The impact of European banks and their business models
on credit supply towards enterprises
1
The impact of European banks and their business models
on credit supply towards enterprises
Tilburg University
Master Thesis Finance
Faculty of Economics and Management
Department of Finance
Author
Lotte Adriana Maria Franssen
ANR
231327
Supervisor
Drs. J.H.G. Gieskens AC CCM QT
Chairperson Prof. dr. O.G. Spalt
Tilburg, August 25, 2014
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Preface
At this point, this master thesis, which I have worked on with full dedication for the last six months,
is brought to completion. On the other hand, you are about to start reading this master thesis. This
master thesis has been written in the context of the Master Finance of Tilburg University. Hopefully
you will read this master thesis with the same compassion as this master thesis has been conducted. I
hope your interest will be aroused and new ideas and insights will occur.
I would like to thank my supervisor from Tilburg University, Mr. Gieskens for his supervision. His
extensive knowledge on this discipline and the time intensive meetings were a great help for me in
finishing this master thesis.
Wish you an informative reading.
Lotte Franssen
Sint-Oedenrode, August 2014
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Abstract
The world of banking institutions is subject to a continuous changing environment of which its
instability, weaknesses and deficiencies became disclosed as a consequence of the recent crises.
Several factors, such as structural developments, for example deregulation, financial innovation as
well as stricter regulations contributed to the negative and major consequences of the crises. As a
consequence, the financial framework of the European Union became affected which has led to
changes in the banking sector. The business models of banks started to change and the supply of
credit, which is the core business of banks, became restricted.
This study investigated the relationship between the different elements of a bank’s business model
and their influence on credit supply towards enterprises in the years 2010-2013, whereby data was
used from Bankscope. Both the asset- and funding structure of banks are of major importance in the
credit supply of banks towards enterprises. In addition, income-structure was of no importance in this
relation for the full model. Furthermore, a post-hoc analysis was performed. In this analysis, the
relation of the elements of banks’ business models on credit supply towards enterprises was
investigated for different banks business models; retail, investment and wholesale banks. The results
showed that banks which adhere an investment bank business model have the largest influence on
credit supply towards enterprises, followed by banks with a wholesale business model. Banks which
adhere a retail bank business model are of less importance in this relation. Hereby, the importance of
business model elements in the relation with credit supply towards enterprises is different for each
type of business model.
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Contents
Preface ................................................................................................................................................... 3
Abstract .................................................................................................................................................. 4
List of figures ......................................................................................................................................... 8
List of tables........................................................................................................................................... 9
1.
Introduction .................................................................................................................................. 10
1.1 Background ................................................................................................................................ 10
1.2 Setting ........................................................................................................................................ 14
1.3 Research Question ..................................................................................................................... 15
1.4 Outline........................................................................................................................................ 16
2. Literature Review............................................................................................................................. 17
2.1 Business models ......................................................................................................................... 17
2.1.1 Definition ............................................................................................................................ 17
2.1.2 Banks and their business models ........................................................................................ 18
2.1.3 Elements business model .................................................................................................... 21
Asset Structure ............................................................................................................................. 22
Capital Structure .......................................................................................................................... 23
Funding Structure......................................................................................................................... 25
2.2 Credit supply .............................................................................................................................. 29
2.3 Conclusion ................................................................................................................................. 30
3. Theoretical Model ............................................................................................................................ 31
3.1 Definition of measurements ....................................................................................................... 31
3.1.1 Credit supply ....................................................................................................................... 31
3.1.2 Asset structure ..................................................................................................................... 31
3.1.3 Capital structure .................................................................................................................. 32
3.1.4 Funding structure ................................................................................................................ 32
3.1.5 Income structure.................................................................................................................. 33
3.2 Hypotheses ................................................................................................................................. 33
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3.3 Research model .......................................................................................................................... 36
4. Methodology .................................................................................................................................... 38
4.1 Sample........................................................................................................................................ 38
4.2 Procedure ................................................................................................................................... 40
4.3 Control variables ........................................................................................................................ 41
5. Data analysis and empirical results .................................................................................................. 42
5.1 Examination of data ................................................................................................................... 42
5.1.1 Missing data ........................................................................................................................ 42
5.1.2 Outliers and extreme values ................................................................................................ 44
5.1.3 Assumptions ........................................................................................................................ 45
5.2 Multiple regression analysis....................................................................................................... 46
5.3 Results ........................................................................................................................................ 47
5.3.1 Asset structure ..................................................................................................................... 47
5.4 Post-hoc analysis ........................................................................................................................ 53
5.4.1 Results ................................................................................................................................. 53
5.4.2 Conclusion post-hoc analysis .............................................................................................. 56
6. Conclusion and recommendations .................................................................................................. 58
6.1 Conclusion ................................................................................................................................. 58
6.2 Theoretical implications ............................................................................................................. 59
6.3 Managerial implications............................................................................................................. 60
6.4 Limitations ................................................................................................................................. 61
6.5 Future research ........................................................................................................................... 61
References ............................................................................................................................................ 62
Glossary ............................................................................................................................................... 68
Abbreviations ................................................................................................................................... 68
Definitions........................................................................................................................................ 69
Securitization ................................................................................................................................... 69
Appendix I Business Model Canvas (BMC)........................................................................................ 70
Appendix II CRD IV capital requirements .......................................................................................... 71
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Appendix III Definition of variables .................................................................................................... 72
Appendix IV List of all banks .............................................................................................................. 73
Appendix V Summary statistics........................................................................................................... 75
Appendix VI Randomness Test ........................................................................................................... 76
Appendix VII Correlation matrix ......................................................................................................... 77
Appendix VIII Regression results of post-hoc analysis ....................................................................... 79
Appendix IX Post-Hoc analysis, means and standard deviations ........................................................ 82
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List of figures
Nr.
Name
Page
1.1
The three pillars of Basel III
11
2.1
Elements of the capital ratio
22
2.2
Pillar I capital ratios
23
3.1
Theoretical research model
35
5.1
Findings in conceptual research model
49
5.2
Diagram of full model results
52
5.3
Diagram of different business model elements
55
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List of tables
Nr.
Name
Page
4.1
List of banks used in this study
38
4.2
Hypotheses
39
5.1
Missing values
41
5.2
Imputation methods
43
5.3
Outliers and extreme values
44
5.4
Assumptions
44
5.5
Assumptions with improved variables
45
5.6
Results of multiple regression analysis, full model
47
5.7
Economic significance
50
5.8
Hypotheses tests
51
5.9
Regression results of post-hoc model with control variables
54
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1. Introduction
The recent financial and economic crisis (hereafter referred to as the crisis), has changed the facets of
the European financial sector and the banking sector in particular. To prevent a recurrence, the
European Commission and the Basel Committee on Banking Supervision have set new rules and
regulations for the banking industry. To be able to comply with this new set of rules and to be able to
respond to a continuous changing environment, banks must adjust their business model framework.
This will result in a transformation in the business models of banks. Another implication of the crisis
is the restricted credit supply of banks which mainly affect enterprises. This section provides further
details on the context of this study. In addition, the research question and outline of this study are
denoted.
1.1 Background
The world of banking institutions is subject to change (Boot & Thakor, 2009). The crisis revealed the
interdependence of the member states of the European Union (EU) and has emphasized the instability,
weaknesses, deficiencies and failures of regulation on national, European and global scale
(Commission, 2012c). Furthermore, Europe had 27 different banking systems, which were based on
national regulations and circumstances before the crisis came up. Because of this large number of
different systems it was not possible to respond to the crisis sufficiently (Commission, 2012b). In fact,
a 2012 report by the European Commission revealed that the economic model of the EU, where the
Economic Monetary Union (EMU) forms a cornerstone, had to be revised in order to prevent a
recurrence (Commission, 2012a).
This revision should contain an improvement on the stability of the EU and EMU, whereby the
interdependence of the member states must be taken into account. Hereby, the integrity of the
financial services should be guaranteed (Commission, 2012a; Commission, 2012c). Therefore, the
European Commission should reform their policy and set new standards which will result in five
building blocks. Within the scope of this study, two building blocks will be highlighted. The first
building block ensures that more integrated economic governance should be established, whereby
stricter supervision of national regulation of EU-member states will be adopted. This will enable the
EU to respond more quickly to deficiencies and instabilities which will result in a more robust
economic model. The second building block ensures that reinforcement of the banking sector will be
important. The objective which the European Commission attempts to reach is (future) credit supply
for enterprises and households. This will be accomplished by changing banks business models and to
oblige more disclosure (Commission, 2012c).
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To guarantee the financial stability in the EU, an addition to the revision of the economic model is
required. Therefore, the European Commission intends to proceed with the implementation of a
‘European Banking Union’. This European Banking Union should not only contribute towards more
stability but should also try to minimize the costs of a bank’s failure at the expense of its taxpayers
(Commission, 2012b). More specifically, a common mechanism for deposit scheme and an integrated
bank risk management system must be incorporated. In addition, the supervision of banks should take
place on European level (Commission, 2012e). A European Banking Union aims to improve the
credibility of the financial sector, whereby banks should serve society and the real economy. To
summarize, a European Banking Union would lead to higher capital requirements, deleveraging
targets, recapitalizations of banks and improving regulatory and supervisory frameworks
(Commission, 2012b).
Another reaction towards the crisis came from the ´Basel Committee on Banking Supervision´. This
committee sets global standards with regard to prudential banking regulation and wants to improve
the financial stability by strengthen supervision, regulation and other practices related to banks
worldwide (Bank for International Settlements, 2013). The underlying cause of the crisis was
characterized by extreme leverage positions, inadequate liquidity positions and little capital of highquality at banks. Furthermore, to anticipate on the consequences, the Basel Committee developed a
program to prevent recurrence and to improve financial stability. This program continues under the
name ‘BASEL III’. Its main objective is to enable banks to counterbalance shocks which are derived
from economic and financial distress and to reduce the risk from the financial sector towards the
economy. In order to achieve the goals of Basel III, banks and other financial institutions should
consider renewed and improved standards. Therefore, the Basel Committee on Banking Supervision
complement the three pillars which were developed in Basel II. These three pillars subsequently
consist of minimum capital requirements, risk management and supervision and market discipline and
are shown in figure 1.1. Basel III mainly pertains on stronger liquidity and capital regulation.
However, as could be seen in figure 1.1, it is of great importance that these liquidity and capital
regulations take place in combination with enhancements in monitoring, governance, risk
management, as well as transparency and disclosure. By incorporating the new pillars, the Basel
Committee on Banking Supervision tries to lower counterparty credit risk, liquidity risk, market risk
and default risk (Bank for International Settlements, 2010).
Changes in the financial sector are not always triggered by a circumstance of such impact as a global
financial crisis. In the past few decades, the financial sector was exposed to several developments
arising from a continuous changing environment. These developments all had their structural impact
on the financial sector and corresponding framework of banks. An example of such a structural
development is deregulation.
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Figuur 1.1 The three pillars of Basel III
For instance, a few decades ago, the financial sectors of several developing and developed countries
proceeded towards a more deregulated financial system (Altunbas, Manganelli, & Marqués-Ibáñez,
2011).
As a result, parties had more and better access to capital markets and the process of issuing securities
became easier. A process of disintermediation came up whereby parties rather prefer to go directly to
capital markets instead of going to a bank. Another consequence of deregulation was the introduction
of new financial instruments (Edwards & Mishkin, 1995). The process of deregulation (which could
be seen as liberalization) enabled banks to loosen their standards, which resulted in an increase in
competition, privatization of banks and adjustments on capital requirements in order to achieve
economic gains (Altunbas et al., 2011; Demirgüç-Kunt & Detragiache, 1998). This process of
deregulation resulted in an expansion of the geographical area of banks activities (Altunbas et al.,
2011).
Another major structural development from the last few decades is financial innovation. A
transformation occurred between the boundaries of financial institutions and financial markets. Direct
funding possibilities via financial markets have increased and the influence of securitization has
gained in proportion. The continuous development from the financial sector coincided with changing
customer preferences as well as preferences from financial institutions as from the financial sector.
This resulted in more integrated financial markets, adjusted products, services and risk management
techniques. As a consequence, the income structure of banks shifted towards a more non-interest
income model (Altunbas et al., 2011; Boot & Thakor, 2009).
Both structural changes of deregulation and financial innovation as well as Basel III and the
integration of a European banking union let to a profound and continuous evolvement of banks and
their business models. Moreover, this evolvement has a major impact on the financial framework in
the EU and is associated with changes in the size, corporate governance and in the income and
funding structure of banks (Altunbas et al., 2011; Ayadi, Arbak, & Groen, 2011). Examples which
affect the EU banking landscape are the spin-off of the ABN Amro takeover and the acquisition of
Alliance and Leicester by Banco Santander (European Central Bank, 2010).
Banks are mostly seen as a source for credit, especially for small and medium-sized enterprises
(SMEs). This mainly relies on the fact that large, publicly trading firms have access to a broader
capital market, while small firms more depend on credit provided by banks. Small firms tend to
borrow mostly from one single bank and thus concentrate their credit demand (Strahan & Weston,
1998). However, due to the recent crisis and the corresponding new banking regulations,
complications arise. One implication of the crisis is the restricted credit supply towards non-financial
SMEs (Blaes, 2011). This decline in bank lending is not just caused by a decline in credit demand
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from enterprises, but also due to a dynamic environment whereby stricter regulations towards capital
requirements are implemented (Hebbink, Kruidhof, & Slingenberg, 2014). Therefore, the European
Commission proposed to increase the paid-in capital to enhance the credit supply towards SMEs. The
European Commission developed key priorities for 2012. One of these priorities is to ‘normalize the
credit supply of the economy’. For European members, restructuring of the banking sector must be
accomplished without excessive deleveraging. However, for some European countries with a weak
financial position, a less positive future and little resources to assist SMEs, it is a difficult exercise to
normalize the credit supply of the economy (Commission, 2012c). Since SMEs represent 99.8 percent
of the total number of firms in the EU with a total turnover of 60 percent, their existence is of
paramount importance. This shows that the future turnover, employment and wealth of business in the
EU as well as the society as a whole might be affected by changes in the economic system (Braaksma
& Smit, 2012; European Central Bank, n.d.).
1.2 Setting
This study is related to finance literature that provides information of the financial sector, especially
the banking industry, that analysis the elements of a bank’s business model and determinants of credit
supply.
Earlier research on elements of a bank’s business model is conducted by Ayadi et al. (2011). In their
research, the authors provide a large variety of determinants of a bank’s business model which were
collected through examining several bank’s business models such as retail and investment banks.
Variables such as structure, ownership, risk and financial activities were taken into account. This
paper was used as a starting point in this study for the determination of the elements of a banks’
business model. Another study from the same authors, with a contribution from Llewellyn in 2012
expands the category of business models with ‘diversified retail banks’ and represents European
banks based on their business model (Ayadi, Arbak, & De Groen, 2012). In addition, an analysis on
the implementation of Basel III and the influence on the elements of business models of banks were
included. Furthermore, Blundell-Wignall, Atkinson, and Roulet (2014) identified elements which
form a key in a bank’s business model. The European Central Bank (further referred to as ECB)
provided additional information on the development of the banking landscape nowadays and its
influence on a bank’s business model (European Central Bank, 2010).
Previous papers also focused on lending behavior of banks. Lown and Morgan (2006) investigated the
relationship between the credit cycle and the business cycle regarding fluctuations in spending and
lending behavior on changes in credit standards. Earlier research from Asea and Blomberg (1998)
focused on the behavior of banks with regard to their lending standards in different states of the
business cycle. Furthermore, several studies already partly focused on the relationship between the
business models of banks and their lending behavior. In fact, Gambacorta and Mistrulli (2003)
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focused on the relationship of one element of a bank’s business model, namely bank capital, and the
effect of this element on the lending behavior of Italian banks. They found that bank capital affects a
bank’s lending behavior considering market imperfections for bank fund-raising and regulatory
capital constraints. Strahan and Weston (1998) conducted research with regard to changes in the
banking industry, defined by changes in size, complexity and bank consolidation and the impact of
those changes on small business lending. The authors conclude that consolidation leads to an increase
in bank lending towards small businesses.
Although previous literature has focused on the lending behavior of banks or on business cycles of
banks, little empirical research has been conducted on the relationship between different elements of a
bank’s business model and the influence of those elements on a bank’s credit supply. This master
thesis will therefore contribute to the literature by an empirical investigation on the impact of
European banks and their business models on credit supply towards enterprises.
Most similar to this study is a working paper of the ECB from Altunbas et al. (2011). In this paper,
the effect of different elements of business models on bank risk during the recent financial crisis has
been expounded. A bank’s business model is hereby determined by capital, diversification, funding
sources, operating efficiency, securitization and links with financial markets and corporate
governance. According to Altunbas et al. (2011), the misery of the recent crisis was due to
undercapitalization, credit expansion, funding structure and bank size. Thereby, riskier banks faced a
different impact of the business models.
On the contrary, this study distinguishes itself from Altunbas et al. (2011) since it is focused on the
impact of the elements of a bank’s business model on their credit supply instead of bank risk.
However, their methodology formed a starting point for this paper.
1.3 Research Question
Considering the transformation of the financial sector, especially for banking institutions, and its
implications for credit supply, the following question will be the focus of this study:
What are the drivers in a bank’s business model on
credit supply towards enterprises for European banks?
The purpose underlying this question is to offer insight in the main elements of a bank’s business
model and to be able to identify the key drivers of a bank business model which are mainly
responsible for credit supply. The identification of these drivers can be used to adjust rules and
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regulations for banks and their business models in the future, to anticipate on a continuous changing
environment so that credit supply can remain unrestricted. This will contribute towards a better
framework for supervision in Europe.
1.4 Outline
The outline of this study is as follows: first, a literature review on business models of banks and its
elements and on credit supply will be presented. This literature review will form a theoretical
framework for the remainder of this study and will lead to the conceptual model. Second, the
hypotheses will be defined. Third, the design of the research is presented with descriptions of the data
and methodology. Fourth, the results of the tests will be analyzed. Finally, the study ends with a
conclusion, summary and a recommendation towards future research.
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2. Literature Review
This section forms the theoretical foundation of the research. Before the relationship between
business models and their impact on credit supply of European banks can be developed, an adequate
definition of both variables needs to be established. Therefore, each paragraph in this section starts
with a definition followed by an overview of existing literature.
2.1 Business models
2.1.1 Definition
In the literature, several definitions of business models are developed. However, scientists have not
yet reached a clear consensus about it (Zott, Amit, & Massa, 2011). To provide an adequate
understanding of what a business model is, several frequently used definitions will be taken into
account in order to develop one encompassing definition. This definition will be used in this entire
study and will be characterized as independent variable in the research model later on.
Following Teece (2010), a business model consists of the supply of data and other sources, which
together contribute to the value creation of the business and consist of an appropriate architecture of
the costs and revenues to be able to deliver this value to the customers. Johnson, Chistensen and
Kagermann (2008) argue that a business model should incorporate four important elements which
together create and deliver value. They argue that a well developed business model should contain the
following elements: key resources and processes, a profit formula and a customer value proposition.
Several other definitions of a business model mainly focus on the value creation process of a business
(Chesbrough & Rosenbloom, 2002; Magretta, 2002), on the creation of competitive advantage
(Morris, Schindehute, & Allen, 2005) or are a description of the realized strategy of a business
(Casadesus-Masanell & Ricart, 2010). More general, a business model is delineated by ‘a pattern’
(Brousseau & Penard, 2006), ‘an architecture’ (Timmers, 1998), ‘a description’ (Weill & Vitale,
2001) or ‘a statement’ (Stewart & Zhao, 2000).
Recently, a new method to define a business model is introduced by Osterwalder and Pigneur (2010)
which is called the ‘Business Model Canvas’ (hereafter referred to as BMC). In the development of
this kind of business model, several definitions and experiences of other scientists are incorporated.
Central to this type of business model is it’s relevance and simplicity. Several organizations
worldwide already implement the BMC, for instance, General Electric, Mastercard and SAP
(Business Model Foundry, n.d.). Following the BMC, a business model consists of nine key elements
which together show how the company creates value and therefore receives earnings. First, the target
group of a company must be defined and questions, such as; ‘For who are we creating value?’ and
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‘Who will we want to serve?’ should be answered. The client preferences will hereby form the start of
a company’s business development. Second, the value proposition(s) must be determined, in other
words, ‘What value do we deliver to the customer?’ The following elements, described in sequence of
importance are the channels, customer relationships and revenue streams. Hereby a company must
focus on, for instance, through which channels the company will contact customers, the kind of
relationship a company wants to keep with its customers and how the company can generate revenue.
Thereafter, the business model should provide a clear insight and understanding of the key partners,
key activities and key resources of the company. However, about the sequence of these elements can
be twisted. Finally, the element of cost structure, which describes the most important costs for a
company, will be outlined. A complete overview of the Business Model Canvas and its nine key
elements is included in Appendix I. Furthermore, in the BMC, the revenue model of a company is
included, since both the cost and revenue structure are incorporated.
Taken the above definitions and descriptions about a business model into account, it can be stated that
a business model definition involves diverse opinions and therefore can be defined in a lot of different
ways. Not all of the above definitions of a business model are used very common. A business model
definition which is easy to understand and nowadays often used is that of Osterwalder and Pigneur
(2010). The authors define a business model as follows:
“A business model describes the rationale of how an organization creates, delivers, and captures
value.” (Osterwalder & Pigneur, 2010, p. 14)
Therefore, this definition and the BMC as a whole will form the basis of the definition of a business
model during this entire study.
2.1.2 Banks and their business models
The economic activity and efficiency of a country is highly dependent on the conduct and efficiency
of the banking sector (Ayadi et al., 2011). As mentioned in the previous paragraph, the BMC uses
clients’ needs and preferences as an input for a company’s business. Since different customers have
different needs, different business models need to persist to fulfill these different needs. Thereby,
business models change over time since they are subject to continuous changing regulation and other
environmental pressures. Because of this, business models of banks are not identical so diversity in
banks business models will persist (Ayadi et al., 2012).
A distinction in banks business models could roughly be made by dividing banks based on the
maturity of their lending activities. Within the banking sector, banks either focus on longer term
maturity dates (over a year) or on shorter term maturity dates (shorter than a year). In this study, the
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focus lies on business models of banks with a maturity term of longer than one year. In addition, the
most important and broadly defined business models of banks, which are retail, investment and
wholesale banks, will be discussed. Other alternative bank business models, such as saving and
mortgage banks are outside the scope of this study. The business models which are described in this
paragraph are not encompassing nor do they exclude each other. Although theoretically, the business
models can be specifically distinguished, taking different banks and their activities into account, there
is not a completely clear consensus to be made on the business models. To provide an adequate
insight in banks business models, first the traditional banking model will be described shortly,
followed by investment, retail and wholesale models.
Traditional bank model
In the traditional banking model, banks fulfill the role of financial intermediation and profile
themselves as “risk managers, financial innovators and providers of liquidity” (Ayadi et al., 2011, p.
3). The key role of banks is to accept deposits and transform them into loans which they provide to
borrowers. The interest which borrowers pay on those loans forms the main source of income. Banks
are able to provide loans since they have a comparative advantage with regard to information,
monitoring and risk analysis. When banks transform the received deposits into loans, the assets will
be entered on the balance sheet of the bank. However, when banks start to issue loans, they are being
exposed to credit risk in case borrowers do not amortize their loans. Therefore, banks monitor their
borrowers and provide for a minimum capital requirement to be able to capture such losses. Another
way to avoid credit risk is to add a premium in the interest rate of borrowers. Since banks dispose of a
comparative advantage with regard to the available information about customers, it is not possible to
shift the credit risk towards other institutions and to insure banks externally against this risk. This
traditional banking model, whereby banks obtain deposits, convert them into loans and are exposed to
credit risk is also characterized as the ‘originate-and-hold’ model (Llewellyn, 2012).
Because of structural changes in the financial sector and some newly introduced rules and regulations,
the financial system has changed. Especially the banking sector has been transforming. A few
examples of changes which caused this development are: an increase in financial innovative and
complex financial products, ‘financialization’ of the economy, integration and globalization of the
financial market and especially the possibility to shift credit risk (Llewellyn, 2012). These changes
have led to a transformation in the nature of banks and therefore in the business models of banks.
Banks became able to outsource some of their services and therefore they no longer had to fulfill all
the functions in the intermediation process. Furthermore, the relationship between borrowers and
lenders changed because of other credit risk instruments. Consequently, the traditional role of banks
changed towards an intermediary in the process of borrowers and credit risk activities (Ayadi et al.,
2011).
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Retail bank model
A business model that is closest to the traditional banking model is the retail bank business model.
This business model relies mainly on customer deposits for funding and issues mostly loans towards
households and SMEs (Köhler, 2014), which forms a source of income. Trading activities are
restricted in this business model. In other words, retail banks provide traditional banking activities
whereby banks try to reach the general public. To be able to reach this public, retail banks have a
broad network of different branches. While retail banks are more relying on customer deposits, they
rely less upon other different forms of market funding. From a study of Ayadi et al. (2011) retail
banks seemed to increase their lending in a period of crisis, and did not receive a lot of governmental
support. Thereby, retail banks have a relatively high level of capital which makes those banks able to
absorb losses. In fact, retail banks have a low default rate which makes them less risky and their stock
returns have a low volatility which all together makes that retail banks appear to have a relatively
stable business model (Ayadi et al., 2011). Following Ayadi et al. (2011) banks which adhere a retail
business model are for instance, Rabobank, SNS, Banco Santander and HSBC. To summarize, retail
banks are banks with a relatively low leverage, take less risks and seems to perform comparable with
banks which enhance other business models. However, recently, retail banks seemed to have moved
towards other business models, such as investment and wholesale banks (Ayadi et al., 2011).
Investment bank model
In contrast to the retail banking model, the key activity of the investment bank model is not to supply
loans to customers but to focus mainly on investment activities. Hereby, investment banks focus in
particular on trading assets and derivatives which are therefore part of their income structure.
Investment banks focus less on interbank lending. Besides the income structure, the funding structure
of investment banks also differs from retail banks. Instead of using customer deposits as a source of
funding, banks with this business model mainly rely on non-traditional funding sources. Examples
hereby are repurchase agreements and reverse repurchase agreements. In a repurchase agreement
banks sell securities, in other words, borrow money, and repurchase them back at a later date in the
future at a higher price. A reverse repurchase agreement is exactly the opposite whereby banks buy
the security and sell them in the future (Jarrow & Chatterjea, 2013). This type of funding is a form of
short-term funding and is also known as market funding. Due to the volatility of the securities,
investment banks must take an active position to be able to manage their balance sheet. Thereby,
investment banks have a high leverage and dispose of low loss-absorbing capital to capture losses
comparable to other banks business models. Therefore, in comparison with retail banks, investment
banks are more risky. During the crisis, investment banks did not perform as well as retail banks and
their lending and trading activities have experienced a decline. As a result, some investment banks
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received governmental support and some even became nationalized. Two examples of investment
banks are Barclays and BNP Paribas (Ayadi et al., 2011).
Wholesale bank model
Another type of a bank business model is the wholesale banking model. Banks which follow this type
of business model distinguish themselves to be mainly domestic and by focusing on the interbank and
wholesale market. The income structure of this business model consists mainly of interest-related
products. Just as the investment banking model, the funding structure of wholesale banks does not
rely upon customer deposits but mainly on funding sources in wholesale markets. The customers of
this type of banks are mainly financial institutions and large and medium-sized enterprises. Following
Ayadi et al. (2011), this banking business model is the most riskiest one in comparison to retail and
investment bank models. As a matter of fact, during the crisis, wholesale banks received the most
governmental support and some were nationalised. This exposure to risk comes from a low
availability of liquid assets and the exposure towards volatile interbank funding. Crédit Agricole and
Danske Bank Group are examples of banks which adhere the wholesale business model (Ayadi et al.,
2011).
2.1.3 Elements business model
Taking previous business models definitions and especially the business model defined by
Osterwalder and Pigneur (2010) into account while projecting them on banks, their business models
and the banking sector, some key elements of a bank’s business model can be defined. Previous
literature already focused on different aspects of a bank which could serve as elements for a bank’s
business model. Examples of such elements are funding structures (Ayadi et al., 2012), risk exposure,
whereby the focus could lie for instance on the degree of derivative concentration or on a share of
liquidity trading assets of banks (Blundell-Wignall & Roulet, 2012), income characteristics (Ayadi et
al., 2012), the process of securitization (Boot & Thakor, 2009) and another common used elements
which are of great importance, for example bank capital (Gambacorta & Mistrulli, 2004). Gambacorta
and Marqués-Ibáñez (2011) showed that some aspects, such as a bank’s business model has even
influenced Europe and the United States in their monetary policy and has led to some structural
changes. For example, the authors found that elements such as securitization, non-interest income and
capital have an impact on bank’s credit supply.
This subsection provides an overview of the literature on the most common used specifications of
elements which frequently form an aspect of a bank’s business model whereby a distinction is made
between four different elements. The key to focus on these four elements is determined by earlier
research, the application of the elements in the BMC and the measurability of the elements.
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Asset Structure
Following Osterwalder and Pigneur (2010), an important element of a business model is the value
proposition. One way in which banks add value to their customers is to provide financial services and
advice. Another way in which banks add value is to sell financial products, such as loans and
securities. Therefore, one of the important elements of a bank’s business model is the asset structure
of banks. Recently, this structure has gone through an intense modification. This modification, which
is partly encouraged by the innovation in credit risk modelling as stated in Basel II, is also known as
the securitization process. Securitization is “the issuance of claims backed by a pool of default-risky
instruments where the new claims frequently have varying exposures to the underlying pool of
collateral” (Marqués-Ibáñez & Scheicher, 2009, p. 2). So with securitization, different risky assets
are spread over different investors. Before the crisis occurred, securitization activity experienced a
peak. As a result, the system of the financial markets and the financial structure of several countries
changed. This has led to a new role for banks and an improvement of the banking sector. Moreover,
the process of securitization has had a significant influence of developments of financial innovation
and has led to better tradability of credit risk since it turned illiquid assets into marketable securities
(Altunbas et al., 2011; Marques Ibanez & Scheicher, 2009). In fact, these changes had an effect on
the monetary policy, credit supply and risk management of banks (Marqués-Ibáñez & Scheicher,
2009). For example, because of securitization, banks are able to transfer part of their credit risk,
which results in lower capital requirements and enable banks to raise more funds (Altunbas et al.,
2011).
The rise in funding could therefore lead to increasing lending possibilities for banks (Marqués-Ibáñez
& Scheicher, 2009). Because the process of securitization provides the opportunity of credit risk
distribution, the lending opportunities for risk seeking borrowers are increased (Marqués-Ibáñez &
Scheicher, 2009). Altunbas, Gambacorta, and Marqués-Ibáñez (2009) examined the relationship of
securitization on the monetary policy of loan supply. The authors found that the proces of
securitization has a major impact on banks’ lending behavior. They argue that the securitization
process has led to an increase in banks’ loan capacity (depends on the conditions of the business cycle)
which results in more credit supply towards households and enterprises, especially in good times.
However, securitization has led to a decline in the importance of the bank’s lending channel. This
could be due the distribution of credit risk of banks which lead to lower capital requirements. In
addition, Dell'Ariccia, Igan and Laeven (2008) found that higher mortgage securitization rates results
in declining lending standards. Another study which confirms the increase in credit supply and looser
credit standards, due to securitization, is published by the ECB (European Central Bank, 2008). They
argue that banks, which securitize assets, are better able to raise funds and therefore could issue more
credit.
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Capital Structure
The bank needs capital for its daily operations and to grow or stay in business. It is capital, derived
either from equity (yearly profit) or debt (loans) that is used for example to build new offices. Thus,
capital structure is the way in which banks assets are financed such as the ratio between debt and
equity, and forms an important part of a bank’s business model. As Osterwalder and Pigneur (2010)
describe, key resources are the necessary recourses to create value and revenue, to maintain
customers contact and to be able to reach markets. Since bank’s are obliged to maintain minimum
capital requirements to be capable to capture future losses and to be able to do their business, capital
requirements form a key resource in a bank’s business model.
Capital requirements are the imposed rules and regulations by supervisors to which banks must
comply. These requirements have played a significant role in the banking sector, especially since the
implementation of the Basel accords I, II and III. In the most recent Basel accord, a new definition of
capital is implemented where common equity plays an important role and the focus lies mainly on
capital requirements, as stated in subsection 1.1. A capital requirement is hereby the definition of
capital divided by total risk exposure amount. In addition, the total risk exposure amount could for
example consist of operational, credit or counterparty credit risk. These are just examples, a full
overview is provided in figure 2.1 and figure 2.2. Furthermore, higher standards for capital ratios are
introduced, to be able to comply with the new and stricter definition of common equity and to be able
to fulfill the capital requirements with regard to counterparty credit risk and other trading activities.
Therefore, not only the common equity tier 1 capital ratio is adjusted, but also the tier 1 capital ratio,
the total capital ratio and the basel 1 floor ratio. An overview of these new capital requirements is
provided in Appendix II (Bank for International Settlements, 2010).
Figure 2.1, Elements of the capital ratio
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Figure 2.2, Pillar I capital ratios
Ten years after the adoption of the Basel I agreement, the Basel Committee on Banking Supervision
has analyzed the effects of increasing capital requirements for banks on their lending behavior and the
effects on the macro economy. To fulfill their capital requirements, it is not unlikely that banks adapt
their lending behavior. By reducing their credit supply, banks are able to meet certain capital
conditions. However, if other financial institutions will not fully respond to this restricted credit
supply, a reduction in credit supply could cause a credit crisis which could affect the economy on a
macroeconomic level. The real-estate sector in the US serves as an example which was highly
affected by the capital requirements banks had to achieve (Jackson, 1999). VanHoose (2007)
analyzed several studies which focussed on the capital regulations of banks. From this analysis, it
became evident that theoretical academic literature has reached consensus about the influence of
restricted capital regulation on bank lending. Bank lending will decrease as a response towards the
restricted capital requirements, especially in the short run. However, on the long run, capital
restrictions will lead to an increase in capital ratios which could probably lead to an increase in
lending. In other words, the modifications on capital requirements have a major influence on banks’
lending behavior, which will either be stricter or looser, dependent on the time horizon.
Hancock and Wilcox (1998) analyzed banks’ lending behavior towards small businesses in the United
States after changes in bank capital occurred. They argued that small banks were more likely than
large banks to change their lending behavior as reaction towards a change in their bank capital. From
Peek and Rosengren (1997), who investigated the effect of credit supply shocks on the real economy,
can be concluded that nor the creditrating of borrowers, nor the volume of loan requested influenced
the lending behavior of Japanese banks. In fact, capital requirements served as a constraint on credit
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supply. Looking at the effects of changes in bank capital on an aggregate level, a negative correlation
between bank capital and credit supply exists. This negative correlation is due to the stability of bank
capital on an aggregate level which hardly changes and therefore almost remains the same, although
changes in bank capital on an individual bank level affect a bank’s credit supply (Jackson, 1999).
Following Van den Heuvel (2002), the negative effect of capital requirements on bank lending
behavior could be due to two factors. Firstly, the adequacy of bank capital plays an important role.
Banks want to secure a sufficient capital buffer and thereby trying to minimize the risk of capital
inadequacy in the future. Secondly, the issuance of new equity has a major effect on bank lending. If
banks are hardly able to issue new equity, their lending behavior will be affected since it would be
difficult to fulfill the capital requirements. In addition, Gambacorta and Mistrulli (2004) also provide
evidence on the impact of bank capital and the negative influence on credit supply. These findings
indeed show that a clear link between a bank’s capital structure and credit supply exists (Gambacorta
& Mistrulli, 2004).
Funding Structure
Similar to the capital structure, the funding structure forms a key resource for the business of banks.
The funding structure of banks is the manner in which banks raise funds to be able to provide their
products and services to their clients. In contrast to capital structure, banks do not rely on equity for
their funding sources but only on debt. For example, savings of customers are a source of funding for
banks. Without such deposits, banks are not capable to exercise their business which makes bank’s
funding structure a key resource. Structural developments such as deregulation and financial
innovation had a major impact on the changing funding structure of banks. The funding structure
transformed from a focus on retail deposits into more dependency on financial markets and wholesale
funding (Altunbas et al., 2011; Huang & Ratnovski, 2011). By combining both wholesale funding and
retail deposits, banks were able to exploit valuable investment opportunities, without facing a
restriction in retail deposits. This restriction could therefore be eliminated, since wholesale funding
attracts cash from for example financial institutions and non-financial corporations, and therefore
relies more on market sources. In contrast to wholesale funding, whereby funding mainly depends on
the situation of the financial markets, retail deposits are a stable source of funding, especially when a
crisis occurs (Huang & Ratnovski, 2011). Hence, banks that relied more upon retail deposits became
less affected and therefore a new transformation of the funding structure seemed to occur. In addition,
results from a survey which was conducted at several European banks showed that, since the crisis,
banks rather prefer retail deposits than wholesale funding (European Central Bank, 2009). Another
consequence of the transformation is the contribution towards a more elastic interest rate which will
fluctuate in line with the situation on financial markets, instead of the inelastic loan rate, which was
applicable under solely deposit funding (Hale & Santos, 2010). The dark side of retail deposits is the
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sluggishness with which it reacts to developments in financing needs instead of wholesale funding
which is able to respond quickly (Altunbas et al., 2011).
In terms of the relationship between funding structure of banks and the impact on their lending
behavior, Gambacorta and Marques-Ibanez (2011) argue that banks lending behavior becomes more
restricted in times of a crisis when banks rely more on market-funding. Thereby, short-term funding is
an important element in the determination of banks´ behavior towards changing monetary rules and
regulations. Moreover, banks reduced their lending on account as a consequence of stricter rules and
regulations which led to higher opportunity costs for holding deposits. Therefore a decrease in overall
lending may be detected since banks may dispose of fewer deposits because of their increased costs.
Ivashina and Scharfstein (2008) analyzed the impact of banking panic on the lending behavior of
banks in the corporate sector. The authors showed that during the crisis, loan demand declined which
caused a decrease in the lending behavior of banks. However, banks at which their funding structure
relied more on deposits were less likely to reduce their lending behavior.
Overall, the funding structure of banks has a major impact on their credit supply. Banks are
continuously responding with their funding structure towards market developments whereby they
already changed from long-term towards short-term funding and even towards different positions in
the retail deposits. The ECB argued that this transformation in a bank’s business model led to a
change in the banking sector and their structures, whereby banks are looking for simplicity (European
Central Bank, 2009).
Income structure
An important element of the BMC is the revenue stream of a company. For banks, this revenue
stream will be determined by their income structure, which will therefore form an important element
in a bank’s business model. As mentioned, structural developments such as deregulation have led to
changes in the banking sector. As a consequence of deregulation, banks had to change their traditional
strategy (which consisted mainly of deposit funded loans) towards a more active one, in order to
respond to an increase in concentration and competition in the banking sector. This change in strategy
was necessary since the rise in competition has led to a decrease in interest margin which resulted in
an incentive for banks to differentiate themselves from others (Lepetit, Nys, Rous, & Tarazi, 2008).
Thereby, banks also started to diversify themselves in order to reduce their risk and to counterbalance
macroeconomic shocks (Stiroh, 2010). Therefore, banks came up with new and innovative services
and products which have led to significant changes in the income structure of banks. Consequently,
the income structure of banks changed from an interest-income structure towards an income structure
which depended also on non-interest income. The non-interest income is hereby divided into income
from commissions and fees and trading income (Lepetit et al., 2008). The European banking sector
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thereby faced a major development of an increase of 15 percent point on non-interest income from
1989 till 1998 (European Central Bank, 2000).
In fact, this combination of an interest income with a non-interest income structure provides a stable
income from lending activities since it is costly for both borrower and lender to switch to another
lending party. However, on the other hand, the non-interest income structure is more subject to
fluctuations since it is easier to change for a client from bank with these kind of services and products
(Lepetit et al., 2008). The transition towards a more non-interest income model also led to higher and
more stable revenues. An explanation for this lies in the independency of business conditions from
non-interest income in contrast to interest income, which leads to a reduction in the cyclical variation
in a bank’s revenue (Stiroh, 2004; Gambacorta & Marqués-Ibáñez, 2011). However, the volatility of
revenue which comes from non-interest income is usually higher in comparison towards revenue from
the traditional model. Overall, it could be stated that the transformation of a bank’s income structure
has an impact on the business models of banks and therefore also on their capability of credit supply.
For instance, during a crisis, banks with larger non-interest income activities are more likely to
restrict their credit supply (Gambacorta & Marqués-Ibáñez, 2011). Looking at the relationship
between the lending behavior of banks and non-interest income, some studies were able to prove that
this relationship could lead to diversification benefits and therefore to a reduction in risk (Lepetit et
al., 2008).
Risk
Finally, a last element which forms an important part of a bank’s business model is described. This
element is risk. Risks play a major role in banks and the banking sector. A way to define banking
risks is “adverse impacts on profitability of several distinct sources of uncertainty” (Bessis, 2002, p.
11). In the banking industry, a number of risks can occur and these risks are the reason why banks
exists, since banks are better able to manage these risks than other institutions (Ayadi et al., 2011;
Bessis, 2002). Managing risks consists of measuring, monitoring and controlling risks. Since banks
could manage risks, they can also be described as “risk machines” (Bessis, 2002, p. viii); taking risks
and embedding them into their products and services. By actively managing risks, banks can protect
themselves from unexpected situations and therefore price the risk, which then leads to a competitve
advantage. As stated, banks need to deal with several kinds of risks, the major ones are systematic,
credit, market, operational and liquidity risk (Bessis, 2002), which will be further explained in this
subsection.
One of the main goals of banking regulation is to prevent banks from systematic risk (Bessis, 2011).
Because of their interdependence, a failure of one single bank could influence other banks which
probably lead to losses and failures of these banks. This type of risk, which may lead to a collapse of
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the whole banking industry, is known as systematic risk. As mentioned, banks are exposed to credit
risk. This is the oldest type of risk and was one of the main features of the Basel I accord (Bessis,
2002; Bessis, 2011). This accord introduced the ‘Cooke ratio’ which is a formula to calculate the
required amount of capital a bank should retain, based on the risk weight of assets and loan value
(Bessis, 2011). A form of credit risk is default risk. This kind of risk, also known as counterparty
credit risk (Pykhtin, 2010), occurs if clients default and therefore will not be able to amortize their
debt. The risk of a decline in the credit standing of an issuer of bonds or stocks is also a form of credit
risk which results in a higher change of default. Credit risk is mainly invisible. When banks make use
of monitoring systems, for example internal ratings and watch lists, thus manage credit risk actively,
it leads to better visibility and measurability of credit risk (Bessis, 2002). Another type of risk which
is outlined in Basel I is market risk. In a 1995 amendment in Basel I, a measure was introduced
whereby “trading positions in bonds, equities, foreign exchange and commodities became subject to
capital charges for market risk” (Bessis, 2011, p. 34). This to be able to offer resistance towards
market risk (Bessis, 2011). Market risk is the risk of fluctuations in the mark-to market value, which
is the ‘fair’ value based on the current market price, of the trading portfolio, caused by market
movements. Real market risk arises due to changes in market parameters such as interest and
exchange rates and consists of losses due to market movements (Bessis, 2002).
With the introduction of the Basel II accord, operational risk became more important. This type of
risk is related to deficits of reporting and information systems and of internal procedures (Bessis,
2002). In Basel II, operational risk is defined as “the risk of loss resulting from inadequate or failed
internal processes, people and systems or from external events” (Basel Committee on Banking
Supervision, 2006, p. 144). Since several different events could lead to potential losses, operational
risk is also classified as event risk (Bessis, 2002). Finally, banks are exposed to liquidity risk.
Recently, banks experienced difficulties to manage liquidity and therefore the Basel III accord
focuses on liquidity risk to ensure that banks will be able to better manage this type of risk (Basel
Committe on Banking Supervision, 2013). Liquidity risk mainly consists of market and asset liquidity
risk. These risks are associated with the costs of funding. If banks are exposed to this kind of risk,
they may end up in bankruptcy (Bessis, 2002).
To be able to manage the risks explained above, banks use quantitative measures. Three types of
quantitative measures can be found in the literature (Bessis, 2002). First, sensitivity is the dependence
of a target variable on an underlying factor. For example, one unit change in the underlying factor on
the target variable can changes the target variable significantly. The variance in the effect is
sensitivity. According to Bessis (2002), sensitivity is mostly market risk related because changes in
the market such as interest rates drive the value of the target variables, for example, house prices.
Second, volatility is a quantitative measure that refers to the fluctuations of a target variable. Hereby,
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up- and downside fluctuations around the average of the target variable will be measured which
indicates the potential instability of the variable. Third, downside measures of risk focuses only on the
negative variations of a target variable, also known as worst-case variations. Examples of target
variables for this type of measurement are credit losses and market values (Bessis, 2002).
2.2 Credit supply
To provide a good understanding of credit supply through banks, an adequate definition is established
which is used during this entire study. Therefore, previously used definitions will be taken into
account, to be able to develop a useful and complete definition.
The most obvious way to measure the lending behavior of banks is by looking at the volume of loans
issued. Several studies, such as Lown and Morgan (2006) used the volume of commercial loans at
banks as an indicator for the commercial credit market. Strahan and Weston (1998) consider the
relation between the lending behavior of banks towards small businesses. To measure this relation,
the size of the commercial and industrial loans was taken into account, since this measure must serve
as a proxy for banks lending behavior towards small businesses. In addition, another study in which
lending behavior of banks is measured in volume is Cebenoyan and Strahan (2004). The authors
measured the effect of actively managing credit risk on lending behavior of banks. Lending is hereby
defined as ‘commercial and industrial loans/assets’ and ‘commercial real estate loans/assets’
A factor that influences the banks’ credit supply is lending standards which banks have to comply.
Lown and Morgan (2006) argue that commercial bank loans are influenced by a change in
commercial credit standards. For instance, tighter credit standards contribute to a drop in credit
supply. Furthermore, lending standards serve as informative elements for future lending. The ECB
produced a survey to investigate the lending behavior of banks towards enterprises and households.
The aim of this survey was to gain insight in banks’ financial regulations and behavior which could
serve as an input for monetary rules and regulations. To define banks’ lending behavior, credit supply
is defined by the standards regarding a bank’s loan policy and the specific conditions on which a
lender and borrower agree. Furthermore, this survey also focuses on changes in credit demand
(European Central Bank, n.d.).
Popov (2013) also focused in his study on banks’ credit supply. Popov used data on both bank- and
firm-level to be able to well define credit supply. This will provide the possibility to make a
distinction between credit supply and credit demand. In his study, banks balance sheets form an input
for credit supply. For data of credit demand, Popov takes credit access of firms into account whereby
the focus lies on whether firms received a loan or not. Two different criteria are maintained to define
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the credit constrainedness of firms in case no loan was granted. First, the criteria ‘credit register’, was
taken into account, which measures the loan rejections when firms have requested a loan. Second, the
criteria ‘survey’ was taken into account. This criteria not only measures the formally rejected loans,
but also the informal rejected ones whereby a firm did not even request a loan since it was supposed
to receive a rejection. This means that firms have a loan demand, but do not exercise this demand.
The ‘survey’ criterion is therefore of great value since this kind of demand is not included in the
official bank records. Taking the above studies and definitions into consideration, during this study,
the following definition of credit supply will be used.
Definition of credit supply
Credit supply is the total amount of outstanding corporate and commercial loans that one or more
European banks provide towards enterprises.
Derived from this definition, gross commercial loans are taken into account which implies that newly
issued loans, in other words, net loans, are included, but also already issued loans are incorporated.
Thereby, in the context of this study credit supply will be characterized as the dependent variable and
only the supply of credit will be taken into account, whereby the demand of credit will be disregarded.
2.3 Conclusion
In this chapter, a comprehensive literature review was provided on the business models of banks and
credit supply. Due to a turbulent environment, different business models of banks exist. These
different business models dispose of common elements, such as asset, capital, funding and income
structure which determine the different business models. In this study, the different elements of banks’
business model form a key to be able to investigate the relationship between the business model
elements of banks and the impact on credit supply.
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3. Theoretical Model
In this section, the key variables that were used to measure the elements of a banks´ business model
and credit supply are defined. Hereby, the definitions of these variables are similar to the definitions
which are used by the financial database ‘Bankscope’. In addition, the hypotheses are developed and
a conceptual research model is stated.
3.1 Definition of measurements
Following the literature (section 2), broadly four key elements define the business model of banks.
Therefore, the four elements; asset structure, capital structure, funding structure and income
structure all together formed the entire business model of banks during this study. The element risk
was used as a control variable in this study which will be further explained in the next section. This
subsection illustrates how each business model element will be measured. Every business model
element is defined in at least two different ways to analyze the relationship between the dependent
and independent variables as accurate as possible. A full overview of the used variables, their
definitions and abbreviations is provided in Appendix III.
3.1.1 Credit supply
As stated in subsection 2.2.1, a way to define credit supply is the total volume of loans banks supply
towards enterprises. Hereby the total volume of all outstanding loans towards enterprises is
incorporated which includes both newly issued and already issued loans. This volume can be
measured through the variable corporate and commercial loans to total assets (ccloans_ta). Following
Bankscope, corporate and commercial loans are loans with a maturity date between six months till
one year. Therefore, in this study, credit supply is defined as short-term credit supply. Although, not
every business model mostly focuses on loans towards enterprises, only corporate and commercial
loans will be taken into account, since consumer and retail loans are out of the scope of this study.
3.1.2 Asset structure
An important indicator for a bank’s business model is to what extent banks provide activities and
services according to the traditional banking model. In other words, this is the asset structure of banks.
As stated in subsection 2.1.2 the key activity of a traditional bank model is to accept deposits and
transform them into loans (Llewellyn, 2012). The retail bank model is the business model of banks
which has the most similarities with the traditional bank model. The main activity of this bank model
is to provide loans towards households (Köhler, 2014). Therefore, a variable which reflects the asset
structure is the ratio of total outstanding (gross) consumer loans to total assets. Hereby, consumer
loans are defined in Bankscope by residential mortgage loans (rml_ta), other mortgage loans (oml_ta)
and other consumer/retail loans (ocrl_ta) to total assets. As also mentioned in subsection 2.1.2, a shift
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in the financial services occurred which had led to a change towards securitization. This resulted in a
new role for banks and a change in the banking sector, which therefore differs from the traditional
banking model (Marqués-Ibáñez & Scheicher, 2009).Therefore, the degree of securitization is an
important variable for asset structure and is in this study measured by the ratio of total securities to
total assets (sec_ta). Hereby, following Bankscope, total securities consist of: reverse repos and cash
collateral + trading securities at future value through income + derivatives + available for sale
securities + held to maturity securities + at-equity investments in associates + other securities. It
seems odd to classify cash collateral under securitization. However, cash collateral is the cash which
remains after liquid assets are converted into cash in case of bankruptcy. Since a holder of securities
dispose of the right over certain assets and cash flows, cash collaterals form an integral part of
securities (Jarrow & Chatterjea, 2013). In general, when the ratio of loans to total assets is higher in
comparison to the ratio of securitization to total assets, banks tend to follow a more traditional
banking approach. If the opposite is true, banks enhance a more innovative business model instead of
a traditional one.
3.1.3 Capital structure
Since the crisis occurred, the Basel Committee on Banking Supervision implemented stricter capital
requirements for banks. Because of these stricter regulations, banks should be able to capture losses in
distress situations. As illustrated in figure 2.1 and 2.2, tier 1 capital forms an important component of
the capital structure and is recognized as ‘primary capital’. The committee increased the required tier
1 capital ratio, from 4 to 6 percent under Basel III (Bank for International Settlements, 2010).
Therefore, capital structure will be measured through the ratio tier 1 capital to total assets (tier_ta).
Following Bankscope this ratio consists of common stockholders’ equity + qualifying cumulative and
noncumulative perpetual preferred stock + minority interest in equity accounts of consolidated
subsidiaries – goodwill (Saunders & Cornett, 2011). Another measure for capital structure is the total
capital to total assets (figure 2.2), which consists of tier 1 capital plus tier 2 capital (tcap_ta). Tier 2
capital hereby consists of stock surplus resulting from the issue of instruments + instruments issued
by consolidated subsidiaries of the bank and held by third parties + certain loan loss provisions +
regulatory adjustments and other criteria for inclusion in Tier 2 Capital (Bank for International
Settlements, 2010).
3.1.4 Funding structure
As mentioned in the literature review, different business models of banks use different funding
structures. Banks which enhance the traditional banking model, or models which are close to that
model, mainly use customer deposits as a source of funding. Therefore, in this study, the funding
structure consists of the variable customer deposits which will be defined as customer deposits to total
assets (cdep_ta). Following Bankscope, customer deposits consists of current, savings and term
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customer deposits. Another way in which banks could fund their lending activities is short-term
funding. In this study, this variable will be measured through the ratio of repurchase agreements and
cash collateral to total assets (reca_ta). Cash collateral is used because it can be converted into cash,
which is a source of funding. Furthermore, another way to measure the funding structure of banks is
by taking the loan-to-customer deposits ratio (loans_dep) into account. This variable is in Bankscope
defined as gross loans – reverse repurchase agreements included in loans divided by total customer
deposits – repurchase agreements included in customer deposits. Hereby, a ratio above 1 indicates
that banks supply more loans than they own deposits and therefore banks need to borrow money. A
ratio below 1 indicates that banks supply loans by using their deposits.
3.1.5 Income structure
Another element which differs between several business models of banks is the income structure. As
stated in subsection 2.1.2, banks changed from an interest based income structure towards a
combination of interest and non-interest based income structure which resulted in a change in banks’
business model (Lepetit et al., 2008). In order to measure the income structure of banks, two variables
are defined. The first variable hereby is interest income and is defined as follows: total interest
income on loans + other interest income divided to total revenues (inti_tr). Hereby, total interest
income is the sum of interest income on mortgage loans + other consumer/retail loans +corporate and
commercial loans + other loans. The second variable is non-interest operating income to total
revenues (ninti_tr). This variable consists of net gains (losses) on trading and derivatives + other
securities + assets at future value through income statement + net insurance income + net fees and
commissions + other operating income. The total revenues of both variables consist of the sum of netinterest income and total non-interest operating income. The explained definitions of measurements
of all business model elements are used in the next subsection to develop the hypotheses.
3.2 Hypotheses
The asset structure of banks is partly measured by the ratio of consumer loans to total assets. Since a
distinction can be made between consumer or corporate loans, the following can be assumed. Banks
which provide loans to consumers, possess less capital to provide loans to enterprises, therefore a
negative relation might exists between the ratio of consumer loans and a bank’s credit supply towards
enterprises. Several authors investigated the relationship between the degree of securitization of assets
and the effect on the lending possibilities for banks. Banks, who engage in the securitization process,
are less exposed to credit risk, since they transfer part of their credit risk to other parties. Therefore, a
bank with securitization activity has to comply with looser capital requirements and is able to raise
more funds. As a consequence, banks could expand their loan capacity and therefore they could issue
more credit (Altunbas et al., 2009; Altunbas et al., 2011; Marqués-Ibáñez & Scheicher, 2009;
Page 33 of 82
European Central Bank, 2008). Thus, in line with these researchers, the following hypotheses are
proposed:

H1: The ratio of consumer loan supply is negatively related to a bank’s credit supply towards
enterprises.

H2: The degree of securitization has a positive effect on a bank’s credit supply towards
enterprises.
Another element of a bank’s business model that affects credit supply is the capital structure of banks.
As already mentioned, by changing capital rules and regulations, it is likely that banks should change
their lending behavior (Jackson, 1999). VanHoose (2007) argued that in the long-run, banks are
probably able to increase their credit supply. This might be because, when assuming that banks go
over the required capital ratio in the long run, these banks are not directly constraint by capital
regulations anymore in a next period and could therefore increase their lending (VanHoose, 2006).
However, evidence from Hancock and Wilcox, (1998); Peek and Rosengren, (1997); and VanHoose,
(2007) suggests that if banks must comply to stricter capital rules and regulations they reduce their
credit supply, especially in the short-run. In line with this reasoning and the fact that Basel accord III
was introduced at the end of 2009, which is assumed to be ‘in the short-run’, a negative relationship
between the capital structure of banks and their lending behavior is expected. Another measure for the
capital structure is the total capital ratio. For the same reason as with the tier 1 ratio, also a negative
relationship between the total capital ratio and a bank’s credit supply is assumed, which leads to the
following hypotheses:

H3:Tier 1 capital is negatively related to a bank’s credit supply towards enterprises.

H4:Total capital is negatively related to a bank’s credit supply towards enterprises.
However, due to the fact that banks are allowed to use an internal ratings based approach (IRB
approach) for capital requirements to respond to credit risk instead of a standardized approach, a
positive relation between tier 1 capital and total capital on credit supply towards enterprises can be
assumed. Banks which adhere an IRB approach are able to develop capital standards which rely upon
banks internal perception of risk (Basel Committee on Banking Supervision, 2001a). By following a
standardized approach, banks need to fulfill capital requirements which are in line with regulations
such as explained in the Basel accords (Basel Committee on Banking Supervision, 2001b). For banks,
especially large ones, it seems attractive to enhance the IRB approach since it enables these banks to
respond optimal towards the risk and risk drivers they face (Basel Committee on Banking Supervision,
2001a). However, not every bank is allowed to use the IRB approach. By using an IRB approach,
Page 34 of 82
banks could make use of ‘gaming and manipulation’ and therefore could “place relatively high-risk
and high-return credits in a lower risk bucket” (Benink & Wihlborg, 2002, p. 108). This could lead to
banks with an IRB approach who manipulate their credit standards into looser ones, in order to
increase their lending behavior. This in contrast to banks which use a standardized approach, since
they are not able to adjust their capital standards. Therefore, assuming that banks also could use an
IRB approach to fulfill their capital requirements, instead of a standardized approach, a positive
relation between both tier 1 capital and total capital on a bank’s credit supply towards enterprises is
expected. However, taking the sample, as shown in Table 4.1, into account, it was hard to distinguish
banks which use a standardized approach from banks which use an IRB approach. For example,
Bankia SA uses a combination of a standardized and an IRB approach (Bankia, S.A., 2013).
Therefore, a distinction in standards on which banks base their credit requirements is considered
outside the scope of this study. In this study, a standardized approach for all banks is assumed which
results in the hypotheses where a negative relation with credit supply is expected.
Following the literature, credit supply of banks could also be influenced by the funding structure of
banks which could consist of deposit funding and/or market funding (Altunbas et al., 2011). The
credit supply of banks should be less affected if banks use deposits as their main source of funding
instead of market funding (Gambacorta & Marqués-Ibáñez, 2011; Ivashina & Scharfstein, 2008).
However, irrespective of which type of funding banks use, in this study it is assumed that banks
which have a higher funding ratio are more likely to supply credit towards enterprises and therefore a
positive relation is expected. It seems likely to occur that this also holds for the loan-to-deposit ratio.
Hereby, a ratio below 1 implies that banks use their deposits to supply loans to their customers; a ratio
above 1 implies that banks borrow money to fulfill the lending behavior of their customers. Therefore,
a higher ratio implies more credit supply as long as banks can borrow money at a lower rate than they
lend money to their customers. Therefore a positive relation between the loan-to-customer deposits
ratio and credit supply towards enterprises is assumed. Hence, the following hypotheses are defined:

H5: A banks funding structure which relies upon deposits has a positive effect on a bank’s
credit supply towards enterprises.

H6: The degree of repurchase agreements and cash collaterals has a positive effect on a
bank’s credit supply towards enterprises.

H7: The loan-to-deposit ratio has a positve effect on a bank’s credit supply towards
enterprises.
Finally, the income structure of banks could have an impact on the credit supply of banks. As
mentioned in subsection 3.1.5, the income structure could be measured by interest income and noninterest income structure. The non-interest income structure results in more volatile revenue streams
Page 35 of 82
and banks with this kind of income structure are more likely to restrict their credit supply than banks
with an interest income structure (Gambacorta & Marqués-Ibáñez, 2011). In addition, one might
expect that non-interest income in a bank is used to fund other non-interest income sources instead of
interest income sources such as credit supply. For example, non-interest income in the form of fees,
might be used to capture fees in the future, instead of capture interest income from credit supply since
credit supply is another source of income. Thus, it might be expected that the source of income will
be used to acquire the same source of income and therefore non-interest income cannot be used to
generate income from credit supply. Therefore, the relationship between non-interest income and
credit supply is expected to be negative. Hence, the following hypotheses are formulated:

H8: Interest income of a bank has a positive effect on a bank’s credit supply towards
enterprises.

H9: Non-interest income of a bank has a negative effect on a bank’s credit supply towards
enterprises.
3.3 Research model
Figure 3.1 Theoretical research model
Page 36 of 82
Taken the research question into account, Figure 3.1 could be developed. This figure is a schematic
illustration of the relations which will be investigated during this study. The relations contain the
influence of different elements of a banks’ business model on a bank’s credit supply towards
enterprises. Control variables are defined for validity and control purposes which will be further
explained in section 4.
Page 37 of 82
4. Methodology
4.1 Sample
The European banking industry consists of a wide variety of banks and corresponding business
models. To provide an adequate view of the effect of these different banks and their business models
on credit supply, this study focuses on retail, investment and wholesale banks. The different business
models are characterized by four main elements: asset structure, capital structure, funding structure
and income structure.
In order to determine the sample size, the program G*Power (Faul, Erdfelder, Buchner, & Lang,
2009) is used. This program suggests a required sample size of 89. In addition, Hair, Black, Babin,
Anderson, & Tatham, (2004) describe a minimum of 50 observations and preferable 100 observations
in multiple regression analysis. Ayadi et al. (2012) used a wide variety of European banks which
overlap with banks which were used during the stress test of June 2011 from the European Banking
Authority (EBA). These banks represent about two-third of the EU-27 banking assets and are
therefore highly representative from a macro-economic viewpoint. Therefore, the same banks are
used in this study. This resulted in a sample size of 82 different European banks. This sample size
also contains banks which are dissolved or merged during recent years, for instance, TT Hellenic
Postbank S.A.. When determining the years in which the empirical analysis was held, the introduction
of Basel III was taken into account. The Basel III accord was announced in late 2009 (Bank for
International Settlements, 2010). This announcement could result in a modification of the business
models of banks after 2009. Therefore in this study, the years before 2010 are not taken into account.
Therefore, the years which are covered in the sample are 2010 till 2013.
Taking the 82 European banks over four years into account (Appendix IV) resulted in 609 cases. This
large number of cases resulted from taking both the consolidated and unconsolidated financial
statements from the 82 European banks into account. Both the consolidated and unconsolidated
financial statements were taken into account since the dependent variable credit supply was for
several banks available in either the consolidated or either in the unconsolidated financial statements.
By taking both financial statements into account, the chance on missing dependent variables could be
minimized. As a consequence, some banks were taken into account both as one single bank and as a
group. Hence, this will contribute to the most thorough insight in the relationship between the
elements of a bank’s business model and the effect of these elements on a bank’s credit supply
towards enterprises. However, cases without a dependent variable were completely rejected. A
number of 53 banks were left in the sample (Table 4.1). Therefore, 202 cases in total were
incorporated into this study. This is almost twice as much as the minimum sample size obtained from
the program G*power and recommendations of Hair et al. (2004).
Page 38 of 82
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
List of banks used in multiple regression analysis
Bankname
ABN Amro Bank NV
ABN Amro Group N.V.
Agricultural Bank of Greece*
Allied Irisch Banks Plc
Alpha Bank AE
Banco Bilbao Vizcaya Argentaria SA (BBVA)
Banco BPI SA
Banco Popular Español SA
Bank of Ireland - Governor and Company of the Bank of Ireland
Banque Et Caisse D'Epargne De L'Etat
Barclays Bank PLC
Barclays PLC
Bayerische Landesbank
BNP Paribas
Caixa Geral De Depósitos
Caja de Ahorros y Pensiones de Barcelona - La Caixa
Commerzbank AG
Cooperatieve Centrala Raiffeisen - Boerenleenbank B.A - Rabobank Nederland
Danske Bank A/S
Deutsche Bank AG
DnB NOR Bank ASA
DZ Bank AG - Deutsche Zentral-Genossenschaftsbank
EFG Eurobank Ergasias SA
Erste Group Bank AG
Espírito Santo Financial Group S.A.
HSBC Holdings Plc
HSH Nordbank AG
ING Bank NV
ING Group NV
Jyske Bank A/S
Jyske Bank A/S (Group)
KBC Bank NV
KBC Groep NV / KBC Groupe SA - KBC Group
Landesbank Baden-Württemberg
Landesbank Berlin AG
Lloyds Banking Group Plc
National Bank of Greece SA
Norddeutsche Landesbank Girozentrale NORD/LB
Nordea Bank AB (publ)
Nykredit Bank A/S
Oesterreichische Volksbank AG
OTP Bank Plc
Piraeus Bank SA
Powszechna Kasa Oszczednosci Bank Polski SA - PKO Bank Polski SA
Royal Bank of Scotland Group Plc (The)
Royal Bank of Scotland Plc (The)
Skandinaviska Enskilda Banken AB
SNS Bank N.V.
Société Générale
Svenska Handelsbanken
Sydbank A/S
TT Hellenic Postbank S.A**
WGZ - Bank AG Westdeutsche Genossenschafts-Zentralbank
* Agricultural Bank of Greece was dissolved since 30 July 2012
** TT Hellenic Postbank S.A. was dissolved since 1 January 2013
Country
NL
NL
GR
IE
GR
ES
PT
ES
IE
LU
UK
UK
DE
FR
PT
ES
DE
NL
DK
DE
NO
DE
GR
AT
PT
UK
DE
NL
NL
DK
DK
BE
BE
DE
DE
UK
GR
DE
SE
DK
AT
HU
GR
PL
UK
UK
SE
NL
FR
SE
DK
GR
DE
Business model
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Investment
Investment
Wholesale
Investment
Retail
Retail
Investment
Retail
Retail
Investment
Retail
Wholesale
Retail
Retail
Retail
Investment
Wholesale
Retail
Retail
Retail
Retail
Retail
Retail
Wholesale
Wholesale
Retail
Retail
Wholesale
Investment
Retail
Wholesale
Retail
Retail
Retail
Investment
Investment
Retail
Retail
Investment
Retail
Retail
Retail
Wholesale
Table 4.1, List of banks used in study
Page 39 of 82
The reason behind this is that more cases will lead to more stable results and a higher chance on
discovering a statistically significant causal relationship. The data was collected from Bankscope and
selected based on their availability. Bankscope contains financial data of banks worldwide.
4.2 Procedure
The aim of this study is to investigate the relationship between the elements of a bank’s business
model and their impact on the credit supply of banks towards enterprises. To test the hypotheses as
stated in Table 4.2, multiple regression analysis was used. By using this statistical technique, the
relationship between a single dependent and multiple independent variables could be analyzed (Hair
et al., 2004). First, the data was derived by using Bankscope through Wharton Research Data Service
(WRDS). WRDS is a web-based business data research service and a common tool for research
(Wharton Research Data Services, n.d.). In this case, an entire dataset with specific variables of all
banks, based on their Bankscope ID number was downloaded. This data was then examined on
missing values, outliers and extreme values, as described in the data analysis section further on.
Business model
element
Hypothesis
Asset structure
H1: The ratio of consumer loan supply is negatively related to a bank’s credit supply towards enterprises.
Asset structure
H2: The degree of securitization has a positive effect on a bank’s credit supply towards enterprises.
Capital structure
H3:Tier 1 capital is negatively related to a bank’s credit supply towards enterprises.
Capital structure
H4:Total capital is negatively related to a bank’s credit supply towards enterprises.
Funding structure
H5: A banks funding structure which relies upon customer deposits has a positive effect on a bank’s credit
supply towards enterprises.
Funding structure
H6:The degree of repurchase agreements and cash collaterals has a positive effect on a bank’s credit supply
towards enterprises.
Funding structure
H7: The loan-to-deposit ratio has a positive effect on a bank’s credit supply towards enterprises
Income structure
H8: Interest income of a bank has a positive effect on a bank’s credit supply towards enterprises.
Table 4.2 Hypotheses
In line with the literature review (section 2), conceptual model (subsection 3.3), hypotheses
(subsection 3.2) and construction of variables (subsection 3.1), the following regression formula was
defined:
In this regression analysis,
is the intercept. In case all independent variables are zero, this is the
value of the dependent variable (
). All other betas (
are standardized
Page 40 of 82
regression coefficients which determine their relative predictive power on the dependent variable. The
error term of the regression analysis is the true random error in the population which is an error in
predicting the sample data. The error term is
in the regression formula (Hair et al., 2004). Finally,
the represents each individual case in the regression analysis. A complete list of all variables is
included in the Glossary and in Appendix III.
4.3 Control variables
In this empirical analysis, also control variables were added. The first variable controls for the size of
banks. It might be possible that larger banks, which have more resources, are more capable to supply
credit towards enterprises. Therefore, the control variable size, which was measured by taking the
total assets of each bank, was taken into account. Another control variable is risk. As mentioned in
subsection 2.1.3., risk is an important element in the way which banks exploit their activities. In line
with earlier research (Köhler, 2014; Stiroh, 2004) risk was measured through the z-score. The z-score
is defined as “the number of standard deviations that profits must fall to drive a firm into bankruptcy”
(Stiroh, 2004, p. 855). The z-score is the sum of the return on assets with the capital adequacy ratio
for each bank in each period, divided by the standard deviation of the return on assets for each bank
in a period (Köhler, 2014). With the following formula, derived from Köhler (2014), z-score was
calculated:
A higher z-score indicates a bank which is less exposed to risk and therefore more stable (Köhler,
2014). Another way to measure risk is by following Ayadi et al., (2012). The authors define a
regulatory measure of risk which is the ratio of risk-weighted assets to total assets (rwa_ta). A higher
ratio implies that banks are more prone to risks and therefore need to hold more regulatory capital.
Page 41 of 82
5. Data analysis and empirical results
5.1 Examination of data
The data was thoroughly examined to ensure a solid dataset for analysis. First, the missing values
were analyzed and second the outliers and extreme values were examined.
5.1.1 Missing data
Some cases missed data. Because it was unknown why some data was missing in Bankscope, the
missing data could not be ignored. Cases with missing data for the predictor variable credit supply
were immediately and completely deleted because obviously, without this data there is nothing to
predict in a regression analysis (Hair et al., 2004). Next, the extent of missing data was examined. In
Table 5.1, the extend of missing data is provided. It seems that seven variables have too much
missing data to be ignored (>10%). In addition, the variable other mortgage loans and the control
variable risk weighted assets have more than 50 percent missing data and were therefore deleted (Hair
et al., 2004). Next a test was performed to check whether the missing data occurred at random. This
to check whether a remedy was possible.
Variable Summary
Missing
N
Percent
CV: Risk weighted assets
AS: Other mortgage loans
FS: Repo's and cash collaterals
AS: Residual mortgage loans
AS: Other consumer retail loans
CS: Total capital
CS: Tier 1 capital
CV: Z-score risk
IS: Total non-interest income
IS: Total interest income
CV: Total assets
FS: Customer deposits
AS: Securitization
FS: Loans to customer deposits
154
158
101
71
60
49
47
7
6
6
0
0
0
0
0
76,20%
78,20%
50,00%
35,10%
29,70%
24,30%
23,30%
3,50%
3,00%
3,00%
0,00%
0,00%
0,00%
0,00%
0,00%
Valid N
48
44
101
131
142
153
155
195
196
196
202
202
202
202
202
Mean
Std. Deviation
83,1
16,8
4,5
19,3
8,7
6,3
5,3
90,6
16,5
83,5
579150,5
42,0
31,4
130,7
22,5
32,6
11,83
5,0
13,5
8,6
2,8
2,8
185,1
14,2
14,2
750032,9
15,3
15,7
48,0
14,5
DV: Commercial and corporate loans
Table 5.1 Missing values
The missing data was tested on level of randomness to determine whether the data was MAR
(missing at random) or MCAR (missing completely at random). The randomness was tested by means
of a ‘separate variance t-test’ by using missing value analysis in SPSS (Hair et al, 2004). The
variables with <5% missing values were automatically ignored in SPSS. Results from the randomness
test are provided in Appendix VI. For each variable the t-value, number of missing data and their
means are presented. In this test, significant results (t-value > 1,96 or < -1,96 for 5% and t-value >
Page 42 of 82
2,575 or < -2,575 for 1%) show non randomness of the data. For almost all variables, the results were
non-significant meaning that the missing data is indeed randomly distributed. For example, for the
variable rml_ta, all but ocrl_ta and z-score were non-significant. Although not all results were nonsignificant, still the majority of variables showed that the missing data was randomly distributed.
Only in variable tcap_ta significant differences on 5 out of 12 variables were found but because this
was only for one variable it was of marginal effect. Because, according to Hair et al. (2004), this
randomness test was used to test for MCAR it was concluded that the missing data was MCAR.
Therefore different imputation methods were possible (Hair et al., 2004).
Four different imputation methods were tested on their mean and standard deviations which can be
found in Table 5.2. In comparing the means, almost all items are alike, except for the listwise method.
This method is mostly applied with low levels of missing data (Hair et al., 2004) and therefore less
appropriate in this study. Similar results are found for the standard deviations. Because Pairwise, EM
and Regression based imputation methods showed similar results, these imputation methods could all
be applied.
In the next step, a correlation matrix of the imputed data with different methods was produced. The
correlation matrix (Appendix VII) showed most differences on the listwise method with the other
methods. The correlations of Pairwise with EM and Regression based methods showed large
similarities. A note has to be made regarding the variable ocrl_ta with rml_ta where EM based
approach showed a negative correlation and the other methods a positive correlation. Some other
differences were found. For example in rml_ta on ccloan_ta, tier1_ta on sec_ta and tcap_ta on sec_ta.
In addition, differences were found from ta on six variables where only the listwise method was
different. As derived from Appendix VII, the differences in correlations are scattered among relations,
making it less of a concern. In addition, the correlations with all variables on the dependent variable
ccloan_ta have no clear anomalies except rml_ta and ta. Finally, the means, standard deviations and
correlations from all imputation methods were taken into account and compared. In addition, SPSS
automatically depicts the best method based on a scan of the data. The regression based approach was
chosen by SPSS and was therefore used. To get the most stable results, the data was first checked on
outliers before imputing. This outlier analysis is described further on. According to Graham,
Olchowski, and Gilreath (2007), imputing the data three times already gives adequate results. Based
on their research, in this study the data was also imputed three times. The imputed data after outlier
analysis was saved as a new dataset which was used in all further analyses in this study. This new
dataset contained a total number of 606 cases.
Page 43 of 82
Imputation
method
DV: Commercial and
corporate loans
AS: Residual mortgage
loans
AS: Other consumer
retail loans
AS: Securitization
CS: Tier 1 capital
CS: Total capital
FS: Customer deposits
FS: Repo's and cash
collatorals
FS: Loans to customer
deposits
IS: Total interest income
IS: Total non-interest
income
CV: Total assets
CV: Z- score risk
Estimated means
Listwise
21,75
23,07
4,75
32,11
4,65
5,50
42,36
4,36
133,15
76,80
23,20
863062
34,78
Pairwise
22,47
19,34
8,68
31,37
5,34
6,35
42,03
4,47
128,02
83,48
16,52
579150
90,61
EM
22,47
18,23
8,66
31,37
5,30
6,33
42,03
4,36
128,87
83,16
16,84
579150
90,30
Regression
22,47
19,85
8,75
31,37
5,39
6,28
42,03
4,16
128,55
83,13
16,95
579150
94,86
AS: Residual mortgage
loans
AS: Other consumer
retail loans
AS: Securitization
CS: Tier 1 capital
CS: Total capital
FS: Customer deposits
FS: Repo's and cash
collatorals
FS: Loans to customer
deposits
IS: Total interest
income
IS: Total non-interest
income
CV: Total assets
Listwise
12,63
12,46
3,78
18,51
2,67
2,86
13,02
4,50
36,46
14,54
14,54
823119
37,34
Pairwise
14,46
13,51
8,56
15,75
2,79
2,85
15,31
5,02
39,50
14,22
14,22
750033
185,09
EM
14,46
13,94
8,64
15,75
2,81
2,84
15,31
4,86
40,28
14,34
14,34
750033
185,34
Regression
14,46
13,52
8,55
15,75
2,87
2,79
15,31
4,66
39,72
14,35
14,41
750033
187,18
CV: Z/score risk
Imputation
method
DV: Commercial and
corporate loans
Estimated standard deviations
Table 5.2 Imputation methods
5.1.2 Outliers and extreme values
Before imputation, the complete dataset needs to be checked on outliers. Following Hair et al (2004, p.
73), outliers are “observations with a unique combination of characteristics identifiable as distinctly
different from the other observations”. A unique combination of characteristics is hereby defined as
“an unusually high or low value on a variable, or a unique combination of values across several
variables that make the observation stand out from the others”. At first, outliers were checked by
eyeballing through the raw data in SPSS. No anomalies were found. Second the outliers were
examined by using the Box-and-Whisker plot approach. For each variable a Box-and-Whisker plot
was produced and analyzed. The boxplot provides outliers and extreme values, whereby outliers were
marked by a dot and extreme values by an asterisk. SPSS defines outliers as observations which are
1.5 times the interquartile range beyond the 25th or 75th percentile. Extreme values are observations
which are 3 times the interquartile range beyond the 25th or 75th percentile (Weinberg & Abramowitz,
2002). According to Hair et al. (2004), outliers can be categorized into four classes. The outliers in
this study were marked as second class outliers which are results of an extraordinary event such as the
crisis. Because this event fits within this research, these outliers were remained (Hair et al., 2004). On
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the contrary, the extreme values, described in Table 5.3 were deleted because their influence could
have biased the regression results since these values are not representative for the total sample.
Outliers
ccloan_ta
rml_ta
ocrl_ta
sec_ta
tier1_ta
tcap_ta
cdep_ta
reca_ta
loans_dep
inti_tr
ninti_tr
ta
z-score
83
175
114
125
64
84
176
171
126
170
85
116
100
127
149
136
137
158
46
102
102
27
4
43
Extreme values
91
101
71
145
72
146
117
147
150
128
53
131
58
138
173
63
174
176
59
45
30
30
146
133
120
31
31
147
120
118
172
172
26
134
126
60
60
118
142
127
42
77
78
132
143
44
118*
144*
175*
160*
175*
176*
161*
160*
171*
125*
125*
172*
11*
24*
117*
44*
33*
70*
136*
168*
168*
137*
60*
161*
138*
12*
115*
71*
23*
114*
72*
32*
87*
95*
59*
60*
3
34*
116*
96*
86*
Table 5.3 Outliers and extreme values
5.1.3 Assumptions
The data had to be checked for assumptions to check if the data was distributed such that multiple
regression analysis was possible. All variables; dependent, independent and control variables, were
individually tested on the most important assumptions of regression analysis.
ccloan_ta
rml_ta
ocrl_ta
sec_ta
tier1_ta
tcap_ta
cdep_ta
reca_ta
loans_dep
inti_tr
ninti_tr
ta
z_score
Linearity
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Homoscedasticity
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Normality
visual check
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Normal
probability plots
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
ShapiroWilks test
,969
,981
,895
,904
,957
,970
,989
,945
,983
,979
,979
,736
,883
Shapiro-Wilks test
sign.
,000
,000
,000
,000
,000
,000
,000
,000
0,00
,000
,000
,000
,000
Table 5.4 Assumptions
These are: linearity, constant variance of the error term (homoscedasticity), independence of the error
terms and normality (Hair et al., 2004). Because the dependent variable is the most important, and to
ensure its validity and reliability, this variable was checked initially. For the dependent variable
ccloan_ta, the graphical examination of the normal probability plot and histogram showed good
results of normality. In addition, the Shapiro-Wilks test should be close to 1 preferably to assume
normality (Razali & Wah, 2011). With a statistic of 0.969 for ccloan_ta, this showed a positive result.
Page 45 of 82
Next, the different independent variables on the dependent variable were assessed. The most
important results are shown in Table 5.4.
LN_ocrl_ta
LN_sec_ta
LN_ta
LN_z_score
SQRT_orcrl_ta
SQRT_sec_ta
SQRT_ta
SQRT_z_score
Linearity
Y
Y
Y
Y
Y
Y
Y
Y
Homoskedasticity
N
Y
Y
Y
N
Y
Y
Y
Normality
visual
check
N
Y
N
N
Y
N
N
Y
Normal
probablity
plots
N
Y
Y
N
Y
N
N
Y
Shapiro-Wilks
test
,845
,981
,955
,912
,949
,960
,878
,977
Shapiro-Wilks
test sign.
,000
,000
,000
,000
,000
,000
,000
,000
Table 5.5 Assumptions with improved variables
According to the assumptions analysis, the variables ocrl_ta, sec_ta, ta and z-score violated normality
and sec_ta also homoscedasticity. All four variables were transformed by taking the square root and
natural logarithm. Table 5.5 shows the assumptions of each of the transformed variables. By
analyzing the plots and normality tests the best method of improvement was chosen. The variables
ocrl_ta and z-score were best improved by taking the square root. Furthermore, the two variables
sec_ta, and ta were best improved by taking the natural logarithm. The newly used variables are
marked in gray.
For independence of the error terms (autocorrelation), the data was checked by assessing the DurbinWatson statistic. A result close to 0 results in positive autocorrelation, a result near 2 shows no
autocorrelation and close to 4 shows a negative autocorrelation. For the multiple regression analysis,
the Durbin-Watson statistic was 0,812. This showed a positive autocorrelation which is a violation of
the assumption for independence of the error terms. Still, because the data was only analyzed by
assessing the effect of multiple independent variables on one dependent variable in a regression
analysis, the timing and sequence of the data was neglected. In addition, the maximum number of
time intervals was 4 (4 years) which showed that problems with autocorrelation, if they exist, are of
minor importance. Because the dataset showed good fit on all other assumptions, the regression
analysis was continued without changing for independence of the error terms.
5.2 Multiple regression analysis
The variables described in the previous section were included in a multiple regression analysis
according to the method described by Hair et al., (2004). Four models were produced. First, model 1,
including all variables without control variables. Second, model 2, including all variables with only
total assets as a control variable. Third, model 3, including all variables with only z-score as a control
variable. Finally, model 4, including all variables and both control variables. A note has to be made
regarding the variable non-interest income, which was removed from the regression analysis because
its results were exactly the opposite of interest income. This reason of its similarity was because both
Page 46 of 82
non-interest and interest income can be summed to 1 since they are both divided by total revenue,
which consists of interest income + non-interest income. The final results of the different regression
analysis are presented in Table 5.6 and will be described in the next subsection.
5.3 Results
In this section the results of Table 5.6 are described. Hereby, a distinction is made between the four
different elements of a bank’s business model as specified in subsection 2.1.3. The relation of each
variable on credit supply is outlined to confirm or reject the hypotheses. Summary statistics of all the
variables in the model are described in Appendix IV. Furthermore, the different models in Table 5.6
all have a relatively good coefficient of determination (R²). The complete model with control
variables explains 41,9% of the regression model.
5.3.1 Asset structure
Derived from the element asset structure, a negative relationship between all variables and corporate
and commercial loans can be specified on all four regression models. More specifically, both the
determinants of consumer loans; residual mortgage loans and other consumer retail loans are
negatively related to credit supply on a statistical significance level of 1 percent. The effect sizes of
both variables are consistent on all four regression models. Because of this negative and statistical
significant relation, hypothesis H1 was confirmed. As stated in subsection 3.2, an explanation for this
negative relation can be found in the assumption that banks which provide loans to consumers are less
likely to provide loans to enterprises since this is another client segment and banks often focus on one
segment.
Another variable which specifies the asset structure is the degree of securitization. Similar to
consumer loans, a negative relation between the degree of securitization and credit supply towards
enterprises was found. This effect is consistent in all four regression models and statistically
significant at a 1 percent level.
However, following the literature, a positive relation between the degree of securitization and credit
supply of banks towards enterprises was expected. Therefore hypothesis H2 was rejected. A possible
explanation for this opposite relation can be found in the implementation of the Basel III accord
because with this, the securitization within financial institutions was called to halt. In the years before,
the degree of securitization was increased. In that point in time, assets were securitized multiple times
to generate future revenues and improve financial ratios in the short-run. However, it became clear
that the credit risk, which was associated with securitization, was not estimated adequate enough
(Vink, 2009).
Page 47 of 82
Asset
structure
Capital
structure
Funding
structure
Income
structure
Control
variables
Notes:
Regression coëfficients and t-values full model
1
2
Residual mortgage loans
-0,411
-0,411
(-8,365) ***
(-8,285) ***
Other consumer retail loans
-0,172
-0,172
(-5,070) ***
(-5,057) ***
Securitization
-0,467
-0,467
(-7,352) ***
(-7,345) ***
Tier 1 capital
0,300
0,300
(3,526) ***
(3,498) ***
Total capital
-0,153
-0,154
(-1,892) *
(-1,835) *
Customer deposits
0,408
0,408
(6,197) ***
(6,160) ***
Repo's and cash collaterals
0,199
0,199
(4,126) ***
(4,110) ***
Loans to customer deposits
0,357
0,357
(6,716) ***
(6,637) ***
Total interest income
0,056
0,055
(1,575)
(1,335)
Total assets
-0,001
(-0,032)
Z-score
3
-0,429
(-8,001)
-0,102
(-2,576)
-0,396
(-5,922)
0,315
(3,315)
-0,118
(-1,291)
0,421
(5,889)
0,238
(4,740)
0,401
(7,142)
-0,033
(-0,876)
***
***
***
***
***
***
***
-0,189
(-4,895) ***
Intercept a
36,525
36,744
35,219
(3,704) ***
(3,049) ***
(3,499) ***
No. Of observations
572
572
515
R²
0,446
0,446
0,450
Standardized coefficients are provided in this table, t-values are between parenthesis
a: Intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10, 5 and
percent levels respectively
4
-0,453
(-8,490)
-0,093
(-2,360)
-0,379
(-5,723)
0,262
(2,760)
-0,012
(-0,128)
0,437
(6,185)
0,222
(4,466)
0,367
(6,510)
0,040
(0,940)
0,177
(3,697)
-0,236
(-5,871)
7,572
(0,609)
515
0,465
Table 5.6, Results of multiple regression analysis, full model
Consequently, securitization had a negative impact on the real economy in the last few years. So, to
prevent a recurrence of all this, Basel III called for a halt on securitization. In addition, this accord
introduced a new definition of capital and stricter regulations which resulted in higher capital
requirements for banks (Bank for International Settlements, 2010; Vink, 2009). Because of these new
stricter capital regulations, banks probably could not comply with the looser capital requirements that
belong to securitization, as stated earlier. Therefore, banks had to downsize their levels of
securitization which resulted in less funding. This affected their loan capacity negatively and may
provide a possible explanation for the negative effect of hypothesis H2.
To summarize, in theory, securitization leads to more credit supply (Altunbas et al., 2009; Altunbas et
al., 2011; Marqués-Ibáñez & Scheicher, 2009; European Central Bank, 2008), while in this study it
was found that banks with a larger degree of securitization seem to supply less credit towards
enterprises. In this research the exact years of Basel III (Bank for international settlements, 2010b)
implementation were used, which might be the cause of this strong opposite effect.
Page 48 of 82
***
**
***
***
***
***
***
***
***
5.3.2 Capital structure
In terms of the capital structure, the variable tier 1 capital is positively related to the credit supply of
banks towards enterprises. The effect seems consistent and is statistically significant at a 1 percent
level on all four regression models. Taking the results into consideration, hypothesis H3 was rejected.
The found positive relation of tier 1 capital on corporate and commercial loans could be explained
following VanHoose (2007). VanHoose (2007) argued that on the long run, capital requirements will
lead to an increase in capital ratios and therefore will lead to an increase in the lending behavior of
banks. This could be explained by the assumption that banks might increase their capital ratios over
the required ratios in the long run. Therefore banks are not directly constraint by capital regulations in
the next period anymore and are able to increase their lending behavior in the periods thereafter
(VanHoose, 2006). On the contrary, in the short run, a negative relation between capital requirements
and the lending behavior of banks might exist since banks first need to build a buffer to meet the new
capital regulations. (Gambacorta & Mistrulli, (2004); Peek & Rosengren (1997); VanHoose (2007).
Thus, in line with these authors’ reasoning, the number of years used in this study (2010-2013) seem
to be ‘on the long run’, although more research should be done to confirm this.
In contrast to the variable tier 1 capital, the variable total capital shows a negative relation with
corporate and commercial loans. However, none of the relations are statistically significant below 10
percent, which means that total capital itself has no direct significant relation with credit supply
towards enterprises. Therefore hypothesis H4 was rejected. A possible speculative explanation for this
could be collaboration of for example, relatively small banks to supply relatively large loans and the
fact that large banks provide relatively small loans. When this occurs, total capital, in other words,
size, does not matter which could explain the relation between total capital and credit supply towards
enterprises. Furthermore, including both control variables in the regression shows a strong decrease of
the t-value of total capital. Thus, the already non-significant direct effect of total capital on credit
supply towards enterprises diminishes further by adding the control variables showing that indeed
there is no effect.
5.3.3 Funding structure
All three variables of funding structure; customer deposits, repo’s and cash collaterals, and loans to
customer deposits show a positive and statistically significant relationship on a 1 percent level.
Therefore, all hypotheses, H5, H6 and H7 were confirmed. From a theoretical perspective, a positive
relation was indeed assumed since more funding should probably lead to more resources to supply
credit.
Page 49 of 82
5.3.4 Income structure
The income structure of banks, as measured in this study seems to have no direct effect on credit
supply towards enterprises. In all models, there was no statistically significant effect between the
variable total interest income on a bank’s credit supply towards enterprises. Therefore hypothesis H8
was rejected. Still, the direction of the relation is positive, which is in line with the reasoning as
described in the theoretical section in this study. The non-significance of the relation could possibly
be explained by last year preferences of financial institutions.
Figure 5.1, Findings in conceptual research model
As stated by the explanation for securitization, financial institutions prefer healthy and strong
financial ratios, especially in the short run (Vink, 2009). This results in a relatively safe risk profile
while in the long run; it may result in affecting the income. Therefore, it might be that there is no
significant relation between income structure and credit supply towards enterprises.
5.3.5 Control variables
Looking at the control variables within the different models in Table 5.6, it can be stated that the
effect of solely the control variable total assets on the independent variables is negligible (effect size 0.001). On the other hand, the control variable z-score had a controlling effect on total capital which
changed from 10 percent significance towards non-significant when including z-score. Although the
variable total interest income was already non-significant, its t-value dropped even further when
including z-score.
Page 50 of 82
The direct effects of the control variables show that z-score itself has a negative effect and is
statistically significant on a 1 percent level. On the contrary, the direct effect of total assets itself was
non-significant. However, when including both the control variables into one regression model, a
statistically significant result was found on the 1 percent level for total assets. Total assets has a
positive relation in contrast to the relation of the z-score. From this can be concluded that larger banks
are more likely to supply loans towards enterprises and that banks with a higher z-score are less likely
to supply loans towards enterprises. Since a higher z-score indicates that a bank incurs less risk and
therefore is more stable, it can be concluded that less risky banks seems to supply less loans towards
enterprises, which is an understandable outcome.
5.3.6 Economic significance
By assessing the economic significance of the model, the strength of the effects could be better
understood. The economic significance is the ‘real’ size of the effect of independent variable X on
dependent variable Y. It was calculated by multiplying the coefficient of each independent variable
by its standard deviation (Da Rin, 2013).
Residual mortgage loans
Asset
structure
Other consumer retail loans
Securitization
Tier 1 capital
Capital
structure
Total capital
Customer deposits
Funding
structure
Repo's and cash collaterals
Loans to customer deposits
Income
structure
Total interest income
Total assets
Control
variables
Z-score
Intercept a
Notes:
Economic significance full model
4
Mean
-0,453
19,40
(-8,490) ***
-0,093
2,75
(-2,360) **
-0,379
3,33
(-5,723) ***
0,262
5,24
(2,760) ***
-0,012
6,30
(-0,128)
0,437
42,03
(6,185) ***
0,222
4,89
(4,466) ***
0,367
128,02
(6,510) ***
0,040
83,82
(0,940)
0,177
12,28
(3,697) ***
-0,236
5,95
(-5,871) ***
7,572
(0,609)
Std.
14,05
Effect
-637%
1,24
-12%
0,48
-18%
2,01
53%
2,26
-3%
15,29
668%
5,27
117%
39,43
1447%
13,27
53%
1,53
27%
3,07
-72%
No. Of observations
515
R²
0,465
Standardized coefficients are provided in this table, t-values are between parentheses.
a: intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10, 5 and 1 percent levels respectively
Table 5.7, Economic significance
Page 51 of 82
For example, the effect of residual mortgage loans is quite large. A one standard deviation equals
14,05. Multiplying this with the coefficient -0,453 results in: 14,05*-0,453 = -6,37, that is a decrease
of 637% in credit supply. Because the mean of credit supply can be read as 2247% (22,47), the effect
is indeed substantial.
The results are as described earlier in the separate sections. Indeed asset- and funding structure are the
business model elements which have a major influence on the credit supply towards enterprises.
5.3.7 Conclusion
Taking Table 5.6 and Figure 5.1 into account, looking at the different business model elements, their
variables and control variables, the following can be concluded. It can be stated that most variables
are significant in every model, except for total capital and total interest income. The business model
elements which have variables with the largest effect size of economic significance and therefore the
highest impact on the credit supply of banks towards enterprises are ‘asset structure’ and ‘funding
structure’. Mainly residual mortgage loans and securitization have relatively large effects of -637%
and -18% respectively. Furthermore, the variables customer deposits and loans to customer deposits
have a major influence on the credit supply of banks towards enterprises with effect sizes of
subsequently 668% and 1447%. This shows that indeed both asset structure and funding structure are
of major importance.
Business model
element
Asset structure
Hypothesis
Confirmed
H1: The ratio of consumer loan supply is negatively related to a bank’s credit supply towards
Yes
enterprises.
Asset structure
H2: The degree of securitization has a positive effect on a bank’s credit supply towards enterprises.
No
Capital structure
H3:Tier 1 capital is negatively related to a bank’s credit supply towards enterprises.
No
Capital structure
H4:Total capital is negatively related to a bank’s credit supply towards enterprises.
N.S.
Funding structure
H5: A banks funding structure which relies upon customer deposits has a positive effect on a bank’s
Yes
credit supply towards enterprises.
Funding structure
H6:The degree of repurchase agreements and cash collaterals has a positive effect on a bank’s
Yes
credit supply towards enterprises.
Funding structure
H7: The loan-to-deposit ratio has a positive effect on a bank’s credit supply towards enterprises
Yes
Income structure
H8: Interest income of a bank has a positive effect on a bank’s credit supply towards enterprises.
N.S.
Table 5.8, Hypotheses tests
Notes: N.S.: model was non-significant
Taking capital structure into account, the variable tier I capital has an effect size of 53% while total
capital was non-significant. Although capital structure has only one strong indicator, still, the element
capital structure indeed has an influence on the credit supply of banks towards enterprises. In contrast
to asset-, funding- and capital structure, the business model element income structure shows no
Page 52 of 82
significant relation between with the credit supply of banks towards enterprises. The variable total
interest income which defines income structure shows no significant effect and thus income structure
is of subordinate importance in the credit supply of banks towards enterprises. To summarize, asset
structure and funding structure are the most important elements of a bank’s business model in the
relation with credit supply towards enterprises. Capital structure is of moderate importance while
income structure is of no importance.
The results were clarified by means of a diagram (Figure 5.2). The means for the variables of each
business model element were taken where non-significant relations were included as zero. Figure 5.2
clearly shows that indeed asset and funding structure are the most important.
Full model
Asset
Capital
Funding
Income
Figure 5.2, Diagram of full model results
5.4 Post-hoc analysis
In this subsection, an additional analysis is provided. This to gain more and better insight into the
specific business models of banks and the effect of their business model elements on a bank credit
supply towards enterprises. In this additional analysis, the roles of different banking business models
are emphasized. Hereby a distinction is made between retail, investment and wholesale banks. Banks
are assigned on these different models, based on earlier research of Ayadi et al., (2012). Similar to the
previous analyses, this post-hoc analysis is performed with multiple regression analysis. The result of
the final model is provided in Table 5.9 while the results of the additional analysis with and without
control variables are provided in Appendix VIII. In each model, the results of the three separate
business models are provided.
5.4.1 Results
In this subsection, the results of the post-hoc analysis are outlined. Hereby, the analysis is based on
the final regression results with both control variables included. The results are described based on
the different business model elements, asset-, capital- funding- and income structure.
Page 53 of 82
Asset structure
The variable residual mortgage loans has a strong negative effect for banks which adhere a retail or a
wholesale business model. The effect of residual mortgage loans on credit supply of banks towards
enterprises is hereby in line with the theory since banks which provide loans to consumers are less
likely to provide loans towards enterprises. In addition, the effect is larger for banks which enhance a
retail business model. This is consistent with the literature on banks business models in subsection
2.1.2 since retail banks mainly provide loans to consumers and therefore this has a stronger effect.
The positive significant effect of residual mortgage loans on credit supply for investment banks
shows that their focus indeed is on corporations and organizations. This finding suggests that banks
with more assets derived from other consumer retail loans provide more credit supply towards
enterprises. A possible speculative explanation could be that these banks use the assets of other
consumer retail loans to attract more funding to provide credit supply towards enterprises. Another
explanation is that banks that have more assets in general, are able to provide more credit supply in
general. The wholesale banking model on the other hand shows the surprising negative effect of other
consumer retail loans on credit supply towards banking. Thus, banks with more assets derived from
consumer retail loans provide less credit supply towards enterprises. This is remarkable because,
according to subsection 2.1.2, banks which adhere a wholesale business model mainly are connected
to financial institutions and large to medium sized enterprises. For the asset structure, the final effect
of securitization shows unexpected negative effects for all three business models. Only retail banking
model is significant below 10% while wholesale is significant on the 10% scale. Banks with a retail
and wholesale business model which apply more securitization provide less credit supply towards
enterprises. These results are similar to the ones in the regular model. In general, for retail banks, the
asset structure is most important on residual mortgage loans and securitization. This suggests that
these banks are more focused on consumers instead of organizations.
Capital structure
Taking the variable tier 1 capital into account for a wholesale business model, this effect is in line
with hypothesis H3 whereby a negative relationship is assumed between the tier 1 capital and the
credit supply of banks towards enterprises. Similar to the regression results of the ‘full model’, retail
banks show a positive relation. Thus, banks with a retail business model that have more tier 1 capital
seem to provide more credit supply towards enterprises. Looking at the variable total capital, just as
with the ‘full model’ a negative relation occurred except for the wholesale bank model whereby a
positive and significant relation occurs. It might be that for tier 1 capital, retail banks use an IRB and
wholesale banks use the standardized approach.
Page 54 of 82
Funding structure
For funding structure it seems that in all three types of business models, repo’s and cash collaterals
are all positively significant related to credit supply towards enterprises. Thus in general, all banks
that have more repo’s and cash collaterals provide more credit supply towards enterprises. This is in
line with the expectation in the hypothesis. Furthermore, it seems that the investment bank business
model has the trongest effect of funding structure on credit supply towards enterprises. For all three
items, a strong significant and positive effect was found. This shows that these types of banks are
more focused on enterprises because all measured funding that is received has a positive relation with
credit supply towards enterprises.
Asset structure
Capital structure
Funding structure
Income structure
Control variables
Regression coefficients and t-values per business model
Econ.
Econ.
Retail
sign. Investment
sign.
Wholesale
Residual mortgage loans
-0,469
0,021
13%
-0,210
-639%
(-7,274) ***
(0,387)
(-2,548) **
Other consumer retail loans
0,088
11%
0,234
-0,427
17%
(1,630)
(5,257) ***
(-4,713) ***
Securitization
-0,426
-0,111
-3%
-0,392
-16%
(-5,385) ***
(-1,544)
(-1,883) *
Tier 1 capital
0,491
-0,129
-16%
-0,270
98%
(3,664) ***
(-1,258)
(-2,545) **
Total capital
-0,186
-42%
0,017
3%
0,358
(-1,298)
(0,171)
(3,718) ***
Customer deposits
0,021
25%
0,737
-0,180
709%
(0,226)
(12,622) ***
(-0,926)
Repo's and cash collaterals
0,163
0,171
0,182
66%
69%
(2,980) ***
(3,259) ***
(2,255) **
Loans to customer deposits
0,111
386%
1,217
-0,065
3669%
(1,282)
(16,252) ***
(-0,331)
Total interest income
0,012
11%
0,182
0,413
201%
(0,221)
(3,521) ***
(2,857) ***
Total assets
0,104
16%
0,535
0,058
23%
(1,416)
(7,469) ***
(0,639)
Z-score
-0,317
-0,099
-0,294
-96%
-26%
(-5,295) ***
(-2,112) **
(-2,305) **
Intercept a
52,878
-180,623
55,051
(2,982) ***
(-6,732) ***
(0,770)
Econ.
sign.
-129%
-59%
-9%
-32%
48%
-171%
100%
-301%
369%
3%
-100%
No. Of observations
333
98
73
R²
0,375
0,905
0,782
Notes: Standardized coefficients are provided in this table, t-values are between parenthesis
a: Intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10, 5 and percent levels respectively
Table 5.9, Regression results of post-hoc model with control variables
Income structure
Corresponding to the full model, for retail banks, no significant relation was found. On the contrary,
for investment and wholesale banks, positive significant relations were depicted. This shows that for
these types of banks, more income from interest has a positive influence on the credit supply towards
enterprises. This provides a new insight to the speculative explanation from the full model in which a
Page 55 of 82
non-significant relation was found and explained by the improvements in short-term financial ratios.
Thus, the income structure does have a positive significant effect as proposed in theory, although only
for investment and wholesale banks.
Control variables
The control variables were included to check for their influences. The results (Appendix VIII) show
that z-score has the most influence on changes of relations. When including both control variables, for
retail banks, the effects of total capital, loans to customer deposits and total interest income
diminished. This shows that these effects were suppressed by risk and total assets. Furthermore, when
including the control variables with investment banks, other consumer retail loans, repo’s and cash
collaterals and total interest income became significant. Thus, these effects take place only for banks
that have more total assets (larger) and are less exposed to risk. Finally, the wholesale banks showed
only a minor change in securitization when including the control variables. Therefore, the effects of
these types of banks do not vary on size or risk. To conclude, for retail and investment banks, the
effects of the control variables have a significant proportion in the strength and significance of the
relations while for wholesale the results are stable.
5.4.2 Conclusion post-hoc analysis
For the analysis of results, the economic significance of each relation was calculated and analyzed.
The used standard deviations can be found in Appendix IX. Taking all three different business models
into account, it can be stated that, as expected, for credit supply towards enterprises, the retail bank
business model has the least significant effects. This is probably because these banks mainly focus on
credit supply towards consumers instead of enterprises. Furthermore, both the investment bank and
wholesale bank models show the strongest results on credit supply towards enterprises. This might
result from their focus, which not only lies on enterprises (investment) but also on interbank lending
(wholesale).
Figure 5.3, Diagram of different business model elements
Page 56 of 82
As shown in Figure 5.3, for each type of business model a different business model element is
important. Similar to the full model, the diagram was produced by calculating the mean of all
statistically significant variables for each business model element. Variables with non-significant
relations were included as zero.
Besides the asset- and funding structure (as shown in the full model), the income structure of banks is
an important driver of the credit supply towards enterprises, but only with investment and wholesale
banks. These results show that it depends on a banks’ business model which business model element
is the main driver in the credit supply of banks towards enterprises. This because the sequence of
importance is different for each individual business model. While in the full model, there was no
significant relation with income structure, in this post-hoc analysis, these effects were found for
investment and wholesale banks. For capital structure, a moderate effect for retail and wholesale
business models can be recognized. Furthermore, a distinction can be made between the sizes of the
different effects of different elements for different kinds of bank business models. Finally, the R² of
the investment and wholesale banks are extremely large, with 90 and 78 percent explained variance.
This shows that the business model variables explain most of the variance on credit supply towards
enterprises for investment and wholesale banks.
Page 57 of 82
6. Conclusion and recommendations
6.1 Conclusion
The banking industry has been subject to several changes of major importance. At the heart of these
changes, certain distinct developments such as deregulation and financial innovation evolved over
time. Furthermore, other developments which contributed towards an evolvement of the banking
industry are the crisis, which emphasized the deficiencies and instability of the financial sector in
Europe (Commission, 2012c), and the Basel III accord which contains standards and regulations to
prevent a recurrence (Bank for International Settlements, 2013).
These developments in the banking industry have led to changes in the funding structure, the income
structure and the size of banks. Moreover, these changes had an impact on the financial framework in
the EU and led to an evolvement of banks, especially towards a change in their business models
(Altunbas et al., (2011); Ayadi et al., (2011). In addition, these changes have led to stricter regulations
and therefore affected the lending behavior of banks (Hebbink et al., 2014). Since banks are a main
source for credit, their restriction in credit supply does not only affect enterprises. The society as a
whole could also be affected by these kind of changes in the economic system (Braaksma & Smit,
2012; European Central Bank, n.d.).
This study has taken advantage of the recent developments which have led to changes in banks
business models and the lending behavior of banks. In addition, the impact of a bank’s business
model on credit supply towards enterprises was conducted. Hereby, insight was gained into the
drivers of a bank’s business model on credit supply towards enterprises.
The main research question to be answered was:
What are the drivers in a bank’s business model on credit supply towards enterprises for European
banks?
The findings show that credit supply towards enterprises was mostly driven by asset- and funding
structure. In addition, the capital structure also seems to be a significant driver, although less strong.
Finally, income structure showed no significant relation with credit supply towards enterprises for the
full model but showed a relatively large effect for the wholesale business model alone. Thus based on
this result, the stimulation of credit supply towards enterprises in the whole economy could focus
more on both asset- and funding structure of banks because these showed the largest impact.
Furthermore, the post-hoc analysis has shown that the retail bank business model is of less
importance for the explanation of credit supply towards enterprises. The investment bank business
Page 58 of 82
model showed the largest effects and explained 90 percent of the variance of credit supply towards
credit supply. This shows that the used business model elements for investment banks clearly explain
the credit supply towards enterprises. For wholesale banks, a similar but slightly weaker relation was
found. Thus, the business model of banks does have a significant influence on the credit supply
towards enterprises. It seems that for each business model, another business model element is the
most important. For the retail business model this is asset structure, for investment banks this is the
funding structure and for wholesale banks this is income structure.
As one might expect that banks supply more credit when their income rises, this study showed that
the relation is more complex. Therefore, solving problems with credit supply towards enterprises
within banks should be more focused on the asset and funding structure of banks instead of
specifically focusing on the income structure. The capital structure of banks should also be considered
although this has already been taken into account partly by the current Basel accords. Generally, in
this context, the specific business model should be taken into account because differences were found
between different business models.
6.2 Theoretical implications
To date, little research has been done on the relation between a banks’ business model and credit
supply towards enterprises. This is remarkable because the banking industry has been discredited
since the crises occurred. It seems that researchers have focused on the broad implications such as the
effects of all Basel accords on the entire sector and neglected more specific relations such as the one
in this study. Therefore, the specific focus provides great contribution to the literature. Furthermore,
this study also contributes to the literature because it is an addition to a study by Altunbas et al.
(2011) who studied the relation between risk and bank business models. This because in both studies,
similar business model elements were used. Where Altunbas et al. (2011) used them in a relation with
bank risk, this study has focused on the relation with credit supply towards enterprises. More
specifically, Altunbas et al. (2011) found that market funding and customer deposits have an
influence on bank risk while this study shows similar importance of the same business model
elements on credit supply. So indeed funding- and asset structure are relatively important business
model elements. This study also contributes to a paper by Gambacorta and Mistrulli (2003) where the
authors have focused on bank capital and its effect on lending behavior of Italian banks. This study
confirms that indeed a bank’s capital structure has a significant impact on its credit supply.
Furthermore, this study combined the measurement indicators of Ayadi et al. (2011) with the business
model elements of Altunbas et al. (2011) and the available variables in Bankscope. This formed a
clear and realistic model of measuring business model elements. Finally, this study can thus be used
as a starting point for future research for both credit supply and business model elements. For
Page 59 of 82
example, the business model elements and the measurement method described in this master thesis
can be used in other studies.
6.3 Managerial implications
Because of the distinction between the three separate business models, banks from other parts of the
world can interpret these results when adhering a similar business model. Although caution is needed
with this because of specific financial rules set by the EU which may not be included in other areas in
the world. Because the results are scattered among business models (refer to the post-hoc analysis),
banks with other business models than the ones in this study are advised to interpret the results with
caution.
Another point of caution is the period of data collection. Because data from the past 4 years was used,
and several changes were made in the banking sector, the results from these past 4 years are no
complete guarantee for the future. Within this period, the focus of banks may have been short instead
of long term since the recent crisis occurred. This focus may change in the future affecting the results.
Still, although the banking sector is due to changes, these appear slow and gradually over the years.
Therefore, the results from this study can be well interpreted within the coming years but more
caution is needed when more time has passed.
Interpreting the findings in this study can be done by both policy makers and banks. In case of policy
makers, stimulating credit supply towards enterprises can be done by focusing more on asset- and
funding regulation or deregulation. In addition, income structure of a bank does have an influence on
the credit supply towards enterprises. Therefore, policy makers as well as banks may want to focus on
income structure as well. Where the Basel accords are now mainly focused on global capital and
liquidity regulation, in other words capital structure, to promote a more resilient banking sector (Bank
for International Settlements, 2010), asset-, funding and income structure could be used as an addition
to make more thoughtful policies. These improved policies might ensure the stability of the banking
sector and thereby enhance the credit supply towards enterprises. Because of the improved policies,
the negative influence of the banking sector on the real economy can be diminished while its positive
influence can be enhanced. Another possible effective method could be to provide rules and
guidelines for each type of bank separately because of the large variety in results for each type of
business model.
In case of banks, this study sheds light on the internal policy regarding credit supply towards
enterprises. Banks can use the results of this study to know where to focus when it comes to
improving its credit supply towards enterprises. In addition, adhering a certain business model has
different implications on the relation between elements of their business model and credit supply
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towards enterprises. For example, the results suggest that indeed, retail banks are limited in their
focus on credit supply towards banking. To conclude, banks may use these findings for improvement
of their focus and internal policy.
6.4 Limitations
Several limitations in this study exist and should be reported. First, banks were assigned on a business
model, based on research of Ayadi et al., (2012). While the authors have done extensive research
towards banks and their business models, one might argue about the allocation of a bank in particular.
Therefore, the assignment of banks on a particular business model might be ambiguous. Another
remark is the sample size of each different business model. For retail banks the sample size is 333,
investment banks have a sample size of 98 and wholesale banks of 73. Having more similarity in the
sample sizes would be better for interpreting the results. Still, the models were statistically significant
which made the results interpretable. Another limitation is that this study only focused on the total
volume of corporate and commercial and consumer loans. No distinction between already issued and
newly issued loans was made. Therefore no direct effect of the implications in newly issued credit
supply, as stated in section 1, was measured. Furthermore, the measurements used in this study were
not incorporated with specific individual situations of certain banks. For example, within the scope of
this study, no clear distinction was made in the way in which banks respond to capital regulations,
following the standardized approach or the IRB approach. Therefore, because all banks are treated in
the same manner, this may not truly reflect reality.
6.5 Future research
This study forms a base in future research on banks’ drivers of credit supply towards enterprises. As
shown, the different types of bank business models and their elements were of significant influence
on the credit supply towards enterprises. Still, this study had some limitations and future scholars
should focus on these limitations in new studies on the same subject. Because this study was merely
general, future research on this topic could take a more specific approach by taking interviews and
case studies of banks in particular. This may contribute to the findings in this study. Furthermore, in
this study, only the credit supply of banks was taken into account and not the credit applications of
enterprises. Because of this, only the relation between the business model elements and their effect on
credit supply of banks towards enterprises was measured. Thus, future research could take the credit
applications of enterprises into account as well. As explained by Popov (2013), who did consider the
credit applications of enterprises, the rejected loans or informally rejected loans by banks should also
be taken into consideration. This could provide a broader picture of credit supply towards enterprises.
In addition, future research may want to focus on including more types of business models to provide
a stronger generalizability of results bank wide. This study may therefore serve as a base for future
research on bank’s business models on credit supply towards enterprises.
Page 61 of 82
References
Altunbas, Y., Gambacorta, L., & Marqués-Ibánez, D. (2009). Securitisation and the Bank Lending
Channel. European Economic Review, 53, 996-1009.
Altunbas, Y., Gambacorta, L., & Marqués-Ibáñez, D. (2009). Securitisation and the Bank Lending
Channel. European Economic Review, 53, 996-1009.
Altunbas, Y., Manganelli, S., & Marqués-Ibánez, D. (2011). Bank Risk During the Financial Crisis,
Do Business Models Matter? European Central Bank (Working Paper Series No. 1394).
Altunbas, Y., Manganelli, S., & Marqués-Ibáñez, D. (2011). Bank Risk During the Financial Crisis,
Do Business Models Matter? European Central Bank (Working Paper Series No. 1394).
Amit, R., & Zott, C. (2001). Value Creation in E-Business. Strategic Management Journal, 22, 493520.
Asea, P. K., & Blomberg, B. (1998). Lending Cycles. Journal of Econometrics, 89-128.
Ayadi, R., Arbak, E., & De Groen, W. (2012). Regulation of European Banks and Business Models:
Towards a New Paradigm? Centre for European Policy Studies Brussels.
Ayadi, R., Arbak, E., & Groen, W. (2011). Business Models in European Banking; A Pre-and PostCrisis Screening. Centre for European Policy Studies.
Bank for International Settlements. (2010). Basel III: A Global Regulatory Framework for more
Resilient Banks and Banking Systems. Basel: Bank for International Settlements.
Bank for International Settlements. (2010). The Basel Committee's Response to the Financial Crisis:
Report to the G20. Basel: Bank for International Settlements.
Bank for international settlements. (October de 2010b). The Basel Committee's response to the
financial crisis: reeport to the G20. Obtenido de BIS: http://www.bis.org/publ/bcbs179.htm
Bank for International Settlements. (2013). About the Basel Committee. Retrieved from www.bis.org:
http://www.bis.org/bcbs/about.htm
Bankia, S.A. (2013). Financial Statements and Management Report. Audit Report.
Basel Committe on Banking Supervision. (2013). Basel III: The Liquidity Coverage Ratio and
Liquidity Risk Monitoring Tools . Basel: Bank for International Settlements.
Basel Committee on Banking Supervision. (2001a). The Internal Ratings-Based Approach. Basel:
Bank for International Settlements.
Basel Committee on Banking Supervision. (2001b). The Standardised Approach to Credit Risk. Basel:
Bank for International Settlements.
Basel Committee on Banking Supervision. (2006). International Convergence of Capital
Measurement and Capital Standards. Basel: Bank for International Settlements.
Benink, H., & Wihlborg, C. (2002). The New Basel Capital Accord: Making it Effective with
Stronger Market Discipline. European Financial Management, 8(1), 103-115.
Page 62 of 82
Bessis, J. (2002). Risk Management in Banking (second edition ed.). West Sussex: John Wiley &
Sons LTD.
Bessis, J. (2011). Risk Management in Banking (third edition ed.). West Sussex: John Wiley & Sons
LTD.
Blaes, B. (2011). Bank-related loan supply factors during the crisis: an analysis based on the German
bank lending survey. Deutsche Bundesbank Eurosystem.
Blundell-Wignall, A., & Roulet, C. (2012). Business Models of Banks, Leverage and he Distance-toDefault. Financial Market Trends.
Blundell-wignall, A., Atkinson, P., & Roulet, C. (2014). Bank Business Models and the Basel System:
Complexity and Interconnectedness. OECD Journal: Financial Market Trends 2014.
Boot, A. W., & Thakor, A. V. (2009). The Accelerating Integration of Banks and Markets and is
Implications for Regulation. The Oxford Handbook of Banking.
Braaksma, R., & Smit, L. (2012). Nieuwe Financieringsvormen voor het MKB. Zoetermeer: EIM bv.
Brousseau, E., & Penard, T. (2006). The Economics of Digital Business Models: A Framework for
Analyzing the Economics of Platforms. Review of Network Economics, 6(2), 81-110.
Business
Model
Foundry.
(n.d.).
Business
Model
Generation.
Retrieved
from
http://www.businessmodelgeneration.com/book
Casadesus-Masanell, R., & Ricart, J. E. (2010). From Strategy to Business Models and onto Tactics.
Long Range Planning, 43(2), 195-215.
Cebenoyan, A. S., & Strahan, P. E. (2004). Risk Management, Capital Structure and Lending at
Banks. Journal of Banking and Finance, 28(1), 19-43.
Chesbrough, H., & Rosenbloom, R. S. (2002). The Role of the Business Model in Capturing Value
from Innovation: Evidence from Xerox Corporation's Technoloy Spinoff Companies.
Industrial and Corporate Change, 11(3), 529-555.
Commission, E. (2012a). Towards a Genuine Economic and Monetary Union: Report by President of
the European Council, Herman van Rompuy. Brussel: European Commission.
Commission, E. (2012b). Towards a Banking Union. Towards a Banking Union. Brussels: European
Commission.
Commission, E. (2012c). Communication from the Commission to the European Parliament, the
European Council, the Council, the European Central Bank, the European Economic and
Social Committee, the Committee of the Regions and the European Investment Bank. Brussels:
European Commission.
Commission, E. (2012d). President Barroso Proposes Banking Union. President Barroso Proposes
Banking Union. European Commission.
Commission, E. (2012e). Communication from the Commission to the European Parliament and the
Council. Brussels: European Commission.
Da Rin, M. (2013). How to Read and Understand a Regression? Tilburg.
Page 63 of 82
De Bondt, G., & Marqués-Ibánez, D. (2005). High-yield Bond Diffusion in the United States, the
United Kingdom, and the Euro Area. Journal of Financial Services Research, 27(2), 163-181.
De Bondt, G., & Marqués-Ibáñez, D. (2005). High-yield Bond Diffusion in the United States, the
United Kingdom, and the Euro Area. Journal of Financial Services Research, 27(2), 163-181.
Dell'Ariccia, G., Igan, D., & Laeven, L. (2008). Credit Booms and Lending Standards: Evidence from
the Subprime Mortgage Market. International Monetary Fund Working Paper.
Demirgüç-Kunt, A., & Detragiache, E. (1998). Financial Liberalization and Financial Fragility.
International Monetary Fund (Working Paper 98/83).
Edwards, F. R., & Mishkin, F. S. (1995). The Decline of Traditional Banking: Implications for
Financial Stability and Regulatory Policy. National Bureau of Economic Research.
European Central Bank. (2000). EU Banks' Income Structure. Frankfurt am Main: European Central
Bank.
European Central Bank. (2008). Securitisation in the Euro Area. Monthly Bulletin February.
European Central Bank. (2009). EU Banks' Funding Structures and Policies. Frankfurt am Main:
European Central Bank.
European Central Bank. (2010). EU Banking Structures. Frankfurt am Main: European Central Bank.
European
Central
Bank.
(n.d.).
ECB:
Access
to
Finance
of
SMEs.
Retrieved
from
http://www.ecb.europa.eu/stats/money/surveys/sme/html/index.en.html#description
European Central Bank. (n.d.). Bank Lending Survey for the Euro Area, the Questionnaire.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analysis using G*Power
3.1: Tests for correlation and regression analysis. 41(4), 1149-1160.
Gambacorta, L., & Marqués-Ibánez, D. (2011). The Bank Lending Channel: Lessons from the Crisis.
Basel: Bank for International Settlements.
Gambacorta, L., & Marqués-Ibáñez, D. (2011). The Bank Lending Channel: Lessons from the Crisis.
Basel: Bank for International Settlements.
Gambacorta, L., & Mistrulli, P. E. (2004). Does Bank Capital Affect Lending Behavior? Journal of
Financial Intermediation, 436-457.
Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How Many Imputations are Really
Needed? Some Practical Clarifications of Multiple Imputation Theory. Prevention Science,
206-213.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2004). Multivariate Data
Analysis (sixth edition ed.). New Jersey: Pearson Prentice Hall.
Hale, G., & Santos, J. A. (2010). Do Banks Propagate Debt Market Shocks? In Federal Reserve BAnk
of San Francisco, 8.
Hancock, D., & Wilcox, J. A. (1998). The "Credit Crunch" and the Availability of Credit to Small
Business. Journal of Banking & Finance, 22, 983-1014.
Page 64 of 82
Hebbink, G., Kruidhof, M., & Slingenberg, J. W. (2014). Kredietverlening en bancair kapitaal. De
Nederlandsche Bank.
Heid, F. (2007). The Cyclical Effects of the Basel II Capital Requirements. Journal of Banking &
Finance, 31, 3885-3900.
Huang, R., & Ratnovski, L. (2011). The Dark Side of Bank Wholesale Funding. Journal of Financial
Intermediation, 20.2, 248-263.
Ivashina, V., & Scharfstein, D. (2008). Bank Lending During the Financial Crisis of 2008. Journal of
Financial Economics, 97(3), 319-338.
Jackson, P. (1999). Capital Requirements ans Bank Behaviour: The Impact of the Basle Accord.
Basle: Basle Committee on Banking Supervision.
Jarrow, R. A., & Chatterjea, A. (2013). An Introduction to Derivative Securities, Financial Markets,
and Risk Management. New York: W.W. Norton & Company .
Johnson, M. W., Chistensen, C. M., & Kagermann, H. (2008). Reinventing Your Business Model.
Harvard Business Review, 86(12), 57-68.
Köhler, M. (2014). Which Banks are more Risky? The Impact of Business Models on Bank Stability.
Journal of Financial Stability.
Lepetit, L., Nys, E., Rous, P., & Tarazi, A. (2008). Bank Income Structure and Risk: An Empirical
Analysis of European Banks. Journal of Banking and Finance, 1452-1467.
Llewellyn, D. T. (2012). The Evolution of Bank Business Models: Pre- and Post-Crisis. In D. T.
Llewellyn, & R. Reid, Future Risks and Fragilities for Financial Stability (pp. 45-67).
Vienna: Larcier.
Llewellyn, D. T., & Reid, R. (2012). The Evolution of Bank Business Models: Pre- and Post-Crisis.
Vienna: Larcier.
Lown, C., & Morgan, D. P. (2006). The Credit Cycle and the Business Cycle: New Findings Using
the Loan Officier Opinion Survey. Journal of Money, Credit and BAnking, 1575-1597.
Magretta, J. (2002). Why Business Models Matter. Harvard Business Review, 80(5), 86-92.
Marques Ibanez, D., & Scheicher, M. (2009). Securitisation: Instruments and Implications. The
Oxford Handbook of Banking, 530-555.
Marqués-Ibáñez, D., & Scheicher, M. (2009). Securitisation: Instruments and Implications. The
Oxford Handbook of Banking, 530-555.
Morris, M., Schindehute, M., & Allen, J. (2005). The Entrepreneur's Business Model: Toward a
Unified Perspective. Journal of business Research, 58(6), 726-735.
Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation: a Handbook for Visionaries,
Game Changers and Challengers. New Jersey: John Wiley & Sons, Inc.
Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation: a Handbook for Visionaries,
Game Changers and Challengers.
Page 65 of 82
Peek, J., & Rosengren, E. S. (1997). Collateral Damage: Effects on the Japanese Real Estate Collapse
on Credit Availability and Real Activity in the United States. Federal Reserve Bank of Boston.
Popov, A. (2013). Monetary Policy, Bank Capital and Credit Supply. A Role for Discouraged and
Informally Rejected Firms. European Central Bank(1593).
Pykhtin, M. (2010). Couterparty Credit Risk. Encyclopedia of Quantitative Finance.
Razali, N. M., & Wah, Y. B. (2011). Power Comparisions of Shapiro-Wilk, Kolmogorov-Smirnov,
Lilliefors and Anderson-Darling Tests. Journal of Statistical Modeling and Analytics, 2(1),
21-33.
Saunders, A., & Cornett, M. M. (2011). Financial Institutions Management. A Risk Management
Approach. Singapore: McGraw-Hill Companies.
Stewart, D. W., & Zhao, Q. (2000). Internet Marketing, Business Models, and Public Policy. Journal
of Public Policy and Marketing, 19(2), 287-296.
Stiroh, K. J. (2004). Diversification in Banking: Is Noninterest Income the Answer? Journal of
Money, Credit and Banking, 36(5), 853-882.
Stiroh, K. J. (2010). Diversification in Banking. In A. N. Berger, P. Molyneux, & J. O. Wilson, The
Oxford Handbook of Banking (pp. 146-171). Oxford: Oxford University Press.
Strahan, P. E., & Weston, J. P. (1998). Small Business Lending and the Changing Structure of the
Banking Industry. Journal of Banking & Finance, 22, 821-845.
Teece, D. J. (2010). Business Models, Business Strategy and Innovation. Long Range Planning, 43(2),
172-194.
Timmers, P. (1998). Business Models for Elecronic Markets. Electronic Markets, 8(2), 3-8.
Van den Heuvel, S. (2002). The Bank Capital Channel of Monetary Policy. Economic Policy Review,
Federal Reserve Bank of New York, 1-7.
VanHoose, D. (2006). Bank Behavior under Capital Regulation: What does the Academic Literature
tell us? Networks Financial Institure Working Paper(WP-04).
VanHoose, D. (2007). Theories of Bank Behavior under Capital Regulation. Journal of Banking &
Finance, 31, 3680-3697.
Vink, D. (2009). "Securitisatie: Hoe nu verder?". Maandblad voor Accountancy en Bedrijfseconomie,
215-223.
Weill, P., & Vitale, M. (2001). Place to Space: Migrating to Ebusiness Models. Boston: Harvard
Business School Press.
Weinberg, S. L., & Abramowitz, S. K. (2002). Data Analysis for the Behavioral Sciences Using SPSS.
Cambridge: Cambridge University Press.
Wharton Research Data Services. (n.d.). About WRDS. Obtenido de WRDS: http://wrdsweb.wharton.upenn.edu/wrds/about/
Zott, C., & Amit, R. (2010). Business Model Design: An Activity System Perspective. Long Range
Planning, 43, 216-226.
Page 66 of 82
Zott, C., Amit, R., & Massa, L. (2011). The Business Model: Recent Developments and Future
Research. Journal of Management, 37(4), 1019-1042.
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Glossary
Abbreviations
BMC
Business Model Canvas
EBA
European Banking Authority
ECB
European Central Bank
EMU
Economic Monetary Union
EU
European Union
IRB
Internal Ratings-Based
MCAR
Missing completely at random
N.S.
Non-significant
SMEs
Small and medium-sized enterprises
WRDS
Wharton Research Data Services
Ccloan
Corporate and commercial loans
Cloan
Consumer loans
Cdep
Customer deposits
Inti
Interest income
Loans_dep
Loans to customer deposits
Ninti
Non-interest income
Reca
Repurchase agreements and cash collateral
Rwa
Risk weighted assets
Ta
Total assets
Tcap
Total capital
Tier
Tier 1 capital
Tsec
Total securities
Z-score
A specified overview of the dependent, independent and control variables is provided in Appendix III.
Page 68 of 82
Definitions
Banking Risk
Adverse impacts on profitability of several distinct sources of uncertainty.
Business Model
A business model describes the rationale of how an organization creates, delivers, and
captures value.
Credit Risk
This type of risk occurs if clients or institutions are not able to amortize their debt also
known as default risk or counterparty credit risk.
Credit Supply
Credit supply is the total amount of outstanding corporate and commercial loans that
one or more European banks provide towards enterprises.
Extreme values
Observations with a unique combination of characteristics identifiable as distinctly
different from the other observations. Extreme values are defined in SPSS as
observations which are 3 times the interquartile range beyond the 25th or 75th
percentile.
Liquidity Risk
The risk of having a lack of liquidity which may cause bankruptcy.
Market Risk
The risk of fluctuations in the mark-to market value, which is the ‘fair’ value based on
the current market price, of the trading portfolio, caused by market movements.
Operational Risk
The risk of loss resulting from inadequate or failed internal processes, people and
systems or from external event.
Outliers
Observations with a unique combination of characteristics identifiable as distinctly
different from the other observations. Outliers in SPSS are defined as observations
which are 1,5 times the interquartile range beyond the 25th or 75th percentile.
Repurchase Agreements
Type of short-term funding. Banks sell securities (borrow money) and repurchase them
back at a later date in the future at a slightly higher price.
Reverse repurchase
Type of short-term funding. Banks buy securities and sell them in the future at a
agreements
slightly higher price. It is the opposite of a repurchase agreement.
Securitization
The issuance of claims backed by a pool of default-risky instruments where the new
claims frequently have varying exposures to the underlying pool of collateral.
Systematic Risk
A failure of one single bank could influence other banks which probably lead to losses
and failures of these banks. This may lead to a collapse of the whole banking industry.
Page 69 of 82
Appendix I Business Model Canvas (BMC)
Figure I.1 The Business Model Canvas (BMC)
70
Appendix II CRD IV capital requirements
Page 71 of 82
Appendix III Definition of variables
Variable
Description
Symbol
Dependent variable
Credit supply
Corporate and commercial loans
SQRT(Corporate and commercial loans to total assets * 100 (2009-2013))
ccloan_ta
Securities
Residential mortgage loans to total assets * 100 (2009-2013)
Other mortgage loans to total assets * 100 (2009-2013)
Other consumer/retail loans to total assets * 100 (2009-2013)
LN(Total securities to total assets * 100 (2009-2013))
rml_ta
oml_ta
ocrl_ta
tsec_ta
Capital structure
Tier 1 capital
Total capital
Tier I capital to total assets * 100 (2009-2013)
Total capital to total assets * 100 (2009-2013)
tier_ta
tcap_ta
Total customer deposits to total assets * 100 (2009-2013)
Repurchase agreements and cash collateral to total assets * 100 (20092013)
cdep_ta
reca_ta
Independent variables
Asset structure
Consumer loans
Funding structure
Deposit funding
Short-term funding
Loans to customer deposits
Income structure
Interest income
Non-interest income
loans_dep
Interest income to total revenues * 100 (2009-2013)
Non-interest income to total revenues * 100 (2009-2013)
inti_tr
ninti_tr
LN(Total assets (2003-2013))
Risk weighted assets to total assets * 100 (2009-2013)
SQRT(
(2009-2013))
ta
rwa_ta
z-score
Control variables
Total assets
Risk weighted assets
Z-score
This table provides the names of all variables which are used during this study. A short description of every variable
is included as even the symbol and source of all variables.
Page 72 of 82
Appendix IV List of all banks
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Bankname
ABN Amro Bank NV
ABN Amro Group N.V.
Agricultural Bank of Greece4
Allied Irisch Banks Plc
Alpha Bank AE
Banca Intesa2
Banca Monte Dei Paschi Di Siena SPA - Gruppo Monte Die Paschi Di Siena
Banco Bilbao Vizcaya Argentaria SA (BBVA)
Banco BPI SA
Banco Comercial Português SA-Millennium bpc
Banco Popolare - Società Cooperative - Banco Popolare
Banco Popular Español SA
Banco Santander SA (ES)
Bank of Cyprus Public Company Limited -Bank of Cyprus Group
Bank of Ireland - Governor and Company of the Bank of Ireland
Bank Of Valletta Plc
Bankia SA
Banque Et Caisse D'Epargne De L'Etat
Banque Populaire3
Barclays Bank PLC
Barclays PLC
Bayerische Landesbank
BNP Paribas
BPCE Group3
BPCE SA3
Caisse D'Epargne3
Caixa Geral De Depósitos
Caja de Ahorros y Pensiones de Barcelona - La Caixa
Commerzbank AG
Cooperatieve Centrala Raiffeisen - Boerenleenbank B.A - Rabobank Nederland
Crédit Agricole S.A.
Danske Bank A/S
Dekabank Deutsche Girozentrale
Deutsche Bank AG
Dexia
DnB NOR Bank ASA
DZ Bank AG - Deutsche Zentral-Genossenschaftsbank
EFG Eurobank Ergasias SA
Erste Group Bank AG
Espírito Santo Financial Group S.A.
HSBC Holdings Plc
HSH Nordbank AG
Hypo Real Estate Holding AG
ING Bank NV
ING Group NV
Intesa Sanpaolo2
Jyske Bank A/S
Jyske Bank A/S (Group)
KBC Bank NV
KBC Groep NV / KBC Groupe SA - KBC Group
Landesbank Baden-Württemberg
Landesbank Berlin AG
Lloyds Banking Group Plc
Marfin popular bank6
Monte Dei Paschi Di Siena SPA
National Bank of Greece SA
NLB dd - Nova Ljubljanska Banka d.d.
Norddeutsche Landesbank Girozentrale NORD/LB
Nordea Bank AB (publ)
Country
NL
NL
GR
IE
GR
IT
IT
ES
PT
PT
IT
ES
ES
CY
IE
MT
ES
LU
FR
UK
UK
DE
FR
FR
FR
FR
PT
ES
DE
NL
FR
DK
DE
DE
BE
NO
DE
GR
AT
PT
UK
DE
DE
NL
NL
IT
DK
DK
BE
BE
DE
DE
UK
CY
IT
GR
SI
DE
SE
Business model
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Investment
Investment
Wholesale
Investment
Wholesale
Wholesale
Wholesale
Retail
Retail
Investment
Retail
Wholesale
Retail
Wholesale
Investment
Wholesale
Retail
Wholesale
Retail
Retail
Retail
Investment
Wholesale
Wholesale
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Wholesale
Wholesale
Retail
Retail
Retail
Retail
Retail
Wholesale
Investment
Page 73 of 82
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
Nova Kreditna Banka Maribor d.d.
Nykredit Bank A/S
Oesterreichische Volksbank AG
OP-Pohjola Group
OTP Bank Plc
Permanent TSB Plc (Irish Life And Permanent)
Piraeus Bank SA
Powszechna Kasa Oszczednosci Bank Polski SA - PKO Bank Polski SA
Raiffeisen Bank International AG
Royal Bank of Scotland Group Plc (The)
Royal Bank of Scotland Plc (The)
Sanpaolo IMI2
Skandinaviska Enskilda Banken AB
SNS Bank N.V.
Société Générale
Svenska Handelsbanken
Swedbank AB
Sydbank A/S
TT Hellenic Postbank S.A5
UniCredit SpA
Unione di Banche Italiane Scpa - UBI Banca
WestLB7
WGZ - Bank AG Westdeutsche Genossenschafts-Zentralbank
SI
DK
AT
FI
HU
IE
GR
PL
AT
UK
UK
IT
SE
NL
FR
SE
SE
DK
GR
IT
IT
DE
DE
Retail
Retail
Wholesale
Retail
Retail
Wholesale
Retail
Retail
Retail
Investment
Investment
Retail
Retail
Retail
Investment
Retail
Retail
Retail
Retail
Retail
Retail
Retail
Wholesale
2
: Intesa Sanpaolo was created on 1 January 2007 bu a merger of Banca Intesa and Sanpaolo IMI
3
: BPCE group is created on July 2009 by the merger of Banque Populaire Group and Groupe Caisse D'Epargne
4
: Agricultural Bank of Greece is dissolved since 30 July 2012
5
: TT Hellenic Postbank S.A. is dissolved since 1 January 2013
6
: Marfin Popular Bank changed its name into Cyprus Popular Bank
7
: WestLB is downsized since 30 june 2012
Page 74 of 82
Appendix V Summary statistics
Descriptive Statistics
Median
Minimum
Maximum
Mean
Std.
Deviation
606
22,70
,20
60,75
22,47
14,44
606
578
606
606
606
606
606
600
606
606
606
546
19,38
2,51
3,27
5,14
6,16
42,48
4,11
130,40
83,86
16,14
12,04
6,00
-28,17
,10
2,12
,17
-,66
6,60
-6,68
24,21
47,38
-36,83
8,76
,56
65,11
5,64
4,39
11,02
14,05
80,70
25,46
272,20
136,83
52,62
14,81
14,12
19,40
2,75
3,33
5,24
6,30
42,03
4,89
128,02
83,82
16,18
12,28
5,95
14,05
1,24
0,48
2,01
2,26
15,29
5,27
39,43
13,27
13,27
1,53
3,07
N
DV: Commercial and corporate
loans
AS: Residual mortgage loans
AS: Other consumer retail loans
AS: Securitization
CS: Tier 1 capital
CS: Total capital
FS: Costumer deposits
FS: Repo's and cash collaterals
FS: Loans to customer deposits
IS: Total interest income
IS: Total non-interest income
CV: Total Assets
CV: Z-score risk
Page 75 of 82
Appendix VI Randomness Test
Separate Variance t Tests
rml_ta
ocrl_ta
tier1_ta
tcap_ta
reca_ta
t
df
# Present
# Missing
Mean(Present)
Mean(Missing)
t
df
# Present
# Missing
Mean(Present)
Mean(Missing)
t
df
# Present
# Missing
Mean(Present)
Mean(Missing)
t
df
# Present
# Missing
Mean(Present)
Mean(Missing)
t
df
# Present
# Missing
Mean(Present)
Mean(Missing)
ccloan_ta
-,6
119,6
131
71
21,9520
23,4264
4,4
110,5
142
60
25,2652
15,8556
2,7
69,7
155
47
24,0543
17,2461
3,1
76,2
153
49
24,2779
16,8259
-1,4
192,1
rml_ta
131
0
19,3415
2,5
45,6
95
36
21,4764
13,7075
4,7
64,2
103
28
21,5218
11,3208
4,9
68,2
103
28
21,5643
11,1646
2,4
124,8
ocrl_ta
-3,7
54,7
95
47
6,4758
13,1414
tier1_ta
-,9
143,7
103
52
5,2345
5,5544
1,9
139,6
118
37
5,5020
4,8311
1,3
46,8
118
24
8,9968
7,1343
-,1
36,4
117
25
8,6518
8,8237
-2,7
93,3
sec_ta
,3
158,0
131
71
31,5927
30,9611
-2,0
120,5
142
60
30,0212
34,5645
-2,2
62,8
155
47
29,8324
36,4439
-2,7
67,2
153
49
29,4739
37,2934
,1
199,8
142
0
8,6820
155
0
5,3418
5,0
34,8
150
5
5,3867
3,9975
-1,4
120,0
tcap_ta
-2,1
144,1
103
50
6,0946
6,8627
1,6
143,9
117
36
6,4784
5,9139
1,5
2,3
150
3
6,3740
4,9239
reca_ta
-,2
59,9
64
37
4,4074
4,5820
-1,6
18,8
84
17
4,0123
6,7397
-,6
19,3
85
16
4,3325
5,2095
-,7
17,5
86
15
4,3046
5,4278
-2,3
120,4
cdep_ta
-,8
146,4
131
71
41,3861
43,2245
6,4
113,8
142
60
46,0764
32,4612
3,4
74,8
155
47
44,0296
35,4451
3,1
84,9
153
49
43,8606
36,3233
1,8
188,5
loans_dep
1,9
111,8
131
69
132,0773
120,3312
-1,3
128,5
140
60
125,8239
133,1605
1,2
65,3
153
47
130,0878
121,3091
1,8
69,3
151
49
131,1195
118,4882
-,7
181,1
inti_tr
-1,4
155,2
128
68
82,4905
85,3540
-,4
144,6
136
60
83,2424
84,0316
,1
54,3
155
41
83,5344
83,2932
-,4
58,5
153
43
83,2366
84,3640
-2,2
193,8
ninti_tr
1,4
155,2
128
68
17,5095
14,6460
,4
144,6
136
60
16,7576
15,9684
-,1
54,3
155
41
16,4656
16,7068
,4
58,5
153
43
16,7634
15,6360
2,2
193,8
,4
162,1
131
71
593319,78
553007,07
,1
115,5
142
60
580914,27
574976,12
1,5
97,5
155
47
617663,90
452138,06
1,6
108,0
153
49
619040,69
454595,27
3,6
197,2
z_score
-3,9
71,9
126
69
43,7389
176,1875
-1,0
97,0
137
58
81,2364
112,7355
-,6
56,6
150
45
85,0061
109,2693
-1,2
59,4
147
48
79,3827
124,9746
1,2
149,5
101
99
98
98
98
98
101
101
97
98
125,9694
81,3040
18,6960
764173,38
106,8932
130,12182
85,6639
14,3361
394127,54
74,4837
153
0
6,3456
101
101
64
67
84
58
101
101
85
70
86
67
101
101
101
0
21,0391
22,1922
6,9919
31,4647
5,0407
5,8658
43,9518
4,4714
23,9014 16,6184 11,1298 31,2767 5,7075 6,9615 40,1127
*For each quantitative variable, pairs of groups are formed by indicator variables (present, missing).
**Indicator variables with less than 5% missing are not displayed.
***Light gray represents t values > 1.96 or <-1.96, Dark gray represents t values >2.575 or <-2.575
ta
Page 76 of 82
Appendix VII Correlation matrix
Correlations for imputation methods
ccloan_ta
rml_ta
ocrl_ta
sec_ta
tier1_t
a
tcap_ta
cdep_ta
reca_ta
loan_de
p
inti_tr
ninti_tr
ta
Z_score
e
ccloan_ta
Listwise
Pairwise
EM
Regression
1,00
1,00
1,00
1,00
rml_ta
Listwise
Pairwise
EM
Regression
0,29
0,30
0,16
0,18
1,00
1,00
1,00
1,00
0,34
-0,03
-0,06
-0,05
0,02
0,19
-0,22
0,08
1,00
1,00
1,00
1,00
-0,57
-0,49
-0,49
-0,49
-0,69
-0,69
-0,64
-0,65
-0,23
-0,21
-0,15
-0,18
1,00
1,00
1,00
1,00
0,19
0,16
0,17
0,19
0,32
0,03
0,04
0,07
-0,13
0,04
0,01
0,04
-0,21
-0,13
-0,05
-0,17
1,00
1,00
1,00
1,00
0,16
0,10
0,12
0,08
0,36
0,05
0,02
0,05
-0,17
0,14
0,11
0,10
-0,22
-0,11
-0,05
-0,02
0,98
0,94
0,94
0,73
1,00
1,00
1,00
1,00
0,42
0,26
0,26
0,26
0,56
0,61
0,50
0,57
0,41
0,30
0,19
0,25
-0,65
-0,47
-0,47
-0,47
0,46
0,37
0,33
0,32
0,48
0,33
0,30
0,28
1,00
1,00
1,00
1,00
-0,52
-0,28
-0,30
-0,30
-0,45
-0,47
-0,40
-0,30
-0,24
-0,14
-0,20
-0,06
0,82
0,77
0,72
0,73
-0,30
-0,24
-0,20
-0,24
-0,28
-0,20
-0,15
-0,11
-0,58
-0,53
-0,51
-0,44
1,00
1,00
1,00
1,00
0,474
0,348
0,340
0,342
0,297
0,251
0,226
0,202
-0,015
-0,029
0,064
0,014
-0,567
-0,542
-0,537
-0,539
-0,034
-0,157
-0,144
-0,133
-0,070
-0,153
-0,117
-0,102
-0,154
-0,262
-0,293
-0,292
-0,430
-0,428
-0,411
-0,307
ocrl_ta
Listwise
Pairwise
EM
Regression
sec_ta
Listwise
Pairwise
EM
Regression
tier1_ta
Listwise
Pairwise
EM
Regression
tcap_ta
Listwise
Pairwise
EM
Regression
cdep_ta
Listwise
Pairwise
EM
Regression
reca_ta
Listwise
Pairwise
EM
Regression
loan_dep
Listwise
Pairwise
EM
Regression
1,00
1,00
1,00
1,00
Page 77 of 82
ccloan_ta
rml_ta
ocrl_ta
sec_ta
Listwise
0,59
0,49
0,30
-0,71
Pairwise
EM
0,28
0,28
0,18
-0,46
0,28
0,19
0,16
-0,46
Regression
0,27
0,24
0,18
-0,45
Listwise
-0,59
-0,49
-0,30
Pairwise
EM
-0,28
-0,28
-0,18
-0,28
-0,19
Regression
-0,28
-0,26
Listwise
-0,46
Pairwise
EM
-0,11
Regression
tier1_ta
tcap_ta
cdep_ta
reca_ta
loan_dep
inti_tr
ninti_tr
LOG_TA
Z_score
0,16
0,13
0,54
0,59
0,466
1,00
-0,05
-0,05
0,22
0,28
0,298
1,00
-0,09
-0,09
0,24
0,28
0,334
1,00
-0,01
-0,10
0,23
0,27
0,327
1,00
0,71
-0,16
-0,13
-0,54
-0,59
-0,466
-1,00
1,00
0,46
0,05
0,05
-0,22
-0,28
-0,298
-1,00
1,00
-0,16
0,46
0,09
0,09
-0,24
-0,28
-0,334
-1,00
1,00
-0,20
0,47
0,01
0,10
-0,24
-0,28
-0,358
-1,00
1,00
-0,48
-0,30
0,55
-0,24
-0,26
-0,60
0,55
-0,143
-0,56
0,56
1,00
-0,22
-0,06
0,22
-0,21
-0,27
-0,32
0,36
0,001
-0,48
0,48
1,00
-0,11
-0,15
-0,05
0,22
-0,21
-0,26
-0,32
0,21
-0,014
-0,48
0,48
1,00
-0,11
-0,16
-0,03
0,22
-0,21
-0,24
-0,32
0,35
-0,013
-0,48
0,47
1,00
Listwise
-0,17
-0,18
-0,09
0,23
0,01
0,01
-0,21
0,17
-0,036
-0,40
0,40
0,47
1,00
Pairwise
EM
-0,27
-0,33
0,42
0,15
-0,07
0,03
0,13
0,00
-0,222
0,06
-0,06
-0,03
1,00
-0,28
-0,31
0,44
0,15
-0,10
0,00
0,14
0,01
-0,233
0,09
-0,09
-0,03
1,00
Regression
-0,30
-0,04
0,42
0,16
-0,03
0,05
0,14
0,03
-0,237
0,07
-0,09
-0,04
1,00
inti_tr
ninti_tr
ta
z_score
Notes: Gray markings indicate relatively strong differences
Page 78 of 82
Appendix VIII Regression results of post-hoc analysis
Without control variables
Asset
structure
Capital
structure
Funding
structure
Income
structure
Control
variables
Regression coëfficients and t-values per business model
Retail
Investment
Residual mortgage loans
-0,496
-0,030
(-8,783) ***
(-0,435)
Other consumer retail
loans
-0,047
0,020
(-1,035)
(0,381)
Securitization
-0,452
-0,219
(-6,357) ***
(-2,447) **
Tier 1 capital
0,421
-0,074
(3,660) ***
(-0,526)
Total capital
-0,199
0,032
(-1,668) *
(0,226)
Customer deposits
0,094
0,635
(1,170)
(8,331) ***
Repo's and cash
collaterals
0,102
0,094
(2,012) **
(1,322)
Loans to customer
deposits
0,236
0,745
(2,850) ***
(10,341) ***
Total interest income
0,129
0,091
(2,867) ***
(1,471)
Total assets
Z-score
Intercept
Notes:
Wholesale
-0,170
(-2,040)
-0,489
(-5,947)
-0,158
(-0,884)
-0,272
(-2,667)
0,361
(3,899)
0,159
(1,110)
**
***
***
***
0,188
(2,320)
**
0,170
(0,993)
0,304
(2,611)
***
a
48,767
-4,956
-13,187
(3,663) ***
(-0,417)
(-0,193)
No. Of observations
385
98
74
R²
0,365
0,812
0,758
Standardized coefficients are provided in this table, t-values are between parenthesis
a: Intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10, 5 and
percent levels respectively
Page 79 of 82
With control variable total assets
Asset
structure
Capital
structure
Funding
structure
Income
structure
Control
variables
Regression coëfficients and t-values per business model
Retail
Investment
Residual mortgage loans
-0,471
0,065
(8,326) ***
(1,244)
Other consumer retail loans
-0,034
0,234
(0,754)
(5,164)
Securitization
-0,473
-0,055
((-0,813)
6,694) ***
Tier 1 capital
0,458
-0,159
(4,000) ***
(-1,537)
Total capital
-0,276
0,026
((0,252)
**
2,288)
Customer deposits
0,024
0,697
(0,287)
(12,374)
Repo's and cash collaterals
0,110
0,186
(2,203) **
(3,511)
Loans to customer deposits
0,229
1,227
(2,797) ***
(16,090)
Total interest income
0,073
0,227
(1,515)
(4,721)
Total assets
-0,164
0,593
((8,761)
***
3,011)
Z-score
Intercept
Notes:
Wholesale
-0,169
***
(-2,022)
-0,486
**
(-5,858)
-0,133
***
(-0,692)
***
***
***
***
***
-0,275
(-2,666)
0,367
***
(3,860)
***
0,164
(1,131)
0,188
(2,301)
0,165
(0,897)
0,334
(2,286)
-0,031
**
**
(-0,347)
a
79,312
-205,763
-14,772
(4,771) ***
(-8,392) ***
(-0,214)
No. Of observations
385
98
74
R²
0,380
0,900
0,758
Standardized coefficients are provided in this table, t-values are between parenthesis
a: Intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10,
5 and percent levels respectively
Page 80 of 82
With control variable z-score
Asset
structure
Capital
structure
Funding
structure
Income
structure
Control
variables
Notes:
Regression coëfficients and t-values per business model
Retail
Investment
Wholesale
Residual mortgage loans
-0,460
-0,111
-0,207
(7,158) ***
(-1,659) *
(-2,521)
Other consumer retail loans
0,083
0,067
-0,427
(1,538)
(1,372)
(-4,735)
Securitization
-0,449
-0,312
-0,330
((-3,665)
(-1,799)
5,790) ***
***
Tier 1 capital
0,518
-0,023
-0,271
(3,904) ***
(-0,176)
(-2,566)
Total capital
-0,241
0,010
0,369
((0,079)
(3,916)
*
1,747)
Customer deposits
-0,015
0,742
-0,144
((9,962)
(-0,777)
0,168)
***
Repo's and cash collaterals
0,162
0,079
0,183
(2,959) ***
(1,218)
(2,278)
Loans to customer deposits
0,118
0,832
-0,054
(1,358)
(12,002) ***
(-0,277)
Total interest income
-0,003
0,017
0,455
((0,285)
(3,579)
0,065)
Total assets
Z-score
-0,276
-0,233
-0,271
((-4,180) ***
(-2,225)
***
5,259)
a
Intercept
69,011
8,433
46,547
(5,071) ***
(0,743)
(0,666)
No. Of observations
333
98
73
R²
0,371
0,844
0,780
Standardized coefficients are provided in this table, t-values are between parenthesis
a: Intercept value is provided as unstandardized
*, ** and *** indicate statistical significance at 10, 5 and percent
levels respectively
**
***
*
**
***
**
***
**
Page 81 of 82
Appendix IX Post-Hoc analysis, means and standard deviations
Retail business model
DV: Commercial and corporate loans
AS: Residual mortgage loans
CS: Tier 1 capital
CS: Total capital
FS: Customer deposits
FS: Repo's and cash collatorals
FS: Loans to customer deposits
IS: Total interest income
AS: Securitization
AS: Other consumer retail loans
CV: Total Assets
CV: Z-index risk
Mean
24,7424
24,4303
6,0607
7,1174
49,9828
2,9981
127,9055
84,7944
3,1379
2,8887
12,1024
5,4554
Std. Deviation
12,58222
13,63477
1,99361
2,27831
12,0441
4,04513
34,76434
9,36051
0,38477
1,28954
1,52416
3,03132
N
333
333
333
333
333
333
333
333
333
333
333
333
Mean
14,2431
11,5495
3,6975
4,6341
29,9564
9,7751
108,6649
66,6766
3,9082
2,1844
14,1295
7,3397
Std. Deviation
8,1004
6,10422
1,22525
1,67539
9,62631
4,01976
30,14676
11,05208
0,29144
0,72931
0,43896
2,60128
N
98
98
98
98
98
98
98
98
98
98
98
98
Mean
15,7841
6,6539
4,1794
5,9513
24,2693
8,5704
155,5944
91,1185
3,5946
2,9176
11,8386
6,7755
Std. Deviation
19,78575
6,11996
1,17364
1,33284
9,47633
5,50716
46,24874
8,93912
0,24024
1,37559
0,59739
3,41746
N
73
73
73
73
73
73
73
73
73
73
73
73
Investment business model
DV: Commercial and corporate loans
AS: Residual mortgage loans
CS: Tier 1 capital
CS: Total capital
FS: Customer deposits
FS: Repo's and cash collatorals
FS: Loans to customer deposits
IS: Total interest income
AS: Securitization
AS: Other consumer retail loans
CV: Total Assets
CV: Z-index risk
Wholesale business model
DV: Commercial and corporate loans
AS: Residual mortgage loans
CS: Tier 1 capital
CS: Total capital
FS: Customer deposits
FS: Repo's and cash collatorals
FS: Loans to customer deposits
IS: Total interest income
AS: Securitization
AS: Other consumer retail loans
CV: Total Assets
CV: Z-index risk
Page 82 of 82