………………………………………………….……….… 46

7. Summary and conclusions ………………………..……………………….… 58

Acknowledgements …………………………………………………………….. 60

References …………………………...……………………………….………… 61

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Chapter 1

Introduction"With a group of bankers I always had the

feeling that success was measured by the

extent one gave nothing away."

Lord Longford

Banks play a fundamental role in the smooth functioning of economies in most

countries around the world. During recent years due to deregulation, technological

change, and globalization of financial markets the banking sector has experienced

major development and changes in its operating environment. There has also been an

increasing trend towards disintermediation. In spite of the changing climate, the

banking system continues to play a central role in financial economic activities in

general as well as particular market segments in developed countries and the

developing world. A healthy banking sector is crucial for resisting negative economic

shocks and contributes to the stability of the financial system as a whole. In light of

this, investigations of bank performance and the determinants of bank profitability

have attracted a great deal of interest on part of academic researchers as well as

banking industry. The questions relating to bank profitability have become even more

relevant and urgent in the view of the ongoing world wide financial crisis, which is

leaving a deep impact on the banking industry in many countries.

A particularly interesting region for investigating bank profitability is Europe,

where competition has intensified significantly during last decades. Switzerland lies in

the heart of Europe and European banking. In the present work I focus on the Swiss

banking sector. Switzerland has traditionally been one of the most important banking

centers in the world. Characterized by client privacy, tax privileges and economic

stability, the banking environment of Switzerland has long enjoyed a beneficial

position in the competitive world market. Furthermore, the banking system plays a

crucial role in the Swiss financial system as well as the economy as a whole. These

factors make Switzerland an attractive candidate to study determinants of bank

profitability. One of the aims of this study is to get a better understanding of the

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present conditions and trends of the Swiss banking sector, and compare our findings

with previous works. In addition, I compare some of the findings on Swiss banks with

analogous studies from other countries.

The main goal of this dissertation is to investigate the effect of bank specific and

macroeconomic determinants on the bank profitability in Switzerland. This study is

similar in style and is partially based on treatments found in Bourke (1989), Molyneux

& Thorton (1992), Demirguc-Kunt & Huizinga (2000), Athanasoglou et al. (2006),

Pasiouras & Kosmidou (2006) and Deitrich & Wanzenried (2009). In the present

work, following the treatment in the mentioned literature, I use a linear regression

model for estimating the effect of various determinants on profitability. The

determinants of profitability used in this work are divided into two groups. The first

group involves eight bank-specific variables characterizing liquidity, bank capital,

credit risk, productivity growth, operating expenses, size, population density of the

region where the bank is situated as well as two dummy variables characterizing the

bank category (Cantonal, Raiffeisen or savings). The second group involves

macroeconomic factors that can affect bank profitability: GDP rate and inflation rate.

The bank profitability was measured in terms of return on average assets (ROAA) and

return on average equities (ROAE). In this study, I employed the data from 16 Swiss

banks covering a period between 2003 and 2008. I restricted the analysis to three

categories of banks, namely Cantonal, Raiffeisen and regional savings banks.

The results of the regression analysis show that five variables show a statistically

significant correlation with bank profitability. These variables are equity to assets ratio

(capital), cost to income ratio (operating expenses), logarithm of the total assets by the

price index (bank size), population density, as well as the macroeconomic variable

inflation. Equity to assets ratio, bank size and population density show a positivecorrelation with bank profitability variables ROAA and ROAE. On the other hand,

cost to assets ratio and inflation show a negative. The current findings are broadly in

agreement with previous studies. In addition, by comparing the resulting regression

coefficients standard deviations of the relevant variables with the mean value ROAA

(as explained in Chapter 6), I analyze the economic significance of the variables for

bank profitability. I find that only equity to assets, population density and bank size

variables are economically significant.

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The rest of the work is organized as follows. Chapter 2 is a literature review,

where I discuss the existing works on bank profitability in general and works on

profitability in Swiss banks in particular. Chapter 3 provides a general overview of

Swiss economy and its banking sector. In this section I discuss the history and

development of the banking sector in Switzerland, and describe its current state. I

focus on the bank classification and mention the regulatory bodies that oversee them.

In Chapter 4, I give a detailed description of the two variables used to characterize

bank profitability as well as various determinants that are used in this study, and

explain the economic significance of these variables. In Chapter 5, I report and

explain the procedure of obtaining real empirical data, as well as describe the data

provide its descriptive statistics. Following this, I specify the regression models used

to study profitability and conduct auxiliary econometric tests on the data. In Chapter

6, I present the main results of the regression analysis along with economic

interpretations and comparisons with previous studies found in literature. Finally, I

conclude in Chapter 7 with an overview of the main results.

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Chapter 2

Literature review

2.1 Overview

In literature, bank profitability is typically measured by two variables, return on equity

(ROE) and/or return on assets (ROA). The determinants of profitability are usually

divided into two categories, factors internal and external to the bank. The internal

determinants arise from the bank’s accounts and are mainly influenced by

management decisions of the bank and its policy goals. For example, among the most

widely used internal determinants are bank size, capital adequacy, expenses, risk

management and liquidity. On the other hand, the external factors are independent of

specific bank management and reflect industry wide and macroeconomic effects. In

this Chapter I outline the main determinants of bank profitability studied in the

literature. Following this, I give a brief overview of literature dealing with profitability

of Swiss banks and profitability of banks in general.

2.2 Internal determinants

Let us begin by looking at the current literature dealing with the internal determinants

of profitability. Several studies concentrate on bank size and profitability relations.

Size is usually used to detect potential economies or diseconomies of scale in the

banking sector. Akhavein et al. (1997), Bourke (1989), Smirlock (1985) and Goddard

et al (2004) have shown a positive relationship between size and bank profitability. In

addition, Short (1979) suggests that bank’s size is linked with it’s capital adequacy,

since large banks raised less expensive capital and consequently seemed more

profitable. Haslem (1968), Short (1979), Goddard (2004) emphasize that size of banks

is closely related to capital ratios, which implies that growth in size will lead to the

increase in profitability. However, Gibson (2001) argues that positive effect of bank

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size on profitability is valid only up to a particular limit. Berger et al (1987) have also

argued that very large banks face scale inefficiencies.

The other important determinant of profitability, considered in a large number of

papers, is bank’s expenses. Bank expenses are linked with the notion of efficient

management. Some of the literature suggests that expenses-related variables should be

included in the cost part of the profit function. Bourke (1989) suggest that reduction of

expenses improve the efficiency and as a consequence increase profitability, implying

a negative relation between profitability and operating expenses. In addition,

Molyneux & Thornton (1992) and Bourke (1989) emphasize a positive correlation

between quality of management and profitability.

Yet another important aspect of the banking business affecting profitability is risk

management. Poor asset quality and low level of liquidity are the two main reasons of

bank failures. Bank’s risks can be divided into liquidity and credit risks. Liquidity risk

arises when banks are unable to meet their liquidity liabilities because of unforeseen

withdrawals of deposits, and can be considered as determinant of bank’s profitability.

Bourke (1989) finds a positive relationship between the level of liquidity and

profitability. In contrast, Molyneux & Thornton (1992) report an opposite results. The

credit risk arises when a customer defaults on a loan or any other type of financial

contracts. The changes in the credit risk can dramatically affect the performance of the

bank and its loan portfolio. There is a significant amount of literature that concentrates

on the relationship between credit risk and profitability in the banking sector. The

studies that include Duca & McLaughlin (1990), Miller & Noulas (1997) indicate

negative effect of credit risk on profitability. These studies show that an increased

exposure to credit risk typically leads to a decline in the bank profitability. Miller &

Noulas (1997) argue that the higher the risk loans held by a financial institution, thehigher is the possibility of unpaid loans and consequently lower the profitability.

Several works argue that leverage is an important factor in explaining bank

performance. In general, leverage refers to the use of debt to supplement investment.

Molyneux (1993) emphasizes that higher level of equity would reduce the cost of

capital, leading to an increase in profitability. Furthermore, an increase in capital can

increase expected earning via reducing the expected cost of financial distress. Bourke

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(1989), Molyneux & Thornton (1992) and Goddard et al. (2004) find a positive

relationship between capital ratios and bank profitability.

2.3 External determinants

Let us move to external determinants of bank profitability. The external variables are

usually divided into the two groups. The first group describes characteristics of the

market, such as concentration, ownership status, competition and industry size. The

second group of variables describes the macroeconomic environment, and includes

interest rate, inflation or cyclical output.

2.3.1. Market Characteristics

All investigations of the structural effect on bank’s profitability typically start with the

statement of two alternative hypotheses: Efficient-Structure (ES) and Market-Power

(MP) hypotheses. The Efficient-structure hypothesis suggests that financial institution

can observe higher profits if they have superior management and productiontechnologies that have lower cost. The increase in managerial and scale efficiencies

lead to higher concentration and consequently to the higher profits. The Market-Power

hypothesis states that increase in market power leads to monopoly profits. More

specifically, Relative-Market-Power hypothesis claims that only firms with large

market shares and well-differentiate products can achieve monopoly power and earn

non-competitive profits.

Numerous studies, such as Berger (1995 a), Smirlock (1985), Berger & Hannan

(1989) and Frame and Kamerschen (1997) analyze the profit–structure relationship in

banking and test the two hypotheses. Frame & Kamerschen (1997) reject the notion

that profits are result of superior x-efficiency (a version of the ES) and conclude that

only market power leads to super-normal profits. However, Smirlock (1985), who

directly tested these competing hypotheses, has provided evidence in favor of ES

hypothesis. Smirlock (1985) used data from a sample of 2700 banks from the 10th

Federal Reserve District in USA during a period between 1973-1978, showed a

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positive relationship between market share and profitability. However he did not find

evidence for correlation between market structure and profitability. Similar

investigations were conducted by Berger (1995a), who showed that managerial

efficiency not only raised profits, but also leads to market share gains and

consequently increased concentration. Berger (1995) emphasizes that the positive

relationship between concentration and profits may be a spurious result due to

correlation with other variables. Consequently, Berger (1995) claimed that, taking into

account other factors, the role of the concentration is small. In contrast, other authors,

such as Bourke (1989) and Short (1979), suggest that increased concentration is not a

result of managerial efficiency, but rather reflects an increasing deviation from

competitive market structure leading to a high level of profitability. Hence, as

suggested by these authors, concentration can positively correlate with profitability.

This is consistent with the traditional structure-conduct-performance paradigm.

Molyneux & Thornton (1992) also found support for this view by taking a sample of

European banks in eighteen countries between 1986-1989. The theoretical links

between the measure of concentration and market power (MP) have been investigated

by a number of works. For instance, Cowling & Waterson (1979), Dansby & Willig

(1979) and Novshek (1980) have shown that a Cournot oligopoly would generate

equilibrium price-cost margins.

The general view on competition and market structure relationships is based on

monopoly power hypothesis. In line with this hypothesis, more concentrated markets

tend to be more collusive with banks achieving monopolistic profits since they operate

with wider margin of intermediation. These arguments are referred to as the

“Structural Model” since they are based on the structure of the banking market.

However, “Non Structural Model” have also been developed, to tackle the theoreticaland empirical deficiencies of the structural model. The works of Rosse & Panzar

(1977) and Panzar & Rosse (1982, 1987) elaborate on the “non structural model”,

developing the so-called H-statistic, which was later used for the examination of the

competitive structure of the banking sector. The Panzar and Rosse (P-R) model

measures competition and investigates the competitive conduct of banks without

employing any information about structure of the market. The P-R methodology was

widely used in literature for investigating the relationship between competition,

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concentration and profitability. Coccorese (2004), in his investigations of Italian

banking industry, used Rosse-Panzar test and to determine the link between observed

level of local banking competition and macroeconomic performance in a given region.

Al-Muharrami et al (2006) investigated the market structure of the GCC (Gulf

Cooperation Council) banking industry during the period 1993-2002 and evaluated the

monopoly power of banks. By using P-R model, they found a mixed bag of

competitive and monopolistic competition as well as monopoly within the GCC

economies. Their findings emphasized that the banking market in GCC, except of

Saudi Arabia and Kuwait, have some way to go in developing a competitive structure.

Several works investigate the relationship between bank regulation and

supervision on the one hand and profitability on the other. Pasiouras et al. (2007),

investigated 677 commercial banks operating in 88 countries covering the period 2000-

2004, and found empirical evidence of correlation of cost and profit efficiency with

regulation and supervision around the world.

Another question, which has been analyzed in the literature, is related to the

ownership status of the bank and its connection to profitability. According to the Short

(1979) the government ownership of the banks is correlated inversely with

profitability. The work of Barth et al. (2004) also emphasizes the relationship betweengovernment ownership and bank efficiency. In contrast, Molyneux & Thornton (1992)

have shown a significant positive correlation between return on capital and

government ownership, suggesting that state-owned banks generate higher returns on

capital than their competitors from the privet sector. Thus, the debate on this question

remains open.

2.3.2. Macroeconomic characteristic

Let us move to the second group of external determinants of bank profitability, which

deal with macroeconomic control variables. The bank profitability is very sensitive to

the changes in macroeconomic conditions, in spite of the trends in the industry

directed at diversification and large use of financial engineering to manage risk

associated with business cycles. In general, when the economy is doing well, the

banks are encouraged to lend more funds and charge higher margins, by improving the

10



quality of assets. The opposite is the case during an economic downturn. The variables

that are usually employed to characterize the macroeconomic conditions are long-term

interest rate, inflation rate, and growth rate of money supply. According to Revell

(1979) the relationship between inflation and profitability depends on whether wages

and other operating expenses rise at a rate faster than inflation. Consequently, in a

mature economy the future inflation can be accurately predicted and banks can

properly manage their operating costs. In this context, Perry (1992) argued that the

way in which inflation affects bank profits depends on whether inflation expectations

are fully predicted. A full anticipation of inflation rate implies that banks could adjust

interest rates faster than the rise in their costs leading to higher profits. Molyneux &

Thornton (1992) have shown a positive correlation between long-term interest rate,

profitability and inflation on the one side and profitability on the other.

Several works concentrate on the relationship between profitability and business

cycle. According to Demirguc-Kunt & Huizinga (2000) and Bikker & Hu (2002) a

positive correlation exists between the business cycle and bank profits. However, the

variables taken to show this relation were not a direct measure of business cycle. In

their investigations Demirguc-Kunt & Huizinga (2000) employed the annual growth

rate of GDP and GNP per capita as the variables to determine dependence of bank

profits on business cycle. On the other hand, Bikker & Hu (2002) used other variables,

such as GDP, unemployment rate and interest rate to show this dependence.

2.4 Studies on Swiss banking system

Overall, there exists a large number of works dealing with the Swiss banking sector.

However, only few of these works deal with determinants of profitability. Early works

on Swiss banking sector were mainly concentrated on the relationship between the

size of the banks and efficiency. Hermann & Maurer (1991) analyzed economies of

scope and scale applying a translog cost function for 1989. Their investigations show

that economies of scale exist both in classical and investment banking sectors, except

for the very large banks. However, the largest banks can have the benefits from

economies of scope. Sheldon (1994) and Sheldon & Haegler (1993) also investigate

economies of scale and scope together with cost efficiency for Swiss banks between

11



1987 and 1990. They employed parametric and non-parametric methods. Their

analysis did not show significant evidence for substantial economy of scale and scope.

Although, inefficient banks did tend to be small overall. According to the Bikker

(1999) who analyzed the cost efficiency of the banking systems in nine European

countries, Swiss banks rank among the best in terms of cost efficiency. However, this

investigation did not deal with the examination of the economy of scale and scope and

the link between efficiency and size. Rime & Stiroh (2003) examined the performance

of the Swiss banks over the period between 1996 and 1999. Their study indicates that

economy of scale exists for the group of small and mid-sized banks, however little

evidence of economy of scale and scope was found for the very large banks. A recent

work by Freuler (2005) investigated cost efficiency and economy of scale for the

panel of 1737 savings and commercial banks of Switzerland, Norway and the

European Union between 1983 and 1997. This study also did not find evidence for

economy of scale. It showed that bank mergers and growth in the size of banks do not

lead to higher efficiency.

Later works on the Swiss banking sector, such as Neuberger & Schacht (2005) and

Neuberger et al (2008), focus mainly on a study of banking relationships. Bichsel &

Blum (2002) have analyzed the relationship between changes in leverage and changes

in risk. Egli & Rime (1999) have examined the influence of UBS-SBC merger on

concentration in the retail banking sector and the effect on consumers. In their work,

Egli & Rime (1999) found no significant dependence between market concentration

and interest rates for mortgages.

2.5 Studies of bank profitability

As was mentioned earlier, the literature on determinants of profitability in Swiss

banks is relatively sparse. Dietrich & Wanzenried (2009) analyze the profitability of

commercial banks in the Switzerland over the period between 1999 and 2006. In order

investigate the effect of the external and internal factors on bank profitability they

employed a linear regression model that included bank-specific, industry-specific and

macroeconomic characteristics as possible determinants. The results of their study

show significant difference in profitability between commercial banks in Switzerland.

12



Their analysis suggests that better capitalized banks seem to be more profitable. In

addition, they find that in the case when a bank’s loan grows faster than the market,

there tends to be a positive impact on profitability.

Another study relevant to the current work was made by Athanasoglou et al.

(2005). This work examined the effect of bank-specific, industry-specific and

macroeconomic determinants on bank profitability in Greece. This work used the

panel for Greek banks covering the period 1985-2001. Their results showed that all

bank-specific determinants affected bank profitability significantly, in an anticipated

way. Furthermore, no evidences were found in support of the Structure Conduct

Performance hypothesis.

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Chapter 3

Switzerland Banking Sector

3.1 An overview of the Swiss economy

Switzerland has one of the most stable economies in the world and a per capita gross

domestic product (GDP) that is higher than in most of the developed countries of the

world. Furthermore, Switzerland has a low budget deficit and unemployment levels.

Its currency, the Swiss Frank (CHF), has traditionally held a reputation for stability.

The inflation rate in recent years was kept below 1%, which is lower than the figures

for the EU and USA. The interest rate has also been quite low, due to a high rate of

savings and a large inflow of foreign money. Switzerland traditionally attracts a lot of

foreign investors and has reputation of safe place to invest because of its policy of

long-term monetary security and bank secrecy.

The impressive economic accomplishments are in part the result of a strong link

between industry and international trade as well as achievements of the service

industry. Many of the Swiss companies have expanded beyond their domestic market,

making Switzerland an important player in world trade. The most important trading

partner for Switzerland has traditionally been the EU. Approximately two-thirds of all

exported goods and four-fifth of all imports are traded with Europe. In Europe,

Switzerland’s most important trading partner has been Germany. Swiss economy is

based on production of export-oriented machinery and electronic equipment, such asgenerators, turbines, textile and tool machines, mills, watches as well as other

innovative high-tech products. Apart from this, Switzerland produces sizable amounts

of agro-chemicals and pharmaceutical drugs.

A major role in the Swiss economy is played by the financial services (banking,

insurance, investment). According to the “Swiss Banking Sector” Compendium

Edition 2006, the financial sector whose net output was 68 bn CHF

14



(14% of Gross Domestic Product (GDP)) is the key business sector in

Switzerland. The Banking

15



Figure 1. An overview of the main macroeconomic parameter for Switzerland.

sector contributed 43 bn CHF (9% of GDP), and the insurance sector

contributed 23 bn CHF (4.8% of GDP). The financial sector employs a

huge number of people (200,000), which represents 5.3% of the

total workforce. In this, 3.2% are employees of banks, 1.4% work for

insurance companies, and 0.6% for other financial service providers.

Historically, Switzerland has observed neutrality and maintained national

sovereignty. It remained neutral and sovereign through both World Wars, and more

recently has stayed outside the European Union. In the 1920, neutrality of Switzerland

towards nations whose territories were fixed by the 1815 Vienna Conference on post-

Napoleon international relations, was confirmed by major nations. This neutrality

contains certain special obligations such as the internment of foreign troops, the

prohibition of their passage and also the prohibition of support of confronting nations

16



by war material or trade with war material. However, Swiss neutrality has supported a

stable environment for the banking sector to operate. In addition, the Bank of

International Settlements (BIS), which encourages cooperation among the world’s

central banks, is also located in Switzerland because of the country’s neutrality.

A key reason for the success of the Swiss banking sector is the bank secrecy and

protection of private banking information. This protection is provided by the Swiss

law. The government of Switzerland emphasizes the right to privacy as fundamental

principle, which should be protected by all democratic countries. Although secrecy is

formally protected, law enforcement agencies may be granted access to information

by a prosecutor or a judge through an issue of a “lifting order” (ECONOMIC PROFILE

OF SWITZERLAND). As a result, nowadays, approximately one-third of all funds kept

outside any country are held in Switzerland. In 2001 Swiss banks managed 2.6 trillion

USD, and by 2007 this figure has increased to a level of 5.7 trillions USD.

3.2 History of Swiss Banking

The rapid growth of Switzerland as financial centre started about 100 years ago. Over

the last 50 years, Switzerland occupies a position of real significance. Along the way

Swiss banks experienced some difficulties, especially in the 1930s, however this

slowed down their development only temporarily. The main consequence of the crisis

in 1930s was the creation of new banking legislation, which laid the legal foundations

for banking secrecy.

It is often argued that the banking secrecy is the key to the success of whole

financial industry of Switzerland. However, Switzerland did not have the national

banking law and official banking secrecy until 1935. The key to success was a

pronounced relationship of trust, which was created between banks and clients over

the century and that was kept as unwritten code of confidentiality similar to the

confidentialities between doctor, priests or lawyers and their clients. Bank secrecy had

existed for a long time, however it was not established officially until relatively late

on. The Swiss Parliament passed the Banking Law in 1934. It contained the rules of

secrecy and criminalizes violation of it. Finally, when the national law on banks and

savings come in to effect, the bank secrecy become a subject of discussion and attacks

17



from within country as wall as from other countries. One of the facts, which helped to

justify this law, was the death punishment of Germans for holding Swiss account

during the era of Hitler.

The era of Nazism holds a lot of dark secrets and open questions about Swiss

banking. During the 1930 and 1940s, a large number of European Jews deposited their

life savings in Swiss banks. However, after the war many were not able to recover

their savings due to the disappearance of their documentation. In recent times the

Swiss banks have come under the fire for this reason. Some historians have argued

that money which German Nazis acquired from defeated countries and their prisoners

was stored in Swiss banks (History of Switzerland).

3.3 Understanding the Swiss Banking System

The modern banking system in Switzerland is usually characterized as being

universal. The legislation in Switzerland does not differentiate between commercial

and investments banks, in a fashion similar to other European countries. An

institution, which authorized to function as a bank, can offer variety of financial

services such as lending and taking deposits, brokerage, trading, portfolio

management and underwriting. On the other hand, the banks also have to fulfill

certain requirements such as liquidity requirement, capital requirements etc. when

they engage in these activities.

Swiss banking system is strictly regulated. All financial institutions, such a banks

or bank-type companies must be registered with the Federal Banking Commission.

The Federal Banking Commission together with the Swiss National Bank strictly

monitor and supervise the functioning of all financial institutions. Another important

function of the Federal Banking Commission is to monitor trading of international

banknotes. Therefore, banks, which want to start banknote business, have to receive

clearance from the commission. Due to the push toward liberalization of the financial

markets in recent time, certain provisions for financial and banking services have been

modified. For example, banking fees were liberalized which included the freeing of

brokerage fees from the cartel. The Swiss banking sector contributes approximately a

18



third of the tax revenue from all companies and fifth of the taxes from individuals and

companies together.

Different banks of Switzerland choose different routes to engage in financial

activities. Some banks act as universal banks, and co-exist as financial organizations

that deal with traditional banking and financial market activities. According to the

Swiss National Bank (2000), Swiss banks can be classified into several groups: big

banks, cantonal banks, regional and savings banks, Raiffeisenkassen banks,

commercial banks, consumer loan banks, stock exchange banks, other banks, foreign,

and private bankers. Different groups of banks are fully or partially specialized. In

order to better understand the banking system of Switzerland, it is useful to provide a

brief description of each type.

Let us begin the description from the category of big banks. The big banks cover

all areas of financial activities. For this reason, they are key figures in most sectors of

the domestic market as well as being major players in the international financial

markets. The two main “big” banks are UBS AG and the Credit Suisse Group. They

account more than 50% of the balance sheet total of all banks in Switzerland. Both

banks have broad branch networks inside the country and abroad. UBS AG is the

leading bank in Switzerland for individual as well as corporative clients, and is also

orientated globally. UBS is the world’s leader in the wealth management and an

important international player in investment banking and securities business. The

Credit Suisse Group can be characterized as a globally-active provider of financial

services. It offers various services such as financial advice to private clients, insurance

companies and as a financial intermediary. It serves global companies and institutions

as well as public corporations.

The cantonal banks are state-owned banks and the major part of their capital isowned by the canton. Nowadays there are a total 24 Cantonal banks in Switzerland.

They are semi-governmental organizations with a state guarantee of liabilities. The

banks in this group vary in size and business activities. The smaller banks are

orientated towards domestic market and traditional banking, focusing mainly on

savings and mortgages, while the larger institutions are involved in a broader array of

financial activities. In spite of the fact that they are closely connected with the state,

cantonal banks must upkeep commercial principles in their financial activity, while

19



their chief target is to promote the canton’s economy. The balance sheet of cantonal

banks normally ranges from 2bn to 80bn CHF. In total, Cantonal banks in Switzerland

account for around 30% of banking business and have a combined total balance sheet

of more than 300bn CHF.

Regional and saving banking is exemplified by smaller universal banks that focus

mostly on domestic, traditional banking with emphasis on lending/deposits business.

The activity of banks is typically restricted to one region or small geographical areas.

Their advantage lies in familiarity with local circumstances and the regional business

cycle together with proximity to customers. As of 2005, regional banks reported a

balance sheet total of 43bn CHF and employed approximately 2,400 staff.

The Raiffeisen banks are small independent banks that are located in rural areas.

They are organized in the form of cooperatives and are mostly orientated towards

mortgage lending. The Raiffeisen banks are affiliated to the Swiss Union of

Raiffeisen. The function of this union is to coordinate banks activities, provide on-the-

ground framework conditions for the business activity as well as supporting local

businesses in various activities, such as advising clients and selling banking services,

thereby helping the banks to concentrate on their core business. Furthermore, the

Union is responsible for the risk management of the participating banks and serves as

their strategic leadership. The Raiffeisen cooperation of banks takes a leading position

among retail banks in Switzerland and its market shares have significantly increased

during the past few years. This group has the largest branch network in Switzerland.

There are in total 390 Raiffeisen banks with 1154 branches all together.

Commercial banks are usually medium size universal banks which combine

commercial and mortgage loans with brokerage and portfolio management activities.

Stock exchange banks are smaller in size in comparison to commercial and deal withonly brokerage and portfolio activities. Their balance sheet only partially reflect their

activity.

Foreign banks are financial institutions which operate under the Swiss banking

law, but their capital is owned by foreigners. These banks vary in size and activity.

For example, some qualify as universal, while other concentrate only on trade or

financial market activities. Foreign banks are mostly European in origin,

predominantly EU (50%), as well as Japanese (20%).

20



Finally, private bankers are individually owned and unincorporated firms. Some of

these are the oldest financial institutions in Switzerland and were created in 18th

century. The private banks mostly focus on portfolio management, and owners are

liable for all the debts of their firms. For this reason private banks only partially

conform to the Swiss banking law. Because they do not accept deposits from third

parties, private bankers do not have to make public their annual financial statements.

Swiss Bankers Association (SBA) was created in 1912 in Basel. This organization

represents all banks, audit firms and securities traders. It performs several functions

such as: representing the interests of banks in dealing with authorities in Switzerland

and abroad; developing a system of self-regulation in consultation with regulatory

bodies; promoting the status of Switzerland as a world financial center; support

training programs for staff; and exchanging information between bank and bank

employees.

One of the main differences of the Swiss banking system from the systems in the

rest of the world lies in the ways in which the banks get access to financial resources

of foreign investors. In order to attract additional resources from abroad banks

generally have to open international branches in the other countries, however, Swiss

banks attract foreign investors without these. The main reason for such strong

popularity of Swiss bank accounts is their legendary privacy. The obligation of

privacy helps the Swiss banks to attract huge resources because information about

investors and their bank account is kept very confidential. If a banker opens to the

public information about individual investor or company without their permission,

he/she will be subject to civil and criminal penalty in Switzerland. The only

exclusions to this rule can be made for accounts which are associated with serious

crimes such as drug trafficking or illegal arms trade.An added advantage of the Swiss privacy laws is that it is not lifted for the tax

evasion. As opposed to most countries, the failure to report income and assets is not a

crime in the Switzerland. Furthermore, neither Swiss government nor other

governments can get access to the bank account information in the case of a suspected

tax fraud. The only possibility to obtain private-protected information about clients is

to convince a Swiss judge that the client has committed a serious crime that is

punishable by the Swiss Penal Code. Swiss law also upholds privacy in cases dealing

21



with private matters, such as inheritance or divorce, if the client wants to keep

information confidential.

An important aspect, which makes Swiss bank accounts very attractive for

foreigners, is that Switzerland does not collect any taxes from non-residents who open

accounts in Swiss banks. However, there are three exclusions to this rule. The first one

is Swiss withholding tax, which covers 35% of withholding tax paid on dividends and

interests by Swiss companies. The second exception is US citizens. A US citizen

cannot invest in US securities from their Swiss bank account before he/she reports it

to the IRS (Internal Revenue Service). Despite the fact that this taxation makes a

Swiss bank account less attractive for US citizens, they continue to hold Swiss

accounts, by following a specific strategy. The American clients use their US accounts

to invest in US securities and use their Swiss account in order to diversify in to the

other (non-US) investments. Finally, the third exception from the Swiss taxation is EU

residents. The clients who live in the European Union have to pay a withholding tax

on the interests from certain investments in the range between 15% and 35 % (Swiss

Banking Sector 2006).

3.4 Future of the Swiss banking system.

Recent economic trends have added pressure on banking costs, together with

regulatory changes and more exacting demands on the part of customers, they have brought about serious changes in the banking market. In addition, market structure,

product innovation, distribution organization, and changing competitiveness of

Switzerland in global economy are also leading to significant changes in the Swiss

banking industry.

According to the study «The Swiss Banking Industry in the Year 2010» (Bernet &

Monnerat), which has been conducted jointly by Accenture and the Swiss Institute of

Banking and Finance at the University of St.Gallen, some of the trends of Swiss

22



banking industry were determined by changes in the financial market regulations. The

trend to increased regulation conditioned by closure of gap in legislation on economic

crime, money laundering and the financing of terrorism is set to continue. However,

this increases risk of overregulation which can affect banking sphere in a negative

manner.

Due to the growing regulation, the Swiss banking system will adapt to the legal

system of the European Union, and the statutory provisions governing banks will be

under the EU regulation. The competitive advantages of Swiss banks, such as secrecy,

will become less important. Even if banking secrecy formally continues to exist, it

will be attenuated by the national legislation of other countries. Consequently, the

image of Switzerland as safe place to invest will diminish. However, despite the

regulations Switzerland will continue holding the leading positions globally in private

banking sector. It will continue to play a dominant in the management of offshore

monies.

The stricter regulations are not likely to stimulate a wave of acquisitions by Swiss

banks in other countries. The takeovers abroad are not likely to take place even though

the competition on the home markets for the international customer is growing and the

volume of offshore monies is reducing. Instead, the Swiss banks will concentrate their

own growth strategies and on their most profitable markets. Their business field will

focus more on specific banking segments or individual customer portfolios.

The banking sector is poised to continue reductions of the workforce, salary level

and production depth by outsourcing part of their chain. The private banks, regional

banks, saving banks and Raiffeisen will show the lowest production depth. As a

consequence, Swiss banks will concentrate on their core activities and will become

increasingly specialized. The banks will be disjoin into distribution banks, portfolioand asset managers, transaction banks and product developers.

The Swiss banking industry which is traditionally associated with tax privileges,

client privacy, and a tradition for stability has for a long time enjoyed a favorable

positions in the competitive world market. However, lately Switzerland is faced with

strong pressure from the international organizations such as the United Nations (UN)

and Organization of Economic Cooperation and Development (OECD) to bring its tax

regulation and bank secrecy laws (precisely the factors that gave it the winning edge

23



over other countries) in line with world standards. It remains to be seen what effects

this coercion is going to have on Swiss banking.

24



Chapter 4

Determinants and variable selection

In order to analyze the profitability of Swiss banks one needs to specify a model and

choose variables that are expected to determine this profitability. This chapter gives an

overview of the various variables that will be used in the current research. I give a

brief description and point out the main economic significance of these variables.

Broadly speaking, the variables are divided into to sets: dependent and independent

variables. I begin this chapter with a description of the dependent variables and their

properties. Following this, I study the independent variables. Firstly, I shall specify

seven bank-specific determinants and indicate the real bank data that can be used as a

proxy for these variables. Following this, I introduce the two macroeconomic

variables that will be used in our analysis.

4.1 Dependent variable

In the current work I use the Return On Average Assets (ROAA) and Return On

Average Equity (ROAE) as the measures of bank profitability. The return on average

assets (ROAA), or Return On Assets (ROA), indicates how profitable a company’s

assets are in generating revenue. Assets, being a stock variable, is measured at a point

in time, while the return is a flow variable, the value of which depends upon the

period of time over which it is measured. For this reason average assets are taken in

order to capture any changes that occurred in assets during the fiscal year. ROA/A is

equal to profit after tax divided by (average) total assets:

AssetsTotal Average

Income Net ROA=

25



This ratio shows how many Swiss francs of earnings are obtained per year by a

financial organization from each Swiss franc controlled by it, and is typically

denominated in percent. ROAA is calculated at period ends, and therefore it does not

show all of the highs/lows, but only an average over the period. Although typically

ROA is useful for comparing competing companies working in the same sphere, it

varies widely across different industries. In equilibrium, return on assets is the

measures of risk premium. Banks and other financial institutions mostly use ROAA as

measure of their performance. It is an indicator of how effective the bank’s assets

management is in generating revenue.

Another measure of profitability is Return On Average Equity (ROAE) or Return

On Equities (ROE). It gauges the rate of return on the ownership interests, such as

shareholders’ equity of the common stock owners. ROAE shows the income per year

generated by one Swiss franc worth of shareholders’ equity, thereby indicating how

efficiently a company uses investment funds in order to generate earnings and growth.

Return on average equity is calculated as net income (after preferred stock dividends

but before common stock dividends have been taken into account) divided by total

equity:

Equityr Shareholde

Income Net ROE =

Companies with a lower leverage (i.e. debt to equity ratio) show higher ROAA,

however the behaviour of their ROAE is undetermined. This rise in ROAA, with a

lowering leverage, can be explained by rising proportion of equity in the ratio

(Assets=debt + equity) and lowering of the debt proportion, while the amount of total

assets holds the same. Due to reduced debt, the bank pays less interest on debt,

thereby reducing bank’s costs. Consequently, the profitability of banks and ROAA

increase. However, the situation is different for ROAE, which has equity in the

denominator. With a rising level of equity, both numerator and denominator in the

definition of ROAE increase. For this reason, in order to determine the total effect of

rising debt to equity ratio, one requires additional information.

ROAE ignores the higher risk that is associated with a higher leverage. For this

reason, ROAA primarily serves as key ratio for the measure of bank profitability. This

26



can be seen as a corollary of the Modigliani-Miller theorem, which states that under a

certain market price process in the deficiency of taxes, bankruptcy cost and

asymmetric information and in an efficient market the value of a firm is unaffected by

the way how the firm is financed.

4.2 Independent variables

4.2.1 Bank-specific determinants of profitability

In this work I use eight bank-specific variables in determining bank profitability:

liquidity, bank capital, credit risk, productivity growth, operating expenses, size,

density of population for the regions where banks situated, and two dummy variables

indicating cantonal and Raiffeisen banks. In order to better understand the relation

between these variables and profitability, it is useful to provide a detailed description

of each variable.

Let us begin with the liquidity variable. Banks can fail to meet their obligations

without essential liquidity. Liquidity risk for the bank is associated with an excess

demand for repayment of deposits over liquid resources of a bank. By definition,

liquid reserves consist of assets that can be converted into cash quickly without loss.

As is well known, banks’ main earnings come from accepting deposits and making

loans based on these. However, in order to be protected from the liquidity risk, banks

should keep part of their funds in the liquid assets. As a result they have less funds to

create loans and as a consequence have lower rate of return than if they used all of the

deposits to make loans. Thus, holding a larger amount of liquid assets is associated

with lower rate of return (Matthews 2008). In this work I use the ratio of liquid assetsto total assets (LATA) as a measure of liquidity. The liquidity ratio measures the

extent to which banks can quickly liquidate assets to cover short-term liabilities. In

general, the higher the value of this ratio, the larger number of liquid assets, held by a

bank, and the larger the margin of safety that bank possesses to cover short-term

debts. Consequently, larger amounts of the liquid assets are associated with lower

rates of return. A number of studies have investigated the relationship between

liquidity and profitability. For example, Molyneux & Thornton (1992) report negative

27



correlation between the two. In contrast, Bourke (1989) finds a positive relationship

between the level of liquidity and profitability. In explaining the results, Bourke

(1989) argues that perhaps depositors accept lower interest rates in return for greater

security provided by liquidity.

I use the ratio of equity to assets (EA) as a proxy for the bank capital strength.

Financial institutions that have a higher capital to assets ratios are expected to be

much safer in comparison with institutions with lower ratios. Moreover, the higher the

equity to assets ratio the lower is the need for external funding. Banks with low value

of the capital ratio (less equity) are riskier and have higher return (in compensation for

the greater risk), than financial institutions that are better capitalized. In their analysis

of the effects of equity to assets ratio on bank profitability, Dietrich & Wanzenried

(2009) present a two-fold argument. On the one hand, since debt is a cheaper form of

finance, one can expect that the capital ratio will affect bank profitability negatively.

On the other hand, better capitalized financial institutions are safer and therefore gain

profits even at times of economical downturn. This fact in turn increases the

creditworthiness of bank, therby reducing its funding cost and external funding. Thus,

capital ratio might also have a positive effect on profitability. As a Consequence, the

direction in which equity to assets ratio affects banks profitability is not well

determined and I hope to shed some light on this question in present work.

The next parameter I investigate is credit risk. Credit risk can be characterized as

the possibility of loss due to default of debtor (Matthews (2008)), for example when a

customer defaults on the loan or other type of financial contract. The default on loans

can take form of failure to pay interest payments or principal on maturity. In this work

I use the ratio of loan loss provisions to loans (LLPL) as a proxy of bank’s credit

quality. To evaluate this ratio, the loan loss provision was taken from the bank’sincome statement and the loans were taken from its balance sheet. Loan loss

provisions are a non-cash expense for the banks to account for possible future losses

on loan defaults. Banks expect a default on a certain percentage of loans, and record

this percentage as an expense when calculating pre-tax income. This serves as a

guaranty of solvency and capitalization for the banks in case of defaults. The value of

loan loss provision increases with the riskiness of the loans that bank makes. Banks

making small number of risky loans normally have a lower level of loan loss provision

28



than banks taking higher risks. Changes in the credit risk can dramatically affect the

performance of the bank and changes in its loans portfolio. Studies, which include

Duca & McLaughlin (1990) and Miller & Noulas (1997), indicate a negative effect of

credit risk on profitability, indicating that an increased exposure to credit risk leads to

a decline in the bank’s profitability. Miller & Noulas (1997) argue that the higher the

risk loans held by a financial institution, the higher is the possibility of unpaid loans,

and consequently lower is the profitability. Dietrich & Wanzenried (2009) and

Athanasoglou et al. (2005) have shown a negative correlation between profitability

and credit risk. Hence, in this work I expect a negative correlation between

ROAA/ROAE and loan loss provisions to loans ratio. However, it worth pointing out

that, banks specialising in riskier loans would in general make greater loss provision

and expect a higher return for the greater risks. In these cases there may be no relation

between actual return and loss provision, or even a positive correlation if banks are

risk averse.

Banks improve profitability by monitoring the credit risk through forecasting the

future levels of risk. Stiglitz & Weiss (1981) have shown that, in equilibrium, a loan

market may be characterized by credit rationing. Banks that make loans are interested

in two factors, the interest rate they receive on loans and their riskiness. However, the

interest rate that banks charge on loans could in turn affect the riskiness of the loans.

In particular, it can affect riskiness, by sorting borrowers whereby more riskier

borrowers deal with higher interest rate (the adverse selection effect), and also

possibly by affecting the action of borrowers making borrowers prefer riskier projects

with higher interest rate instead of the less riskier ones (the incentive effect) (Stiglitz

& Weiss (1981)). Thus, banks may not compensate an increase in risk by increasing

the rate of interest. In fact, the dependence of profitability on interest rate has the formshown on figure 2. The rising rate of interest has two opposite effects on bank loan

revenue. The first effect is positive, which means that revenue increases with rise in

interest rate. The second effect is negative, implying a decrease in the expected

revenue as the interest rate increases, due to the increase in risk of default. After the

certain point the second factor will exceed the first one and the total expected profit

will decrease (Matthews & Thompson 2008).

29



diversification and increase of operational efficiency with the economy of scales, it

has been shown in a large number of works that a larger size has a positive affect on

bank profitability. However, in the case of extremely large banks, the impact of size

on profitability could be negative due to the agency cost, bureaucratic processes and

other negative aspects of large organizations. There is a large amount of literature

investigating the correlation between bank’s size and profitability. For example,

Bourke (1989) examined the correlation of bank size and profitability using 90 banks

between 1972 and 1981 in twelve countries and territories such as Australia, USA

(California, Massachusetts, New York), Canada, Ireland, England, Wales, Belgium,

Holland, Denmark, Norway and Spain. Smirlock (1985) used over 2700 unit state

banks operating in the seven-state area under the jurisdiction of the Federal Reserve

Bank of Kansas City between 1973 and 1978. Both Bourke (1989) and Smirlock

(1985) found a positive relationship between size and bank profitability. On the other

hand, Gibson (2001) who investigated profitability of European banks argued that

positive effect of large bank’s size on profitability holds only up to a particular limit.

In the present work, I use dummy variables to describe bank category. As was

described in Chapter 3, Swiss banks can be divided into several categories (big banks,

cantonal banks, regional and savings banks, Raiffeisenkassen banks, commercial

banks, consumer loan banks, stock exchange banks, other banks, foreign, and private

bankers). In the current investigation I will use only banks from the Cantonal,

Raiffeisen and savings groups. In order to specify the category to which a particular

bank belongs, I shall use two dummy variables, one for the Cantonal banks and one

for the Raiffeisen, CN and RFF , respectively. The banks for which both of the dummy

variables are zero will be considered as savings.

The last bank specific variable involved in the present analysis is density of population (DEN) for the region where the bank is situated. The functioning of

Cantonal, Raiffeisen and saving banks is limited to a geographically small area.

Switzerland is officially separated into 26 cantons that have varied area, density of

population and per capital income. All these factors may have an impact on the

profitability of corresponding banks. For instance, the cantons with higher density of

population have more bank activities than areas with less population. For this reason,

one can expect a positive correlation between density of population and profitability.

31



The Cantons used in this research are presented in Table 1, along with their

population, area and density of population.

Table 1. Canton description with number of banks.

Name of canton Number of

banks

Population Area (km²) Pop. density

St. Gallen 4 465937 2 026 230/ km²Basel- Stadt 1 185227 37 5 006/ km²Geneva 1 438 177 282 1552/ km²

Lucerne 1 363 475 1493 243/ km²

Obwalden 1 33 997 491 69/ km²Fribourg 1 263 241 1 671 158/ km²Aargau 2 581 562 1 404 414/ km²Schaffhausen 1 74 527 298 250/ km²Zurich 3 1307567 1729 756/ km²Schwyz 1 141 024 908 155/ km²

4.2.2 Macroeconomic determinants

Let us now turn to a description of the external variables that reflect macroeconomic

conditions. In the current research I use two macroeconomic variables: GDP growth

rate and Inflation.

The gross domestic product growth (GDP) is a measure of a country’s economic

activity. GDP is market value of all final goods and services produced in the country

in a year and is equal to total consumption, investment and government spending plus

the value of exports minus the value of import (Mishkin (1997)). This variable has an

impact on factors related to the supply and demand for loans and deposits. The GDP

growth rate was used as a control for cyclical output effects. In general, the

profitability of banks is expected to be procyclical. There are several reasons for this.

First of all, lending falls during cyclical downturn, since such a period is associated

with increased risk. The banks provisions, during downturns, will be higher because

of deterioration in the quality of loans and capital. All of these factors reduce banks’

returns during economic downturns. On the other hand, demand for credit and stock

32



market transaction will rise during economic growth, leading to a growth in interest

margins. Consequently, revenue rise faster than cost of lending thereby increasing

profits, during periods of economic growth. Demirguc-Kunt & Huizinga (1999) found

a positive correlation between the business cycle and bank profitability. Similarly,

Athanasoglou et al. (2005) find a positive effect of GDP growth rate on bank

profitability in the Greek banking industry, although the cyclical output was found

significant for profitability only in the upper phase of the cycle.

Another macroeconomic variable that was used in the current research was

inflation. In economy inflation means a rise in the general level of prices of goods and

services over a period of time. With a rising price level, each unit of currency has the

ability to purchase fewer goods and services, and therefore inflation has a negative

impact on purchasing power of money. To measure inflation one uses the inflation

rate, which is calculated as the annualized percentage change in the general price

index. High level of inflation and uncertainty about inflation in the future discourage

investments and savings and may negatively affect the bank’s lending to the private

sector. Moderate inflation has positive aspects as well, since it mitigates the effects of

economic recession (however there are some exclusion, for example supply shocks

and stagflation of 1970s) and acts as a debt relief by reducing the level of real debt.

Inflation affects both the cost and revenue of financial institutions. The correlation

between inflation and bank profitability crucially depends on whether inflation is

anticipated or unanticipated (Perry (1992)). Anticipated inflation implies the ability of

banks and businesses to predict inflation and and therefore take steps to protect

themselves from its effects (lenders demand higher nominal interest rates). In the case

of the anticipated inflation, banks can adjust interest rate, and this can result in

revenues increasing faster than cost, leading to a positive impact on profitability.Bordes et al. (1991), investigating the profitability of anticipated inflation in banking,

show the mechanism through which anticipated inflation leads to higher profitability

of banks. In anticipation of inflation banks raise the nominal interest rate charged on

loans, leading to a rise in the equilibrium return on the bank assets relative to the

equilibrium interest that banks pay on deposits, consequently leading to an increase in

bank profitability. This, happens because banks can anticipate inflation in a better way

that their clients. Conversely, in case of unanticipated inflation banks will be slow to

33



adjust their interest rate, and as a result bank costs increase faster then bank revenue,

leading to a negative impact on bank profitability.

4.3 Conclusion

In Table 2 I summarize the list of bank-specific and macroeconomic variables used in

this work. The bank’s profitability is measured in terms of ROAE and ROAA. The

variables expected to determine bank profitability can be divided into two groups:

bank-specific and macroeconomic. The bank-specific variables used in this work are:

liquidity assets to total assets, Loan loss provisions to loans, cost to income ratio, total

assets to price index, two dummy variables characterizing the bank category and

population density. Two macroeconomic variables, GDP growth rate and inflation,

have been used as possible determinants of profitability. In the next chapter, I present

the descriptive statistic for the variables introduced above. Based on these variables, I

shall specify a regression model to analyze bank profitability.

Table 2. Definition, notation and the expected effect of the variables.

Variable Measure Notation Expected effect

Dependent variable

34



Profitability

Net profit over total

assets (in %)

Net profit over average

total equity (in %)

ROAA

ROAE

Determinants

Bank-specific characteristics (internal factors)

Liquidity Liquid asses / total

assets

LATA ? (+/-)

Capital Equity to assets (in %) EA ? (+/-)

Capital risk Loan loss provisions /

loans

LLPL Negative

Operating expenses

management

Cost-income ratio

CIR Negative

Bank size Total assets/ Price

index

TA ? (+/-)

Bank category

Two dummy variables CN and RFF ? (+/-)

Region Population Density DEN Positive

Macroeconomic characteristic (external factors)

(Gross domestic

product)

GDP growth

GDP growth rate

(average)

GDP Positive

35



Inflation Current period

inflation rate

INF ? (+/-)

36



Chapter 5

Methodology and data description

The previous chapter enlisted the variables that will be used to analyze the bank

profitability in Switzerland, and described their expected effect. The purpose of this

chapter is to link the variables described in previous chapter with real bank data and

specify the regression model for the analysis of profitability. I begin in this chapter by

describing how and where the data was obtained. Next, a brief discussion of models

used in previous studies of profitability is given. Following this, I specify the

regression model used in the current work and describe econometric procedures.

Finally, I conclude this chapter with an overview of the descriptive statistic of the

data.

5.1 Data

In the current work I have used annual bank data and macroeconomic characteristics

of Switzerland over the period 2003-2008. The data have been obtained from an

unbalanced panel dataset for 16 banks in Switzerland, consisting of 86 observations

over period between 2003 and 2008.

There are several reasons for the choosing the particular time period 2003-2008.

Firstly, earlier periods have been investigated for various countries, including

Switzerland, in previous works. For example, Dietrich & Wanzenried (2009) analyze

the profitability of the commercial banks in Switzerland over the time period between

1999 and 2006. Athanasoglou et al. (2005) examined bank profitability of Greek

banks covering the period 1985-2001. To the best of my knowledge, the time period

from 2003 to 2008 has not been analyzed in literature. Secondly, the data for other

time periods has significant gaps in all of the available databases. Therefore, 2003-

2008 was also chosen for simplicity of collecting complete data.

37



The choice of 16 banks for the analysis was guided by practical considerations.

The BankScope database, used in this work, contains a total of 839 Swiss banks. Out

of these, approximately 200 fall into the categories of our interest (Raiffeisen,

cantonal and savings). However, not all of these banks had complete set of data for the

period of our interest. As a result, I was strongly limited in the number of banks that

could be used for the analysis. For my analysis I have chosen 16 banks covering

different regions of Switzerland. Out of these 7 were Cantonal banks, 5 were

Raiffeisen banks and 4 were savings banks.

The data on bank variables was obtained from the BankScope database.

Bankscope is a comprehensive international database that includes information on

29,000 banks around the world both public and private. It obtains data from Fitch

Ratings information provider as well as nine other sources. The normal bank report

contains detailed information about bank: location, ownership, bank age, address,

detailed balance sheet and income statement, in total up to 200 data items. The data is

afforded in varying degree of standardization and detail, which make it easy to search

and analyze. The data was obtained in the form of a report and then was partly

imported in to a special Excel file.

The data on macroeconomic variables, inflation rate and real GDP growth rate

(measured on the power parity basis), was taken from the Trading Economics (Global

Economic Research) web page and Swiss National Bank web page.

5.2 The regression model

In the current work, in order to test the dependence of bank profitability on bank-

specific and macroeconomic determinants, I use the following regression model:

t m

k

t m

K

k

k

i

t m

I

i

it m uY Y c P ,,,

1

, +++= ∑∑=

β β (1)

Where t m P , is the measure of profitability for the bank m and time t. Index m refers

to the individual bank m =1..N , with N=16 in the current work. Index t refers to the

time period spanning the six year period for data from 2003 to 2008. Y’ s represent the

38



external and internal factors/determinants, which are grouped into bank-specific (i)

and macroeconomic (k) categories. c is a constant term, and finally, t mu , is the error

term.

The liner regression model (1) has been widely used in a number of works.

Molyneux & Thornton (1992) and Pasiouras & Kosmidou (2006) in their

investigations use a regression model analogous to (1) for analyzing the determinants

of bank profitability in Europe. Some authors, in their study of profitability, use

models similar to (1) but with slight modifications. Demirguc-Kunt & Huizinga

(2000), who investigated financial structure and bank profitability for a large number

of developed and developing countries, used a regression equation in following form:

ji j ji ji S X B I ,, ε δ γ β α ++++=

Where ji I , is the dependent variable which can be profitability (ROA/ROE) or Net

Margin for bank I, in country j. Apart from the bank variables i B and

macroeconomic variables jS , this work used country variables j X .

Another group, Athanasoglou et al. (2005) and Athanasoglou et al. (2006)

investigated determinants of bank profitability in Greece and in the south eastern

European region. These works have argued that bank profits show a trend to persist

over time, i.e. show serial correlation, which is a reflection of the barrier to market

competition, informational uncertainty and sensitivity to macroeconomic shocks. As a

result, they adopted a dynamic specification of the model including lagged dependent

variable among the regressors. Their model had following form:

t i

M

m

m

t im

L

l

l

t il

J

j

j

t i jt it i X X X П c П ,

1

,

1

,

1

,1,, ε β β β δ +++++= ∑∑∑===

−

Where, apart from the terms similar to Eq. (1), there is a one-period lagged

profitability term 1, −t i П .

39



5.3 Econometric specification

Expanding equation (1) to reflect the selected variables (which are reported in the

Table 2) the models are organized as follows:

t it t t t

t t t t t t

RFF CN DEN INF

GDP TACIR LLPL EA LATAc ROAA

,,10,9,8,7

,6,5,4,3,2,1 ln

ε β β β β

β β β β β β

+++++

+++++++=

(2)

t it t t t

t t t t t t

u RFF CN DEN INF

GDP TACIR LLPL EA LATAa ROAE

,,10,9,8,7

,6,5,4,3,2,1 ln

+++++

+++++++=

γ γ γ γ

γ γ γ γ γ γ

(3)

Where ROAA and ROAE are main measure of bank profitability and are calculated as

Net Income/Total assets and Net Income/Total Equity respectively. LATA is the

liquidity variable measured as liquid assets/total assets. EA is the proxy for the bank

capital strength and is measured as the ratio equity/total assets. LLPL is ratio of loan

loss provisions to loans and was used as a proxy of bank’s credit quality. CIR is

expenses management variable, measured as cost to income ratio. TA is bank size

determinant, measured as total assets/price index, and ln TA is the logarithm of thisratio. GDP stands for the growth rate of gross domestic product, and INF is inflation

rate for the corresponding period. DEN is the density of population in the

corresponding region. CN and RFF are the two dummy variables that indicate the

bank category (Cantonal and Raiffeisen).

In the current work I also investigate models in which only bank specific factors

such as liquidity, capital, capital risk, operating expenses management, bank size,

population density in cantons and category of bank (i.e. endogenous factors) are

considered. These models are specified as follows:

t it t t

t t t t t

RFF CN DEN

TACIR LLPL EA LATAc ROAA

,,8,7,6

,5,4,3,2,1 ln

ε β β β

β β β β β

++++

++++++=

(4)

t it t t

t t t t t

u RFF CN DEN

TACIR LLPL EA LATAa ROAE

,,8,7,6

,5,4,3,2,1 ln

++++

++++++=

γ γ γ

γ γ γ γ γ (5)

40



The variables entering the above equations have been specified above.

In order to evaluate the regression models, I have used the EViews 6 statistical

package. EViews is used for statistical and econometric analysis, such as time series

estimation and forecasting as well as cross-section and panel data analysis. In the

current work I deal with panel data, since our data contain both time series and cross-

sectional elements. The data taken from the BankScope database was organized in the

form of a table in an Excel file. This file was then imported into Eviews, where a

panel workfile was created.

All regression models were estimated using the least squares method. The effect

specification for cross-section was chosen as random. In general, there are two

different effect specifications for cross-section: fixed and random. The fixed effect

approach allows the intercept in the regression model to differ cross-sectionally but

not over time, however all of the slopes estimates are fixed both cross-sectionally and

over time (Brooks (2008)). However, the tested model contains two dummy variables

(CN and RFF), and this makes it impossible to use fixed effect approach in the

regression. Due to this fact I apply random effect. In order to test the appropriateness

of choosing the random effect, I performed the Hausman Test. The results of the

Hausman testing for the ROAA and ROAE are presented in the Table 3.

Table 3. Hausman Test

Correlated Random Effects - Hausman Test

Equation: ROAA_RANDOM

Test cross-section random effects

Test SummaryChi-Sq.Statistic Chi-Sq. d.f. Prob.

Cross-section random 6.276736 8 0.6163

Correlated Random Effects - Hausman Test

Equation: ROAE_RANDOM

Test cross-section random effects

Test Summary

Chi-Sq.

Statistic Chi-Sq. d.f. Prob.

41



Cross-section random 7.454522 8 0.4885

If the Chi-Square Statistic value, taken from the Hausman test, is smaller than the

critical Chi-square for 10 degrees of freedom 342,910,5,0 = χ (6.276736 < 9.342 and

7.454522 < 9.342), then the random effect is an appropriate choice (also, p-value for

the test is more than 10% indicating that the random effect model is appropriate).

Thus, the Hausman test shows that random effect specification is to be preferable to

fixed. Moreover, the choice of random effect is supported by the absence of

significant heteroscedasticity in the residuals. I have also used the test of

heteroscedasticity in the residuals in order to support the random effect choice. I

conducted the Breusch-Pagan test, which tests whether the estimated variance of the

residuals from a regression are dependent on the value of the independent variables.

The hull and alternative hypothesis of the Breusch-Pagan test are:

0 H : No heteroscedasticity (homoscedasticity)

1 H : Heteroscedasticity

In order to conduct the test the following procedure was followed. Firstly, models (2),

(3), (4) and (5) were estimated. Secondly, the squared residuals of these models were

regressed on independent variables. The results of this regression are presented in

Table 4 (for all models). In order to investigate whether there is evidence of

heteroscedasticity in the residual variance, the Lagrange multiplier (LM) were

calculated. LM statistic is calculated as:

2

2

12/1 R

RSS R LM

−

∗

= .

Table 4. Breusch-Pagan Hetero test statistic

42



Dependent Variable: residual_ROAA_all_variables

R-squared 0.365774 Mean dependent var 0.209417

Sum squared resid17.67923 S.D. dependent var 0.572665

Dependent Variable: residual_ROAE_all_variables


Sum squared resid 108.2148 S.D. dependent var 1.437453

Dependent Variable: residual_ROAA_bank_specific


Sum squared resid44.95735 S.D. dependent var 0.912119

Dependent Variable: residual_ROAE_bank_specific


Sum squared resid 26.81780 S.D. dependent var 0.713474

The 0 H is rejected if LM > 188.25

10,005,0= χ (the critical chi-square for the 10

degrees of freedom). The models give:

,18.23,54.4 var _ _ var _ _ == all ROAE all ROAA LM LM

.08.7,56.11 _ _ _ _ == specificbank ROAE specificbank ROAA LM LM

All of these values are less than the critical value. Consequently, there is no evidence

to reject the null hypothesis for all of the models (2), (3), (4) and (5). For this reason,

it is reasonable to assume that these models are homoscadastic.

Models (2), (3), (4) and (5) were estimated with a random effect using least square

method. The coefficient covariance method was chosen as white cross section, which

also controls the cross-section heteroscedasticity of the variables.

5.4 Data description

43



Table 5. Descriptive statistics

Variables Mean Median Std.dev. Max Min

Dependent variable: profitability

ROAA (%) 0.47 0.43 0.33 2.15 0.05

ROAE (%) 6.91 6.45 2.95 15.13 1.85

Independent variables

Bank specific characteristics

Liquid asses / total assets (%) 53.68 41.30 25.90 98.2 0

Equity to assets (in %) 7.04 7.5 3.08 14.74 0.59

Loan loss provisions / loans (%) 0.068 0.051 0.19 0.47 -1.33

Cost-income ratio (%) 58.7 58.4 8.11 81.01 37.89

Bank size (log) 8.18 7.51 1.78 11.8 5.47

Population density (/km²) 716.7 249.6 1171.2 5009 66

Dummy variable cantonal bank 0.44 0

Dummy variable Raiffeisen bank 0.312 0

Macroeconomic characteristic

GDP growth rate (average)(%) 2.21 2.54 1.23 3.38 -0.2

Current period inflation rate 1.14 0.94 0.61 2.43 0.63

Table 5 shows the descriptive statistics for all variables used in the regression

analyses. The mean, media, standard deviation maximum and minimum value are

reported for each variable. The average value of return on average assets (ROAA) for

all bank samples is 0.47% over period between 2003 and 2008. The median value of

ROAA for this sample is 0.43% is close to the value of mean. This indicates an

absence of skewness in the distribution of ROAA, i.e. in other words, indicates a

symmetric distribution of ROAA for the various banks and different time periods

around the mean value. A similar conclusion applies for the other profitability

measure, return on average equity (ROAE).

44



The ratio of liquid assets to total assets is used as a measure of liquidity. The mean

value of liquid assets to total assets is 53.68% and varies significantly between the

banks in the chosen samples. The maximum value was 98.2% and the minimum value

was 0%. The ratio of equity to assets was used as a proxy for the bank capital strength.

The best capitalized bank in the chosen sample had a capital ratio of 14.74%, while

the least capitalizes institution had only 0.59. The ratio of loan loss provisions to

loans, which is a proxy of bank’s credit quality, averaged only 0.068%, which is quite

low and can be explained by narrow sampling of bank categories. For example, the

highest value of loan loss provisions to loans ratio was only 0.47%. The mean value of

the cost to income ration was 58.7%. The relatively small heterogeneity in data can be

seen as a result of narrow sampling, since the present work restricts its analysis only to

Cantonal, Raiffeisen and savings banks. Furthermore, the chosen banks are quite small

and are restricted to compact geographical regions.

Bank size was calculated by dividing total assets by the price index for a given

year, in order to take in to account inflation, and then taking the logarithm of this

value. The mean value of the bank size was 8.18, with a maximum value amount 11.8

and minimum 5.47.

The average value of population density was 716.7 people/km2

, however the

median value is 249.6 people/km2, which is three times less. That difference can be

explained by the presence of a few banks from highly populated regions such as

Basel-Stadt with a huge population density (5 006/ km²) that significantly skew the

mean towards a higher value. The mean values of dummy variables show the relative

proportion of cantonal (0.44) and Raiffeisen (0.31) banks in the sample. In total 7

Cantonal banks, 5 Raiffeisen banks and 4 saving banks from different regions of

Switzerland were considered.Let us turn to a description of macroeconomic characteristics. On average, the

(real) GDP growth rate amounts to 2.21%, which is quite significant for a developed

economy. These growth figures reflect the fact that the time period between 2003 and

2008 has been relatively prosperous for Switzerland. The highest GDP growth for

Switzerland was achieved in 2006 and amounted to 3.38%, however in 2003 the GDP

growth was negative in 2003 at -0.2%.

45



Finally, the inflation rate in Switzerland over the period of our interest was 1.14%

on average, and is a quite low rate of inflation. The highest rate was recorded in 2008

at 2.43%. The lowest inflation rate was recorded at 0.62% in 2003.

5.5 Conclusion

To sum up, I began the chapter by presenting an explanation of how and why the

particular data were chosen for the current study. I mention the source of the data and

how this data was imported for the purposes of this work. Following this, I presented

the regression model and described the econometric approach used for determining

bank profitability. I have also tried to give a detailed explanation of the model

estimation procedure. Finally, I presented an detailed overview of the descriptive

statistics of the data. In the following chapter, I shall present the results of the

regression analysis. I shall give an interpretative overview of the main findings, and

further substantiate some of the findings by comparing them with those found

elsewhere in the literature.

46



Chapter 6

Empirical results

In previous chapter I have specified a model for investigating the determinants of

bank profitability, and gave a detailed overview of the variables that are included in

this model and are expected to have an impact on bank performance. In the present

chapter I present the results of the regression analysis and highlight the main findings.

I shall compare our findings with results of other studies. The two tables, Table 6 and

Table 7, present the main regression results for outputs for ROAA and ROAE taken as

the dependent variables, respectively.

Table 6. Estimation results for return on average assets (ROAA) as dependent

variable

Independentvariables Only bank specific variables All variables

Coeff. Std.error

t-stat. Prob. Coeff. Std.error t-stat. Prob.

Intercept -0.0202 0.56071 -0.036 0.971 -0.1752 0.5728 -0.3059 0.7605

LATA 0.00118 0.00107 1.102 0.274 0.00099 0.00102 0.97561 0.3324

EA 0.08742 0.05191 1.684 0.096 0.10026 0.05973 1.67843 0.0974

LLPL 0.04552 0.02508 1.815 0.073 0.05377 0.03341 1.60937 0.1117

CIR -0.0086 0.00296 -2.918 0.005 -0.0070 0.00263 -2.6762 0.0091

lnTA 0.03652 0.02885 1.265 0.209 0.04138 0.02785 1.48541 0.1416

DEN 7.6E-05 5.1E-05 1.471 0.145 7.6E-05 5.7E-05 1.34019 0.1842

47



CN 0.09859 0.0964 1.023 0.309 0.10006 0.09621 1.03999 0.3017

RFF0.32363 0.28307 1.143 0.256 0.38350 0.32848 1.16750 0.2467

GDP - - - - -0.0020 0.00975 -0.2055 0.8377

INF - - - - -0.0468 0.02898 -1.6135 0.1108

Test characteristics:

R-squared 0.661469 0.647098

Adjusted R ² 0,656231 0,649945

Sum squaredresidual

1.685384 1.599208

F-statistics 7.7402 6.117770

Prob(F-statistic) 0.000000 0.000000

S.D. dependent var. 0.189125 0.184822

Breusch-Pagan test(LM)

11.56

(

χ 0,005,8

2=21.95)

4.54

(

χ 0,005,102=25.18)

Note: ROAA: Net Income /Total assets; LATA: Liquid assets/total assets; EA: Equity/total

assets; LLPL: Loan loss provision/loan; CIR: cost-income ratio; TA: Total assets/ price index;lnTA: log of total assets/price index; DEN: population density in cantons; CN and RFF:

dummy variables of category of banks – cantonal and Raiffeisen; GDP: growth domestic

product rate; INF: current period inflation rate; LM: Lagrange Multiplier.

Let us firstly begin by looking at details of data presented in Table 6. The table

reports the regression results of the estimation of model (2) (containing all variables)

and model (4) (containing only bank specific variables) for ROAA (return on average

assets) taken as the profitability variable. The first set of four columns show the

results when only bank specific variables including the two dummy variables are

considered. The final four columns contain estimation results for all variables,

including macroeconomic factors. One can notice that there are no significant

differences in results for the values of the regression coefficients and corresponding

significance level for the two models. It is also noticeable, that the explanatory power

(in terms of adjusted R ²) decreases insignificantly from 0.661469 to 0.647098 with

the insertion of additional macroeconomic factors. Thus, for the sample used in this

work, external factors do not substantially influence the overall descriptive power of

48



the models. However, if one employed longer time series, the difference between the

two models might become significant. In general, the relation between the additional

macroeconomic variables and performance of banks can be useful for policy

decisions. The value of Lagrange Multiplier in the BP test is 11.56 for bank specific

model and 4.54 for the all-variable model, both of which are less than the

corresponding critical values, indicating that both models do not have

heteroscedasticity.

I begin the analysis of the regression results presented on Table 6 with the

liquidity variable (LATA). The correlation coefficient for this variable is positive in

both the models indicating that LATA might have a positive effect on bank

profitability (measured by ROAA). However, the value of probability for the two

models shows that there is a 27% and 33% chance respectively, that the true value of

the liquidity coefficient is zero. This implies that liquidity does not have a statistically

significant effect on bank profitability. The standard error is comparable to the

coefficient estimate, thereby making the estimate unreliable. Therefore, the positive

value of the correlation coefficient might be a result of estimation error. On the other

hand, if the sign of the correlation is determined correctly, the result would indicate

innapropriateness of a simplistic explanation. As outlined earlier, in order to be

protected from the liquidity risk bank should keep a part of their funds in the liquid

assets, and as a result have fewer funds to create loans. Hence, a larger amount of the

liquid assets can be associated with lower rates of return. In contrast, the present

results show a positive correlation. This might be a result of the fact that better run

banks make higher profits and manage risk better, which implies holding more liquid

assets thereby reducing the chances of liquidity crises. Our findings are consistent

with the findings of Bourke (1989), who has also shown a positive correlation between liquidity ratio and profitability. Bourke (1989) analyzed the internal and

external determinants of bank profitability in twelve countries in Europe, North

America and Australia.

The next variable EA is a measure of bank capital strength (equity over total

assets). Capital ratio EA has one of the largest regression coefficients in the present

study, and therefore seems to be one of the main determinants of performance of

Swiss banks. The parameter is characterized by a relatively high significance

49



coefficient. The probability that true value of capital variable is zero is less than 10%.

EA has a positive effect on bank profitability in Switzerland (an increase in the equity

ratio of say 0.1 would raise profitability by 0.87 percent). The regression coefficient is

comparable for the two models (2) and (3). Moreover, the error bars are typically

smaller that the coefficients indicating a robustness in determining their signature. As

was mentioned before, financial institutions, which have higher capital to assets ratios,

are supposed to be much than institutions with have lower ratios. The obtained results

have a clear theoretical justification. Financial institutions with a reliable capital

position are able to realize business opportunities more effectively and have more

flexibility and time to deal with problems, such as unexpected losses, and therefore

can achieve an increased profitability. It is also reasonable to suppose that better

capitalized banks (because they are less risky) get access to cheaper sources of funds,

or that caution implied by high capital ratio is preserved in the loan portfolio, which

also improves the rate of profit (Bourke1989). Our findings are consistent with

Bourke (1989). Similar results were obtained by Molyneux & Thornton (1992) for

European banking industry and Demirguc-Kunt & Huizinga (1999) for large panel of

banks in 80 countries.

Let us turn to the LLPL variable, which defined as the ratio of loan loss provision

to total loans. In contrast to expectations, results show positive correlation between

credit risk and profitability. The probability that there is no correlation of LLPL with

dependent variable ROAA is just 7.3%. Therefore, this variable has a statistically

significant effect on bank profitability. The standard error (0.02508) is smaller than

the coefficient (0.04552), implying robustness in determining the signature of the

correlation (a similar conclusion follows from the value of the T-statistic (1.8153)).

The positive correlation implies that higher risks result in higher margins, andtherefore higher profits. This can be the case for the banks specialising in riskier loans

that would make greater loss provision and expect a higher return for the greater risks.

In such cases there may be no association between actual return and loss provision or

even a positive one – if banks are risk averse. Similar results were obtained by

Kosmidou et al. (2005) for the UK commercial banking industry over the period 1995-

2002. In contrast, other studies show a negative correlation between the credit risk and

profitability, implying that an increased exposure to credit risks lead to decline in a

50



bank’s profitability. Athanasoglou et al. (2005) show this for the Greece banking

system where, as they argue, that bank managers attempt to maximize profits mainly

through policies that improve screening and monitoring credit risk. Miller & Noulas

(1997) also show similar results for the large banks in USA. However, in case of

Switzerland, Dietrich & Wanzenried (2009) found that credit risk variable does not

have statistically significant effect on bank profitability.

As expected, CIR (cost to income ratio) appears to be an important determinant of

bank profitability in Switzerland. The numbers in Table 6 show a negative, and

statistically significant (0.0046), effect of CIR on the dependent variable ROAA. The

high t-statistic value (-2.918) shows that standard error is smaller in comparison with

the value of the coefficient, indicating a confident determination of sign of the

regression coefficient. The difference between the value of coefficients when only

bank specific variables are included in the regression (-0.0086) and all variables are

included (-0.00704) is not significant and does not change main results. The negative

correlation implies that an increase in expenses reduce the profit of the Swiss banks.

Therefore, efficient cost management is prerequisite for improved profitability of

banks. These findings are consistent with previous studies Kosmidou et al. (2005) and

Guru et al. (1999) who found a negative correlation between measure of cost and bank

performance in Greece and Australia, respectively. In addition, Kosmidou &

Pasiouras (2006) show that cost to income ratio appears to be the most significant

determinant of profitability for both foreign and domestic banks in the European

Union.

Let us next consider the bank size variable lnTA. Size of the bank was measured

by logarithm of the ratio total assets over price index in order to take in to account

changes in price every year. The results show a positive (0.03652), however nothighly significant (0.2094), effect of bank size on dependent variable ROAA.

Numerical value of the coefficient does not change significantly (from 0.03652 to

0.04138) when additional macroeconomic variables are added to the regression model.

The value of t-statistics (1.2656) shows that the standard error is smaller than the

value of the coefficient and therefore it is likely that the sign of the coefficient was

determined appropriately. The large size might result in economy of scale that will

reduce the cost of accumulation and processing information. Moreover, larger banks

51



have a higher degree of product and loan diversification compared to the small banks.

This is especially true for the banks considered in this work: small savings, cantonal

and Raiffeisen banks, where it is not necessary to take in to account negative aspects

of large financial organizations, such as bureaucratic processes. Similar results were

obtained by Smirlock (1985) in his investigation of over 2700 unit state banks in USA

between 1973 and 1978.

The population density DEN of the canton where each bank situated has a positive

and significant effect on bank profitability in Switzerland as measured by ROAA. The

value of coefficient (7.6E-05) and significant level (0.1454) stay completely

unchanged with the addition of macroeconomic variables to the regression model. The

t-statistic shows the standard error is smaller than the value of the coefficient, thereby

indicating that the sign of the correlation was determined correctly. A positive

correlation between population density and profitability implies that with an increase

in population of a canton the number of deposits, loans and operations with the banks

increase, which leads to a rise in profitability.

The two dummy variables CN and RFF indicating the bank category do not show

a statistically significant correlation with bank profitability. These results may be a

result of narrow sampling. In current work I have used only small saving banks

situated in the rural areas and cantons. However, Dietrich & Wanzenried (2009) who

investigate 453 commercial banks in Switzerland, including the two giants, Credit

Swiss and UBS, also found no significant correlation between bank category and

profitability.

Let us now turn to the external factors related to the macroeconomic environment

in Switzerland. The GDP growth rate (GDP) shows a negative (-0.00200), but not a

statistically significant, impact on bank profitability measured in the return on averageassets ROAA. The standard error (0.00975) is significantly larger than the regression

coefficient (-0.00200), implying a significant uncertainty in determination of the effect

(a similar conclusion follows from the small value of the T-statistic (0.20554)). On

first thoughts, as was discussed in Chapter 4, one might expect a positive correlation

between the GDP growth rate and bank profitability. However, current results do not

provide substantive support to the arguments that the business cycle impacts on the

performance of banks. This could be seen as a result of narrow sampling, both in

52



terms of the number of banks considered and the time period over which they were

considered. On the other hand, several studies such as Kosmidou et al. (2005), Hassan

and Bashir (2003), Athanasoglou et al. (2005) have shown that a positive correlation

exists between GDP growth rate and the performance of the financial sector.

Kosmidou (2005) shows a positive correlation between business cycle and

profitability for the UK commercial banking industry over the period 1995 -2002.

Athanasoglou et al. (2005) find evidences that business cycle significantly affects

bank profitability in Greece in the period 1985 and 2001.

Finally, in our analysis of results presented in Table 6, let us study the implications

of the inflation rate variable INF for bank profitability. The regression coefficient is

negative (-0.04677), however there is 11% probability for an absence of correlation.

Thus, although there is an indication for the negative impact of inflation on bank

profitability, this determination remains not completely conclusive. This finding lends

support to assumptions that the inflation in Switzerland is largely unanticipated, since

(as was explained in Chapter 4) banks are generally expected to increase their

profitability in the case of anticipated inflation.

Let us now move to Table 7. This table indicates the results for the regression

analysis with the profitability measured by the variable ROAE (return on average

equity). The regression models are given by (3) and (5). As before, (neglecting the

column describing the variables) first four columns report the regression results when

only bank specific characteristics were included in regression model. The final four

columns present the result when all of the variables, including macroeconomic factors,

were included. As in the previous case, the Breusch-Pagan test (LM) shows no

heteroscedasticity in both the models, (3) and (5).

Table 7. Estimation results for return on average equity (ROAE) as dependent

variable

Independent

variables Only bank specific variables All variables

Coeff. Std.error t-stat. Prob. Coeff. Std.error t-stat. Prob.

Intercept 8.25634 5.77147 1.4305 0.1567 5.93760 5.58557 1.06303 0.2912

53



LATA 0.00548 0.00984 0.5572 0.5750 0.0027 0.00978 0.28195 0.7788

EA 0.13922 0.44566 0.3124 0.7556 0.3274 0.50022 0.65463 0.5147

LLPL 0.56915 0.20556 2.7687 0.0071 -0.09025 0.02712 -3.32808 0.0014

CIR -0.11186 0.03599 -3.106 0.0027 -0.09025 0.02711 -3.32808 0.0014

lnTA 0.46213 0.44738 1.0330 0.3049 0.56566 0.44861 1.26092 0.2113

DEN 0.00078 0.00078 1.0064 0.317 0.00078 0.00087 0.89197 0.3753

CN 1.39306 1.00572 1.3851 0.1701 1.32754 1.06012 1.25226 0.2144

RFF 3.8428 2.91767 1.3193 0.1910 4.72690 3.42994 1.37813 0.1723

GDP - - -- -- -0.03047 0.07615 -0.40018 0.6902

INF - - - - -0.62072 0.22332 -2.77951 0.0069

Test characteristics:

R-squared 0.497574 0.468778

Adjusted R ² 0.453574 0.438252

Sum squared

residual

144.196 131.3411

F-statistics 3.560887 3.34926

Prob(F-statistic) 0.0001488 0.0001

S.D. dependent

var.

1.527343 1.499775

Breusch-Pagantest

(LM)

7.082

( 955.212

8,005,0= χ )

23.18

( 188.252

10,005,0= χ )

Note: ROAE: Net Income /Total Equity; LATA: Liquid assets/total assets; EA: Equity/total

assets; LLPL: Loan loss provision/loan; CIR: cost-income ratio; TA: Total assets/ price index;

lnTA: log of total assets/price index; DEN: population density in cantons; CN and RFF:

dummy variables of category of banks – Cantonal and Raiffeisen; GDP: growth domestic

product rate; INF: current period inflation rate; LM: Lagrange Multiplier.

54



In general, the main results of these regressions are consistent with results

discussed in the context of ROAA. Table 7 reports positive and statistically significant

correlation between credit risk (LLPL) and profitability (0.56915; 7.1%). There is a

positive, but insignificant, correlation between bank size (lnTA) and profitability

(0.46213; 30%). A negative and statistically significant correlation between cost to

income ratio (CIR) and profitability (-0.11186; 2%). In addition, there is no significant

correlation between bank category variables CN, RFF and profitability. All of these

results are consistent with results obtained for ROAA. The results of the regression

analysis do not significantly alter when macroeconomic variables are added to the

model. As before, in the case of ROAA, The GDP growth rate (GDP) variable shows

negative but not statistically significant effect on bank profitability measured in terms

of ROAE. The inflation variable INF shows a negative and highly significant (0.6%)

impact on bank profitability in case of ROAE.

In comparing the results for ROAA and ROAE there are also certain differences.

Although the coefficient for ratio equity to the total assets EA remains positive

(0.13922; 76%), it becomes statistically non significant. Moreover, the standard error

(0.44566) is bigger than the coefficient (0.13922), implying that the effect of the

variable on bank profitability is uncertain in sign. In addition, the coefficient for the

liquidity variable LATA, although remains positive, has larger standard errors.

Therefore, the precise signature of the effect of LATA on bank profitability becomes

undetermined. In addition the density of population variable DEN has become

statistically insignificant (30%) in the case of profitability measured by ROAE.

Overall, the power of the model to explain the return on average equity (measured

by the adjusted R ²) is 45%, and is lower than for the case of ROAA (66%).

In order to study the significant determinants of bank profitability in more detail, it

is instructive to remove the variables found to be insignificant above from the

regression models of ROAA and ROAE. In the new restricted regression models, the

following variables were removed: LATA, CN, RFF, GDP and EA (removed for

ROAE only). The output for the restricted models is shown in Table 8. The results

show that, in the regression of the restricted model, all of the variables, with an

exception of LLPL, have become statistically significant. However, LLPL has a

55



significance level of only 50%. For this reason, it is follows that LLPL is not a

significant determinant of bank profitability in the current context. On the other hand,

CIR, EA, lnTA, DEN and INF display an more statistically significant correlation to

bank profitability variables ROAA and ROAE, than in case of models (2)-(5).

Table 8. The estimation results for the restricted models (ROAA, ROAE)

Variable: ROAA_restricted

Variable Coefficient Std. Error t-Statistic Prob.

C 0.413539 0.214924 1.924114 0.0579

EA 0.059480 0.018062 3.293181 0.0015

LLPL 0.041759 0.062612 0.666952 0.5067CIR -0.009522 0.003216 -2.960576 0.0041

LNTA 0.057823 0.020145 2.870382 0.0053

DEN 6.26E-05 1.55E-05 4.043602 0.0001

INF -0.038938 0.029189 -1.334002 0.0857

Variable: ROAE_restricted

Variable Coefficient Std. Error t-Statistic Prob.

C 10.85188 2.841796 3.818670 0.0003

LLPL 0.589661 0.725051 0.813268 0.4185CIR -0.102386 0.041883 -2.444574 0.0167

LNTA 0.639457 0.303881 2.104302 0.0385

DEN 0.000682 0.000221 3.082445 0.0028

INF -0.564262 0.262358 -2.150733 0.0346

Although, I have verified the statistical significance of variables CIR, EA, lnTA,

DEN and INF, their economic significance remains to be shown. It is crucial to note

that statistical significant correlation does not imply an economically significant

impact on bank profitability. In order to quantify the economic significance of the

analyzed variables a simple statistic is computed. The value of the regression

coefficient for each of the significant variable from Table 8 is multiplied by the

standard deviation for that variable (taken from Table 5). The obtained result is

compared with the mean value of ROAA or ROAE. If the obtained value is

significantly less than the mean ROAA/ROAE, one concludes that the chosen variable

does not have a significant economic effect on bank profitability. On the other hand if

56



the obtained value is comparable to the mean ROAA/ROAE, one concludes that the

given variable has a significant economic effect on profitability.

Table 9. The calculated value of variables for determination of economic

significance.

Variable Calculated value Mean ROAA

CIR 0.072 0.43

DEN 0.33; 0.0044 0.43

EA 0.2 0.43

lnTA 0.1 0.43

INF 0.018 0.43

The results show that cost to income ratio CIR has a value of the statistic (0.072)

that is significantly lower than the mean ROAA (0.43). Therefore, one can conclude

that despite statistical significance, there is no sizable economic effect on bank

profitability due to CIR. The result shows that the increase of cost efficiency does not

lead to the significant increase in bank profitability.

A similar quantity was calculated for the population density variable DEN.However, instead of multiplying the regression coefficient into standard deviation, it

was multiplied into the maximum and minimum value of density in the considered

Cantons, respectively. The obtained value for most dense Canton is 0.33 and for the

least dense is 0.0044. Comparing with mean of ROAA (0.33), one can conclude that

population density variable is an economically significant. Thus, the density of

population has a significant influence on profitability. In other words, the profitability

of saving banks varies significantly depending on which region or Canton they are

situated in. This can be explained by dispersion bank efficiency and different revenue

generated in different regions.

The equity to assets variable EA shows a value 0.2. Comparing with mean ROAA

(0.43), I conclude that equity to assets variable is not only statistically but also

economically significant and has considerable positive effect the bank profitability. As

was mentioned previously, this is a direct reflection of the fact that financial

57



institutions that have a higher capital to assets ratio are expected to be much safer

thereby reducing their funding cost and costs of external funds.

Bank size variable lnTA gives a value 0.1, which implies a marginal economic

significance. The sign and the value of the correlation coefficient indicate that bank

size has a positive effect on bank profitability. This conclusion is in direct

correspondence with expectations based on economy of scale, as was mentioned

previously.

Finally, inflation yields a low value (0.018) for the statistic, in comparison with

the mean of ROAA (0.43). Therefore, one should conclude that despite the

statistically significant correlation, inflation does not have an important economic

impact on bank profitability. This result may be interpreted as a result of narrow

sampling. The current work concentrated only on small savings banks (Cantonal,

Raiffeisen and regional savings). These banks are not significantly influences by

macroeconomic changes, in the same manner as the big commercial banks.

To sum up, the present results show that only some of the chosen variables have

significant economical effect on profitability of small savings banks in Switzerland.

The main variables that have a significant economic impact on profitability are EA,

DEN and lnTA. All of these variables show a positive impact on bank profitability.

Thus, current study implies that capital (equity to assets), bank size (total assets) and

population density in the region where the bank is operating are the main determinants

of bank profitability for small savings banks in Switzerland. On the other hand, CIR

and INF variables, although statistically significant, do not show a strong economic

impact on bank profitability. Finally, LATA, CN, RFF and GDP do not show a

statistically significant correlation with bank profitability. The absence of statisticallysignificant correlation might be a result of narrow sampling, both in terms of the

number of banks considered and their categories, as well as a short time span of

analyzed data.

58



Chapter 7

Summary and conclusions

In the current work, I have studied the determinants of bank profitability in savings

banks in Switzerland. The chosen sample consisted of 16 banks (7 Cantonal banks, 5

Raiffeisen banks and 4 other saving banks) from across Switzerland during the period

from 2003 to 2008. Using linear regression models, I studied the impact of bank

specific and macroeconomic factors on bank profitability measured in terms of return

on average assets (ROAA) and return on average equity (ROAE). Based on the

regression models, I identified the statistically significant determinants, and further

determined the economically significant determinants of profitability.

The result of this study show that the economically significant determinants of

bank profitability are: capital (as measured by equity to assets), bank size (measured

in terms of the logarithm of total assets), and population density in the region where

the bank is operating. All of these factors show a significant positive correlation with

bank profitability measured in terms of both ROAA and ROAE, implying a rise in

profitability with a rise in any of these factors. My findings are broadly consistent

with the literature on profitability. However, for reasons mentioned in previous

chapters, this work was limited to a narrow data sample, both in terms of bank

categories and the time frame considered. This might be a reason for not finding a

statistically significant correlation of profitability with some of the other variables like

liquidity and GDP growth rate.The Swiss banking industry, like the banking system world wide, is currently

facing strenuous times. The lessons of the current financial crisis are yet to be

completely comprehended. However, already there are visible trend towards a more

tightly regulated banking sphere. Moreover, there is pressure for more transparency.

All of these factors are having a profound influence on Swiss banking, the results of

which remain to be seen. In general, traditional banking has faired comparatively well

59



in the current crisis. In light of this, a study of small banks and their profitability

seems to be a worthwhile endeavour.

60



Acknowledgements

I would like to thank Prof. Foreman-Peck for his supervision and useful discussions.

His comments and suggestions helped to improve this work considerably.

61



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